"The Journals of Gerontology - Series A: Biological Sciences and Medical Sciences"

Di Cesare, Francesca; Luchinat, Claudio; Tenori, Leonardo; Saccenti, Edoardo

doi: 10.1093/gerona/glab335pmid: 34748631

Abstract In this study, we investigated how the concentrations, pairwise correlations and ratios of 202 free circulating blood metabolites and lipids vary with age in a panel of n = 1 882 participants with an age range from 48 to 94 years. We report a statistically significant sex-dependent association with age of a panel of metabolites and lipids involving, in women, linoleic acid, α-linoleic acid, and carnitine, and, in men, monoacylglycerols and lysophosphatidylcholines. Evaluating the association of correlations among metabolites and/or lipids with age, we found that phosphatidylcholines correlations tend to have a positive trend associated with age in women, and monoacylglycerols and lysophosphatidylcholines correlations tend to have a negative trend associated with age in men. The association of ratio between molecular features with age reveals that decanoyl-l-carnitine/lysophosphatidylcholine ratio in women “decrease” with age, while l-carnitine/phosphatidylcholine and l-acetylcarnitine/phosphatidylcholine ratios in men “increase” with age. These results suggest an age-dependent remodeling of lipid metabolism that induces changes in cell membrane bilayer composition and cell cycle mechanisms. Furthermore, we conclude that lipidome is directly involved in this age-dependent differentiation. Our results demonstrate that, using a comprehensive approach focused on the changes of concentrations and relationships of blood metabolites and lipids, as expressed by their correlations and ratios, it is possible to obtain relevant information about metabolic dynamics associated with age. Correlation analysis, Gender differences, Human aging, Lipids, Metabolomics Aging is a very complex process, influenced by genetic, environmental, and lifestyle factors (1,2), and involves progressive systemic dysregulation, affecting all levels of an organism, from molecules to organs (3,4). Metabolomics, that is the comprehensive analysis of small molecule profiles measured in a biological sample like blood or urine (5,6), is an excellent approach to obtain a global representation of the metabolic status of an organism with respect to a healthy status or a particular pathophysiological condition (7–9). The analysis of metabolomic profiles obtained from participants of different ages, performed using an integrative systems biology approach (10), allows the comprehensive description of the metabolic dynamics and can help to quantify and decipher the relationships between molecular features and aging process (4,11). Studies have been conducted in humans, highlighting how the metabolome is sex and age-dependent, indicating sex-specific association of certain genetic loci with several metabolites and lipid species: the levels of many metabolites (among them fatty acids, including 10 long-chain fatty acids, polyunsaturated fatty acids, glutamine, tyrosine, and histidine) and variation thereof are highly dependent on sex and age, and that sex differentially influences the levels and variation over time of many metabolites (12,13). Correlations and ratios among molecules, and not only their levels, bear relevant biological information: Because molecules behave in an orchestrated way through metabolic pathways, changes in their association patterns, as represented by correlations and ratios (14,15), can provide information on the remodulation of biochemical reaction networks and metabolic pathways associated with age or sexual dimorphism, thus suggesting mechanisms through which molecules may modify cell membranes and affect hormonal activities, mitochondrial metabolism, and cell responses to oxidative stress (11,16). In this study, making use of publicly available data, we took a comprehensive system biology approach, focusing on the association of the blood circulating unconjugated metabolites and lipids with age and sex in a large population cohort with an age range between 48 and 98 years (17). We investigated how metabolite and lipid abundances correlate with age groups, but also how the correlation and the ratios between metabolites and lipids change in groups of participants of different (increasing) ages. Material and Methods Experimental Data We used data from the TwinGene project (17) that includes a longitudinal cohort from the Swedish Twin Register and a matched subcontrol cohort stratified on age and sex. The cohort was selected by Ganna et al. (17). This data set is a valid representation of a population consisting of not related participants and with a wide age range. It contains 202 quantified blood metabolites and lipids measured on n = 2 139 participants (nW = 921 women [43%] and nM = 1 218 men [57%]) with an overall age range of 47.6–93.9 years (women age range = 48.4–93.9 years and men age range = 47.6–93.3 years) and with an overall average age of 68.8 (women average age = 68.8 years and men average age = 68.7 years). This data set was used to identify potential molecular features and metabolic pathways associated with the sex-related aging process. Data were downloaded from the MetaboLights database (https://www.ebi.ac.uk/metabolights/) with accession number MTBLS93. Briefly, metabolomic profiling was performed on ultra-performance liquid chromatography to quadrupole time-of-flight mass spectrometry with an atmospheric electrospray interface operating in positive ion mode. The first step was the detection, alignment, grouping, and assignment of metabolites, performed by Ganna et al. (17), using the XCSM software. For the metabolic annotation, 4 approaches were performed by the authors: (a) based on matching accurate mass, fragmentation pattern, and retention time with their in-house spectral library of authentic standards collected; (b) based on spectrum and/or m/z similarities, but not retention time, and the annotation relies on the information of public databases; (c) based on the combination of spectral data, accurate mass, and retention time to assign the metabolite to a specific chemical class; (d) the other approaches failed in the annotation of the metabolite and the metabolite was annotated as “unknown.” Combining these approaches, m = 202 molecular features, divided into m1 = 36 metabolites and m2 = 166 lipids and lipid precursors, were assigned in the original publication (Supplementary Table 1). For further details, we refer the reader to the original publication (17). Data Preprocessing Removal of outliers To obtain a uniform study population, we removed those participants showing outlying blood metabolites and lipid profiles under the assumption of the presence of possibly undiagnosed pathophysiological conditions. Outliers were removed using a Principal Components Analysis (PCA) based approach. Hotelling’s T2 values were calculated from PCA scores; samples whose T2 values exceeded the 95% confidence ellipsis were considered outliers and were removed from subsequent analysis. The optimal number of significant principal components to be retained (at the α = 0.05 level) was determined using a statistical test based on the Tracy–Widom distribution (18). A total of 117 women (18%) and 140 men (11%) were removed from the analysis. This left n = 1 882 (nW = 804 women, 43%, nM = 1 078 men, 57%) samples/participants available for further analysis. Subject stratification The nW = 804 women and the nM = 1 078 men were separately stratified by age in 20 groups, Wt (for women) and Mt (for men) with t = 1,2, …, 20 of size wi and mi by taking the 20 quantiles QT1, QT2, …, QT20 of the women and men age distributions, reflecting the 5th, 10th, …, 95th, and 100th percentiles of the sex-specific age distribution. Consequently, each Wt group and Mt group had approximately 5% of the sex-specific sample (≅40 for women and ≅54 for men). The age characteristics for each women and men group are given in Supplementary Table 2. A graphical illustration is shown in Figure 1. For each Wt and Mt group, we defined the corresponding data matrices Wt and Mt of size wi × p and mi× p containing the concentrations of the p = 202 metabolites and lipids measured on the wi and mi participants in the corresponding group. Each set of data matrices is associated with a 1 × 20 vector tM (respectively tF) containing the average age of the M1, M2, …, M20 group (respectively W1, W2, …, W20). Figure 1. Open in new tabDownload slide Overview of stratification of the study participants. Participants are first stratified by sex and then by age. Women and men are divided into 20 groups according to the 20 quantiles obtained from the age distribution of the 2 sex-specific groups. Statistical Analysis Estimation of the average concentration of molecular features specific to age groups For each data set Wt and Mt we calculated the mean abundance mi between each molecular feature xi. As for the correlation case, we obtained thus 20 values for each metabolite–lipid, representing the changes of the average abundance of molecular feature xi associated with the age groups (a graphical representation is shown in Figure 2A): Figure 2. Open in new tabDownload slide (A) Overview of the statistical procedure used to establish the association rcA(xi, xj) (eqn (6)) between the metabolite and lipids pairwise correlations C(xi, xj) (eqn (3)), with the group age (Figure 1). In the case of ratios (eqn (7)), the correlation matrix is replaced with the matrix of pairwise ratios, for average abundances is replaced by the vectors of means (eqn (8)). (B). Overview of the data-splitting procedure used to validate the results. Each subject group is randomly split into 2 halves, obtaining 2 sets of 20 groups. The analysis is performed on the first set, while the second set is used for validation. The procedure is repeated 100 times: Only results validated more than 50% of the times are considered significant. We considered the standard mean estimation: ai= 1n∑nk=1xi (1) For each feature, we thus obtained 20 mean values: A(xi)={ai (t=1), ai (t=2), …, ai (t=20)}(2) Estimation of correlations between molecular features specific to age groups For each data set Wt and Mt of size wi × p and mi × p, we calculated the correlation rij between each pair of molecular features xi, xj. For each pair, we obtained thus 20 correlation values, representing the evolution of the strength of the relationship between molecular features xi, xj associated with different age groups (Figure 2A): C(xi, xj)={rij (t=1), rij (t=2), …, rij (t=20)}(3) We used Winsorized correlation coefficients that are robust toward the shape, sample size, and outliers in the metabolite concentration distribution (19) to estimate the correlation rij within molecular features pairs. The Winsorized correlation coefficient is obtained by replacing the k smallest observations with the (k + 1)st smallest observation, and the k largest observations with the (k + 1)st largest observation. In this way, the observations are Winsorized at each end of both xi, and xj. The Pearson’s correlation coefficient is then calculated on the Winsorized variables (20). A 10% Winsorization was used. Among the ½p(p−1) possible correlations we retained for further analysis only those pairs of molecular features for which the correlation rijwas found to be significant at the α = 0.01 level in at least 10 of the 20 data sets Wt and Mt. Estimation of ratios between molecular features specific to age groups For each data set Wt and Mt, we calculated the ratio qij between each pair of molecular features xi, xj. As for the correlation case, we thus obtained 20 values for each pair, representing the evolution of the ratio magnitude of molecular features xi and xj (Figure 2A). We considered the unbiased ratio estimator proposed by van Kempen and van Vliet (21) which is defined as: qij= xi¯xj¯− 1n(xi¯xj¯3var(xi)− cov(xi,xj)xj¯2) (4) where xi¯is the mean of xi, xj¯is the mean of xj, var(xi) is the variance of xi, cov(xi,xj) is the covariance between xi and xj, and n is the sample size. For each ratio, we thus obtained 20 ratio values: Q(xi, xj)={qij (t=1), qij (t=2), …, qij (t=20)}(5) Because we were looking for ratio values varying over the 20 age groups, we retained for further analysis only those ratios qij for which the relative variation between qij(t = 1) and qij(t = 20) was larger than 10%. Estimation of the association with average group age of the correlation and ratios among molecular features The association rcA(xi, xj) of the correlation of each pair of molecular features xi, xj with the average group age tM was estimated by taking the Winsorized correlation between the vectors of correlations C(xi, xj) defined in eqn (3) and the average group age vector tM (respectively, tF): rcA(xi, xj)=corr(C(xi, xj), tM)(6) The association rqA(xi, xj) of the ratio of each pair of molecular features xi, xj with the average group age tM was estimated in a similar fashion: rqA(xi, xj)=corr(Q(xi, xj), tM)(7) The association raA(xi, xj) of the mean abundance of each molecular features xi with the average group age tM was estimated as: raA(xi)=corr(A(xi), tM)(8) We considered to be associated with age only the correlations, ratios, or mean abundances of those molecular features for which |rcA(xi, xj)|≥ 0.65, |rqA(xi, xj)|≥ 0.65 and |raA(xij)|≥ 0.65 and p < .01 after correction (fdr) for multiple testing with the Benjamini–Hochberg method. Correction for multiple testing (Benjamini–Hochberg) was applied at all analysis stages. This choice is based on both statistical and biological considerations. There are 20 age groups, which means that the sample size available to estimate the correlation between metabolite concentrations and associations (correlations and ratios) is 20: With 20 observations, it is possible to assess significance at α = 0.01 with 80% power only of correlations |r| ≥ 0.65. In addition, there are ~20 participants per age group, thus metabolite–metabolite correlation |r| ≥ 0.65 can be estimated. Biologically the 0.65 threshold is justified by considering that the majority of correlations observed in metabolomics studies are below 0.6 (22,23): Setting a higher threshold allows to focus on correlations that really stand out of the background correlation. Validation of the results To validate the results of the analysis described in the previous sections, that is, the existence of an association between average age group and metabolite and lipid concentration (eqn (8)), correlations (eqn (6)), and ratios (eqn (7)), we implemented a data splitting approach (24,25). Basically, we randomly split each of the 20 age groups into 2 halves and performed the analysis independently on the 2 data splits to ascertain if the results could be reproduced. To consider the variability due to the random splitting, the overall procedure was repeated generating k = 100 different pairs of data splits: Analysis was repeated on the 100 pairs of data. We considered to be valid only those results that were confirmed in at least 50% of the splits (Figure 2B). In this way, we could obtain an estimation of the reproducibility and robustness of the results by mimicking validation in an external cohort: A portion of the data is used to suggest a hypothesis, and a second, independent portion is used to test it. Note that this approach can be rephrased in an inferential setting and implies that Type I error (ie, the risk of false positives) is controlled (conservatively) at the 0.01 level (26) after correction for multiple testing. The downside of such an approach is a potential loss of power, due to the reduction of the sample size used to estimate correlation. However, this approach is effective in giving valid inference after the selection of a hypothesis, estimating nuisance parameters, and avoiding overfitting (26). Software All calculations and plots were performed in R (version 3.3.2). The function “win.cor,” implemented in WRS2 package, was used to calculate the Winsorized correlations. Results Association of Metabolite and Lipid Abundances With Age Starting from a total of n = 202 metabolites and lipids, a total of pW = 3 (women) and pM = 3 (men) compounds were found statistically significant (adjusted p ≤ .01 and absolute value of raA ≥ 0.65, see eqn (8)) in more than 50% of splits obtained performing the validation method. In particular, in the women cohort, we observed a positive correlation of the concentrations of carnitine with raA = 0.79 and an adjusted p = .0009 in the 79% of validation splits, linoleic acid with raA = 0.66 and an adjusted p = .001 in the 59% of validation splits, and α-linoleic acid with raA = 0.65 and an adjusted p = .01 in the 66% of validation splits with the average age of women group (Figure 3A). Figure 3. Open in new tabDownload slide Correlations between average metabolites and lipids concentrations and the average age of the 20 subject groups: women (A) and men (B). LPC = lysophosphatidylcholine; PC = phosphatidylcholine; PE = phosphatidylethanolamine; MAG = monoacylglycerol. See Figure 2 for an overview of the statistical procedure. The age groups that we used here are data-driven and are not physiologically informed. In particular, the first group of women (W1) corresponds to a 6-year age bin that likely represents perimenopausal women, given that the average age of menopause in women in the Western world is 51 years (27). Although this does not affect statistical analysis, we shall consider that menopausal transition aligns with age. In the men cohort, we observed negative correlation with age of monoacylglycerol (MAG), especially MAG (18:0) with raA = −0.65 and an adjusted p = .005 in the 53% of validation splits, and lysophosphatidylcholines (LPCs), especially 1-palmitoyl-2-hydroxy-sn-glycero-3-phosphocholine (LPC [16:0]) with raA = −0.67 and an adjusted p = .005 in the 58% of validation splits, and LPC (0:0/18:0) with raA = −0.65 and an adjusted p = .008 in the 62% of validation splits (Figure 3B). For a complete overview of the results for all metabolites see Supplementary Table 3. Association of the Correlation Among Molecular Features With Age Starting from a total of n = 20 301 metabolites and lipids correlations, a total of cW = 2 (women) and cM = 4 (men) correlations among molecules result to be statistically significant (adjusted p ≤ .01 and absolute value of rcA ≥ 0.65, see eqn (6)) in more than 50% of splits after the validation method. In the women cohort, the correlations between phosphocholines (PCs), especially between (a) PC (28:2)–PC (32:1) with rcA = 0.69 and an adjusted p = .008 in the 57% of validation splits and (b) PC (32:1)–PC (35:3) | PE (38:3) with rcA = 0.72 and an adjusted p = .008 in the 54% of validation splits, tend to increase with age (Figure 4A). Figure 4. Open in new tabDownload slide Correlations between metabolites and lipids correlations and the average age of the 20 subject groups: women (A) and men (B). LPC = lysophosphatidylcholine; PC = phosphatidylcholine; PE = phosphatidylethanolamine; MAG = monoacylglycerol. See Figure 2 for an overview of the statistical procedure. In Figure 4B, correlations between MAG and LPC, especially (a) MAG (16:0)–LPC (16:1/0:0) with rcA = −0.81 and an adjusted p = .002 in the 53% of validation splits, (b) MAG (16:0)–LPC (0:0/16:1) with rcA= −0.80 and an adjusted p = .002 in the 59% of validation splits, (c) MAG (18:1)–LPC (16:1/0:0) with rcA = −0.78 and an adjusted p = .003 in the 60% of validation splits, and (d) MAG (18:0)–LPC (0:0/16:1) with rcA = −0.76 and an adjusted p = .004 in the 53% of validation splits, decrease with the average age of men-specific groups. The levels of these lipids vary in a similar fashion, decreasing with the age. For a complete overview of the results for all metabolites see Supplementary Table 3. Association of the Ratios Among Molecular Features With Age Alterations in the ratios between 2 single lipids and/or metabolites may point at perturbations in pathways relevant for a certain specific phenotype and they could influence the physiological course of aging. In this light, pairwise ratios may serve as potential biomarkers of the aging process (28,29). Starting from a total of n = 20 301 metabolites and lipids ratios, after the validation method, we found only qW =1 (women) and qM = 2 (men) ratios between molecules whose variation is significantly associated with the average age (adjusted p ≤ 0.01 and absolute value of rqA ≥ 0.65, see eqn (7)). In particular, the ratio between decanoyl-l-carnitine/LPC (0:0/18:2) with rqA = −0.67 and an adjusted p = .002 in the 56% of validation splits shows a negative association with the average age of the women cohort (Figure 5A). Figure 5. Open in new tabDownload slide Correlations between average metabolites and lipids ratios and the average age of the 20 subject groups: women (A) and men (B). GCA = glycocholic acid; PC = phosphatidylcholine; PE = phosphatidylethanolamine; MAG = monoacylglycerol. See Figure 2 for an overview of the statistical procedure. In Figure 5B, the ratios between l-carnitine/PC (37:5) with rqA = 0.85 and an adjusted p = 1 × 10−4 in the 55% of validation splits and l-acetylcarnitine/PC (37:5) with rqA = 0.85 and an adjusted p = 2 × 10−4 in the 51% of validation splits tend to be positively correlated with the average age of men-specific groups. For a complete overview of the results for all metabolites see Supplementary Table 3. Discussion To shed light on the molecular mechanisms possibly associated with age, we studied how the concentration, correlations, and ratios of and among circulating blood metabolites and lipids vary with subject age groups, considering men and women separately to highlight possible dependencies on sex. Using different approaches, in the original paper, Ganna et al. (17) demonstrated that LPC (18:1) and LPC (18:2) are not directly associated with coronary heart disease (CHD), but they found an age-dependent negative trend of these 2 lipids in association with CHD risk. Moreover, MAG (18:2) and sphingomyelin (28:1) have a positive correlation with the CHD risk. Our results support the usefulness of the metabolomic analysis conjugated with a system biology approach for the identification of age-related metabolites and their association patterns, providing additional information compared to what is already known from the literature. In women, the levels of carnitine, linoleic acid, and α-linoleic acid show a positive correlation with (group) age. These significant correlations are of particular interest because previous studies showed that the age-dependent carnitine serum levels increase more with age in adult women than men (30,31), and the endogenous biosynthesis of carnitine depends on the production, by lysosomal protein degradation, of trimethyl-lysine (32) whose homeostasis is regulated by dietary intake, intestinal absorption, and renal reabsorption. Carnitine also plays an important role in carnitine-shuttle biochemical reactions and in the energy pool metabolism, inducing an expression of intramitochondrial alterations (30,33), fundamental in linoleic acid metabolism. Previous studies report that the reduction of estrogens activity and the increase of testosterone levels induce modification of the rate of conversion of linoleic acid and α-linolenic acid into n − 3 long-chain polyunsaturated fatty acids, inducing changes in cell membrane composition and in cell cycle mechanisms (34,35). Endogenous biosynthesis of carnitine depends on the production, by lysosomal protein degradation, of trimethyl-lysine (32). The homeostasis of this molecule is regulated by dietary intake, intestinal absorption, and renal reabsorption. Carnitine also plays an important role in carnitine-shuttle biochemical reactions and in the energy pool metabolism, inducing an expression of intramitochondrial alterations (30,33), fundamental in linoleic acid metabolisms, whose activity shows age-dependent dysregulation (36). In addition to the role of polyunsaturated fatty acids as energy sources, they have several functions, as cellular signaling pathways (37) and as structural components of cell membranes (38), inducing age-dependent changes (39). The negative correlation of LPCs concentrations with age, molecularly associated with the reduction of MAGs levels by the MAG lipase enzyme activity (36,40), induces a skeletal muscle mitochondrial dysfunction (41); the decreasing of LPCs is, generally, also associated with the increase of body mass index (BMI) but, in an older population, this effect is associated, firstly, with the increasing of age-dependent inflammation, depending on an overall remodulation of cell membrane and mitochondrial dysfunction. Because the pairwise correlations among molecules can be used as a proxy to describe the underlying metabolic network (10), here we consider the correlations observed as the result of the combination of all reactions and regulatory processes occurring in the metabolic network (18,42) at a given age. In women, the correlations between PC (28:2)–PC (32:1) and PC (32:1)–PC(35:3) | PE(38:3) tend to increase with age. During the menopause period, a global dysregulation on liver enzymes is induced, causing the synthesis of PCs from choline (43,44). The interactions of PCs are associated with the remodulation of membranes integrity, promoting their conservation and directly affecting the membrane permeability, increasing the fluidity of the bilayer and protecting it from peroxidative damage (38,45), a frequent phenomenon in advanced age (46). Correlations between MAG (16:0)–LPC (16:1/0:0), MAG (16:0)–LPC (0:0/16:1), MAG (18:1)–LPC (16:1/0:0), and MAG (18:0)–LPC (0:0/16:1) decrease with age in men, and this has been related to the increase of the MAG lipase enzyme activity that determines the hydrolysis of MAG into glycerol and fatty acid alkyl ester (36,40) and to the impaired mitochondrial oxidative capacity associated with low levels of LPCs in advanced age (36,41). The alterations in the ratios between 2 single lipids and/or metabolites may point at perturbations in pathways relevant for a certain specific phenotype. We considered the pairwise ratios as potential biomarkers (28,29) of the aging process. We found that only the ratio between decanoyl-l-carnitine/LPC(0:0/18:2) shows a negative association with the average age in women, and, at best of our knowledge, this association has never been reported. We can speculate that decreasing levels of LPCs and the increasing levels of decanoyl-l-carnitine induce, synergistically, a mitochondrial dysfunction (36,41), contributing to age-dependent metabolic changes and being an indirect result of aging (47). In contrast, the ratios between l-carnitine/PC(37:5) and l-acetylcarnitine/PC(37:5) tend to be positively correlated with the average age of men-specific groups. Little is known about these molecular ratios. As said before, carnitine plays a role in carnitine-shuttle biochemical reactions: carnitine palmitoyltransferase 1 enzyme is involved in the reversible acylation of l-carnitine, producing l-acetylcarnitine, and this event is fundamental in fatty acid beta-oxidation, maintenance of acyl-coenzyme A pools, and energy metabolism (30). The carnitine-shuttle activity could generate a specific remodeling of mitochondrial fatty acids oxidation, promoting a modification in the mitochondrial membrane lipidome (48), increasing PCs fraction (49,50). Although, actually, the overall aging molecular mechanisms are unclear, our results show that lipids (ie, LPC, MAG, PC, PE, linoleic acid) and carnitine are fundamental in the age-related metabolic pathways. Strengths and Limitations One of the strengths of this study is a large number of patients with a very wide age range (47.6–93.9 years) whose metabolome was analyzed. We implemented a stringent validation of the results using a repeated data resampling to account for varaibility and to obtain robust estimate of metabolite concentrations, correlations, and ratios calculated at the age group level to eliminate subject-to-subject variability. One limitation of this study is the lack of availability of the clinical data (ie, BMI, waist circumference, systolic and diastolic blood pressures) associated with the participants’ metabolite data, publicly available on the MetaboLights public database, resulting in an incomplete representation of the pathophysiological conditions of the cohort, indicating that we could not correct at the individual level for such factors in the analysis. Conclusions In this study, we presented a comprehensive biology approach to highlight potential molecular features concentrations, associations, and ratios directly associated with the increase of the age of a sex- and age-matched population. We showed that linoleic acid, α-linoleic acid, and carnitine have, in the women cohort, a positive correlation trend with age, while MAGs and LPCs have, in the men cohort, a negative correlation trend with age. These results highlight, in women, the effect of the reduction of estrogens activity and the increase of testosterone levels on the linoleic acid metabolism and on the energy pool metabolism that induces the overall changes in cell membrane composition and cell cycle mechanisms. In men, low levels of LPCs concentrations are directly connected with the reduction of MAGs levels by the MAG lipase enzymatic activity that induces mitochondrial dysfunction. Analyzing the pairwise correlations among molecules, we observed that PCs/PCs correlations tend to have a positive trend associated with the average ages of women, while MAGs/LPCs correlations tend to have a negative trend associated with men average ages. These results, in both cases, suggest an age-dependent remodeling of fatty acid metabolism that induces, overall, remodeling of cell and mitochondrial membranes and modification in terms of fluidity of membranes bilayers. We studied the pairwise ratios as potential biomarkers of aging. In women, the decanoyl-l-carnitine/LPC ratio has a negative association with the increasing of the average ages, while in men the ratios between l-carnitine/PC and l-acetylcarnitine/PC have a positive association with the increase of age, suggesting, in both cases, a radical remodeling of the dynamic membrane fluidity and carnitine-shuttle activity. This study brings forward the concept that correlation and ratios among molecular features, and not only abundances along, could be used to investigate the dynamic of molecular mechanisms and their association with age. 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Herpich, Catrin; Kochlik, Bastian; Weber, Daniela; Ott, Christiane; Grune, Tilman; Norman, Kristina; Raupbach, Jana

