Recent developments on the role of epigenetics in obesity and metabolic disease
- Susan J. van Dijk1,
- Ross L. Tellam2,
- Janna L. Morrison3,
- Beverly S. Muhlhausler†4, 5 and
- Peter L. Molloy†1Email author
© van Dijk et al. 2015
Received: 9 March 2015
Accepted: 29 June 2015
Published: 11 July 2015
The increased prevalence of obesity and related comorbidities is a major public health problem. While genetic factors undoubtedly play a role in determining individual susceptibility to weight gain and obesity, the identified genetic variants only explain part of the variation. This has led to growing interest in understanding the potential role of epigenetics as a mediator of gene-environment interactions underlying the development of obesity and its associated comorbidities. Initial evidence in support of a role of epigenetics in obesity and type 2 diabetes mellitus (T2DM) was mainly provided by animal studies, which reported epigenetic changes in key metabolically important tissues following high-fat feeding and epigenetic differences between lean and obese animals and by human studies which showed epigenetic changes in obesity and T2DM candidate genes in obese/diabetic individuals. More recently, advances in epigenetic methodologies and the reduced cost of epigenome-wide association studies (EWAS) have led to a rapid expansion of studies in human populations. These studies have also reported epigenetic differences between obese/T2DM adults and healthy controls and epigenetic changes in association with nutritional, weight loss, and exercise interventions. There is also increasing evidence from both human and animal studies that the relationship between perinatal nutritional exposures and later risk of obesity and T2DM may be mediated by epigenetic changes in the offspring. The aim of this review is to summarize the most recent developments in this rapidly moving field, with a particular focus on human EWAS and studies investigating the impact of nutritional and lifestyle factors (both pre- and postnatal) on the epigenome and their relationship to metabolic health outcomes. The difficulties in distinguishing consequence from causality in these studies and the critical role of animal models for testing causal relationships and providing insight into underlying mechanisms are also addressed. In summary, the area of epigenetics and metabolic health has seen rapid developments in a short space of time. While the outcomes to date are promising, studies are ongoing, and the next decade promises to be a time of productive research into the complex interactions between the genome, epigenome, and environment as they relate to metabolic disease.
KeywordsEpigenetics DNA methylation Obesity Type 2 diabetes Developmental programming
Obesity is a complex, multifactorial disease, and better understanding of the mechanisms underlying the interactions between lifestyle, environment, and genetics is critical for developing effective strategies for prevention and treatment .
In a society where energy-dense food is plentiful and the need for physical activity is low, there is a wide variation in individuals’ susceptibility to develop obesity and metabolic health problems. Estimates of the role of heredity in this variation are in the range of 40–70 %, and while large genome-wide association studies (GWAS) have identified a number of genetic loci associated with obesity risk, the ~100 most common genetic variants only account for a few percent of variance in obesity [2, 3]. Genome-wide estimates are higher, accounting for ~20 % of the variation ; however, a large portion of the heritability remains unexplained.
Recently, attention has turned to investigating the role of epigenetic changes in the etiology of obesity. It has been argued that the epigenome may represent the mechanistic link between genetic variants and environmental factors in determining obesity risk and could help explain the “missing heritability.” The first human epigenetic studies were small and only investigated a limited number of loci. While this generally resulted in poor reproducibility, some of these early findings, for instance the relationship between PGC1A methylation and type 2 diabetes mellitus (T2DM)  and others as discussed in van Dijk et al. , have been replicated in later studies. Recent advances and increased affordability of high-throughput technologies now allow for large-scale epigenome-wide association studies (EWAS) and integration of different layers of genomic information to explore the complex interactions between the genotype, epigenome, transcriptome, and the environment [6–9]. These studies are still in their infancy, but the results thus far have shown promise in helping to explain the variation in obesity susceptibility.
There is increasing evidence that obesity has developmental origins, as exposure to a suboptimal nutrient supply before birth or in early infancy is associated with an increased risk of obesity and metabolic disease in later life [10–13]. Initially, animal studies demonstrated that a range of early life nutritional exposures, especially those experienced early in gestation, could induce epigenetic changes in key metabolic tissues of the offspring that persisted after birth and result in permanent alterations in gene function [13–17]. Evidence is emerging to support the existence of the same mechanism in humans. This has led to a search for epigenetic marks present early in life that predict later risk of metabolic disease, and studies to determine whether epigenetic programming of metabolic disease could be prevented or reversed in later life.
This review provides an update of our previous systematic review of studies on epigenetics and obesity in humans . Our previous review showcased the promising outcomes of initial studies, including the first potential epigenetic marks for obesity that could be detected at birth (e.g., RXRA) . However, it also highlighted the limited reproducibility of the findings and the lack of larger scale longitudinal investigations. The current review focuses on recent developments in this rapidly moving field and, in particular, on human EWAS and studies investigating the impact of (pre- and postnatal) nutritional and lifestyle factors on the epigenome and the emerging role of epigenetics in the pathology of obesity. We also address the difficulties in identifying causality in these studies and the importance of animal models in providing insight into mechanisms.
Epigenetic changes in animal models of obesity
Animal models provide unique opportunities for highly controlled studies that provide mechanistic insight into the role of specific epigenetic marks, both as indicators of current metabolic status and as predictors of the future risk of obesity and metabolic disease. A particularly important aspect of animal studies is that they allow for the assessment of epigenetic changes within target tissues, including the liver and hypothalamus, which is much more difficult in humans. Moreover, the ability to harvest large quantities of fresh tissue makes it possible to assess multiple chromatin marks as well as DNA methylation. Some of these epigenetic modifications either alone or in combination may be responsive to environmental programming. In animal models, it is also possible to study multiple generations of offspring and thus enable differentiation between transgenerational and intergenerational transmission of obesity risk mediated by epigenetic memory of parental nutritional status, which cannot be easily distinguished in human studies. We use the former term for meiotic transmission of risk in the absence of continued exposure while the latter primarily entails direct transmission of risk through metabolic reprogramming of the fetus or gametes.
Animal studies have played a critical role in our current understanding of the role of epigenetics in the developmental origins of obesity and T2DM. Both increased and decreased maternal nutrition during pregnancy have been associated with increased fat deposition in offspring of most mammalian species studied to date (reviewed in [11, 13–15, 19]). Maternal nutrition during pregnancy not only has potential for direct effects on the fetus, it also may directly impact the developing oocytes of female fetuses and primordial germ cells of male fetuses and therefore could impact both the offspring and grand-offspring. Hence, multigenerational data are usually required to differentiate between maternal intergenerational and transgenerational transmission mechanisms.
