Limited knowledge exists on the long-term implications of maternal gestational weight gain (GWG) on offspring health. Our objective was to examine whether high GWG in normal weight women is associated with adult offspring cardio-metabolic risk factors.
We used a cohort of 308 Danish women who gave birth in 1988–89 and whose offspring participated in a clinical examination at 20 years of age. Main outcome measures were offspring body mass index (BMI), waist circumference, weight-regulating hormones, blood lipids and glucose metabolism. Associations were assessed using multivariable linear and logistic regression models.
A weak positive association was observed between GWG during the first 30 weeks and offspring anthropometry. Each 1-kg increase in maternal GWG was associated with 0.1-kg m−2 higher (95% confidence interval (CI): 0.01, 0.2) offspring BMI and 10% (95% CI: 0.1%, 20%) higher odds of offspring overweight at the age of 20 years, with similar associations observed in both sexes. However, sex differences were observed for the association between maternal GWG and specific cardio-metabolic risk factors. Hence, a 1-kg increase in GWG was associated with 3.4% (95% CI; 0.8, 6.0%) higher homeostasis model assessment-estimated insulin resistance (HOMA-IR), 3.7% (95% CI: 1.4%, 6.2%) higher insulin and 10.7% (95% CI: 5.7%, 15.9%) higher leptin levels in male offspring. These associations were not observed in females, which may partly be explained by more frequent reports of dieting and physical exercise at follow-up among female offspring.
In normal-weight women, high GWG may have modest long-term implications on offspring cardio-metabolic risk factors at adult age.
Maternal nutrition is important for foetal health, and this presumption is supported by substantial evidence showing that the in utero environment is a strong determinant for later risk of disease in the offspring.1 For example, both maternal under- and overnutrition have been linked to greater adiposity in the offspring and a number of different metabolic events later in life.2, 3, 4 Excessive weight gain during pregnancy is primarily caused by imbalance between energy expenditure and intake, although other factors such as fluid retention (oedema) may play a role. For pregnant women, such imbalance may affect the health of the mother and the offspring, as high gestational weight gain (GWG) has been associated with unfavourable outcomes, including macrosomia, emergency caesarean delivery and postpartum weight retention.5 As a result, the US Institute of Medicine (IOM) has issued guidelines for optimal pregnancy weight gain.5 These guidelines vary according to pre-pregnancy body mass index (BMI) and are specifically aimed at promoting optimal pregnancy and birth outcomes. The IOM has, however, acknowledged that more information is needed on the role of excessive GWG on offspring long-term health.
Recent findings suggest that maternal GWG may be associated with offspring anthropometric measures during different life stages,6, 7, 8 whereas other studies have found no association.9, 10, 11 A limitation of many studies investigating the relationship between GWG and offspring adiposity is the difficulty to factor-out potential confounding by familial dietary and lifestyle habits postpartum. Association between GWG and comorbidities of adiposity such as weight-regulating hormones and other cardio-metabolic risk factors has also been minimally explored.
High maternal pre-pregnancy BMI is considered an independent risk factor for offspring overweight and obesity.3, 12, 13 Although the causality of this relationship is unclear,14 maternal overweight and obesity may mask the potential association of GWG with offspring later health. Hence, the potential influence of GWG on offspring adiposity and disease may be different in normal weight women.15, 16, 17 The aim of this study was to examine the associations between GWG in women of normal weight and offspring anthropometry and cardio-metabolic risk factors at 20 years of age.
Subjects and methods
The birth cohort
Details about the cohort (DAFO88) and its dietary component have been described elsewhere.18, 19, 20 A total of 965 out of 1212 eligible women participated in the study and were recruited from April 1988 to January 1989 in Aarhus, Denmark. These women all had a singleton pregnancy and were scheduled to attend a routine midwife visit in gestational week 30. At the time of the follow-up study (in 2008–2009), the offspring were 19–20 years old. The study was approved by the Danish Data Protection Agency and the Central Denmark Region Committees on Biomedical Research Ethics (Reference No. 20070157). Participants provided written informed consent at recruitment.
