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. Author manuscript; available in PMC: 2014 Jun 1.
Published in final edited form as: Curr Cardiovasc Risk Rep. 2013 Apr 9;7(3):217–223. doi: 10.1007/s12170-013-0308-y

Pregnancy Complications and the Risk of Metabolic Syndrome for the Offspring

Kelli K Ryckman 1,, Kristi S Borowski 2, Nisha I Parikh 3, Audrey F Saftlas 4
PMCID: PMC3755758  NIHMSID: NIHMS465140  PMID: 23997844

Abstract

Metabolic syndrome is a growing problem globally, and is a contributor to non-communicable diseases such as type II diabetes and cardiovascular disease. The risk of developing specific components of the metabolic syndrome such as obesity, hyperlipidemia, hypertension, and elevated fasting blood sugar has been largely attributed to environmental stressors including poor nutrition, lack of exercise, and smoking. However, large epidemiologic cohorts and experimental animal models support the “developmental origins of adult disease” hypothesis, which posits that a significant portion of the risk for adult metabolic conditions is determined by exposures occurring in the perinatal period. Maternal obesity and the rate of complications during pregnancy such as preterm birth, preeclampsia, and gestational diabetes continue to rise. As our ability to reduce perinatal morbidity and mortality improves the long-term metabolic consequences remain uncertain, pointing to the need for further research in this area.

Keywords: Maternal obesity, Gestational diabetes, Preeclampsia, Metabolic syndrome, Hypertension, Hyperlipidemia, Type II diabetes, Cardiovascular disease, Pregnancy complications

Introduction

Metabolic syndrome is defined as the presence of at least 3 of the following metabolic risk factors: abdominal obesity, high triglycerides, low high-density lipoprotein cholesterol, high blood pressure, and elevated fasting blood sugar [1]. Metabolic syndrome is a growing global health issue and is a risk factor for incident diabetes and cardiovascular disease (CVD). There is a substantial body of evidence demonstrating that environmental exposures in adolescence and adulthood such as poor nutrition, a sedentary lifestyle, and smoking contribute significantly to the risk of metabolic syndrome. In addition to childhood and adulthood environmental exposures, there is growing evidence that in utero exposures also contribute to metabolic syndrome. Therefore, the identification of metabolic syndrome in children and young adults may facilitate primary prevention efforts, before the onset of overt CVD and type 2 diabetes mellitus (T2DM).

“Developmental programming” also referred to as “fetal origins of adult disease” or the “Barker Hypothesis”, is the basis for the observation that low birth weight is not only associated with immediate morbidities for the neonate but also leads to later risk for adult diseases [24]. The theory posits that there are critical time periods during fetal and postnatal development, when an individual is sensitive to environmental stressors. During these early periods of “plasticity”, changes to an individual’s metabolism can remain permanent. The adaptations that occur during critical periods of fetal and postnatal development promote survival in an inadequate environment (ie, poor nutrition or growth restriction). In these surviving individuals, however, later-in-life exposure to nutritional abundance and growth can cause metabolic disturbances that promote the development of diseases such as hypertension, obesity, and diabetes.

Another possible explanation for the associations between in utero exposures and adult disease is that there are shared genetic risk factors that impact both early and later-life outcomes. This explanation is well supported by the “fetal insulin hypothesis”, which posits that the same genetic factors that predispose to decreased fetal insulin secretion in utero may also affect insulin resistance in adulthood [5]. Evidence from animal models also suggests that the intrauterine milieu influences not only the development of the fetus but also the reproductive fitness of that fetus such that subsequent generations may continue to be affected [6•, 7, 8]. This observation is described as “intergenerational programming.” For example, female rodents (F0) fed a low protein diet give birth to offspring (F1) with low birth weight, reduced insulin sensitivity, and high cholesterol [9, 10]. The F1 females also give birth to offspring (F2) with metabolic conditions, despite being fed a normal diet [9, 10]. The presence of metabolic conditions in the F2 generation indicates that even in the absence of the original environmental stressor (poor nutrition), these offspring remain susceptible to metabolic conditions through “intergenerational programming.” One possible mechanism for the transmission of chronic diseases between generations is epigenetic changes that are inherited to the subsequent generations; however, more research in this area is needed.

In the following review, we examine the evidence from human studies focused on the relationship between maternal complications of pregnancy and the subsequent risks for metabolic syndrome later in life for both the affected mothers and their offspring.

Birth Weight, Intrauterine Environment, and Metabolic Syndrome

The associations between birth weight and conditions that comprise “metabolic syndrome” ie, hypertension, glucose intolerance, and obesity are observed in studies across the world, and are well supported by large meta-analyses (Table 1). Other conditions in both childhood and adulthood that are associated with birth weight include bone health, chronic kidney disease, asthma, type 1 diabetes, cancer, and a host of other conditions [2028]. While the focus has largely been on the relationship of low birth weight (LBW) and later-life metabolic disease, there is strong evidence that the association of birth weight is “U”-shaped, meaning both high birth weight (macrosomia) and LBW carry significant risk for later-life conditions [29••].

Table 1.