Evidence for a role of epigenetics in animal models of obesity separated by transmission type
Intergenerational maternal effect
Periconceptional undernutrition in normal and overweight ewes using artificial insemination and embryo transfer
Fat deposition and adrenal changes in offspring
Decreased expression of IGF2 and decreased DNA methylation of a proximal imprinting control region; changes in adrenal IGF2 DNA methylation; hypermethylation of pituitary glucocorticoid receptor
Intergenerational maternal effect
Maternal undernutrition prior to conception and during early gestation
Programming of obesity
Altered offspring histone methylation and acetylation in fetal hypothalamic energy regulating pathways
Intergenerational maternal effect
Different maternal dietary energy sources during that last half of gestation
Late gestation fetal gene expression and DNA methylation from a variety of tissues
Changes in late gestation fetal DNA methylation of CpG islands associated with IGF2R and H19 in muscle and adipose tissue
Intergenerational maternal effect
Methylating micronutrient supplementation during gestation—impacts on F2
Back fat percentage, adipose tissue, and fat thickness at 10th rib, croup, and shoulder in F2
Differentially expressed metabolic genes in F2 liver and muscle, DNA methylation change in IYD
Intergenerational maternal effect
Maternal low-protein diet during gestation and maternal diet restriction during gestation
Body weight, food intake, and adiposity
Altered germline DNA methylation of F1 adult males in a locus specific manner; changed expression and DNA methylation of LXRA in liver; demethylation of leptin promoter in adipocytes
Intergenerational maternal effect
Maternal high-fat diet during gestation; maternal obesity model; maternal high-fat diet using a Glut4+/− genetic background; maternal diet-induced obesity
Offspring chromatin organization; metabolic syndrome in offspring unmasked by exposure to western diet; glucose intolerance, insulin resistance, hepatic steatosis; obesity; exacerbated metabolic syndrome in offspring; insulin levels, insulin resistance in adipose tissue
Changes in offspring hepatic histone marks H3K14ac and H3K9me3; changes in offspring hepatic gene expression and widespread subtle changes in cytosine methylation; DNA methylation change in PEG3 in spermatozoa of offspring; cell autonomous transmission of altered insulin signaling. Reduced IRS1 expression associated with elevated miR126
Intergenerational maternal effect
Maternal diet restriction during gestation; suboptimal diet during early gestation
Catch up growth, obesity, and liver weight; T2D
Change in offspring liver IGF1 expression and IGF1 H3K4 methylation; decreased PPARA expression and increased DNA methylation in the PPARA promoter in liver; dhange in offspring growth hormone and PPARA expression and DNA methylation in liver; chromatin changes affecting enhancer/promoter interactions at HNF4A promoter in pancreatic islets from offspring
Intergenerational maternal effect
Maternal overfeeding model during preconception and gestation
Adipogenesis, gene expression and reduced representation DNA methylation in offspring
Changes in gene expression and proximal DNA methylation in genes in lipogenic pathways of adipocytes from offspring
Intergenerational maternal effect
Maternal high-fat diet during gestation
Altered expression of Npas2
Changes in offspring fetal liver chromatin mark H3K14ac in the NPAS2 promoter
Intergenerational paternal effect
Obesity in offspring
Chromatin (H3K9me3 and H3K27me3)-dependent reprogramming of offspring metabolic genes; a similar system may regulate obesity susceptibility and phenotypic variation in mice and humans
Intergenerational paternal effect
Paternal low-protein diet
High cholesterol in offspring
Changes in hepatic gene expression and DNA methylation in offspring
Intergenerational paternal effect
Intrauterine growth restriction
F1 offspring become obese and glucose intolerant with aging
F1 males show change in methylation of LXRA in sperm that is transmitted to somatic cells in the F2
Intergenerational paternal effect
F1 has increased susceptibility to diabetes
F1 show changes in pancreatic gene expression and DNA methylation linked to insulin signaling. A large portion of these genes are also differentially methylated in sperm
Potential transgenerational effect
Avy mouse—change in coat color and adult onset obesity through maternal transmission to the next generation; modulation by methyl donors and genistein during gestation.
Coat color and adult onset obesity in offspring
DNA methylation of a retrotransposon promoter adjacent to the agouti gene; evidence for germ line transmission of methylation status
Effect resulting from direct exposure of adult
Weight, fasting glucose, glucose, and insulin tolerance tests; obesity
Differential DNA methylation at numerous sites in adipose tissue; changes in DNA methylation of metabolism-related genes in liver and oocytes
Epigenetic changes in offspring associated with maternal nutrition during gestation
Effects of paternal nutrition on offspring epigenetic marks
Potential transgenerational epigenetic changes promoting fat deposition in offspring
Direct exposure of individuals to excess nutrition in postnatal life
While many studies have identified diet-associated epigenetic changes in animal models using candidate site-specific regions, there have been few genome-wide analyses undertaken. A recent study focussed on determining the direct epigenetic impact of high-fat diets/diet-induced obesity in adult mice using genome-wide gene expression and DNA methylation analyses . This study identified 232 differentially methylated regions (DMRs) in adipocytes from control and high-fat fed mice. Importantly, the corresponding human regions for the murine DMRs were also differentially methylated in adipose tissue from a population of obese and lean humans, thereby highlighting the remarkable evolutionary conservation of these regions. This result emphasizes the likely importance of the identified DMRs in regulating energy homeostasis in mammals.
Drawing on the evidence from animal studies and with the increasing availability of affordable tools for genome-wide analysis, there has been a rapid expansion of epigenome studies in humans. These studies have mostly focused on the identification of site-specific differences in DNA methylation that are associated with metabolic phenotypes.
Genetic association studies. Genetic polymorphisms that are associated with an increased risk of developing particular conditions are a priori linked to the causative genes. The presence of differential methylation in such regions infers functional relevance of these epigenetic changes in controlling expression of the proximal gene(s). There are strong cis-acting genetic effects underpinning much epigenetic variation [7, 45], and in population-based studies, methods that use genetic surrogates to infer a causal or mediating role of epigenome differences have been applied [7, 46–48]. The use of familial genetic information can also lead to the identification of potentially causative candidate regions showing phenotype-related differential methylation .
Timing of epigenetic changes. The presence of an epigenetic mark prior to development of a phenotype is an essential feature associated with causality. Conversely, the presence of a mark in association with obesity, but not before its development, can be used to exclude causality but would not exclude a possible role in subsequent obesity-related pathology.
Plausible inference of mechanism. This refers to epigenetic changes that are associated with altered expression of genes with an established role in regulating the phenotype of interest. One such example is the association of methylation at two CpG sites at the CPT1A gene with circulating triglyceride levels . CPT1A encodes carnitine palmitoyltransferase 1A, an enzyme with a central role in fatty acid metabolism, and this is strongly indicative that differential methylation of this gene may be causally related to the alterations in plasma triglyceride concentrations.
