. 2015;7(6):937-50.

doi: 10.2217/epi.15.45. Epub 2015 May 26.

HIF3A association with adiposity: the story begins before birth

Hong Pan^{1

2}, Xinyi Lin¹, Yonghui Wu¹, Li Chen¹, Ai Ling Teh¹, Shu E Soh^{3

4}, Yung Seng Lee^{1

4

5}, Mya Thway Tint⁴, Julia L MacIsaac⁶, Alexander M Morin⁶, Kok Hian Tan⁷, Fabian Yap⁷, Seang Mei Saw³, Michael S Kobor⁶, Michael J Meaney^{1

8}, Keith M Godfrey⁹, Yap-Seng Chong^{1

4}, Peter D Gluckman^{1

10}, Neerja Karnani^{1

4}, Joanna D Holbrook¹; GUSTO Study Group

Collaborators, Affiliations

Collaborators

GUSTO Study Group:
P Agarwal, A Biswas, C L Bong, B F P Broekman, S Cai, J K Y Chan, Y H Chan, C Y I Chee, H Y H Chen, Y B Cheung, A Chia, A Chinnadurai, C K Chng, M F F Chong, S C Chong, M C Chua, C M Ding, E A Finkelstein, D Fok, M Fortier, A E N Goh, Y T D Goh, J J Gooley, W M Han, M Hanson, C J Henry, C-Y Hsu, H Inskip, J Kapur, K Kwek, I Y-M Lau, B W Lee, N Lek, S B Lim, Y-L Low, I Magiati, L M Daniel, C Ngo, K Naiduvaje, W W Pang, A Qiu, B L Quah, V S Rajadurai, M Rauff, S A Rebello, J L Richmond, A Rifkin-Graboi, L P-C Shek, A Sheppard, B Shuter, L Singh, W Stunkel, L L Su, O H Teoh, H P S van Bever, R M van Dam, I B Y Wong, P C Wong, G S H Yeo

Affiliations

¹ Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, 117609, Singapore.
² School of Computer Engineering, Nanyang Technological University (NTU), 639798, Singapore.
³ Saw Swee Hock School of Public Health, National University of Singapore (NUS), 117597, Singapore.
⁴ Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228, Singapore.
⁵ Division of Paediatric Endocrinology & Diabetes, Khoo Teck Puat-National University Children's Medical Institute, National University Health System, 119228, Singapore.
⁶ Department of Medical Genetics, Centre for Molecular Medicine & Therapeutics, Child & Family Research Institute, University of British Columbia, Vancouver, BC, V5Z 4H4 Canada.
⁷ KK Women's and Children's Hospital, 229899, Singapore.
⁸ Ludmer Centre for Neuroinformatics & Mental Health, Douglas University Mental Health Institute, McGill University, Montreal, (Quebec) H4H 1R3, Canada.
⁹ MRC Lifecourse Epidemiology Unit & NIHR Southampton Biomedical Research Centre, University of Southampton & University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK.
¹⁰ Centre for Human Evolution, Adaptation & Disease, Liggins Institute, University of Auckland, Auckland, 1142, New Zealand.

PMID: 26011824
PMCID: PMC4863876
DOI: 10.2217/epi.15.45

HIF3A association with adiposity: the story begins before birth

Hong Pan et al. Epigenomics. 2015.

. 2015;7(6):937-50.

doi: 10.2217/epi.15.45. Epub 2015 May 26.

Authors

Collaborators

GUSTO Study Group:
P Agarwal, A Biswas, C L Bong, B F P Broekman, S Cai, J K Y Chan, Y H Chan, C Y I Chee, H Y H Chen, Y B Cheung, A Chia, A Chinnadurai, C K Chng, M F F Chong, S C Chong, M C Chua, C M Ding, E A Finkelstein, D Fok, M Fortier, A E N Goh, Y T D Goh, J J Gooley, W M Han, M Hanson, C J Henry, C-Y Hsu, H Inskip, J Kapur, K Kwek, I Y-M Lau, B W Lee, N Lek, S B Lim, Y-L Low, I Magiati, L M Daniel, C Ngo, K Naiduvaje, W W Pang, A Qiu, B L Quah, V S Rajadurai, M Rauff, S A Rebello, J L Richmond, A Rifkin-Graboi, L P-C Shek, A Sheppard, B Shuter, L Singh, W Stunkel, L L Su, O H Teoh, H P S van Bever, R M van Dam, I B Y Wong, P C Wong, G S H Yeo