At follow-up, a total of 915 offspring of the 965 mothers were contacted and asked to fill out a web-based questionnaire concerning their anthropometry, current health and lifestyle. Of the 688 offspring who agreed to participate in the follow-up, 438 offspring attended a clinical examination. Of these offspring, 35 were born to mothers in underweight (pre-pregnancy BMI <18.5), 308 to mothers in normal weight (pre-pregnancy BMI 18.5–24.9), 32 to overweight or obese mothers (pre-pregnancy BMI ⩾25) and 63 had missing values on their mother's weight gain during pregnancy. As the original cohort was a lean population (81% of the women had pre-pregnancy BMI 18.5–24.9 kg m−2), we decided to restrict our analysis to normal weight women. The final data set therefore consisted of 308 mother–offspring pairs (45% of the follow-up cohort).
Mothers (in normal weight) of offspring not attending the clinical examination had a lower energy intake compared with mothers of offspring attending the examination (8.4 vs 8.7 MJ d−1). Lower educational level and primi- and multiparity (43 vs 34%) were also more common among mothers of offspring not attending the clinical examination (data not shown).
Measurement of exposure variables and covariates
Prior to the routine midwife visit in gestational week 30, dietary and lifestyle questionnaires, including questions on: dietary habits, pre-pregnancy BMI lifestyle and socioeconomic factors, were mailed to the women. During the visit, the questionnaires were returned, the responses were corroborated by trained personnel and a dietary interview was conducted. Information on offspring birth weight was retrieved from birth certificates and weight measurements at week 30 and at the end of pregnancy were retrieved from clinical records and records from antenatal visits. The exposure variables were maternal GWG at week 30 (GWG30) and the total GWG (GWGtotal). GWGtotal was calculated as the difference between the greatest obtained weight in pregnancy and pre-pregnancy weight. GWG30 was used as a continuous term and the GWGtotal was used to classify women's weight gain according to the IOM guidelines (suboptimal (<11.5 kg), optimal (11.5–16 kg) and excessive (>16 kg)).5 Examining weight gain up to week 30 has the advantage that GWG at that time should be minimally influenced by foetal weight and maternal oedema, compared with using GWGtotal. In contrast, the definition of optimal weight gain during pregnancy is based on GWGtotal and therefore facilitates comparison with current IOM recommendations.
For the 308 subjects included in our analyses, there were missing values of either GWGtotal or GWG30 (but not both) for 12 subjects. Missing GWG values for these 12 subjects were imputed based on the predicted value (GWG30=2.50+0.55*GWGtotal) using linear regression for the 296 women with complete data on both GWG measures.
Measurement of outcome variables
Offspring's clinical examination included standard anthropometric measurements, that is, height, weight and waist circumference, and a collection of a fasting (10 h) blood sample. The blood sample was centrifuged and frozen at −80 °C. Serum leptin and adiponectin concentrations were determined at the Medical Research Laboratories in Aarhus, Denmark, by in house-validated assays and carried out as described previously.23 Briefly, serum leptin concentrations were measured by time-resolved immunofluorometric assay based on commercially available reagents (R&D Systems, Abingdon, UK) and recombinant human leptin as standard. Adiponectin levels were also determined by a time-resolved immunofluorometric assay based on two antibodies and recombinant human adiponectin (R&D Systems). Plasma insulin levels were measured using a commercial ELISA kit (DAKO, Glostrup, DK). The homeostasis model assessment-estimated insulin resistance (HOMA-IR) was calculated as: Fasting glucose (mmol l−1)*fasting insulin (mU l−1)/22.5. Serum triglycerides, total cholesterol, low-density lipoprotein (LDL) and high-density lipoprotein were measured according to standard methods on a Modular P (Roche Diagnostics, Basel, Switzerland). Three readings of systolic blood pressure, diastolic blood pressure (DBP) and resting pulse were recorded with an automatic blood pressure device (OMRON M6 Comfort (HEM-7000-E), OMRON HEALTHCARE CO., LTD., Kyoto, Japan) and the mean of each was used.
Multivariable regression models were used to investigate the associations between maternal GWG and offspring outcomes at follow-up, with effect estimates presented either as mean change (linear regression) or odds ratios (logistic regression) with 95% confidence intervals (CI). As a measure of an association, we employed trend tests (t-test for linear regression, chi-square test (type III test) for logistic regression) entering GWG either as a continuous variable or as dummy variables (1=suboptimal, 2=optimal, 3=excessive GWG) when coding GWG according to IOM guidelines. Owing to skewed distributions, insulin, leptin, adiponectin, total cholesterol, triglyceride, LDL and high-density lipoprotein levels were transformed using the natural logarithm. The estimates from regression models were back-transformed via exponentiation to facilitate interpretation as a percent change.
