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Does physical activity during pregnancy adversely influence markers of the metabolic syndrome in adult offspring? A prospective study over two decades
  1. Inge Danielsen1,
  2. Charlotta Granström1,
  3. Dorte Rytter2,
  4. Bodil Hammer Bech2,
  5. Tine Brink Henriksen3,
  6. Allan Arthur Vaag4,
  7. Sjurdur Frodi Olsen1
  1. 1Department of Epidemiology Research, Centre for Fetal Programming, Statens Serum Institute, Copenhagen, Denmark
  2. 2Department of Public Health, Section of Epidemiology, Centre for Fetal Programming, Aarhus University, Aarhus, Denmark
  3. 3Department of Paediatrics, Aarhus University Hospital, Skejby, Denmark
  4. 4Department of Endocrinology, Rigshospitalet and Copenhagen University, Copenhagen, Denmark
  1. Correspondence to Inge Danielsen, Department of Epidemiology Research, Centre for Fetal Programming, Statens Serum Institut, Denmark, Artillerivej 5, Building 206, Copenhagen 2300, Denmark; ind{at}ssi.dk

Abstract

Background It is unknown whether physical activity during pregnancy (PA) has long-term impact on the metabolic profile of the offspring. We investigated associations of PA with markers of the metabolic syndrome (MS) in 20y old offspring.

Methods Longitudinal study where 965 pregnant women during 1988–1989 had four dimensions of PA assessed by questionnaires in gestation week 30: PA at work; leisure time PA, daily amount of walking-biking and sport participation. The following MS markers were assessed in the offspring (n=439): body mass index (BMI), waist circumference, blood pressure, homeostasis model assessment insulin resistance as well as fasting plasma glucose, triglycerides, cholesterol (high-density lipoprotein (HDL), low-density lipoprotein and total cholesterol), insulin and leptin levels.

Results Walking-biking PA in pregnancy is associated with unchanged or subtle, adverse changes of distinct MS markers among offspring including lower levels of HDL cholesterol (ratio 0.95 (95% CI 0.92 to 0.98) per 1 h increment in walking-biking), a higher diastolic blood pressure (difference 1.12 (95% CI 0.03 to 2.20) mm Hg/1 h increment) and a higher BMI (ratio 1.03 (95% CI 1.01 to 1.05) per 1 h increment). In separate analyses in males, these associations persisted and additional adverse associations were found for triglycerides, systolic blood pressure, waist circumference and leptin. No associations were detected with other measures of PA.

Conclusions The study did not substantiate any protective effects of PA in pregnancy. In contrast, data suggested that high amounts of daily walking-biking in pregnancy may have adverse effects on levels of HDL cholesterol, diastolic blood pressure and BMI in young adult offspring.

  • EPIDEMIOLOGY
  • FETAL
  • PHYSICAL ACTIVITY
  • PREGNANCY

https://doi.org/10.1136/jech-2012-202017

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    Introduction

    The metabolic syndrome (MS) has 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 high-density lipoprotein (HDL) cholesterol and raised triglyceride levels, as well as raised blood pressure and fasting plasma glucose levels.1

    Several lines of evidence indicate that MS is rooted in fetal life.2 ,3 Still, the underlying causes are unclear. Much attention has been diverted to the maternal diet, such as a high-fat intake,4 endocrine disrupting chemicals5 ,6 and diabetes during pregnancy.7 However, the intrauterine environment is likely to be influenced by a range of different factors in addition to maternal diet. Until today, the level of physical activity, known to be tightly linked to the risk of diabetes, MS and cardiovascular disease in adult life, has received limited attention as the possible link between pregnant women's everyday life and risk of metabolic disease in the offspring.

