Advertisement
Research Article

Dietary Glycemic Index during Pregnancy Is Associated with Biomarkers of the Metabolic Syndrome in Offspring at Age 20 Years

  • Inge Danielsen mail,

    ind@ssi.dk

    Affiliation: Centre for Fetal Programming, Department of Epidemiology Research, Statens Serum Institute, Copenhagen, Denmark

    X
  • Charlotta Granström,

    Affiliation: Centre for Fetal Programming, Department of Epidemiology Research, Statens Serum Institute, Copenhagen, Denmark

    X
  • Thorhallur Haldorsson,

    Affiliations: Centre for Fetal Programming, Department of Epidemiology Research, Statens Serum Institute, Copenhagen, Denmark, The Unit for Nutrition Research, Faculty of Food Science and Nutrition, School of Health Sciences, University of Iceland, Reykjavik, Iceland

    X
  • Dorte Rytter,

    Affiliation: Centre for Fetal Programming, Department of Public Health, Section for Epidemiology, Aarhus University, Aarhus, Denmark

    X
  • Bodil Hammer Bech,

    Affiliation: Centre for Fetal Programming, Department of Public Health, Section for Epidemiology, Aarhus University, Aarhus, Denmark

    X
  • Tine Brink Henriksen,

    Affiliation: Department of Paediatrics, Aarhus University Hospital, Skejby, Denmark

    X
  • Allan Arthur Vaag,

    Affiliations: Department of Endocrinology, Rigshospitalet, Copenhagen, Denmark, Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark

    X
  • Sjurdur Frodi Olsen

    Affiliation: Centre for Fetal Programming, Department of Epidemiology Research, Statens Serum Institute, Copenhagen, Denmark

    X
  • Published: May 31, 2013
  • DOI: 10.1371/journal.pone.0064887
Corrections

2 Jul 2013: Danielsen I, Granström C, Haldorsson T, Rytter D, Hammer Bech B, et al. (2013) Correction: Dietary Glycemic Index during Pregnancy Is Associated with Biomarkers of the Metabolic Syndrome in Offspring at Age 20 Years. PLoS ONE 8(7): 10.1371/annotation/e72c7dcd-f5f9-4deb-a185-f468b04d336a. doi: 10.1371/annotation/e72c7dcd-f5f9-4deb-a185-f468b04d336a | View correction

Abstract

Objective

Growing evidence indicates that metabolic syndrome is rooted in fetal life with a potential key role of nutrition during pregnancy. The objective of the study was to assess the possible associations between the dietary glycemic index (GI) and glycemic load (GL) during pregnancy and biomarkers of the metabolic syndrome in young adult offspring.

Methods

Dietary GI and GL were assessed by questionnaires and interviews in gestation week 30 and offspring were clinically examined at the age of 20 years. Analyses based on 428 mother-offspring dyads were adjusted for maternal smoking during pregnancy, height, pre-pregnancy body mass index (BMI), education, energy intake, and the offspring’s ambient level of physical activity. In addition, possible confounding by gestational diabetes mellitus was taken into account.

Outcome Measures

Waist circumference, blood pressure, HOMA insulin resistance (HOMA-IR) and plasma levels of fasting glucose, triglycerides, HDL cholesterol, LDL cholesterol, total cholesterol, insulin, and leptin were measured in the offspring.

Results

Significant associations were found between dietary GI in pregnancy and HOMA-IR (the relative increase in HOMA-IR per 10 units’ GI increase was 1.09 [95% CI: 1.01, 1.16], p = 0.02), insulin (1.09 [95% CI: 1.02, 1.16], p = 0.01) and leptin (1.21 [95% CI: 1.06, 1.38], p = 0.01) in the offspring; whereas no associations were detected for GL.

Conclusions

Our data suggests that high dietary GI in pregnancy may affect levels of markers for the metabolic syndrome in young adult offspring in a potentially harmful direction.

Introduction

Metabolic diseases, including the metabolic syndrome (MS), have been on the rise worldwide during the last two decades. MS is associated with increased risk of cardiovascular disease and type 2 diabetes and consists of the components: central obesity, reduced HDL cholesterol and raised triglyceride levels, as well as raised blood pressure and fasting plasma glucose levels [1]. Growing evidence indicates that MS is rooted in fetal life with a potential key role of nutrition during pregnancy [2]. The intrauterine environment, influenced by the maternal diet, may “programme” the fetus and thus influence susceptibility for MS in later life [3], [4].