Meta-analyses of significant associations between birth weight and components/correlates of metabolic syndrome

Study N Exposure Birth Weight
Reference Group
Outcome Effect OR [95 % CI]
Silveira et al. 2008 [11] 5867 LBW (<2.5 kg) >3.4 kg Metabolic syndrome 2.53 [1.57, 4.08]
Harder et al. 2007 [12] 132,180 HBW (>4 kg) 2.5–4 kg T2DM 1.36 [1.07, 1.73]
LBW (<2.5 kg) 2.5–4 kg T2DM 1.47 [1.26, 1.72]
Whincup et al. 2008 [13] 152,084 BW continuous per 1–kg rise T2DM 0.80 [0.72, 0.89]
Mu et al. 2012 [14] 39,641 LBW (<2.5 kg) >2.5 kg hypertension 1.21 [1.13, 1.30]
32,951 HBW (>4 kg) <4 kg hypertension 0.78 [0.71, 0.86]
32,351 LBW (<2.5 kg) >2.5 kg SBP 2.58 [1.51, 3.64]
28,144 HBW (>4 kg) <4 kg SBP −2.08 [−2.98, −1.17]
de Jong et al. 2012 [15] 3080 preterm or VLBW term SBP 2.50 [1.67, 3.32]
Gamborg et al. 2007 [16] 197,954 BW continuous per 1–kg rise SBP in females −2.80 [−3.85, −1.76]
SBP in males −1.52 [−2.27, −0.77]
Zhao et al. 2012 [17] 211,457 HBW (>4 kg) 2.5–4 kg BMI>25 1.46 [1.27, 1.68]
Yu et al. 2011 [18] 39,216 HBW (>4 kg) <4 kg BMI>30 2.07 [1.91, 2.24]
Owen et al. 2003 [19] 23,247 BW continuous per 1–kg rise Total cholesterol −0.05 [−0.08, −0.02]

HBW high birth weight, LBW low birth weight, SBP systolic blood pressure, T2DM type 2 diabetes mellitus

While many studies show birth weight is independently associated with risk for later-life disorders, others suggest it is not the cause but rather a surrogate marker of risk for adulthood metabolic syndrome [29••]. Specifically, it is argued that over-nutrition and accelerated “catch-up growth” increase the long-term risks for metabolic syndrome in individuals born LBW. Infants born LBW can be described as appropriately grown for gestational age, but born early (preterm delivery at 32 weeks, for example) or as growth restricted and/or small for gestational age (SGA). SGA is most often defined as a birth weight below the 10th percentile based on growth curves standardized to gestational age and infant gender. SGA can represent infants that are constitutionally small due to genetic or environmental causes or infants that are small due to intrauterine growth restriction (IUGR). IUGR suggests that there is growth restriction or that the fetus’ growth potential is not being met. IUGR is often accompanied by oligohydramnios and abnormal umbilical artery Doppler flow. LBW and SGA likely have distinct etiologies in their contributions to adult metabolic syndrome.

Maternal conditions such as obesity, gestational diabetes (GDM), and preeclampsia may contribute to the development of in utero stress that is responsible for adverse birth outcomes such as LBW or macrosomia; and through developmental programming, these maternal conditions may also increase susceptibility to adult metabolic disease in the offspring [30, 31]. While the underlying biological and etiologic mechanisms for these various hypotheses are poorly understood, it is clear that the predisposition to adult disease lies in key areas of fetal and postnatal development where there is a complex network of genetic, epigenetic, metabolic, and environmental influences contributing to future disease risk.

Maternal Obesity

Obesity is a significant global health problem; with one-third of adults in the United States classified as overweight (BMI 25–29.9 mg/kg2) and another third classified as obese (BMI >30 mg/kg2) [32]. Current estimates by the World Health Organization predict that by 2015, 2.3 billion adults will be overweight and 700 million will be obese [32]. Even more concerning is that childhood obesity is also on the rise with 12.5 million (17 %) children classified as obese in the United States [33]. In addition to the environment, fetal exposure to maternal obesity in utero plays a significant role in the risk of childhood obesity [34]. Obesity and T2DM, which often coexist, are common metabolic disorders encountered by women during their pregnancies, with an estimated one-third of all pregnancies complicated by maternal obesity [35]. Obese women are at an increased risk for maternal complications including GDM, preeclampsia, and thromboembolic events [36, 37]; and obstetric complications such as fetal macrosomia, stillbirth, shoulder dystocia, cesarean delivery, and preterm delivery [3638].

Maternal obesity exerts a “U” shaped influence on birth weight in that this condition confers risk for high birth weight as well as LBW, which is partly due to the increased risk for preterm birth in obese women [3941]. Macrosomic infants have increased amounts of adipose tissue and are therefore at heightened risk for childhood obesity and diabetes later in life [34]. LBW infants have periods of rapid catch-up growth, which is thought to program them for a higher risk of obesity and metabolic syndrome [42•]. However, a systematic review by Harder et al. shows that most published studies examining birth weight and adulthood obesity demonstrate a linear relationship, whereby higher birth weight predisposes to obesity in adulthood [29••, 43]. No studies report an inverse relationship and only a small portion (8 %) show a “U” shaped distribution between birth weight and later-life obesity [29••, 43]. In addition, several studies observe that high maternal pre-pregnancy BMI and excessive weight gain during pregnancy are better predictors for adulthood metabolic syndrome and obesity in the offspring than birth weight (high or low) alone [42•, 4446].

