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Maternal Dietary Patterns and Gestational Diabetes Risk: A Case-Control Study

Maternal Dietary Patterns and Gestational Diabetes Risk: A Case-Control Study Hindawi Journal of Diabetes Research Volume 2017, Article ID 5173926, 8 pages https://doi.org/10.1155/2017/5173926 Research Article Maternal Dietary Patterns and Gestational Diabetes Risk: A Case-Control Study 1 2 2 3 Fatemeh Sedaghat, Mahdieh Akhoondan, Mehdi Ehteshami, Vahideh Aghamohammadi, 1 4 2 Nila Ghanei, Parvin Mirmiran, and Bahram Rashidkhani Department of Basic Medical Sciences, Faculty of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran Department of Community Nutrition, Faculty of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran Department of Paramedical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran Nutrition and Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Correspondence should be addressed to Bahram Rashidkhani; rashidkhani@yahoo.com Received 26 April 2017; Revised 22 August 2017; Accepted 24 August 2017; Published 6 December 2017 Academic Editor: Eusebio Chiefari Copyright © 2017 Fatemeh Sedaghat et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Maternal dietary patterns play an important role in the progress of gestational diabetes mellitus (GDM). The aim of the present study was to explore this association. Method. A total of 388 pregnant women (122 case and 266 control) were included. Dietary intake were collected using a food frequency questionnaire (FFQ). GDM was diagnosed using a 100-gram, 3-hour oral glucose tolerance test. Dietary pattern was identified by factor analysis. To investigate the relation between each of the independent variables with gestational diabetes, the odds ratio (OR) was calculated. Results. Western dietary pattern was high in sweets, jams, mayonnaise, soft drinks, salty snacks, solid fat, high-fat dairy products, potatoes, organ meat, eggs, red meat, processed foods, tea, and coffee. The prudent dietary pattern was characterized by higher intake of liquid oils, legumes, nuts and seeds, fruits and dried fruits, fish and poultry whole, and refined grains. Western dietary pattern was associated with increased risk of gestational diabetes mellitus before and after adjustment for confounders (OR = 1.97, 95% CI: 1.27–3.04, OR = 1.68, 95% CI: 1.04–2.27). However, no significant association was found for a prudent pattern. Conclusion. These findings suggest that the Western dietary pattern was associated with an increased risk of GDM. 1. Introduction the prevalence of GDM is thought to shadow that of type II diabetes, we expect to see a rise in GDM incidence during the coming years [3]. Gestational diabetes mellitus (GDM), defined as any degree Thus, it is crucial to detect modifiable risk factors that of glucose intolerance with onset during pregnancy, is a common complication, and its prevalence ranges between may contribute to the prevention of GDM. Food and dietary factors have been reported to affect glucose homeostasis, and 7 and 14% worldwide [1]. diet may be associated with GDM risk factors [6, 7]. Several Incidence of GDM among Iranian women living in studies found a positive association between GDM risk and urban areas has been reported similar to developing coun- intake of total fat [8, 9], saturated fat [10, 11], and an inverse tries [2–5]. However, with the prevalence of type II diabetes increasing across the world and considering that relation between the risk of GDM and polyunsaturated 2 Journal of Diabetes Research 5284 kcal) were excluded. The final sample for statistical fat [10, 12]. In contrast, no significant association was found between total dietary, saturated, and polyunsaturated fat analysis was 122 cases and 266 controls. intake and the risk of GDM in another study [13]. Informed consent was obtained from each participant However, most previous studies have traditionally prior to enrollment. The study protocol was approved by focused on the role of the single food items [14], nutrients the ethics committee at the National Nutrition and Food [15, 16], or food groups [17, 18]. Considering the limitations Technology Research Institute of Shahid Beheshti University of these kinds of studies, such as neglecting the interactions of Medical Science. and synergistic effects of nutrients [19], detecting dietary patterns offers a comprehensive and complimentary method 2.2. Dietary Intake. One year before pregnancy, dietary intake in investigating the relationship between diet and disease risk of all participants were collected through validated semi- [20]. Dietary patterns are population specific and are influ- quantitative food frequency questionnaires (FFQs) by trained enced by sociocultural factors and food availability [20]. interviewers. The FFQ consisted of 147 food items, including The results of recent systematic reviews showed that adher- some of the most common Iranian meal recipes, and has ence to a healthier dietary pattern, like Mediterranean dietary been previously shown to be valid and reproducible for use pattern, and reducing the intake of sugar sweetened cola, in Iranian adults [27]. Subjects were asked to mention their potatoes, fatty foods, sweets, and food with high heme iron consumption frequency of a given serving of each food content can decrease the incidence of GDM, especially in item during the past year, on a daily, weekly, monthly, women at higher risk and before getting pregnant [7, 21]. or yearly basis. Common household measures (measuring However, the majority of the studies to date have been cups, spoons, and palm of hand) were used for better esti- conducted in western populations [17, 22, 23]. Conversely, mation of the real portion consumed by the subjects [28]. studies investigating dietary patterns in relation to GDM in Portion sizes consumed from each food item were then Asian women, who are at a much greater risk of developing converted to daily gram intake using the household scales GDM compared to others ethnics, are sparse [24, 25]. Due [28]. In addition to the daily energy, macronutrient and to the growing trend of gestational diabetes and a shortage micronutrient consumption for participants was calculated of research on the effect of dietary patterns on the prevention using the United States Department of Agriculture Food of GDM in Asian women, this study aimed to investigate the Composition Databases (USDA FCT). However, for some association between prepregnancy dietary patterns and risk dairy products such as Kashk, wild plum, mint, sweet canned of GDM in Iranian women. cherry, and sour cherry that are not listed in the USDA FCT, Iranian FCT was used alternatively [28]. 