Epigenome-wide association studies: identifying epigenetic biomarkers of metabolic health
Representative human studies showing evidence for a role of epigenetics in obesity and related comorbidities
Association with epigenetic marks or changes in epigenetic marksa
Methylation in 37 CpGs associated with BMI and 1 probe with WC in blood (n = 5465); of those 16 CpGs (e.g., CpGs in CPT1A, ABCG1, and SREBPF1) were also associated with BMI in subcutaneous adipose tissue (n = 648)
Yes, 3 other cohorts
DNA methylation of 4979 CpGs (e.g., in FTO, TCF7L2, FASN, PGC1A, CCRL2) in subcutaneous adipose tissue (n = 190). Several BMI-related methylation sites were also associated with age (e.g., ELOVL2), and with gene expression levels
Yes, 2nd cohort
HIF3A methylation 3 CpGs in whole blood (n = 2587) and subcutaneous adipose tissue (n = 635), not in skin (n = 395)
Yes, 3 other cohorts
LY86 methylation 1 CpG in blood leukocytes adolescents and adults (n = 1534)
Yes, 4 other cohorts
249 DMRs in subcutaneous adipose tissue (n = 21), methylation in all these 249 regions also changed with high-fat feeding in mice and includes regions overlapping T2DM loci such as in TCF7L2
No, only in mice
WHR, blood pressure LDL cholesterol
Association between WHR and ADRB3 methylation in whole blood and between blood pressure and ADRB3 methylation in visceral adipose tissue of obese (n = 25). Association between ADRB3 methylation and LDL cholesterol in whole blood of men with familial hypercholesterolemia (n = 41)
Not validated in 2nd cohort
ADCY3 meQTL in subcutaneous adipose tissue from twins (n = 648)
Methylation differences at 1236 CpGs in leukocytes of monozygotic twins discordant for BMI and liver fat (n = 13)
Methylation in 101 genes in subcutaneous adipose tissue (n = 106) including AOC3, SOD3, DOCK9, AQP7, ANGPT4, ANGPT2, TIMP4, ADAMST4, HOXA3, and LIPE, not found in blood leukocytes same individuals
No validation performed
CPT1A methylation 1 CpG in CD4+T cells (n = 991) and leukocytes (n = 1261)
Yes, technical and 2nd cohort
VLDL and LDL
CPT1A methylation 2 CpGs in CD4+T cells (n = 994)
Yes, in the same cohort
Cholesterol and TG
Methylation in 9 CpGs, including in ABCG1, CPT1A and SREBF1, in whole blood (n = 2747), 5 of these CpGs also showed associations in subcutaneous adipose tissue (n = 634)
Yes, 2 other cohorts
Insulin and HOMA-IR
ABCG1 1 CpG in CD4+T cells (n = 837)
Yes, in the same cohort
DNA methylation of 711 CpGs in subcutaneous adipose tissue (n = 96 males). Not validated in female cohort (n = 94) but 30 CpGs validated in T2DM case-control cohort
Yes, validated in 1 of 2 cohorts
MALT1 methylation whole blood (n = 27 twins + n = 263 individuals) and other less significant DMRs (FDR < 0.1) overlapping T2DM GWAS loci
Yes, other cohort
Methylation in 1649 CpG sites, some overlapping T2DM, and obesity GWAS loci such as TCF7L2, FTO, and KCNQ1 in pancreatic islets (n = 49)
No differentially methylated sites (after FDR correction) in T2DM discordant monozygotic twins (n = 14 pairs). Differential methylation at 15,627 CpGs, including in T2DM GWAS loci PPARG, KCNQ1, TCF7L2, and IRS1 in subcutaneous adipose tissue (n = 56). 1410 of these CpGs were also differentially methylated (P < 0.05) in the T2DM discordant twins
Yes, other cohort
Adiposity measured annually age 9–14 years
Increase PGC1A promoter methylation in whole blood children measured annually from 5–7 years (n = 40)
No, but measures at multiple time points
Maternal exposure or phenotype and epigenetic marks in offspring
181 DMRs in adult whole blood (n = 48), including in CDH23, SMAD7, INSR, CPT1A, KLF13, RFTN1 (validated in n = 120)
Yes, technical and 2nd cohort
Variation methyl donor intake
Changes in mean methylation across BOLA3, LOC654433, EXD3, ZFYVE28, RBM46, and ZNF678 in blood leukocytes (n = 126) and hair follicles (n = 82) of 2- to 8-month infants
No validation performed
Decreased mean methylation across BOLA3, LOC654433, EXD3, ZFYVE28, RBM46, and ZNF678 in blood leukocytes (n = 126) and hair follicles (n = 82) of 2- to 8-month infants
No validation performed
Gestational weight gain early pregnancy
Increased methylation 4 CpGs in MMP7, KCNK4, TRPM5, and NFKB1 in newborn cord blood (n = 88), no association in 2nd cohort (n = 170)
Not validated (technical and 2nd cohort)
Differential methylation in ZCCHC10 in newborn cord blood (n = 308), other less significant sites were found in WNT16, ACPL2, C18orf8, ANGPTL2, SAPCD2, and ADCY3
42 CpGs in newborn cord blood (n = 136). ~1/3 of CpGs overlapped with sites that were associated with maternal glucose levels (n = 36) or micronutrient supplementation (n = 59) in 2 other child cohorts
Yes, technical and 2 other cohorts
No differentially methylated sites (after FDR correction) in cord blood and placenta (n = 44) but enrichment of sites in genes metabolic disease pathway
Weight loss surgery
Change in methylation at 3601 CpGs (195 DMRs) in subcutaneous adipose tissue and 15 CpGs in omental adipose tissue (n = 15), some DMRs overlapping known obesity and T2DM loci
Weight loss surgery
227 DMRs, methylation in these regions also changed with high-fat feeding in mice
No, only in mice
Weight loss surgery in liver disease
Before surgery 467 differentially methylated CpGs between control (n = 18), healthy obese (n = 18), steatosis (n = 12), and NASH (n = 15) liver samples. After surgery changes in methylation at 113 CpGs, disease-associated methylation was reversible at the HOXB1, PRKCZ, SLC38A10, and SECTM1 loci
No, but for baseline technical and 2nd cohort
Methylation profiles of RYR1, TUBA3C and BDNF in PBMCs of successful weight loss maintainers (n = 16) more closely resembled lean (n = 16) than obese subjects (n = 16)
Endurance and strength exercise
Changes in DNA methylation in skeletal muscle of obese T2DM subjects (n = 17) after 16 weeks, most pronounced with endurance exercise
High-fat diet (5 days)
PPARGC1A DNA methylation across 4 CpGs increased in subcutaneous adipose tissue of lean adults born with low birth weight (n = 19) but not in controls (n = 26)
From these studies, altered methylation of PGC1A, HIF3A, ABCG1, and CPT1A and the previously described RXRA  have emerged as biomarkers associated with, or perhaps predictive of, metabolic health that are also plausible candidates for a role in development of metabolic disease.