doi: 10.1093/gerona/glab331pmid: 34726231

Abstract Dicarbonyl stress describes the increased formation of 1,2-dicarbonyl compounds and is associated with age-related pathologies. The role of dicarbonyl stress in healthy aging is poorly understood. In a preliminary study, we analyzed 1,2-dicarbonyl compounds, namely 3-deoxyglucosone (3-DG), glyoxal (GO), and methylglyoxal (MGO) in plasma of older (25 months, n = 11) and younger (5 months, n = 14) male C57BL/6J (B6) mice via ultra performance liquid chromatography tandem mass spectrometry. Postprandial 3-DG was higher in younger compared to older mice, whereas no differences were found for GO and MGO. Subsequently, in the main study, we analyzed fasting serum of older women (OW, 72.4 ± 6.14 years, n = 19) and younger women (YW, 27.0 ± 4.42 years, n = 19) as well as older men (OM, 74.3 ± 5.20 years, n = 15) and younger men (YM, 27.0 ± 3.34, n = 15). Serum glucose, insulin, 1,2-dicarbonyl concentrations, and markers of oxidative stress were quantified. In a subgroup of this cohort, an oral dextrose challenge was performed, and postprandial response of 1,2-dicarbonyl compounds, glucose, and insulin were measured. In women, there were no age differences regarding fasting 1,2-dicarbonyl concentrations nor the response after the oral dextrose challenge. In men, fasting MGO was significantly higher in OM compared to YM (median: 231 vs 158 nM, p = .006), whereas no age differences in fasting 3-DG and GO concentrations were found. Glucose (310 ± 71.8 vs 70.8 ± 11.9 min·mmol/L) and insulin (7 149 ± 1 249 vs 2 827 ± 493 min·µIU/mL) response were higher in OM compared to YM, which did not translate into a higher 1,2-dicarbonyl response in older individuals. Overall, aging does not necessarily result in dicarbonyl stress, indicating that strategies to cope with 1,2-dicarbonyl formation can remain intact. Aging, Fasting, Glyoxal, Methylglyoxal, Postprandial, 3-Deoxyglucosone Dicarbonyl stress is a consequence of mitochondrial dysfunction and cellular senescence, which are hallmarks of aging (1). The accumulation of reactive carbonyl compounds and oxygen species causes cellular damage, therefore contributing to the aging process (2). In vivo, the main source of 1,2-dicarbonyl compounds, such as 3-deoxyglucosone (3-DG), methylglyoxal (MGO), and glyoxal (GO), is the degradation of monosaccharides as well as triosephosphates (3). Only a minor amount is ingested with the diet (4,5). Fasting blood concentrations of 1,2-dicarbonyl compounds in healthy adults range between 100 nM and 1 µM (3,6). In normoglycemic participants, acute glucose ingestion resulted in increased plasma concentrations of 3-DG, GO, and MGO after 30 minutes due to their endogenous formation (7). Subsequently, glucose and 1,2-dicarbonyl concentrations return to fasting levels within 120 minutes after ingestion. However, participants with an impaired glucose metabolism or type 2 diabetes exhibit a delayed postprandial peak of 1,2-dicarbonyl compounds as well as a prolonged time until fasting concentrations are reached (7). The abnormal accumulation of 1,2-dicarbonyl compounds due to reduced degradation or increased formation is termed dicarbonyl stress. Naturally, the body possesses defense systems to maintain 1,2-dicarbonyl concentrations at low levels. 3-DG can be reduced to 3-deoxyfructose via aldose and aldehyde reductase or oxidized to 3-deoxy-2-ketogluconate via aldehyde or 2-oxoaldehyde dehydrogenase (3,8–10). MGO and GO are mainly metabolized via the glyoxalase system, which consists of the enzymes glyoxalase 1 and 2 (Glo1, Glo2) as well as glutathione (3). Glo1 activity is known to decrease with age (11). When 1,2-dicarbonyls are chronically elevated, the reactive carbonyl groups can irreversibly modify proteins via nonenzymatic glycation, resulting in the formation of advanced glycation end products and their pathophysiological consequences (12). Associations of dicarbonyl stress and various age-related pathologies such as diabetes (7), obesity (13), cancer (14), and cognitive decline (15) have been found. Furthermore, dicarbonyl stress is associated with oxidative stress and vice versa (3). Because current studies analyzed 1,2-dicarbonyl compounds in samples of older participants with underlying pathologies (16,17), it is difficult to derive the role of dicarbonyl compounds in older individuals without severe health impairments. In a preliminary experiment, we examined postprandial 1,2-dicarbonyl concentrations in male mice of different ages. Subsequently, we analyzed fasting 1,2-dicarbonyl concentrations, markers of glucose metabolism, as well as oxidative stress in older and younger adults. Furthermore, a subgroup of the human cohort was subjected to a dextrose challenge to investigate postprandial 1,2-dicarbonyl response. We hypothesized that fasting concentrations and response to a dextrose challenge of 3-DG, GO, and MGO were different: (a) in older age due to the age-related activity decline of detoxification enzymes and (b) between women and men as there are sex-specific responses to oxidative stress and Glo1 activity differences described (18,19). Method Animal Experimental Procedure Male C57Bl/6J (B6, Janvier’s Labs: CS 4105 Le Genest St Isle, 53941 Saint Berthevin Cedex, France) were housed in open cages of 4 to 5 animals in a controlled environment (20 ± 2°C, 12/ 12 hours light/dark cycle) with ad libitum access to a standard diet (SD; V1534-300 Ssniff, Soest, Germany) and water. At an age of 5 months (younger group) and 25 months (older group), body weight was measured and mice were subsequently sacrificed by acute isoflurane exposure. Blood samples were taken and blood glucose was immediately analyzed by using a Contour XT glucometer (Bayer, Leverkusen, Germany). Subsequently, blood was centrifuged for 5 minutes, 13 000×g, and plasma samples were stored at −80°C until analysis of 1,2-dicarbonyl compounds. Blood samples were taken in the morning between 7 and 10 am. Because mice are nocturnal animals and were not fastened before sacrifice, the morning blood drawing was considered to be postprandial. All mice were kept in agreement with the National Institutes of Health guidelines for care and use of laboratory animals and with the guidelines of the German Law on the Protection of Animals. Final organ removal and blood collection were approved by the local authorities. Study Population This is a secondary analysis of a larger study which is described in detail elsewhere (20). In brief, the study was performed in community-dwelling older (65–85 years) and younger adults (18–35 years). Exclusion criteria were type 1 and 2 diabetes, a stroke or heart attack in the past 6 months, food allergies and intolerances, pregnancy, or any severe or malignant disease. The study was approved by the ethics committee of the University of Potsdam and registered at drks.de as DRKS00017090. All participants signed a written informed consent. In this study, 19 older (OW) and younger women (YW), as well as 15 older (OM) and younger men (YM), were included in the analyses. From these participants, blood samples after an oral dextrose challenge were available in n = 14 OW, n = 10 YW, n = 5 OM, and n = 5 YM. Study Protocol Participants were instructed to refrain from vigorous exercise and alcohol on the day prior to the study. After an overnight fast, participants arrived between 07:30 and 08:15 am at the study facility; body weight and height were measured. A cannula was then inserted into an antecubital vein for repeated blood sampling. After taking a blood sample in the fasted state, a subgroup of participants received a dextrose drink consisting of 50 g dextrose in 300 mL water that had to be consumed within 15 minutes. Blood samples were taken 15, 30, 60, 120, and 240 minutes after ingestion. During this time, participants were allowed to drink water ad libitum. Blood serum and EDTA plasma were obtained and stored at −80°C until analysis. Measurement of Blood Parameters of the Glucose Metabolism Serum insulin (intraassay coefficient of variance: 4.8–6.0%, interassay coefficient of variance: 8.1–9.0%; BioVendor, Brno, Czech Republic) was quantified using an immunosorbent assay. Furthermore, serum glucose was measured using a colorimetric method (ABX Pentra 400; Horiba, Ltd, Montpellier, France). Homeostasis model assessment (19) was used to estimate insulin resistance (HOMA-IR). Analysis of 1,2-Dicarbonyl Compounds Serum 1,2-dicarbonyl concentrations were quantified after derivatization with o-phenylenediamine (oPD) using ultra performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) (6). In brief, 25 µL serum samples were mixed with 75 µL oPD solution (10 mg oPD in 10 mL 1.6 mol/L perchloric acid) for derivatization of 3-DG, MGO, and GO in the corresponding quinoxalines. The samples were kept in the dark for 20 hours. Optimization of derivatization procedure and information regarding the stability of 1,2-dicarbonyl compounds are described by Scheijen et al. (6). After incubation, 10 µL of an internal standard solution containing isotope-labeled quinoxalines was added. Internal standard solution was prepared according to Scheijen et al. (6). After centrifugation (10 000 rpm, 10 min), an aliquot of 80 µL was transferred to an high performance liquid chromatography (HPLC) vial and 5 µL was subjected to UPLC-MS/MS analysis. An Acquity I-class system coupled to a Xevo TQ-XS mass spectrometer (both Waters Corporation, Milford, MA) was used for analysis. For chromatographic separation, a Cortecs Solid Core C18 column (2.1 × 50 mm, 1.6 μm) at a column temperature of 40°C was used. Solvent A was 0.2% formic acid in LC-MS grade water and solvent B was LC-MS grade ACN. The solvents were pumped at a flow rate of 0.6 mL/min in gradient mode (0 min, 1% B; 1 min, 1% B; 6 min, 60% B; 6.1 min, 1% B; 8 min, 1% B). The injection volume was 5 μL. The ESI source was operated in positive mode, and nitrogen was utilized as the nebulizing gas with a gas flow of 650 L/h and a gas temperature of 350°C. The capillary voltage was set to 2.7 kV and the source temperature was 150°C. Analytes were measured in MRM mode with the following transitions used for quantification and optimized collision energies (CEs) and cone voltages (CVs). MGO: 145.1 → 77.1 (CV 24, CE 25 V), d4-MGO: 149.1 → 81.1 (CV 24, CE 20 V), 3-DG: 235.1 → 171.1 (CV 22, CE 18 V), d4-3-DG: 239.1 → 175.1 (CV 20, CE 18 V), GO: 131.1 → 77.1 (CV 20, CE 22 V), d4-GO: 135.1 → 81.1 (CV 20, CE 22 V). Data were acquired and evaluated with the MassLynx Software (Waters, version 4.2). A 6-point calibration curve was prepared for 3-DG (2 500 – 0 nmol/L), GO (1 600 – 0 nmol/L), and MGO (1 300 – 0 nmol/L). Calibration curves were linear over the respective concentration ranges (r2 = 0.99) in water and serum. Interassay variation was determined using 2 calibration standards that were analyzed over 8 months. Interassay variation was 15.5%, 7.4%, and 12.1% for 3-DG, GO, and MGO, respectively. Intraassay variation was determined by replicate analysis of a pooled serum sample (n = 7) and was 9.0%, 3.9%, and 6.4% for 3-DG, GO, and MGO, respectively. Limit of detection (LOD) and limit of quantification (LOQ) were calculated on the basis of the signal-to-noise ratio (LOD s/N = 3 and LOQ s/N = 10). The LOD for 3-DG, GO, and MGO was 0.04, 0.41, and 0.13 nM, which corresponds to 0.2, 2.0, and 0.7 fmol on the column. The LOQ for 3-DG, GO, and MGO was 0.15, 1.36, and 0.44 nM, which corresponds to 0.7, 6.8, and 2.2 fmol on the column. For recovery analysis, 2 levels of 1,2-dicarbonyl compounds were added to a pooled serum sample and analyzed in triplicate. Concentrations 1 and 2 for addition were 1 465 and 586 nM for 3-DG, 870 and 174 nM for GO, and 692 and 138 nM for MGO. Recovery of 3-DG was 108.3% and 98.0%, for GO 89.4% and 87.3%, and for MGO 109.8% and 89.9% for concentrations 1 and 2, respectively. Measurement of Oxidative Stress Markers As markers of oxidative stress, protein carbonyls (PC), 3-nitrotyrosine (3-NT), and malondialdehyde (MDA) were quantified in plasma samples. PC and 3-NT were measured using an in-house ELISA as previously described (21). Plasma MDA concentrations were determined after protein precipitation and derivatization with thiobarbituric acid by reversed-phase HPLC coupled with fluorescence detection as previously described by Weber et al. (21) with a Reprosil Pur 120 C18 AQ column (250 × 4.6 mm; 5 μm, Dr. Maisch GmbH, Ammerbuch, Germany). Statistical Analysis Statistical analyses were performed with SPSS (IBM version 25; SPSS Inc., Chicago, IL) and GraphPad Prism (version 8.00 for Windows; GraphPad Software, La Jolla, CA). When necessary, variables were log-transformed to achieve normal distribution. Mann–Whitney U test and Student’s t-test were used accordingly to assess age and sex differences. Fasting concentrations are shown as median (interquartile range [IQR]). Violin plots present median and the upper and lower quartile. To evaluate changes in 1,2-dicarbonyl compound concentrations over time, repeated measures analysis of variance was performed. Postprandial response was represented by the positive incremental area under the curve (iAUC), which was calculated using the trapezoid rule and considering only values above the baseline. Here data are shown as mean ± SD. Possible associations between 1,2-dicarbonyl response, glucose, and insulin are shown with Pearson’s correlations coefficients. Results Postprandial 1,2-Dicarbonyl Concentrations in Mice Mice were fed a standard diet for 5 and 25 months before sacrifice. Blood glucose concentrations and body weight were not significantly different between the age groups (data not shown). Postprandial 3-DG concentrations of younger mice ranged between 1 400 and 2 100 nM and were significantly higher than concentrations of older mice, which ranged between 1 200 and 1 800 nM (Figure 1). Postprandial GO and MGO concentrations were between 400–1 100 nM and 300–1 200 nM, respectively, and no age differences were found. Figure 1. Open in new tabDownload slide Postprandial 1,2-dicarbonyl compound concentrations in older (25 months) and younger (5 months) male mice. Data are shown as Violin plots; dotted lines represent median and quartiles. Student’s t-test for normally distributed data and Mann–Whitney U test for nonnormally distributed data were used to assess differences between age groups. 25 months old (n = 11), 5 months old (n = 14). 3-DG = 3-deoxyglucosone; GO = glyoxal; MGO = methylglyoxal; 25 M = 25 months old; 5 M = 5 months old; p < .05. Figure 1. Open in new tabDownload slide Postprandial 1,2-dicarbonyl compound concentrations in older (25 months) and younger (5 months) male mice. Data are shown as Violin plots; dotted lines represent median and quartiles. Student’s t-test for normally distributed data and Mann–Whitney U test for nonnormally distributed data were used to assess differences between age groups. 25 months old (n = 11), 5 months old (n = 14). 3-DG = 3-deoxyglucosone; GO = glyoxal; MGO = methylglyoxal; 25 M = 25 months old; 5 M = 5 months old; p < .05. Participants Characteristics Younger adults were overall healthy without any apparent disease. In older adults, high blood pressure was most prevalent (n = 15), followed by dyslipidemia (n = 6) and thyroid-associated diseases (n = 5). A basic description of the study cohort is displayed in Table 1. For both sexes, older adults had significantly higher HOMA-IR values and MDA concentrations compared to younger adults. Regarding sex differences, women had a significantly lower body mass index (BMI) than men in both age groups. Fasting concentrations of oxidative stress markers were similar for older women and older men. Younger women had significantly lower PC concentrations than younger men. Table 1. Baseline Participants Characteristics . YW (n = 19) . OW (n = 19) . YM (n = 15) . OM (n = 15) . p * . p † . p ‡ . p § . Age (years) 27.0 ± 4.42 72.4 ± 6.14 27.0 ± 3.34 74.3 ± 5.20 .348 .964 BMI (kg/m²) 22.3 ± 2.64 23.9 ± 2.93 24.9 ± 3.68 26.3 ± 3.78 .089 .325 .041 .019 HOMA-IR 1.92 ± 0.45 2.40 ± 0.88 2.11 ± 0.53 3.14 ± 1.46 .025 .029 .120 .319 PC (nmol/mg)‖ 1.03 ± 0.32 1.16 ± 0.29 1.49 ± 0.49 1.30 ± 0.54 .123 .367 .837 .008 3-NT (nmol/mg)‖ 1.99 ± 1.40 2.15 ± 2.01 2.72 ± 2.49 2.77 ± 2.35 .729 .744 .515 .607 MDA (µmol/L)‖ 0.69 ± 0.35 0.98 ± 0.44 0.60 ± 0.25 1.03 ± 0.48 .009 .002 .811 .607 Glucose (mmol/L) 4.42 ± 0.37 5.15 ± 0.51 4.74 ± 0.30 5.67 ± 0.87 <.001 .001 .039 .012 Insulin (µIU/mL)‖ 9.71 ± 1.85 10.4 ± 3.29 10.0 ± 2.45 12.1 ± 4.05 .885 .412 .271 .758 . YW (n = 19) . OW (n = 19) . YM (n = 15) . OM (n = 15) . p * . p † . p ‡ . p § . Age (years) 27.0 ± 4.42 72.4 ± 6.14 27.0 ± 3.34 74.3 ± 5.20 .348 .964 BMI (kg/m²) 22.3 ± 2.64 23.9 ± 2.93 24.9 ± 3.68 26.3 ± 3.78 .089 .325 .041 .019 HOMA-IR 1.92 ± 0.45 2.40 ± 0.88 2.11 ± 0.53 3.14 ± 1.46 .025 .029 .120 .319 PC (nmol/mg)‖ 1.03 ± 0.32 1.16 ± 0.29 1.49 ± 0.49 1.30 ± 0.54 .123 .367 .837 .008 3-NT (nmol/mg)‖ 1.99 ± 1.40 2.15 ± 2.01 2.72 ± 2.49 2.77 ± 2.35 .729 .744 .515 .607 MDA (µmol/L)‖ 0.69 ± 0.35 0.98 ± 0.44 0.60 ± 0.25 1.03 ± 0.48 .009 .002 .811 .607 Glucose (mmol/L) 4.42 ± 0.37 5.15 ± 0.51 4.74 ± 0.30 5.67 ± 0.87 <.001 .001 .039 .012 Insulin (µIU/mL)‖ 9.71 ± 1.85 10.4 ± 3.29 10.0 ± 2.45 12.1 ± 4.05 .885 .412 .271 .758 Notes: YW = younger women; OW = older women; YM = younger men; OM = older men; BMI = body mass index; HOMA-IR = homeostasis model assessment—insulin resistance; PC = protein carbonyls; 3-NT = 3-nitrotyrosine; MDA = malondialdehyde. All data are shown as mean ± SD. Age and sex differences were calculated using Student’s t-test, unless otherwise indicated. *Age difference between women. †Age differences between men. ‡Sex difference between older adults. §Sex differences between younger adults. ‖Mann–Whitney U test. p < .05. Open in new tab Table 1. Baseline Participants Characteristics . YW (n = 19) . OW (n = 19) . YM (n = 15) . OM (n = 15) . p * . p † . p ‡ . p § . Age (years) 27.0 ± 4.42 72.4 ± 6.14 27.0 ± 3.34 74.3 ± 5.20 .348 .964 BMI (kg/m²) 22.3 ± 2.64 23.9 ± 2.93 24.9 ± 3.68 26.3 ± 3.78 .089 .325 .041 .019 HOMA-IR 1.92 ± 0.45 2.40 ± 0.88 2.11 ± 0.53 3.14 ± 1.46 .025 .029 .120 .319 PC (nmol/mg)‖ 1.03 ± 0.32 1.16 ± 0.29 1.49 ± 0.49 1.30 ± 0.54 .123 .367 .837 .008 3-NT (nmol/mg)‖ 1.99 ± 1.40 2.15 ± 2.01 2.72 ± 2.49 2.77 ± 2.35 .729 .744 .515 .607 MDA (µmol/L)‖ 0.69 ± 0.35 0.98 ± 0.44 0.60 ± 0.25 1.03 ± 0.48 .009 .002 .811 .607 Glucose (mmol/L) 4.42 ± 0.37 5.15 ± 0.51 4.74 ± 0.30 5.67 ± 0.87 <.001 .001 .039 .012 Insulin (µIU/mL)‖ 9.71 ± 1.85 10.4 ± 3.29 10.0 ± 2.45 12.1 ± 4.05 .885 .412 .271 .758 . YW (n = 19) . OW (n = 19) . YM (n = 15) . OM (n = 15) . p * . p † . p ‡ . p § . Age (years) 27.0 ± 4.42 72.4 ± 6.14 27.0 ± 3.34 74.3 ± 5.20 .348 .964 BMI (kg/m²) 22.3 ± 2.64 23.9 ± 2.93 24.9 ± 3.68 26.3 ± 3.78 .089 .325 .041 .019 HOMA-IR 1.92 ± 0.45 2.40 ± 0.88 2.11 ± 0.53 3.14 ± 1.46 .025 .029 .120 .319 PC (nmol/mg)‖ 1.03 ± 0.32 1.16 ± 0.29 1.49 ± 0.49 1.30 ± 0.54 .123 .367 .837 .008 3-NT (nmol/mg)‖ 1.99 ± 1.40 2.15 ± 2.01 2.72 ± 2.49 2.77 ± 2.35 .729 .744 .515 .607 MDA (µmol/L)‖ 0.69 ± 0.35 0.98 ± 0.44 0.60 ± 0.25 1.03 ± 0.48 .009 .002 .811 .607 Glucose (mmol/L) 4.42 ± 0.37 5.15 ± 0.51 4.74 ± 0.30 5.67 ± 0.87 <.001 .001 .039 .012 Insulin (µIU/mL)‖ 9.71 ± 1.85 10.4 ± 3.29 10.0 ± 2.45 12.1 ± 4.05 .885 .412 .271 .758 Notes: YW = younger women; OW = older women; YM = younger men; OM = older men; BMI = body mass index; HOMA-IR = homeostasis model assessment—insulin resistance; PC = protein carbonyls; 3-NT = 3-nitrotyrosine; MDA = malondialdehyde. All data are shown as mean ± SD. Age and sex differences were calculated using Student’s t-test, unless otherwise indicated. *Age difference between women. †Age differences between men. ‡Sex difference between older adults. §Sex differences between younger adults. ‖Mann–Whitney U test. p < .05. Open in new tab Fasting 1,2-Dicarbonyl, Glucose, and Insulin Concentrations in Human Participants Overall, the median concentrations of 3-DG were 853 nM (IQR: 608 nM), 385 nM (IQR: 360 nM) for GO and 204 nM (IQR: 141 nM) for MGO in all participants. In men and women, fasting glucose concentrations were significantly higher in older age (Table 1). Fasting insulin concentrations were similar between age groups in both men and women. In women, 3-DG, MGO, or GO did not differ between age groups (Figure 2A). In men, only fasting MGO concentrations were significantly higher in older compared to younger individuals (Figure 2B). Figure 2. Open in new tabDownload slide Fasting 1,2-dicarbonyl compound concentrations in older and younger women (A) and men (B). Data are shown as Violin plots; dotted lines represent median and quartiles. Student’s t-test for normally distributed data and Mann–Whitney U test for nonnormally distributed data were used to assess differences between age groups. YW (n = 19), OW (n = 19), YM (n = 15), OM (n = 15). 3-DG = 3-deoxyglucosone; GO = glyoxal; MGO = methylglyoxal; YW = younger women; OW = older women; YM = younger men; OM = older men; p < .05. Figure 2. Open in new tabDownload slide Fasting 1,2-dicarbonyl compound concentrations in older and younger women (A) and men (B). Data are shown as Violin plots; dotted lines represent median and quartiles. Student’s t-test for normally distributed data and Mann–Whitney U test for nonnormally distributed data were used to assess differences between age groups. YW (n = 19), OW (n = 19), YM (n = 15), OM (n = 15). 3-DG = 3-deoxyglucosone; GO = glyoxal; MGO = methylglyoxal; YW = younger women; OW = older women; YM = younger men; OM = older men; p < .05. The evaluation of sex differences revealed that fasting glucose concentrations were significantly higher in men when compared to women (Table 1). Regarding fasting 1,2-dicarbonyl concentrations, fasting GO concentrations were significantly lower in older women compared to older men (349 nM, IQR: 406 nM vs 556 nM, IQR: 654 nM; p = .048). Glucose, Insulin, and 1,2-Dicarbonyl Response to Dextrose Challenge in Human Participants Participants’ characteristics of the oral dextrose challenge are given in Supplementary Table 1. Postprandial 1,2-dicarbonyl concentrations and response (iAUC) following dextrose ingestion are displayed in Figure 3. 1,2-dicarbonyl concentrations significantly changed over time in both sexes, with the exception of GO in men. In women, postprandial 1,2-dicarbonyl concentrations and response were not different between age groups. In men, postprandial 3-DG concentrations were higher in older compared to younger men at 15, 30, and 60 minutes. Furthermore, MGO concentrations were also higher in older compared to younger men at 30 and 60 minutes. However, this did not translate into a significantly higher response in older men. Glucose and insulin response were also not different in women, whereas, in men, older men had a significantly higher glucose iAUC (OM: 310 ± 160 min·mmol/L vs YM: 70.8 ± 26.7 min·mmol/L) as well as insulin iAUC (OM: 7 149 ± 2 792 min·µIU/mL vs YM: 2 827 ± 1 102 min·µIU/mL) compared to younger men. In addition, glucose response was positively associated with 3-DG as well as GO response (Figure 4A and B), but only in men. Moreover, higher insulin response was associated with higher MGO response (Figure 4C). Regarding age differences, glucose response was positively associated with 3-DG response (r = 0.778, p < .001) in older adults, whereas in younger adults, higher insulin response was positively correlated with 3-DG response (r = 0.639, p = .010). Figure 3. Open in new tabDownload slide Postprandial 1,2-dicarbonyl concentrations and response (iAUC) to a dextrose challenge in older and younger women and men. Data are shown as mean ± SD. Repeated measures analysis of variance was used to examine changes over time and differences between age groups, and Mann–Whitney U test was used to assess age differences of the postprandial response (iAUC). *Significantly different between age groups. YW (n = 19), OW (n = 19), YM (n = 15), OM (n = 15). iAUC = incremental area under the curve; 3-DG = 3-deoxyglucosone; GO = glyoxal; MGO = methylglyoxal; YW = younger women; OW = older women; YM = younger men; OM = older men; p < .05. Figure 3. Open in new tabDownload slide Postprandial 1,2-dicarbonyl concentrations and response (iAUC) to a dextrose challenge in older and younger women and men. Data are shown as mean ± SD. Repeated measures analysis of variance was used to examine changes over time and differences between age groups, and Mann–Whitney U test was used to assess age differences of the postprandial response (iAUC). *Significantly different between age groups. YW (n = 19), OW (n = 19), YM (n = 15), OM (n = 15). iAUC = incremental area under the curve; 3-DG = 3-deoxyglucosone; GO = glyoxal; MGO = methylglyoxal; YW = younger women; OW = older women; YM = younger men; OM = older men; p < .05. Figure 4. Open in new tabDownload slide Associations of glucose response with 3-DG (A) and GO (B) response as well as insulin response with MGO response (C) to a dextrose challenge. (A) No significant correlation in women, men: r = 0.782, p = .008. (B) No significant correlation in women, men: r = 0.707, p = .022. (C) No significant correlation in women, men: r = 0.784, p = .007. YW (n = 19), OW (n = 19), YM (n = 15), OM (n = 15). iAUC = incremental area under the curve; 3-DG = 3-deoxyglucosone; GO = glyoxal; MGO = methylglyoxal; YW = younger women; OW = older women; YM = younger men; OM = older men; p < .05. Figure 4. Open in new tabDownload slide Associations of glucose response with 3-DG (A) and GO (B) response as well as insulin response with MGO response (C) to a dextrose challenge. (A) No significant correlation in women, men: r = 0.782, p = .008. (B) No significant correlation in women, men: r = 0.707, p = .022. (C) No significant correlation in women, men: r = 0.784, p = .007. YW (n = 19), OW (n = 19), YM (n = 15), OM (n = 15). iAUC = incremental area under the curve; 3-DG = 3-deoxyglucosone; GO = glyoxal; MGO = methylglyoxal; YW = younger women; OW = older women; YM = younger men; OM = older men; p < .05. Discussion It has been postulated previously that dicarbonyl stress contributes to the aging phenotype (22). So far, little is known about the role of dicarbonyl compounds in older individuals without underlying pathologies and also whether concentrations of 1,2-dicarbonyl compounds exhibit sex differences. In a preliminary experiment, we analyzed concentrations of the reactive 1,2-dicarbonyl compounds 3-DG, MGO, and GO in male C57Bl/6J mice. Our main study included older and younger men and women. The age of the mice was comparable to the demographics of our human cohort as 25 months are equal to 70 human years and 5 months to 25 human years (23). In contrast to our hypothesis that aging increases plasma 1,2-dicarbonyl concentrations due to cellular imbalance of formation and degradation of reactive carbonyl species, GO and MGO concentrations did not differ between older and younger mice. Moreover, plasma 3-DG concentrations were even higher in younger compared to older mice. In our human study, older women did not have higher concentrations of 1,2-dicarbonyl compounds compared to the younger control group. The same was valid for plasma concentrations of 3-DG and GO, but not MGO, in men. During an oral dextrose challenge, older women had no increased response in 1,2-dicarbonyl compounds, whereas in older men it appeared to be slightly elevated. Recently, the influence of long-term intake of MGO on its urinary excretion and plasma concentrations was investigated in nonfasted C57BL/6N mice aged 6–24 months (24). Neither the experimental nor the control group without MGO intake showed changes in plasma MGO levels in older age. For plasma GO, a slight decrease over time was shown (24). Our results confirm that in healthy mice, 1,2-dicarbonyl compounds are not higher in older age and, as shown for 3-DG, are even lower. Lower concentrations of 1,2-dicarbonyl compounds in older mice might be due to changes in energy regulation or decreased absorption of macronutrients during aging (25). In our preliminary experiment, 1,2-dicarbonyl analysis was performed postprandially in mice. Thus, an influence of the diet on the results cannot be excluded. Mice are model organisms living in a strictly controlled environment with standardized diets, and factors affecting human metabolism and aging are more diverse. To examine whether the absence of higher 1,2-dicarbonyl compounds in older age can be confirmed in humans, we analyzed fasting serum samples of older and younger men and women in our main study. In women, fasting glucose and MDA concentrations were higher in older adults, but no age differences for 1,2-dicarbonyl compounds were observed. For men, again higher fasting glucose and MDA concentrations, as well as higher fasting MGO, were found in older adults. The higher concentrations of glucose (26) and the oxidative stress marker MDA (27) in older adults were reported previously and indicate cellular dysfunction as well as increased glucose intolerance. Recently, it has been shown that the activity of the glyoxalase system, which is the main detoxification route of MGO, decreases in the human eye lens in older age (28). The authors did not find sex-specific differences between the lenses of men and women. The analysis of glyoxalase activity might provide more insight into the mechanism of increased MGO concentrations in older men, but this was unfortunately not feasible in plasma nor serum samples in our study. Hence, whether the higher MGO in older men is due to decreased glyoxalase activity needs to be further elucidated. It has been shown in Glo1 knock-out cells that a compensatory detoxification can be achieved through aldose reductase (29). Whether this mechanism plays a role in the detoxification in humans has yet to be confirmed. Very recently it was postulated that protein deglycase DJ-1 can also act as a glyoxalase, thus contributing to MGO detoxification (30). Due to the various detoxification routes of MGO, there are different possibilities of how the metabolism might adjust to increased dicarbonyl concentrations during aging. Whether these enzymatic systems possess sex-specific differences is unknown. Our results of fasting concentrations in older and younger women and men indicate that older adults, despite having higher MDA and blood glucose levels, do not generally show higher plasma 1,2-dicarbonyl compounds. The nonenzymatic formation of 1,2-dicarbonyl compounds is a constant process in the presence of glucose. However, when high loads of glucose are present, for example, after food intake, higher amounts of 1,2-dicarbonyl compounds are formed. Therefore, we analyzed the 1,2-dicarbonyl compound response of older and younger human participants to an oral dextrose challenge. Interestingly, the response of plasma glucose and insulin in older women was not significantly higher compared to younger women. Accordingly, 1,2-dicarbonyl response was not higher in older women. It has been postulated that aging itself does not solely account for glucose intolerance, but that exercise and body fat mass play an important role (31). Although our cohort was matched for BMI, we cannot exclude that some of the women participating in the dextrose challenge were particularly physically active resulting in a lower 1,2-dicarbonyl response. In men, we observed a higher glucose and insulin response after the dextrose challenge in older compared to younger men. Although this did not translate into a significantly increased dicarbonyl response in older men, concentrations of 1,2-dicarbonyl compounds tended to be higher in older compared to younger men. As the number of older men performing the dextrose challenge was low (n = 5), this has to be interpreted carefully. Furthermore, these older men exhibited higher insulin resistance, estimated by HOMA-IR, than younger men, which potentially explains the higher glucose and insulin response (Supplementary Table 1). Glucose response of older adults was positively associated with 3-DG response but not with GO and MGO response. This indicates that 3-DG formation and degradation in older individuals might not be as well-regulated as for MGO and GO. In contrast to 3-DG, GO and MGO are degraded by multiple detoxification systems as mentioned above. This could be due to the lower reactivity of 3-DG toward proteins and thus lower risk of glycation reactions compared to MGO (32,33). Moreover, in men, glucose response is positively correlated with 3-DG and GO response, which was not found in women. Potentially, the male detoxification system is more vulnerable to the aging process, which is seen in a more pronounced reduction of Glo1 activity in older men compared with older women (18). For aldose and aldehyde reductase, the main 3-DG degrading enzymes, no such studies are available. Overall, we showed that although it is known that glucose tolerance decreases with age (34), this does not necessarily affect 1,2-dicarbonyl compound levels. In the present study, the sample size with n < 20 per group is small, especially regarding the dextrose challenge. This limits the possibilities of statistical analyses. We are aware of the limitations of this study; nevertheless, our results indicate that 1,2-dicarbonyl formation and degradation routes might be differently affected in older men and women. The sex-specific differences in plasma 1,2-dicarbonyl concentrations in older age have not been reported before and provide a basis for further studies of glycation and aging research. Overall, our results suggest that aging in the absence of severe health impairments does not necessarily result in dicarbonyl stress, indicating that endogenous strategies to cope with 1,2-dicarbonyl formation may be unaffected by aging. The results help understand the impact of glycation during aging and present new perspectives regarding the process of healthy aging. Acknowledgments The authors would like to thank Andrea Katschak (German Institute of Human Nutrition, Potsdam-Rehbrücke, Department of Nutrition and Gerontology) for supporting the human study and analytical work, as well as Stefanie Deubel (German Institute of Human Nutrition, Potsdam-Rehbrücke, Department of Molecular Toxicology) for supporting the animal work. Funding This work was supported by the Deutsche Forschungsgemeinschaft (RA 3524/2-1 to J.R.). Conflict of Interest None declared. 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This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact [email protected] © The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America.