The composition and amount of carbohydrates consumed during pregnancy are of particular interest. Dietary GI is a measure of the postprandial effect on the plasma glucose of the carbohydrate of a food item compared to the effect of the same amount of carbohydrate in pure glucose or white bread. GL in addition takes into account the amount of carbohydrate in the food item and thus is a more quantitative measure [5]. The concept of GI was first introduced by Jenkins et al. in 1981 [6] and is a commonly used measure of carbohydrate quality. Evidence has emerged from human and animal studies that maternal glucose levels may be predictive of metabolic disorders in the offspring [7][9]. Among a group of non-diabetic and generally healthy pregnant women, it has been shown that dietary GI is associated with the levels of glycosylated hemoglobin and plasma glucose [10]. There is also some evidence to support that a low GI diet during pregnancy may improve fetal glucose and insulin regulation and reduce birth weight and fetal adiposity [2], [10][13], although not all studies have supported this [14]. We are unaware of any previous studies in humans investigating the association between dietary GI or GL during pregnancy and the metabolic profile in the adult offspring. Accordingly, this association was studied in a unique prospective birth cohort with 20 years’ follow-up.

Materials and Methods

Ethics Statement

The study was approved by the Danish Data Protection Agency and the Central Denmark Region Committees on Biomedical Research Ethics (Reference No. 20070157). Written consent was obtained from all participants.

The Danish Fetal Origin Cohort 1988

In 1988 a total of 965 out of 1212 eligible women with singleton pregnancies were recruited for a birth cohort study in Denmark [15]. Prior to the routine antenatal visit in gestational week 30, the pregnant women received a postal questionnaire to complete and return to the antenatal care clinic. Following the antenatal visit, a 15-minute face-to-face interview was conducted by a trained person who corroborated the response to the self-administered questionnaire and completed a second interviewer-guided questionnaire with the women. The two questionnaires covered medical history, diet and other lifestyle as well as socio-economic factors. Further information about the women’s health, birth outcomes, medical history, and anthropometry was extracted from hospital records and from the Danish Medical Birth Registry as well as from the records kept by the midwives and general practitioners. Moreover, screening for gestational diabetes mellitus (GDM) was done with fasting glucose measurements in woman who were obese, had a family history of diabetes mellitus, GDM in a previous pregnancy, a previous delivery of an infant above 4500 g, previous stillbirth, age above 38 years, or glucosuria in the current pregnancy. When two independent fasting capillary plasma glucose values were above 4.6 mmol/L the woman was referred to an OGTT [16].

Offspring Follow Up

During 2008 and 2009, mothers and offspring were contacted and offspring invited to complete a web-based questionnaire including inquiries on current health, lifestyle and dietary habits as well as height, weight and waist circumference.

All potential participants were asked to participate in a clinical examination. The participants were examined between 8:00 AM and 12:30 PM after an overnight fasting. Height, weight and waist circumference were measured. After 7 min. of rest, blood pressure was measured three times in the horizontal position (2 min. intervals in between) using an automatic blood pressure device (OMRON M6 Comfort HEM-7000-E). The average value of the last two measurements was used in the analyses. A venous blood sample was drawn and immediately centrifuged and frozen at −80°C.

From a total number of 965 women we traced 894 singleton offspring. The remaining study group included twins, mothers and children with an incorrect personal identification number (in use for every citizen in Denmark), stillbirths, mothers and children who had died or were abroad, or with unknown addresses, or offspring that was unable to participate because of illness.

A total of 688 subjects (77% of the eligible population) participated in the follow up study by filling out the questionnaire, providing information on the offspring’s level of physical activity, and of these 439 attended the clinical examination.

Offspring Biomarkers

Plasma glucose levels were measured using bedside equipment (Accu-chek, Roche Diagnostics, Germany) immediately after blood sampling. Serum leptin concentrations were determined at the Medical Research Laboratories, Aarhus University Hospital, Denmark, by a time-resolved immunofluorometric assay based on commercially available reagents and recombinant human leptin as standard [17]. Plasma insulin concentrations were determined using a commercial ELISA kit. Insulin resistance was estimated using the homeostasis model assessment for insulin resistance (HOMA-IR) by means of the formula: fasting glucose (mmol/L) x fasting insulin (mU/L)/22.5 [18]. Serum triglycerides and cholesterol fractions (total cholesterol, LDL, HDL) were measured according to standard methods on a Modular P from Roche Diagnostics, Basel, Switzerland.

Markers for MS

Primary outcome variables were chosen among the continuous variables inherent in the definition of MS, i.e. waist circumference (cm), fasting levels of plasma glucose (mmol/L), triglycerides (mmol/L) and HDL cholesterol (mmol/L), as well as systolic and diastolic blood pressure measurements (mmHg). Additionally, the primary outcome variables were supplemented with secondary variables associated with MS including BMI (kg/m2), plasma levels of LDL and total cholesterol (mmol/L), concentrations of fasting plasma insulin (pmol/L) and leptin (ug/L), as well as HOMA-IR. Waist circumference and BMI were determined solely by means of data from the clinical examinations (439 subjects) to eliminate the possibility of under-reported data on BMI and waist circumference. BMI>18.5 and <25 kg/m2 was considered normal, whereas BMI≤18.5, BMI≥25.0 kg/m2 and BMI≥30.0 kg/m2 is termed underweight, overweight and obese, respectively.

