Gestational Diabetes Is Associated with High Energy and Saturated Fat Intakes and with Low Plasma Visfatin and Adiponectin Levels Independent of Prepregnancy BMI

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ORIGINAL ARTICLE Gestational diabetes is associated with high energy and saturated fat intakes and with low plasma visfatin and adiponectin levels independent of prepregnancy BMI

S Park1, M-Y Kim2, SH Baik3, J-T Woo4, YJ Kwon1, JW Daily1, Y-M Park5, J-H Yang2 and S-H Kim6

BACKGROUND/OBJECTIVES: Gestational diabetes mellitus (GDM) risk factors are well established for Caucasians, but not for Asians. We hypothesized that nutrient intakes, plasma adipokines and/or gestational hormones might be linked to GDM development among pregnant Korean women. This study sought to identify new risk factors for GDM and adverse pregnancy outcomes according to body weight at prepregnancy. SUBJECTS/METHODS: All subjects were pregnant women visiting the Cheil General Hospital and Women’s Healthcare Center between June 2006 and March 2009. Non-GDM (n ¼ 531) and GDM (n ¼ 215) participants were divided into normal-weight and overweight groups according to prepregnancy body mass index (BMI) above or below 23 kg/m2 at 24–28th week of gestation. At that time, glucose tolerance, insulin resistance as homeostatic model assessment for insulin resistance, insulin secretory capacity as homeostatic model assessment for b-cell function, anthropometric measurement, nutrient intakes, and plasma levels of adipokines and gestational hormones were determined. RESULTS: GDM women gained more weight in early pregnancy than non-GDM among normal-weight women. GDM was mainly associated with increased insulin resistance in overweight women and decreased insulin secretory capacity in normal-weight women. Plasma visfatin and adiponectin were lower and progesterone levels higher in GDM than non-GDM independent of BMI while plasma resistin levels were higher in non-GDM, but not GDM, overweight women. Energy and saturated fat intakes were higher in GDM independent of body weight, whereas taurine intakes were lower in GDM than non-GDM only in normal-weight women. CONCLUSIONS: Low visfatin and adiponectin and high progesterone levels in the circulation and high energy and saturated fat intakes were common risk factors for GDM and pregnancy outcome such as large for gestational age. Daily reference intakes for energy and fat during pregnancy need to be re-evaluated according to prepregnancy BMI.

European Journal of Clinical Nutrition (2013) 67, 196–201; doi:10.1038/ejcn.2012.207 Keywords: gestational diabetes; weight gain; BMI; adipokines; prolactin; progesterone

INTRODUCTION Inflammation can result in the destruction of pancreatic b-cells 6 Well-characterized risk factors for gestational diabetes and may also contribute to the progression of insulin resistance. mellitus (GDM) include overweight and obesity, older maternal Visfatin, a newly identified adipokine, is essential for normal age, previous GDM and family history of diabetes; however, insulin action but at higher levels, as seen with obesity, it is 7 these known risk factors are not reliable predictors of the associated with impaired insulin secretion and insulin resistance. occurrence of GDM in individuals or even in some populations.1 Adiponectin, which decreases with increased adipose tissue GDM screening using traditional risk factors cannot detect almost deposits although it is secreted by adipocytes, is a well- half of GDM women.2 Ethnicity is also known to affect the characterized insulin sensitizer; low levels of high molecular occurrence and severity of GDM and type 2 diabetes; a possible weight adiponectin have been associated with increased 8 consequence of low insulin secretory capacity of pancreatic b-cells incidence of type 2 diabetes in Japanese men. Furthermore, among high-risk populations, including Asians.3,4 Specific changes in gestational hormonal status during pregnancy may 9 nutritional risk factors have not been clearly identified for GDM contribute to dysregulation of glucose homeostasis. Changes in development. insulin resistance and insulin secretory capacity during pregnancy Adipokines and inflammatory cytokines are also known to are concomitant with increases in circulating prolactin, 10 impact the development of both type 2 diabetes and GDM.5 progesterone and estrogen levels.