The following confounders were chosen a priori: offspring's sex, mother's age (in quartiles), pre-pregnancy BMI (in quartiles), parity (0, 1, ⩾2), smoking status (non-smoker, <10, ⩾10 cigarettes per day), educational level (elementary schooling, high school or technical schooling, university education (bachelor's degree), higher academic (master's and doctoral degrees), other education) and whether offspring thought their father was overweight (no, yes, missing category). In sensitivity analyses, adjustments were made for intermediary factors such as birth weight, gestational age, offspring's smoking status (non-smoker, occasional, daily), offspring's alcohol consumption (⩾1, 2–3, 4–6, ⩾7 times per month), whether offspring reported being on a diet (yes/no) or exercising (yes/no) to lose weight, offspring leptin levels and BMI (when analysing serum biomarkers) at 20 years of age.
Multiple imputations as implemented in SPSS were used to impute missing covariates (mother;s smoking status (3.2%), educational level (5.8%), gestational age (3.2%), offspring's alcohol (8.4%) and smoking habits (3.9%).
Additional analyses regarding the association between offspring dieting and exercising and cardio-metabolic outcomes were performed with Student's t test and Mann Whitney U test. All analyses were done in SPSS 20.0 (IBM Corp., Armonk, NY, USA).
Maternal characteristics are described in Table 1. The mean total GWG was 14.4±4.9 kg and the mean duration of gestation was 283±10 days. According to the IOM guidelines, 27% of the mothers gained inadequate weight, 44% gained appropriate weight and 29% gained excessive weight. Women with excessive GWG had longer pregnancy duration and their offspring had, as expected a higher birth weight compared with women with optimal or suboptimal weight gain.
At ~20 years of age, the mean BMI of offspring attending clinical examination was 22.3±3.0 kg m−2 and 17.2% of the offspring were overweight or obese (BMI ⩾25 kg m−2), which is comparable to the results from The Danish Health Examination Survey 2007-2008.21 Offspring characteristics at follow-up are shown in Table 2. Cigarette smoking among females was the only examined questionnaire-based characteristic that was associated with GWG. Distribution of offspring cardio-metabolic biomarkers by sex can be found in online supplementary information.
Multivariable association between GWG and offspring BMI and waist circumference
After adjusting for covariates, each 1-kg increase in maternal GWG30 was associated with 0.1 kg m−2 higher (95% CI 0.01; 0.2) offspring BMI and 1.1 (95% CI; 1.01; 1.2) higher odds of offspring being overweight at age 20 (Table 3). Furthermore, 1.8 higher odds (95% CI; 0.9; 3.8) of offspring being overweight was observed when mothers had excessive GWG compared with mothers with optimal weight gain (P for trend=0.01). A positive, non-significant association was also observed between maternal GWG30 and offspring waist circumference. For each 1-kg increase in GWG30 the increase in offspring BMI was similar for both sexes, 0.12 kg m−2 (95% CI: −0.03, 0.27) and 0.10 kg m−2 (95% CI: −0.02, 0.23) in males and females, respectively, and comparable estimates were also observed for waist circumference (data not shown).
Table 4 shows the mean increment in offspring metabolic biomarkers per 1-kg change in maternal GWG30 (mean difference by IOM categories of maternal GWG can be found in online supplementary information. In adjusted models, maternal GWG30 (each 1-kg increase) was associated with 3.7% higher offspring serum leptin levels (95% CI: 1.2, 6.4). The association was primarily driven by male offspring, where each 1-kg increase in maternal GWG was associated with 10.7% (95%CI: 5.7, 15.9) higher leptin levels among male offspring compared with 0.4% (95%CI: −2.4, 3.3) among female offspring. For mothers who gained excessive weight during pregnancy, this increment corresponded to 93% (95%CI: 30, 186) higher leptin levels among male offspring (online supplementary data). Moreover, each 1-kg increase in GWG30 was also positively associated with DBP, resting pulse, HOMA-IR and insulin levels among the male offspring only. When taking into account multiple comparisons in Table 4, the associations for HOMA-IR and DBP would not be considered significant whereas the associations for resting pulse, serum leptin and insulin levels were robust for this correction.