Exposure Variables

The dietary assessment method used was a self-administered semi-quantitative food questionnaire combined with an interview in which photographic aids were used to assess portion sizes. The women were systematically asked about all possible categories of food items. The questions gave information on how often per week or per day the food item was consumed and how much per portion. Assessment of dietary intake was done by means of a national food composition database using standard recipes and standard portion sizes supplementing the answers from the questionnaire.

Dietary GI is a measure of the increase in plasma glucose after intake of a food item (containing 50 or 100 g of carbohydrate) and defined as the incremental area under the postprandial glucose response curve in percentage of the corresponding area following intake of a standard reference food item (same amount of carbohydrate), which can be either glucose or white bread [5]. Thus, GI measures the effect of the carbohydrate in the specific food item on the plasma glucose and thus represents a quality aspect of the food. GL represents both the quality and the quantity of the food, taking into account that the glycemic effect of a food item depends not only on the GI but also on the amount of carbohydrate eaten. Application of GI and GL in research and partly in clinical settings is widespread though the concept of GI is still contentious because of controversies relating to methodology and clinical applicability [19], [20].

For foods included in the questionnaire, we used the GI-table from Foster-Powell et al. 2002 [5], though a newer table exists [21], with white bread as the standard reference. The table contains GI-values measured during a time period close to the time when the dietary data were collected in the cohort. This is important as the composition of processed foods, and thereby the GI of these food items, changes over time. Daily dietary GI was calculated as the product of the GI and carbohydrate content for each food or beverage, summed for all items consumed either as a snack or as part of a meal in average per day and divided by the total carbohydrate intake per day. Daily GL was calculated as the product of the GI and carbohydrate content for each food or beverage and summed for all items consumed in average per day and then energy-adjusted by the residual method [22]. Both variables were analysed as continuous variables and in quintiles based on the diet of all women in the cohort (n = 894).

Statistical Analyses

Baseline characteristics of pregnant women either with participating or non-participating offspring were tested for differences by χ2-test. Distributions of covariates according to maternal exposure of GI and GL were tested for trends across quintiles of GI and GL by using Mantel-Haentzel χ2-test for trend for categorical covariates. Associations between maternal GI and GL and offspring outcome variables were examined by multivariate linear regression analyses. Waist circumference was adjusted for BMI using the residual method [23], providing an uncorrelated measure of BMI and waist circumference. Due to skewed distributions, all outcome variables except adjusted waist circumference and blood pressure were log-transformed. We a priori decided to include the following covariates: maternal height (continuous, 3% missing), education (five categories, 5% missing), smoking (yes or no, 5% missing), pre-pregnancy BMI (continuous, 3% missing), energy intake (five quintiles, 0% missing), and offspring’s current physical activity (four categories, 0% missing). Observations with missing covariate values were excluded from the analyses.

Maternal height, pre-pregnancy BMI and offspring’s physical activity were included as these variables are possible determinants of anthropometric and metabolic measures in the offspring. Energy intake was included as it is associated with the diet and possibly also the outcome variables. Maternal education and smoking were included to account for potential social and lifestyle confounding. In the presented data, women diagnosed with GDM were excluded from the analyses. Furthermore, the influence of GDM status was investigated by conducting the analyses after inclusion of the GDM cases.

The analyses were performed for combined sexes as well as males and females separately. The combined analysis included sex in the model along with the other covariates. Among the women with participating offspring, the GI varied between 49.3 and 88.3, whereas GL varied between 108.6 and 267.6. Considering this exposure range, analyses were made using exposures of 10 units’ increments with the aim to create results with magnitudes of clinical relevance. Mean changes in outcome variables for 10 units’ increment in GI and GL are presented as absolute increments for waist circumference and blood pressure and relative increments for BMI and offspring biomarkers. These measures of association are expressed as ‘difference per 10U GI/GL increase’ and ‘ratio per 10U GI/GL increase’. Associations were considered statistically significant at the 5% level and all regression coefficients are presented with 95% CI. All analyses were performed using the SAS GLM procedure (Version 9.3; SAS Institute, Cary, NC).

Results

Mothers of participating offspring were more often normal weight and non-smokers and had a higher education compared to mothers of non-participating offspring (table 1). Furthermore they had a higher energy intake. No significant differences were observed between the two groups regarding dietary GI and GL.

thumbnail

Table 1. Characteristics of 894 pregnant women in the birth cohort dependent on their offspring’s participation in the follow up.

doi:10.1371/journal.pone.0064887.t001

There was a significant association between increasing GI and GL and decreasing education, and fewer women with a low GL intake were smoking compared to women with at high GL intake (supplementary table S1). No significant association was found with any other covariate included in the model.