Finesso, Giovanni E; McDevitt, Ross A; Roy, Roshni; Brinster, Lauren R; Di Francesco, Andrea; Meade, Theresa; de Cabo, Rafael; Ferrucci, Luigi; Perdue, Kathy A

doi: 10.1093/gerona/glab328pmid: 34718551

Abstract Age-dependent differences in methylation at specific cytosine–guanine (CpG) sites have been used in “epigenetic clock” formulas to predict age. Deviations of epigenetic age from chronological age are informative of health status and are associated with adverse health outcomes, including mortality. In most cases, epigenetic clocks are performed on methylation from DNA extracted from circulating blood cells. However, the effect of neoplastic cells in the circulation on estimation and interpretation of epigenetic clocks is not well understood. Here, we explored this using Fischer 344 (F344) rats, a strain that often develops large granular lymphocyte leukemia (LGLL). We found clear histological markers of LGLL pathology in the spleens and livers of 27 out of 61 rats aged 17–27 months. We assessed DNA methylation by reduced representation bisulfite sequencing with coverage of 3 million cytosine residues. Although LGLL broadly increased DNA methylation variability, it did not change epigenetic aging. Despite this, the inclusion of rats with LGLL in clock training sets significantly altered predictor selection probability at 83 of 121 commonly utilized CpG sites. Furthermore, models trained on rat samples that included individuals with LGLL had greater absolute age error than those trained exclusively rats free of LGLL (39% increase; p < .0001). We conclude that the epigenetic signals for aging and LGLL are distinct, such that LGLL assessment is not necessary for valid measures of epigenetic age in F344 rats. The precision and architecture of constructed epigenetic clock formulas, however, can be influenced by the presence of neoplastic hematopoietic cells in training set populations. Aging, Cancer, Mononuclear cell leukemia Alterations in epigenetic modifications of DNA are a hallmark of aging. The age-dependent divergence in methylation profiles of monozygotic twins suggests that external lifestyle factors and/or stochastic internal processes contribute to lifelong drift in DNA methylation (1). Large-scale interrogation of methylation at individual cytosine residues has identified sites where percent methylation is strongly correlated with age (2,3). These findings led to the development of “epigenetic clocks” that accurately estimate age across multiple tissues using weighted averages of methylation at a selected group of cytosine–guanine (CpG) sites (4,5). It has been argued that individuals with a methylation signature “older” than chronological age are experiencing accelerated aging. Accelerated methylation age has been observed in Werner’s syndrome (6) and HIV infection (7) and has been associated with mortality risk in the general population (8). Another line of epigenetic clock research has assessed genetic and genomic properties of the specific CpG sites composing clock formulas to gain insights into the molecular mechanisms of aging (5,9–11). Most epigenetic clocks were developed using whole-blood DNA. However, because white blood cell composition changes systematically with age and may be affected by acute and chronic diseases (12,13), this raises the possibility that changes attributed to intrinsic cellular aging may be due to different cell composition in the source material. Blood-related diseases such as leukemias, lymphomas, or myelomas, which are characterized by dramatic changes in composition and epigenetic status of white blood cells and the presence of abnormal circulating cells (14), may be particularly disruptive to epigenetic clock modeling. The inclusion of individuals with hematopoietic malignancies within training data could affect subsequently generated epigenetic clock formulas. The few studies applying previously generated epigenetic clock formulas to test populations with hematopoietic malignancies have yielded discordant results: Age acceleration was observed with B-cell lymphoma (15) but not with acute myeloid leukemia (16). Rodents provide an attractive model system for aging studies, as their shorter life spans allow for aging studies to be conducted on rapid time scales. Accordingly, epigenetic clocks have been developed in mice and rats and used to track interventions that shorten or prolong life span (17–19). Furthermore, the investigation into genetic and genomic architectures of these clocks has provided insight into mechanisms underlying the aging process (9,18,20). Initial rodent epigenetic clocks were developed in mice, which are extensively used for studies involving genetic manipulation. However, rats confer several advantages over mice for longitudinal aging studies. Their larger body size allows for sufficient volumes of blood to be drawn repeatedly, as well as better spatial resolution of the brain and heart tissue during in vivo imaging (21). Additionally, rats offer a well-established model for cognitive aging that mimics individual differences seen in humans (22). A potential bias in rodent studies using blood-based epigenetic clock assessment is that laboratory rodents are frequently affected by hematopoietic malignancies. Indeed, hematological cancers are the most frequent cause of death in aged mice and rats (23). This problem is especially pronounced with Fischer 344 (F344) rats, a strain widely used in aging research in general and first used to calculate a rat epigenetic clock (9). F344 rats often spontaneously develop large granular lymphocyte leukemia (LGLL), which is characterized by atypical mononuclear cells in spleen tissue and circulating blood; in more advanced cases, lesions are seen in the liver and then in the bone marrow (24). Rats with LGLL display marked hemolytic anemia and leukocytosis (25). LGLL typically emerges in F344 populations at 18 months of age and is the leading cause of death after 20 months (24,26). The goal of the present study was to assess the impact of hematopoietic disease on epigenetic clock function using spontaneous LGLL in aged F344 rats. In particular, we sought to address (a) whether the presence of LGLL would accelerate epigenetic aging, and (b) how it would affect the precision and CpG composition of newly generated epigenetic clock formulas. Method Animals All animals used herein were part of a previously published study (9) whose procedures were approved by the Institutional Animal Care and Use Committee of the National Institute on Aging Intramural Research Program (NIA-IRP) in accordance with the National Research Council’s Guide for the Care and Use of Laboratory Animals. In brief, male F344 CDF rats were obtained from the NIA Aged Rodent Colony. Rats were singly housed after arrival at the NIA-IRP animal facility, where they had ad libitum access to standard house chow and water and were maintained on a 12 hours on–12 hours off light cycle. A total of 162 animals were used in the study (6 for each month of age between 1 and 27 months). Three animals died before a blood sample could be collected and another 2 died after the blood was collected but before the scheduled necropsy for liver and spleen collection. One juvenile rat and one middle-aged rat died prematurely and were therefore excluded from subsequent analysis. Rats assessed for LGLL were euthanized within 4 weeks of blood draw. At the time of euthanasia, internal organs were dissected and stored in neutral buffered 10% formalin for subsequent pathological analysis. LGLL Staging Previous research indicates that the presence of LGLL is extremely rare before the age of 18 months (24). Therefore, we assessed LGLLpathology in animals 17 months of age and older (n = 61). As a negative control, we also evaluated spleens and livers of six 6-month-old rats and six 12-month-old rats. Following established diagnostic procedures (26), organs were cut into 5–6 µm sections and stained with hematoxylin and eosin (H&E; Histoserv, Inc., Germantown, MD). Each slide was evaluated on a light microscope at progressively higher magnifications from 4× to 40× by a member of the study team (G.E.F.) and reviewed by a certified veterinary pathologist (L.R.B.). Disagreements were resolved by joint review and discussion. Splenic tissues were assessed for infiltration of the red pulp by neoplastic cells and effacement of the white pulp; liver tissues were assessed for infiltration of hepatic sinusoids by neoplastic cells and degeneration of hepatocytes. Each animal was assigned a score of 0 (absence of LGLL) to 3 on the basis of established criteria (27–29), summarized in Table 1. Rats were not evaluated for other age-related pathologies aside from LGLL. Table 1. Descriptions of Splenic and Liver Pathology by Large Granular Lymphocyte Leukemia Stage Stage . Description . 0 Lesion free. 1 Leukemic cells in the splenic red pulp. 2 Infiltration of splenic red pulp with leukemic cells and evident effacement of the marginal zone. Leukemic cells evident in the hepatic sinusoids. 3 Severe infiltration of the splenic red pulp with effacement of the marginal zone and peri-arteriolar lymphatic sheath, resulting in complete loss of splenic architecture. Centrilobular hepatocellular degeneration and necrosis are evident. Stage . Description . 0 Lesion free. 1 Leukemic cells in the splenic red pulp. 2 Infiltration of splenic red pulp with leukemic cells and evident effacement of the marginal zone. Leukemic cells evident in the hepatic sinusoids. 3 Severe infiltration of the splenic red pulp with effacement of the marginal zone and peri-arteriolar lymphatic sheath, resulting in complete loss of splenic architecture. Centrilobular hepatocellular degeneration and necrosis are evident. Adopted From Ref. (29). Open in new tab Table 1. Descriptions of Splenic and Liver Pathology by Large Granular Lymphocyte Leukemia Stage Stage . Description . 0 Lesion free. 1 Leukemic cells in the splenic red pulp. 2 Infiltration of splenic red pulp with leukemic cells and evident effacement of the marginal zone. Leukemic cells evident in the hepatic sinusoids. 3 Severe infiltration of the splenic red pulp with effacement of the marginal zone and peri-arteriolar lymphatic sheath, resulting in complete loss of splenic architecture. Centrilobular hepatocellular degeneration and necrosis are evident. Stage . Description . 0 Lesion free. 1 Leukemic cells in the splenic red pulp. 2 Infiltration of splenic red pulp with leukemic cells and evident effacement of the marginal zone. Leukemic cells evident in the hepatic sinusoids. 3 Severe infiltration of the splenic red pulp with effacement of the marginal zone and peri-arteriolar lymphatic sheath, resulting in complete loss of splenic architecture. Centrilobular hepatocellular degeneration and necrosis are evident. Adopted From Ref. (29). Open in new tab DNA Methylation Scores The raw data and procedures used to generate methylation scores for these samples have already been described (9). In brief, blood was treated with proteinase K and RNAse A, followed by DNA extraction. For each rat, a single sample of 100 ng DNA was digested with Msp I and ligated to TruSeq barcoded adapters. DNA fragments between 200 and 300 bp were selected with magnetic beads, bisulfite-treated, PCR-amplified, and sequenced on an Illumina HiSeq2500 sequencer (8 libraries per lane) with 100 bp single-end reads. Sequences were trimmed of adapter DNA using CutAdapt, aligned with BS-Seeker2, and methylation beta values calculated at all cytosine residues. Aligned data were filtered to keep only sites with greater than 10× coverage in at least 80% of the samples. Samples that failed bisulfite sequencing quality control or had more than 20% missing methylation scores were excluded (Supplementary Table 1), leaving a total of 84 young rats, 27 aged rats free of LGLL (“LGLL−”), and 24 aged rats with LGLL at any stage (“LGLL+”). Subsequent methylation analysis was restricted to the 3 002 770 sites with complete coverage in all 135 analyzed rats. Genomic annotations were derived of database “rn6” from the Rat Genome Sequencing Consortium. Statistical Analysis Analysis was performed using GraphPad Prism or R on an NIA Computational Biology Core high-performance workstation. Threshold for statistical significance in epigenome-wide linear modeling was adjusted using Bonferroni correction to account for a number of tests (methylation at 3 × 106 cytosine residues). Predictor selection was performed using the elastic net algorithm implemented in the R package “glmnet” (30), with alpha fixed at 0.5 to weight penalization midway between ridge and LASSO. The regularization parameter lambda was adjusted to minimize mean-squared error using either 5-fold or 10-fold cross-validation, depending on whether the modeling was performed only on the 51 aged rats or on the all-ages 135 rat cohort, respectively. A probability threshold of 0.5 was used to interpret predictions from logistic regression models. Identification of de novo DNA motifs was performed using the HOMER (31) function “findMotifsGenome” with a window of 200 bp surrounding target sites. Monte Carlo analyses (Figures 3 and 4) contained 200 simulations, each with 84 rats, a number representing 75% of the total LGLL− population. Half of the simulations contained 84 randomly selected LGLL− rats. The other half (“mixed”) contained 18 randomly selected LGLL+ (all 3 stages) rats and 66 randomly selected LGLL− rats, thereby maintaining a consistent ratio of LGLL− and LGLL+ rats in each simulation. To test LGLL classification models (Supplementary Figures 4 and 5), a series of Monte Carlo analyses were run, each with 100 simulations. In each simulation, 75% of LGLL+ and 75% of aged LGLL− rats were partitioned into a training set to build a model, which was then applied to the remaining test set animals and evaluated for sensitivity and specificity. LGLL presence was treated as a binary condition in all Monte Carlo analyses. Influence of LGLL on elastic net predictor selection was assessed by calculating selection bias, defined as the number of times a particular site or region was selected in 100 simulations with LGLL-training data, subtracted by the number of times it was selected in corresponding mixed population simulations. A large number of CpG sites were used in very few models; to prevent our analysis from overemphasizing small biases at sites rarely used (lower-left corner in Figure 4A), we restricted the analysis to sites selected in at least 10% of simulations. To assess whether a bias was statistically significant, we used a random permutation-based false discovery rate (FDR) approach. We ran 100 additional simulations in which LGLL scores were randomly permuted, and used the observed standard deviation of bias scores in this data set to normalize scores from real and permuted data into number of standard deviations beyond the null hypothesis (bias = 0). Treating these as z-scores, we calculated p values based on a normal distribution and took the ratio of indexed real:permuted p values to compute FDR-adjusted q values. Cutoff for significance was set at q < .05. Results LGLL Pathology Among the 61 rats older than 17 months evaluated for the LGLL staging, 27 had morphological changes consistent with LGLL (Supplementary Figure 1): 11 rats at Stage 1, 6 rats at Stage 2, and 10 rats at Stage 3 (Figure 1A). Within this cohort, LGLL+ rats skewed older, p = .0007. LGLL was not associated with a significant difference in body weight (Figure 1B; age-adjusted effect of LGLL p = .33). Figure 1. Open in new tabDownload slide LGLL prevalence by age. (A) Prevalence of LGLL stage by age of rats. White-stippled bar segments indicate rats that were assessed for LGLL but excluded from final DNA methylation analysis. (B) Individual body weights of rats. LGLL = large granular lymphocyte leukemia. Figure 1. Open in new tabDownload slide LGLL prevalence by age. (A) Prevalence of LGLL stage by age of rats. White-stippled bar segments indicate rats that were assessed for LGLL but excluded from final DNA methylation analysis. (B) Individual body weights of rats. LGLL = large granular lymphocyte leukemia. LGLL-Associated Changes in DNA Methylation Global methylation, calculated by averaging methylation scores across all 3 million sites, displayed greater variability among LGLL+ rats than aged LGLL− controls (0.0596 ± 0.0025 [SD] vs 0.0690 ± 0.0006; F23,26 = 17.69, p < .0001). Methylation at individual cytosines was more variable in LGLL+ rats (Figure 2; Supplementary Table 2); the standard deviation of methylation scores was increased by at least 0.1 at 6 689 sites but similarly decreased at only 51 sites (exact binomial test p <2e−16). The increase in variability was not likely a byproduct of the older age of LGLL+ rats, because methylation variability was nearly identical in non-LGLL rats above or below the group median age (Supplementary Figure 2A). Additionally, the influence of LGLL on DNA methylation variability was stronger, not weaker, when the analysis was restricted to rats 22 months of age or older (Supplementary Figure 2B), a subgroup in which there was not an age difference between LGLL+ and LGLL− rats (p = .3). None of the 3 million CpG sites examined displayed average methylation that differed significantly between LGLL+ and LGLL− rats (Figure 2C). However, restricting the analysis to rats with the most advanced stage of LGLL did reveal a limited number of differentially methylated sites (Supplementary Figure 3 and Supplementary Table 3). Figure 2. Open in new tabDownload slide Increased variability in CpG methylation in rats with LGLL. (A) Scatter plot of 3 million individual cytosine residues. X-axis, difference in the standard deviation of methylation fraction between LGLL+ and LGLL− rats. Points to the right of zero (vertical line) indicate sites with greater variability in LGLL+ rats. Y-axis, average methylation fraction in all animals. Inset, individual methylation scores at representative CpG site demonstrating increased variability in LGLL+ rats. (B) Frequency distribution of sites based on the standard deviations of methylation scores. (C) Manhattan plot depicting the age-adjusted influence of LGLL on average methylation at individual CpG sites across rat genome. Horizontal line at top of graph indicates the Bonferroni-adjusted threshold for statistical significance. LGLL = large granular lymphocyte leukemia; CpG = cytosine–guanine. Figure 2. Open in new tabDownload slide Increased variability in CpG methylation in rats with LGLL. (A) Scatter plot of 3 million individual cytosine residues. X-axis, difference in the standard deviation of methylation fraction between LGLL+ and LGLL− rats. Points to the right of zero (vertical line) indicate sites with greater variability in LGLL+ rats. Y-axis, average methylation fraction in all animals. Inset, individual methylation scores at representative CpG site demonstrating increased variability in LGLL+ rats. (B) Frequency distribution of sites based on the standard deviations of methylation scores. (C) Manhattan plot depicting the age-adjusted influence of LGLL on average methylation at individual CpG sites across rat genome. Horizontal line at top of graph indicates the Bonferroni-adjusted threshold for statistical significance. LGLL = large granular lymphocyte leukemia; CpG = cytosine–guanine. We performed HOMER analysis to identify transcription factor binding motifs enriched close to CpG sites affected by LGLL. To obtain statistically significant and robust motifs, large numbers of sites are required. Therefore, we used 2 approaches to broaden the definition of differentially methylated CpG sites: grouping all LGLL stages together or splitting LGLL Stage 3, and relaxing statistical thresholds for positive identification (Supplementary Table 4). The most significant hit in all cases contained a CCGG consensus sequence that appeared in close proximity to both hyper- and hypo-methylated CpG sites and was predicted to bind the transcription factors Elk4 or Elf1. To test whether there might be a signal across a constellation of sites that is not evident at individual CpG residues, we attempted multivariate modeling using linear and nonlinear approaches. Linear and logistic elastic net regression models (Supplementary Figure 4), as well as support vector machine and decision tree modeling (Supplementary Figure 5), all failed to reliably identify rats with LGLL. Most models had poor sensitivity, usually only identifying leukemic status in rats with Stage 3 LGLL. Collectively these data suggest that LGLL introduces random variability in DNA methylation, with a systematic signal only becoming evident at the most advanced stage of disease pathology. Impact of LGLL on the Epigenetic Clock We next explored the influence of LGLL on epigenetic clock modeling. We first generated a new epigenetic clock model using elastic net regularization trained on all rats (Figure 3A). LGLL was not associated with a significant change in age acceleration/deceleration (Figure 3B). Even the subset of rats with Stage 3 LGLL did not display significant age acceleration/deceleration (Figure 3B). To further explore the influence of LGLL on epigenetic clock modeling, we ran 100 pairs of analyses on randomly selected subsets of rats. In each analysis, we constructed 2 epigenetic clock models: one trained exclusively on LGLL− rats, the other on a mix of LGLL+ (all stages) and LGLL− rats, using identical proportions in each simulation. All models were trained on 84 rats, a number representing 75% of the total LGLL− population. When each rat’s average epigenetic age was taken across simulations using mixed populations, there was again no significant effect of LGLL on age acceleration or absolute age error (Supplementary Figure 6). Predictions from each epigenetic clock model were fitted to their respective training data for evaluation. Models trained on populations containing LGLL+ rats had worse precision, most evident in a 39% increase in absolute age error (Figure 3C). Furthermore, the formulas fitting actual age as a function of epigenetic age from simulations containing LGLL+ rats had significantly lower Pearson correlation coefficients (Figure 3D), slopes further from 1 (Figure 3E), and intercepts further from 0 (Figure 3F). Training a final epigenetic clock model on all 111 LGLL− rats resulted in a model containing 129 CpG sites (Supplementary Table 5), 117 of which were common to models generated in the Monte Carlo simulations. This model was very well fit to its training set (r2 = 0.999; mean absolute age error 7.19 days). Figure 3. Open in new tabDownload slide LGLL reduces epigenetic clock precision without accelerating epigenetic aging. (A) Sample epigenetic clock trained on all rats, with chronological and epigenetic ages plotted. r2 = 0.99. (B) Age acceleration of rats presented in panel A, grouped by LGLL status and score. (C–F) Results from 2 sets of 100 simulations generating epigenetic clock models trained exclusively on LGLL− rats or on mixed populations that included LGLL− and LGLL+ rats from all 3 stages of pathology. Each training set contained 84 randomly selected rats; proportions of LGLL+ and LGLL− were constant in the mixed population simulations. (C) Comparison of absolute age error. (D–F) Evaluation of models fitting actual age versus epigenetic age: fit (D), slope (E), and intercept (F). All plots are shown as mean ± SEM. LGLL = large granular lymphocyte leukemia. *p <.05; ***p <.001. Figure 3. Open in new tabDownload slide LGLL reduces epigenetic clock precision without accelerating epigenetic aging. (A) Sample epigenetic clock trained on all rats, with chronological and epigenetic ages plotted. r2 = 0.99. (B) Age acceleration of rats presented in panel A, grouped by LGLL status and score. (C–F) Results from 2 sets of 100 simulations generating epigenetic clock models trained exclusively on LGLL− rats or on mixed populations that included LGLL− and LGLL+ rats from all 3 stages of pathology. Each training set contained 84 randomly selected rats; proportions of LGLL+ and LGLL− were constant in the mixed population simulations. (C) Comparison of absolute age error. (D–F) Evaluation of models fitting actual age versus epigenetic age: fit (D), slope (E), and intercept (F). All plots are shown as mean ± SEM. LGLL = large granular lymphocyte leukemia. *p <.05; ***p <.001. The 200 regression models generated in the Monte Carlo simulations used anywhere from 31 to 127 individual cytosine residues’ percent methylation as predictors. A total of 4 612 unique sites were selected at least once as predictors for epigenetic clock models. Only 12 of these sites were significantly affected by Stage 3 LGLL using FDR criteria; none were among the 18 sites significant at the more stringent Bonferroni level. These results suggest a minimal overlap between epigenetic responses to aging and LGLL. Many sites used in the Monte Carlo simulations showed selection bias in which the rate of inclusion in models differed in simulations trained on LGLL-rats versus mixed populations containing both LGLL− and LGLL+ rats (Figure 4A). To identify sites with statistically significant bias, we used a permutation-based FDR adjustment, comparing observed results to 100 simulations in which rats’ LGLL scores were randomly permuted. We found that 83 of the 123 CpG sites appearing in at least 10% of simulations showed significant selection bias at the q < .05 level. The most dramatic bias in selection was for the cytosine at position 1:216661895 in exon 2 of the Cdkn1c gene. This CpG was selected as a predictor in 98/100 LGLL- simulations, but only 6/100 simulations trained on mixed populations. Most sites with significant bias were selected less frequently when LGLL+ rats were introduced into training data (Figure 4A; points below diagonal line). There were fewer sites biased toward selection in mixed population simulations (above diagonal); interestingly, 2 of the sites showing the greatest bias in this direction were in close proximity to one another, immediately upstream of the Trnak-cuu transcription start site. Figure 4. Open in new tabDownload slide LGLL affects elastic net predictor selection during epigenetic clock generation. (A) Scatter plot of individual cytosine residues selected in epigenetic clock models. X-axis, rate of selection in 100 models trained exclusively on LGLL− rats. Y-axis, rate of selection in 100 models trained with mixed populations containing both LGLL+ (all 3 stages) and LGLL− rats. Diagonal line indicates equivalent selection rate (slope = 1); sites below it were biased toward usage in LGLL− simulations; sites above the line were biased toward usage in mixed simulations. Lightly-colored squares/diamonds close to the diagonal line represent sites where bias was not statistically significant. Gray circles represent selection rates from an additional 100 simulations with randomly permuted LGLL scores. (B) Scatter plot of regions of DNA, defined as stretches of DNA with no gaps between residues greater than 250 bp. X-axis, the total number of sites within region used as predictors across 100 simulations training epigenetic clock models on populations of LGLL− rats. Y-axis, number of sites used as predictors in 100 simulations with mixed populations of LGLL− and LGLL+ rats. (C) Net selection bias measures of regions plotted in panel B. Net selection bias is calculated as the absolute value of difference between the total number of sites selected in mixed simulations versus LGLL− simulations. The top 50 regions from real data (diamonds and squares) and random permutations (gray circles) are listed in rank order. (D) Selection rates of individual sites present within the region of DNA immediately upstream of the Trnak-cuu gene. Chromosomal annotations are drawn to scale; reduced representation bisulfite sequencing (RRBS) coverage DNA fragment is 344 bp long. LGLL = large granular lymphocyte leukemia. Figure 4. Open in new tabDownload slide LGLL affects elastic net predictor selection during epigenetic clock generation. (A) Scatter plot of individual cytosine residues selected in epigenetic clock models. X-axis, rate of selection in 100 models trained exclusively on LGLL− rats. Y-axis, rate of selection in 100 models trained with mixed populations containing both LGLL+ (all 3 stages) and LGLL− rats. Diagonal line indicates equivalent selection rate (slope = 1); sites below it were biased toward usage in LGLL− simulations; sites above the line were biased toward usage in mixed simulations. Lightly-colored squares/diamonds close to the diagonal line represent sites where bias was not statistically significant. Gray circles represent selection rates from an additional 100 simulations with randomly permuted LGLL scores. (B) Scatter plot of regions of DNA, defined as stretches of DNA with no gaps between residues greater than 250 bp. X-axis, the total number of sites within region used as predictors across 100 simulations training epigenetic clock models on populations of LGLL− rats. Y-axis, number of sites used as predictors in 100 simulations with mixed populations of LGLL− and LGLL+ rats. (C) Net selection bias measures of regions plotted in panel B. Net selection bias is calculated as the absolute value of difference between the total number of sites selected in mixed simulations versus LGLL− simulations. The top 50 regions from real data (diamonds and squares) and random permutations (gray circles) are listed in rank order. (D) Selection rates of individual sites present within the region of DNA immediately upstream of the Trnak-cuu gene. Chromosomal annotations are drawn to scale; reduced representation bisulfite sequencing (RRBS) coverage DNA fragment is 344 bp long. LGLL = large granular lymphocyte leukemia. The observation of clustered sites with similar selection bias propelled us to look at more broadly defined regions of DNA in which predictor selection by elastic net was affected by LGLL. We operationally defined a “region” of DNA to continue until the first gap of more than 250 bp between sites, a threshold chosen based on the distribution of observed site–site gaps (Supplementary Figure 7). We then added the total number of sites selected within each region across all 100 simulations for each population type (Figure 4B). We found that 50 of 82 analyzed regions had a bias that was statistically significant at the FDR-adjusted q < .05 level. A majority of the most biased regions were selected more in the LGLL− only simulations (Figure 4C), meaning that the inclusion of LGLL+ rats in a training set reduces the likelihood that they are selected as predictors in model generation. The single most biased region was a 344 bp fragment of DNA immediately upstream of the Trnak-cuu transcription start site containing 5 individual cytosine residues that were used as predictors in epigenetic clock models. All 5 of these residues showed bias toward mixed populations (Figure 4D). Age-adjusted methylation scores in this region were not significantly affected by LGLL status, whether analyzed individually (p > .39) or averaged into a single regional methylation score calculated for each rat (p = .28). Discussion Here we show that the presence of LGLL in F344 rats is associated with broadly increased interindividual variability in DNA methylation, assessed by reduced representation bisulfite sequencing at 3 million cytosine residues. A limited number of sites were differentially methylated in the presence of LGLL, but only in rats with an advanced stage of disease pathology. Epigenetic aging was not accelerated in rats with any stage of LGLL. However, the inclusion of leukemic rats in training data reduced the precision of subsequently generated clock models and profoundly affected their CpG composition. The F344 is one of the 3 rat strains that have been provided by the National Institute on Aging’s Aged Research Colony since its inception in 1974 (32), which may account in part for its widespread use in aging research. F344 rats frequently develop LGLL after 17 months of age, often resulting in death (24,26). It is unclear whether the development of this condition introduces a systematic bias in the epigenetic signal that accompanies biological aging. To address this question, we tested the influence of LGLL on white blood cell DNA methylation in male F344 rats ranging from 1 to 27 months old. We found that LGLL did not affect epigenetic aging, regardless of whether all LGLL+ rats were analyzed together as a group or separated by disease stage. The lack of an effect was surprising, considering that the average survival of an F344 rat from the first identification of leukemic cells in peripheral blood is 5 weeks (33); therefore, in these rats, the epigenetic clock is not a good predictor of survival. The majority of our rats with Stage 2 LGLL had neoplastic cells visible in the liver. As LGLL has been shown to originate in the spleen (24), liver involvement indicates that neoplastic cells were circulating in the peripheral blood. Indeed, there was minimal overlap between the sites with significantly different methylation in the presence of Stage 3 LGLL and the sites selected in 200 simulations of epigenetic clock formula generation. These results indicate that epigenetic age is a valid measure in F344 rats even without assessment for LGLL pathology, although its association with mortality is questionable. In contrast to the minimal impact on epigenetic age calculations, the inclusion of LGLL+ rats in training sets profoundly affected the creation of epigenetic clock formulas. These models had reduced precision in accurately calculating chronological age across all rats. Interestingly, the lack of precision in our models trained on mixed populations of rats appeared to be driven at least in part by increased variability in the methylation scores of LGLL+ rats at certain residues, where methylation was otherwise strongly associated with chronological age. In addition to reduced precision, these models used different CpG sites as predictors. Because hematopoietic neoplasia is common in aged mice and rats (23), these results suggest that caution should be employed when analyzing CpG composition of rodent epigenetic clocks. Much has been discussed on the use of epigenetic clocks in humans to estimate mortality. While it has been clearly demonstrated that epigenetic age acceleration—the discrepancy between epigenetic clock prediction and actual chronological age—can predict health outcomes including mortality, the effect size of this information is relatively small. For example, a human study found that the epigenetic clock was modestly sensitive to risk factors for coronary heart disease, but did not predict outcomes (34). Furthermore, a mouse study using different pools of CpGs and selection algorithms found an explicit trade-off between model accuracy in predicting chronological age and sensitivity to antiaging dwarfism mutations (35). Newer generation epigenetic clocks that are tuned on health characteristics instead of age perform better, especially in predicting mortality (36), but their reliability is still too small for translational purposes. This is the first study to document epigenetic changes associated with spontaneous LGLL in F344 rats. We found an increase in variability of methylation at individual sites, reminiscent of observations in human acute myeloid leukemia and several leukemic cell lines (37). We could not identify any individual cytosine bases where there were reliable changes in methylation that occurred broadly across all stages of LGLL. This may be because of low signal to noise due to healthy cells with presumably normal patterns of DNA methylation; abnormal leukocytes account for anywhere from 20% to 90% of circulating white blood cells in rats with LGLL at varying stages of progression (38). When we restricted the analysis to the most advanced stage of LGLL, we found a limited number of differentially methylated sites. The most significantly associated site was hypermethylated in rats with LGLL and located 70 bp upstream of the transcription start site for the inositol transporter Slc2a13. Interestingly, a human study found that expression of SLC2A13 in bone marrow was a prognostic indicator of survival time in patients with acute myeloid leukemia (39). Under the traditional assumption that hypermethylation at the promoter of a gene suppresses transcription, our findings are congruent with the observation in humans. Several other sites that we found to be most significantly altered in LGLL have been implicated in various human cancers, though not necessarily related to hematopoietic tissues. TUBB4A expression is a prognostic indicator in clear cell renal cell carcinoma (40). MEIS2 is hypermethylated and transcriptionally downregulated in prostate cancer, and its methylation status is a prognostic biomarker for recurrence (41). NFIX, which regulates hematopoietic lineage specification (42), is proposed as a “master regulator” for metastasis in lung cancer (43). Finally, our data identified DNA-binding motifs for Elk4 protein proximal to sites with altered methylation. This transcription factor is associated with a number of human cancers (44,45). Collectively these results suggest that epigenetic events in rat LGLL bear similarities to a variety of human cancers involving both blood and solid tumors. There were several important limitations to this study. First, we used only male rats due to availability from the NIA Aged Rodent Colony at the time this work was conducted. LGLL has been documented in female rats (24,26), and it would be important for future work to explore potential sex differences in the phenomena addressed here. Second, we did not assess rats for other age-related pathologies; rats with advanced stages of LGLL might be expected to develop complications due to impaired immune function. Thus, while the LGLL associated changes that we documented may be direct consequences of transformation within white blood cells, we cannot exclude the possibility that they are mediated indirectly through other health complications. Finally, sample sizes in certain analyses were limited. There were only 9 rats with Stage 3 LGLL in our sample, which may have reduced the generalizability of some of our findings. In most analyses, we combined all LGLL stages together into a single group (n = 24); however, to identify differentially methylated CpG sites, we had to isolate rats with Stage 3 LGLL (n = 9). Nevertheless, the key findings of our study—the impact of LGLL on the architecture and function of epigenetic clock models—remain. In summary, we show that LGLL, a disease that is common in aged F344 rats, has a broad impact on methylation variability in white blood cell DNA. Despite this, the presence of LGLL did not accelerate epigenetic aging. Given that rats with LGLL survive only for a few weeks and that as many as 50% of F344 rats develop LGLL, our findings also suggest a strong disconnect between epigenetic signals associated with chronological aging and risk of death. However, the inclusion of LGLL+ rats adversely affected the precision and composition of newly generated epigenetic clock formulas. Thus, diverse potential effects of circulating neoplastic cells on epigenetic clock modeling should be considered. Acknowledgments We thank Toshiko Tanaka for advising on statistical analyses and assistance with the Manhattan plots, Supriyo De for access to NIA Computational Biology Core high-performance workstations, Osorio Meirelles for advising on randomized permutation, Ravi Tharakan for advising on DNA methylation, Brian Clopper for logistical support with rat spleen and liver tissue, and Heather DeMali for digital microscopy imaging. Funding This work was supported entirely by the National Institute on Aging Intramural Research Program. Conflict of Interest None declared. Author Contributions G.E.F., K.A.P., R.D., and L.F. conceived the project. K.A.P. and T.M. performed necropsies and collected rat tissue; L.R.B. and G.E.F. assessed pathology. 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Nat Rev Cancer . 2017 ; 17 ( 6 ): 337 – 351 . doi:10.1038/nrc.2017.20 Google Scholar Crossref Search ADS PubMed WorldCat Author notes Equal contribution. Published by Oxford University Press on behalf of The Gerontological Society of America 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US. Published by Oxford University Press on behalf of The Gerontological Society of America 2021.