Gestational Diabetes

GDM, like maternal obesity, is a growing concern within the obstetrics field. It is estimated that at least 7 % of all pregnancies are complicated with GDM, which is believed to be underdiagnosed [47]. A recent study of 25,000 non-diabetic pregnancies collected from 9 countries observe that GDM is present in approximately 18 % of the studied pregnancies when careful diagnostic criteria are employed [48]. Maternal obesity often coexists with GDM, with up to 25 % of obese women having concurrent GDM [49]. This combination of maternal disease adds additional risk and complexity to evaluating both the immediate and long-term outcomes for the mother and offspring.

GDM is a significant problem with lasting long-term metabolic consequences for both the mother and offspring. Among women diagnosed with GDM, approximately 5 %–10 % are found to have diabetes (most often type II) immediately after pregnancy [47]. Additionally, women with GDM are at a 35 %–65 % increased risk of developing diabetes later-in-life, and their offspring are more likely to develop obesity and T2DM in childhood and adulthood [47]. Several studies observe that children born to diabetic mothers have a higher BMI than children born to mothers without GDM; this effect appears to be more pronounced in older (9–14 years) rather than younger (5–8 years) children [30, 50•, 51]. In a study that compared children 6–11 years according with maternal GDM status, those born large for gestational age to mothers with GDM were at higher risk of metabolic syndrome than infants that were born to mothers who were neither obese or had GDM [40]. Another study found that children (10–16 years) born to mothers with GDM had higher systolic blood pressure, BMI, and 2-hour glucose and insulin levels than control children [52]. This risk appears to carry well into adulthood with findings that young adults (19–27 years) born to mothers with GDM are at an 8-fold increased risk for T2DM [53].

Preeclampsia

Preeclampsia is a serious multi-system disorder of pregnancy that is defined as hypertension occurring after the 20th week of pregnancy in the presence of proteinuria. The prevalence of preeclampsia is between 2 % and 10 % worldwide with ~8.5 million women affected each year [54, 55]. Preeclampsia results in ~18 % of maternal deaths each year and a significant portion of fetal morbidity and mortality, which is generally related to iatrogenic delivery preterm due to severe preeclampsia [55]. The cause of preeclampsia remains uncertain and the only established cure for preeclampsia is delivery of the fetus. A minority of women (1 out of ~3000 pregnancies per year) with severe preeclampsia will develop eclampsia, characterized by the occurrence of generalized convulsions or seizures. Severe preeclampsia, remote from delivery, presents a complicated medical scenario posing significant risks to both the mother with continuing the pregnancy and the fetus with preterm delivery. More so, reduced placental blood flow often accompanies preeclampsia and can result in fetal hypoxia and intrauterine growth restriction (IUGR) [55]. Women who have had preeclampsia are at a 4-fold increased risk for chronic hypertension and a 2-fold increased risk for ischaemic heart disease and stroke within 10–15 years after their affected pregnancy [56].

In addition to increased maternal risk for CVD and metabolic syndrome after a preeclamptic pregnancy, the offspring also carries additional metabolic risks. A large study of the Helsinki Birth Cohort population found that offspring born to preeclamptic mothers were at approximately a 2-fold increased risk for stroke; however, there was no evidence of an increased risk for coronary heart disease [31]. A systematic review and meta-analysis of cardiovascular risk factors in children and young adults born to preeclamptic mothers revealed that children and young adults aged 4–30 years old born to preeclamptic mothers have higher systolic (2.39 mm Hg increase) and diastolic (1.35 mm Hg increase) blood pressures than their counterparts born to non-preeclamptic mothers [57••]. The long-term clinical significance of this increase is unclear. A modest yet significant increase in BMI was observed among children born to preeclamptic mothers, even among term, normal weight infants. Few studies have examined the risks of preeclampsia and adult offspring cholesterol and glucose metabolism; therefore, no concrete conclusions could be drawn from the meta-analysis on these factors [57••].

Genetic and Epigenetic Mechanisms

Most of the biology and etiology underlying the connections between the intrauterine environment, fetal growth, and later-life metabolic disease is unknown; however, emerging evidence suggests that shared genetic risk factors mediate these effects [58••]. Genome-wide association studies and large meta-analyses have examined genetic associations with birth weight and the risk of developing later-life metabolic disorders, such as T2DM [5964]. A recent systematic review described significant overlap in genes that are related to both birth weight and T2DM (Table 2) [58••]. Genotypes in the ADCY5, CDKAL1, and HHEX-IDE genes are associated with low birth weight as well as later-life high fasting plasma glucose levels and T2DM [64]. Maternal genetic variants in GCK and TCF7L2 are associated with high infant birth weight and T2DM [62]. These associations likely arise due to the link between GCK and TCF7L2 and maternal glucose levels throughout pregnancy. Because maternal glucose crosses the placenta, increased levels can result in an increase in fetal insulin secretion, which in turn, can lead to an increase in birth weight [65].