Of the 439 women included in the analyses, 121 women with high risk of GDM were screened and 11 (9.0% of the screened group and 2.5% of the total group) were diagnosed. The estimated prevalence of GDM in Denmark in 1999–2000 was 2.4% [24], corresponding to between 10 and 11 GDM cases in a population of 439 women. The 11 GDM cases were excluded from the analyses reported in tables 24. For unadjusted results, please see the supplementary tables S2, S3, and S4.

thumbnail

Table 2. MS markers in male and female offspring dependent on their mothers’ dietary GI in 2nd trimester.

doi:10.1371/journal.pone.0064887.t002
thumbnail

Table 3. MS markers in female offspring dependent on their mothers’ dietary GI in 2nd trimester.

doi:10.1371/journal.pone.0064887.t003
thumbnail

Table 4. MS markers in male offspring dependent on their mothers’ dietary GI in 2nd trimester.

doi:10.1371/journal.pone.0064887.t004

In the adjusted analyses of offspring of both sexes (table 2), we found no associations among the primary outcome variables. Among the secondary variables, with increasing GI we found higher levels of total cholesterol (ratio per 10U GI increase 1.03 (95% CI: 1.00, 1.06)), higher HOMA-IR (ratio 1.09 (95% CI: 1.01, 1.16)), and higher levels of insulin (ratio 1.09 (95% CI: 1.02, 1.16)) and leptin (ratio 1.21 (95% CI: 1.06, 1.38)). Analyses with quintiles of GI did not consistently result in significant associations but were generally in agreement with the analyses with the continuous variable. No associations were observed for GL (data not shown).

In female offspring exposed to maternal GI (table 3) we found no associations among the primary outcome variables. Among the secondary variables, with increasing GI we found higher levels of total cholesterol (ratio 1.05 (95% CI: 1.01, 1.09)) and leptin (ratio 1.21 (95% CI: 1.05, 1.40)) and in addition a borderline significant association with higher levels of insulin (ratio 1.08 (95% CI: 0.99, 1.19)). Furthermore, with increasing GL the data showed significant associations with the primary variables: systolic blood pressure (difference per 10U GL increase −0.46 (95% CI: −0.91, −0.01)) and waist circumference (difference 0.26 (95% CI: 0.01, 0.51).

No significant associations between maternal GI and GL and any outcome measure were observed in male offspring (table 4 and data not shown).

Inclusion of the 11 GDM cases in the analyses did not change the associations (data not shown). Adding the offspring’s waist circumference to the statistical model did not the change the associations either (data not shown).

Discussion

In this study we showed significant associations between high dietary GI in pregnancy and key markers of the metabolic syndrome (MS) in the young adult offspring. The results may be of clinical relevance. Thus, an increase in maternal GI of 10 units was associated with increased HOMA-IR, as well as increased levels of insulin and leptin among offspring of 9, 9 and 21%, respectively.

GL was not associated with MS markers among the offspring in the combined analyses. Nevertheless, among the females the observed association between increasing GL and higher waist circumference could be clinically relevant, as the possible effect of an increase of only 10 GL units – out of a total exposure range of 159 GL units - was 0.3 cm. A borderline significant association between increasing GL and lower systolic blood pressure among female offspring only. The extent to which these associations may be of clinical relevance remains to be determined.

Additional adjustment for the offspring’s waist circumference did not change the associations between GI in pregnancy and MS markers in the offspring, suggesting that factors other than current abdominal adiposity and body composition may explain the associations. These results are in full agreement with a recent study reporting that carbohydrate-rich diet supplementation in pregnant rats was associated with increased insulin, leptin and glucose levels among their adult male offspring, while no association was found with the offspring’s body weight [25]. These results in rats together with ours in humans support the idea that GI (and perhaps GL) during pregnancy may exhibit a causal programming effect in the adult offspring. Nevertheless, in our observational study we cannot exclude the theoretical possibility that mother’s and child’s lifestyles, including eating and physical activity habits, even after 20 years, may be similar as a result of social and familial factors. However, excluding the offspring’s physical activity from the statistical model did not change the associations between maternal GI and offspring MS markers (data not shown). The extent to which the mother’s diet during pregnancy is associated with the diet and eating habits of the offspring is unknown. Nevertheless, leptin in the offspring, the level of which was associated with the mother’s GI, may influence upon appetite and eating patterns [26]. Accordingly, it is possible that the offspring’s diet and levels of leptin may act in concert as mediators in the pathways between maternal GI and offspring MS markers. In this perspective it would be incorrect to adjust for the offspring’s diet.

Epigenetic mechanisms may be involved in developmental programming of MS by GI in pregnancy. Thus, a recent study found associations between maternal carbohydrate content in early pregnancy and DNA methylation of candidate genes from umbilical cord tissue and between the methylation status at birth and child’s obesity at the age of 9 [27].