Among female offspring, significant inverse associations were, however, found with offspring blood-lipid levels. Differences in behavioural responses to increased weight may account for these sex differences as more frequent reports of dieting (16 vs 8%) and physical exercise (41 vs 26%) at follow-up was observed among female offspring compared with males (Table 2).
In comparison with offspring of mothers with optimal GWG, children of those with sub-optimal GWG tended to have lower BMI (Table 3) and more favourable cardio-metabolic outcomes (online supplementary data), for example, offspring of mothers with suboptimal GWG tended to have 10% lower insulin levels (95% CI: −20, 1) and 7% lower leptin levels (95% CI: −25, 15) compared with offspring of mothers with optimal weight gain (P for trend <0.05 for both outcomes).
In our sensitivity analyses, we found that taking birth weight and gestational age into account, the effect sizes were attenuated slightly for offspring BMI (β went from 0.10–0.08 per 1-kg increase during the first 30 weeks of gestation). However, these additional adjustments did not attenuate the observed associations of GWG with offspring cardio-metabolic outcomes. For example, β for serum leptin levels went from 3.7 to 3.6% per 1-kg increase in GWG during the first 30 weeks of gestation when also adjusting for birth weight and gestational age. Adjusting for offspring smoking, alcohol habits and BMI (when analysing serum biomarkers) at the age of 20 did not appreciably alter effect estimates (online supplementary information).
However, effect sizes were significantly attenuated for DBP, HOMA-IR and insulin levels when adjusting separately for offspring leptin levels (data not shown).
In our models, we adjusted for paternal overweight reported by the offspring at follow-up (yes/no) as a proxy for confounding for familial lifestyle. Information about maternal overweight at follow-up reported by the offspring (see Table 2) was not included because this variable did not affect our estimates (data not shown), most likely because this adjustment was already accounted for by pre-pregnancy BMI.
Given the sex-specific differences observed for cardio-metabolic outcomes in Table 4, we formally tested effect modification by sex by including GWG (continuous variable), sex (binary variable) and an interaction term between the two in the regression model, along with the remaining covariates. Statistically significant interactions (P<0.05) were observed for serum leptin levels, insulin, HOMA-IR, resting pulse, total cholesterol and LDL cholesterol.
In a study of women with normal pre-pregnancy weight, we observed a weak positive association between GWG and BMI in both male and female offspring at 20 years. In addition, our results suggest that GWG is adversely associated with offspring biomarkers of cardio-metabolic health in male but not female offspring. GWG was, however, inversely associated with levels of total cholesterol and LDL levels among females. Differences in lifestyle habits may account for these differences as we observed higher prevalence of physical activity and dieting among female offspring.
Accumulating evidences indicate that GWG is associated with offspring BMI in childhood.7 Our findings suggest that this association may extend to adulthood, which is in accordance with other recent findings.6 Current recommendations on optimal weight gain are primarily based on limiting pregnancy complications and promoting optimal foetal growth. In accordance with previous studies,6, 7, 22 we found that in comparison with offspring of mothers with optimal GWG, children of those with sub-optimal GWG tended to have lower BMI and more favourable cardio-metabolic outcomes (online supplementary data). Establishing optimal GWG for short- and long-term outcomes may therefore be complex and there is a need to examine whether the modest shifts observed in our study and by others6, 7, 22 become clinically relevant later in adulthood.