    Intervention as well as observational studies consistently support that physical activity in adult life protects against MS.8–12 These protective effects are supposed to be related to the positive effects on the plasma lipid profile, and improved glycaemic control, and are translated into improved blood pressure control, increased insulin sensitivity and decreased central adiposity.3 Physical activity in pregnancy may also influence the intrauterine environment, for instance, by altered substrate availability influencing fetal growth and development in a beneficial manner.13 ,14 However, the data reported so far are inconsistent with one study reporting increased physical activity during pregnancy to be associated with reduced body mass index (BMI) among offspring at age 5 years,15 and two other studies showing no influence of physical activity on body fat16 or BMI17 among offspring aged 1 and 7 years, respectively. We studied the influence of physical activity in pregnancy on markers of MS among 20-year-old offspring in a unique Danish birth cohort.

    Methods

    Danish fetal origin cohort 1988

    In 1988, a total of 965 of 1212 eligible women with singleton pregnancies were recruited for a birth cohort study in Denmark.18 In gestational week 30, the women completed a postal questionnaire. A 15 min face-to-face interview was conducted by a trained person who verified the response to the self-administered questionnaire and completed a second interviewer-guided questionnaire with the women. The questionnaires covered medical history, diet and other lifestyle factors as well as socioeconomical factors. Further information about the women's health, pregnancy outcomes, medical history and anthropometry status was extracted from the hospital records and from the Danish Medical Birth Registry as well as from the records kept by the midwives and general practitioners. The self-administered questionnaire included questions about the level of physical activity during the previous 3 months corresponding to the second trimester.

    Exposure variables:

    1. Physical activity at work: Possible answers: (a) no job during the last 3 months, (b) mainly sedentary work (eg, desk job), (c) light physical work (eg, light industrial work and teaching), (d) mainly walking around, often climbing stairs, carrying things (eg, cleaning, home caring and nursing) and (e) heavy physical work, lifting heavy burdens (stronger physical exertion than in the previous categories). In the present paper, categories (b)–(e) are designated sedentary, light, moderate and high physical activity at work, respectively. Moderate and high were pooled due to a low number of observations in the high category and designated moderate-high. Category (a) (no job, n=59) was excluded from the analysis of physical activity at work.

    2. Physical activity in leisure time: Possible answers: (a) mainly sitting, reading or watching TV, or other sedentary activities, (b) walking, bike riding or otherwise somewhat physically active (eg, table tennis and light gardening) at least 3 h a week, (c) very active (eg, doing sports and heavy gardening) at least 3 h a week, (d) competitive sports and either swim, play ball or run long distances several times a week. For the analyses, categories (a) and (b) were designated sedentary and light physical activity in leisure time, respectively, and (c) and (d) were pooled due to low numbers of observations and designated moderate-high physical activity in leisure time.

    3. Duration of walking and bike riding: The women were asked how much time (in minutes) they typically spent walking and bike riding on a weekday. The data were converted to hours/day and used as a continuous variable.

    4. Sport activities: The women were asked if they participated in sports or other physical exercise more than 1 h a week, and, if so, what kind of sport and for how many hours per week. All types of sports reported were grouped according to intensity and given corresponding metabolic equivalent (MET) scores, using the International Physical Activity Questionnaire scoring system and Compendium of Physical Activity by Ainsworth et al,19 ,20 where one MET is equivalent to the energy expenditure of a standard 60 kg person in the resting state (resting metabolic rate (RMR), by convention set to 3.5 ml of O2/kg/min). By definition, the MET score of a work activity is the ratio of the work metabolic rate and RMR. MET scores were adjusted for weight and MET hours calculated by multiplying with the duration of the activity: MET score × duration (in hours)=MET hours. MET hours were further divided by 10 to derive a continuous measure of ‘per 10 MET hours’ when examining the association with MS markers.

    Offspring follow-up

    In 2008 and 2009, offspring were invited to complete a web-based questionnaire including inquiries on current health, lifestyle and dietary habits and to participate in a clinical examination. The participants were examined after overnight fasting. Height, weight and waist circumference were measured. Following 7 min of rest, blood pressure was measured three times in the horizontal position. The average value of the last two measurements was used in the analysis. A venous blood sample was drawn and immediately centrifuged, and subsequently frozen at −80°C.

    From a total number of 965 women, 894 singleton offspring were traced. The remaining group consisted of twins, mothers and children with an incorrect personal identification number, mothers and children who were dead or abroad, or with unknown addresses, or offspring that were unable to participate because of illness.