According to the “Pedersen hypothesis” and the theory of “fuel mediated teratogenesis”, maternal glucose crosses the placenta and results in intrauterine hyperglycemia and fetal hyperinsulinemia leading to increased fetal growth and adiposity with consequences for later health [28][30]. This may explain the right side of the U-shaped association between birth weight and subsequent development of type 2 diabetes [31], [32]. Interestingly, the HAPO study suggested that mild elevations of maternal plasma glucose levels may impact on birth weight as well as risk of maternal and fetal pregnancy complications even at a level below conventional diagnostic GDM plasma glucose cut off levels [33]. In the present study, also after exclusion of the 11 GDM cases, the data revealed associations between dietary GI in pregnancy and distinct offspring MS markers. Given the known influence of GI on plasma glucose levels even in non-diabetic pregnant women (10), we speculate that subtle elevations of plasma glucose in pregnant women may be a mediator of the programming effect of GI in pregnancy.

The associations observed between maternal GI and offspring levels of HOMA-IR, leptin and insulin did not differ significantly between male and female offspring (p>0.05). Notably, however, when restricting the analyses to either sex, the associations for leptin and insulin persisted in females but not in males. This finding may reflect a lower participation rate among the latter or sex-specific mechanisms in the fetal programming of metabolic traits. Recently, in the same birth cohort, we detected obesogenic effects in female but not in male offspring as a result of fetal exposure to perfluorooctanoate [34]. Our findings are in line with a recent study on data from the great Chinese famine, where exposure to severe undernutrition in fetal life was associated with higher risk of MS in adult women but not in men [35] and with another study on data from the Dutch famine study, where maternal malnutrition during early gestation was associated with obesity only in women [36]. Sexual dimorphism in developmental programming may be explained by the sexually dimorphic embryo-derived tissue of the placenta which plays a significant role in determing fetal size, nutrition, morbidity and survival [37]. During recent years, evidence has emerged from rodent and human studies that the growth of the placenta and gene expression as well as DNA methylation differ between the sexes and furthermore that the sexes respond differently to environmental insults [37][39]. Differences in response to quality or composition of the maternal diet on the sex-dependent placental gene expression have been observed as well, providing insight in different sensitivity of male and female fetuses to the maternal diet. Thus the female placenta seems to be more adaptive to changes in maternal diet compared to the male placenta [40]. Furthermore, it has been shown that the maternal diet composition influences the cortisol level in the mother [41][43] and, in addition, changes the gene expression and the activity of the HPA axis, which regulate the glucocorticoid production, differently in male and female fetuses and offspring [37], [44]. Among humans, higher maternal cortisol was associated with marginally higher Fat Mass Index (FMI) in 5 year old girls, but marginally lower FMI in boys [45], indicating that diet-dependent maternal cortisol levels may influence the offspring with long-term consequences.

Our data indicates that GI during pregnancy has stronger influence on the offspring’s metabolic profile than GL. In previous studies investigating the associations between dietary GI and GL on biomarkers for metabolic disease in adults, the associations were not consistent between GI and GL [46][48]. Recent meta-analyses have reported statistically significant associations between GI or GL and chronic diseases including cardiovascular disease and type 2 diabetes and came to different conclusions when addressing the question of whether GI or GL had the most powerful effect on development of disease [47], [49]. Interestingly, the associations between GI and GL on one hand and adult disease on the other in general have been reported to be stronger in women than in men [46], [47], which may further support our sex-dependent findings in the offspring.

A limitation of the study is the use of biomarkers for MS and not MS per se in the offspring, as none of the young people had developed overt MS at the time of follow-up. The study had several strengths. Information on maternal diet was provided prospectively with no knowledge about offspring conditions, physical activity or body proportions, and the study had a long follow-up period. We could not identify any participation bias with regards to the exposure levels (cf. table 1).

In conclusion, our data suggests that dietary GI in second trimester of pregnancy may be a determinant of HOMA-IR and plasma levels of insulin, leptin and cholesterol in the adult offspring. GI and GL did not seem to predict markers of MS in males, whereas both GI and GL were associated with specific markers of MS among the females. We speculate that the programming effect(s) of maternal dietary GI in pregnancy may be mediated via subtle elevations of plasma glucose levels within the non-diabetic range. This remains to be addressed in future studies.

Supporting Information

Table S1.

Characteristics of the pregnant women in relation to dietary GI and GL in 2nd trimester.

doi:10.1371/journal.pone.0064887.s001

(DOCX)

Table S2.

MS markers in male and female offspring dependent on their mothers’ dietary GI in 2nd trimester.

doi:10.1371/journal.pone.0064887.s002

(DOCX)

Table S3.

MS markers in female offspring dependent on their mothers’ dietary GI in 2nd trimester.

doi:10.1371/journal.pone.0064887.s003

(DOCX)

Table S4.

MS markers in male offspring dependent on their mothers’ dietary GI in 2nd trimester.

doi:10.1371/journal.pone.0064887.s004

(DOCX)

Acknowledgments

All contributors fulfil the PLOS ONE authorship criteria.