Table 2.

Shared significant (P<10−3) genetic associations with birth weight and T2DM

Gene SNP Risk
allele
Fetal or
Maternal
Obs. for BW
meta-analysisa
Effect size for BW
[95 % CI]b
P-value Effect for T2DM
OR [95 % CI]
P value References
ADCY5 rs9883204 C Fetal 38,214 −30 [−38, −23] 7×10−15 c1.12 [1.09, 1.15] 1×10−20 [58••, 59, 63]
CDKAL1 rs7754840 C Fetal 24,885 −20 [−29, −11] 5×10−6 1.16 [1.12, 1.20] 5×10−19 [58••, 61, 64]
HHEX-IDE rs1111875 C Fetal 25,164 −16 [−24, −8] 8×10−5 1.16 [1.13, 1.20] <5.0×10−4 [58••, 64, 66]
GCK rs1799884 T Maternal 12,643 30 [15, 44] 5×10−5 c1.08 [1.04, 1.11] 2×10−6 [58••, 61, 62]
TCF7L2 rs7903146 T Maternal 13,406 23 [11,35] 3×10−4 1.44 [1.40, 1.49] 1×10−109 [58••, 61, 62]
a

Most recent meta-analysis reported

b

Change in BW per fetal risk allele in grams

c

SNP for T2DM risk is rs11708067 for ADCY5 and rs1990458 for GCK

Epigenetic changes, or those that affect gene expression through mechanisms other than those mediated by the underlying DNA, comprise another mechanism that may mediate the link between the intrauterine environment and later-life disease [67••]. Specific epigenetic changes include DNA methylation and histone modifications. A recent study demonstrated that epigenetic profiles measured at birth are associated with childhood adiposity [68]. This epigenetic mechanism explained ~25 % of the variation in fat mass. Evidence also suggests that epigenetic changes occurring during a time of developmental ‘plasticity’, such as during periods of fetal growth, can be passed on from one generation to the next [69]. Studies are also demonstrating that maternal nutrition is not only directly important for fetal growth but also influences adulthood diseases through epigenetic mechanisms [7072].

Conclusions

The growing body of epidemiologic evidence presented here, and experimental evidence in animal models reviewed elsewhere [29••], indicates that pregnancy complications and adverse birth outcomes strongly predispose both mother and offspring of affected pregnancies to metabolic syndrome, CVD, and T2DM later in life. These reproductive factors, however, are rarely taken into consideration when evaluating an individual’s risk for metabolic diseases. Approximately 1 in 12 infants are born LBW each year in the United States and an even greater proportion is exposed to adverse pregnancy conditions such as gestational diabetes, preeclampsia, or maternal obesity. As our ability improves to reduce the immediate morbidity and mortality of these infants, many more persons with low and high birth weight are surviving to adulthood, and are therefore at an increased risk for later-life morbidity and mortality. In addition to increasing the offspring’s risk for metabolic syndrome, pregnancy complications also increase the mother’s risk for developing CVD later in life. More research is needed to understand the biological etiology underlying these relationships, including genetic and epigenetic mechanisms. However, with an increasing body of evidence indicating that the intrauterine environment influences an offspring’s risk for metabolic syndrome in adulthood, further clinical consideration is warranted through increased monitoring, risk stratification, and follow-up of individuals with such adverse reproductive histories.

Footnotes

Conflict of Interest Kelli K. Ryckman declares that she has no conflict of interest.

Kristi S. Borowski declares that she has no conflict of interest.

Nisha I. Parikh declares that she has no conflict of interest.

Audrey F. Saftlas declares that she has no conflict of interest.

Contributor Information

Kelli K. Ryckman, Email: kelli-ryckman@uiowa.edu, Department of Epidemiology, University of Iowa, 105 River St, Iowa City, IA 52242, USA.

Kristi S. Borowski, Email: Borowski.kristi@mayo.edu, Obstetrics and Gynecology, Mayo Clinic, Rochester, MN, USA.

Nisha I. Parikh, Email: nparikh@queens.org, Cardiovascular Division, The Queens Medical Center, Honolulu, HI 96813, USA.

Audrey F. Saftlas, Email: aubrey-saftlas@uiowa.edu, Department of Epidemiology, University of Iowa, 105 River St, Iowa City, IA 52242, USA.

