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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].
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.
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.
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.
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).
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).
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