In our study, the relationship between GWG and offspring BMI was similar for both male and female offspring, whereas for biomarkers of cardio-metabolic health, maternal GWG was relatively strongly associated with higher leptin and insulin levels, HOMA-IR index, DBP and resting pulse in male offspring only. The association for HOMA-IR was driven by greater insulin levels as fasting blood sugar was not related to GWG, which is not surprising given the young age of the offspring.23 Effect sizes were significantly attenuated for DBP, HOMA-IR and insulin levels when adjusting separately for offspring leptin levels, which indicates that these modest shifts in offspring cardio-metabolic biomarkers may be mediated through increased adiposity. A more favourable inverse association between GWG and total and LDL cholesterol was, however, observed for female offspring. Although most studies have not reported sex-specific differences, Mamun et al.15 observed a stronger relationship between maternal GWG and offspring BMI in males compared with females at the age of 21. Furthermore, animal studies have reported that male and female offspring exhibit different programmed outcomes following insults in utero.24 For example, disturbed glucose homeostasis has been reported in male offspring of over-nourished mothers, despite both sexes displayed elevated levels of adiposity compared with controls.25, 26
The sex difference we observed for biomarkers of cardio-metabolic health could also be related to differences in behavioural responses to increased weight. Compared with males, female offspring of mothers gaining excessive weight during pregnancy were almost twice as likely to report that they thought their weight was too high and that they were trying to lose weight by dieting and exercising (Table 2). We also noted that although BMI levels were slightly higher among females on a diet compared with those not dieting, total cholesterol levels were significantly lower (P= 0.02) in females dieting compared with females not on a diet (data not shown). This may explain why inverse association between maternal GWG and blood lipids were observed in female but not in male offspring. These speculations are strengthened by differences observed in offspring resting pulse, that is, maternal weight gain was associated with higher resting pulse among male offspring, whereas a non-significant inverse association was observed for females (Table 4 and online supplementary data). Physical activity and fitness is known to improve biomarkers of cardio-metabolic health relatively rapidly,27, 28 while reducing weight takes longer time to achieve. However, adjusting for offspring dieting and physical activity did not change estimates which could be related to the fact that we did not have information about the intensity of the diet. Not answering yes to ‘being on a diet’ does not exclude that females were not in general still relatively more active and had more preferences for healthy foods compared with males. Being able to account for the inverse association between GWG and blood lipids had required an accurate assessment of dieting and physical activity. We therefore speculate that ‘programming’ by maternal GWG may be mitigated by offspring behavioural responses that are more prevalent among female offspring.
The mechanism by which maternal GWG could influence later health of offspring is currently not well understood. The association with offspring anthropometry could be mediated by the effect of GWG on birth weight and therefore reflect tracking in size across the life course.
The magnitude of the associations between GWG and offspring BMI decreased slightly after birth weight and gestational age were added to the model, which suggest that this association might be partly mediated by foetal growth. Additional adjustment for birth weight did, however, not alter associations regarding cardio-metabolic outcomes. We also had two measurements of maternal weight gain, that is, at week 30 and highest obtained GWG, but slightly stronger associations were observed with GWG at week 30 compared with total GWG, when analysed both as continuous variables (data not shown). This may indicate that our observations are likely associated with maternal fat mass accumulation rather than only with foetal growth, which makes up a larger proportion of GWG in late pregnancy. Shared familial genetic and lifestyle characteristics, like high energy diet and low levels of physical activity may also link greater GWG with greater offspring BMI and adverse cardio-metabolic profile in adulthood. We had information regarding whether offspring thought their mother, father or sibling was overweight in addition to information on offspring's smoking and alcohol habits. We were therefore able to take into account confounding by important familial dietary and lifestyle habits postpartum (online supplementary data) and this in addition to long-term follow-up is the major strength of our study. The persistent relationship observed after adjustment for these factors suggests that, at least in part, high GWG may affect offspring's weight and metabolism by modifying the intrauterine environment, possibly by influencing maternal and foetal hormonal profile, which may affect offspring appetite control and adiposity later in life.29, 30 Our results therefore indicate that interventions promoting healthy GWG should not only target overweight and obese women, but also women in normal weight.
Concerning weaknesses, we cannot, as with all observational studies exclude residual confounding or confounding by unmeasured covariate(s). Even though our covariate adjustments had minimal influence on our effect estimates compared with unadjusted models (data not shown), the role of residual confounding particularly during long-term follow-up can never fully be excluded. Furthermore, we were unable to account for genetics. Relying on only one measure for biomarkers of cardio-metabolic health such as leptin can also be considered a limitation.31, 32 Our measure of pre-pregnancy weight was based on self-report, possibly leading to bias because of underreporting; however, we suspect that such bias should be small given our restriction to women of normal weight.33, 34 In addition, the population studied was white, fairly well educated, with normal BMI and for the most part normal GWG. Whether the result could be applied to other populations, perhaps at higher cardiovascular risk, remains to be studied.