Author Contributions

Conceived and designed the experiments: SFO. Performed the experiments: DR BHB TBH. Analyzed the data: ID CG. Contributed reagents/materials/analysis tools: DR CGS TH. Wrote the paper: ID. Editing of the manuscript: AV SFO TBH BHB DR TH. Contribution to the discussion: AV SFO.

References

  1. 1. International Diabetes Federation (2012) http://www.idf.org/webdata/docs/MetS_def​_update2006.pdf.
  2. 2. Tzanetakou IP, Mikhailidis DP, Perrea DN (2011) Nutrition During Pregnancy and the Effect of Carbohydrates on the Offspring's Metabolic Profile: In Search of the "Perfect Maternal Diet". Open Cardiovasc Med J 5: 103–109 10.2174/1874192401105010103 [doi];TOCMJ-5-103 [pii].
  3. 3. Hales CN, Barker DJ (2001) The thrifty phenotype hypothesis. Br Med Bull 60: 5–20. doi: 10.1093/bmb/60.1.5
  4. 4. Xita N, Tsatsoulis A (2010) Fetal origins of the metabolic syndrome. Ann N Y Acad Sci 1205: 148–155 10.1111/j.1749-6632.2010.05658.x [doi]. doi: 10.1111/j.1749-6632.2010.05658.x
  5. 5. Foster-Powell K, Holt SH, Brand-Miller JC (2002) International table of glycemic index and glycemic load values: 2002. Am J Clin Nutr 76: 5–56.
  6. 6. Jenkins DJ, Wolever TM, Taylor RH, Barker H, Fielden H, et al. (1981) Glycemic index of foods: a physiological basis for carbohydrate exchange. Am J Clin Nutr 34: 362–366.
  7. 7. Clausen TD, Mathiesen ER, Hansen T, Pedersen O, Jensen DM, et al. (2008) High prevalence of type 2 diabetes and pre-diabetes in adult offspring of women with gestational diabetes mellitus or type 1 diabetes: the role of intrauterine hyperglycemia. Diabetes Care 31: 340–346 dc07-1596 [pii];10.2337/dc07-1596 [doi]. doi: 10.2337/dc07-1596
  8. 8. Keely EJ, Malcolm JC, Hadjiyannakis S, Gaboury I, Lough G, et al. (2008) Prevalence of metabolic markers of insulin resistance in offspring of gestational diabetes pregnancies. Pediatr Diabetes 9: 53–59 PDI258 [pii];10.1111/j.1399-5448.2007.00258.x [doi]. doi: 10.1111/j.1399-5448.2007.00258.x
  9. 9. Poston L (2010) Developmental programming and diabetes - The human experience and insight from animal models. Best Pract Res Clin Endocrinol Metab 24: 541–552 S1521-690X(10)00040-0 [pii];10.1016/j.beem.2010.05.007 [doi]. doi: 10.1016/j.beem.2010.05.007
  10. 10. Scholl TO, Chen X, Khoo CS, Lenders C (2004) The dietary glycemic index during pregnancy: influence on infant birth weight, fetal growth, and biomarkers of carbohydrate metabolism. Am J Epidemiol 159: 467–474. doi: 10.1093/aje/kwh068
  11. 11. Moses RG, Luebcke M, Davis WS, Coleman KJ, Tapsell LC, et al.. (2006) Effect of a low-glycemic-index diet during pregnancy on obstetric outcomes. Am J Clin Nutr 84: 807–812. 84/4/807 [pii].
  12. 12. McGowan CA, McAuliffe FM (2010) The influence of maternal glycaemia and dietary glycaemic index on pregnancy outcome in healthy mothers. Br J Nutr 104: 153–159 S0007114510000425 [pii];10.1017/S0007114510000425 [doi]. doi: 10.1017/s0007114510000425
  13. 13. Clapp JF III (2002) Maternal carbohydrate intake and pregnancy outcome. Proc Nutr Soc 61: 45–50. doi: 10.1079/pns2001129
  14. 14. Walsh JM, McGowan CA, Mahony R, Foley ME, McAuliffe FM (2012) Low glycaemic index diet in pregnancy to prevent macrosomia (ROLO study): randomised control trial. BMJ 345: e5605. doi: 10.1136/bmj.e5605
  15. 15. Olsen SF, Hansen HS, Sandstrom B, Jensen B (1995) Erythrocyte levels compared with reported dietary intake of marine n-3 fatty acids in pregnant women. Br J Nutr 73: 387–395. doi: 10.1079/bjn19950041
  16. 16. Lauszus FF, Paludan J, Klebe JG (1999) Birthweight in women with potential gestational diabetes mellitus-an effect of obesity rather than glucose intolerance? Acta Obstet Gynecol Scand 78: 520–525. doi: 10.1080/j.1600-0412.1999.780608.x
  17. 17. Frystyk J, Tarnow L, Hansen TK, Parving HH, Flyvbjerg A (2005) Increased serum adiponectin levels in type 1 diabetic patients with microvascular complications. Diabetologia 48: 1911–1918 10.1007/s00125-005-1850-z [doi]. doi: 10.1007/s00125-005-1850-z