References

Papers of particular interest, published recently, have been highlighted as:

• Of importance

•• Of major importance

  • 1.Povel CM, Beulens JW, van der Schouw YT, et al. Metabolic Syndrome Model definitions predicting type 2 diabetes and cardiovascular disease. Diabetes Care. 2013;36:362–368. doi: 10.2337/dc11-2546. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Barker DJ. The developmental origins of adult disease. J Am Coll Nutr. 2004;23:588S–595S. doi: 10.1080/07315724.2004.10719428. [DOI] [PubMed] [Google Scholar]
  • 3.Barker DJ. The developmental origins of chronic adult disease. Acta Paediatr Suppl. 2004;93:26–33. doi: 10.1111/j.1651-2227.2004.tb00236.x. [DOI] [PubMed] [Google Scholar]
  • 4.de Boo HA, Harding JE. The developmental origins of adult disease (Barker) hypothesis. Aust N Z J Obstet Gynaecol. 2006;46:4–14. doi: 10.1111/j.1479-828X.2006.00506.x. [DOI] [PubMed] [Google Scholar]
  • 5.Hattersley AT, Tooke JE. The fetal insulin hypothesis: an alternative explanation of the association of low birthweight with diabetes and vascular disease. Lancet. 1999;353:1789–1792. doi: 10.1016/S0140-6736(98)07546-1. [DOI] [PubMed] [Google Scholar]
  • 6. Roseboom TJ, Watson ED. The next generation of disease risk: are the effects of prenatal nutrition transmitted across generations? Evidence from animal and human studies. Placenta. 2012;33(Suppl 2):e40–e44. doi: 10.1016/j.placenta.2012.07.018. This article focuses on existing animal and human evidence for intergenerational and transgenerational transmission of disease. Potential mechanisms for transmission of disease between generations are discussed.
  • 7.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. doi: 10.1056/NEJMra0708473. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Skinner MK. What is an epigenetic transgenerational phenotype? F3 or F2. Reprod Toxicol. 2008;25:2–6. doi: 10.1016/j.reprotox.2007.09.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Peixoto-Silva N, Frantz ED, Mandarim-de-Lacerda CA, Pinheiro-Mulder A. Maternal protein restriction in mice causes adverse metabolic and hypothalamic effects in the F1 and F2 generations. Br J Nutr. 2011;106:1364–1373. doi: 10.1017/S0007114511001735. [DOI] [PubMed] [Google Scholar]
  • 10.Zambrano E, Martinez-Samayoa PM, Bautista CJ, et al. Sex differences in transgenerational alterations of growth and metabolism in progeny (F2) of female offspring (F1) of rats fed a low protein diet during pregnancy and lactation. J Physiol. 2005;566:225–236. doi: 10.1113/jphysiol.2005.086462. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Silveira VM, Horta BL. Birth weight and metabolic syndrome in adults: meta-analysis. Rev Saude Publica. 2008;42:10–18. doi: 10.1590/s0034-89102008000100002. [DOI] [PubMed] [Google Scholar]
  • 12.Harder T, Rodekamp E, Schellong K, et al. Birth weight and subsequent risk of type 2 diabetes: a meta-analysis. Am J Epidemiol. 2007;165:849–857. doi: 10.1093/aje/kwk071. [DOI] [PubMed] [Google Scholar]
  • 13.Whincup PH, Kaye SJ, Owen CG, et al. Birth weight and risk of type 2 diabetes: a systematic review. JAMA. 2008;300:2886–2897. doi: 10.1001/jama.2008.886. [DOI] [PubMed] [Google Scholar]
  • 14.Mu M,Wang SF, Sheng J, et al. Birth weight and subsequent blood pressure: a meta-analysis. Arch Cardiovasc Dis. 2012;105:99–113. doi: 10.1016/j.acvd.2011.10.006. [DOI] [PubMed] [Google Scholar]
  • 15.de Jong F, Monuteaux MC, van Elburg RM, et al. Systematic review and meta-analysis of preterm birth and later systolic blood pressure. Hypertension. 2012;59:226–234. doi: 10.1161/HYPERTENSIONAHA.111.181784. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Gamborg M, Byberg L, Rasmussen F, et al. Birth weight and systolic blood pressure in adolescence and adulthood: meta-regression analysis of sex- and age-specific results from 20 Nordic studies. Am J Epidemiol. 2007;166:634–645. doi: 10.1093/aje/kwm042. [DOI] [PubMed] [Google Scholar]
  • 17.Zhao Y, Wang SF, Mu M, Sheng J. Birth weight and overweight/obesity in adults: a meta-analysis. Eur J Pediatr. 2012;171:1737–1746. doi: 10.1007/s00431-012-1701-0. [DOI] [PubMed] [Google Scholar]
  • 18.Yu ZB, Han SP, Zhu GZ, et al. Birth weight and subsequent risk of obesity: a systematic review and meta-analysis. Obes Rev. 2011;12:525–542. doi: 10.1111/j.1467-789X.2011.00867.x. [DOI] [PubMed] [Google Scholar]
  • 19.Owen CG, Whincup PH, Odoki K, et al. Birth weight and blood cholesterol level: a study in adolescents and systematic review. Pediatrics. 2003;111:1081–1089. doi: 10.1542/peds.111.5.1081. [DOI] [PubMed] [Google Scholar]
  • 20.Xu X, Dailey AB, Peoples-Sheps M, et al. Birth weight as a risk factor for breast cancer: a meta-analysis of 18 epidemiological studies. J Womens Health. 2009;18:1169–1178. doi: 10.1089/jwh.2008.1034. [DOI] [PubMed] [Google Scholar]