In conclusion, our results provide evidence that maternal GWG among normal weight mothers may affect offspring cardio-metabolic health at young adult age. Although the observed associations were modest, we cannot exclude that these modest shifts may become more apparent later in life. Measurements over longer periods of time are needed to add to the current understanding of the long-term influence of non-optimal GWG on offspring's anthropometry and cardio-metabolic health.
Gluckman PD, Hanson MA, Cooper C, Thornburg KL . Effect of in utero and early-life conditions on adult health and disease. N Engl J Med 2008; 359: 61–73.
Ravelli AC, van Der Meulen JH, Osmond C, Barker DJ, Bleker OP . Obesity at the age of 50 y in men and women exposed to famine prenatally. Am J Clin Nutr 1999; 70: 811–816.
Drake AJ, Reynolds RM . Impact of maternal obesity on offspring obesity and cardiometabolic disease risk. Reproduction 2010; 140: 387–398.
Lillycrop KA, Burdge GC . Epigenetic changes in early life and future risk of obesity. Int J Obes (Lond) 2011; 35: 72–83.
Committee to Reexamine IOM Pregnancy Weight Guidelines. In: Institute of medicine, National research Consil. 2010/07/30 ed. National Academies Press: Washington DC, 2009.
Mamun AA, Mannan M, Doi SA . Gestational weight gain in relation to offspring obesity over the life course: a systematic review and bias-adjusted meta-analysis. Obes Rev 2014; 15: 338–347.
Nehring I, Lehmann S, von Kries R . Gestational weight gain in accordance to the IOM/NRC criteria and the risk for childhood overweight: a meta-analysis. Pediatr Obes 2013; 8: 218–224.
Deierlein AL, Siega-Riz AM, Adair LS, Herring AH . Effects of pre-pregnancy body mass index and gestational weight gain on infant anthropometric outcomes. J Pediatr 2011; 158: 221–226.
Whitaker RC . Predicting preschooler obesity at birth: the role of maternal obesity in early pregnancy. Pediatrics 2004; 114: e29–e36.
Gale CR, Javaid MK, Robinson SM, Law CM, Godfrey KM, Cooper C . Maternal size in pregnancy and body composition in children. J Clin Endocrinol Metab 2007; 92: 3904–3911.
Koupil I, Toivanen P . Social and early-life determinants of overweight and obesity in 18-year-old Swedish men. Int J Obes (Lond) 2008; 32: 73–81.
Hochner H, Friedlander Y, Calderon-Margalit R, Meiner V, Sagy Y, Avgil-Tsadok M et al. Associations of maternal prepregnancy body mass index and gestational weight gain with adult offspring cardiometabolic risk factors: the Jerusalem Perinatal Family Follow-up Study. Circulation 2012; 125: 1381–1389.
Yu Z, Han S, Zhu J, Sun X, Ji C, Guo X . Pre-pregnancy body mass index in relation to infant birth weight and offspring overweight/obesity: a systematic review and meta-analysis. PLoS One 2013; 8: e61627.
Patro B, Liber A, Zalewski B, Poston L, Szajewska H, Koletzko B . Maternal and paternal body mass index and offspring obesity: a systematic review. Ann Nutr Metab 2013; 63: 32–41.
Mamun AA, O'Callaghan M, Callaway L, Williams G, Najman J, Lawlor DA . Associations of gestational weight gain with offspring body mass index and blood pressure at 21 years of age: evidence from a birth cohort study. Circulation 2009; 119: 1720–1727.
von Kries R, Ensenauer R, Beyerlein A, Amann-Gassner U, Hauner H, Rosario AS . Gestational weight gain and overweight in children: Results from the cross-sectional German KiGGS study. Int J Pediatr Obes 2011; 6: 45–52.
Beyerlein A, Nehring I, Rzehak P, Heinrich J, Muller MJ, Plachta-Danielzik S et al. Gestational weight gain and body mass index in children: results from three german cohort studies. PLoS One 2012; 7: e33205.
Olsen SF, Hansen HS, Sandstrom B, Jensen B . Erythrocyte levels compared with reported dietary intake of marine n-3 fatty acids in pregnant women. Br J Nutr 1995; 73: 387–395.