  18. 18. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, et al. (1985) Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28: 412–419. doi: 10.1007/bf00280883
  19. 19. Brand-Miller J, Buyken AE (2012) The glycemic index issue. Curr Opin Lipidol 23: 62–67 10.1097/MOL.0b013e32834ec705 [doi]. doi: 10.1097/mol.0b013e32834ec705
  20. 20. Grant SM, Wolever TM (2011) Perceived barriers to application of glycaemic index: valid concerns or lost in translation? Nutrients 3: 330–340 10.3390/nu3030330 [doi];nutrients-03-00330 [pii]. doi: 10.3390/nu3030330
  21. 21. Atkinson FS, Foster-Powell K, Brand-Miller JC (2008) International tables of glycemic index and glycemic load values: 2008. Diabetes Care 31: 2281–2283 dc08-1239 [pii];10.2337/dc08-1239 [doi]. doi: 10.2337/dc08-1239
  22. 22. Willett WC, Howe GR, Kushi LH (1997) Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr 65: 1220S–1228S.
  23. 23. Willet W (1998) Nutritional Epidemiology. New York: Oxford University Press.
  24. 24. Jensen DM, Molsted-Pedersen L, Beck-Nielsen H, Westergaard JG, Ovesen P, et al.. (2003) Screening for gestational diabetes mellitus by a model based on risk indicators: a prospective study. Am J Obstet Gynecol 189: 1383–1388. S000293780300601X [pii].
  25. 25. Beck B, Richy S, Archer ZA, Mercer JG (2012) Ingestion of Carbohydrate-Rich Supplements during Gestation Programs Insulin and Leptin Resistance but not Body Weight Gain in Adult Rat Offspring. Front Physiol 3: 224 10.3389/fphys.2012.00224 [doi]. doi: 10.3389/fphys.2012.00224
  26. 26. Djiane J, Attig L (2008) Role of leptin during perinatal metabolic programming and obesity. J Physiol Pharmacol 59 Suppl 155–63.
  27. 27. Godfrey KM, Sheppard A, Gluckman PD, Lillycrop KA, Burdge GC, et al. (2011) Epigenetic gene promoter methylation at birth is associated with child's later adiposity. Diabetes 60: 1528–1534 db10-0979 [pii];10.2337/db10-0979 [doi]. doi: 10.2337/db10-0979
  28. 28. Catalano PM, Hauguel-De MS (2011) Is it time to revisit the Pedersen hypothesis in the face of the obesity epidemic? Am J Obstet Gynecol 204: 479–487 S0002-9378(10)02400-2 [pii];10.1016/j.ajog.2010.11.039 [doi]. doi: 10.1016/j.ajog.2010.11.039
  29. 29. Freinkel N (1980) Banting Lecture 1980. Of pregnancy and progeny. Diabetes 29: 1023–1035. doi: 10.2337/diabetes.29.12.1023
  30. 30. Pedersen J (1954) Weight and length at birth of infants of diabetic mothers. Acta Endocrinol (Copenh) 16: 330–342. doi: 10.1530/acta.0.0160330
  31. 31. Pettitt DJ, Jovanovic L (2001) Birth weight as a predictor of type 2 diabetes mellitus: the U-shaped curve. Curr Diab Rep 1: 78–81. doi: 10.1007/s11892-001-0014-x
  32. 32. McCance DR, Pettitt DJ, Hanson RL, Jacobsson LT, Knowler WC, et al. (1994) Birth weight and non-insulin dependent diabetes: thrifty genotype, thrifty phenotype, or surviving small baby genotype? BMJ 308: 942–945. doi: 10.1136/bmj.308.6934.942
  33. 33. Metzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, et al. (2008) Hyperglycemia and adverse pregnancy outcomes. N Engl J Med 358: 1991–2002 358/19/1991 [pii];10.1056/NEJMoa0707943 [doi]. doi: 10.1097/01.aoa.0000344706.95925.dc
  34. 34. Halldorsson TI, Rytter D, Haug LS, Bech BH, Danielsen I, et al. (2012) Prenatal exposure to perfluorooctanoate and risk of overweight at 20 years of age: a prospective cohort study. Environ Health Perspect 120: 668–673 10.1289/ehp.1104034 [doi]. doi: 10.1289/ehp.1104034
  35. 35. Zheng X, Wang Y, Ren W, Luo R, Zhang S, et al. (2012) Risk of metabolic syndrome in adults exposed to the great Chinese famine during the fetal life and early childhood. Eur J Clin Nutr 66: 231–236 ejcn2011161 [pii];10.1038/ejcn.2011.161 [doi]. doi: 10.1038/ejcn.2011.161