1Department of Food and Nutrition, Hoseo University, Asan, Korea; 2Department of Obstetrics and Gynecology, Cheil General Hospital and Women’s Healthcare Center, Kwandong University College of Medicine, Seoul, Korea; 3Department of Internal Medicine, Korea University College of Medicine, Seoul, Korea; 4Department of Endocrinology and Metabolism, Kyung Hee University School of Medicine, Seoul, Korea; 5Department of Preventive Medicine, The Catholic University of Korea College of Medicine, Seoul, Korea and 6Department of Endocrinology and Metabolism, Cheil General Hospital & Women’s Healthcare Center, Kwandong University College of Medicine, Seoul, Korea. Correspondence: Dr S-H Kim, Department of Endocrinology and Metabolism, Cheil General Hospital and Women’s Healthcare Center, Kwandong University, College of Medicine, 1-19 Mook-Dong Jung-Gu, Seoul, 100-380 Korea. E-mail: [email protected] Received 23 March 2012; revised 18 November 2012; accepted 19 November 2012 Gestational diabetes, diet and adipokines S Park et al 197 Although obesity is the most consistent risk factor for type 2 Statistical analysis diabetes and GDM, nearly half of Korean GDM is not associated Data are expressed as mean±s.d. for normal distribution or median with obesity. We hypothesized that the development of GDM in (interquatile range) for skewed distribution. Statistical analyses were normal-weight and overweight women who followed weight gain performed using SAS Version 9.1. (SAS Institute Inc., Cary, NC, USA). guidelines during pregnancy, but still developed GDM, might be Subjects were divided into two groups according to prepregnancy BMI explained by secretion profiles of adipokine/cytokines and with a cutoff value of 23 for overweight according to the WHO expert gestational hormones, and by diet. Therefore, our overall objective consultation because Asians generally have a higher percentage of body fat than Caucasians of the same age, sex and BMI and a substantial risk of was to identify major risk factors that would help identify women type 2 diabetes and cardiovascular disease among normal-weight at risk for GDM and facilitate the establishment of interventions to individuals when using a BMI cutoff point of 25 kg/m2.13,14 In order to prevent the onset of GDM and improve pregnancy outcome. determine the effects of GDM and prepregnancy BMI, we conducted two- way analysis of variance according to GDM and prepregnancy BMI for continuous variables and a w2 test or Fisher’s exact test for categorical variables. Adjustments were made for the following variables: maternal age, gestational age, family history and parity. In addition, if a difference SUBJECTS AND METHODS among the four groups was found in a continuous variable, we utilized Subjects and screening for GDM post hoc Tukey’s testing to determine which groups were significantly All pregnant women visiting the Cheil General Hospital and Women’s different while Cochran–Mantel–Haenszel test for a categorical variable Healthcare Center (Seoul, Korea) between June 2006 and March 2009 for was performed. Kruskal–Wallis test was used for the parameters with a their first visit at 8–10 weeks of pregnancy took a short survey recording skewed distribution. Backward stepwise logistic regression analysis was prepregnancy body weights, medication use, and measurements of body used to determine which factors in prepregnancy and during pregnancy weight and blood glucose levels, and received a general education session were independently associated with normal-weight and overweight GDM for managing pregnancy including food intake. The pregnant women were women. In addition, factors independently affecting large for gestational asked to meet weight gain guidelines according to their body mass index age (LGA) in normal-weight and overweight GDM women were assessed (BMI) by regulating food intake during the remaining pregnancy to reduce by backward regression analysis. A P-valueo0.05 was considered obesity-related complications. statistically significant. GDM screening at the 24–28th week of gestation used a universal two- step GDM screening program with a 50 g glucose challenge test. Women with a negative result were deemed non-GDM and those testing positive RESULTS were administered a 100 g, 3-h oral glucose tolerance test and GDM was Characteristics of pregnant women identified according to criteria outlined by Carpenter and Coustan.10 Of the 18 215 pregnant women screened, 554 out of 15 780 (3.8%) normal-weight Mean prepregnancy BMIs of non-GDM and GDM subjects were 2 or underweight women and 532 of the 3643 (14.6%) overweight or obese 19.8±1.5 and 24.7±3.2 kg/m , respectively, and GDM was women were diagnosed