Markey, Senator Edward J

doi: 10.1093/gerona/glac027pmid: 35137076

Article PDF first page preview Close This content is only available as a PDF. © The Author(s) 2022. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Guo, Yanfei F; Ng, Nawi; Kowal, Paul; Lin, Hualiang; Ruan, Ye; Shi, Yan; Wu, Fan

doi: 10.1093/gerona/glac022pmid: 35134914

Abstract Background A series of studies have explored the health effects of long-term exposure to ambient PM2.5 among older adults. However, few studies have investigated the adverse effect of long-term exposure to ambient PM2.5 on frailty, and the results are inconclusive. This study sought to investigate the associations between long-term exposure to ambient PM2.5 and frailty in 6 low- and middle-income countries. Methods We included an analytical sample of 34 138 individuals aged 50 and older from the Study on global AGEing and adult health Wave 1 (2007/2010). Air pollution estimates were generated using a standard methodology derived from Moderate Resolution Imaging Spectroradiometer observations and Multiangle Imaging Spectroradiometer instruments from the Terra satellite, along with simulations from the GEOS-Chem chemical transport model. A 3-level hierarchical logistic model was used to evaluate the association between frailty index and long-term PM2.5 exposure at 3 levels (individual, province, and country). Results In rural areas, each 10 μg/m3 increase in ambient PM2.5 was associated with a 30% increase in the odds of frailty (OR = 1.30, 95% CI: 1.21–1.39) after adjusting for various potential confounding factors. The gender-stratified analysis showed that the association seemed to be slightly stronger in men (OR = 1.31, 95% CI: 1.18–1.46) than in women (OR = 1.21, 95% CI: 1.07–1.36) in rural areas. Conclusion In a large sample of community-based older adults from 6 middle-income countries, we found evidence that long-term PM2.5 exposure was associated with frailty in rural areas. Air pollution, Ambient PM2.5, Frailty, Older adults A growing body of literature supports the adverse effect of long-term ambient air pollution exposure on lung function and other health outcomes (1,2). In older adults and susceptible individuals, short- and long-term exposures to air pollution have been associated with poor lung function, mortality, and cancer (3,4). Compared to other age groups, older adults are more susceptible to the impact of air pollution. At the same time, this population is also more susceptible to increased vulnerability to risk factors leading to frailty (5). Given the current global trends of demographic aging, economic development, and urbanization, air pollution and frailty among older people have become emerging and intertwined public health issues. For example, individuals with compromised lung function do not have sufficient normal pulmonary defense mechanisms to respond to air pollution and particulate matter (6,7). Consequently, when the individuals experience an exacerbation of chronic obstructive pulmonary diseases due to pneumonia comorbid, they might become transiently frail or become frailer for those with existing frailty due to their inability to cope with the additional stress posed by air pollution. The situation can lead to poorer health outcomes and manifest as cardiovascular diseases or depression (8,9). While a series of studies have explored the health effects of long-term exposure to ambient fine particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5) among older adults, these studies generally assess the association between exposure to PM2.5 with a single disease endpoint, such as the incidence of acute coronary event (10) or cognitive performance (11), rather than a complex outcome such as frailty that is usually measured as an index generated from multiple indicators. Though no single-standard assessment tool of frailty is available, the frailty index has been well tested in several populations (12). To the best of our knowledge, only one study has specifically investigated the relationship between air pollution and the incidence of frailty assessed by the frailty index (9). The study reported that PM2.5 exposure was associated with an increased risk of developing frailty among patients suffering from myocardial infarction (OR = 1.53; 95% CI: 1.22–1.91, when comparing the 75th vs 25th percentiles of the exposure distribution), after adjusting for sociodemographic and clinical variables. However, a similar association has not been reported in a population-based study. This article aims to examine the association between long-term exposure to PM2.5 and frailty among older adults in 6 middle-income countries (MICs) who participated in the Study on global AGEing and adult health (SAGE) during 2007–2010. We further evaluate the dose–response association between PM2.5 concentrations and frailty stratified by urban/rural settings. Method Study Population The study population was drawn from the SAGE Wave 1 (2007–2010), a longitudinal cohort survey of aging and older adults in 6 MICs: China, Ghana, India, Mexico, Russian Federation, and South Africa. SAGE employed multistage cluster sampling strategies in all countries (13). The strata selected were defined by provinces/states and locality (urban/rural). Enumeration areas constituted the primary sampling units and were selected using the probability proportional to size method. Finally, household enumerations were randomly selected as the final sampling unit. The sampling design in Mexico was similar to the other 5 countries but included a supplementary, random sample from urban and rural households to compensate for the overrepresentation of metropolitan strata. Face-to-face interviews were used to complete the questionnaires. The response rates ranged from 51% in Mexico to 93% in China (India 68%, Ghana 80%, Russia 83%, and South Africa 77%). Ethics and Consent WHO’s Ethical Review Committee approved SAGE (RPC146). Additionally, each country obtained ethical clearance through their respective review bodies, and SAGE obtained written informed consent from each respondent. Study Variables Outcome variable—the frailty index Frailty was defined as the accumulation of deficits (14). In this study, the frailty index was constructed as the proportion of deficits present in 40 variables (15). The index included variables on medical symptoms, medically diagnosed conditions, activities of daily living, and performance tests (walking speed and grip strength). The likelihood of frailty increases with more decrements in health (16). The complete list of variables and the coding is presented in Supplementary Table 3. The individual frailty scores ranged from 0 (no deficits) to 1 (highest level of deficits in all variables). A cutoff value of 0.2 has been recognized as a threshold for approaching a frail state (17,18). Hence, we classified individuals into not frail (0 to less than 0.2) and frail (0.2–1.0) groups. Exposure variable—air pollution assessment This study estimated the ambient PM2.5 levels using remotely sensed data. These estimates were derived from a combination of observations from the Moderate Resolution Imaging Spectroradiometer (19) and Multiangle Imaging Spectroradiometer instruments from the Terra satellite, along with simulations from the GEOS-Chem chemical transport model (www.geos-chem.org). The analysis resulted in estimates of the average long-term level of exposure to PM2.5 at an approximate resolution of 10 km × 10 km. According to a previous validation study, the estimated PM2.5 data had an expected 1-sigma uncertainty of 1 μg/m3 + 25% (20). The location of respondents was geocoded using coordinates from the SAGE data sets and mapped to Google Earth at the community level. We refer to these as community addresses to indicate the varying size and meaning of the aerial unit to which each address was geocoded. A corresponding PM2.5 concentration estimate was assigned and used in the regression models. The “community” in our study refers to the township in China, enumeration block in Ghana and South Africa, village and enumeration block in India, a geostatistic area in Mexico, and atenum in Russia. We obtained the 1–5 years averaged PM2.5 concentration estimate before SAGE Wave 1 with this method. We used the 3-year averaged PM2.5 concentration in the main models. Considering the effect of temperature and humidity on the length of time people are exposed to PM2.5 outdoors, we also collected the 1–5 years averaged temperature and humidity for each “community.” Other covariates Other variables used in the analyses included age, gender, education, wealth quintiles, tobacco use, vegetable and fruit intake, and physical activity levels. According to the International Standard Classification of Education, education was divided into 5 levels. Wealth quintiles were derived from the household ownership of durable goods, dwelling characteristics (floor, wall, and stove), and access to services such as clean water, sanitation, and cooking fuel. Tobacco use was assessed by self-reported questions and categorized into never smokers, noncurrent smokers, current nondaily smokers, and current daily smokers. We used the average number of daily servings consumed in a typical day to assess fruit and vegetable consumption. Five or more servings of fruits and/or vegetables were defined as sufficient daily intake (equivalent to at least 400 g/day). Fewer than 5 servings were categorized as insufficient. We generated a categorical indicator of physical activity (low, moderate, and high levels) from the self-reported total time spent on physical activity, the number of days, and the intensity of physical activity (21). The study asked fuel types for domestic cooking and usage of ventilation to assess indoor air pollution. It categorized them as clean fuels (electricity and natural gas) and unclean fuels (coal, wood, dung, and agricultural residues). Statistical Methods We used descriptive statistics to describe the sociodemographic characteristics and selected covariates for respondents in the 6 countries, including observations and weighted proportions for categorical variables. We generated country-specific frequency histograms to describe the 3-year averaged PM2.5 concentration distribution. A 3-level hierarchical logistic model was used to evaluate the association between long-term 3-year PM2.5 exposure and frailty, considering that individuals were nested within provinces, which were nested within countries. Covariates of interest at the individual level (Level 1) included age, gender, education, tobacco use, vegetable and fruit intake, and physical activity. All models were stratified by urban/rural setting. To avoid small regression coefficients, we set 10 μg/m3 instead of 1 μg/m3 as the minimum concentration unit. We also performed a stratified analysis to compare whether the associations varied by country, age group, or gender to identify the potential effect modifiers. We evaluated the dose–response association between PM2.5 concentrations and frailty by modeling the PM2.5 concentrations using restricted cubic splines with knots at the PM2.5 concentration quartiles. We generated the plots separately for urban and rural areas. We also conducted sensitivity tests to assess the robustness of the analyses to different PM2.5 exposure duration and different scales of frailty as the outcome variable. First, in addition to the 3-year PM2.5 exposure, we also tested for different exposure durations, including 1-year, 2-year, 4-year, and 5-year PM2.5 exposures. Second, we repeated all the analyses and used the frailty index as a continuous variable in the model. All analyses were performed using Stata v16.0 (StataCorp, College Station, TX). We used the p value <.05 to determine statistical significance. Role of the Funding Source The funder had no role in the study design; collection, analysis, and interpretation of data; writing of the report; or the decision to submit the report for publication. Results A total of 38 670 individuals aged 50 and older participated in SAGE Wave 1, among which 4 532 individuals who could not complete or partially completed an interview or with missing sociodemographic variables were excluded from the analyses. China had the largest sample (N = 13 070), and Mexico (N = 2 249) had the smallest. The demographic and socioeconomic characteristics of the respondents differed widely across the 6 countries (Table 1). The proportion of women was higher in China, Mexico, Russia, and South Africa and the 50–59 age group category was the largest proportion of respondents in all countries. More than two thirds of Indians resided in rural areas. Most older Mexicans, Russians, and South Africans lived in urban areas. Compared to other SAGE countries, participants in Ghana and India had lower educational levels, with 64.2% and 60.8%, respectively, of the older population having less than primary education. Men were more likely than women to use tobacco in all 6 countries. The prevalence of inadequate fruit and vegetable intake was high in all countries, particularly among India’s older population. The prevalence of low physical activity level was highest in South Africa at 59.2%. Table 1. Demographic Characteristics Among SAGE Respondents Aged 50 Years and Older (Weighted), by Country . China (N = 13 070) . Ghana (N = 4 271) . India (N = 6 415) . Mexico (N = 2 249) . Russian Federation (N = 3 751) . South Africa (N = 3 796) . . Weighted Percentages . . . . . . Age group 50–59 45.1 40 49.2 48.5 44.9 50 60–69 31.9 27.5 31 25.7 26.8 30.7 70–79 18.5 22.9 15.7 17.6 21 13.8 80+ 4.5 9.6 4.2 8.2 7.3 5.5 Gender Men 49.8 52.6 51.5 47 42.1 44 Women 50.2 47.4 48.5 53 57.9 56 Residence Urban 47.3 40.9 29.0 79 70.7 64.8 Rural 52.7 59.1 71.0 21 29.3 35.2 Education Less than primary 42 64.2 60.8 55.2 1.5 49.2 Primary school completed 21 11 14.9 24.1 5.3 22.3 Secondary school completed 19.8 4 10.3 10 17.6 14.3 High school completed 12.6 17.2 8.7 2.4 54.6 8.5 College completed and above 4.5 3.6 5.2 8.2 21 5.8 Wealth quintile Q1 （lowest） 16.2 18.3 18 15 13.2 20.8 Q2 18.1 19.1 19.5 24.8 16.9 19.9 Q3 20.4 20.4 18.6 16.7 19.2 18.2 Q4 23.4 20.7 19.8 16.7 22.3 19.7 Q5 (highest) 21.8 21.5 24.1 26.8 28.4 21.4 Fruit and vegetable intake Sufficient 64.4 31.1 9.4 18.6 19.1 31.3 Insufficient 35.6 68.9 90.6 81.4 80.9 68.7 Tobacco use Never smoker 64.1 75.4 45.4 60.5 65 67.6 Noncurrent smokers 6.6 14.3 4.6 19.2 13.4 9.6 Current nondaily smokers 2.5 2.6 2.9 7 2.1 3.4 Current daily smokers 26.9 7.7 47.1 13.3 19.5 19.4 Physical activity High level 44.6 62 52.7 40 62.6 28.5 Moderate level 27.4 12.5 23 22.5 15.7 12.3 Low level 28.1 25.5 24.3 37.5 21.7 59.2 . China (N = 13 070) . Ghana (N = 4 271) . India (N = 6 415) . Mexico (N = 2 249) . Russian Federation (N = 3 751) . South Africa (N = 3 796) . . Weighted Percentages . . . . . . Age group 50–59 45.1 40 49.2 48.5 44.9 50 60–69 31.9 27.5 31 25.7 26.8 30.7 70–79 18.5 22.9 15.7 17.6 21 13.8 80+ 4.5 9.6 4.2 8.2 7.3 5.5 Gender Men 49.8 52.6 51.5 47 42.1 44 Women 50.2 47.4 48.5 53 57.9 56 Residence Urban 47.3 40.9 29.0 79 70.7 64.8 Rural 52.7 59.1 71.0 21 29.3 35.2 Education Less than primary 42 64.2 60.8 55.2 1.5 49.2 Primary school completed 21 11 14.9 24.1 5.3 22.3 Secondary school completed 19.8 4 10.3 10 17.6 14.3 High school completed 12.6 17.2 8.7 2.4 54.6 8.5 College completed and above 4.5 3.6 5.2 8.2 21 5.8 Wealth quintile Q1 （lowest） 16.2 18.3 18 15 13.2 20.8 Q2 18.1 19.1 19.5 24.8 16.9 19.9 Q3 20.4 20.4 18.6 16.7 19.2 18.2 Q4 23.4 20.7 19.8 16.7 22.3 19.7 Q5 (highest) 21.8 21.5 24.1 26.8 28.4 21.4 Fruit and vegetable intake Sufficient 64.4 31.1 9.4 18.6 19.1 31.3 Insufficient 35.6 68.9 90.6 81.4 80.9 68.7 Tobacco use Never smoker 64.1 75.4 45.4 60.5 65 67.6 Noncurrent smokers 6.6 14.3 4.6 19.2 13.4 9.6 Current nondaily smokers 2.5 2.6 2.9 7 2.1 3.4 Current daily smokers 26.9 7.7 47.1 13.3 19.5 19.4 Physical activity High level 44.6 62 52.7 40 62.6 28.5 Moderate level 27.4 12.5 23 22.5 15.7 12.3 Low level 28.1 25.5 24.3 37.5 21.7 59.2 Open in new tab Table 1. Demographic Characteristics Among SAGE Respondents Aged 50 Years and Older (Weighted), by Country . China (N = 13 070) . Ghana (N = 4 271) . India (N = 6 415) . Mexico (N = 2 249) . Russian Federation (N = 3 751) . South Africa (N = 3 796) . . Weighted Percentages . . . . . . Age group 50–59 45.1 40 49.2 48.5 44.9 50 60–69 31.9 27.5 31 25.7 26.8 30.7 70–79 18.5 22.9 15.7 17.6 21 13.8 80+ 4.5 9.6 4.2 8.2 7.3 5.5 Gender Men 49.8 52.6 51.5 47 42.1 44 Women 50.2 47.4 48.5 53 57.9 56 Residence Urban 47.3 40.9 29.0 79 70.7 64.8 Rural 52.7 59.1 71.0 21 29.3 35.2 Education Less than primary 42 64.2 60.8 55.2 1.5 49.2 Primary school completed 21 11 14.9 24.1 5.3 22.3 Secondary school completed 19.8 4 10.3 10 17.6 14.3 High school completed 12.6 17.2 8.7 2.4 54.6 8.5 College completed and above 4.5 3.6 5.2 8.2 21 5.8 Wealth quintile Q1 （lowest） 16.2 18.3 18 15 13.2 20.8 Q2 18.1 19.1 19.5 24.8 16.9 19.9 Q3 20.4 20.4 18.6 16.7 19.2 18.2 Q4 23.4 20.7 19.8 16.7 22.3 19.7 Q5 (highest) 21.8 21.5 24.1 26.8 28.4 21.4 Fruit and vegetable intake Sufficient 64.4 31.1 9.4 18.6 19.1 31.3 Insufficient 35.6 68.9 90.6 81.4 80.9 68.7 Tobacco use Never smoker 64.1 75.4 45.4 60.5 65 67.6 Noncurrent smokers 6.6 14.3 4.6 19.2 13.4 9.6 Current nondaily smokers 2.5 2.6 2.9 7 2.1 3.4 Current daily smokers 26.9 7.7 47.1 13.3 19.5 19.4 Physical activity High level 44.6 62 52.7 40 62.6 28.5 Moderate level 27.4 12.5 23 22.5 15.7 12.3 Low level 28.1 25.5 24.3 37.5 21.7 59.2 . China (N = 13 070) . Ghana (N = 4 271) . India (N = 6 415) . Mexico (N = 2 249) . Russian Federation (N = 3 751) . South Africa (N = 3 796) . . Weighted Percentages . . . . . . Age group 50–59 45.1 40 49.2 48.5 44.9 50 60–69 31.9 27.5 31 25.7 26.8 30.7 70–79 18.5 22.9 15.7 17.6 21 13.8 80+ 4.5 9.6 4.2 8.2 7.3 5.5 Gender Men 49.8 52.6 51.5 47 42.1 44 Women 50.2 47.4 48.5 53 57.9 56 Residence Urban 47.3 40.9 29.0 79 70.7 64.8 Rural 52.7 59.1 71.0 21 29.3 35.2 Education Less than primary 42 64.2 60.8 55.2 1.5 49.2 Primary school completed 21 11 14.9 24.1 5.3 22.3 Secondary school completed 19.8 4 10.3 10 17.6 14.3 High school completed 12.6 17.2 8.7 2.4 54.6 8.5 College completed and above 4.5 3.6 5.2 8.2 21 5.8 Wealth quintile Q1 （lowest） 16.2 18.3 18 15 13.2 20.8 Q2 18.1 19.1 19.5 24.8 16.9 19.9 Q3 20.4 20.4 18.6 16.7 19.2 18.2 Q4 23.4 20.7 19.8 16.7 22.3 19.7 Q5 (highest) 21.8 21.5 24.1 26.8 28.4 21.4 Fruit and vegetable intake Sufficient 64.4 31.1 9.4 18.6 19.1 31.3 Insufficient 35.6 68.9 90.6 81.4 80.9 68.7 Tobacco use Never smoker 64.1 75.4 45.4 60.5 65 67.6 Noncurrent smokers 6.6 14.3 4.6 19.2 13.4 9.6 Current nondaily smokers 2.5 2.6 2.9 7 2.1 3.4 Current daily smokers 26.9 7.7 47.1 13.3 19.5 19.4 Physical activity High level 44.6 62 52.7 40 62.6 28.5 Moderate level 27.4 12.5 23 22.5 15.7 12.3 Low level 28.1 25.5 24.3 37.5 21.7 59.2 Open in new tab The 3-year averaged PM2.5 concentration in the 6 countries was 22.6 μg/m3. China and India had the highest average PM2.5 concentrations (32.9 and 31.0 μg/m3, respectively), while South Africa had the lowest level of PM2.5 (6.0 μg/m3; Figure 1). Figure 1. Open in new tabDownload slide The 3-year averaged PM2.5 concentration across 6 middle-income countries. *Unique value of PM2.5 concentration. Figure 1. Open in new tabDownload slide The 3-year averaged PM2.5 concentration across 6 middle-income countries. *Unique value of PM2.5 concentration. The prevalence of frailty for each country and by age group, gender, and residential areas is displayed in Table 2. The prevalence of frailty was the highest in India, while China had the lowest prevalence at 14.5%. In all 6 countries, frailty prevalence was higher in older age groups, men, and rural residents. Table 2. The Prevalence of Frailty* Among Older Men and Women, by Country . China . . India . . Mexico . . Russian Federation . . South Africa . . Ghana . . . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Total 14.5 13 028 51.6 6 377 34.3 2 257 39.9 3 751 41.3 3 780 39.2 4 266 Age group 50–59 7.8 5 687 40.2 2 919 24.5 430 19.9 1 427 35.8 1 681 22.0 1 686 60–69 15.0 3 900 55.3 2 187 34.6 924 42.9 1 026 41.5 1 221 37.2 1 194 70–79 24.5 2 733 70.4 1 017 49.2 600 63.4 971 49.2 650 56.8 973 80+ 41.2 750 87.0 292 59.2 302 86.6 327 67.8 245 74.5 418 Gender Men 11.9 6 124 41.2 3 250 29.5 894 35.7 1 334 36.2 1 618 34.1 2 237 Women 17.4 6 946 62.6 3 165 38.5 1 355 43.2 2 417 45.0 2 179 44.8 2 034 Residence Urban 13.7 6 362 47.1 1 657 31.9 1 657 38.1 2 894 40.7 2 532 38.6 1 741 Rural 15.6 6 709 53.4 4 758 43.2 604 44.3 862 42.0 1 264 39.6 2 530 . China . . India . . Mexico . . Russian Federation . . South Africa . . Ghana . . . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Total 14.5 13 028 51.6 6 377 34.3 2 257 39.9 3 751 41.3 3 780 39.2 4 266 Age group 50–59 7.8 5 687 40.2 2 919 24.5 430 19.9 1 427 35.8 1 681 22.0 1 686 60–69 15.0 3 900 55.3 2 187 34.6 924 42.9 1 026 41.5 1 221 37.2 1 194 70–79 24.5 2 733 70.4 1 017 49.2 600 63.4 971 49.2 650 56.8 973 80+ 41.2 750 87.0 292 59.2 302 86.6 327 67.8 245 74.5 418 Gender Men 11.9 6 124 41.2 3 250 29.5 894 35.7 1 334 36.2 1 618 34.1 2 237 Women 17.4 6 946 62.6 3 165 38.5 1 355 43.2 2 417 45.0 2 179 44.8 2 034 Residence Urban 13.7 6 362 47.1 1 657 31.9 1 657 38.1 2 894 40.7 2 532 38.6 1 741 Rural 15.6 6 709 53.4 4 758 43.2 604 44.3 862 42.0 1 264 39.6 2 530 *Frailty was defined as the accumulation of deficits using the frailty index approach. Index values of 0 to less than 0.2 were categorized as not frail, index values of 0.2–1.0 were categorized as frail. Open in new tab Table 2. The Prevalence of Frailty* Among Older Men and Women, by Country . China . . India . . Mexico . . Russian Federation . . South Africa . . Ghana . . . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Total 14.5 13 028 51.6 6 377 34.3 2 257 39.9 3 751 41.3 3 780 39.2 4 266 Age group 50–59 7.8 5 687 40.2 2 919 24.5 430 19.9 1 427 35.8 1 681 22.0 1 686 60–69 15.0 3 900 55.3 2 187 34.6 924 42.9 1 026 41.5 1 221 37.2 1 194 70–79 24.5 2 733 70.4 1 017 49.2 600 63.4 971 49.2 650 56.8 973 80+ 41.2 750 87.0 292 59.2 302 86.6 327 67.8 245 74.5 418 Gender Men 11.9 6 124 41.2 3 250 29.5 894 35.7 1 334 36.2 1 618 34.1 2 237 Women 17.4 6 946 62.6 3 165 38.5 1 355 43.2 2 417 45.0 2 179 44.8 2 034 Residence Urban 13.7 6 362 47.1 1 657 31.9 1 657 38.1 2 894 40.7 2 532 38.6 1 741 Rural 15.6 6 709 53.4 4 758 43.2 604 44.3 862 42.0 1 264 39.6 2 530 . China . . India . . Mexico . . Russian Federation . . South Africa . . Ghana . . . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Weighted Percent（%） . Number . Total 14.5 13 028 51.6 6 377 34.3 2 257 39.9 3 751 41.3 3 780 39.2 4 266 Age group 50–59 7.8 5 687 40.2 2 919 24.5 430 19.9 1 427 35.8 1 681 22.0 1 686 60–69 15.0 3 900 55.3 2 187 34.6 924 42.9 1 026 41.5 1 221 37.2 1 194 70–79 24.5 2 733 70.4 1 017 49.2 600 63.4 971 49.2 650 56.8 973 80+ 41.2 750 87.0 292 59.2 302 86.6 327 67.8 245 74.5 418 Gender Men 11.9 6 124 41.2 3 250 29.5 894 35.7 1 334 36.2 1 618 34.1 2 237 Women 17.4 6 946 62.6 3 165 38.5 1 355 43.2 2 417 45.0 2 179 44.8 2 034 Residence Urban 13.7 6 362 47.1 1 657 31.9 1 657 38.1 2 894 40.7 2 532 38.6 1 741 Rural 15.6 6 709 53.4 4 758 43.2 604 44.3 862 42.0 1 264 39.6 2 530 *Frailty was defined as the accumulation of deficits using the frailty index approach. Index values of 0 to less than 0.2 were categorized as not frail, index values of 0.2–1.0 were categorized as frail. Open in new tab Table 3 presents the associations between the prevalence of frailty and ambient PM2.5 exposure and the separate stratified analyses. Overall, for rural residence, each 10 μg/m3 increase in ambient PM2.5 was associated with a 29.7% increase in the odds of frailty (OR = 1.297, 95% CI: 1.211–1.388) after adjusting for various potential confounding factors including age, sex, education, wealth index, tobacco use, physical activity levels, temperature, and humidity. We did not observe significant associations in urban areas. In China, though, higher ambient PM2.5 was associated with lower odds of frailty in urban areas. But in South Africa, there was a significant positive association between ambient PM2.5 and frailty in both urban and rural areas. The gender-stratified association between ambient PM2.5 and frailty showed that the association seems to be slightly stronger in men (OR = 1.314, 95% CI: 1.182–1.460) than in women (OR = 1.207, 95% CI: 1.071–1.359) in rural areas. In spline regression models (Figure 2), the dose–response relationship was progressive over the range of ambient PM2.5 concentrations in rural areas. Table 3. Results From Stratified Analyses Examining the Association Between Ambient PM2.5 Exposure and Frailty PM2.5 (per 10 μg/m3 increase) . . Urban . . Rural . . . . OR . 95% CI . OR . 95% CI . Overall 1.030 0.717–1.480 1.297** 1.211–1.388 Country China 0.835* 0.705–0.991 0.926 0.761–1.126 Ghana 1.358 0.109–16.943 2.337 0.457–11.958 India 1.069 0.845–1.353 0.988 0.906–1.078 Mexico 1.693 0.483–5.931 5.195* 1.056–25.555 Russian Federation 0.309 0.018–5.442 0.180 0.001–54.057 South Africa 10.473** 3.616–30.332 4.780** 1.625–14.060 Age group 50–59 1.101 0.644–1.882 1.231** 1.145–1.325 60–69 0.925 0.632–1.354 1.335** 1.111–1.603 70–79 0.767** 0.702–0.838 1.016 0.841–1.227 80+ 0.868 0.752–1.001 0.885 0.572–1.368 Gender Men 0.947 0.711–1.260 1.314** 1.182–1.460 Women 0.925 0.608–1.406 1.207** 1.071–1.359 PM2.5 (per 10 μg/m3 increase) . . Urban . . Rural . . . . OR . 95% CI . OR . 95% CI . Overall 1.030 0.717–1.480 1.297** 1.211–1.388 Country China 0.835* 0.705–0.991 0.926 0.761–1.126 Ghana 1.358 0.109–16.943 2.337 0.457–11.958 India 1.069 0.845–1.353 0.988 0.906–1.078 Mexico 1.693 0.483–5.931 5.195* 1.056–25.555 Russian Federation 0.309 0.018–5.442 0.180 0.001–54.057 South Africa 10.473** 3.616–30.332 4.780** 1.625–14.060 Age group 50–59 1.101 0.644–1.882 1.231** 1.145–1.325 60–69 0.925 0.632–1.354 1.335** 1.111–1.603 70–79 0.767** 0.702–0.838 1.016 0.841–1.227 80+ 0.868 0.752–1.001 0.885 0.572–1.368 Gender Men 0.947 0.711–1.260 1.314** 1.182–1.460 Women 0.925 0.608–1.406 1.207** 1.071–1.359 Notes: OR = odds ratio; CI = confidence interval. Odds ratios are adjusted for age, education, gender, wealth index, fruit and vegetable intake, tobacco use, physical activity level, cooking fuel use, temperature, and humidity. *p < .05, **p < .01. Open in new tab Table 3. Results From Stratified Analyses Examining the Association Between Ambient PM2.5 Exposure and Frailty PM2.5 (per 10 μg/m3 increase) . . Urban . . Rural . . . . OR . 95% CI . OR . 95% CI . Overall 1.030 0.717–1.480 1.297** 1.211–1.388 Country China 0.835* 0.705–0.991 0.926 0.761–1.126 Ghana 1.358 0.109–16.943 2.337 0.457–11.958 India 1.069 0.845–1.353 0.988 0.906–1.078 Mexico 1.693 0.483–5.931 5.195* 1.056–25.555 Russian Federation 0.309 0.018–5.442 0.180 0.001–54.057 South Africa 10.473** 3.616–30.332 4.780** 1.625–14.060 Age group 50–59 1.101 0.644–1.882 1.231** 1.145–1.325 60–69 0.925 0.632–1.354 1.335** 1.111–1.603 70–79 0.767** 0.702–0.838 1.016 0.841–1.227 80+ 0.868 0.752–1.001 0.885 0.572–1.368 Gender Men 0.947 0.711–1.260 1.314** 1.182–1.460 Women 0.925 0.608–1.406 1.207** 1.071–1.359 PM2.5 (per 10 μg/m3 increase) . . Urban . . Rural . . . . OR . 95% CI . OR . 95% CI . Overall 1.030 0.717–1.480 1.297** 1.211–1.388 Country China 0.835* 0.705–0.991 0.926 0.761–1.126 Ghana 1.358 0.109–16.943 2.337 0.457–11.958 India 1.069 0.845–1.353 0.988 0.906–1.078 Mexico 1.693 0.483–5.931 5.195* 1.056–25.555 Russian Federation 0.309 0.018–5.442 0.180 0.001–54.057 South Africa 10.473** 3.616–30.332 4.780** 1.625–14.060 Age group 50–59 1.101 0.644–1.882 1.231** 1.145–1.325 60–69 0.925 0.632–1.354 1.335** 1.111–1.603 70–79 0.767** 0.702–0.838 1.016 0.841–1.227 80+ 0.868 0.752–1.001 0.885 0.572–1.368 Gender Men 0.947 0.711–1.260 1.314** 1.182–1.460 Women 0.925 0.608–1.406 1.207** 1.071–1.359 Notes: OR = odds ratio; CI = confidence interval. Odds ratios are adjusted for age, education, gender, wealth index, fruit and vegetable intake, tobacco use, physical activity level, cooking fuel use, temperature, and humidity. *p < .05, **p < .01. Open in new tab Figure 2. Open in new tabDownload slide The concentration–response curves for PM2.5 on frailty in urban and rural areas of 6 low- and middle-income countries. Note: Odds ratios (95% confidence intervals) of frailty according to PM2.5 concentrations based on restricted cubic splines with knots at the quartiles. The reference value is set as the minimum value. Odds ratios are adjusted for age, education, gender, wealth index, fruit and vegetable intake, tobacco use, physical activity levels, cooking fuel use, temperature, and humidity. Lines represent the odds ratio (thick line) and 95% confidence interval (dashed lines). Figure 2. Open in new tabDownload slide The concentration–response curves for PM2.5 on frailty in urban and rural areas of 6 low- and middle-income countries. Note: Odds ratios (95% confidence intervals) of frailty according to PM2.5 concentrations based on restricted cubic splines with knots at the quartiles. The reference value is set as the minimum value. Odds ratios are adjusted for age, education, gender, wealth index, fruit and vegetable intake, tobacco use, physical activity levels, cooking fuel use, temperature, and humidity. Lines represent the odds ratio (thick line) and 95% confidence interval (dashed lines). The sensitivity analyses suggested that the length of exposure had little effect on the association between PM2.5 and frailty in rural areas. Similarly, the association remains unchanged significantly after including the frailty index as a continuous variable in the model (Supplementary Tables 1 and 2). Discussion In a large sample of community-based adults aged 50 years and older from 6 MICs, we found strong evidence that long-term PM2.5 exposure was associated with frailty in rural areas. The association was robust, remaining significant when using the average concentrations of 1-year, 2-year, 4-year, and 5-year PM2.5 exposure before the survey after adjusting for demographic characteristics, behavioral risk factors, and indoor air pollution. To our best knowledge, this is the first study to investigate the association between ambient PM2.5 exposure and frailty as measured using a frailty index among community-dwelling older adults in MICs. Numerous epidemiological studies have demonstrated a positive correlation between exposure to ambient PM2.5 and risk of cardiovascular disease (22,23), the incidence of acute coronary events, and cerebrovascular events (10,24). Some studies have also highlighted an association between exposure to ambient PM2.5 and depression in low-income countries and MICs (8). Very few studies have investigated the effect of long-term exposure to ambient PM2.5 on frailty. In a nationwide analysis of 28 million individuals 55 years and older across 3 034 counties in the United States, higher levels of PM2.5 air pollution were associated with lower population-based probabilities of exceptional aging, defined as reaching age 85 years (25). Another study found increased odds of developing frailty as measured using a frailty index associated with exposure to PM2.5 among postmyocardial infarction patients (9). Our study has for the first time described the association between long-term exposure to ambient PM2.5 and frailty among the general population aged 50 and older in middle-income settings. We also found an approximately linear dose–response relationship in rural areas in each country. Air pollution, including long-term exposure to ambient PM2.5, induces chronic systemic oxidative stress, inflammation, hormonal changes, and genetic and epigenetic modifications (26,27), all of which can cause damage to cellular and molecular structures. This damage may promote cumulative decline in multiple physiological systems like the endocrine, immune, and skeletal muscle. Abnormal results in 3 or more systems are a significant predictor of frailty (28). This suggested the onset of frailty when physiological decline reaches an aggregate critical mass. However, while markers of inflammation related to air pollution were not predictive of the onset of frailty in one study (3), underlying environmental factors like long-term exposure to ambient PM2.5 in combination with genetic factors may play an essential role in this process. In this study, physiological decline and genetic factors might be underlying mechanisms contributing to the observed association between long-term exposure to ambient PM2.5 and frailty. The country-stratified analyses showed that the association between ambient PM2.5 and frailty is quite different in urban China and South Africa; the reason for the difference is poorly understood. Still, there are significant differences in PM2.5 exposure and sample size between the 2 countries. That is why we further used pooled data to analyze the association between PM2.5 and frailty after adjusting for the impact at the national level. The gender-stratified analysis also showed that the association seems to be slightly stronger in men than in women in rural areas. This result is consistent with a Danish cohort study that showed higher hazard ratios for all-cause and cardiovascular disease mortality in men than in women for PM2.5 and PM10 (29). Our findings suggested that PM2.5 exposure was associated with an increased risk of frailty in rural-dwelling older adults, but no consistent associations were observed in urban areas. The differences between urban and rural areas are unknown, especially with the widespread media coverage of urban pollution in Beijing and New Delhi, for example, but could be related to the type of employment. Public health and policy measures would be crucial in preventing air pollution. Differences in how the policy is implemented and monitored in urban and rural areas may contribute to the differences. Health care access and supply often differ between urban and rural areas and may play a role in compensating for health loss caused by environmental pollution. Even though association estimates were generally inconsistent in urban areas, we noted a more robust association among males, older respondents, and those with lower education levels. Our findings warrant further investigation of location-specific mechanisms and other community or individual characteristics that may play a role in the onset of frailty. Our study was based on a large population-based sample of respondents with relatively high response rates across the 6 MICs. Furthermore, we included vital risk factors that may have affected the results, including socioeconomic status, behavioral risk factors, indoor air pollution, and other factors, for potential confounding. The inclusion should help to improve the study’s ability to detect any real associations. There are also limitations to acknowledge. This study can only state associations between long-term PM2.5 and frailty but cannot infer causality, in part because of the cross-sectional data used in this study SAGE Wave 1. The use of remote sensing data in estimating PM2.5 in grid cell of 100 km2 in our study resulted in relatively small exposure contrast, contributing to measurement errors in PM2.5 exposure. However, this method is widely applied to estimate the global burden of ambient PM2.5 (20,30). A study in China during 2013–2015 also reported a high correlation between ground-based PM2.5 concentrations and estimates from monitoring stations (23). In addition, due to the limited data, we only used the annual average PM2.5 concentration values as the exposure variable. The use of annual concentration may result in less accurate results, especially in areas with high variable PM2.5 concentrations over the year. However, as our health outcome, frailty index, reflects a more stable, chronic long-term situation collected at a one time point, we argue that higher resolution PM2.5 concentrations and temperature data might not provide additional information to address our research question. Finally, we also did not consider the effect of population migration on PM2.5 exposure assessment. The effect of population movement on effect estimation is more complex; the population in the exposed area can move out or in or both. If the population only moves out from the exposed area, the effect estimate is underestimated to a greater extent as the migration rate increases. In summary, our study suggests that long-term exposure to ambient PM2.5 is a significant risk factor of frailty in the rural-dwelling population aged 50 years and older in 6 MICs. More efforts are needed to protect older adults from ambient outdoor and indoor air pollution, especially in rural areas. In addition, more studies are needed with a particular focus on whether or how environmental pollutants affect frailty in older adult populations. Acknowledgments We accomplished the study within the context of the Swedish National Graduate School for Competitive Science on Ageing and Health (SWEAH) funded by the Swedish Research Council. Funding This work was supported by the World Health Organization and the US National Institute on Aging through Interagency Agreements (OGHA 04034785, YA1323-08-CN-0020, Y1-AG-1005-01) and a research grant (R01-AG034479); the Shanghai New Three-year Action Plan for Public Health (grant No. GWV-10.1-XK16). The Network for International Longitudinal Studies on Ageing, funded by the Swedish Forte Network grant (Dnr: 2015-01499), supported N.N.’s contribution in this article. It was also supported by Shanghai Municipal Health Commission, Shanghai, China (201840118). Conflict of Interest None declared. Author Contributions All authors contributed to the study concept and design, acquisition, analysis, or interpretation of data. Y.F.G. and N.N. did the statistical analyses. Y.F.G. conducted the literature search and wrote the first draft of the manuscript. All authors critically revised the manuscript for important intellectual content and approved the final version. F.W. obtained the funding. References 1. Hoek G , Krishnan RM, Beelen R, et al. Long-term air pollution exposure and cardio-respiratory mortality: a review . Environ Health. 2013 ; 12 ( 1 ): 43 . doi:10.1186/1476-069X-12-43 Google Scholar Crossref Search ADS PubMed WorldCat 2. 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This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact [email protected] © The Author(s) 2022. Published by Oxford University Press on behalf of The Gerontological Society of America.