Interaction between genotype and the epigenome
Epigenetic variation is highly influenced by the underlying genetic variation, with genotype estimated to explain ~20–40 % of the variation [6, 8]. Recently, a number of studies have begun to integrate methylome and genotype data to identify methylation quantitative trait loci (meQTL) associated with disease phenotypes. For instance, in adipose tissue, an meQTL overlapping with a BMI genetic risk locus has been identified in an enhancer element upstream of ADCY3 . Other studies have also identified overlaps between known obesity and T2DM risk loci and DMRs associated with obesity and T2DM [43, 48, 62]. Methylation of a number of such DMRs was also modulated by high-fat feeding in mice  and weight loss in humans . These results identify an intriguing link between genetic variations linked with disease susceptibility and their association with regions of the genome that undergo epigenetic modifications in response to nutritional challenges, implying a causal relationship. The close connection between genetic and epigenetic variation may signify their essential roles in generating individual variation [65, 66]. However, while these findings suggest that DNA methylation may be a mediator of genetic effects, it is also important to consider that both genetic and epigenetic processes could act independently on the same genes. Twin studies [8, 63, 67] can provide important insights and indicate that inter-individual differences in levels of DNA methylation arise predominantly from non-shared environment and stochastic influences, minimally from shared environmental effects, but also with a significant impact of genetic variation.
The impact of the prenatal and postnatal environment on the epigenome
Two recently published studies made use of human populations that experienced “natural” variations in nutrient supply to study the impact of maternal nutrition before or during pregnancy on DNA methylation in the offspring [68, 69]. The first study used a Gambian mother-child cohort to show that both seasonal variations in maternal methyl donor intake during pregnancy and maternal pre-pregnancy BMI were associated with altered methylation in the infants . The second study utilized adult offspring from the Dutch Hunger Winter cohort to investigate the effect of prenatal exposure to an acute period of severe maternal undernutrition on DNA methylation of genes involved in growth and metabolism in adulthood . The results highlighted the importance of the timing of the exposure in its impact on the epigenome, since significant epigenetic effects were only identified in individuals exposed to famine during early gestation. Importantly, the epigenetic changes occurred in conjunction with increased BMI; however, it was not possible to establish in this study whether these changes were present earlier in life or a consequence of the higher BMI.
Other recent studies have provided evidence that prenatal overnutrition and an obese or diabetic maternal environment are also associated with DNA methylation changes in genes related to embryonic development, growth, and metabolic disease in the offspring [70–73]. While human data are scarce, there are indications that paternal obesity can lead to altered methylation of imprinted genes in the newborn , an effect thought to be mediated via epigenetic changes acquired during spermatogenesis.
The epigenome is established de novo during embryonic development, and therefore, the prenatal environment most likely has the most significant impact on the epigenome. However, it is now clear that changes do occur in the “mature” epigenome under the influence of a range of conditions, including aging, exposure to toxins, and dietary alterations. For example, changes in DNA methylation in numerous genes in skeletal muscle and PGC1A in adipose tissue have been demonstrated in response to a high-fat diet [75, 76]. Interventions to lose body fat mass have also been associated with changes in DNA methylation. Studies have reported that the DNA methylation profiles of adipose tissue [43, 64], peripheral blood mononuclear cells , and muscle tissue  in formerly obese patients become more similar to the profiles of lean subjects following weight loss. Weight loss surgery also partially reversed non-alcoholic fatty liver disease-associated methylation changes in liver  and in another study led to hypomethylation of multiple obesity candidate genes, with more pronounced effects in subcutaneous compared to omental (visceral) fat . Accumulating evidence suggests that exercise interventions can also influence DNA methylation. Most of these studies have been conducted in lean individuals [80–82], but one exercise study in obese T2DM subjects also demonstrated changes in DNA methylation, including in genes involved in fatty acid and glucose transport . Epigenetic changes also occur with aging, and recent data suggest a role of obesity in augmenting them [9, 84, 85]. Obesity accelerated the epigenetic age of liver tissue, but in contrast to the findings described above, this effect was not reversible after weight loss .
Collectively, the evidence in support of the capacity to modulate the epigenome in adults suggests that there may be the potential to intervene in postnatal life to modulate or reverse adverse epigenetic programming.
Effect sizes and differences between tissue types
DNA methylation changes associated with obesity or induced by diet or lifestyle interventions and weight loss are generally modest (<15 %), although this varies depending on the phenotype and tissue studied. For instance, changes greater than 20 % have been reported in adipose tissue after weight loss  and associations between HIF3A methylation and BMI in adipose tissue were more pronounced than in blood .
The biological relevance of relatively small methylation changes has been questioned. However, in tissues consisting of a mixture of cell types, a small change in DNA methylation may actually reflect a significant change in a specific cell fraction. Integration of epigenome data with transcriptome and other epigenetic data, such as histone modifications, is important, since small DNA methylation changes might reflect larger changes in chromatin structure and could be associated with broader changes in gene expression. The genomic context should also be considered; small changes within a regulatory element such as a promotor, enhancer, or insulator may have functional significance. In this regard, DMRs for obesity, as well as regions affected by prenatal famine exposure and meQTL for metabolic trait loci have been observed to overlap enhancer elements [8, 43, 68]. There is evidence that DNA methylation in famine-associated regions could indeed affect enhancer activity , supporting a role of nutrition-induced methylation changes in gene regulation.
The study of the role of epigenetics in obesity and metabolic disease has expanded rapidly in recent years, and evidence is accumulating of a link between epigenetic modifications and metabolic health outcomes in humans. Potential epigenetic biomarkers associated with obesity and metabolic health have also emerged from recent studies. The validation of epigenetic marks in multiple cohorts, the fact that several marks are found in genes with a plausible function in obesity and T2DM development, as well as the overlap of epigenetic marks with known obesity and T2DM genetic loci strengthens the evidence that these associations are real. Causality has so far been difficult to establish; however, regardless of whether the associations are causal, the identified epigenetic marks may still be relevant as biomarkers for obesity and metabolic disease risk.
Effect sizes in easily accessible tissues such as blood are small but do seem reproducible despite variation in ethnicity, tissue type, and analysis methods . Also, even small DNA methylation changes may have biological significance. An integrative “omics” approach will be crucial in further unraveling the complex interactions between the epigenome, transcriptome, genome, and metabolic health. Longitudinal studies, ideally spanning multiple generations, are essential to establishing causal relationships. We can expect more such studies in the future, but this will take time.
While animal studies continue to demonstrate an effect of early life nutritional exposure on the epigenome and metabolic health of the offspring, human data are still limited. However, recent studies have provided clear evidence that exposure to suboptimal nutrition during specific periods of prenatal development is associated with methylation changes in the offspring and therefore have the potential to influence adult phenotype. Animal studies will be important to verify human findings in a more controlled setting, help determine whether the identified methylation changes have any impact on metabolic health, and unravel the mechanisms underlying this intergenerational/transgenerational epigenetic regulation. The identification of causal mechanisms underlying metabolic memory responses, the mode of transmission of the phenotypic effects into successive generations, the degree of impact and stability of the transmitted trait, and the identification of an overarching and unifying evolutionary context also remain important questions to be addressed. The latter is often encapsulated by the predictive adaptive response hypothesis, i.e., a response to a future anticipated environment that increases fitness of the population. However, this hypothesis has increasingly been questioned as there is limited evidence for increased fitness later in life .