Olsen SF, Hansen HS, Secher NJ, Jensen B, Sandstrom B . Gestation length and birth weight in relation to intake of marine n-3 fatty acids. Br J Nutr 1995; 73: 397–404.
Halldorsson TI, Rytter D, Haug LS, Bech BH, Danielsen I, Becher G et al. Prenatal exposure to perfluorooctanoate and risk of overweight at 20 years of age: a prospective cohort study. Environ Health Perspect 2012; 120: 668–673.
Christensen AI, Severin M, Holmberg T, Eriksen L, Toftager M, Zachariassen A et al KRAM-undersøgelsen i tal og billeder. Retrieved from http://www.si-folkesundhed.dk/upload/rapporten_kram_2010.pdf.
Fraser A, Tilling K, Macdonald-Wallis C, Sattar N, Brion MJ, Benfield L et al. Association of maternal weight gain in pregnancy with offspring obesity and metabolic and vascular traits in childhood. Circulation 2010; 121: 2557–2564.
Venn BJ, Williams SM, Perry T, Richardson S, Cannon A, Mann JI . Age-related differences in postprandial glycaemia and glycaemic index. Age Ageing 2011; 40: 755–758.
Aiken CE, Ozanne SE . Sex differences in developmental programming models. Reproduction 2013; 145: R1–13.
Samuelsson AM, Matthews PA, Argenton M, Christie MR, McConnell JM, Jansen EH et al. Diet-induced obesity in female mice leads to offspring hyperphagia, adiposity, hypertension, and insulin resistance: a novel murine model of developmental programming. Hypertension 2008; 51: 383–392.
Nivoit P, Morens C, Van Assche FA, Jansen E, Poston L, Remacle C et al. Established diet-induced obesity in female rats leads to offspring hyperphagia, adiposity and insulin resistance. Diabetologia 2009; 52: 1133–1142.
Thompson PD, Crouse SF, Goodpaster B, Kelley D, Moyna N, Pescatello L . The acute versus the chronic response to exercise. Med Sci Sports Exerc 2001; 33 (6 Suppl): S438–S445.
Warburton DE, Nicol CW, Bredin SS . Health benefits of physical activity: the evidence. CMAJ 2006; 174: 801–809.
Armitage JA, Taylor PD, Poston L . Experimental models of developmental programming: consequences of exposure to an energy rich diet during development. J Physiol 2005; 565 (Pt 1) 3–8.
Poston L . Gestational weight gain: influences on the long-term health of the child. Curr Opin Clin Nutr Metab Care 2012; 15: 252–257.
Ahima RS, Flier JS . Leptin. Annu Rev Physiol 2000; 62: 413–437.
Geisthovel F, Jochmann N, Widjaja A, Horn R, Brabant G . Serum pattern of circulating free leptin, bound leptin, and soluble leptin receptor in the physiological menstrual cycle. Fertil Steril 2004; 81: 398–402.
Lederman SA, Paxton A . Maternal reporting of prepregnancy weight and birth outcome: consistency and completeness compared with the clinical record. Matern Child Health J 1998; 2: 123–126.
Brunner Huber LR . Validity of self-reported height and weight in women of reproductive age. Matern Child Health J 2007; 11: 137–144.
We acknowledge the financial support of the Danish Council for Strategic Research. The present study was supported by the Danish Council for Strategic Research (Grant no.: 09-067124 (Centre for Fetal Programming), 2101-07-0025 (Danish Centre for Obesity Research) and 2101-06-0005).
The authors declare no conflict of interest.
Supplementary Information accompanies this paper on International Journal of Obesity website
About this article
Cite this article
Hrolfsdottir, L., Rytter, D., Olsen, S. et al. Gestational weight gain in normal weight women and offspring cardio-metabolic risk factors at 20 years of age. Int J Obes 39, 671–676 (2015). https://doi.org/10.1038/ijo.2014.179
Environment International (2020)
Journal of Pediatric Endocrinology and Metabolism (2020)
International Journal of Obesity Supplements (2020)
The effect of pre-pregnancy weight and gestational weight gain on blood pressure in children at 6 years of age
Journal of Public Health (2020)
Gestational Exercise and Maternal and Child Health: Effects until Delivery and at Post-Natal Follow-up
Journal of Clinical Medicine (2020)