with GDM; 531 non-GDM and 215 GDM women positively correlated with BMI. GDM women were older if enrolled in the study. Blood collections and questionnaire surveys were overweight and were shorter than non-GDM regardless of weight. obtained at 25.1±0.95 weeks of gestation and gestational age was not Prepregnancy BMI of normal-weight women was not different significantly different among the groups. All GDM women attended at least between non-GDM and GDM women, but was higher at the 24– three nutritional and lifestyle education sessions on maintaining body 28th week in GDM women (Supplementary Table 1). In overweight weight and glucose control, and all GDM women maintained normal blood glucose levels using diet, exercise and/or insulin injection and non-GDM women, prepregnancy and 24–28th week BMIs were greater in the women also maintained normoglycemia. GDM group than the non-GDM group (Supplementary Table 1). The study protocol was approved by the Ethical Committee of the However, BMI at delivery was not significantly different between Institutional Review Board of Cheil General Hospital and all subjects the non-GDM and GDM women. Weight gain during pregnancy provided written informed consent on the GDM screening day. Subjects was associated with prepregnancy BMI and GDM (Supplementary then completed an interviewer-administered questionnaire and were Table 1). Women with a higher parity were more overweight assessed for blood pressure, height and body weight. Blood was collected regardless of GDM and women with a family history of diabetes for assessing lipid and adipokine profiles and gestational hormones. In exhibited a higher prevalence of GDM. addition, food intakes were determined by a skilled dietician using the 24-h recall method. Questionnaires were used to obtain the following information: age, prepregnancy weight, obstetric history including Glucose and lipid metabolism at 24- to 28-week pregnancy gestational age and parity, and family history of diabetes among first- degree relatives. Plasma glucose levels while fasting and at 1 h after a 50 g glucose challenge were not affected by body weight (Table 1). Fasting plasma insulin levels were higher in overweight women, and were only elevated in GDM women who were overweight. Area under Laboratory measurements, physiologic indexes and nutrient the curve of glucose was not affected by body weight, but area intakes at 24–28th week of pregnancy under the curve of insulin was significantly higher in overweight Plasma glucose concentrations were measured by the glucose oxidase than normal-weight women (Table 1). HOMA-IR, an index of method using a YSI 2300 STAT (YSI; Yellow Springs, OH, USA). Plasma insulin resistance, was higher in GDM than non-GDM but the insulin, prolactin and progesterone concentrations were measured using a human-specific radioimmunoassay kit (Linco Research, St Charles, MO, increase was minimal in the normal-weight GDM group, but was USA). Homeostatic model assessments for insulin resistance (HOMA-IR) and much higher in the overweight group. By contrast, HOMA-B, an b-cell function (HOMA-B) were calculated using the equations developed index of insulin secretion capacity, was decreased in all GDM 11 by Matthews et al. Hemoglobin A1c in GDM subjects was measured using women regardless of body weight, was more important in normal- a Variant II HbA1c dual kit from Bio-Rad Laboratories (Hercules, CA, USA). weight GDM women considering the minimal changes in insulin Circulating adipokines (adipsin, adiponectin, resistin, leptin and visfatin) resistance. Thus, GDM development in normal-weight women was were measured on a multiplex suspension array system (Bio-Plex, Bio-Rad mostly related to insulin secretory dysfunction. Although blood Laboratories). Plasma lipid profiles (total, high-density lipoprotein and low- pressure remained in a normal range for all groups, it was higher density lipoprotein cholesterol, and triglyceride) and C-reactive protein in overweight and GDM women (Supplementary Table 2). Plasma were determined by enzymatic colorimetry. Plasma low-density lipoprotein cholesterol was calculated using the Friedewald equation. total cholesterol levels were not changed by body weight and Nutrient intakes were analyzed from the food intakes using CAN-PRO GDM states while plasma high-density lipoprotein concen- (version 3; Korean Nutrient Society, Seoul, Korea) and some were trations were lower in overweight women than normal-weight calculated as the percentage of the Korean Dietary Reference Intake for women, but GDM women had higher concentrations of pregnant women of similar age.12 high-density lipoprotein than non-GDM women (Supplementary