    A total of 688 subjects (77% of the eligible population) participated in the follow-up by filling out the questionnaire and of these, 439 attended the clinical examination.

    Offspring biomarkers

    Plasma glucose levels were measured using bedside equipment 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.21 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)×fasting insulin (mU/l)/22.5.22 Serum triglycerides and cholesterol fractions (total cholesterol, low-density lipoprotein (LDL), HDL) were measured according to standard methods.

    Markers for MS

    Primary outcome variables consisted of the single continuous variables inherent in the definition of MS including waist circumference, fasting levels of plasma glucose, triglycerides and HDL cholesterol, as well as systolic and diastolic blood pressure. Secondary outcome variables were chosen among supplementary variables associated with MS including BMI, plasma levels of LDL and total cholesterol, fasting plasma insulin and leptin concentrations, as well as HOMA-IR. Offspring waist circumference was adjusted for BMI using the residual method,23 providing an uncorrelated measure of BMI and waist circumference. Owing to the skewed distributions, all outcome variables except adjusted waist circumference and blood pressure were log-transformed.

    Statistical analyses

    Baseline characteristics of pregnant women with non-participating offspring and of those with participating offspring were tested for differences by χ2 test.

    Distributions of covariates according to maternal physical activity in categories were tested for trends by the Mantel-Haentzel χ2 test for trend for categorical covariates. For continuous maternal physical activity, analysis of variance was used to test for trend.

    Associations between maternal physical activity and offspring outcome measures were examined by multiple linear regression analyses. We decided a priori on the following covariates: maternal height (continuous, 3% missing), education (five categories, 5% missing), smoking (yes or no, 5% missing) and pre-pregnancy BMI (continuous, 3% missing). Observations with any single missing covariate value were excluded from analysis.

    Maternal height and pre-pregnancy BMI were included as these variables are possible determinants of anthropometric and metabolic measures in the offspring. Parity was not considered as initial analyses showed no association to any outcome measures. Maternal education and smoking were included to account for potential social and lifestyle confounding. The four measures of maternal physical activity were separately added to this model.

    All analyses were performed for combined sexes and subsequently for male and female offspring separately. The combined analysis included sex as a covariate. Changes in outcome variables for a one unit increment in the continuous measures of physical activity, and changes in outcome variables for each increment from one category to the next in the categorical measures of physical activity, are presented as absolute changes for waist circumference and blood pressure and as relative changes for BMI and biomarkers. These measures of association are expressed as ‘difference (95% CI)’ and ‘ratio (95% CI)’. Associations were considered statistically significant at the 5% level and all regression results are presented with 95% CI. All analyses were performed using the SAS GLM procedure (V.9.3; SAS Institute, Cary, North Carolina, USA).

    Results

    The mean (±SD) daily walking and bike riding among the pregnant women was 52 (±36.8) minutes. Twenty per cent of the women participated in sports, and among these the mean total MET hours/week was 12 (SD=8.4). Sixteen per cent of the women did not work. Among the women who worked, less than 2% reported high, 30% moderate, 37% light and 32% sedentary physical activity at work. Two per cent of the women reported high, 6% moderate, 72% light and 22% sedentary leisure activity.

    Associations between physical activity measures and covariates were analysed (tables 1 and 2). Higher leisure activity was associated with larger height and higher education. Pregnant women's participation in sport tended to be more common the higher the pre-pregnancy BMI of the pregnant women.