Weir, Kristie Rebecca; Ailabouni, Nagham J; Schneider, Carl R; Hilmer, Sarah N; Reeve, Emily

doi: 10.1093/gerona/glab222pmid: 34390339

Abstract Background Harmful and/or unnecessary medication use in older adults is common. This indicates deprescribing (supervised withdrawal of inappropriate medicines) is not happening as often as it should. This study aimed to synthesize the results of the Patients’ Attitudes Towards Deprescribing (PATD) questionnaire (and revised versions). Methods Databases were searched from January 2013 to March 2020. Google Scholar was used for citation searching of the development and validation manuscripts to identify original research using the validated PATD, revised PATD (older adult and caregiver versions), and the version for people with cognitive impairment (rPATDcog). Two authors extracted data independently. A meta-analysis of proportions (random-effects model) was conducted with subgroup meta-analyses for setting and population. The primary outcome was the question: “If my doctor said it was possible, I would be willing to stop one or more of my medicines.” Secondary outcomes were associations between participant characteristics and primary outcome and other (r)PATD results. Results We included 46 articles describing 40 studies (n = 10,816 participants). The meta-analysis found the proportion of participants who agreed or strongly agreed with this statement was 84% (95% CI 81%–88%) and 80% (95% CI 74%–86%) in patients and caregivers, respectively, with significant heterogeneity (I2 = 95% and 77%). Conclusion Consumers reported willingness to have a medication deprescribed although results should be interpreted with caution due to heterogeneity. The findings from this study moves toward understanding attitudes toward deprescribing, which could increase the discussion and uptake of deprescribing recommendations in clinical practice. Caregivers, Inappropriate prescribing, Medications, Older adults, Polypharmacy Internationally, there has been focus on the increasing prevalence and harms of multiple medication use in the older population (1). As people age, there may be changes in medical conditions and other medications, as well as a change in their preferences and treatment goals, which can shift medications toward an unfavorable benefit to risk ratio (2). A medication is considered inappropriate when potential harms outweigh potential benefits in the individual (3). An American study of older veterans (n = 462,405) found that 50% were dispensed one or more potentially inappropriate medications (4). The use of potentially inappropriate medications in older adults increases the risk of adverse drug reactions, functional impairment (5), hospitalization, and mortality (3,6–8). This places a high burden on older adults and health care systems due to associated costs (9,10). This highlights the need for deprescribing, which has been defined as the process of withdrawal of an inappropriate medication, supervised by a health care professional with the goal of managing polypharmacy and improving outcomes (11). Systematic reviews of randomized controlled trials assessing the effectiveness of deprescribing interventions showed that deprescribing is feasible and safe to implement in a research setting (12,13). To implement deprescribing in “real life” clinical practice, it is essential to understand the barriers and enablers for deprescribing. Clinicians commonly report consumers (patients and their caregivers) as being resistant to deprescribing, and patients can have internally contradictory beliefs in that they perceive all their medications are necessary but also want to take fewer (14–16). The most frequently used patient questionnaires for the assessment of self-reported attitudes toward deprescribing is the Patients’ Attitudes Towards Deprescribing (PATD) questionnaire (17). It was developed in 2013 as an exploratory research tool and revised with versions for older adults, caregivers, and people with cognitive impairment (rPATD (18) and rPATDcog) (19). This manuscript uses “(r)PATD” to denote all versions of the questionnaire. The original PATD underwent face, content, criterion, internal validity, and sensitivity and reliability testing. The questionnaire was then revised due to limitations of the original PATD (designed to be exploratory, no scoring ability, limited scope of potential barriers and enablers) and to simultaneously develop a version for informal caregivers. The rPATD underwent face, construct, content, criterion-related validity testing, internal consistency (Chronbach’s α > .65 for all factors), and test–retest consistency (gamma values between 0.57 and 0.89, p < .00 for factor scores). The rPATDcog was adapted from the older adult’s version of the rPATD, including shortening the questionnaire and simplifying the wording and response options, making it researcher/clinician administered (rather than self-administered), and conducting face validity. The retained questions were those with the greatest item-to-total correlation to the overall factor score. (r)PATD has been used internationally in multiple research studies with variable findings. Substantial differences exist between the published studies using the (r)PATD in terms of population, method of measurement, and associations with participant characteristics. The aims of this systematic review were (i) to determine the willingness of adults, caregivers, and people living with cognitive impairment to have a medication deprescribed; (ii) to describe the participant characteristics associated with willingness to have a medication deprescribed; and (iii) to report the attitudes and beliefs of adults and caregivers about their medications and deprescribing as reported through use of the (r)PATD. Method We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA). The protocol was preregistered in PROSPERO (CRD42020150007). Inclusion and Exclusion Criteria Studies were eligible if they were original studies that enrolled adults (>18 years) with any medical condition taking at least one medication or caregivers of such adults. All study types and settings were included if one or more of the questionnaires of interest were administered and quantitative results captured. No language or other limits were applied. Search Medline via Ovid, EMBASE, Scopus, International Pharmaceutical Abstracts, and Web of Science core collections for conference abstracts were searched from the date of first publication of the original PATD manuscript, January 2013, to March 2020. Google Scholar was used for citation searching of the development and validation manuscripts of the (r)PATD questionnaires (17–19). We emailed anyone who had contacted the primary author of the (r)PATD (ER) for permission to use the questionnaires to identify gray literature. Title/abstract and full text screening was conducted independently by 2 researchers. Disagreements were resolved by discussion. Data Extraction Data extracted independently by 2 authors using a standardized form included author, year of publication, study setting, design, participant characteristics, self-reported attitudes toward deprescribing ((r)PATD), and associations between willingness to deprescribe and participants’ characteristics. Modifications to any (r)PATD questions and details regarding translations were captured. Studies written in a language other than English were translated using a professional translation service. Corresponding authors were contacted when the primary outcome was not clearly reported. Two authors independently assessed the quality of reporting using the SUrvey Reporting GuidelinE (SURGE) (20) (modified slightly for the purposes of this review). Outcomes The primary outcome of interest was self-reported willingness to have a medication deprescribed, defined as the proportion of participants who responded “agree” or “strongly agree” to “Would you be willing to have one or more of your medicines stopped if your doctor said it was possible?” A version of this question is present in all versions of the (r)PATD. Secondary outcomes were associations between the primary outcome and participant characteristics and other (r)PATD results. Analysis For the primary outcome, a random-effects meta-analysis of proportions using the restricted maximum likelihood method was performed in R v3.5.1 using the “meta” package. The proportion was recalculated from the relevant numerator (number who responded agree or strongly agree) and denominator (number who responded to the questionnaire). Proportions were transformed for meta-analysis via the Freeman–Tukey double arsine function to normalize distributions. Funnel plots were used to identify publication bias by plotting the proportion against the standard error and sample size. To investigate heterogeneity, we performed analyses of predefined subgroups based on study setting, population, survey administration, and peer-reviewed status. Secondary outcomes were synthesized and presented narratively. Caregiver and rPATDcog results are presented separately. Results Study Characteristics We identified and included 40 eligible studies reported in 46 articles (Supplementary Figure 1). Sample sizes ranged from 18 to 1981 participants with a total of 10,816 participants (Table 1). The studies were conducted in Australia (n = 12) (17–19,21–32), Malaysia (n = 4) (33–37), the United States (n = 4) (38–42), Canada (n = 3) (43–45), the Netherlands (n = 3) (46–49), Denmark (n = 2) (50,51), Singapore (n = 2) (52,53), Jordan (54), Belgium (55), Ethiopia (56), India (57), Italy (58), Spain (59), Japan (60), Pakistan (61), Ireland (62), and the United Kingdom (63) (one each). Twenty-two studies used the original PATD (17,21–24,26,27,29,30,32,33,38,43–45,48,49,51,53,57–61), 17 used the older adults version of the rPATD (18,25,28,31,34–37,39–42,46,47,50,52,54–56,62,63), and 1 used the rPATDcog (19). Six studies that used the rPATD/rPATDcog also used the caregiver version of the rPATD (19,31,34,35,52,55,63). Most studies used the (r)PATD questionnaires specifically for measuring attitudes in a cross-sectional study. However, some studies (n = 4) (21,22,41,44,62) used the questionnaires as a baseline and/or outcome measure in a deprescribing intervention study. More than half of the 40 studies (n = 24, 60%) (18,19,21,22,24–27,29,31,33,39,40,43–45,48–52,55–58,62,63) focused on older adults. The median age of participants included in the studies ranged from 51 to 87 years old. Seventeen (43%) studies (18,21,22,24,30,31,33–35,38,41–44,46,47,49,52,53,61,62) were conducted in the community or primary care setting, 9 (23%) in the hospital setting (17,25,29,32,45,52,58,61,63), and 8 (20%) (19,23,39,40,53,54,56,59,60) in the outpatient setting. Six studies translated the PATD (43,48,49,51,58,59), and 7 studies translated the rPATD (34,35,37,46,47,50,54–56); 13 studies in total (Supplementary Table S2). Four studies used medication-specific questions in adapted (r)PATD questionnaires on statins (29), alpha-blockers (46,47), benzodiazepines (25), and proton pump inhibitors (Supplementary Table S3) (39,40). Table 1. Study and Participant Characteristics Source, Year, Country . Sample Size, Study Design . Study Population . Age, Years (Median) . Female % . Number of Medications (Median) . Translated, Language . Questionnaire Modified, How . PATD questionnaire Anderson et al., 2020 (21,22), Australia 78, pragmatic controlled, pre-post, mixed methods study Community setting, aged 65+ y, taking ≥5 medications 74 59 8 N Y, only first 10 questions reported Aoki et al., 2019 (60), Japan 1483, cross-sectional survey Outpatient, adults aged 18+ y, taking ≥1 medication NR 49 NR N N Candela et al., 2019 (thesis) (59), Spain 210, cross-sectional survey Outpatient, adults aged 18+ y, HIV-positive patients on antiretroviral therapy 51 23 5 Y, Spanish Y, translated Cross et al., 2020 (23), Australia 50, feasibility study, pre-post intervention study Outpatient, patients at risk of a medication-related problem 81 36 11 N Y, only first 10 questions reported Frankowski et al., 2019 (48), Netherlands 47, observational descriptive study Geriatric psychiatry residential ward, taking ≥5 medications 67 51 11 Y*, Dutch Y, deleted Q8, Q14 and Q15 Galazzi et al., 2016 (58), Italy 100, cross-sectional survey Hospital setting, aged 65+ y 79 47 6 Y, Italian Y, translated and deleted Q14 Gillespie et al., 2019 (24), Australia 137, cross-sectional survey Community setting, aged 65+ y, taking ≥5 medications 76 61 7 N Y, deleted Q14 and Q15 Goulding unpublished (38), United States 75, pre-post intervention study Community setting, patients with serious mental illness enrolled in a medication adherence program 60‡ 56 NR N N Hao et al., 2018 (33), Malaysia 222, cross-sectional survey Community setting, aged 65+ y, taking ≥5 medications 70 58 6 NR Y, Q11 modified Hendrix et al., 2019 (26), Australia 383, cross-sectional survey Residential aged care facility, aged 65+ y 88‡ 76 10 N N Kalogianis et al., 2016 (27), Australia 232, cross-sectional survey Residential aged care facility, aged 65+ y 87‡ 76 15†,‡ N Y, minor wording changes to allow for interviewer administered Ng et al., 2017 (53), Singapore 136, cross-sectional survey Outpatient health care centers, adults aged 45+ y, taking ≥5 medications 68 41 6 N NR Qi et al., 2015 (29), Australia 180, cross-sectional survey Hospital setting, aged 65+ y, taking a statin medication 78 47 8/10§ N Y, 5 statin specific questions added Reeve et al., 2014 (thesis) (30),‖ Australia 77, cross-sectional survey Community pharmacies, adults aged 18+ y, taking ≥1 medication 69 51 5 N Y, Q11 was not used Reeve et al., 2013 (PATD development + results) (17,32), Australia 100, development of a questionnaire, cross-sectional survey Outpatients, adults aged 18+ y, taking ≥1 medication 72 55 10 N N Saraswathy et al., 2018 (57),¶ India 257, observational study Residential aged care facility NR 48 NR NR NR Schiøtz et al., 2018 (51), Denmark 100, cross-sectional survey Outpatient clinics, aged 65+ y, taking ≥10 medications 75 63 12 Y, Danish Y, translated and Q9 modified Sirois et al., 2017 (43), Canada 129, cross-sectional survey Community setting, aged 65+ y, taking ≥1 medication 76 63 6 Y, French Y, translated. 2 questions added about nurse involvement and follow-up for deprescribing Turner et al., 2018 (44), Canada 489, secondary analysis of a randomized controlled trial Community setting, aged 65+ y, taking ≥1 medication, taking specific medication 75‡ 66 9‡ N Y, only first 10 questions reported ul Haq et al., 2016 (61),¶ Pakistan 207, cross-sectional survey Hospitals and community pharmacies NR NR NR NR NR Van Marum et al., 2016 (49), Netherlands 40, interview and cross-sectional survey Community setting, older adults aged 70+ y, taking ≥7 medications 79 55 11‡ Y, Dutch Y, translated. Deleted Q8, Q14 and Q15 Whitty et al., 2018 (45), Canada 53, pilot study Hospital setting, seriously ill or frail older patients 80‡ 43 13‡ NR Y, Q11 response items changed to 2-point scale (Yes and No), deleted Q12 and Q13 rPATD, rPATDcog questionnaires Cardwell et al., 2020 (62), Ireland 786, non-randomized pilot study Community setting, aged 65+ y, taking ≥10 medications 70‡ 65 10‡ N N Edelman et al., 2019 (46,47), The Netherlands 179, cross-sectional survey Community setting, men aged 30+ y, taking an alpha-blocker, diagnosed with lower urinary tract symptoms 69‡ 0 4 Y, Dutch Y, translated. Modified questions to create alpha-blocker-specific rPATD factors Gnjidic et al., 2019 (25), Australia 42, feasibility study Hospital setting, aged 65+ y, taking a benzodiazepine 72 55 10 N Y, 5 benzodiazepine-specific questions were added Ikeji et al., 2019 (39,40), United States 19, cross-sectional survey Outpatient, aged 65+ y, taking a Proton Pump Inhibitor NR 60 NR N Y, the questionnaire was modified to focus on proton pump inhibitors Kua C-H et al., 2020 (52), Singapore 615, cross-sectional survey Hospitals, community pharmacies and primary care clinics, aged 65+ y, taking ≥1 medication. Caregivers 73‡ 44 5‡ N N Kua K et al., 2019 (34,35,63), Malaysia 502, cross-sectional survey Community pharmacies and primary care clinics, aged 60+ y, taking ≥1 medication. Caregivers 67 50 3 Y, Mandarin and Malay Y, translated Lundby et al., 2019 (50),¶ Denmark 159, validation study and cross-sectional survey Residential aged care facility 82 61 NR Y, Danish Y, translated Major et al., 2019 (28),# Australia 66, intervention study and survey Community setting NR NR 12‡ N Y, Q7 (primary outcome) was not asked Martinez et al., 2020 (41), United States 30, pre-post intervention study Community setting, adults aged 18+ y, with insomnia 56‡ 100 4‡ N NR Ng et al., 2019,(36),¶ Malaysia¶ 18, cross-sectional survey Community setting, adults aged 18+ y, diagnosed with Parkinson’s disease 64 44 5** NR NR Nusair et al., 2020 (54), Jordan 358, validation study and survey Outpatient, adults aged 18+ y, taking ≥5 medications 60‡ 52 7‡ Y, Arabic Y, translated Omar et al., 2019 (37), Malaysia 182, cross-sectional survey Primary care clinics, aged 65+ y, taking ≥1 medication 72 52 6 Y, Malay Y, translated Paque et al., 2019 (55), Belgium 296, cross-sectional survey Residential aged care facility, aged 65+ y, limited life expectancy. Caregivers 86‡ 74 7‡ Y, Dutch Y, translated and added a question about patients’ willingness to speak to their GP about their medications Reeve et al., 2019 (rPATD development and results) (18,31), Australia 386, cross-sectional survey Community setting, aged 65+ y, taking ≥1 medication. Caregivers 74 57 NR N N Reeve et al., 2018 (42),†† United States 1981, cross-sectional survey Community setting, aged 65+ y NR 55 NR N Y, combined 10 questions from the PATD and rPATD (older adults’ version),modified to a 4-point Likert scale (deleted unsure) Reeve et al., 2018 (rPATDcog) (19), Australia 21, development and pilot study of the rPATDcog Outpatient, adults aged 18+ y, taking ≥1 medication, with a diagnosis of mild cognitive impairment or dementia. Caregivers 77‡ 48 7‡ N Y, the rPATD questionnaire for older adults was used to develop the rPATDcog questionnaire Scott et al., 2019 (63), United Kingdom 75, cross-sectional survey Hospital setting, aged 70+ y, with physical frailty or comorbidities. Caregivers 87 45 8 N Y, Q10 minor changes to fit the UK context regarding cost of medicines Tegegn et al., 2018 (56), Ethiopia 316, cross-sectional survey Outpatient, aged 65+ y, taking ≥1 medication 70 45 3 Y, Amharic Y, translated and modified to a 4-point Likert scale (deleted unsure) Source, Year, Country . Sample Size, Study Design . Study Population . Age, Years (Median) . Female % . Number of Medications (Median) . Translated, Language . Questionnaire Modified, How . PATD questionnaire Anderson et al., 2020 (21,22), Australia 78, pragmatic controlled, pre-post, mixed methods study Community setting, aged 65+ y, taking ≥5 medications 74 59 8 N Y, only first 10 questions reported Aoki et al., 2019 (60), Japan 1483, cross-sectional survey Outpatient, adults aged 18+ y, taking ≥1 medication NR 49 NR N N Candela et al., 2019 (thesis) (59), Spain 210, cross-sectional survey Outpatient, adults aged 18+ y, HIV-positive patients on antiretroviral therapy 51 23 5 Y, Spanish Y, translated Cross et al., 2020 (23), Australia 50, feasibility study, pre-post intervention study Outpatient, patients at risk of a medication-related problem 81 36 11 N Y, only first 10 questions reported Frankowski et al., 2019 (48), Netherlands 47, observational descriptive study Geriatric psychiatry residential ward, taking ≥5 medications 67 51 11 Y*, Dutch Y, deleted Q8, Q14 and Q15 Galazzi et al., 2016 (58), Italy 100, cross-sectional survey Hospital setting, aged 65+ y 79 47 6 Y, Italian Y, translated and deleted Q14 Gillespie et al., 2019 (24), Australia 137, cross-sectional survey Community setting, aged 65+ y, taking ≥5 medications 76 61 7 N Y, deleted Q14 and Q15 Goulding unpublished (38), United States 75, pre-post intervention study Community setting, patients with serious mental illness enrolled in a medication adherence program 60‡ 56 NR N N Hao et al., 2018 (33), Malaysia 222, cross-sectional survey Community setting, aged 65+ y, taking ≥5 medications 70 58 6 NR Y, Q11 modified Hendrix et al., 2019 (26), Australia 383, cross-sectional survey Residential aged care facility, aged 65+ y 88‡ 76 10 N N Kalogianis et al., 2016 (27), Australia 232, cross-sectional survey Residential aged care facility, aged 65+ y 87‡ 76 15†,‡ N Y, minor wording changes to allow for interviewer administered Ng et al., 2017 (53), Singapore 136, cross-sectional survey Outpatient health care centers, adults aged 45+ y, taking ≥5 medications 68 41 6 N NR Qi et al., 2015 (29), Australia 180, cross-sectional survey Hospital setting, aged 65+ y, taking a statin medication 78 47 8/10§ N Y, 5 statin specific questions added Reeve et al., 2014 (thesis) (30),‖ Australia 77, cross-sectional survey Community pharmacies, adults aged 18+ y, taking ≥1 medication 69 51 5 N Y, Q11 was not used Reeve et al., 2013 (PATD development + results) (17,32), Australia 100, development of a questionnaire, cross-sectional survey Outpatients, adults aged 18+ y, taking ≥1 medication 72 55 10 N N Saraswathy et al., 2018 (57),¶ India 257, observational study Residential aged care facility NR 48 NR NR NR Schiøtz et al., 2018 (51), Denmark 100, cross-sectional survey Outpatient clinics, aged 65+ y, taking ≥10 medications 75 63 12 Y, Danish Y, translated and Q9 modified Sirois et al., 2017 (43), Canada 129, cross-sectional survey Community setting, aged 65+ y, taking ≥1 medication 76 63 6 Y, French Y, translated. 2 questions added about nurse involvement and follow-up for deprescribing Turner et al., 2018 (44), Canada 489, secondary analysis of a randomized controlled trial Community setting, aged 65+ y, taking ≥1 medication, taking specific medication 75‡ 66 9‡ N Y, only first 10 questions reported ul Haq et al., 2016 (61),¶ Pakistan 207, cross-sectional survey Hospitals and community pharmacies NR NR NR NR NR Van Marum et al., 2016 (49), Netherlands 40, interview and cross-sectional survey Community setting, older adults aged 70+ y, taking ≥7 medications 79 55 11‡ Y, Dutch Y, translated. Deleted Q8, Q14 and Q15 Whitty et al., 2018 (45), Canada 53, pilot study Hospital setting, seriously ill or frail older patients 80‡ 43 13‡ NR Y, Q11 response items changed to 2-point scale (Yes and No), deleted Q12 and Q13 rPATD, rPATDcog questionnaires Cardwell et al., 2020 (62), Ireland 786, non-randomized pilot study Community setting, aged 65+ y, taking ≥10 medications 70‡ 65 10‡ N N Edelman et al., 2019 (46,47), The Netherlands 179, cross-sectional survey Community setting, men aged 30+ y, taking an alpha-blocker, diagnosed with lower urinary tract symptoms 69‡ 0 4 Y, Dutch Y, translated. Modified questions to create alpha-blocker-specific rPATD factors Gnjidic et al., 2019 (25), Australia 42, feasibility study Hospital setting, aged 65+ y, taking a benzodiazepine 72 55 10 N Y, 5 benzodiazepine-specific questions were added Ikeji et al., 2019 (39,40), United States 19, cross-sectional survey Outpatient, aged 65+ y, taking a Proton Pump Inhibitor NR 60 NR N Y, the questionnaire was modified to focus on proton pump inhibitors Kua C-H et al., 2020 (52), Singapore 615, cross-sectional survey Hospitals, community pharmacies and primary care clinics, aged 65+ y, taking ≥1 medication. Caregivers 73‡ 44 5‡ N N Kua K et al., 2019 (34,35,63), Malaysia 502, cross-sectional survey Community pharmacies and primary care clinics, aged 60+ y, taking ≥1 medication. Caregivers 67 50 3 Y, Mandarin and Malay Y, translated Lundby et al., 2019 (50),¶ Denmark 159, validation study and cross-sectional survey Residential aged care facility 82 61 NR Y, Danish Y, translated Major et al., 2019 (28),# Australia 66, intervention study and survey Community setting NR NR 12‡ N Y, Q7 (primary outcome) was not asked Martinez et al., 2020 (41), United States 30, pre-post intervention study Community setting, adults aged 18+ y, with insomnia 56‡ 100 4‡ N NR Ng et al., 2019,(36),¶ Malaysia¶ 18, cross-sectional survey Community setting, adults aged 18+ y, diagnosed with Parkinson’s disease 64 44 5** NR NR Nusair et al., 2020 (54), Jordan 358, validation study and survey Outpatient, adults aged 18+ y, taking ≥5 medications 60‡ 52 7‡ Y, Arabic Y, translated Omar et al., 2019 (37), Malaysia 182, cross-sectional survey Primary care clinics, aged 65+ y, taking ≥1 medication 72 52 6 Y, Malay Y, translated Paque et al., 2019 (55), Belgium 296, cross-sectional survey Residential aged care facility, aged 65+ y, limited life expectancy. Caregivers 86‡ 74 7‡ Y, Dutch Y, translated and added a question about patients’ willingness to speak to their GP about their medications Reeve et al., 2019 (rPATD development and results) (18,31), Australia 386, cross-sectional survey Community setting, aged 65+ y, taking ≥1 medication. Caregivers 74 57 NR N N Reeve et al., 2018 (42),†† United States 1981, cross-sectional survey Community setting, aged 65+ y NR 55 NR N Y, combined 10 questions from the PATD and rPATD (older adults’ version),modified to a 4-point Likert scale (deleted unsure) Reeve et al., 2018 (rPATDcog) (19), Australia 21, development and pilot study of the rPATDcog Outpatient, adults aged 18+ y, taking ≥1 medication, with a diagnosis of mild cognitive impairment or dementia. Caregivers 77‡ 48 7‡ N Y, the rPATD questionnaire for older adults was used to develop the rPATDcog questionnaire Scott et al., 2019 (63), United Kingdom 75, cross-sectional survey Hospital setting, aged 70+ y, with physical frailty or comorbidities. Caregivers 87 45 8 N Y, Q10 minor changes to fit the UK context regarding cost of medicines Tegegn et al., 2018 (56), Ethiopia 316, cross-sectional survey Outpatient, aged 65+ y, taking ≥1 medication 70 45 3 Y, Amharic Y, translated and modified to a 4-point Likert scale (deleted unsure) Notes: *Implied in the article: a translated questionnaire was based on comparative research (van Marum et al. (49)). †Regular and medications taken as required. ‡Mean. §Discrepancy in the manuscript text and table. ‖This reference contains results from 2 cohorts; one of these cohorts was published separately (and so are reported separately: Reeve 2013). Data presented here are from the second cohort only (community pharmacy participants). ¶This is an abstract. #This is an editorial comment. **Including supplements. ††Combined PATD and rPATD questions, for clarity we have classified this reference as using the rPATD questionnaire. Open in new tab Table 1. Study and Participant Characteristics Source, Year, Country . Sample Size, Study Design . Study Population . Age, Years (Median) . Female % . Number of Medications (Median) . Translated, Language . Questionnaire Modified, How . PATD questionnaire Anderson et al., 2020 (21,22), Australia 78, pragmatic controlled, pre-post, mixed methods study Community setting, aged 65+ y, taking ≥5 medications 74 59 8 N Y, only first 10 questions reported Aoki et al., 2019 (60), Japan 1483, cross-sectional survey Outpatient, adults aged 18+ y, taking ≥1 medication NR 49 NR N N Candela et al., 2019 (thesis) (59), Spain 210, cross-sectional survey Outpatient, adults aged 18+ y, HIV-positive patients on antiretroviral therapy 51 23 5 Y, Spanish Y, translated Cross et al., 2020 (23), Australia 50, feasibility study, pre-post intervention study Outpatient, patients at risk of a medication-related problem 81 36 11 N Y, only first 10 questions reported Frankowski et al., 2019 (48), Netherlands 47, observational descriptive study Geriatric psychiatry residential ward, taking ≥5 medications 67 51 11 Y*, Dutch Y, deleted Q8, Q14 and Q15 Galazzi et al., 2016 (58), Italy 100, cross-sectional survey Hospital setting, aged 65+ y 79 47 6 Y, Italian Y, translated and deleted Q14 Gillespie et al., 2019 (24), Australia 137, cross-sectional survey Community setting, aged 65+ y, taking ≥5 medications 76 61 7 N Y, deleted Q14 and Q15 Goulding unpublished (38), United States 75, pre-post intervention study Community setting, patients with serious mental illness enrolled in a medication adherence program 60‡ 56 NR N N Hao et al., 2018 (33), Malaysia 222, cross-sectional survey Community setting, aged 65+ y, taking ≥5 medications 70 58 6 NR Y, Q11 modified Hendrix et al., 2019 (26), Australia 383, cross-sectional survey Residential aged care facility, aged 65+ y 88‡ 76 10 N N Kalogianis et al., 2016 (27), Australia 232, cross-sectional survey Residential aged care facility, aged 65+ y 87‡ 76 15†,‡ N Y, minor wording changes to allow for interviewer administered Ng et al., 2017 (53), Singapore 136, cross-sectional survey Outpatient health care centers, adults aged 45+ y, taking ≥5 medications 68 41 6 N NR Qi et al., 2015 (29), Australia 180, cross-sectional survey Hospital setting, aged 65+ y, taking a statin medication 78 47 8/10§ N Y, 5 statin specific questions added Reeve et al., 2014 (thesis) (30),‖ Australia 77, cross-sectional survey Community pharmacies, adults aged 18+ y, taking ≥1 medication 69 51 5 N Y, Q11 was not used Reeve et al., 2013 (PATD development + results) (17,32), Australia 100, development of a questionnaire, cross-sectional survey Outpatients, adults aged 18+ y, taking ≥1 medication 72 55 10 N N Saraswathy et al., 2018 (57),¶ India 257, observational study Residential aged care facility NR 48 NR NR NR Schiøtz et al., 2018 (51), Denmark 100, cross-sectional survey Outpatient clinics, aged 65+ y, taking ≥10 medications 75 63 12 Y, Danish Y, translated and Q9 modified Sirois et al., 2017 (43), Canada 129, cross-sectional survey Community setting, aged 65+ y, taking ≥1 medication 76 63 6 Y, French Y, translated. 