& 2013 Macmillan Publishers Limited European Journal of Clinical Nutrition (2013) 196 – 201 Gestational diabetes, diet and adipokines S Park et al 198 Table 1. Overnight-fasted plasma glucose and insulin levels at 24–28th weeks of pregnancy according to prepregnancy BMI and GDM

Normal weight (BMIo23) Overweight (BMIX23)

Non-GDM (n ¼ 395) GDM (n ¼ 98) Non-GDM (n ¼ 136) GDM (n ¼ 117)

Fasting glucose (mmol/l) 4.4±0.3a 4.7±0.5b 4.5±0.3a,b 5.2±0.7c,d,e Glucose at 1 h after 50 g glucose challenge (mmol/l) 6.2±0.8b 9.1±1.0c 6.3±0.8b 9.4±1.5c,d,e Fasting insulin (pmol/l) 80.6±38.9a 77.1±25.7a 88.9±40.3b 109.0±43.1c,d,e Area under the curve of glucose at 100 g OGTT — 26.1±3.3 — 27.2±5.4 Area under the curve of insulin at 100 g OGTT — 1368±645 — 7258±3120e b c b c,d HbA1C (%) 5.3±0.3 5.8±0.4 5.4±0.4 5.8±0.5 HOMA-IR 2.1±1.0f 2.3±0.9a 2.5±1.2b 3.6±1.6c,d,e HOMA-B 293.7±229.2c 197.7±86.6b 288.7±153.5c 219.1±129.9b,d Adiponectin (mg/l) 5.93 (6.04) 4.40 (6.13) 5.94 (4.96) 4.18 (3.19)d Adipsin (mg/l) 0.43 (0.33) 0.39 (0.33) 0.53 (0.24) 0.58 (0.26)e Leptin (mg/l) 5.77 (4.87) 6.19 (6.73) 10.30 (7.94) 11.27 (9.57)e Resistin (mg/l) 6.83 (6.46) 6.58 (5.06) 13.37 (10.97) 5.44 (4.43)d Visfatin (mg/l) 1.38 (0.743) 0.86 (0.59) 1.17 (1.78) 0.63 (0.53)d,,e C-reactive protein (nmol/l) 97.1 (139.1) 100.0 (121.0) 164.8 (210.5) 218.1 (298.1)d,,e Prolactin (nmol/l) 6.01±3.75a 6.36±2.26a 5.64±2.47b 5.61±1.96b,e Progesterone (nmol/l) 568.9±178.4b 707.2±227.4c 442.7±146.3c 642.0±226.4c,d,e

Abbreviations: BMI, body mass index; GDM, gestational diabetes mellitus; HbA1c, hemoglobin A1c, HOMA-B, homeostatic model assessment for b-cell function; HOMA-IR, homeostatic model assessment for insulin resistance; OGTT, oral glucose tolerance test. Values are mean±s.d. or median (interquatile range). a,b,c,fMeans in the same row with different superscripts were significantly different among the groups by Tukey’s test at Po0.05. dSignificantly different by GDM at Po0.05. eSignificantly different by BMI at Po0.05.

Table 2. Nutrient intake at 24–28 weeks of pregnancy according to prepregnancy BMI and GDM

Normal weight (BMIo23) Overweight (BMIX23)

Non-GDM (n ¼ 395) GDM (n ¼ 98) Non-GDM (n ¼ 136) GDM (n ¼ 117)