    View this table:
    Table 1

    Distribution of covariates among the pregnant women according to the exposure variables: leisure activity in categories of increasing intensity and daily duration of walking and bike riding in categories of increasing duration (in minutes)

    View this table:
    Table 2

    Distribution of covariates among the pregnant women according to physical activity at work in categories of increasing intensity and participation in sports in categories of increasing activity (MET hours/week)

    As shown in online supplementary table S1, more women with participating offspring were normal weight non-smokers and working during pregnancy and had a lower level of physical activity at work compared with women with non-participating offspring. The data showed no differences in the level of physical activity between the women who had a job and those who did not work during pregnancy and were therefore excluded from the analyses on physical activity at work (see online supplementary table S2). In addition, the data did not show considerable differences between participating women and women, who were excluded from the analyses due to missing data on covariates or on measures of physical activity except for parity (see online supplementary table S3). However, women with missing data on sport participation were less educated and more were smoking (see online supplementary table S3), and, moreover, they had a lower level of leisure activity (data not shown). More time spent on daily walking and bike riding in pregnancy was associated with lower plasma levels of HDL cholesterol (ratio 0.95 (95% CI 0.92 to 0.98), higher diastolic blood pressure (difference 1.12 mm Hg (95% CI (0.03 to 2.20)) and higher BMI (ratio 1.03 (95% CI (1.01 to 1.05)) in the offspring (table 3). In addition, there was a tendency towards higher levels of leptin (ratio 1.12 (95% CI (0.98 to 1.29)). In these analyses with combined sexes, the data showed no associations between the other three measures of maternal physical activity and MS markers in the offspring (see online supplementary tables S4, S7 and S10).

    View this table:
    Table 3

    MS markers in male and female offspring/1 h increment/day in their mothers’ daily amount of walking and bike riding (hours/day) in the second trimester*

    The associations with the pregnant women's daily walking and bike riding persisted in the subgroup of male offspring (table 4) where a higher amount of daily walking and bike riding was associated with lower levels of HDL cholesterol, higher diastolic blood pressure, higher BMI and higher levels of leptin. In addition, among the male offspring, more time spent on walking and bike riding was associated with higher levels of triglycerides, higher systolic blood pressure and a larger waist circumference. Among the female offspring (table 5), only the association with levels of HDL cholesterol persisted.

    View this table:
    Table 4

    MS markers in male offspring/1 h increment/day in their mothers’ daily amount of walking and bike riding (h/day) in the second trimester*

    View this table:
    Table 5

    MS markers in female offspring/1 h increment/day in their mothers’ daily amount of walking and bike riding (hours/day) in the second trimester*

    In addition, in the subgroup of female offspring, the data also showed U-formed associations between the level of physical activity at work during pregnancy and the female plasma levels of LDL cholesterol and total cholesterol (see online supplementary table S6).

    In the analyses with separated sexes, the data showed no further associations (see online supplementary tables S5, S6, S8, S9, S11 and S12).

    Additional analyses were made to study the possible associations between length of gestation and maternal physical activity. However, no associations were detected. Moreover, supplementary analyses were made with additional adjustments for offspring's physical activity resulting in a few and insignificant changes to the results. For instance, in the analyses with combined sexes, per 1 h increment in mothers’ walking and bike riding, the associations changed as follows: plasma levels of HDL cholesterol ratio 0.95 (95% CI (0.92 to 0.99)); diastolic blood pressure difference 1.16 mm Hg (95% CI (0.07 to 2.25)); BMI ratio 1.03 ((95% CI (1.01 to 1.05)) and levels of leptin ratio 1.14 (95% CI (0.99 to 1.31)). In addition, birth weight and birth length were added to the model in two separate analyses; however, these supplementary adjustments did not change the results either.

    Discussion

    We hypothesised that physical activity during pregnancy may benefit the metabolic profile of the offspring. Surprisingly, however, our data pointed—with a few exceptions—towards non-beneficial metabolic effects on the adult offspring. Notably, the effects differed according to sex and measures of physical activity.

    Only the daily walking and bike riding was associated with MS markers in the offspring. Daily walking and bike riding is the most exact of the four measures of physical activity, as walking and bike riding is unambiguous and the unit of the measure (minutes/day) is exact. Daily walking and bike riding overlap to some extent with leisure activity. Limited answer possibilities may explain why the data showed no associations with leisure activity. The same degree of unreliability holds for physical activity at work. The variable measuring participation in sports was encumbered with other limitations: first, only 20% of the women entered the analysis of mixed sexes, resulting in relatively low statistical power. Second, the processing of the original data involved several assumptions related to MET scores of the reported sports, reducing the complexity and quality of the original data.