In summary, outcomes are promising, as the epigenetic changes are linked with adult metabolic health and they act as a mediator between altered prenatal nutrition and subsequent increased risk of poor metabolic health outcomes. New epigenetic marks have been identified that are associated with measures of metabolic health. Integration of different layers of genomic information has added further support to causal relationships, and there have been further studies showing effects of pre- and postnatal environment on the epigenome and health. While many important questions remain, recent methodological advances have enabled the types of large-scale population-based studies that will be required to address the knowledge gaps. The next decade promises to be a period of major activity in this important research area.
This work has been supported by a grant from the Science and Industry Endowment Fund (Grant RP03-064). JLM and BSM are supported by the National Health and Medical Research Council Career Development Fellowships (JLM, APP1066916; BSM, APP1004211). We thank Lance Macaulay and Sue Mitchell for critical reading and comments on the manuscript.
- WHO. WHO | Overweight and obesity. http://www.who.int/gho/ncd/risk_factors/overweight/en/index.html. Accessed 29 January 2015.
- Visscher PM, Brown MA, McCarthy MI, Yang J. Five years of GWAS discovery. Am J Hum Genet. 2012;90:7–24.PubMed CentralPubMedView ArticleGoogle Scholar
- Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR, et al. Genetic studies of body mass index yield new insights for obesity biology. Nature. 2015;518:197–206.PubMed CentralPubMedView ArticleGoogle Scholar
- Ling C, Del Guerra S, Lupi R, Rönn T, Granhall C, Luthman H, et al. Epigenetic regulation of PPARGC1A in human type 2 diabetic islets and effect on insulin secretion. Diabetologia. 2008;51:615–22.PubMed CentralPubMedView ArticleGoogle Scholar
- Van Dijk SJ, Molloy PL, Varinli H, Morrison JL, Muhlhausler BS. Epigenetics and human obesity. Int J Obes (Lond). 2015;39:85–97.View ArticleGoogle Scholar
- Teh AL, Pan H, Chen L, Ong M-L, Dogra S, Wong J, et al. The effect of genotype and in utero environment on interindividual variation in neonate DNA methylomes. Genome Res. 2014;24:1064–74.PubMed CentralPubMedView ArticleGoogle Scholar
- Olsson AH, Volkov P, Bacos K, Dayeh T, Hall E, Nilsson EA, et al. Genome-wide associations between genetic and epigenetic variation influence mRNA expression and insulin secretion in human pancreatic islets. PLoS Genet. 2014;10:e1004735.PubMed CentralPubMedView ArticleGoogle Scholar
- Grundberg E, Meduri E, Sandling JK, Hedman AK, Keildson S, Buil A, et al. Global analysis of DNA methylation variation in adipose tissue from twins reveals links to disease-associated variants in distal regulatory elements. Am J Hum Genet. 2013;93:876–90.PubMed CentralPubMedView ArticleGoogle Scholar
- Ronn T, Volkov P, Gillberg L, Kokosar M, Perfilyev A, Jacobsen AL, et al. Impact of age, BMI and HbA1c levels on the genome-wide DNA methylation and mRNA expression patterns in human adipose tissue and identification of epigenetic biomarkers in blood. Hum Mol Genet. 2015;24:3792–813.PubMedGoogle Scholar
- Waterland RA, Michels KB. Epigenetic epidemiology of the developmental origins hypothesis. Annu Rev Nutr. 2007;27:363–88.PubMedView ArticleGoogle Scholar
- McMillen IC, Rattanatray L, Duffield JA, Morrison JL, MacLaughlin SM, Gentili S, et al. The early origins of later obesity: pathways and mechanisms. Adv Exp Med Biol. 2009;646:71–81.PubMedView ArticleGoogle Scholar
- Ravelli A, van der Meulen J, Michels R, Osmond C, Barker D, Hales C, et al. Glucose tolerance in adults after prenatal exposure to famine. Lancet. 1998;351:173–7.PubMedView ArticleGoogle Scholar
- McMillen IC, MacLaughlin SM, Muhlhausler BS, Gentili S, Duffield JL, Morrison JL. Developmental origins of adult health and disease: the role of periconceptional and foetal nutrition. Basic Clin Pharmacol Toxicol. 2008;102:82–9.PubMedView ArticleGoogle Scholar
- Zhang S, Rattanatray L, McMillen IC, Suter CM, Morrison JL. Periconceptional nutrition and the early programming of a life of obesity or adversity. Prog Biophys Mol Biol. 2011;106:307–14.PubMedView ArticleGoogle Scholar
- Bouret S, Levin BE, Ozanne SE. Gene-environment interactions controlling energy and glucose homeostasis and the developmental origins of obesity. Physiol Rev. 2015;95:47–82.PubMedView ArticleGoogle Scholar
- Borengasser SJ, Zhong Y, Kang P, Lindsey F, Ronis MJ, Badger TM, et al. Maternal obesity enhances white adipose tissue differentiation and alters genome-scale DNA methylation in male rat offspring. Endocrinology. 2013;154:4113–25.PubMed CentralPubMedView ArticleGoogle Scholar
- Gluckman PD, Lillycrop KA, Vickers MH, Pleasants AB, Phillips ES, Beedle AS, et al. Metabolic plasticity during mammalian development is directionally dependent on early nutritional status. Proc Natl Acad Sci U S A. 2007;104:12796–800.PubMed CentralPubMedView ArticleGoogle Scholar
- Godfrey KM, Sheppard A, Gluckman PD, Lillycrop KA, Burdge GC, McLean C, et al. Epigenetic gene promoter methylation at birth is associated with child’s later adiposity. Diabetes. 2011;60:1528–34.PubMed CentralPubMedView ArticleGoogle Scholar
- McMillen IC, Adam CL, Muhlhausler BS. Early origins of obesity: programming the appetite regulatory system. J Physiol. 2005;565(Pt 1):9–17.PubMed CentralPubMedView ArticleGoogle Scholar
- Begum G, Stevens A, Smith EB, Connor K, Challis JR, Bloomfield F, et al. Epigenetic changes in fetal hypothalamic energy regulating pathways are associated with maternal undernutrition and twinning. FASEB J. 2012;26:1694–703.PubMed CentralPubMedView ArticleGoogle Scholar
- Ge ZJ, Liang QX, Hou Y, Han ZM, Schatten H, Sun QY, et al. Maternal obesity and diabetes may cause DNA methylation alteration in the spermatozoa of offspring in mice. Reprod Biol Endocrinol. 2014;12:29.PubMed CentralPubMedView ArticleGoogle Scholar
- Jousse C, Parry L, Lambert-Langlais S, Maurin AC, Averous J, Bruhat A, et al. Perinatal undernutrition affects the methylation and expression of the leptin gene in adults: implication for the understanding of metabolic syndrome. FASEB J. 2011;25:3271–8.PubMedView ArticleGoogle Scholar
- Lan X, Cretney EC, Kropp J, Khateeb K, Berg MA, Penagaricano F, et al. Maternal diet during pregnancy induces gene expression and DNA methylation changes in fetal tissues in sheep. Front Genet. 2013;4:49.PubMed CentralPubMedView ArticleGoogle Scholar