Energy intake (kcal/day) 2018.3±410.8a 2198.3±543.5b 1907.4±371.9c 2159.3±595.3b,d Energy (% EER) 84.1±17.1a 91.4±22.2b 76.3±15.2c 85.8±22.7a,d,e Carbohydrate (g/day) 285.4±59.2a 317.1±90.9b 271.3±50.6a 308.8±85.2b,d Fiber (g/day) 21.83±5.9a 26.28±9.2b 20.4±5.4a 24.35±8.9b,d Protein (g/day) 80.4±20.7a 87.5±23.9b 78.5±22.9a 90.8±33.2b,d Protein (% RI) 115.5±12.8a 125.2±23.7b 112.2±22.6a 129.4±31.9b,d Plant protein (g/day) 35.42±9.0c 40.08±12.6a 36.85±18.2c 42.97±19.6b,d,e Animal protein (g/day) 45.42±18.0 47.9±20.7 41.9±15.7 47.82±25.5 Fat (g/day) 64.5±21.7a 68.7±29.7b 60.2±19.5c 67.4±26.2b,d Fat (% of energy) 28.5±5.8b 28.1±7.1b 27.3±5.1a 26.9±7.1a,e Taurine (mg/day) 21.2±5.2b 7.5±2.6c 14.1±3.1a 16.6±5.4a,d Abbreviations: BMI, body mass index; EER, estimated energy requirement; GDM, gestational diabetes mellitus; RI, recommended intake. Values are mean±s.d. a,b,cMeans in the same row with different superscripts were significantly different among the groups by Tukey’s test at Po0.05. dSignificantly different by GDM at Po0.05. eSignificantly different by BMI at Po0.05.

Table 1). Plasma triglyceride levels were higher in overweight and the recommended ranges (Table 2). When total fat intake was GDM women than normal-weight and non-GDM women. categorized by different fatty acids, the consumption of saturated and monounsaturated fatty acids was higher in subjects with GDM Plasma adipokines and gestational hormones at 24- to 28-week than non-GDM subjects regardless of BMI (Table 2). However, the pregnancy intakes of polyunsaturated fatty acids and cholesterol were not GDM, but not body weight, was associated with lower plasma significantly different among the groups (Supplementary Table 3). adiponectin concentrations and overweight non-GDM women As GDM women had higher intakes of energy than non-GDM, had greatly elevated resistin levels (Table 1). Plasma adipsin and they consumed higher amounts of most nutrients. However, leptin concentrations were higher in overweight than normal- normal-weight GDM women had lower intakes of taurine, which is weight women but were not affected by GDM. Plasma visfatin related to b-cell survival and insulin secretion (Table 2). concentrations were lower in GDM and overweight women while plasma C-reactive protein concentrations increased in women Parameters that influenced GDM in normal-weight and with GDM and overweight. Plasma prolactin concentrations were overweight pregnant women lower in overweight women regardless of GDM while plasma In normal-weight women, family history, plasma progesterone and progesterone concentrations were increased in GDM women but triglyceride levels, and saturated fatty acid intakes exhibited decreased by overweight (Table 1). positive associations with GDM whereas HOMA-B and plasma visfatin levels were negatively associated (Table 3). These Nutrient intakes at 24- to 28-week pregnancy parameters explained 33.8% of the variation in GDM development GDM women had higher energy, carbohydrate, fiber and total fat in normal-weight pregnant women. However, in overweight intakes than non-GDM in both normal-weight and overweight pregnant women, diastolic blood pressure, HOMA-IR, plasma subjects although their intakes, except for total fat, were within progesterone and triglyceride levels and caloric intake were

European Journal of Clinical Nutrition (2013) 196 – 201 & 2013 Macmillan Publishers Limited Gestational diabetes, diet and adipokines S Park et al 199 negatively associated with LGA (Table 5). However, in overweight Table 3. Parameters explaining the variation of GDM development in GDM women, diastolic blood pressure and caloric intake were the normal and overweight women by backward stepwise logistic positively associated with LGA whereas plasma visfatin levels and regression analysis potassium and Zn intake were negatively associated with LGA 95% Conﬁdence Odds P-value (Table 5). These parameters explained 29.5% of the variation in interval ratio LGA in overweight GDM.