  36. 36. Ravelli AC, van Der Meulen JH, Osmond C, Barker DJ, Bleker OP (1999) Obesity at the age of 50 y in men and women exposed to famine prenatally. Am J Clin Nutr 70: 811–816.
  37. 37. Gallou-Kabani C, Gabory A, Tost J, Karimi M, Mayeur S, et al. (2010) Sex- and diet-specific changes of imprinted gene expression and DNA methylation in mouse placenta under a high-fat diet. PLoS One 5: e14398 10.1371/journal.pone.0014398 [doi]. doi: 10.1371/journal.pone.0014398
  38. 38. O'Connell BA, Moritz KM, Walker DW, Dickinson H (2013) Sexually dimorphic placental development throughout gestation in the spiny mouse (Acomys cahirinus). Placenta 34: 119–126 S0143-4004(12)00444-4 [pii];10.1016/j.placenta.2012.11.009 [doi]. doi: 10.1016/j.placenta.2012.11.009
  39. 39. Gabory A, Roseboom TJ, Moore T, Moore LG, Junien C (2013) Placental contribution to the origins of sexual dimorphism in health and diseases: sex chromosomes and epigenetics. Biol Sex Differ 4: 5 2042-6410-4-5 [pii];10.1186/2042-6410-4-5 [doi]. doi: 10.1186/2042-6410-4-5
  40. 40. Mao J, Zhang X, Sieli PT, Falduto MT, Torres KE, et al. (2010) Contrasting effects of different maternal diets on sexually dimorphic gene expression in the murine placenta. Proc Natl Acad Sci U S A 107: 5557–5562 1000440107 [pii];10.1073/pnas.1000440107 [doi]. doi: 10.1073/pnas.1000440107
  41. 41. Kanitz E, Otten W, Tuchscherer M, Grabner M, Brussow KP, et al. (2012) High and low proteinratio carbohydrate dietary ratios during gestation alter maternal-fetal cortisol regulation in pigs. PLoS One 7: e52748 10.1371/journal.pone.0052748 [doi];PONE-D-12-15897 [pii]. doi: 10.1371/journal.pone.0052748
  42. 42. Otten W, Kanitz E, Tuchscherer M, Grabner M, Nurnberg G, et al.. (2013) Effects of low and high protein:carbohydrate ratios in the diet of pregnant gilts on maternal cortisol levels and the adrenocortical and sympathoadrenal reactivity in their offspring. J Anim Sci. jas.2012-6080 [pii];10.2527/jas.2012-6080 [doi].
  43. 43. Garcia-Prieto MD, Tebar FJ, Nicolas F, Larque E, Zamora S, et al. (2007) Cortisol secretary pattern and glucocorticoid feedback sensitivity in women from a Mediterranean area: relationship with anthropometric characteristics, dietary intake and plasma fatty acid profile. Clin Endocrinol (Oxf) 66: 185–191 CEN2705 [pii];10.1111/j.1365-2265.2006.02705.x [doi]. doi: 10.1111/j.1365-2265.2006.02705.x
  44. 44. Gabory A, Attig L, Junien C (2011) Developmental programming and epigenetics. Am J Clin Nutr 94: 1943S–1952S ajcn.110.000927 [pii];10.3945/ajcn.110.000927 [doi]. doi: 10.3945/ajcn.110.000927
  45. 45. Van Dijk AE, Van EM, Stronks K, Gemke RJ, Vrijkotte TG (2012) The relation of maternal job strain and cortisol levels during early pregnancy with body composition later in the 5-year-old child: the ABCD study. Early Hum Dev 88: 351–356 S0378-3782(11)00306-9 [pii];10.1016/j.earlhumdev.2011.09.009 [doi]. doi: 10.1016/j.earlhumdev.2011.09.009
  46. 46. Kim K, Yun SH, Choi BY, Kim MK (2008) Cross-sectional relationship between dietary carbohydrate, glycaemic index, glycaemic load and risk of the metabolic syndrome in a Korean population. Br J Nutr 100: 576–584 S0007114508904372 [pii];10.1017/S0007114508904372 [doi]. doi: 10.1017/s0007114508904372
  47. 47. Ma XY, Liu JP, Song ZY (2012) Glycemic load, glycemic index and risk of cardiovascular diseases: Meta-analyses of prospective studies. Atherosclerosis. S0021-9150(12)00338-3 [pii];10.1016/j.atherosclerosis.2012.05.​028[doi].
  48. 48. Dong JY, Zhang YH, Wang P, Qin LQ (2012) Meta-analysis of dietary glycemic load and glycemic index in relation to risk of coronary heart disease. Am J Cardiol 109: 1608–1613 S0002-9149(12)00601-7 [pii];10.1016/j.amjcard.2012.01.385 [doi]. doi: 10.1016/j.amjcard.2012.01.385
  49. 49. Barclay AW, Petocz P, McMillan-Price J, Flood VM, Prvan T, et al.. (2008) Glycemic index, glycemic load, and chronic disease risk-a meta-analysis of observational studies. Am J Clin Nutr 87: 627–637. 87/3/627 [pii].