- Li CC, Young PE, Maloney CA, Eaton SA, Cowley MJ, Buckland ME, et al. Maternal obesity and diabetes induces latent metabolic defects and widespread epigenetic changes in isogenic mice. Epigenetics. 2013;8:602–11.PubMed CentralPubMedView ArticleGoogle Scholar
- Lillycrop KA, Phillips ES, Jackson AA, Hanson MA, Burdge GC. Dietary protein restriction of pregnant rats induces and folic acid supplementation prevents epigenetic modification of hepatic gene expression in the offspring. J Nutr. 2005;135:1382–6.PubMedGoogle Scholar
- Radford EJ, Ito M, Shi H, Corish JA, Yamazawa K, Isganaitis E, et al. In utero effects. In utero undernourishment perturbs the adult sperm methylome and intergenerational metabolism. Science. 2014;345(80):1255903.PubMedView ArticleGoogle Scholar
- Suter M, Bocock P, Showalter L, Hu M, Shope C, McKnight R, et al. Epigenomics: maternal high-fat diet exposure in utero disrupts peripheral circadian gene expression in nonhuman primates. FASEB J. 2011;25:714–26.PubMed CentralPubMedView ArticleGoogle Scholar
- Suter MA, Ma J, Vuguin PM, Hartil K, Fiallo A, Harris RA, et al. In utero exposure to a maternal high-fat diet alters the epigenetic histone code in a murine model. Am J Obs Gynecol. 2014;210:463 e1–463 e11.View ArticleGoogle Scholar
- Tosh DN, Fu Q, Callaway CW, McKnight RA, McMillen IC, Ross MG, et al. Epigenetics of programmed obesity: alteration in IUGR rat hepatic IGF1 mRNA expression and histone structure in rapid vs. delayed postnatal catch-up growth. Am J Physiol Gastrointest Liver Physiol. 2010;299:G1023–9.PubMed CentralPubMedView ArticleGoogle Scholar
- Sandovici I, Smith NH, Nitert MD, Ackers-Johnson M, Uribe-Lewis S, Ito Y, et al. Maternal diet and aging alter the epigenetic control of a promoter-enhancer interaction at the Hnf4a gene in rat pancreatic islets. Proc Natl Acad Sci U S A. 2011;108:5449–54.PubMed CentralPubMedView ArticleGoogle Scholar
- Braunschweig M, Jagannathan V, Gutzwiller A, Bee G. Investigations on transgenerational epigenetic response down the male line in F2 pigs. PLoS One. 2012;7, e30583.PubMed CentralPubMedView ArticleGoogle Scholar
- Carone BR, Fauquier L, Habib N, Shea JM, Hart CE, Li R, et al. Paternally induced transgenerational environmental reprogramming of metabolic gene expression in mammals. Cell. 2010;143:1084–96.PubMed CentralPubMedView ArticleGoogle Scholar
- Ost A, Lempradl A, Casas E, Weigert M, Tiko T, Deniz M, et al. Paternal diet defines offspring chromatin state and intergenerational obesity. Cell. 2014;159:1352–64.PubMedView ArticleGoogle Scholar
- Martínez D, Pentinat T, Ribó S, Daviaud C, Bloks VW, Cebrià J, et al. In utero undernutrition in male mice programs liver lipid metabolism in the second-generation offspring involving altered Lxra DNA methylation. Cell Metab. 2014;19:941–51.PubMedView ArticleGoogle Scholar
- Wei Y, Yang C-R, Wei Y-P, Zhao Z-A, Hou Y, Schatten H, et al. Paternally induced transgenerational inheritance of susceptibility to diabetes in mammals. Proc Natl Acad Sci U S A. 2014;111:1873–8.PubMed CentralPubMedView ArticleGoogle Scholar
- Grossniklaus U, Kelly WG, Kelly B, Ferguson-Smith AC, Pembrey M, Lindquist S. Transgenerational epigenetic inheritance: how important is it? Nat Rev Genet. 2013;14:228–35.PubMed CentralPubMedView ArticleGoogle Scholar
- Pembrey M, Saffery R, Bygren LO. Human transgenerational responses to early-life experience: potential impact on development, health and biomedical research. J Med Genet. 2014;51:563–72.PubMed CentralPubMedView ArticleGoogle Scholar
- Wolff GL, Kodell RL, Moore SR, Cooney CA. Maternal epigenetics and methyl supplements affect agouti gene expression in Avy/a mice. FASEB J. 1998;12:949–57.PubMedGoogle Scholar
- Jirtle RL, Skinner MK. Environmental epigenomics and disease susceptibility. Nat Rev Genet. 2007;8:253–62.PubMedView ArticleGoogle Scholar
- Morgan HD, Sutherland HG, Martin DI, Whitelaw E. Epigenetic inheritance at the agouti locus in the mouse. Nat Genet. 1999;23:314–8.PubMedView ArticleGoogle Scholar
- Cropley JE, Suter CM, Beckman KB, Martin DI. Germ-line epigenetic modification of the murine A vy allele by nutritional supplementation. Proc Natl Acad Sci U S A. 2006;103:17308–12.PubMed CentralPubMedView ArticleGoogle Scholar
- Hoile SP, Lillycrop KA, Thomas NA, Hanson MA, Burdge GC. Dietary protein restriction during F0 pregnancy in rats induces transgenerational changes in the hepatic transcriptome in female offspring. PLoS One. 2011;6, e21668.PubMed CentralPubMedView ArticleGoogle Scholar
- Multhaup ML, Seldin MM, Jaffe AE, Lei X, Kirchner H, Mondal P, et al. Mouse-human experimental epigenetic analysis unmasks dietary targets and genetic liability for diabetic phenotypes. Cell Metab. 2015;21:138–49.PubMedView ArticleGoogle Scholar
- Michels KB, Binder AM, Dedeurwaerder S, Epstein CB, Greally JM, Gut I, et al. Recommendations for the design and analysis of epigenome-wide association studies. Nat Methods. 2013;10:949–55.PubMedView ArticleGoogle Scholar
- Dayeh TA, Olsson AH, Volkov P, Almgren P, Rönn T, Ling C. Identification of CpG-SNPs associated with type 2 diabetes and differential DNA methylation in human pancreatic islets. Diabetologia. 2013;56:1036–46.PubMed CentralPubMedView ArticleGoogle Scholar
- Relton CL, Davey Smith G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol. 2012;41:161–76.PubMed CentralPubMedView ArticleGoogle Scholar
- Liu Y, Aryee MJ, Padyukov L, Fallin MD, Hesselberg E, Runarsson A, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013;31:142–7.PubMed CentralPubMedView ArticleGoogle Scholar
- Yuan W, Xia Y, Bell CG, Yet I, Ferreira T, Ward KJ, et al. An integrated epigenomic analysis for type 2 diabetes susceptibility loci in monozygotic twins. Nat Commun. 2014;5:5719.PubMed CentralPubMedView ArticleGoogle Scholar
- Nitert MD, Dayeh T, Volkov P, Elgzyri T, Hall E, Nilsson E, et al. Impact of an exercise intervention on DNA methylation in skeletal muscle from first-degree relatives of patients with type 2 diabetes. Diabetes. 2012;61:3322–32.PubMed CentralPubMedView ArticleGoogle Scholar
- Gagnon F, Aïssi D, Carrié A, Morange P-E, Trégouët D-A. Robust validation of methylation levels association at CPT1A locus with lipid plasma levels. J Lipid Res. 2014;55:1189–91.PubMed CentralPubMedView ArticleGoogle Scholar
- Demerath EW, Guan W, Grove ML, Aslibekyan S, Mendelson M, Zhou Y-H, et al. Epigenome-wide association atudy (EWAS) of BMI, BMI change, and waist circumference in African American adults identifies multiple replicated loci. Hum Mol Genet. 2015:ddv161–.