2. Materials and Methods 2.3. Assessment of Nondietary Exposures. Information on age, 2.1. Participants. This hospital-based case-control study was prepregnancy weight, education level, socioeconomic status, conducted in Tehran, a province of Iran at high-risk of diabe- cigarette smoking, family history of diabetes, and taking tes. Cases (n = 123) with pregnant women aged 18–40 years supplements were obtained from all cases and controls by who visited major general hospitals in different regions of trained professional interviewers using a questionnaire. Tehran (11 million inhabitants) were included. Pregnant The weight of each subject was measured with minimum women were screened for gestational diabetes between the clothing, and 100 g sensitivity and height was measured 24th and 28th weeks of gestation with a 50 g, 1 hr glucose with using a nonstretch tape-meter fixed to a wall and challenge test (GCT). If the screening test was positive (blood was recorded to the nearest 0.5 cm. Body mass index (BMI) glucose greater than 130 mg per ml), diagnostic testing was was computed subsequently by the formula (weight in kg)/ performed using a 100 g, 3-hour oral glucose tolerance test (height in meter) . (OGTT). Women meeting the Carpenter and Coustan criteria Physical activity level before pregnancy and average [26], fasting 5.3 mmol/l, 1 h 10.0 mmol/l, 2 h 8.6 mmol/l, and time per day spent on different intensity activities was 3 h, 7.8 mmol/l, were diagnosed with GDM (any two values evaluated using a validated self report-based questionnaire at or above established thresholds). [29] and was expressed as metabolic equivalents hours/day Controls were pregnant women (n = 268) whose GCT (METs-h/d) in which nine different MET levels were ranged tests at 24–28 weeks of pregnancy were in the normal range. on a scale from sleep/rest (0.9 METs) to high-intensity phys- The exclusion criteria were multiple pregnancies, history of ical activities (>6 METs). By multiplying the time spent on gestational diabetes or diabetes (prepregnancy), and under- each activity level by the MET value of each activity, the going a weight-reduction diet one year before pregnancy. MET-time for an activity was computed. Based on question- Furthermore, controls were followed up until the end of naire data, we estimated a total activity score by adding the pregnancy. If they developed GDM, they were excluded specific activities together. from the study. In this study, two controls were recruited within the same medical center for each case. Controls were matched to cases on age (within 5 years). During anal- 2.4. Statistical Analysis. All analyses were performed using the ysis, three patients (1 case and 2 controls) with extreme Statistical Package for Social Sciences software version 16 energy intakes that probably reflected careless completion (SPSS Inc., Chicago, IL, USA), and a two-sided p value < 0.05 of the dietary questionnaire (below or above the mean ± 3 was considered significant. The Kaiser–Meyer–Olkin test SD for log-transformed calories; cutpoints: 244 kcal and (KMO = 0.7) indicated adequacy of sampling. Bartlett’s test Journal of Diabetes Research 3 Table 1: Food groupings used in the dietary pattern analysis. Food groups Food items Refined grains White breads (lavash, baguettes), rice, pasta, noodles, biscuits Fast foods Sausages, bologna (beef), pizza Potatoes Potatoes (cooked and fried potatoes, French fries) Salty snacks Crackers, potato chips, corn puffs, pickled vegetables Mayonnaise Mayonnaise Sugar sweetened beverages Soft drinks, synthetic fruit juices Eggs Eggs Lettuce, spinach, green leafy vegetables, onions, cucumber, turnip, cabbage, Vegetables cauliflower, kale, eggplant, squash, celery, green pepper, garlic, mushrooms, green peas, green beans, broad beans, carrots, pumpkin, tomatoes, tomato sauce Whole grains Iranian dark breads, barley, corn, bulgur Apple, orange, tangerine, date, melon, watermelon and Persian melon, cantaloupe, banana, lemon, lime, apricots, grapes, cherries, strawberries, pomegranates, kiwi, Fruits and dried fruits grapefruit, persimmons, pear, peach, plums, nectarine, mulberry, fig, dried fruits, and natural fruit juices Poultry and fish Chicken, canned tuna fish, and every kind of fish Whole fat milk, cocoa milk, whole fat yoghurt, concentrated and creamy yoghurt, High fat dairy cream cheese, kashk, ice cream Low-fat dairy Low-fat milk, plain yoghurt, cheese, and yoghurt drink Jam and tinned fruits Jam, honey, and tinned fruits Liquid oils Vegetable oils, olives, and olive oil Solid fats Butter, margarine, cream, hydrogenated vegetable oils, and animal fats Cakes, cookies, Iranian confectioneries (gaz, sohan, noghl), confections, Sweet snacks sugars, chocolates, candies Red meats Beef, lamb, hamburger, ground meat Organ meats Liver, heart, kidney, tongue, feet, head, tripe, and brain Tea and coffee Tea, coffee Nuts and seeds Walnuts, almonds, hazelnuts, pistachios, peanuts, and roasted seeds Legumes Lentils, beans, chickpea, split peas, and soya beans of sphericity was significant (p <0 001) indicating that factor variables were not normal. Unconditional logistic regression analysis was suitable for the data. was used in estimating odds ratio (OR) with 95% confidence For identifying the dietary patterns, food items obtained interval (CI) after controlling for confounding variables (pre- from the FFQ were categorized into 22 groups based on the pregnancy weight, gestational age, physical activity, family similarity of nutrients (Table 1). Principal component analy- history of diabetes, housing ownership, and building area). sis was used to identify major dietary patterns based on the 22 food groups. Two interpretable factors were retained 3. Results based on the Scree test [19]; an orthogonal rotation proce- dure, the Varimax rotation, was then applied to simplify Table 2 compares the characteristics of 122 cases and 266 con- the factor structure facilitating interpretation. The labeling trols. By design, age was similar in both groups (29.7 years of derived factors was done on the basis of interpretation of versus 29.6 years in controls and cases, resp.). Cases had the data and of the earlier literature. The factor score for each higher prepregnancy BMI and family history of diabetes com- pattern was calculated by summing intakes of food groups pared to controls, while gestational age and physical activity weighted by factor loading, and individuals were assigned a were higher significantly in the control group (p <0 001). factor score for each dietary pattern [19]. Controls reported higher building area and housing owner- Two dietary pattern scores were divided into two catego- ship. No significant differences were observed for maternal ries based on the medians. To evaluate the differences in age, energy intake, smoking status, and education status. distribution of categorical variables, chi-square test was Factor-loading matrix for the 2 retained factors is shown applied. To assess the differences in distribution of continu- in Table 3. Two dietary patterns were derived with eigen- ous variables across the dietary pattern score categories, values above 2 from the scree plot, as well as factor loadings independent sample t-test was used in case of normality, using factor analysis; these two patterns accounted for and Mann–Whitney test was used where the distribution of 19.4% of the total variation in food intakes. Dietary patterns 4 Journal of Diabetes Research Table 2: Characteristics of patients in an Iranian GDM case-control study between 2009 and 2010, Tehran, Iran. Controls Cases a,b Characteristics p value N = 266 N = 122 Age (years), mean ± SD 29.76 ± 4.26 29.64 ± 4.52 0.81 Gestational age (week), mean ± SD 31.19 ± 3.53 29.39 ± 4.74 <0.0001 Prepregnancy BMI (kg/m ), mean ± SD 24.64 ± 3.32 27.25 ± 3.82 <0.0001 Energy intake (kcal), mean ± SD 2672 ± 706 2818 ± 755 0.06 Physical activity (METs-h/d), mean ± SD 21.75 ± 26.37 12.92 ± 16.43 0.001 