1, GDM normal weight R2 ¼ 0.3376 Family history 1.082–9.721 3.243 0.0225 DISCUSSION HOMA-B 0.989–0.997 0.993 0.0007 Like type 2 diabetes, GDM is most commonly associated with both Plasma progesterone 1.003–1.019 1.011 0.0147 insulin resistance and inadequate compensatory insulin secretion. (nmol/l) However, unlike type 2 diabetes, GDM only occurs during Plasma triglyceride 1.004–1.019 1.012 0.0477 (mmol/l) pregnancy and spontaneously resolves after childbirth. During Plasma visfatin (mg/l) 0.678–1.038 0.858 0.0848 normal pregnancy, progesterone induces insulin resistance that Saturated fatty acid 1.005–1.048 1.027 0.0020 does not result in diabetes because of increased compensatory intake (% RI) insulin secretion because of prolactin-induced b-cell prolifera- 16,17 2 tion. However, in some pregnancies GDM develops as a 1, GDM overweight R ¼ 0.5910 consequence of either unusually high insulin resistance, perhaps DBP (mm Hg) 1.024–1.131 1.076 0.0012 HOMA-IR 1.067–1.544 1.283 0.0465 because of the contribution of pre-existing insulin resistance in Plasma progesterone 1.014–1.032 1.023 o0.0001 overweight women, or because of inadequate b-cell expansion (nmol/l) and concomitant insulin insufficiency. In this study, HOMA-IR and Plasma triglyceride 1.003–1.013 1.008 0.0002 HOMA-B analyses revealed that in overweight women GDM was (mmol/l) mostly due to insulin resistance, which is similar to most GDM in Plasma adiponectin 0.176–0.851 0.387 0.0183 Western countries; however, the 50% of GDM women who were (mg/l) lean were no more insulin resistant than overweight non-GDM Plasma resistin (mg/l) 0.237–0.685 0.403 0.0008 women and low insulin secretory capacity was the primary defect Plasma CRP (nmol/l) 0.991–1.197 1.094 0.0528 causing the GDM, although prolactin secretion was highest in that Calorie intake (% EER) 1.025–1.100 1.062 0.0005 group. Therefore, GDM in normal-weight women was not due to Abbreviations: CRP, C-reactive protein; DBP, diastolic blood pressure; either excessive insulin resistance or to low prolactin secretion, but EER, estimated energy requirement; GDM, gestational diabetes mellitus; due either to inherent low insulin secretory capacity or to other HOMA-B, homeostatic model assessment for b-cell function; HOMA-IR, endogenous or exogenous factors that impaired insulin secretion. homeostatic model assessment for insulin resistance; RI, recommended intake. A prominent factor that led to GDM in both weight groups was a higher energy consumption that was accompanied by positively associated with GDM while plasma adiponectin and higher carbohydrate consumption. It is possible that the higher resistin levels were negatively associated with GDM (Table 3). carbohydrate consumption led to diabetes because of These parameters explained 59.1% of the variation in overweight a carbohydrate load that neither group of GDM women could pregnant women. Therefore, insulin secretion capacity was a more accommodate and that simply lowering their dietary carbohydrate important factor for inducing GDM in normal-weight pregnant content would have prevented much of the GDM. women whereas insulin resistance was for overweight pregnant On the other hand, there were some protective factors against women. Plasma triglyceride and progesterone levels were developing GDM. In overweight women, elevated resistin levels common factors associated with the development of GDM. and higher taurine intakes appeared to provide significant protection against GDM, and resistin appeared to protect against infants being born LGA. Higher levels of both adiponectin and Pregnancy outcomes visfatin were protective against GDM regardless of body weight. After diagnosis of GDM, dietary consumption was managed Visfatin is the same molecule as nicotinamide phosphoribosyltransferase, the rate-limiting enzyme in nicotinamide adenine resulting in blood glucose levels that were well maintained. This is 7 not uncommon as a diagnosis of GDM motivates both the patients dinucleotide biosynthesis. Nicotinamide adenine dinucleotide and health-care providers to manage the GDM.15 As a result, has a regulatory role in modulating the activity of the gestational age at delivery, delivery methods and incidence of a nicotinamide adenine dinucleotide-dependent deacetylase, low Apgar score (an indicator of neonatal morbidity) were not sirtuin-1, which improves insulin sensitivity in liver, adipose tissue and skeletal muscle entirely or in part by upregulating the affected by prepregnancy BMI and GDM (Table 4), indicating that 18–20 prepregnancy BMI and GDM does not affect neonatal morbidity in biosynthesis of adiponectin. Furthermore, sirtuin-1 also well-controlled GDM. In addition, macrosomia and small for potentiates glucose-stimulated insulin secretion by pancreatic b-cells21 and low visfatin/nicotinamide phosphoribosyltransferase gestational age did not differ according to GDM and prepreg- 22 nancy BMI. However, LGA frequency was significantly higher only impairs glucose-stimulated insulin secretion. Low visfatin levels in normal-weight GDM women compared with normal-weight would be expected to increase insulin resistance and decrease non-GDM women. The birth weight of babies and the preterm insulin secretory capacity; and would thus exacerbate impaired incidence were higher in overweight groups at prepregnancy whole body glucose regulation in both lean and overweight regardless of GDM (Table 4). Thus, these results suggested that Korean GDM women. GDM and prepregnancy overweight resulted in minimal adverse The other important observations of this study were the effects of pregnancy outcomes when blood glucose levels and weight gain nutrient intakes and dietary habits. This study revealed that both during the last trimester pregnancy are well controlled. normal-weight and overweight GDM women had higher energy and saturated fat intakes than non-GDM women although energy intakes of all groups were lower than the recommended energy Parameters that influenced LGA in normal-weight and overweight requirementsforpregnantwomen,buttheyhadproperweightgain pregnant GDM women until the 24–28th week of the pregnancy. Fat intake was 27–28% of In normal-weight GDM women, Na intake was significantly energy intake in all groups, but the GDM groups consumed more positively associated with LGA whereas taurine intake was saturated fat than non-GDM. Women who had a habit of consuming