  • 21.Baird J, Kurshid MA, Kim M, et al. Does birthweight predict bone mass in adulthood? A systematic review and meta-analysis. Osteoporos Int. 2011;22:1323–1334. doi: 10.1007/s00198-010-1344-9. [DOI] [PubMed] [Google Scholar]
  • 22.Andersen LG, Angquist L, Gamborg M, et al. Birth weight in relation to leisure time physical activity in adolescence and adulthood: meta-analysis of results from 13 Nordic cohorts. PLoS One. 2009;4:e8192. doi: 10.1371/journal.pone.0008192. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Cardwell CR, Stene LC, Joner G, et al. Birthweight and the risk of childhood-onset type 1 diabetes: a meta-analysis of observational studies using individual patient data. Diabetologia. 2010;53:641–651. doi: 10.1007/s00125-009-1648-5. [DOI] [PubMed] [Google Scholar]
  • 24.Caughey RW, Michels KB. Birth weight and childhood leukemia: a meta-analysis and review of the current evidence. Int J Cancer. 2009;124:2658–2670. doi: 10.1002/ijc.24225. [DOI] [PubMed] [Google Scholar]
  • 25.Cook MB, Akre O, Forman D, et al. A systematic review and metaanalysis of perinatal variables in relation to the risk of testicular cancer—experiences of the son. Int J Epidemiol. 2010;39:1605–1618. doi: 10.1093/ije/dyq120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Flaherman V, Rutherford GW. A meta-analysis of the effect of high weight on asthma. Arch Dis Child. 2006;91:334–339. doi: 10.1136/adc.2005.080390. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Lawlor DA, Ebrahim S, Davey Smith G. Association of birth weight with adult lung function: findings from the British Women's Heart and Health Study and a meta-analysis. Thorax. 2005;60:851–858. doi: 10.1136/thx.2005.042408. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.White SL, Perkovic V, Cass A, et al. Is low birth weight an antecedent of CKD in later life? A systematic review of observational studies. Am J Kidney Dis. 2009;54:248–261. doi: 10.1053/j.ajkd.2008.12.042. [DOI] [PubMed] [Google Scholar]
  • 29. Neitzke U, Harder T, Plagemann A. Intrauterine growth restriction and developmental programming of the metabolic syndrome: a critical appraisal. Microcirculation. 2011;18:304–311. doi: 10.1111/j.1549-8719.2011.00089.x. This review summarizes the current literature on low birth weight, intrauterine growth restriction, and the risk for adult metabolic syndrome. Both systematic reviews and meta-analysis of human data as well as animal models are discussed. The authors conclude that low birth weight, IUGR, and/or SGA are not independent risk factors for metabolic syndrome in adulthood but rather neonatal overfeeding and rapid neonatal weight gain increase the lifetime risks for metabolic syndrome.
  • 30.Silverman BL, Rizzo TA, Cho NH, Metzger BE. Long-term effects of the intrauterine environment. The Northwestern University Diabetes in Pregnancy Center. Diabetes Care. 1998;21(Suppl 2):B142–B149. [PubMed] [Google Scholar]
  • 31.Kajantie E, Eriksson JG, Osmond C, et al. Pre-eclampsia is associated with increased risk of stroke in the adult offspring: the Helsinki birth cohort study. Stroke. 2009;40:1176–1180. doi: 10.1161/STROKEAHA.108.538025. [DOI] [PubMed] [Google Scholar]
  • 32.World Health Organization. Obesity: Preventing and managing the global epidemic. [Accessed December 2012]; Available at http://libdoc.who.int/trs/WHO_TRS_894.pdf. [PubMed]
  • 33.Ogden C, Carroll M. National Center for Health Statistics; 2010. [Accessed December 2012]. Prevalence of obesity among children and adolescents: United States, trends 1963–1965 through 2007–2008. Available at: http://www.cdc.gov/nchs/data/hestat/obesity_child_07_08/obesity_child_07_08.htm. [Google Scholar]
  • 34.Armitage JA, Poston L, Taylor PD. Developmental origins of obesity and the metabolic syndrome: the role of maternal obesity. Front Horm Res. 2008;36:73–84. doi: 10.1159/000115355. [DOI] [PubMed] [Google Scholar]
  • 35.Gunatilake RP, Perlow JH. Obesity and pregnancy: clinical management of the obese gravida. Am J Obstet Gynecol. 2011;204:106–119. doi: 10.1016/j.ajog.2010.10.002. [DOI] [PubMed] [Google Scholar]
  • 36.Reece EA. Perspectives on obesity, pregnancy, and birth outcomes in the United States: the scope of the problem. Am J Obstet Gynecol. 2008;198:23–27. doi: 10.1016/j.ajog.2007.06.076. [DOI] [PubMed] [Google Scholar]
  • 37.Yogev Y, Catalano PM. Pregnancy and obesity. Obstet Gynecol Clin North Am. 2009;36:285–300. doi: 10.1016/j.ogc.2009.03.003. viii. [DOI] [PubMed] [Google Scholar]
  • 38.Langer O. Management of obesity in GDM: old habits die hard. J Matern Fetal Neonatal Med. 2008;21:165–171. doi: 10.1080/14767050801929877. [DOI] [PubMed] [Google Scholar]