Family history of diabetes, n (%) 89 (33.46) 66 (54.55) <0.0001 Supplement use a year before pregnancy, n (%) 84 (31.58) 45 (37.19) 0.28 Smoking exposure, n (%) 24 (9.02) 8 (6.61) 0.42 Education (%) 0.35 Illiterate and primary 29.7 24.4 High school 41.3 48.8 Diploma and over 29.0 26.8 Housing ownership (yes), (%) 79.2 20.8 0.002 History of abortion, n (%) Building area (m ) 69 75 0.03 a b Values are presented either as mean ± SD or n (%) for quantitative and qualitative variables, respectively; chi-square test and independent sample t-test were applied for categorical variables and continuous variables, respectively; n = 388. were denoted “Western” and “prudent” according to previ- Table 3: Factor loadings matrix for the major dietary patterns ous studies. The “Western” dietary pattern had high positive identified by factor analysis in an Iranian GDM case-control study factor loadings for sweet snacks, jam and tinned fruits, may- between 2009 and 2010, Tehran, Iran (n = 388). onnaise, sugar-sweetened beverages, salty snacks, solid fats, high-fat dairy, potatoes, organ meats, eggs, red and processed Western dietary Prudent dietary Food groups meat, and tea and coffee, as well as negative factor loading for pattern pattern low fat dairy, legumes, and whole grains. The “prudent” Refined grains — 0.24 dietary pattern had high positive factor loadings for liquid Fast foods 0.22 — oils, legumes, nuts and seeds, fruits and dried fruits, fish Potatoes 0.32 — and poultry whole, and refined grains. Salty snacks 0.44 — Characteristics of the study participants across median Mayonnaise 0.43 0.27 categories of the dietary pattern scores are shown in Sugar sweetened beverages 0.40 — Table 4. BMI, family history of diabetes, parity, and energy intake was higher in pregnant women with higher scores of Eggs 0.29 “Western” dietary pattern compared to those with lower Vegetables —— scores (p <0 05). Women with higher scores of the “prudent” Whole grains −0.24 0.35 dietary pattern had higher energy intake and education level Fruits and dried fruits — 0.51 compared to those with lower scores (p <0 05). Poultry and fish — 0.43 The ORs and their 95% CI for GDM by the median of High-fat dairy 0.34 — dietary pattern scores are displayed in Table 5. Risk of Low-fat dairy −0.21 — developing GDM among women in the second median of “Western” dietary pattern scores was higher compared Jam and tinned fruits 0.50 — to the first median (OR = 1.97, 95% CI: 1.27–3.04). After Liquid oils — 0.69 adjusting for prepregnancy weight, gestational age, physi- Solid fats 0.38 — cal activity, family history of diabetes, housing ownership, Sweet snacks 0.48 — and building area, the association was still significant Red and processed meats 0.23 0.21 (OR = 1.68, 95% CI: 1.04–2.27). However, no significant Organ meats 0.31 — association was found between “prudent” dietary pattern Tea and coffee 0.25 — scores and risk of GDM. Nuts and seeds 0.28 0.58 Legumes −0.38 0.59 4. Discussion Explained variance (%) 10.3 8.9 In the present study, we identified two distinct dietary Values < 0.20 were excluded for simplicity. patterns among participants: Western dietary pattern (high in sweets, jams, mayonnaise, soft drinks, salty snacks, solid Journal of Diabetes Research 5 Table 4: Participants’ characteristics according to dietary pattern scores in an Iranian GDM case-control study between 2009 and 2010, Tehran, Iran. a a Western dietary pattern Prudent dietary pattern Characteristics Low High Low High Age (years) 29.6 ± 4.3 29.8 ± 4.4 29.6 ± 4.3 29.8 ± 4.4 p value 0.5 0.8 Prepregnancy BMI (kg/m ) 25.1 ± 3.5 25.9 ± 3.8 25.6 ± 3.9 25.4 ± 3.5 p value 0.02 0.54 Gestational age (week) 30.5 ± 3.9 30.8 ± 4.1 30.5 ± 3.9 30.8 ± 4.1 p value 0.56 0.50 Energy intake (kcal) 2489 ± 673 2984 ± 787 2275 ± 549 3194 ± 684 p value <0.001 <0.001 Family history of diabetes (%) 34.4 46.6 40.3 40.5 p value 0.02 0.85 Smokers (%) 1.5 0 0.5 1.1 p value 0.24 0.62 Physical activity (%) Low 49.0 51.0 54.6 45.4 High 51.0 49.0 45.4 54.6 p value 0.65 0.56 Education (%) Illiterate and primary 5.5 8.3 9.7 4.1 High school 18.7 23.7 26.5 15.8 Diploma and over 75.8 68.0 63.8 80.1 p value 0.49 0.005 a b Low category comprises below median values, and high category corresponds to above median values; values are presented either as mean ± SD or (%) for quantitative and qualitative variables, respectively; n = 388. Table 5: Unadjusted and adjusted odds ratios (OR) and 95% confidence intervals (CI) for gestational diabetes mellitus by median categories of dietary patterns in an Iranian GDM case-control study between 2009 and 2010, Tehran, Iran. Cases, n (%) Controls, n (%) Unadjusted OR (95% CI) Adjusted OR (95% CI) Western dietary pattern Low 48 (39.0) 150 (55.8) 1.00 (reference) 1.00 (reference) High 75 (61.0) 119 (44.2) 1.97 (1.27–3.04) 1.68 (1.04–2.72) Prudent dietary pattern Low 63 (51.2) 133 (49.4) 1.00 (reference) 1.00 (reference) High 60 (48.8) 136 (50.6) 0.93 (0.60–1.42) 0.97 (0.61–1.56) a b Low category comprises below median values, and high category corresponds to above median values; adjusted for confounding variables (prepregnancy BMI, gestational age, physical activity, family history of diabetes, housing ownership, and building area); n = 388. fat, high-fat dairy products, potatoes, organ meat, eggs, red however, few studies have investigated the association between prepregnancy dietary patterns and risk of GDM meat, processed foods, tea and coffee and low-fat dairy product, and whole grains) and prudent dietary pattern [17, 22, 23], and to our knowledge, only few has been (which includes high intake of liquid oils, legumes, nuts conducted in Asian females [24, 25]. and seeds, fruits and dried fruits, fish and poultry whole, In a large prospective cohort study in 3063 pregnant and refined grains). Western dietary pattern was positively Chinese women, no significant association was found associated with an increased risk of gestational diabetes, after between prudent pattern and GDM risk, while the sweets adjustment for potential confounders. However, there was no and seafood pattern was associated with an increased risk significant association between healthy dietary pattern and of GDM [25]. risk of GDM. Our findings are further supported by the findings from Healthy dietary patterns have consistently been associ- the Nurses’ Health Study II [17]. Women in the highest quintile of Western dietary pattern were associated with ated with a reduced risk of type 2 diabetes mellitus (T2DM); 6 Journal of Diabetes Research seems that the adverse effects of refined grains (high glycemic increased likelihood of GDM compared to those women in the lowest quintile [17]. index and low fiber content) on glucose metabolism out- Pregnancy is characterized by progressive hyperlipid- weigh other benefits of healthy dietary pattern identified in our study. emia, insulin resistance, and a deterioration of glucose tolerance as the pregnancy advances to the third trimester Discrepancies between results of studies could be [30]. Prior evidence suggests that women who develop referring to differences in study design, sample size, food GDM have altered functions of β-cells and insulin resistance, questionnaire, definition, and number of the food groups. compromising their capacity to deal with the metabolic Since dietary patterns reflect the