    The results suggest that males may be more sensitive to the physical activity level of their mothers during fetal life compared with females. Thus, among male offspring, not only blood lipid levels but also BMI, systolic and diastolic blood pressure as well as plasma leptin concentrations were associated with the level of maternal physical activity. Among the females, only the plasma HDL cholesterol levels were associated with maternal physical activity. On the other hand, physical activity at work was associated with levels of LDL and total cholesterol among female but not male offspring. Notably, these associations in females may appear U-shaped with trends towards higher levels after exposure to sedentary and moderate-to-high physical activity at work compared with light activity. However, such trends towards U-shaped associations were not seen in males or in the combined analyses, questioning the validity of this subanalysis in females.

    A potential explanation for the observed sex differences could be that health-related behaviour is differently dependent on parental bonding and parental behaviour in boys compared with girls.24

    The adverse effects of physical activity in pregnancy on offspring MS markers are quantitatively comparable to the effects of maternal BMI and smoking during pregnancy on offspring MS markers. For instance, increasing BMI from normal (18.6–24.9 kg/m2) to overweight or obese (25+ kg/m2) was associated with a 4.6 mm Hg higher diastolic blood pressure and 8% increase in BMI among the offspring (data not shown). Correspondingly, the offspring of women with a high level of daily walking and bike riding (more than 100 min) had a 2.9 mm Hg higher diastolic blood pressure and a 7% higher BMI compared with offspring of women with a low level (less than 20 min). Smoking during pregnancy tended to increase the BMI in the offspring with 3%. However, given that our findings were in the opposite direction of our a priori hypothesis of beneficial effects of maternal physical activity on MS markers in the offspring, these results need to be validated in other cohorts before the true quantitative impact can be determined.

    Physical activity in pregnancy acutely changes the blood distribution and supply of oxygen and nutrients to the fetus. It is possible that the fetus in a physically active woman adapts to a fetal environment characterised by periods of reduced oxygen, insulin and glucose levels and increased levels of catecholamines.13 This environment may to some extent intermittently resemble that of a fetus exposed to undernutrition, where epidemiological studies have shown adverse effects on various markers for MS in the adult offspring.25 ,26

    A limitation of our study is that the questionnaire may not have picked up important aspects of physical activity such as domestic work, which is of relevance for women of childbearing age, and activities that either cannot be characterised as competitive sports or are performed less than 3 h/week.

    The several strengths of the study include outcome variables in adults and may therefore all together be more contemporary compared with many earlier studies in the field of developmental programming.

    Developmental programming is likely to be linked to the specific cultural context and living conditions and seems to differ between populations.27 Thus, it is likely that the effects of physical activity during pregnancy on markers for MS in the offspring may be confounded differently in different settings and different generations.

    As each of the markers for MS studied in the offspring is known to develop and deteriorate further with ageing, we would expect the same associations to be present with time and age in these subjects, and assumably with larger effect sizes. Moreover, it is indeed possible that other associations, for example, with overt metabolic disease, will appear with increasing age. However, this of course needs to be demonstrated in future follow-up studies of this or other cohorts.

    In conclusion, our data suggest that physical activity in the second trimester of pregnancy does not exhibit beneficial effects on markers of MS in the young adult offspring. If anything, increased physical activity may be detrimental for metabolic outcome variables in the offspring. Male and female offspring appeared to respond differently depending on the type of physical activity. The results need to be confirmed by new studies on large cohorts with high quality exposure data throughout the entire period of pregnancy.

    What is already known on this subject?

    • The impact of physical activity in pregnancy on metabolic health among adult offspring is unknown.

    What does this study add?

    • We hypothesised that increased physical activity in pregnancy has beneficial effects on markers of metabolic health in adult offspring.

    • We tested the hypothesis by measuring a range of markers of insulin resistance and the metabolic syndrome among adult offspring from a unique cohort of women who had different aspects of physical activity determined by questionnaires during pregnancy two decades earlier.

    • We were unable to confirm our a priori hypothesis and some markers even suggested adverse effects of physical activity in pregnancy on metabolic health in the offspring.