& 2013 Macmillan Publishers Limited European Journal of Clinical Nutrition (2013) 196 – 201 Gestational diabetes, diet and adipokines S Park et al 200 Table 4. Pregnancy outcomes according to prepregnancy BMI and GDM

Normal weight (BMIo23) Overweight (BMIZ23)

Non-GDM (n ¼ 395) GDM (n ¼ 98) Non-GDM (n ¼ 136) GDM (n ¼ 117)

Gestational age at delivery (weeks) 39.1±1.95 38.7±1.50 39.0±1.51 38.6±1.89 Delivery methods (%) Normal labor 54.7 53.7 54.0 43.2 Cesarean section 45.3 46.3 46.0 56.8 Preterm (%) 5.5 3.6 6.9 13.3c Baby sex (male %) 50.9 51.2 51.4 56.7d Baby weight (g) 3186±475a 3138±471a 3300±458b 3281±537b,d Large for gestational age (%) 3.2 10.1 5.1 7.0d Small for gestational age (%) 5.5 2.2 1.9 3.7 Macrosomia (44000 g, %) 1.1 4.1 4.6 5.3 Abbreviations: BMI, body mass index; GDM, gestational diabetes mellitus. Values are mean±s.d. or percentage.a,bMeans in the same row with different superscripts were significantly different among the groups by Tukey’s test at Po0.05. cSignificantly different by GDM at Po0.05. dSignificantly different by BMI at Po0.05.