culture, food preferences, and environmental factors (such as food availability), it can challenges of pregnancy [17, 22, 30]. The Western dietary pattern and a dietary pattern similar to that were associated be expected that different dietary patterns are identified in with a significant increase in fasting insulin and C-peptide different populations and time periods [19]. Dietary patterns levels and higher plasma glucose concentrations in healthy derived from factor analysis capture the effect of the combi- individuals [31]. nation of many interacting and synergic foods facilitating the application of nutritional findings for public health Red and processed meats as one of the main components of the Western dietary pattern are sources of saturated fat, recommendations and providing dietary guidelines which heme iron, nitrosamines, and other constituents which have might be useful in the prevention of diseases. To our knowl- been associated with beta cell damage, oxidative stress, and edge, the present study is the first one in a Middle-Eastern insulin resistance as well as incident GDM [17, 22]. Toxicity country to report the association between major dietary pattern effects of nitrosamines which are produced by the reaction s and GDM. Studies in developing countries can of nitrite (commonly used as a preservative in processed provide unique opportunities to assess the relation between meats) with amine compounds has been reported in dete- dietary patterns and GDM risk. Generally, where economic rioration of beta cell function, increased lipid peroxidation resources are severely restricted, food intake is strongly and proinflammatory cytokine activation [32]. In addition, linked to income, so that even small economic differences early limited N-nitrosodiethylamine (NDEA) exposures play are directly reflected in dietary intakes [37]. Other strengths critical roles in the pathogenesis of major insulin resistance of this analysis are that we were able to control for several diseases including T2DM in animal models [33]. Further- potential confounders such as physical activity, prepreg- more, advanced glycation end products (AGEs) produced nancy BMI, and gestational age. In addition, high participa- during the heating process in red meat and high-fat products tion rate in this study, as 100% of cases and 97% of controls are suggested as another possible mediator of association who were initially invited to participate in the research were between red and processed meat and GDM [34]. Moreover, retained in the final analyses, decreased the risk of selection it has been observed that high intakes of saturated and trans bias. Since cases were more likely to have changed their diets fatty acids by reducing insulin binding ability to its receptors due to the disease symptoms, the incident cases of GDM were and impairing glucose transport could be one of the impor- registered in the present study to reduce the possibility of tant risk factors triggering GDM [21, 35]. recall bias. One other strength is that the study was con- Another potential explanation is related to a high ducted in a province with a very high point prevalence consumption of sugar-sweetened beverages contributing to of GDM [4]. a high glycemic load (GL) diet leading to inflammation, The study has several limitations. First, because of the insulin resistance, and impaired β-cell function [14, 30]. In observational nature of the current study, we cannot rule addition, soft drinks contain caramel colouring which are out the possibility of residual confounding by unmeasured rich in AGEs promoting inflammatory mediators that factors such as abnormal metabolic factors. However, signif- might be important in the development of diabetes, such icant associations remained after carefully controlling for as C-reactive protein and TNF-α [34]. It is also plausible major well-documented risk factors for GDM. Second, the that low intakes of whole grains in Western dietary pattern results of factor analysis approach might be affected by can contribute to GDM risk. Whole grains are high in several arbitrary but important decisions including the insoluble fiber which delay gastric emptying and slow number of factors to extract, the components labelling, absorption of glucose, resulting in a small increment in insu- the method of rotation, and even their interpretation lin levels [6, 30]. [38]. Third, since dietary intake was assessed using FFQ, On the other hand, some studies have shown an inverse measurement errors were unavoidable which can cause relationship between a healthy dietary pattern and GDM underestimation of associations. However, the FFQ applied [17]. The effects of a healthy dietary pattern on reducing risk in this study has relative good reproducibility and validity of GDM may be due to a lower dietary energy density and among the Iranian population [27], and we also excluded glycemic load and higher amount of fruits and vegetables rich subjects overreporting their energy intakes. Fourth, due to in antioxidants and phytochemicals, dietary fiber, and micro- the case-control study design, we cannot provide evidence nutrients such as magnesium and vitamin C [22, 30]. In our of a causal relationship between prepregnancy dietary pat- study, in line with some other studies [25], no significant terns and the risk for GDM. association was found between healthy dietary pattern and Finally, the sample size of this study was small and the gestational diabetes. This probably could be due to the pres- study was only conducted on women living in Tehran city. ence of rice and refined grains in our healthy dietary pattern These could limit the generalization of study results to the since rice and refined grains are Iranian staple foods [36]. It entire Iranian population. Journal of Diabetes Research 7 [9] V. Wijendran, R. B. Bendel, S. C. Couch et al., “Maternal 5. Conclusion plasma phospholipid polyunsaturated fatty acids in pregnancy Overall, we found strong associations between Western with and without gestational diabetes mellitus: relations with dietary pattern and higher GDM risk, while no association maternal factors,” The American Journal of Clinical Nutrition, vol. 70, no. 1, pp. 53–61, 1999. was found between prudent pattern and GDM. However, case-control studies may prove an association but these do [10] S. Bo, G. Menato, A. Lezo et al., “Dietary fat and gestational not demonstrate causation. As a result, these findings need hyperglycaemia,” Diabetologia, vol. 44, no. 8, pp. 972–978, to be confirmed in future prospective studies for etiological purposes to identify whether improving maternal’s dietary [11] K. Bowers, D. K. Tobias, E. Yeung, F. B. Hu, and C. Zhang, “A prospective study of prepregnancy dietary fat intake and risk pattern adherence before pregnancy is associated with a of gestational diabetes,” The American Journal of Clinical decreased risk of GDM. Nutrition, vol. 92, no. 2, pp. 446–453, 2012. [12] Y. Wang, L. H. Storlien, A. B. Jenkins et al., “Dietary variables Conflicts of Interest and glucose tolerance in pregnancy,” Diabetes Care, vol. 23, no. 4, pp. 460–464, 2000. None of the authors had any personal or financial conflicts [13] J. S. Radesky, E. Oken, S. L. Rifas-Shiman, K. P. Kleinman, of interest. J. W. Rich-Edwards, and M. W. 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Maternal Dietary Patterns and Gestational Diabetes Risk: A Case-Control Study