    References

    1. ↵
      International Diabetes Federation. 2012. http://www.idf.org/webdata/docs/MetS_def_update2006.pdf. Ref Type: Online Source.
    2. ↵
      1. Hales CN,
      2. Barker DJ
      . The thrifty phenotype hypothesis. Br Med Bull 2001;60:5–20.
      OpenUrlAbstract/FREE Full Text
    3. ↵
      1. Xita N,
      2. Tsatsoulis A
      . Fetal origins of the metabolic syndrome. Ann N Y Acad Sci 2010;1205:148–55.
      OpenUrlCrossRefPubMedWeb of Science
    4. ↵
      1. Sullivan EL,
      2. Smith MS,
      3. Grove KL
      . Perinatal exposure to high-fat diet programs energy balance, metabolism and behavior in adulthood. Neuroendocrinology 2011;93:1–8.
      OpenUrlCrossRefPubMedWeb of Science
    5. ↵
      1. Halldorsson TI,
      2. Rytter D,
      3. Haug LS,
      4. et al
      . Prenatal exposure to perfluorooctanoate and risk of overweight at 20 years of age: a prospective cohort study. Environ Health Perspect 2012;120:668–73.
      OpenUrlCrossRefPubMedWeb of Science
    6. ↵
      1. Newbold RR
      . Impact of environmental endocrine disrupting chemicals on the development of obesity. Hormones (Athens) 2010;9:206–17.
      OpenUrlPubMed
    7. ↵
      1. Yessoufou A,
      2. Moutairou K
      . Maternal diabetes in pregnancy: early and long-term outcomes on the offspring and the concept of “metabolic memory”. Exp Diabetes Res 2011;2011:218598.
      OpenUrlPubMed
    8. ↵
      1. Qi L,
      2. Hu FB,
      3. Hu G
      . Genes, environment, and interactions in prevention of type 2 diabetes: a focus on physical activity and lifestyle changes. Curr Mol Med 2008;8:519–32.
      OpenUrlCrossRefPubMedWeb of Science
    9. ↵
      1. Bo S,
      2. Ciccone G,
      3. Baldi C,
      4. et al
      . Effectiveness of a lifestyle intervention on metabolic syndrome. A randomized controlled trial. J Gen Intern Med 2007;22:1695–703.
      OpenUrlCrossRefPubMedWeb of Science
    10. ↵
      1. Wessel TR,
      2. Arant CB,
      3. Olson MB,
      4. et al
      . Relationship of physical fitness vs body mass index with coronary artery disease and cardiovascular events in women. JAMA 2004;292:1179–87.
      OpenUrlCrossRefPubMedWeb of Science
    11. ↵
      1. Ford ES,
      2. Kohl HW III.,
      3. Mokdad AH,
      4. et al
      . Sedentary behavior, physical activity, and the metabolic syndrome among U.S. adults. Obes Res 2005;13:608–14.
      OpenUrlCrossRefPubMedWeb of Science
    12. ↵
      1. Pan Y,
      2. Pratt CA
      . Metabolic syndrome and its association with diet and physical activity in US adolescents. J Am Diet Assoc 2008;108:276–86.
      OpenUrlCrossRefPubMedWeb of Science
    13. ↵
      1. Clapp JF
      . Influence of endurance exercise and diet on human placental development and fetal growth. Placenta 2006;27:527–34.
      OpenUrlCrossRefPubMedWeb of Science
    14. ↵
      1. Riemann MK,
      2. Kanstrup Hansen IL
      . Effects on the foetus of exercise in pregnancy. Scand J Med Sci Sports 2000;10:12–19.
      OpenUrlCrossRefPubMed
    15. ↵
      1. Clapp JF III.
      . Morphometric and neurodevelopmental outcome at age five years of the offspring of women who continued to exercise regularly throughout pregnancy. J Pediatr 1996;129:856–63.
      OpenUrlCrossRefPubMedWeb of Science
    16. ↵
      1. Clapp JF III.,
      2. Simonian S,
      3. Lopez B,
      4. et al
      . The one-year morphometric and neurodevelopmental outcome of the offspring of women who continued to exercise regularly throughout pregnancy. Am J Obstet Gynecol 1998;178: 594–9.
      OpenUrlCrossRefPubMedWeb of Science