  • 39.Oken E, Rifas-Shiman SL, Field AE, et al. Maternal gestational weight gain and offspring weight in adolescence. Obstet Gynecol. 2008;112:999–1006. doi: 10.1097/AOG.0b013e31818a5d50. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Boney CM, Verma A, Tucker R, Vohr BR. Metabolic syndrome in childhood: association with birth weight, maternal obesity, and gestational diabetes mellitus. Pediatrics. 2005;115:e290–e296. doi: 10.1542/peds.2004-1808. [DOI] [PubMed] [Google Scholar]
  • 41.Djelantik AA, Kunst AE, van der Wal MF, et al. Contribution of overweight and obesity to the occurrence of adverse pregnancy outcomes in a multi-ethnic cohort: population attributive fractions for Amsterdam. BJOG. 2012;119:283–290. doi: 10.1111/j.1471-0528.2011.03205.x. [DOI] [PubMed] [Google Scholar]
  • 42. Desai M, Beall M, Ross MG. Developmental origins of obesity: programmed adipogenesis. Curr Diab Rep. 2013;13:27–33. doi: 10.1007/s11892-012-0344-x. This report summarizes human epidemiological and animal model studies focused on the contribution of maternal obesity, with or without gestational diabetes, to the offspring's risk for childhood and adult obesity. The effects of postnatal catch-up growth and fat accrual are discussed, as well as potential causative mechanisms including epigenetic programming, altered organ development, and cellular signaling.
  • 43.Harder T, Schellong K, Stupin J, et al. Where is the evidence that low birthweight leads to obesity? Lancet. 2007;369:1859. doi: 10.1016/S0140-6736(07)60847-2. [DOI] [PubMed] [Google Scholar]
  • 44.Hull HR, Thornton JC, Ji Y, et al. Higher infant body fat with excessive gestational weight gain in overweight women. Am J Obstet Gynecol. 2011;205:211.e1–211.e7. doi: 10.1016/j.ajog.2011.04.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Gluckman PD, Hanson MA, Morton SM, Pinal CS. Life-long echoes—a critical analysis of the developmental origins of adult disease model. Biol Neonate. 2005;87:127–139. doi: 10.1159/000082311. [DOI] [PubMed] [Google Scholar]
  • 46.Schack-Nielsen L, Michaelsen KF, Gamborg M, et al. Gestational weight gain in relation to offspring body mass index and obesity from infancy through adulthood. Int J Obes. 2010;34:67–74. doi: 10.1038/ijo.2009.206. [DOI] [PubMed] [Google Scholar]
  • 47.Centers for Disease Control and Prevention. National diabetes fact sheet: National estimates and general information on diabetes and prediabetes in the United States. [Accessed December 2012];2011 Available at: http://www.cdc.gov/diabetes/pubs/pdf/ndfs_2011.pdf.
  • 48.Coustan DR, Lowe LP, Metzger BE, et al. The Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study: paving the way for new diagnostic criteria for gestational diabetes mellitus. Am J Obstet Gynecol. 2010;202:654.e1–654.e6. doi: 10.1016/j.ajog.2010.04.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Yogev Y, Visser GH. Obesity, gestational diabetes, and pregnancy outcome. Semin Fetal Neonatal Med. 2009;14:77–84. doi: 10.1016/j.siny.2008.09.002. [DOI] [PubMed] [Google Scholar]
  • 50. Ornoy A. Prenatal origin of obesity and their complications: gestational diabetes, maternal overweight, and the paradoxical effects of fetal growth restriction and macrosomia. Reprod Toxicol. 2011;32:205–212. doi: 10.1016/j.reprotox.2011.05.002. This article reviews the literature on the effects of gestational diabetes, maternal obesity, and fetal growth restriction on the offspring's risk for metabolic syndrome. The roles of insulin resistance, leptin, hypothalamic programming, and epigenetics are also discussed.
  • 51.Gillman MW, Rifas-Shiman S, Berkey CS, et al. Maternal gestational diabetes, birth weight, and adolescent obesity. Pediatrics. 2003;111:e221–e226. doi: 10.1542/peds.111.3.e221. [DOI] [PubMed] [Google Scholar]
  • 52.Cho NH, Silverman BL, Rizzo TA, Metzger BE. Correlations between the intrauterine metabolic environment and blood pressure in adolescent offspring of diabetic mothers. J Pediatr. 2000;136:587–592. doi: 10.1067/mpd.2000.105129. [DOI] [PubMed] [Google Scholar]
  • 53.Damm P. Future risk of diabetes in mother and child after gestational diabetes mellitus. Int J Gynaecol Obstet. 2009;104(Suppl 1):S25–S26. doi: 10.1016/j.ijgo.2008.11.025. [DOI] [PubMed] [Google Scholar]
  • 54.Osungbade KO, Ige OK. Public health perspectives of preeclampsia in developing countries: implication for health system strengthening. J Pregnancy. 2011;2011:481095. doi: 10.1155/2011/481095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 55.Anderson UD, Olsson MG, Kristensen KH, et al. Review: biochemical markers to predict preeclampsia. Placenta. 2012;33(Suppl):S42–S47. doi: 10.1016/j.placenta.2011.11.021. [DOI] [PubMed] [Google Scholar]