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Hindawi Journal of Diabetes Research Volume 2017, Article ID 5173926, 8 pages https://doi.org/10.1155/2017/5173926 Research Article Maternal Dietary Patterns and Gestational Diabetes Risk: A Case-Control Study 1 2 2 3 Fatemeh Sedaghat, Mahdieh Akhoondan, Mehdi Ehteshami, Vahideh Aghamohammadi, 1 4 2 Nila Ghanei, Parvin Mirmiran, and Bahram Rashidkhani Department of Basic Medical Sciences, Faculty of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran Department of Community Nutrition, Faculty of Nutrition Sciences and Food Technology, National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran Department of Paramedical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran Nutrition and Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Correspondence should be addressed to Bahram Rashidkhani; rashidkhani@yahoo.com Received 26 April 2017; Revised 22 August 2017; Accepted 24 August 2017; Published 6 December 2017 Academic Editor: Eusebio Chiefari Copyright © 2017 Fatemeh Sedaghat et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. Maternal dietary patterns play an important role in the progress of gestational diabetes mellitus (GDM). The aim of the present study was to explore this association. Method. A total of 388 pregnant women (122 case and 266 control) were included. Dietary intake were collected using a food frequency questionnaire (FFQ). GDM was diagnosed using a 100-gram, 3-hour oral glucose tolerance test. Dietary pattern was identified by factor analysis. To investigate the relation between each of the independent variables with gestational diabetes, the odds ratio (OR) was calculated. Results. Western dietary pattern was high in sweets, jams, mayonnaise, soft drinks, salty snacks, solid fat, high-fat dairy products, potatoes, organ meat, eggs, red meat, processed foods, tea, and coffee. The prudent dietary pattern was characterized by higher intake of liquid oils, legumes, nuts and seeds, fruits and dried fruits, fish and poultry whole, and refined grains. Western dietary pattern was associated with increased risk of gestational diabetes mellitus before and after adjustment for confounders (OR = 1.97, 95% CI: 1.27–3.04, OR = 1.68, 95% CI: 1.04–2.27). However, no significant association was found for a prudent pattern. Conclusion. These findings suggest that the Western dietary pattern was associated with an increased risk of GDM. 1. Introduction the prevalence of GDM is thought to shadow that of type II diabetes, we expect to see a rise in GDM incidence during the coming years [3]. Gestational diabetes mellitus (GDM), defined as any degree Thus, it is crucial to detect modifiable risk factors that of glucose intolerance with onset during pregnancy, is a common complication, and its prevalence ranges between may contribute to the prevention of GDM. Food and dietary factors have been reported to affect glucose homeostasis, and 7 and 14% worldwide [1]. diet may be associated with GDM risk factors [6, 7]. Several Incidence of GDM among Iranian women living in studies found a positive association between GDM risk and urban areas has been reported similar to developing coun- intake of total fat [8, 9], saturated fat [10, 11], and an inverse tries [2–5]. However, with the prevalence of type II diabetes increasing across the world and considering that relation between the risk of GDM and polyunsaturated 2 Journal of Diabetes Research 5284 kcal) were excluded. The final sample for statistical fat [10, 12]. In contrast, no significant association was found between total dietary, saturated, and polyunsaturated fat analysis was 122 cases and 266 controls. intake and the risk of GDM in another study [13]. Informed consent was obtained from each participant However, most previous studies have traditionally prior to enrollment. The study protocol was approved by focused on the role of the single food items [14], nutrients the ethics committee at the National Nutrition and Food [15, 16], or food groups [17, 18]. Considering the limitations Technology Research Institute of Shahid Beheshti University of these kinds of studies, such as neglecting the interactions of Medical Science. and synergistic effects of nutrients [19], detecting dietary patterns offers a comprehensive and complimentary method 2.2. Dietary Intake. One year before pregnancy, dietary intake in investigating the relationship between diet and disease risk of all participants were collected through validated semi- [20]. Dietary patterns are population specific and are influ- quantitative food frequency questionnaires (FFQs) by trained enced by sociocultural factors and food availability [20]. interviewers. The FFQ consisted of 147 food items, including The results of recent systematic reviews showed that adher- some of the most common Iranian meal recipes, and has ence to a healthier dietary pattern, like Mediterranean dietary been previously shown to be valid and reproducible for use pattern, and reducing the intake of sugar sweetened cola, in Iranian adults [27]. Subjects were asked to mention their potatoes, fatty foods, sweets, and food with high heme iron consumption frequency of a given serving of each food content can decrease the incidence of GDM, especially in item during the past year, on a daily, weekly, monthly, women at higher risk and before getting pregnant [7, 21]. or yearly basis. Common household measures (measuring However, the majority of the studies to date have been cups, spoons, and palm of hand) were used for better esti- conducted in western populations [17, 22, 23]. Conversely, mation of the real portion consumed by the subjects [28]. studies investigating dietary patterns in relation to GDM in Portion sizes consumed from each food item were then Asian women, who are at a much greater risk of developing converted to daily gram intake using the household scales GDM compared to others ethnics, are sparse [24, 25]. Due [28]. In addition to the daily energy, macronutrient and to the growing trend of gestational diabetes and a shortage micronutrient consumption for participants was calculated of research on the effect of dietary patterns on the prevention using the United States Department of Agriculture Food of GDM in Asian women, this study aimed to investigate the Composition Databases (USDA FCT). However, for some association between prepregnancy dietary patterns and risk dairy products such as Kashk, wild plum, mint, sweet canned of GDM in Iranian women. cherry, and sour cherry that are not listed in the USDA FCT, Iranian FCT was used alternatively [28]. 