- Dick KJ, Nelson CP, Tsaprouni L, Sandling JK, Aïssi D, Wahl S, et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet. 2014;6736:1–9.Google Scholar
- Su S, Zhu H, Xu X, Wang X, Dong Y, Kapuku G, et al. DNA methylation of the LY86 gene is associated with obesity, insulin resistance, and inflammation. Twin Res Hum Genet. 2014;17:183–91.PubMed CentralPubMedView ArticleGoogle Scholar
- Clarke-Harris R, Wilkin TJ, Hosking J, Pinkney J, Jeffery AN, Metcalf BS, et al. PGC1α promoter methylation in blood at 5–7 years predicts adiposity from 9 to 14 years (EarlyBird 50). Diabetes. 2014;63:2528–37.PubMedView ArticleGoogle Scholar
- Guay S-P, Brisson D, Lamarche B, Biron S, Lescelleur O, Biertho L, et al. ADRB3 gene promoter DNA methylation in blood and visceral adipose tissue is associated with metabolic disturbances in men. Epigenomics. 2014;6:33–43.PubMedView ArticleGoogle Scholar
- Agha G, Houseman EA, Kelsey KT, Eaton CB, Buka SL, Loucks EB. Adiposity is associated with DNA methylation profile in adipose tissue. Int J Epidemiol. 2014:1–11.
- Irvin MR, Zhi D, Joehanes R, Mendelson M, Aslibekyan S, Claas SA, et al. Epigenome-wide association study of fasting blood lipids in the genetics of lipid-lowering drugs and diet network study. Circulation. 2014;130:565–72.PubMedView ArticleGoogle Scholar
- Frazier-Wood AC, Aslibekyan S, Absher DM, Hopkins PN, Sha J, Tsai MY, et al. Methylation at CPT1A locus is associated with lipoprotein subfraction profiles. J Lipid Res. 2014;55:1324–30.PubMed CentralPubMedView ArticleGoogle Scholar
- Pfeifferm L, Wahl S, Pilling LC, Reischl E, Sandling JK, Kunze S, et al. DNA methylation of lipid-related genes affects blood lipid levels. Circ Cardiovasc Genet. 2015.
- Petersen A-K, Zeilinger S, Kastenmüller G, Römisch-Margl W, Brugger M, Peters A, et al. Epigenetics meets metabolomics: an epigenome-wide association study with blood serum metabolic traits. Hum Mol Genet. 2014;23:534–45.PubMed CentralPubMedView ArticleGoogle Scholar
- Hidalgo B, Irvin MR, Sha J, Zhi D, Aslibekyan S, Absher D, et al. Epigenome-wide association study of fasting measures of glucose, insulin, and HOMA-IR in the genetics of lipid lowering drugs and diet network study. Diabetes. 2014;63:801–7.PubMed CentralPubMedView ArticleGoogle Scholar
- Dayeh T, Volkov P, Salö S, Hall E, Nilsson E, Olsson AH, et al. Genome-wide DNA methylation analysis of human pancreatic islets from type 2 diabetic and non-diabetic donors identifies candidate genes that influence insulin secretion. PLoS Genet. 2014;10, e1004160.PubMed CentralPubMedView ArticleGoogle Scholar
- Nilsson E, Jansson PA, Perfilyev A, Volkov P, Pedersen M, Svensson MK, et al. Altered DNA methylation and differential expression of genes influencing metabolism and inflammation in adipose tissue from subjects with type 2 diabetes. Diabetes. 2014;63:2962–76.PubMedView ArticleGoogle Scholar
- Benton MC, Johnstone A, Eccles D, Harmon B, Hayes MT, Lea RA, et al. An analysis of DNA methylation in human adipose tissue reveals differential modification of obesity genes before and after gastric bypass and weight loss. Gene. 2015;16:1–21.Google Scholar
- Bateson P, Gluckman P. Plasticity and robustness in development and evolution. Int J Epidemiol. 2012;41:219–23.PubMedView ArticleGoogle Scholar
- Feinberg AP, Irizarry RA, Feinberg AP, Irizarry RA. Evolution in health and medicine Sackler colloquium: stochastic epigenetic variation as a driving force of development, evolutionary adaptation, and disease. Proc Natl Acad Sci U S A. 2010;107(Suppl):1757–64.PubMed CentralPubMedView ArticleGoogle Scholar
- Martino D, Loke YJ, Gordon L, Ollikainen M, Cruickshank MN, Saffery R, et al. Longitudinal, genome-scale analysis of DNA methylation in twins from birth to 18 months of age reveals rapid epigenetic change in early life and pair-specific effects of discordance. Genome Biol. 2013;14:R42.PubMed CentralPubMedView ArticleGoogle Scholar
- Tobi EW, Goeman JJ, Monajemi R, Gu H, Putter H, Zhang Y, et al. DNA methylation signatures link prenatal famine exposure to growth and metabolism. Nat Commun. 2014;5:5592.PubMed CentralPubMedView ArticleGoogle Scholar
- Dominguez-Salas P, Moore SE, Baker MS, Bergen AW, Cox SE, Dyer RA, et al. Maternal nutrition at conception modulates DNA methylation of human metastable epialleles. Nat Commun. 2014;5:3746.PubMed CentralPubMedView ArticleGoogle Scholar
- Quilter CR, Cooper WN, Cliffe KM, Skinner BM, Prentice PM, Nelson L, et al. Impact on offspring methylation patterns of maternal gestational diabetes mellitus and intrauterine growth restraint suggest common genes and pathways linked to subsequent type 2 diabetes risk. FASEB J. 2014:1–12.