    17. ↵
      1. Schou AC,
      2. Juhl M,
      3. Gamborg M,
      4. et al
      . Maternal recreational exercise during pregnancy in relation to children's BMI at 7 years of age. Int J Pediatr 2012;2012:920583.
      OpenUrlPubMed
    18. ↵
      1. Olsen SF,
      2. Hansen HS,
      3. Sandstrom B,
      4. et al
      . Erythrocyte levels compared with reported dietary intake of marine n-3 fatty acids in pregnant women. Br J Nutr 1995;73: 387–95.
      OpenUrlCrossRefPubMedWeb of Science
    19. ↵
      1. Ainsworth BE,
      2. Haskell WL,
      3. Leon AS,
      4. et al
      . Compendium of physical activities: classification of energy costs of human physical activities. Med Sci Sports Exerc 1993;25:71–80.
      OpenUrlCrossRefPubMedWeb of Science
    20. ↵
      1. Ainsworth BE,
      2. Haskell WL,
      3. Whitt MC,
      4. et al
      . Compendium of physical activities: an update of activity codes and MET intensities. Med Sci Sports Exerc 2000;32(Suppl 9):S498–504.
      OpenUrlCrossRefPubMedWeb of Science
    21. ↵
      1. Frystyk J,
      2. Tarnow L,
      3. Hansen TK,
      4. et al
      . Increased serum adiponectin levels in type 1 diabetic patients with microvascular complications. Diabetologia 2005;48: 1911–18.
      OpenUrlCrossRefPubMedWeb of Science
    22. ↵
      1. Matthews DR,
      2. Hosker JP,
      3. Rudenski AS,
      4. et al
      . Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412–19.
      OpenUrlCrossRefPubMedWeb of Science
    23. ↵
      1. Willet W
      . Nutritional epidemiology. 2nd edn. New York: Oxford University Press, 1998.
    24. ↵
      1. Meesters C,
      2. Muris P,
      3. Hoefnagels C,
      4. et al
      . Social and family correlates of eating problems and muscle preoccupation in young adolescents. Eat Behav 2007;8:83–90.
      OpenUrlCrossRefPubMed
    25. ↵
      1. Ravelli AC,
      2. van Der Meulen JH,
      3. Osmond C,
      4. et al
      . Obesity at the age of 50 y in men and women exposed to famine prenatally. Am J Clin Nutr 1999;70: 811–16.
      OpenUrlAbstract/FREE Full Text
    26. ↵
      1. Zheng X,
      2. Wang Y,
      3. Ren W,
      4. et al
      . Risk of metabolic syndrome in adults exposed to the great Chinese famine during the fetal life and early childhood. Eur J Clin Nutr 2012;66:231–6.
      OpenUrlCrossRefPubMedWeb of Science
    27. ↵
      1. Vaag AA,
      2. Grunnet LG,
      3. Arora GP,
      4. et al
      . The thrifty phenotype hypothesis revisited. Diabetologia 2012;55:2085–8.
      OpenUrlCrossRefPubMedWeb of Science

    Supplementary materials

    • Supplementary Data

      This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

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    Footnotes

    • Contributors ID wrote the manuscript and researched the data. CG researched the data and conducted the analyses. DR collected the data and reviewed/edited the manuscript. BHB collected the data and reviewed/edited the manuscript. TBH collected the data and reviewed/edited the manuscript. AAV contributed to the discussion and reviewed/edited the manuscript. SFO collected the data and initiated the follow-up and he also contributed to the discussion and reviewed/edited the manuscript.

    • Funding This work was supported by the Danish Council for Strategic Research, grant numbers 09-067124, 09-063072, 2101-06-0005.

    • Competing interests None.

    • Ethics approval 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.

    • Provenance and peer review Not commissioned; externally peer reviewed.