2. Materials and Methods 2.3. Assessment of Nondietary Exposures. Information on age, 2.1. Participants. This hospital-based case-control study was prepregnancy weight, education level, socioeconomic status, conducted in Tehran, a province of Iran at high-risk of diabe- cigarette smoking, family history of diabetes, and taking tes. Cases (n = 123) with pregnant women aged 18–40 years supplements were obtained from all cases and controls by who visited major general hospitals in different regions of trained professional interviewers using a questionnaire. Tehran (11 million inhabitants) were included. Pregnant The weight of each subject was measured with minimum women were screened for gestational diabetes between the clothing, and 100 g sensitivity and height was measured 24th and 28th weeks of gestation with a 50 g, 1 hr glucose with using a nonstretch tape-meter fixed to a wall and challenge test (GCT). If the screening test was positive (blood was recorded to the nearest 0.5 cm. Body mass index (BMI) glucose greater than 130 mg per ml), diagnostic testing was was computed subsequently by the formula (weight in kg)/ performed using a 100 g, 3-hour oral glucose tolerance test (height in meter) . (OGTT). Women meeting the Carpenter and Coustan criteria Physical activity level before pregnancy and average [26], fasting 5.3 mmol/l, 1 h 10.0 mmol/l, 2 h 8.6 mmol/l, and time per day spent on different intensity activities was 3 h, 7.8 mmol/l, were diagnosed with GDM (any two values evaluated using a validated self report-based questionnaire at or above established thresholds). [29] and was expressed as metabolic equivalents hours/day Controls were pregnant women (n = 268) whose GCT (METs-h/d) in which nine different MET levels were ranged tests at 24–28 weeks of pregnancy were in the normal range. on a scale from sleep/rest (0.9 METs) to high-intensity phys- The exclusion criteria were multiple pregnancies, history of ical activities (>6 METs). By multiplying the time spent on gestational diabetes or diabetes (prepregnancy), and under- each activity level by the MET value of each activity, the going a weight-reduction diet one year before pregnancy. MET-time for an activity was computed. Based on question- Furthermore, controls were followed up until the end of naire data, we estimated a total activity score by adding the pregnancy. If they developed GDM, they were excluded specific activities together. from the study. In this study, two controls were recruited within the same medical center for each case. Controls were matched to cases on age (within 5 years). During anal- 2.4. Statistical Analysis. All analyses were performed using the ysis, three patients (1 case and 2 controls) with extreme Statistical Package for Social Sciences software version 16 energy intakes that probably reflected careless completion (SPSS Inc., Chicago, IL, USA), and a two-sided p value < 0.05 of the dietary questionnaire (below or above the mean ± 3 was considered significant. The Kaiser–Meyer–Olkin test SD for log-transformed calories; cutpoints: 244 kcal and (KMO = 0.7) indicated adequacy of sampling. Bartlett’s test Journal of Diabetes Research 3 Table 1: Food groupings used in the dietary pattern analysis. Food groups Food items Refined grains White breads (lavash, baguettes), rice, pasta, noodles, biscuits Fast foods Sausages, bologna (beef), pizza Potatoes Potatoes (cooked and fried potatoes, French fries) Salty snacks Crackers, potato chips, corn puffs, pickled vegetables Mayonnaise Mayonnaise Sugar sweetened beverages Soft drinks, synthetic fruit juices Eggs Eggs Lettuce, spinach, green leafy vegetables, onions, cucumber, turnip, cabbage, Vegetables cauliflower, kale, eggplant, squash, celery, green pepper, garlic, mushrooms, green peas, green beans, broad beans, carrots, pumpkin, tomatoes, tomato sauce Whole grains Iranian dark breads, barley, corn, bulgur Apple, orange, tangerine, date, melon, watermelon and Persian melon, cantaloupe, banana, lemon, lime, apricots, grapes, cherries, strawberries, pomegranates, kiwi, Fruits and dried fruits grapefruit, persimmons, pear, peach, plums, nectarine, mulberry, fig, dried fruits, and natural fruit juices Poultry and fish Chicken, canned tuna fish, and every kind of fish Whole fat milk, cocoa milk, whole fat yoghurt, concentrated and creamy yoghurt, High fat dairy cream cheese, kashk, ice cream Low-fat dairy Low-fat milk, plain yoghurt, cheese, and yoghurt drink Jam and tinned fruits Jam, honey, and tinned fruits Liquid oils Vegetable oils, olives, and olive oil Solid fats Butter, margarine, cream, hydrogenated vegetable oils, and animal fats Cakes, cookies, Iranian confectioneries (gaz, sohan, noghl), confections, Sweet snacks sugars, chocolates, candies Red meats Beef, lamb, hamburger, ground meat Organ meats Liver, heart, kidney, tongue, feet, head, tripe, and brain Tea and coffee Tea, coffee Nuts and seeds Walnuts, almonds, hazelnuts, pistachios, peanuts, and roasted seeds Legumes Lentils, beans, chickpea, split peas, and soya beans of sphericity was significant (p <0 001) indicating that factor variables were not normal. Unconditional logistic regression analysis was suitable for the data. was used in estimating odds ratio (OR) with 95% confidence For identifying the dietary patterns, food items obtained interval (CI) after controlling for confounding variables (pre- from the FFQ were categorized into 22 groups based on the pregnancy weight, gestational age, physical activity, family similarity of nutrients (Table 1). Principal component analy- history of diabetes, housing ownership, and building area). sis was used to identify major dietary patterns based on the 22 food groups. Two interpretable factors were retained 3. Results based on the Scree test [19]; an orthogonal rotation proce- dure, the Varimax rotation, was then applied to simplify Table 2 compares the characteristics of 122 cases and 266 con- the factor structure facilitating interpretation. The labeling trols. By design, age was similar in both groups (29.7 years of derived factors was done on the basis of interpretation of versus 29.6 years in controls and cases, resp.). Cases had the data and of the earlier literature. The factor score for each higher prepregnancy BMI and family history of diabetes com- pattern was calculated by summing intakes of food groups pared to controls, while gestational age and physical activity weighted by factor loading, and individuals were assigned a were higher significantly in the control group (p <0 001). factor score for each dietary pattern [19]. Controls reported higher building area and housing owner- Two dietary pattern scores were divided into two catego- ship. No significant differences were observed for maternal ries based on the medians. To evaluate the differences in age, energy intake, smoking status, and education status. distribution of categorical variables, chi-square test was Factor-loading matrix for the 2 retained factors is shown applied. To assess the differences in distribution of continu- in Table 3. Two dietary patterns were derived with eigen- ous variables across the dietary pattern score categories, values above 2 from the scree plot, as well as factor loadings independent sample t-test was used in case of normality, using factor analysis; these two patterns accounted for and Mann–Whitney test was used where the distribution of 19.4% of the total variation in food intakes. Dietary patterns 4 Journal of Diabetes Research Table 2: Characteristics of patients in an Iranian GDM case-control study between 2009 and 2010, Tehran, Iran. Controls Cases a,b Characteristics p value N = 266 N = 122 Age (years), mean ± SD 29.76 ± 4.26 29.64 ± 4.52 0.81 Gestational age (week), mean ± SD 31.19 ± 3.53 29.39 ± 4.74 <0.0001 Prepregnancy BMI (kg/m ), mean ± SD 24.64 ± 3.32 27.25 ± 3.82 <0.0001 Energy intake (kcal), mean ± SD 2672 ± 706 2818 ± 755 0.06 Physical activity (METs-h/d), mean ± SD 21.75 ± 26.37 12.92 ± 16.43 0.001 Family history