- Morales E, Groom A, Lawlor DA, Relton CL. DNA methylation signatures in cord blood associated with maternal gestational weight gain: results from the ALSPAC cohort. BMC Res Notes. 2014;7:278.PubMed CentralPubMedView ArticleGoogle Scholar
- Ruchat SM, Houde AA, Voisin G, St-Pierre J, Perron P, Baillargeon JP, et al. Gestational diabetes mellitus epigenetically affects genes predominantly involved in metabolic diseases. Epigenetics. 2013;8:935–43.PubMed CentralPubMedView ArticleGoogle Scholar
- Liu X, Chen Q, Tsai H-J, Wang G, Hong X, Zhou Y, et al. Maternal preconception body mass index and offspring cord blood DNA methylation: exploration of early life origins of disease. Environ Mol Mutagen. 2014;55:223–30.PubMedView ArticleGoogle Scholar
- Soubry A, Murphy SK, Wang F, Huang Z, Vidal AC, Fuemmeler BF, et al. Newborns of obese parents have altered DNA methylation patterns at imprinted genes. Int J Obes (Lond). 2015;39:650–7.View ArticleGoogle Scholar
- Jacobsen SC, Brøns C, Bork-Jensen J, Ribel-Madsen R, Yang B, Lara E, et al. Effects of short-term high-fat overfeeding on genome-wide DNA methylation in the skeletal muscle of healthy young men. Diabetologia. 2012;55:3341–9.PubMedView ArticleGoogle Scholar
- Gillberg L, Jacobsen SC, Rönn T, Brøns C, Vaag A. PPARGC1A DNA methylation in subcutaneous adipose tissue in low birth weight subjects--impact of 5 days of high-fat overfeeding. Metabolism. 2014;63:263–71.PubMedView ArticleGoogle Scholar
- Huang Y-T, Maccani JZJ, Hawley NL, Wing RR, Kelsey KT, McCaffery JM. Epigenetic patterns in successful weight loss maintainers: a pilot study. Int J Obes (Lond). 2015;39:865–8.View ArticleGoogle Scholar
- Barres R, Kirchner H, Rasmussen M, Yan J, Kantor FR, Krook A, Näslund E, Zierath JR. Weight loss after gastric bypass surgery in human obesity remodels promoter methylation. Cell Rep. 2013:1–8.
- Ahrens M, Ammerpohl O, von Schönfels W, Kolarova J, Bens S, Itzel T, et al. DNA methylation analysis in nonalcoholic fatty liver disease suggests distinct disease-specific and remodeling signatures after bariatric surgery. Cell Metab. 2013;18:296–302.PubMedView ArticleGoogle Scholar
- Voisin S, Eynon N, Yan X, Bishop DJ. Exercise training and DNA methylation in humans. Acta Physiol (Oxf). 2014;213:39–59.View ArticleGoogle Scholar
- Lindholm ME, Marabita F, Gomez-Cabrero D, Rundqvist H, Ekström TJ, Tegnér J, et al. An integrative analysis reveals coordinated reprogramming of the epigenome and the transcriptome in human skeletal muscle after training. Epigenetics. 2014;9:1557–69.PubMedView ArticleGoogle Scholar
- Denham J, O’Brien BJ, Marques FZ, Charchar FJ. Changes in the leukocyte methylome and its effect on cardiovascular related genes after exercise. J Appl Physiol. 2014:jap.00878.2014.
- Rowlands DS, Page RA, Sukala WR, Giri M, Ghimbovschi SD, Hayat I, et al. Multi-omic integrated networks connect DNA methylation and miRNA with skeletal muscle plasticity to chronic exercise in type 2 diabetic obesity. Physiol Genomics. 2014;46:747–65.PubMedView ArticleGoogle Scholar
- Horvath S, Erhart W, Brosch M, Ammerpohl O, von Schonfels W, Ahrens M, et al. Obesity accelerates epigenetic aging of human liver. Proc Natl Acad Sci. 2014;111:15538–43.PubMed CentralPubMedView ArticleGoogle Scholar
- Almén MS, Nilsson EK, Jacobsson JA, Kalnina I, Klovins J, Fredriksson R, et al. Genome-wide analysis reveals DNA methylation markers that vary with both age and obesity. Gene. 2014.;548:61–7
- Houseman EA, Molitor J, Marsit CJ. Reference-free cell mixture adjustments in analysis of DNA methylation data. Bioinformatics. 2014;30:1431–9.PubMed CentralPubMedView ArticleGoogle Scholar
- Wells JC. A critical appraisal of the predictive adaptive response hypothesis. Int J Epidemiol. 2012;41:229–35.PubMedView ArticleGoogle Scholar
- Williams-Wyss O, Zhang S, MacLaughlin SM, Kleemann D, Walker SK, Suter CM, et al. Embryo number and periconceptional undernutrition in the sheep have differential effects on adrenal epigenotype, growth, and development. Am J Physiol Endocrinol Metab. 2014;307:E141–50.PubMedView ArticleGoogle Scholar
- Zhang S, Rattanatray L, Morrison JL, Nicholas LM, Lie S, McMillen IC. Maternal obesity and the early origins of childhood obesity: weighing up the benefits and costs of maternal weight loss in the periconceptional period for the offspring. Exp Diabetes Res. 2011;2011:585749.PubMed CentralPubMedView ArticleGoogle Scholar
- Zhang S, Williams-Wyss O, MacLaughlin SM, Walker SK, Kleemann DO, Suter CM, et al. Maternal undernutrition during the first week after conception results in decreased expression of glucocorticoid receptor mRNA in the absence of GR exon 17 hypermethylation in the fetal pituitary in late gestation. J Dev Orig Heal Dis. 2013;4:391–401.View ArticleGoogle Scholar
- Lie S, Morrison JL, Williams-Wyss O, Suter CM, Humphreys DT, Ozanne SE, et al. Periconceptional undernutrition programs changes in insulin-signaling molecules and microRNAs in skeletal muscle in singleton and twin fetal sheep. Biol Reprod. 2014;90:5.PubMedView ArticleGoogle Scholar
- Van Straten EM, van Meer H, Huijkman NC, van Dijk TH, Baller JF, Verkade HJ, et al. Fetal liver X receptor activation acutely induces lipogenesis but does not affect plasma lipid response to a high-fat diet in adult mice. Am J Physiol Endocrinol Metab. 2009;297:E1171–8.PubMedView ArticleGoogle Scholar
- Fernandez-Twinn DS, Alfaradhi MZ, Martin-Gronert MS, Duque-Guimaraes DE, Piekarz A, Ferland-McCollough D, et al. Downregulation of IRS-1 in adipose tissue of offspring of obese mice is programmed cell-autonomously through post-transcriptional mechanisms. Mol Metab. 2014;3:325–33.PubMed CentralPubMedView ArticleGoogle Scholar
- Waterland RA, Travisano M, Tahiliani KG. Diet-induced hypermethylation at agouti viable yellow is not inherited transgenerationally through the female. FASEB J. 2007;21:3380–5.PubMedView ArticleGoogle Scholar
- Ge ZJ, Luo SM, Lin F, Liang QX, Huang L, Wei YC, et al. DNA methylation in oocytes and liver of female mice and their offspring: effects of high-fat-diet-induced obesity. Env Heal Perspect. 2014;122:159–64.Google Scholar
- Ollikainen M, Ismail K, Gervin K, Kyllönen A, Hakkarainen A, Lundbom J, et al. Genome-wide blood DNA methylation alterations at regulatory elements and heterochromatic regions in monozygotic twins discordant for obesity and liver fat. Clin Epigenetics. 2015;7:1–13.View ArticleGoogle Scholar
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.