  • 56.Bellamy L, Casas JP, Hingorani AD, Williams DJ. Pre-eclampsia and risk of cardiovascular disease and cancer in later life: systematic review and meta-analysis. BMJ. 2007;335:974. doi: 10.1136/bmj.39335.385301.BE. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57. Davis EF, Lazdam M, Lewandowski AJ, et al. Cardiovascular risk factors in children and young adults born to preeclamptic pregnancies: a systematic review. Pediatrics. 2012;129:e1552–e1561. doi: 10.1542/peds.2011-3093. This systematic review and meta-analysis demonstrates that in utero exposure to preeclampsia is associated with higher childhood and adulthood blood pressure in the offspring. The role of preeclampsia and offspring BMI, cholesterol levels, and glucose metabolism are also reviewed.
  • 58. Yaghootkar H, Freathy RM. Genetic origins of low birth weight. Curr Opin Clin Nutr Metab Care. 2012;15:258–264. doi: 10.1097/MCO.0b013e328351f543. This article reviews current literature on common genetic variants that are associated with both birth weight and type 2 diabetes. To-date there are 6 genetic loci that show strong evidence for association with birth weight; of these 5 are also associated with type II diabetes indicating a genetic mechanism for the relationship between low birth weight and the development of adult diabetes.
  • 59.Dupuis J, Langenberg C, Prokopenko I, et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42:105–116. doi: 10.1038/ng.520. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 60.Saxena R, Hivert MF, Langenberg C, et al. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet. 2010;42:142–148. doi: 10.1038/ng.521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 61.Saxena R, Elbers CC, Guo Y, et al. Large-scale gene-centric meta-analysis across 39 studies identifies type 2 diabetes loci. Am J Hum Genet. 2012;90:410–425. doi: 10.1016/j.ajhg.2011.12.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 62.Freathy RM, Hayes MG, Urbanek M, et al. Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study: common genetic variants in GCK and TCF7L2 are associated with fasting and postchallenge glucose levels in pregnancy and with the new consensus definition of gestational diabetes mellitus from the International Association of Diabetes and Pregnancy Study Groups. Diabetes. 2010;59:2682–2689. doi: 10.2337/db10-0177. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 63.Freathy RM, Mook-Kanamori DO, Sovio U, et al. Variants in ADCY5 and near CCNL1 are associated with fetal growth and birth weight. Nat Genet. 2010;42:430–435. doi: 10.1038/ng.567. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 64.Andersson EA, Pilgaard K, Pisinger C, et al. Type 2 diabetes risk alleles near ADCY5, CDKAL1, and HHEX-IDE are associated with reduced birthweight. Diabetologia. 2010;53:1908–1916. doi: 10.1007/s00125-010-1790-0. [DOI] [PubMed] [Google Scholar]
  • 65.HAPO Study Cooperative Research Group. Metzger BE, Lowe LP, et al. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med. 2008;358:1991–2002. doi: 10.1056/NEJMoa0707943. [DOI] [PubMed] [Google Scholar]
  • 66.Li X, Li Y, Song B, et al. Hematopoietically-expressed homeobox gene three widely-evaluated polymorphisms and risk for diabetes: a meta-analysis. PLoS One. 2012;7:e49917. doi: 10.1371/journal.pone.0049917. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67. Godfrey KM, Gluckman PD, Hanson MA. Developmental origins of metabolic disease: life course and intergenerational perspectives. Trends Endocrinol Metab. 2010;21:199–1205. doi: 10.1016/j.tem.2009.12.008. This article reviews the existing literature and evidence for the developmental origins of metabolic disease. Epigenetic mechanisms are discussed and the existing literature on this topic is nicely reviewed.
  • 68.Godfrey KM, Sheppard A, Gluckman PD, et al. Epigenetic gene promoter methylation at birth is associated with child's later adiposity. Diabetes. 2011;60:1528–1534. doi: 10.2337/db10-0979. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Jablonka E, Raz G. Transgenerational epigenetic inheritance: prevalence, mechanisms, and implications for the study of heredity and evolution. Q Rev Biol. 2009;84:131–176. doi: 10.1086/598822. [DOI] [PubMed] [Google Scholar]
  • 70.Lillycrop KA, Burdge GC. The effect of nutrition during early life on the epigenetic regulation of transcription and implications for human diseases. J Nutrigenet Nutrigenomics. 2011;4:248–260. doi: 10.1159/000334857. [DOI] [PubMed] [Google Scholar]
  • 71.Hanson M, Godfrey KM, Lillycrop KA, et al. Developmental plasticity and developmental origins of non-communicable disease: theoretical considerations and epigenetic mechanisms. Prog Biophys Mol Biol. 2011;106:272–280. doi: 10.1016/j.pbiomolbio.2010.12.008. [DOI] [PubMed] [Google Scholar]
  • 72.Lillycrop KA, Burdge GC. Epigenetic mechanisms linking early nutrition to long term health. Best Pract Res Clin Endocrinol Metab. 2012;26:667–676. doi: 10.1016/j.beem.2012.03.009. [DOI] [PubMed] [Google Scholar]

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