2 questions added about nurse involvement and follow-up for deprescribing Turner et al., 2018 (44), Canada 489, secondary analysis of a randomized controlled trial Community setting, aged 65+ y, taking ≥1 medication, taking specific medication 75‡ 66 9‡ N Y, only first 10 questions reported ul Haq et al., 2016 (61),¶ Pakistan 207, cross-sectional survey Hospitals and community pharmacies NR NR NR NR NR Van Marum et al., 2016 (49), Netherlands 40, interview and cross-sectional survey Community setting, older adults aged 70+ y, taking ≥7 medications 79 55 11‡ Y, Dutch Y, translated. Deleted Q8, Q14 and Q15 Whitty et al., 2018 (45), Canada 53, pilot study Hospital setting, seriously ill or frail older patients 80‡ 43 13‡ NR Y, Q11 response items changed to 2-point scale (Yes and No), deleted Q12 and Q13 rPATD, rPATDcog questionnaires Cardwell et al., 2020 (62), Ireland 786, non-randomized pilot study Community setting, aged 65+ y, taking ≥10 medications 70‡ 65 10‡ N N Edelman et al., 2019 (46,47), The Netherlands 179, cross-sectional survey Community setting, men aged 30+ y, taking an alpha-blocker, diagnosed with lower urinary tract symptoms 69‡ 0 4 Y, Dutch Y, translated. Modified questions to create alpha-blocker-specific rPATD factors Gnjidic et al., 2019 (25), Australia 42, feasibility study Hospital setting, aged 65+ y, taking a benzodiazepine 72 55 10 N Y, 5 benzodiazepine-specific questions were added Ikeji et al., 2019 (39,40), United States 19, cross-sectional survey Outpatient, aged 65+ y, taking a Proton Pump Inhibitor NR 60 NR N Y, the questionnaire was modified to focus on proton pump inhibitors Kua C-H et al., 2020 (52), Singapore 615, cross-sectional survey Hospitals, community pharmacies and primary care clinics, aged 65+ y, taking ≥1 medication. Caregivers 73‡ 44 5‡ N N Kua K et al., 2019 (34,35,63), Malaysia 502, cross-sectional survey Community pharmacies and primary care clinics, aged 60+ y, taking ≥1 medication. Caregivers 67 50 3 Y, Mandarin and Malay Y, translated Lundby et al., 2019 (50),¶ Denmark 159, validation study and cross-sectional survey Residential aged care facility 82 61 NR Y, Danish Y, translated Major et al., 2019 (28),# Australia 66, intervention study and survey Community setting NR NR 12‡ N Y, Q7 (primary outcome) was not asked Martinez et al., 2020 (41), United States 30, pre-post intervention study Community setting, adults aged 18+ y, with insomnia 56‡ 100 4‡ N NR Ng et al., 2019,(36),¶ Malaysia¶ 18, cross-sectional survey Community setting, adults aged 18+ y, diagnosed with Parkinson’s disease 64 44 5** NR NR Nusair et al., 2020 (54), Jordan 358, validation study and survey Outpatient, adults aged 18+ y, taking ≥5 medications 60‡ 52 7‡ Y, Arabic Y, translated Omar et al., 2019 (37), Malaysia 182, cross-sectional survey Primary care clinics, aged 65+ y, taking ≥1 medication 72 52 6 Y, Malay Y, translated Paque et al., 2019 (55), Belgium 296, cross-sectional survey Residential aged care facility, aged 65+ y, limited life expectancy. Caregivers 86‡ 74 7‡ Y, Dutch Y, translated and added a question about patients’ willingness to speak to their GP about their medications Reeve et al., 2019 (rPATD development and results) (18,31), Australia 386, cross-sectional survey Community setting, aged 65+ y, taking ≥1 medication. Caregivers 74 57 NR N N Reeve et al., 2018 (42),†† United States 1981, cross-sectional survey Community setting, aged 65+ y NR 55 NR N Y, combined 10 questions from the PATD and rPATD (older adults’ version),modified to a 4-point Likert scale (deleted unsure) Reeve et al., 2018 (rPATDcog) (19), Australia 21, development and pilot study of the rPATDcog Outpatient, adults aged 18+ y, taking ≥1 medication, with a diagnosis of mild cognitive impairment or dementia. Caregivers 77‡ 48 7‡ N Y, the rPATD questionnaire for older adults was used to develop the rPATDcog questionnaire Scott et al., 2019 (63), United Kingdom 75, cross-sectional survey Hospital setting, aged 70+ y, with physical frailty or comorbidities. Caregivers 87 45 8 N Y, Q10 minor changes to fit the UK context regarding cost of medicines Tegegn et al., 2018 (56), Ethiopia 316, cross-sectional survey Outpatient, aged 65+ y, taking ≥1 medication 70 45 3 Y, Amharic Y, translated and modified to a 4-point Likert scale (deleted unsure) Source, Year, Country . Sample Size, Study Design . Study Population . Age, Years (Median) . Female % . Number of Medications (Median) . Translated, Language . Questionnaire Modified, How . PATD questionnaire Anderson et al., 2020 (21,22), Australia 78, pragmatic controlled, pre-post, mixed methods study Community setting, aged 65+ y, taking ≥5 medications 74 59 8 N Y, only first 10 questions reported Aoki et al., 2019 (60), Japan 1483, cross-sectional survey Outpatient, adults aged 18+ y, taking ≥1 medication NR 49 NR N N Candela et al., 2019 (thesis) (59), Spain 210, cross-sectional survey Outpatient, adults aged 18+ y, HIV-positive patients on antiretroviral therapy 51 23 5 Y, Spanish Y, translated Cross et al., 2020 (23), Australia 50, feasibility study, pre-post intervention study Outpatient, patients at risk of a medication-related problem 81 36 11 N Y, only first 10 questions reported Frankowski et al., 2019 (48), Netherlands 47, observational descriptive study Geriatric psychiatry residential ward, taking ≥5 medications 67 51 11 Y*, Dutch Y, deleted Q8, Q14 and Q15 Galazzi et al., 2016 (58), Italy 100, cross-sectional survey Hospital setting, aged 65+ y 79 47 6 Y, Italian Y, translated and deleted Q14 Gillespie et al., 2019 (24), Australia 137, cross-sectional survey Community setting, aged 65+ y, taking ≥5 medications 76 61 7 N Y, deleted Q14 and Q15 Goulding unpublished (38), United States 75, pre-post intervention study Community setting, patients with serious mental illness enrolled in a medication adherence program 60‡ 56 NR N N Hao et al., 2018 (33), Malaysia 222, cross-sectional survey Community setting, aged 65+ y, taking ≥5 medications 70 58 6 NR Y, Q11 modified Hendrix et al., 2019 (26), Australia 383, cross-sectional survey Residential aged care facility, aged 65+ y 88‡ 76 10 N N Kalogianis et al., 2016 (27), Australia 232, cross-sectional survey Residential aged care facility, aged 65+ y 87‡ 76 15†,‡ N Y, minor wording changes to allow for interviewer administered Ng et al., 2017 (53), Singapore 136, cross-sectional survey Outpatient health care centers, adults aged 45+ y, taking ≥5 medications 68 41 6 N NR Qi et al., 2015 (29), Australia 180, cross-sectional survey Hospital setting, aged 65+ y, taking a statin medication 78 47 8/10§ N Y, 5 statin specific questions added Reeve et al., 2014 (thesis) (30),‖ Australia 77, cross-sectional survey Community pharmacies, adults aged 18+ y, taking ≥1 medication 69 51 5 N Y, Q11 was not used Reeve et al., 2013 (PATD development + results) (17,32), Australia 100, development of a questionnaire, cross-sectional survey Outpatients, adults aged 18+ y, taking ≥1 medication 72 55 10 N N Saraswathy et al., 2018 (57),¶ India 257, observational study Residential aged care facility NR 48 NR NR NR Schiøtz et al., 2018 (51), Denmark 100, cross-sectional survey Outpatient clinics, aged 65+ y, taking ≥10 medications 75 63 12 Y, Danish Y, translated and Q9 modified Sirois et al., 2017 (43), Canada 129, cross-sectional survey Community setting, aged 65+ y, taking ≥1 medication 76 63 6 Y, French Y, translated. 2 questions added about nurse involvement and follow-up for deprescribing Turner et al., 2018 (44), Canada 489, secondary analysis of a randomized controlled trial Community setting, aged 65+ y, taking ≥1 medication, taking specific medication 75‡ 66 9‡ N Y, only first 10 questions reported ul Haq et al., 2016 (61),¶ Pakistan 207, cross-sectional survey Hospitals and community pharmacies NR NR NR NR NR Van Marum et al., 2016 (49), Netherlands 40, interview and cross-sectional survey Community setting, older adults aged 70+ y, taking ≥7 medications 79 55 11‡ Y, Dutch Y, translated. Deleted Q8, Q14 and Q15 Whitty et al., 2018 (45), Canada 53, pilot study Hospital setting, seriously ill or frail older patients 80‡ 43 13‡ NR Y, Q11 response items changed to 2-point scale (Yes and No), deleted Q12 and Q13 rPATD, rPATDcog questionnaires Cardwell et al., 2020 (62), Ireland 786, non-randomized pilot study Community setting, aged 65+ y, taking ≥10 medications 70‡ 65 10‡ N N Edelman et al., 2019 (46,47), The Netherlands 179, cross-sectional survey Community setting, men aged 30+ y, taking an alpha-blocker, diagnosed with lower urinary tract symptoms 69‡ 0 4 Y, Dutch Y, translated. Modified questions to create alpha-blocker-specific rPATD factors Gnjidic et al., 2019 (25), Australia 42, feasibility study Hospital setting, aged 65+ y, taking a benzodiazepine 72 55 10 N Y, 5 benzodiazepine-specific questions were added Ikeji et al., 2019 (39,40), United States 19, cross-sectional survey Outpatient, aged 65+ y, taking a Proton Pump Inhibitor NR 60 NR N Y, the questionnaire was modified to focus on proton pump inhibitors Kua C-H et al., 2020 (52), Singapore 615, cross-sectional survey Hospitals, community pharmacies and primary care clinics, aged 65+ y, taking ≥1 medication. Caregivers 73‡ 44 5‡ N N Kua K et al., 2019 (34,35,63), Malaysia 502, cross-sectional survey Community pharmacies and primary care clinics, aged 60+ y, taking ≥1 medication. Caregivers 67 50 3 Y, Mandarin and Malay Y, translated Lundby et al., 2019 (50),¶ Denmark 159, validation study and cross-sectional survey Residential aged care facility 82 61 NR Y, Danish Y, translated Major et al., 2019 (28),# Australia 66, intervention study and survey Community setting NR NR 12‡ N Y, Q7 (primary outcome) was not asked Martinez et al., 2020 (41), United States 30, pre-post intervention study Community setting, adults aged 18+ y, with insomnia 56‡ 100 4‡ N NR Ng et al., 2019,(36),¶ Malaysia¶ 18, cross-sectional survey Community setting, adults aged 18+ y, diagnosed with Parkinson’s disease 64 44 5** NR NR Nusair et al., 2020 (54), Jordan 358, validation study and survey Outpatient, adults aged 18+ y, taking ≥5 medications 60‡ 52 7‡ Y, Arabic Y, translated Omar et al., 2019 (37), Malaysia 182, cross-sectional survey Primary care clinics, aged 65+ y, taking ≥1 medication 72 52 6 Y, Malay Y, translated Paque et al., 2019 (55), Belgium 296, cross-sectional survey Residential aged care facility, aged 65+ y, limited life expectancy. Caregivers 86‡ 74 7‡ Y, Dutch Y, translated and added a question about patients’ willingness to speak to their GP about their medications Reeve et al., 2019 (rPATD development and results) (18,31), Australia 386, cross-sectional survey Community setting, aged 65+ y, taking ≥1 medication. Caregivers 74 57 NR N N Reeve et al., 2018 (42),†† United States 1981, cross-sectional survey Community setting, aged 65+ y NR 55 NR N Y, combined 10 questions from the PATD and rPATD (older adults’ version),modified to a 4-point Likert scale (deleted unsure) Reeve et al., 2018 (rPATDcog) (19), Australia 21, development and pilot study of the rPATDcog Outpatient, adults aged 18+ y, taking ≥1 medication, with a diagnosis of mild cognitive impairment or dementia. Caregivers 77‡ 48 7‡ N Y, the rPATD questionnaire for older adults was used to develop the rPATDcog questionnaire Scott et al., 2019 (63), United Kingdom 75, cross-sectional survey Hospital setting, aged 70+ y, with physical frailty or comorbidities. Caregivers 87 45 8 N Y, Q10 minor changes to fit the UK context regarding cost of medicines Tegegn et al., 2018 (56), Ethiopia 316, cross-sectional survey Outpatient, aged 65+ y, taking ≥1 medication 70 45 3 Y, Amharic Y, translated and modified to a 4-point Likert scale (deleted unsure) Notes: *Implied in the article: a translated questionnaire was based on comparative research (van Marum et al. (49)). †Regular and medications taken as required. ‡Mean. §Discrepancy in the manuscript text and table. ‖This reference contains results from 2 cohorts; one of these cohorts was published separately (and so are reported separately: Reeve 2013). Data presented here are from the second cohort only (community pharmacy participants). ¶This is an abstract. #This is an editorial comment. **Including supplements. ††Combined PATD and rPATD questions, for clarity we have classified this reference as using the rPATD questionnaire. Open in new tab Regarding the quality of reporting, all studies described or partially described the questionnaire used (100%, 38/38) and most referenced the original work (95%, 36/38; see Supplementary Tables 4 and 5). Assessment of quality reporting was unable to be performed on 2 of the studies (38,59). Most studies gave a description of the desired population (89%, 34/38), 79% (30/38) reported how the survey was administered, and 74% (28/38) at least partially reported the psychometric properties of the (r)PATD. However, 26 studies (68%) did not report the format of the survey (paper, online, or both) and half (19/38) did not present a sample size calculation or justification of sample size. Willingness to Have a Medication Deprescribed Overall, 49%–98% (n = 36 studies) of patients in the included studies were willing to stop 1 or more of their medications if their doctor said it was possible (Tables 2 and 3). Three studies did not report the results to this question as a proportion. From the rPATDcog (n = 1), 82% of patients (with cognitive impairment) were willing to have a medication deprescribed if their doctor said it was possible (19). Our meta-analysis showed the pooled proportion was 84% (95% CI 81%–88%, I2 = 95%) of patients who responded “agree” or “strongly agree” to the question: “Would you be willing to have one or more of your medicines stopped if your doctor said it was possible?” (Figure 1). There was significant heterogeneity overall and the subgroup analyses (Supplementary Figure 2) were not able to explain the heterogeneity. We found limited evidence of publication bias based on visual inspection of the funnel plots (Supplementary Figure 3). Table 2. PATD Questionnaire Results Open in new tab Table 2. PATD Questionnaire Results Open in new tab Table 3. Older Adults’ Results From the rPATD Questionnaire Open in new tab Table 3. Older Adults’ Results From the rPATD Questionnaire Open in new tab Figure 1. Open in new tabDownload slide Forest plots of proportion of participants who agreed or strongly agreed with the question “If my doctor said it was possible, I would be willing to stop one or more of my medicines”. (A) Forest plot patients. (B) Forest plot caregivers. The majority of caregivers (65%–87%, n = 5 studies) reported that they would be willing for one or more of their care recipient’s medications to be stopped if their care recipient’s doctor said it was possible (Supplementary Table S6) (19,31,34,35,52,55,63). The pooled effect estimate was 80% (95% CI 74%–86%, I2 = 77%). Responses to the (r)PATD Questionnaires The questions from the PATD which had the smallest ranges of responses (ie, least variation in findings across studies) were “I feel that I may be taking one or more medications that I no longer need” (studies found between 8% and 38% agreement in 32/39 studies (as this question is in both the PATD and rPATD questionnaires), “I believe one or more of my medications is giving me side effects” (11%–44% over 19/22 studies), and “I believe that all my medications are necessary” (56%–92% in 18/22 studies). Although the questions with the greatest variation across studies were “I would like to reduce the number of medications that I am taking” (17%–89% over 18/22 studies) and “I would accept taking more medications for my health conditions” (10%–84% in 17/22 studies; see Table 2). Studies that used the rPATD questionnaire (Table 3) found that 27%–52% of participants would be reluctant to stop a medicine they had taken for a long time (12/17 studies). Most participants (67%–93%) reported they were satisfied with their current medicines (12/17 studies), whereas 24%–100% of participants felt that they knew exactly which medicines they take and/or have an up-to-date list (12/17 studies) and 7%–90% of participants felt that one or more of their medicines may not be working (11/17 studies). In response to the statement: “I would like to try stopping one of my medicines to see how I feel without it,” 9%–44% of participants agreed (14/17; see Table 3). Findings of the caregivers’ version of the rPATD are presented in Supplementary Table S6. Associations Between Participant Characteristics and Willingness to Have a Medication Deprescribed Fourteen studies examined relationships between participant characteristics and the primary outcome willingness to have a medication deprescribed (Table 4 and Supplementary Table S7). The most common patient characteristics examined were age (n = 12), gender (n = 6), education level (n = 6), number of medications (n = 11), and chronic health conditions (n = 4). Five of 12 studies reported a significant association between age and willingness to have a medication deprescribed, although the direction of this association varied (eg, older age compared with younger age were both found to be associated with greater willingness to have a medication deprescribed). Three studies examined relationships between caregiver characteristics and the primary outcome willingness to have a medication deprescribed (Supplementary Table S8). Table 4. Associations With the Primary Outcome Question “If my doctor said it was possible, I would be willing to stop one or more of my regular medicines” . Variables (Statistical Significance, Direction of Association) . Source, Year . Age . Number of Medications . Number of Chronic Health Conditions . Gender (Female) . Education Level . Access Discount Medications* (Yes) . PATD  Aoki et al., 2019 (60) S, + S, + S, + NS NS /  Gillespie et al., 2019 (24) / NS / / / /  Hao et al., 2018 (33) S, − / / / / /  Kalogianis et al., 2016 (27) / NS / / / /  Qi et al., 2015 (29) NS NS / / / /  Reeve et al., 2013  (PATD development + results) (17,32) NS NS NS / / S, −  Reeve et al., 2014 (thesis) (30)† NS NS / / / /  ul Haq et al., 2016 (61) S, + NS / / / NS rPATD  Kua C-H et al., 2020 (52) NS S‡ / NS NS /  Kua K et al., 2019 (34) S, + NS / NS S, − /  Ng et al., 2017 (53) S, − / / / / /  Reeve et al., 2019 (rPATD results) (18,31) NS NS / NS NS S, +  Reeve et al., 2018 (42) NS S, + S, + NS NS NS  Tegegn et al., 2018 (56) NS / NS§ NS NS / Total examined 11 10 4 6 5 4 Total significant 5 3 2 0 1 2 . Variables (Statistical Significance, Direction of Association) . Source, Year . Age . Number of Medications . Number of Chronic Health Conditions . Gender (Female) . Education Level . Access Discount Medications* (Yes) . PATD  Aoki et al., 2019 (60) S, + S, + S, + NS NS /  Gillespie et al., 2019 (24) / NS / / / /  Hao et al., 2018 (33) S, − / / / / /  Kalogianis et al., 2016 (27) / NS / / / /  Qi et al., 2015 (29) NS NS / / / /  Reeve et al., 2013  (PATD development + results) (17,32) NS NS NS / / S, −  Reeve et al., 2014 (thesis) (30)† NS NS / / / /  ul Haq et al., 2016 (61) S, + NS / / / NS rPATD  Kua C-H et al., 2020 (52) NS S‡ / NS NS /  Kua K et al., 2019 (34) S, + NS / NS S, − /  Ng et al., 2017 (53) S, − / / / / /  Reeve et al., 2019 (rPATD results) (18,31) NS NS / NS NS S, +  Reeve et al., 2018 (42) NS S, + S, + NS NS NS  Tegegn et al., 2018 (56) NS / NS§ NS NS / Total examined 11 10 4 6 5 4 Total significant 5 3 2 0 1 2 Notes: / = not examined; NS = not significant; S = significant. “+” denotes increasing/higher variable (or female gender or possession of a medication concession card) associated with increasing willingness to deprescribe. “−” denotes decreasing/lower variable (or male gender or no medication concession card) associated with increasing willingness to deprescribe. *Participants had a medication concession card or drug cost was covered/fully subsidised. †This reference contains results from 2 cohorts; one of these cohorts was published separately (and so are reported separately: Reeve 2013). Data presented here are from the second cohort only (community pharmacy participants). ‡Unclear if the direction of the finding is “+” or “−” ; significant difference was found between groups (1–5, 6–10, and >10), but authors report “No significant differences in sub-group analysis.” §Charlson Comorbidity Index. Open in new tab Table 4. Associations With the Primary Outcome Question “If my doctor said it was possible, I would be willing to stop one or more of my regular medicines” . Variables (Statistical Significance, Direction of Association) . Source, Year . Age . Number of Medications . Number of Chronic Health Conditions . Gender (Female) . Education Level . Access Discount Medications* (Yes) . PATD  Aoki et al., 2019 (60) S, + S, + S, + NS NS /  Gillespie et al., 2019 (24) / NS / / / /  Hao et al., 2018 (33) S, − / / / / /  Kalogianis et al., 2016 (27) / NS / / / /  Qi et al., 2015 (29) NS NS / / / /  Reeve et al., 2013  (PATD development + results) (17,32) NS NS NS / / S, −  Reeve et al., 2014 (thesis) (30)† NS NS / / / /  ul Haq et al., 2016 (61) S, + NS / / / NS rPATD  Kua C-H et al., 2020 (52) NS S‡ / NS NS /  Kua K et al., 2019 (34) S, + NS / NS S, − /  Ng et al., 2017 (53) S, − / / / / /  Reeve et al., 2019 (rPATD results) (18,31) NS NS / NS NS S, +  Reeve et al., 2018 (42) NS S, + S, + NS NS NS  Tegegn et al., 2018 (56) NS / NS§ NS NS / Total examined 11 10 4 6 5 4 Total significant 5 3 2 0 1 2 . Variables (Statistical Significance, Direction of Association) . Source, Year . Age . Number of Medications . Number of Chronic Health Conditions . Gender (Female) . Education Level . Access Discount Medications* (Yes) . PATD  Aoki et al., 2019 (60) S, + S, + S, + NS NS /  Gillespie et al., 2019 (24) / NS / / / /  Hao et al., 2018 (33) S, − / / / / /  Kalogianis et al., 2016 (27) / NS / / / /  Qi et al., 2015 (29) NS NS / / / /  Reeve et al., 2013  (PATD development + results) (17,32) NS NS NS / / S, −  Reeve et al., 2014 (thesis) (30)† NS NS / / / /  ul Haq et al., 2016 (61) S, + NS / / / NS rPATD  Kua C-H et al., 2020 (52) NS S‡ / NS NS /  Kua K et al., 2019 (34) S, + NS / NS S, − /  Ng et al., 2017 (53) S, − / / / / /  Reeve et al., 2019 (rPATD results) (18,31) NS NS / NS NS S, +  Reeve et al., 2018 (42) NS S, + S, + NS NS NS  Tegegn et al., 2018 (56) NS / NS§ NS NS / Total examined 11 10 4 6 5 4 Total significant 5 3 2 0 1 2 Notes: / = not examined; NS = not significant; S = significant. “+” denotes increasing/higher variable (or female gender or possession of a medication concession card) associated with increasing willingness to deprescribe. “−” denotes decreasing/lower variable (or male gender or no medication concession card) associated with increasing willingness to deprescribe. *Participants had a medication concession card or drug cost was covered/fully subsidised. †This reference contains results from 2 cohorts; one of these cohorts was published separately (and so are reported separately: Reeve 2013). Data presented here are from the second cohort only (community pharmacy participants). ‡Unclear if the direction of the finding is “+” or “−” ; significant difference was found between groups (1–5, 6–10, and >10), but authors report “No significant differences in sub-group analysis.” §Charlson Comorbidity Index. Open in new tab Discussion Main Findings We synthesized results of 40 studies that used the (r)PATD questionnaires. The included studies were diverse in study design, intended purpose, and characteristics examined. Overall, many participants were willing to have a medication deprescribed if their doctor said it was possible (84%, 95% CI 81%–88%). Caregiver data provided a similar result, 80% (95% CI 74%–86%). However, there was significant heterogeneity (I2 = 95% patients, 77% for carers) and no explanation for this was identified through the subgroup analyses. Approximately one third of the studies examined associations between participant characteristics and the primary outcome. However, there was inconsistency in whether there was statistical significance between characteristics and the primary outcome. In the studies where there was an association found, there was inconsistency in the direction of the association (ie, if the characteristic was associated with higher or lower willingness). As such, it is still unclear whether individual characteristics (such as age or number of medications) could predict participant willingness to have their medications deprescribed. Strengths and Limitations A strength of this review is that we included articles published in any language, conference abstracts, and gray literature. We identified unpublished (or locally published) articles/reports through contacting those who had requested permission to use the (r)PATD. Our multipronged search strategy, which included methods outside of traditional database searching, led to additional studies being included. Studies within the review were diverse in terms of setting, country, and design. Most studies in this review were from high-income countries, which may reflect missing studies, or that studies have not been done in these lower income countries. Few studies examined caregivers’ attitudes, and only a single study used the rPATDcog. Many of the included studies were cross-sectional and as such do not allow conclusions on causality (when examining the associations between participant characteristics and willingness to deprescribe). The (r)PATD, as a self-reported measure, is susceptible to social desirability bias. Although no difference was found in the subanalysis looking at method of administration (self-report, researcher administered) and several studies collected responses anonymously. Convenience samples were used in several studies; representativeness of the sample was described as one of the limitations in many of the included articles, and how nonrespondents differed from participants was rarely described. Overall, participants may have been healthier or more involved in managing their medications, particularly in studies of self-selected participants. One U.S. study (42) was conducted in a representative population and several studies targeted disadvantaged populations (38,41,48) without any obvious differences in (r)PATD responses in these studies. A checklist to assess the quality of reporting was used in place of a risk of bias tool (20). Such reporting checklists do not technically assess a study’s quality; however, no quality assessment tool was identified for surveys. The sections that were generally well reported included background, discussion, and ethics, whereas methods and results were less well reported. The studies within this review were somewhat heterogeneous, including how the (r)PATD was used. Although a number of translations have been published, it is unclear if they are all semantically equivalent to the English version. There was variation in the use of items and response scales, and few of the studies that modified the (r)PATD reported validation for their local context. However, most translated versions of the (r)PATD involved some piloting (5–28 patients), and modified questions were often reviewed by the research team. It is possible that cultural or country-specific differences exist in relation to patients’ attitudes toward deprescribing that may affect responses to the (r)PATD. Comparison With Other Studies There is an increasing understanding that medication optimization can be achieved by engaging older adults and their caregivers in deprescribing decisions and prioritizing patient-centered care. The synthesized results from this review can be interpreted in the context of findings using complementary surveys. The Patient Perceptions of Deprescribing (PPoD) survey (64,65) found that one third of participants (34%, n = 803) had experienced stopping a medication. Significant factors associated with past deprescribing experience included being told by a doctor or the patient asking to stop a medication, interest in deprescribing, shared decision making, and higher education. Alternatively, factors associated with decreased likelihood to deprescribe included polypharmacy and participants having higher trust in their doctor. Qualitative findings of patient-related barriers to deprescribing (66,67) recognize the often coexisting positive and negative attitudes toward deprescribing that patients have, as well as the complex interplay that exists between attitudes, beliefs, and decision making. The (r)PATD results reflect these seemingly contradictory attitudes in that individuals may say they are open to deprescribing but also report high satisfaction with their medications. Indeed, qualitative findings show clinicians perceive their patients are reluctant to deprescribe medications (68,69). Additionally, in previous studies, 30%–40% of participants have refused to participate in a deprescribing intervention study (70–72), irrespective of taking potentially inappropriate medications (73). Presently, the predictive ability of the (r)PATD has not been established, and it may be difficult to discriminate patient behavior from hypothetical willingness to deprescribe. Additionally, even though participants in the included studies overwhelmingly report agreement with deprescribing if their doctor said it was possible, the factors influencing acceptance of deprescribing in clinical practice at a single point in time are complex and multifaceted (74). Research, Clinical, and Policy Implications We found inconsistency in the participant characteristics that were associated with willingness to deprescribe. Understanding predictors of positive attitudes toward deprescribing more generally could enable tailored deprescribing practices as singular, external, or measurable factors might not consistently predict attitudes to deprescribing. There were some participant characteristics, such as frailty and dementia, that were only captured in a few studies or were measured in different ways. This highlights the need to consistently measure characteristics to add to the evidence base, particularly for these patients that stand to benefit the most from deprescribing (75). Deprescribing is a process that should involve the patient (76), therefore, an ongoing conversation with the patient and caregiver, and consideration of the complex internal and external factors that affect the implementation of deprescribing is required (77). Although mostly used in the research setting, the (r)PATD is increasingly used as part of a deprescribing intervention strategy, such as the Australian G-MEDSS (Goal-directed Medication Review Electronic Decision Support System) study (78,79) and the US OPTIMISE study (80,81). This highlights a shift toward implementation of self-assessment surveys in clinical practice to promote “real-time” support for deprescribing conversations. Further work is required to determine how and when the (r)PATD can be best utilized in clinical practice. Different public health and policy initiatives may be implemented to increase deprescribing activities. For instance, raising public awareness and acceptance of deprescribing as a normal and positive part of patient care may alleviate concerns patients may have to trial stopping a medication (this review found between 27% and 52% of participants were reluctant to stop a long-term medication) (82). Additionally, it is important to prioritize shared decision making (76) with a focus on patients’ goals and preferences, to navigate the seemingly contradictory beliefs of willingness to deprescribe yet feeling that their medications are appropriate. Remuneration and dedicated clinical consultations for these discussions may be needed to increase widespread deprescribing in practice. Conclusions Overall, clinicians should be reassured of their patients’ and caregivers’ willingness to have medications deprescribed. As such, this could encourage clinicians to initiate a conversation about deprescribing with those they care for. The findings from this study moves toward understanding attitudes toward deprescribing, which could, in turn, increase the discussion and uptake of deprescribing recommendations in clinical practice. Funding E.R. is an Australian National Health and Medical Research Council (NHMRC)–Australian Research Council (ARC) Dementia Research Development Fellow and K.R.W and N.J.A.’s salaries are supported by this award. Conflict of Interest Dr. Reeve was the lead author of the development of the PATD, rPATD, and rPATDcog (the questionnaire of interest in this systematic review). Acknowledgments We acknowledge Ms. Lorien Delaney, Academic Librarian at the University of South Australia for her support in refining the strategy used for the peer-review literature search. Thank you to all the researchers who responded to our requests for more information. Author Contributions All authors were involved in designing the study. K.R.W. and N.J.A. were involved in searching the database. K.R.W. and N.J.A. screened citations for inclusion. K.R.W., E.R., and N.J.A. were involved in extracting data and interpretation. K.R.W. and E.R. synthesized the data, and C.R.S. conducted the meta-analysis. 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Res Social Adm Pharm . 2019 ; 15 ( 6 ): 801 – 805 . doi:10.1016/j.sapharm.2018.08.013 Google Scholar Crossref Search ADS PubMed WorldCat © The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact [email protected] © The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America.