of diabetes, n (%) 89 (33.46) 66 (54.55) <0.0001 Supplement use a year before pregnancy, n (%) 84 (31.58) 45 (37.19) 0.28 Smoking exposure, n (%) 24 (9.02) 8 (6.61) 0.42 Education (%) 0.35 Illiterate and primary 29.7 24.4 High school 41.3 48.8 Diploma and over 29.0 26.8 Housing ownership (yes), (%) 79.2 20.8 0.002 History of abortion, n (%) Building area (m ) 69 75 0.03 a b Values are presented either as mean ± SD or n (%) for quantitative and qualitative variables, respectively; chi-square test and independent sample t-test were applied for categorical variables and continuous variables, respectively; n = 388. were denoted “Western” and “prudent” according to previ- Table 3: Factor loadings matrix for the major dietary patterns ous studies. The “Western” dietary pattern had high positive identified by factor analysis in an Iranian GDM case-control study factor loadings for sweet snacks, jam and tinned fruits, may- between 2009 and 2010, Tehran, Iran (n = 388). onnaise, sugar-sweetened beverages, salty snacks, solid fats, high-fat dairy, potatoes, organ meats, eggs, red and processed Western dietary Prudent dietary Food groups meat, and tea and coffee, as well as negative factor loading for pattern pattern low fat dairy, legumes, and whole grains. The “prudent” Refined grains — 0.24 dietary pattern had high positive factor loadings for liquid Fast foods 0.22 — oils, legumes, nuts and seeds, fruits and dried fruits, fish Potatoes 0.32 — and poultry whole, and refined grains. Salty snacks 0.44 — Characteristics of the study participants across median Mayonnaise 0.43 0.27 categories of the dietary pattern scores are shown in Sugar sweetened beverages 0.40 — Table 4. BMI, family history of diabetes, parity, and energy intake was higher in pregnant women with higher scores of Eggs 0.29 “Western” dietary pattern compared to those with lower Vegetables —— scores (p <0 05). Women with higher scores of the “prudent” Whole grains −0.24 0.35 dietary pattern had higher energy intake and education level Fruits and dried fruits — 0.51 compared to those with lower scores (p <0 05). Poultry and fish — 0.43 The ORs and their 95% CI for GDM by the median of High-fat dairy 0.34 — dietary pattern scores are displayed in Table 5. Risk of Low-fat dairy −0.21 — developing GDM among women in the second median of “Western” dietary pattern scores was higher compared Jam and tinned fruits 0.50 — to the first median (OR = 1.97, 95% CI: 1.27–3.04). After Liquid oils — 0.69 adjusting for prepregnancy weight, gestational age, physi- Solid fats 0.38 — cal activity, family history of diabetes, housing ownership, Sweet snacks 0.48 — and building area, the association was still significant Red and processed meats 0.23 0.21 (OR = 1.68, 95% CI: 1.04–2.27). However, no significant Organ meats 0.31 — association was found between “prudent” dietary pattern Tea and coffee 0.25 — scores and risk of GDM. Nuts and seeds 0.28 0.58 Legumes −0.38 0.59 4. Discussion Explained variance (%) 10.3 8.9 In the present study, we identified two distinct dietary Values < 0.20 were excluded for simplicity. patterns among participants: Western dietary pattern (high in sweets, jams, mayonnaise, soft drinks, salty snacks, solid Journal of Diabetes Research 5 Table 4: Participants’ characteristics according to dietary pattern scores in an Iranian GDM case-control study between 2009 and 2010, Tehran, Iran. a a Western dietary pattern Prudent dietary pattern Characteristics Low High Low High Age (years) 29.6 ± 4.3 29.8 ± 4.4 29.6 ± 4.3 29.8 ± 4.4 p value 0.5 0.8 Prepregnancy BMI (kg/m ) 25.1 ± 3.5 25.9 ± 3.8 25.6 ± 3.9 25.4 ± 3.5 p value 0.02 0.54 Gestational age (week) 30.5 ± 3.9 30.8 ± 4.1 30.5 ± 3.9 30.8 ± 4.1 p value 0.56 0.50 Energy intake (kcal) 2489 ± 673 2984 ± 787 2275 ± 549 3194 ± 684 p value <0.001 <0.001 Family history of diabetes (%) 34.4 46.6 40.3 40.5 p value 0.02 0.85 Smokers (%) 1.5 0 0.5 1.1 p value 0.24 0.62 Physical activity (%) Low 49.0 51.0 54.6 45.4 High 51.0 49.0 45.4 54.6 p value 0.65 0.56 Education (%) Illiterate and primary 5.5 8.3 9.7 4.1 High school 18.7 23.7 26.5 15.8 Diploma and over 75.8 68.0 63.8 80.1 p value 0.49 0.005 a b Low category comprises below median values, and high category corresponds to above median values; values are presented either as mean ± SD or (%) for quantitative and qualitative variables, respectively; n = 388. Table 5: Unadjusted and adjusted odds ratios (OR) and 95% confidence intervals (CI) for gestational diabetes mellitus by median categories of dietary patterns in an Iranian GDM case-control study between 2009 and 2010, Tehran, Iran. Cases, n (%) Controls, n (%) Unadjusted OR (95% CI) Adjusted OR (95% CI) Western dietary pattern Low 48 (39.0) 150 (55.8) 1.00 (reference) 1.00 (reference) High 75 (61.0) 119 (44.2) 1.97 (1.27–3.04) 1.68 (1.04–2.72) Prudent dietary pattern Low 63 (51.2) 133 (49.4) 1.00 (reference) 1.00 (reference) High 60 (48.8) 136 (50.6) 0.93 (0.60–1.42) 0.97 (0.61–1.56) a b Low category comprises below median values, and high category corresponds to above median values; adjusted for confounding variables (prepregnancy BMI, gestational age, physical activity, family history of diabetes, housing ownership, and building area); n = 388. fat, high-fat dairy products, potatoes, organ meat, eggs, red however, few studies have investigated the association between prepregnancy dietary patterns and risk of GDM meat, processed foods, tea and coffee and low-fat dairy product, and whole grains) and prudent dietary pattern [17, 22, 23], and to our knowledge, only few has been (which includes high intake of liquid oils, legumes, nuts conducted in Asian females [24, 25]. and seeds, fruits and dried fruits, fish and poultry whole, In a large prospective cohort study in 3063 pregnant and refined grains). Western dietary pattern was positively Chinese women, no significant association was found associated with an increased risk of gestational diabetes, after between prudent pattern and GDM risk, while the sweets adjustment for potential confounders. However, there was no and seafood pattern was associated with an increased risk significant association between healthy dietary pattern and of GDM [25]. risk of GDM. Our findings are further supported by the findings from Healthy dietary patterns have consistently been associ- the Nurses’ Health Study II [17]. Women in the highest quintile of Western dietary pattern were associated with ated with a reduced risk of type 2 diabetes mellitus (T2DM); 6 Journal of Diabetes Research seems that the adverse effects of refined grains (high glycemic increased likelihood of GDM compared to those women in the lowest quintile [17]. index and low fiber content) on glucose metabolism out- Pregnancy is characterized by progressive hyperlipid- weigh other benefits of healthy dietary pattern identified in our study. emia, insulin resistance, and a deterioration of glucose tolerance as the pregnancy advances to the third trimester Discrepancies between results of studies could be [30]. Prior evidence suggests that women who develop referring to differences in study design, sample size, food GDM have altered functions of β-cells and insulin resistance, questionnaire, definition, and number of the food groups. compromising their capacity to deal with the metabolic Since dietary patterns reflect the culture, food