appropriate methods to determine actual and usual intake. In Table 5. Parameters explaining the variation of large for gestational addition, our preliminary study showed that 24-h recall was close age in the normal and overweight GDM women by backward stepwise to 3-day record in their actual intakes. regression analysis In conclusion, this study clearly demonstrated that GDM development in normal-weight Korean women is primarily related Estimate of 95% Confidence to poor insulin secretory capacity, whereas in overweight Korean coefficient interval GDM women it is associated with both insulin resistance and to Normal weight R2 ¼ 0.3269 inadequate insulin secretion. The data also suggest that current Sodium intake (g/day) 0.3567 0.0699 to 0.6435 recommended energy intakes during pregnancy needs to be Vitamin C intake (mg/day) 0.0038 À 0.0002 to 0.0078 re-evaluated. This study also found that in addition to traditional Taurine intake (mg/day) À 0.0627 À 0.1141 to À 0.0011 risk factors such as BMI, age, parity and family history of diabetes; 2 HOMA-B assessment of insulin secretory capacity might be a Overweight R ¼ 0.2952 valuable tool for early assessment of GDM risk. Other useful DBP (mm Hg) 0.0943 0.027 to 0.1859 Plasma visfatin (mg/l) À 0.0093 À 0.0177 to À 0.0009 predictors of GDM may be high triglyceride, low visfatin and Plasma leptin (mg/l) À 0.0005 À 0.0011 to 0.0001 adiponectin, and high progesterone levels in the circulation. Calorie intake (% EER) 0.0629 0.0027 to 0.1231 Dietary interventions for preventing the onset of GDM and Potassium intake (mg/day) À 0.0072 À 0.0145 to À 0.00003 improving pregnancy outcome should include careful manage- Zinc intake (mg/day) À 0.1207 À 0.2388 to À 0.0026 ment of energy intake, especially saturated fat intake, and possibly increasing the intake of taurine-rich foods. Abbreviations: DBP, diastolic blood pressure; EER, estimated energy requirement; GDM, gestational diabetes mellitus. CONFLICT OF INTEREST The authors declare no conflict of interest. less fatty foods and snacks and more milk products were at lower risk of developing GDM (data not shown). Taurine consumption was much lower in normal-weight GDM women than non-GDM. ACKNOWLEDGEMENTS Therefore, high energy intakes with higher saturated fats and lower This work was supported by grants from the Korean Research Foundation in Korea taurine intake may contribute to the development of GDM. Owing (R04-2008-000-10078-0) and the Korea Health 21 R&D Project, Ministry of Health and to its antioxidant and anti-inflammatory action, taurine protects Welfare, Republic of Korea (Grant no. A102065-1011-1070100). against apoptosis of b-cells and enhances their regeneration.23 Thus, a low intake of taurine might block b-cell expansion during pregnancy contributing to GDM and might be related to LGA AUTHOR CONTRIBUTIONS development because the increase in b-cell mass is necessary to The author’s responsibilities were as follows: SP contributed to designing the maintain glucose homeostasis. study, analyzing data and preparing the manuscript; M-YK, J-TW and J-HY This study had some limitations. First, as a cross-sectional study, analyzed data and reviewed the manuscript; YJK performed data analysis; JWD it did not determine cause-and-effect. Second, the division of BMI and Y-MP joined to prepare the manuscript; SHB and S-HK collected samples may not be reflective of weight status at all-time points since and data and prepared the manuscript. prepregnancy BMI was used for dividing the two groups such as normal-weight and overweight. Third, the biochemical levels at the beginning of pregnancy were not determined to compare REFERENCES with later trimesters. Finally, the nutrient intakes determined by 1 Hedderson MM, Williams MA, Holt VL, Weiss NS, Ferrara A. Body mass index and 24-h recall might introduce some bias in determining usual intake weight gain prior to pregnancy and risk of gestational diabetes mellitus. Am J since 24-h recall is based on the memory, and may not be fully Obstet Gynecol 2008; 198: 409 e1-e7. recalled especially by obese women, and there are differences in 2 Coustan DR, Nelson C, Carpenter MW, Carr SR, Rotondo L, Widness JA. Maternal age and screening for gestational diabetes: a population based study. Obstet intakes during weekdays and weekends. Many studies have Gynecol 1989; 73: 557–561. revealed that the 3-day food record is considered as the gold 3 Park S, Park JE, Daily JW, Kim SH. Low gestational weight gain improves infant and 24 standard for determining usual intakes. However, considering maternal pregnancy outcomes in overweight and obese Korean women with expenses and accuracy of the measurement, 24-h recall can be gestational diabetes. Gynecol Endocrinol 2011; 27: 775–781.

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