Gerlach, Lauren B; Myra Kim, Hyungjin; Ignacio, Rosalinda V; Strominger, Julie; Maust, Donovan T

doi: 10.1093/gerona/glab241pmid: 34410381

Abstract Background Previous findings regarding the association between benzodiazepine exposure and dementia have conflicted, though many have not accounted for anticholinergic exposure. The goal of this study was to evaluate the association of benzodiazepine exposure with the risk of developing dementia, accounting for the anticholinergic burden. Methods Using a retrospective cohort design, we identified veterans 65 or older without dementia during a 10-year baseline period and then followed participants for 5 years to evaluate the risk of dementia diagnosis. The primary exposure was cumulative benzodiazepine exposure. Cox proportional hazards survival model was used to examine the association between benzodiazepine exposure and dementia, adjusting for anticholinergic burden and other demographic and clinical characteristics associated with increased dementia risk. Results Of the 528 006 veterans in the study cohort, 28.5% had at least one fill for a benzodiazepine. Overall, 7.9% developed a diagnosis of dementia during the observation period. Compared to veterans with no exposure to benzodiazepines, the adjusted hazard ratios for dementia risk were 1.06 (95% confidence interval [CI] 1.02–1.10) for low benzodiazepine exposure, 1.05 (95% CI 1.01–1.09) for medium benzodiazepine exposure, and 1.05 (95% CI 1.02–1.09) for high benzodiazepine exposure. Conclusions Cumulative benzodiazepine exposure was minimally associated with increased dementia risk when compared with nonuse but did not increase in a dose-dependent fashion with higher exposure. Veterans with low benzodiazepine exposure had essentially the equivalent risk of developing dementia as veterans with high exposure. While benzodiazepines are associated with many side effects for older adults, higher cumulative use does not appear to increase dementia risk. Cognitive impairment, Sedative hypnotic, Veterans affairs Benzodiazepines are widely prescribed to older adults (1) despite the association of their use with a variety of adverse effects, including falls (2), fractures (3), and motor vehicle accidents (4). In addition, following opioids, benzodiazepines are the second most common medication class implicated in prescription drug overdose (5). Particularly concerning for older adults, benzodiazepines adversely affect cognition: In a review of 68 randomized, placebo-controlled trials, benzodiazepines consistently induced both amnestic and non-amnestic cognitive impairment, with larger effects among participants aged 65 years or older (6). Given these concerns, multiple professional societies have cautioned against long-term use of benzodiazepines among older adults given the high potential for side effects (7,8). Benzodiazepine use is associated with sedation and inattention as well as declines across several cognitive domains including reduced visuospatial ability, speed of processing, and visual learning (9). Additionally, conditions for which benzodiazepines may be prescribed (eg, depression, anxiety, posttraumatic stress disorder, and anxiety) have also been independently associated with an increased risk of dementia (10). While benzodiazepines’ adverse impact on cognition is clear, the link between benzodiazepine exposure and development of dementia is less so. Several studies have supported an association between benzodiazepine use and increased risk for dementia, including 2 recent analyses published by Billioti de Gage et al. (11,12). However, 2 other recent cohort studies (13,14) and a case–control analysis (15) did not find increased risk related to benzodiazepine exposure. An additional recent study specifically limited to patients with mood disorders also did not find an association between benzodiazepine exposure and dementia (16). One potential reason for the conflicting results is that these prior analyses, while accounting for a variety of potential confounders, did not account for anticholinergic exposure, which, if chronic, can cause Alzheimer-type pathology (17) and does appear to be associated with incident dementia (18). Adults who are prescribed benzodiazepines may also be more likely to take anticholinergic medications, exposure to which then confounds the apparent relationship of benzodiazepines with dementia. We sought to evaluate the association of cumulative benzodiazepine exposure and incident dementia utilizing veterans administration (VA) data over a 15-year period from FY2000 to FY2015, accounting for the important confounder of anticholinergic exposure. We hypothesized that high cumulative exposure to benzodiazepines would be associated with an increased risk of dementia. Method We linked prescription data from the VA Pharmacy Benefits Management service with the Corporate Data Warehouse patient data to evaluate the association between cumulative benzodiazepine exposure and incident dementia. The VA Ann Arbor Healthcare System institutional review board approved this study. Study Cohort Data were drawn from a 100% sample of VHA data from FY2000 to FY2015. This time span was split into 3 periods: a 10-year exposure period (FY2000–FY2009), a 1-year lag period (FY2010), and a 5-year outcome period (FY2011–FY2015). Veterans were eligible for the study cohort if they were older than 54 years at the start of the baseline (ie, October 1, 1999) and, during each year of the baseline exposure period, they met the following criteria: (a) had at least one outpatient or inpatient service encounter, (b) had at least one prescription claim, and (c) did not have a diagnosis of dementia. We required that the cohort meet the above criteria for each year of the baseline exposure period in order to ascertain cumulative prescription benzodiazepine exposure. By requiring patients to be older than 54 years at the start of baseline, all cohort veterans were aged 65 years or older at the start of the outcome period for dementia ascertainment. Incident Dementia Diagnosis Incident dementia was determined during the 5-year study outcome period from FY2011 to FY2015 (Figure 1). We identified a diagnosis of Alzheimer’s disease or related dementia during the observation period from outpatient and inpatient service encounters each year (see Supplementary Table 1 for International Classification of Disease, Ninth Revision codes). Figure 1. Open in new tabDownload slide Study timeline for primary (1a) and sensitivity (1b) analyses. Figure 1. Open in new tabDownload slide Study timeline for primary (1a) and sensitivity (1b) analyses. Cumulative Benzodiazepine Exposure Using prescription data from VA Pharmacy Benefits Management, we identified benzodiazepine exposure during the 10-year baseline exposure period. All benzodiazepine prescriptions were converted to lorazepam equivalents (Supplementary Table 2) (19,20). A daily dose equivalent of lorazepam 1 mg per day was considered the standardized daily dose (SDD). We then calculated the total medication dose for each benzodiazepine prescription fill in lorazepam equivalents by multiplying the tablet strength by the number of tablets dispensed. Using methods similar to Gray et al. (14,18), we calculated the total standardized daily dose (TSDD) by summing the SDD for all benzodiazepine prescriptions during the 10-year exposure window. We categorized cumulative use as no use, low use (1–30 TSDD), medium use (31–365 TSDD), and high use (366+ TSDD) based on the distribution and clinically meaningful cut-points. For example, a TSDD of 365 meant that a patient received 365 days of daily dose-equivalent of lorazepam 1 mg during the 10-year baseline exposure period. (That exposure could have occurred during 365 continuous days or been intermittent throughout the 10-year exposure period.) To limit protopathic bias, we excluded benzodiazepine exposure in the year preceding the observation period (ie, FY2010) because benzodiazepine prescriptions that close to an incident diagnosis could have been prescribed for prodromal symptoms of dementia (21). Figure 1 demonstrates the study baseline exposure and outcome periods. Covariates We selected covariates for inclusion that are potentially associated with incident dementia risk based on a review of the literature (10). Sociodemographic factors included age, sex, education, and income; education and median income were determined from Census data based on the veteran’s ZIP Code (22). Clinical conditions included indicators of cardiovascular risk (eg, stroke, diabetes, hypertension, myocardial infarction, congestive heart failure, angina, arrhythmia, hyperlipidemia, and peripheral artery disease) as well as depression, anxiety, posttraumatic stress disorder, insomnia, traumatic brain injury, tobacco use, and alcohol use. Clinical conditions were assessed from ICD-9-CM codes during the 10-year baseline and 1-year lag period. Lastly, studies have consistently demonstrated an association between exposure to medications with strong anticholinergic properties and an increased risk of dementia (18,23,24). To account for such exposure, we calculated cumulative strong anticholinergic medication burden during the same 10-year baseline exposure period from which benzodiazepine exposure was ascertained (ie, FY2000–FY2009). We calculated the total medication dose for each anticholinergic medication fill by multiplying the tablet strength by the number of tablets dispensed. We then calculated the SDD by dividing by the minimum effective dose per day recommended for use in older adults (18,24). For each veteran, the TSDD was calculated by summing the SDD for all anticholinergic medication fills during the baseline exposure period. As with benzodiazepines, prescriptions during the FY2010-lag period were excluded. TSDD anticholinergic medication exposures were categorized as no use, 1–30, 31–365, 366–1 095, and >1 095 based on previous studies (18,24). Statistical Analysis For descriptive purposes, we identified the most commonly prescribed benzodiazepines among veterans during the baseline period, characterized by half-life (20). Next, we summarized the sociodemographic and clinical characteristics of the cohort by benzodiazepine exposure groups. We used a Cox proportional hazards survival model to evaluate the association between cumulative benzodiazepine exposure and incident dementia. Participants were censored for (a) death, (b) gap in care for greater than 12 months from a last inpatient or outpatient service encounter (ie, on Day 366 following their last encounter), or (c) end of the study period. We fit the following models sequentially to meet our study objectives: Model 1 was unadjusted, Model 2 added patient sociodemographic characteristics (eg, age, income, education) and clinical conditions associated with incident dementia risk, and Model 3 then added the 10-year cumulative anticholinergic medication exposure during the baseline exposure period. The assumption of proportional hazards was assessed by examining plots of Schoenfeld residuals by transformed time for each variable. Plots did not suggest nonproportional hazards. We conducted several sensitivity analyses. First, given the potential that benzodiazepine use preceding a dementia diagnosis may be in response to prodromal symptoms of dementia, we extended the lag time to 3 years to help minimize potential protopathic bias. Second, we assessed whether results remained consistent for the risk of developing Alzheimer’s disease specifically (ICD-9-CM codes 290.0, 290.1x, 290.2x, 290.3, 294.10, 294.11, 331.0). Third, among veterans with high benzodiazepine exposure (ie, those with a TSDD 366+), we evaluated if the risk of incident dementia differed by the recency of benzodiazepine exposure (ie, recent users, past users, or continuous users) (14). Lastly, to differentially account for the competing risk of death, we reran the analysis with death as a competing risk using Fine and Gray subdistribution hazard models. All analyses were conducted in SAS version 9.4 (SAS Institute, Cary, NC). Results At the start of the outcome period in FY2010, the analytic cohort included 528 066 veterans. The mean age was 77.0 years (SD 7.4) and 97.7% were male. About 28.5% of veterans in the study cohort filled at least one prescription for a benzodiazepine during the exposure period; 7.9% of veterans in the study cohort received a diagnosis of dementia during the outcome period, of which 34.0% received benzodiazepines during the baseline exposure period. Baseline sociodemographic, clinical characteristics, and anticholinergic exposure by the level of benzodiazepine exposure are given in Table 1. Generally, those with higher benzodiazepine exposure had a higher prevalence of mental health disorders and insomnia, though they did not have a higher burden of other medical conditions. Those with the highest benzodiazepine exposure also had the highest burden of high-level anticholinergic exposure (ie, TSDD >1 095) and the smallest proportion with no anticholinergic exposure: Among those in the top tertile of benzodiazepine exposure, 43.9% had the highest category of anticholinergic exposure, while 19.8% had no exposure. Among the study cohort, lorazepam, temazepam, diazepam, clonazepam, and alprazolam were the most commonly prescribed benzodiazepines during the baseline exposure period (Table 2). Table 1. Baseline Sociodemographic and Clinical Characteristics of Veterans at the Start of Observation Period, FY2010 . . Benzodiazepine Exposure* . Characteristics, N (%) . Overall (N = 528 066) . None (N = 377 784), N (%) . Low (N = 33 613), N (%) . Medium (N = 46 551), N (%) . High (N = 70 118), N (%) . Age, mean (SD) 77.0 (7.4) 77.4 (7.2) 75.8 (7.3) 76.2 (7.5) 75.4 (7.6) Male 515 979 (97.7) 370 295 (98.0) 32 419 (96.4) 45 125 (96.9) 68 140 (97.2) % with less than high school education, mean (SD) 15.7 (9.0) 15.6 (9.0) 15.8 (9.1) 15.9 (9.1) 15.9 (8.9) Median household income ($), mean (SD) 49 620 (19 106.9) 49 825 (19 248.7) 49 787 (19 124.0) 48 989 (18 815.6) 48 860 (18 485.8) Depression 67 643 (12.8) 30 316 (8.0) 5 230 (15.6) 9 955 (21.4) 22 142 (31.6) Anxiety 33 556 (6.4) 8 960 (2.4) 2 008 (6.0) 5 120 (11.0) 17 468 (24.9) Posttraumatic stress disorder 33 567 (6.4) 13 182 (3.5) 2 252 (6.7) 4 743 (10.2) 13 390 (19.1) Alcohol disorder 15 560 (2.9) 9 164 (2.4) 1 551 (4.6) 1 838 (3.9) 3 007 (4.3) Diabetes 201 714 (38.2) 145 371 (38.5) 13 151 (39.1) 18 053 (38.8) 25 139 (35.9) Hypertension 388 628 (73.6) 279 973 (74.1) 25 023 (74.4) 34 123 (73.3) 49 509 (70.6) Insomnia 19 351 (3.7) 7 299 (1.9) 1 529 (4.5) 3 442 (7.4) 7 081 (10.1) Stroke 22 290 (4.2) 14 921 (3.9) 1 806 (5.4) 2 298 (4.9) 3 265 (4.7) Traumatic brain injury 2 531 (0.5) 1 443 (0.4) 223 (0.7) 350 (0.8) 515 (0.7) Tobacco use disorder 47 481 (9.0) 30 472 (8.1) 3 573 (10.6) 4 877 (10.5) 8 559 (12.2) Myocardial infarction 23 416 (4.4) 15 279 (4.0) 1 863 (5.5) 2 624 (5.6) 3 650 (5.2) Coronary heart failure 48 723 (9.2) 32 416 (8.6) 3 872 (11.5) 5 342 (11.5) 7 093 (10.1) Angina 12 628 (2.4) 7 807 (2.1) 1 138 (3.4) 1 549 (3.3) 2 134 (3.0) Arrhythmia 98 457 (18.6) 69 106 (18.3) 7 001 (20.8) 9 580 (20.6) 12 770 (18.2) Hyperlipidemia 358 767 (67.9) 259 062 (68.6) 22 372 (66.6) 31 132 (66.9) 46 201 (65.9) Peripheral artery disease 44 881 (8.5) 30 919 (8.2) 3 376 (10.0) 4 394 (9.4) 6 192 (8.8) Cumulative anticholinergic medication use†  None 218 301 (41.3) 184 238 (48.8) 8 926 (26.6) 11 238 (24.1) 13 899 (19.8)  TSDD 1–30 42 132 (8.0) 32 118 (8.5) 3 297 (9.8) 3 322 (7.1) 3 395 (4.8)  TSDD 31–365 107 502 (20.4) 74 047 (19.6) 9 218 (27.4) 11 645 (25.0) 12 592 (18.0)  TSDD 366–1 095 53 454 (10.1) 32 349 (8.6) 4 634 (13.8) 7 025 (15.1) 9 446 (13.5)  TSDD >1 095 106 677 (20.2) 55 032 (14.6) 7 538 (22.4) 13 321 (28.6) 30 786 (43.9) . . Benzodiazepine Exposure* . Characteristics, N (%) . Overall (N = 528 066) . None (N = 377 784), N (%) . Low (N = 33 613), N (%) . Medium (N = 46 551), N (%) . High (N = 70 118), N (%) . Age, mean (SD) 77.0 (7.4) 77.4 (7.2) 75.8 (7.3) 76.2 (7.5) 75.4 (7.6) Male 515 979 (97.7) 370 295 (98.0) 32 419 (96.4) 45 125 (96.9) 68 140 (97.2) % with less than high school education, mean (SD) 15.7 (9.0) 15.6 (9.0) 15.8 (9.1) 15.9 (9.1) 15.9 (8.9) Median household income ($), mean (SD) 49 620 (19 106.9) 49 825 (19 248.7) 49 787 (19 124.0) 48 989 (18 815.6) 48 860 (18 485.8) Depression 67 643 (12.8) 30 316 (8.0) 5 230 (15.6) 9 955 (21.4) 22 142 (31.6) Anxiety 33 556 (6.4) 8 960 (2.4) 2 008 (6.0) 5 120 (11.0) 17 468 (24.9) Posttraumatic stress disorder 33 567 (6.4) 13 182 (3.5) 2 252 (6.7) 4 743 (10.2) 13 390 (19.1) Alcohol disorder 15 560 (2.9) 9 164 (2.4) 1 551 (4.6) 1 838 (3.9) 3 007 (4.3) Diabetes 201 714 (38.2) 145 371 (38.5) 13 151 (39.1) 18 053 (38.8) 25 139 (35.9) Hypertension 388 628 (73.6) 279 973 (74.1) 25 023 (74.4) 34 123 (73.3) 49 509 (70.6) Insomnia 19 351 (3.7) 7 299 (1.9) 1 529 (4.5) 3 442 (7.4) 7 081 (10.1) Stroke 22 290 (4.2) 14 921 (3.9) 1 806 (5.4) 2 298 (4.9) 3 265 (4.7) Traumatic brain injury 2 531 (0.5) 1 443 (0.4) 223 (0.7) 350 (0.8) 515 (0.7) Tobacco use disorder 47 481 (9.0) 30 472 (8.1) 3 573 (10.6) 4 877 (10.5) 8 559 (12.2) Myocardial infarction 23 416 (4.4) 15 279 (4.0) 1 863 (5.5) 2 624 (5.6) 3 650 (5.2) Coronary heart failure 48 723 (9.2) 32 416 (8.6) 3 872 (11.5) 5 342 (11.5) 7 093 (10.1) Angina 12 628 (2.4) 7 807 (2.1) 1 138 (3.4) 1 549 (3.3) 2 134 (3.0) Arrhythmia 98 457 (18.6) 69 106 (18.3) 7 001 (20.8) 9 580 (20.6) 12 770 (18.2) Hyperlipidemia 358 767 (67.9) 259 062 (68.6) 22 372 (66.6) 31 132 (66.9) 46 201 (65.9) Peripheral artery disease 44 881 (8.5) 30 919 (8.2) 3 376 (10.0) 4 394 (9.4) 6 192 (8.8) Cumulative anticholinergic medication use†  None 218 301 (41.3) 184 238 (48.8) 8 926 (26.6) 11 238 (24.1) 13 899 (19.8)  TSDD 1–30 42 132 (8.0) 32 118 (8.5) 3 297 (9.8) 3 322 (7.1) 3 395 (4.8)  TSDD 31–365 107 502 (20.4) 74 047 (19.6) 9 218 (27.4) 11 645 (25.0) 12 592 (18.0)  TSDD 366–1 095 53 454 (10.1) 32 349 (8.6) 4 634 (13.8) 7 025 (15.1) 9 446 (13.5)  TSDD >1 095 106 677 (20.2) 55 032 (14.6) 7 538 (22.4) 13 321 (28.6) 30 786 (43.9) *Cumulative benzodiazepine exposure: total standardized daily dose (TSDD) based on lorazepam equivalents summed over the 10-year exposure period, categorized as none (0), low (1–30), medium (31–365), and high (366+). †Cumulative anticholinergic exposure: TSDD based on the minimum effective daily dose and summed over the 10-year exposure period. Open in new tab Table 1. Baseline Sociodemographic and Clinical Characteristics of Veterans at the Start of Observation Period, FY2010 . . Benzodiazepine Exposure* . Characteristics, N (%) . Overall (N = 528 066) . None (N = 377 784), N (%) . Low (N = 33 613), N (%) . Medium (N = 46 551), N (%) . High (N = 70 118), N (%) . Age, mean (SD) 77.0 (7.4) 77.4 (7.2) 75.8 (7.3) 76.2 (7.5) 75.4 (7.6) Male 515 979 (97.7) 370 295 (98.0) 32 419 (96.4) 45 125 (96.9) 68 140 (97.2) % with less than high school education, mean (SD) 15.7 (9.0) 15.6 (9.0) 15.8 (9.1) 15.9 (9.1) 15.9 (8.9) Median household income ($), mean (SD) 49 620 (19 106.9) 49 825 (19 248.7) 49 787 (19 124.0) 48 989 (18 815.6) 48 860 (18 485.8) Depression 67 643 (12.8) 30 316 (8.0) 5 230 (15.6) 9 955 (21.4) 22 142 (31.6) Anxiety 33 556 (6.4) 8 960 (2.4) 2 008 (6.0) 5 120 (11.0) 17 468 (24.9) Posttraumatic stress disorder 33 567 (6.4) 13 182 (3.5) 2 252 (6.7) 4 743 (10.2) 13 390 (19.1) Alcohol disorder 15 560 (2.9) 9 164 (2.4) 1 551 (4.6) 1 838 (3.9) 3 007 (4.3) Diabetes 201 714 (38.2) 145 371 (38.5) 13 151 (39.1) 18 053 (38.8) 25 139 (35.9) Hypertension 388 628 (73.6) 279 973 (74.1) 25 023 (74.4) 34 123 (73.3) 49 509 (70.6) Insomnia 19 351 (3.7) 7 299 (1.9) 1 529 (4.5) 3 442 (7.4) 7 081 (10.1) Stroke 22 290 (4.2) 14 921 (3.9) 1 806 (5.4) 2 298 (4.9) 3 265 (4.7) Traumatic brain injury 2 531 (0.5) 1 443 (0.4) 223 (0.7) 350 (0.8) 515 (0.7) Tobacco use disorder 47 481 (9.0) 30 472 (8.1) 3 573 (10.6) 4 877 (10.5) 8 559 (12.2) Myocardial infarction 23 416 (4.4) 15 279 (4.0) 1 863 (5.5) 2 624 (5.6) 3 650 (5.2) Coronary heart failure 48 723 (9.2) 32 416 (8.6) 3 872 (11.5) 5 342 (11.5) 7 093 (10.1) Angina 12 628 (2.4) 7 807 (2.1) 1 138 (3.4) 1 549 (3.3) 2 134 (3.0) Arrhythmia 98 457 (18.6) 69 106 (18.3) 7 001 (20.8) 9 580 (20.6) 12 770 (18.2) Hyperlipidemia 358 767 (67.9) 259 062 (68.6) 22 372 (66.6) 31 132 (66.9) 46 201 (65.9) Peripheral artery disease 44 881 (8.5) 30 919 (8.2) 3 376 (10.0) 4 394 (9.4) 6 192 (8.8) Cumulative anticholinergic medication use†  None 218 301 (41.3) 184 238 (48.8) 8 926 (26.6) 11 238 (24.1) 13 899 (19.8)  TSDD 1–30 42 132 (8.0) 32 118 (8.5) 3 297 (9.8) 3 322 (7.1) 3 395 (4.8)  TSDD 31–365 107 502 (20.4) 74 047 (19.6) 9 218 (27.4) 11 645 (25.0) 12 592 (18.0)  TSDD 366–1 095 53 454 (10.1) 32 349 (8.6) 4 634 (13.8) 7 025 (15.1) 9 446 (13.5)  TSDD >1 095 106 677 (20.2) 55 032 (14.6) 7 538 (22.4) 13 321 (28.6) 30 786 (43.9) . . Benzodiazepine Exposure* . Characteristics, N (%) . Overall (N = 528 066) . None (N = 377 784), N (%) . Low (N = 33 613), N (%) . Medium (N = 46 551), N (%) . High (N = 70 118), N (%) . Age, mean (SD) 77.0 (7.4) 77.4 (7.2) 75.8 (7.3) 76.2 (7.5) 75.4 (7.6) Male 515 979 (97.7) 370 295 (98.0) 32 419 (96.4) 45 125 (96.9) 68 140 (97.2) % with less than high school education, mean (SD) 15.7 (9.0) 15.6 (9.0) 15.8 (9.1) 15.9 (9.1) 15.9 (8.9) Median household income ($), mean (SD) 49 620 (19 106.9) 49 825 (19 248.7) 49 787 (19 124.0) 48 989 (18 815.6) 48 860 (18 485.8) Depression 67 643 (12.8) 30 316 (8.0) 5 230 (15.6) 9 955 (21.4) 22 142 (31.6) Anxiety 33 556 (6.4) 8 960 (2.4) 2 008 (6.0) 5 120 (11.0) 17 468 (24.9) Posttraumatic stress disorder 33 567 (6.4) 13 182 (3.5) 2 252 (6.7) 4 743 (10.2) 13 390 (19.1) Alcohol disorder 15 560 (2.9) 9 164 (2.4) 1 551 (4.6) 1 838 (3.9) 3 007 (4.3) Diabetes 201 714 (38.2) 145 371 (38.5) 13 151 (39.1) 18 053 (38.8) 25 139 (35.9) Hypertension 388 628 (73.6) 279 973 (74.1) 25 023 (74.4) 34 123 (73.3) 49 509 (70.6) Insomnia 19 351 (3.7) 7 299 (1.9) 1 529 (4.5) 3 442 (7.4) 7 081 (10.1) Stroke 22 290 (4.2) 14 921 (3.9) 1 806 (5.4) 2 298 (4.9) 3 265 (4.7) Traumatic brain injury 2 531 (0.5) 1 443 (0.4) 223 (0.7) 350 (0.8) 515 (0.7) Tobacco use disorder 47 481 (9.0) 30 472 (8.1) 3 573 (10.6) 4 877 (10.5) 8 559 (12.2) Myocardial infarction 23 416 (4.4) 15 279 (4.0) 1 863 (5.5) 2 624 (5.6) 3 650 (5.2) Coronary heart failure 48 723 (9.2) 32 416 (8.6) 3 872 (11.5) 5 342 (11.5) 7 093 (10.1) Angina 12 628 (2.4) 7 807 (2.1) 1 138 (3.4) 1 549 (3.3) 2 134 (3.0) Arrhythmia 98 457 (18.6) 69 106 (18.3) 7 001 (20.8) 9 580 (20.6) 12 770 (18.2) Hyperlipidemia 358 767 (67.9) 259 062 (68.6) 22 372 (66.6) 31 132 (66.9) 46 201 (65.9) Peripheral artery disease 44 881 (8.5) 30 919 (8.2) 3 376 (10.0) 4 394 (9.4) 6 192 (8.8) Cumulative anticholinergic medication use†  None 218 301 (41.3) 184 238 (48.8) 8 926 (26.6) 11 238 (24.1) 13 899 (19.8)  TSDD 1–30 42 132 (8.0) 32 118 (8.5) 3 297 (9.8) 3 322 (7.1) 3 395 (4.8)  TSDD 31–365 107 502 (20.4) 74 047 (19.6) 9 218 (27.4) 11 645 (25.0) 12 592 (18.0)  TSDD 366–1 095 53 454 (10.1) 32 349 (8.6) 4 634 (13.8) 7 025 (15.1) 9 446 (13.5)  TSDD >1 095 106 