preferences, and environmental factors (such as food availability), it can challenges of pregnancy [17, 22, 30]. The Western dietary pattern and a dietary pattern similar to that were associated be expected that different dietary patterns are identified in with a significant increase in fasting insulin and C-peptide different populations and time periods [19]. Dietary patterns levels and higher plasma glucose concentrations in healthy derived from factor analysis capture the effect of the combi- individuals [31]. nation of many interacting and synergic foods facilitating the application of nutritional findings for public health Red and processed meats as one of the main components of the Western dietary pattern are sources of saturated fat, recommendations and providing dietary guidelines which heme iron, nitrosamines, and other constituents which have might be useful in the prevention of diseases. To our knowl- been associated with beta cell damage, oxidative stress, and edge, the present study is the first one in a Middle-Eastern insulin resistance as well as incident GDM [17, 22]. Toxicity country to report the association between major dietary pattern effects of nitrosamines which are produced by the reaction s and GDM. Studies in developing countries can of nitrite (commonly used as a preservative in processed provide unique opportunities to assess the relation between meats) with amine compounds has been reported in dete- dietary patterns and GDM risk. Generally, where economic rioration of beta cell function, increased lipid peroxidation resources are severely restricted, food intake is strongly and proinflammatory cytokine activation [32]. In addition, linked to income, so that even small economic differences early limited N-nitrosodiethylamine (NDEA) exposures play are directly reflected in dietary intakes [37]. Other strengths critical roles in the pathogenesis of major insulin resistance of this analysis are that we were able to control for several diseases including T2DM in animal models [33]. Further- potential confounders such as physical activity, prepreg- more, advanced glycation end products (AGEs) produced nancy BMI, and gestational age. In addition, high participa- during the heating process in red meat and high-fat products tion rate in this study, as 100% of cases and 97% of controls are suggested as another possible mediator of association who were initially invited to participate in the research were between red and processed meat and GDM [34]. Moreover, retained in the final analyses, decreased the risk of selection it has been observed that high intakes of saturated and trans bias. Since cases were more likely to have changed their diets fatty acids by reducing insulin binding ability to its receptors due to the disease symptoms, the incident cases of GDM were and impairing glucose transport could be one of the impor- registered in the present study to reduce the possibility of tant risk factors triggering GDM [21, 35]. recall bias. One other strength is that the study was con- Another potential explanation is related to a high ducted in a province with a very high point prevalence consumption of sugar-sweetened beverages contributing to of GDM [4]. a high glycemic load (GL) diet leading to inflammation, The study has several limitations. First, because of the insulin resistance, and impaired β-cell function [14, 30]. In observational nature of the current study, we cannot rule addition, soft drinks contain caramel colouring which are out the possibility of residual confounding by unmeasured rich in AGEs promoting inflammatory mediators that factors such as abnormal metabolic factors. However, signif- might be important in the development of diabetes, such icant associations remained after carefully controlling for as C-reactive protein and TNF-α [34]. It is also plausible major well-documented risk factors for GDM. Second, the that low intakes of whole grains in Western dietary pattern results of factor analysis approach might be affected by can contribute to GDM risk. Whole grains are high in several arbitrary but important decisions including the insoluble fiber which delay gastric emptying and slow number of factors to extract, the components labelling, absorption of glucose, resulting in a small increment in insu- the method of rotation, and even their interpretation lin levels [6, 30]. [38]. Third, since dietary intake was assessed using FFQ, On the other hand, some studies have shown an inverse measurement errors were unavoidable which can cause relationship between a healthy dietary pattern and GDM underestimation of associations. However, the FFQ applied [17]. The effects of a healthy dietary pattern on reducing risk in this study has relative good reproducibility and validity of GDM may be due to a lower dietary energy density and among the Iranian population [27], and we also excluded glycemic load and higher amount of fruits and vegetables rich subjects overreporting their energy intakes. Fourth, due to in antioxidants and phytochemicals, dietary fiber, and micro- the case-control study design, we cannot provide evidence nutrients such as magnesium and vitamin C [22, 30]. In our of a causal relationship between prepregnancy dietary pat- study, in line with some other studies [25], no significant terns and the risk for GDM. association was found between healthy dietary pattern and Finally, the sample size of this study was small and the gestational diabetes. This probably could be due to the pres- study was only conducted on women living in Tehran city. ence of rice and refined grains in our healthy dietary pattern These could limit the generalization of study results to the since rice and refined grains are Iranian staple foods [36]. It entire Iranian population. Journal of Diabetes Research 7 [9] V. Wijendran, R. B. Bendel, S. C. Couch et al., “Maternal 5. Conclusion plasma phospholipid polyunsaturated fatty acids in pregnancy Overall, we found strong associations between Western with and without gestational diabetes mellitus: relations with dietary pattern and higher GDM risk, while no association maternal factors,” The American Journal of Clinical Nutrition, vol. 70, no. 1, pp. 53–61, 1999. was found between prudent pattern and GDM. However, case-control studies may prove an association but these do [10] S. Bo, G. Menato, A. Lezo et al., “Dietary fat and gestational not demonstrate causation. As a result, these findings need hyperglycaemia,” Diabetologia, vol. 44, no. 8, pp. 972–978, to be confirmed in future prospective studies for etiological purposes to identify whether improving maternal’s dietary [11] K. Bowers, D. K. Tobias, E. Yeung, F. B. Hu, and C. Zhang, “A prospective study of prepregnancy dietary fat intake and risk pattern adherence before pregnancy is associated with a of gestational diabetes,” The American Journal of Clinical decreased risk of GDM. Nutrition, vol. 92, no. 2, pp. 446–453, 2012. [12] Y. Wang, L. H. Storlien, A. B. Jenkins et al., “Dietary variables Conflicts of Interest and glucose tolerance in pregnancy,” Diabetes Care, vol. 23, no. 4, pp. 460–464, 2000. None of the authors had any personal or financial conflicts [13] J. S. Radesky, E. Oken, S. L. Rifas-Shiman, K. P. Kleinman, of interest. J. W. Rich-Edwards, and M. W. 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