677 (20.2) 55 032 (14.6) 7 538 (22.4) 13 321 (28.6) 30 786 (43.9) *Cumulative benzodiazepine exposure: total standardized daily dose (TSDD) based on lorazepam equivalents summed over the 10-year exposure period, categorized as none (0), low (1–30), medium (31–365), and high (366+). †Cumulative anticholinergic exposure: TSDD based on the minimum effective daily dose and summed over the 10-year exposure period. Open in new tab Table 2. Cumulative Benzodiazepine Exposure During the Study Baseline Exposure Period by Medication Type* Medication . Patients With Benzodiazepine Use (N = 150 282) N (%) . Total Benzodiazepine Exposure Person-Days (N = 271 625 248) N (%) . Short-acting †  Lorazepam 53 464 (35.6) 58 543 918 (21.6)  Temazepam 50 910 (33.9) 30 421 821 (11.2)  Clonazepam 31 094 (20.7) 81 344 862 (29.9)  Alprazolam 30 315 (20.2) 62 472 212 (23.0)  Oxazepam 8 917 (5.9) 7 276 693 (2.7)  Triazolam 734 (0.5) 262 178 (0.1)  Estazolam 4 (0.0) 539 (0.0) Long-acting  Diazepam 32 360 (21.5) 25 861 540 (9.5)  Chlordiazepoxide 4 138 (2.8) 3 753 102 (1.4)  Clorazepate 895 (0.6) 1 219 814 (0.4)  Flurazepam 662 (0.4) 689 124 (0.3)  Halazepam 1 (0.0) 420 (0.0)  Prazepam 4 (0.0) 106 (0.0) Medication . Patients With Benzodiazepine Use (N = 150 282) N (%) . Total Benzodiazepine Exposure Person-Days (N = 271 625 248) N (%) . Short-acting †  Lorazepam 53 464 (35.6) 58 543 918 (21.6)  Temazepam 50 910 (33.9) 30 421 821 (11.2)  Clonazepam 31 094 (20.7) 81 344 862 (29.9)  Alprazolam 30 315 (20.2) 62 472 212 (23.0)  Oxazepam 8 917 (5.9) 7 276 693 (2.7)  Triazolam 734 (0.5) 262 178 (0.1)  Estazolam 4 (0.0) 539 (0.0) Long-acting  Diazepam 32 360 (21.5) 25 861 540 (9.5)  Chlordiazepoxide 4 138 (2.8) 3 753 102 (1.4)  Clorazepate 895 (0.6) 1 219 814 (0.4)  Flurazepam 662 (0.4) 689 124 (0.3)  Halazepam 1 (0.0) 420 (0.0)  Prazepam 4 (0.0) 106 (0.0) *Column percentages do not sum to 100% because patients could receive >1 benzodiazepine during the exposure period. †Benzodiazepines are classified as short- or long-acting by half-life (ie, time taken for blood concentration to fall to half its peak value) (20). Open in new tab Table 2. Cumulative Benzodiazepine Exposure During the Study Baseline Exposure Period by Medication Type* Medication . Patients With Benzodiazepine Use (N = 150 282) N (%) . Total Benzodiazepine Exposure Person-Days (N = 271 625 248) N (%) . Short-acting †  Lorazepam 53 464 (35.6) 58 543 918 (21.6)  Temazepam 50 910 (33.9) 30 421 821 (11.2)  Clonazepam 31 094 (20.7) 81 344 862 (29.9)  Alprazolam 30 315 (20.2) 62 472 212 (23.0)  Oxazepam 8 917 (5.9) 7 276 693 (2.7)  Triazolam 734 (0.5) 262 178 (0.1)  Estazolam 4 (0.0) 539 (0.0) Long-acting  Diazepam 32 360 (21.5) 25 861 540 (9.5)  Chlordiazepoxide 4 138 (2.8) 3 753 102 (1.4)  Clorazepate 895 (0.6) 1 219 814 (0.4)  Flurazepam 662 (0.4) 689 124 (0.3)  Halazepam 1 (0.0) 420 (0.0)  Prazepam 4 (0.0) 106 (0.0) Medication . Patients With Benzodiazepine Use (N = 150 282) N (%) . Total Benzodiazepine Exposure Person-Days (N = 271 625 248) N (%) . Short-acting †  Lorazepam 53 464 (35.6) 58 543 918 (21.6)  Temazepam 50 910 (33.9) 30 421 821 (11.2)  Clonazepam 31 094 (20.7) 81 344 862 (29.9)  Alprazolam 30 315 (20.2) 62 472 212 (23.0)  Oxazepam 8 917 (5.9) 7 276 693 (2.7)  Triazolam 734 (0.5) 262 178 (0.1)  Estazolam 4 (0.0) 539 (0.0) Long-acting  Diazepam 32 360 (21.5) 25 861 540 (9.5)  Chlordiazepoxide 4 138 (2.8) 3 753 102 (1.4)  Clorazepate 895 (0.6) 1 219 814 (0.4)  Flurazepam 662 (0.4) 689 124 (0.3)  Halazepam 1 (0.0) 420 (0.0)  Prazepam 4 (0.0) 106 (0.0) *Column percentages do not sum to 100% because patients could receive >1 benzodiazepine during the exposure period. †Benzodiazepines are classified as short- or long-acting by half-life (ie, time taken for blood concentration to fall to half its peak value) (20). Open in new tab Benzodiazepine Exposure and Dementia Risk We fit 3 separate Cox proportional hazards survival models to characterize the relationship between cumulative benzodiazepine exposure and dementia risk. In unadjusted analysis (Model 1), cumulative benzodiazepine exposure was associated with increased dementia risk in a dose-dependent fashion when compared with nonuse (high benzodiazepine exposure [TSDD 366+]: hazard ratio [HR] 1.39, 95% confidence interval [CI] 1.35–1.43; medium benzodiazepine exposure [TSDD 31–365]: HR 1.31, 95% CI 1.27–1.35; low benzodiazepine exposure [1–30]: HR 1.20, 95% CI 1.15–1.24; Table 3). Model 2 included patient sociodemographic and clinical characteristics associated with incident dementia risk; inclusion of these variables reduced the strength of the association of benzodiazepine exposure and dementia risk. Compared to nonuse, high exposure (HR 1.12, 95% CI 1.08–1.15), medium exposure (HR 1.09, 95% CI 1.05–1.13), and low benzodiazepine exposure (HR 1.09, 95% CI 1.05–1.14) were only slightly associated with dementia risk. In Model 3, which included 10-year cumulative anticholinergic exposure, benzodiazepine exposure remained minimally associated with increased dementia risk when compared with nonuse, but did not increase in a dose-dependent fashion with higher exposure. Veterans with low benzodiazepine exposure (HR 1.06, 95% CI 1.02–1.10) had essentially the equivalent, slightly elevated risk of developing dementia as veterans with high benzodiazepine exposure (HR 1.05, 95% CI 1.02–1.09). Table 3. Association of Incident Dementia During Follow-up Years With 10-Year Benzodiazepine Exposure* . . . Hazard Ratio (95% Confidence Interval) . Benzodiazepine Exposure† . Follow-up Time‡, Person-Years . Incident Dementia (N = 41 630), N (%) . Model 1: Unadjusted . Model 2: Demographics + Clinical Characteristics§ . Model 3: Model 2 + Anticholinergic Exposure . None 1 509 015 27 529 (66.1) 1 (Reference) 1 (Reference) 1 (Reference) Low 133 754 2 923 (7.0) 1.20 (1.15–1.24) 1.09 (1.05–1.14) 1.06 (1.02–1.10) Medium 180 894 4 314 (10.4) 1.31 (1.27–1.35) 1.09 (1.05–1.13) 1.05 (1.01–1.09) High 271 486 6 864 (16.5) 1.39 (1.35–1.43) 1.12 (1.08–1.15) 1.05 (1.02–1.09) . . . Hazard Ratio (95% Confidence Interval) . Benzodiazepine Exposure† . Follow-up Time‡, Person-Years . Incident Dementia (N = 41 630), N (%) . Model 1: Unadjusted . Model 2: Demographics + Clinical Characteristics§ . Model 3: Model 2 + Anticholinergic Exposure . None 1 509 015 27 529 (66.1) 1 (Reference) 1 (Reference) 1 (Reference) Low 133 754 2 923 (7.0) 1.20 (1.15–1.24) 1.09 (1.05–1.14) 1.06 (1.02–1.10) Medium 180 894 4 314 (10.4) 1.31 (1.27–1.35) 1.09 (1.05–1.13) 1.05 (1.01–1.09) High 271 486 6 864 (16.5) 1.39 (1.35–1.43) 1.12 (1.08–1.15) 1.05 (1.02–1.09) *7.9% (N = 34 766) of veterans in the study cohort received a diagnosis of dementia during the follow-up period, of which 34.0% received benzodiazepines during the baseline period. †Benzodiazepine exposure: total standardized daily dose summed over the 10-year exposure window, categorized as none (0), low (1–30), medium (31–365), and high (366+). ‡Follow-up time starts from October 1, 2010 until September 30, 2015, incident dementia, gap in VA care more than 365 days or death, whichever is earliest. §Model 2: Adjusted for patient sociodemographic (age, income, education) and clinical conditions (stroke, diabetes, hypertension, myocardial infarction, congestive heart failure, angina, arrhythmia, hyperlipidemia, peripheral artery disease, depression, anxiety, posttraumatic stress disorder, insomnia, traumatic brain injury, tobacco, and alcohol use). Open in new tab Table 3. Association of Incident Dementia During Follow-up Years With 10-Year Benzodiazepine Exposure* . . . Hazard Ratio (95% Confidence Interval) . Benzodiazepine Exposure† . Follow-up Time‡, Person-Years . Incident Dementia (N = 41 630), N (%) . Model 1: Unadjusted . Model 2: Demographics + Clinical Characteristics§ . Model 3: Model 2 + Anticholinergic Exposure . None 1 509 015 27 529 (66.1) 1 (Reference) 1 (Reference) 1 (Reference) Low 133 754 2 923 (7.0) 1.20 (1.15–1.24) 1.09 (1.05–1.14) 1.06 (1.02–1.10) Medium 180 894 4 314 (10.4) 1.31 (1.27–1.35) 1.09 (1.05–1.13) 1.05 (1.01–1.09) High 271 486 6 864 (16.5) 1.39 (1.35–1.43) 1.12 (1.08–1.15) 1.05 (1.02–1.09) . . . Hazard Ratio (95% Confidence Interval) . Benzodiazepine Exposure† . Follow-up Time‡, Person-Years . Incident Dementia (N = 41 630), N (%) . Model 1: Unadjusted . Model 2: Demographics + Clinical Characteristics§ . Model 3: Model 2 + Anticholinergic Exposure . None 1 509 015 27 529 (66.1) 1 (Reference) 1 (Reference) 1 (Reference) Low 133 754 2 923 (7.0) 1.20 (1.15–1.24) 1.09 (1.05–1.14) 1.06 (1.02–1.10) Medium 180 894 4 314 (10.4) 1.31 (1.27–1.35) 1.09 (1.05–1.13) 1.05 (1.01–1.09) High 271 486 6 864 (16.5) 1.39 (1.35–1.43) 1.12 (1.08–1.15) 1.05 (1.02–1.09) *7.9% (N = 34 766) of veterans in the study cohort received a diagnosis of dementia during the follow-up period, of which 34.0% received benzodiazepines during the baseline period. †Benzodiazepine exposure: total standardized daily dose summed over the 10-year exposure window, categorized as none (0), low (1–30), medium (31–365), and high (366+). ‡Follow-up time starts from October 1, 2010 until September 30, 2015, incident dementia, gap in VA care more than 365 days or death, whichever is earliest. §Model 2: Adjusted for patient sociodemographic (age, income, education) and clinical conditions (stroke, diabetes, hypertension, myocardial infarction, congestive heart failure, angina, arrhythmia, hyperlipidemia, peripheral artery disease, depression, anxiety, posttraumatic stress disorder, insomnia, traumatic brain injury, tobacco, and alcohol use). Open in new tab Sensitivity Analyses We conducted several sensitivity analyses. First, to address potential protopathic bias (21), we increased the lag period to 3 years (Figure 1). In adjusted models controlling for patient sociodemographic, clinical characteristics, and anticholinergic medication exposure (ie, Model 3), only the highest level of benzodiazepine exposure was slightly associated with increased risk of dementia (HR 1.03, 95% CI 1.00–1.07). Low and medium levels of benzodiazepine exposure were not significantly associated with incident dementia (Supplementary Table 2). Next, we evaluated the association between benzodiazepine exposure and incident risk specifically of Alzheimer’s disease. In adjusted models, compared to nonuse, none of the benzodiazepine exposure levels were associated with the risk of Alzheimer’s disease (Supplementary Table 3). Among veterans with the highest use of benzodiazepines (TSDD 366+), we found that the timing of benzodiazepine exposure (ie, recent, past, or continuous use) was not associated with dementia risk: Veterans with continuous use of benzodiazepines during the 10-year baseline exposure period were just as likely as veterans with predominately recent or past use of benzodiazepines to develop dementia (Supplementary Table 4). Lastly, when we fit the Fine and Gray models with death as the competing risk, we found that while low levels of benzodiazepine exposure were associated with a higher risk of dementia (subdistribution HR 1.06, 95% CI 1.02–1.10), medium and high levels were not (subdistribution HR 1.03, 95% CI 0.99–1.06 and subdistribution HR 1.01, 95% CI 0.98–1.05, respectively; Supplementary Table 5). Discussion In this analysis of a large national sample of older veterans, we did not find a strong association between benzodiazepine use and dementia risk. When controlling for important covariates including cumulative anticholinergic exposure, Veterans with low benzodiazepine exposure had a nearly identical—or slightly higher—risk of developing dementia when compared with veterans with high benzodiazepine exposure, which argues against a causal relationship (25). In sensitivity analyses, no level of benzodiazepine exposure was associated with increased risk of developing Alzheimer’s disease specifically nor did the timing of benzodiazepine exposure (ie, recent, past, or continuous use) influence dementia risk. The results from previous studies evaluating the association between cumulative benzodiazepine exposure and dementia risk have been inconsistent. Some studies have found an increased risk of dementia with benzodiazepine use (11,12), while others consistent with our findings, found no association (13–16). Our results differ from 2 recent analyses published by Billioti de Gage et al. (11,12), demonstrating an association between benzodiazepine exposure and dementia. In their first analysis, a prospective analysis of 1 063 adults who were dementia-free at baseline, new use of a benzodiazepine was associated with increased risk of dementia, with an adjusted HR of 1.60 (11). Their finding was consistent across multiple sensitivity analyses. However, in that analysis, the benzodiazepine prescribing could have been for prodromal or early symptoms of dementia such as depression or impaired sleep, which may occur before dementia is diagnosed—in other words, the prescribed benzodiazepine may have been for a symptom related to a neurodegenerative process not yet diagnosed as frank dementia. In a subsequent analysis designed to address this potential protopathic bias (21), the authors again found that benzodiazepine use was associated with an increased risk of Alzheimer’s disease (adjusted odds ratio 1.51) (12). While these studies controlled for a variety of covariates linked to dementia risk, neither study controlled for cumulative anticholinergic medication exposure, which can potentially confound the relationship between benzodiazepine exposure and dementia risk. A more recent analysis of a prospective, population-based cohort of 3 434 older adults in the United States by Gray et al. (14) did not find that benzodiazepine exposure was associated with incident dementia. Furthermore, in a large nationwide cohort and nested case–control study in Denmark, the authors found that among 235 465 patients with affective disorders, benzodiazepine exposure was not associated with subsequent dementia (16). In a cohort study of 10 263 patients in Canada, Nafti et al. (13) found that while benzodiazepine exposure was associated with an increased risk of cognitive impairment (adjusted HR 1.32), there was no significant association between benzodiazepine use and the risk of dementia or Alzheimer’s disease. Furthermore, in a case–control study among 26 459 patients in the United Kingdom, Imfeld et al. (15) found that when controlling for protopathic bias by increasing the lag period to 3 years, there was no association between earlier benzodiazepine exposure and subsequent risk of Alzheimer’s disease or vascular dementia. Relatively few studies examining the association between benzodiazepine exposure and dementia have accounted for anticholinergic use. Anticholinergic medications impair short-term cognition by blocking acetylcholine in the central nervous system, where it plays an essential role in cognitive function including memory and attention (26). Beyond these short-term effects, Alzheimer’s disease is associated with loss of cholinergic neurons in the basal forebrain (27), while cholinesterase inhibitors (eg, donepezil), which decrease the breakdown of acetylcholine, are the current mainstays of treatment for Alzheimer’s disease. In a cohort study in the United Kingdom, Grossi et al. (28) found no association between benzodiazepine exposure and dementia risk when controlling for anticholinergic exposure as a dichotomous variable. However, the study only included a small number of participants who had any use of benzodiazepines (N = 227) over a 10-year follow-up period. In a more recent cohort study among 3 526 community-dwelling patients within primary care in the Netherlands that controlled for anticholinergic exposure as a dichotomous variable, the authors again found no association between benzodiazepine use and dementia risk (29). Neither study evaluated the impact of cumulative dose and anticholinergic exposure. This analysis represents one of the largest prospective cohort studies to evaluate the association between benzodiazepine exposure and dementia. We included a variety of covariates for inclusion that are associated with incident dementia risk (10) and ours is the first to also account for the cumulative dose of anticholinergic exposure as a potential confounder. Our analysis has several limitations. First, prescribing is a proxy for benzodiazepine exposure but does not capture actual medication use. Second, detection of baseline comorbidities and a dementia diagnosis are based on administrative claims data, which may contribute to misclassification bias and residual confounding. Third, our sample includes a high proportion of men, and our study results may not generalize to other clinical populations. Fourth, our study observation period ends after 5 years, which leaves dementia diagnoses past that period unobserved, which may also underestimate dementia risk. Lastly, our estimates of benzodiazepine and anticholinergic exposures may be underestimated because they do not account for medications received outside of the VA, such as through Medicare Part D. However, Part D did not begin until 2006 and did not cover benzodiazepines until 2013, limiting the potential for unobserved exposures. While our study does not show a link between benzodiazepine exposure and dementia risk, benzodiazepines clearly impair cognition, including changes in memory, concentration, and attention (6,13). In a review of 68 randomized, placebo-controlled trials where participants underwent neuropsychological testing before and after benzodiazepine administration, Tannenbaum et al. (6) demonstrated that benzodiazepines consistently induced both amnestic and non-amnestic cognitive impairment. Such impairments can limit functional ability and threaten independence for older adults. Furthermore, the use of benzodiazepines among older adults has been associated with a variety of other noncognitive significant adverse effects including falls and sedation, which should limit their routine use among older adults to avoid medication-related harms. Conclusions This study represents one of the largest prospective studies evaluating the association between benzodiazepine use and dementia. Cumulative benzodiazepine exposure was not meaningfully associated with an increased risk of incident dementia. Differences from previous studies may be due to failure to account for other important confounding factors such as cumulative anticholinergic medication use. While benzodiazepines are associated with many serious side effects for older adults, higher cumulative use does not appear to increase dementia risk. Funding This work was supported by the National Institute of Drug Abuse (R01 DA045705-S1). L.B.G. was also supported, in part, by grant K23AG066864 from the National Institute on Aging. The funding source had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit. Conflict of Interest None declared. Author Contributions L.B.G. had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: L.B.G. and D.T.M; acquisition, analysis, or interpretation of data: all authors; drafting of the manuscript: L.B.G., R.V.I., and D.T.M.; critical revision of the manuscript for important intellectual content: all authors; statistical analysis: R.V.I. and H.M.K.; obtaining funding: D.T.M.; administrative, technical, or material support: D.T.M.; supervision: D.T.M. No related articles have been published or submitted from this study. References 1. 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J Am Med Dir Assoc . 2020 ; 21 ( 2 ): 188 – 193.e3 . doi:10.1016/j.jamda.2019.05.010 Google Scholar Crossref Search ADS PubMed WorldCat Published by Oxford University Press on behalf of The Gerontological Society of America 2021. This work is written by (a) US Government employee(s) and is in the public domain in the US. Published by Oxford University Press on behalf of The Gerontological Society of America 2021.

Orkaby, Ariela R; Ward, Rachel; Chen, Jiaying; Shanbhag, Akshay; Sesso, Howard D; Gaziano, J Michael; Djousse, Luc; Driver, Jane A

doi: 10.1093/gerona/glac006pmid: 35018441

BackgroundInflammation is a central pathway leading to frailty but whether commonly used nonaspirin nonsteroidal anti-inflammatory drugs (NSAIDs) can prevent frailty is unknown.MethodsProspective cohort study of male physicians ≥60 who participated in the Physicians’ Health Study. Annual questionnaires collected data on NSAID use, lifestyle, and morbidity. Average annual NSAID use was categorized as 0 days/year, 1–12 days/year, 13–60 days/year, and >60 days/year. Frailty was assessed using a validated 33-item frailty index. Propensity score inverse probability of treatment weighting was used to address confounding by indication and logistic regression models estimated odds ratios (ORs) of prevalent frailty according to nonaspirin NSAID use.ResultsA total of 12 101 male physicians were included (mean age 70 ± 7 years, mean follow-up 11 years). Reported NSAID use was 0 days/year for 2 234, 1–12 days/year for 5 812, 13–60 days/year for 2 833, and >60 days/year for 1 222 participants. A total of 2 413 participants (20%) were frail. Higher self-reported NSAID use was associated with greater alcohol use, smoking, arthritis, hypertension, and heart disease, while less NSAID use was associated with coumadin use and prior bleeding. After propensity score adjustment, all characteristics were balanced. ORs (95% confidence intervals) of prevalent frailty were 0.90 (0.80–1.02), 1.02 (0.89–1.17), and 1.26 (1.07–1.49) for average NSAID use of 1–12 days/year, 13–60 days/year, and >60 days/year, compared to 0 days/year (p-trend < .001).ConclusionsLong-term use of NSAIDs at high frequency is associated with increased risk of frailty among older men. Additional study is needed to understand the role of anti-inflammatory medication in older adults and its implication for overall health.

Sakai, Tomomichi; Shiroshita, Akihiro; Momosaki, Ryo; Umegaki, Hiroyuki

doi: 10.1093/gerona/glac031pmid: 35137062

Article PDF first page preview Close This content is only available as a PDF. © The Author(s) 2022. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: [email protected]. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Byrd, DeAnnah R; Jiang, Yanping; Zilioli, Samuele; Thorpe, Roland J; Lichtenberg, Peter A; Whitfield, Keith E

doi: 10.1093/gerona/glac038pmid: 35259257

In the article “The Interactive Effects of Education and Social Support on Blood Pressure in African Americans,” Yanping Jiang’s affiliation was incorrect. The incorrect affiliation was Department of Psychology, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA The correct affiliation is Institute for Health, Health Care Policy and Aging Research, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, USA This has now been corrected online. Published by Oxford University Press on behalf of The Gerontological Society of America 2022. This work is written by (a) US Government employee(s) and is in the public domain in the US. Published by Oxford University Press on behalf of The Gerontological Society of America 2022.