Affiliations

¹ Singapore Institute for Clinical Sciences (SICS), A*STAR, Brenner Centre for Molecular Medicine, 117609, Singapore.
² School of Computer Engineering, Nanyang Technological University (NTU), 639798, Singapore.
³ Saw Swee Hock School of Public Health, National University of Singapore (NUS), 117597, Singapore.
⁴ Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228, Singapore.
⁵ Division of Paediatric Endocrinology & Diabetes, Khoo Teck Puat-National University Children's Medical Institute, National University Health System, 119228, Singapore.
⁶ Department of Medical Genetics, Centre for Molecular Medicine & Therapeutics, Child & Family Research Institute, University of British Columbia, Vancouver, BC, V5Z 4H4 Canada.
⁷ KK Women's and Children's Hospital, 229899, Singapore.
⁸ Ludmer Centre for Neuroinformatics & Mental Health, Douglas University Mental Health Institute, McGill University, Montreal, (Quebec) H4H 1R3, Canada.
⁹ MRC Lifecourse Epidemiology Unit & NIHR Southampton Biomedical Research Centre, University of Southampton & University Hospital Southampton NHS Foundation Trust, Southampton, SO16 6YD, UK.
¹⁰ Centre for Human Evolution, Adaptation & Disease, Liggins Institute, University of Auckland, Auckland, 1142, New Zealand.

PMID: 26011824
PMCID: PMC4863876
DOI: 10.2217/epi.15.45

Abstract

Aim: Determine if the association of HIF3A DNA methylation with weight and adiposity is detectable early in life.

Material & methods: We determined HIF3A genotype and DNA methylation patterns (on hybridization arrays) in DNA extracted from umbilical cords of 991 infants. Methylation levels at three CpGs in the HIF3A first intron were related to neonatal and infant anthropometry and to genotype at nearby polymorphic sites.

Results & conclusion: Higher methylation levels at three previously described HIF3A CpGs were associated with greater infant weight and adiposity. The effect sizes were slightly smaller than those reported for adult BMI. There was also an interaction within cis-genotype. The association between higher DNA methylation at HIF3A and increased adiposity is present in neonates. In this study, no particular prenatal factor strongly influenced HIF3A hypermethylation. Our data nonetheless suggest shared prenatal influences on HIF3A methylation and adiposity.

Keywords: DNA methylation; HIF3A protein; birth weight; embryonic and fetal development; epigenomics; human; obesity.

PubMed Disclaimer

Conflict of interest statement

Financial & competing interests disclosure KM Godfrey, PD Gluckman and Y-S Chong have received reimbursement for speaking at conferences sponsored by companies selling nutritional products. They are part of an academic consortium that has received research funding from Abbott Nutrition, Nestec and Danone. This work was supported by the Translational Clinical Research (TCR) Flagship Program on Developmental Pathways to Metabolic Disease funded by the National Research Foundation (NRF) and administered by the National Medical Research Council (NMRC), Singapore – NMRC/TCR/004-NUS/2008. Additional funding is provided by the Singapore Institute for Clinical Sciences (SICS) – Agency for Science, Technology and Research (A*STAR), Singapore. KM Godfrey is supported by the National Institute for Health Research through the NIHR Southampton Biomedical Research Centre and by the European Union's Seventh Framework Programme (FP7/2007–2013), project EarlyNutrition under grant agreement no 289346. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed. No writing assistance was utilized in the production of this manuscript.

Figures

<b>Figure 1.</b> — **Figure 1.**. **Association between percentage methylation at cg16672562 and birth weight.**
**(A)** Box plot of birth weight (horizontal axis) binned into four bins of equal numbers of unique values, against % methylation at cg16672562 (vertical axis). Number of individual data points, median and mean % methylation of each bin is displayed in the table below the horizontal axis. **(B)** Heatmap displaying % methylation at cg16672562, maximum, medium and minimum methylation are shown in red, white and blue, respectively. Subjects are split by binned (four bins of equal number of unique values) birth weight (horizontal axis) and binned (four bins of equal number of unique values, last two bins are combined as there are few observations in the last bin) gestational age (vertical axis). The progression from blue to red (low to high methylation) as birth weight increases (left to right) is visible in each gestational age bin (top to bottom). **(C)** Scatter plots of birth weight (horizontal axis) against % methylation at cg16672562 (vertical axis), each panel displays data for binned gestational ages (four bins of equal number of unique values, last two bins are combined as there are few observations in the last bin), ranges for each bin are displayed in panel headers. **(D)** Heatmap displaying % methylation at cg16672562, maximum, medium and minimum methylation are shown in red, white and blue, respectively. Subjects are split by binned (four bins of equal number of unique values) birth weight (horizontal axis) and binned (four bins of equal number of unique values) birth length (vertical axis). The progression from blue to red (low to high methylation) as birth weight increases (left to right) is visible in each birth length bin (top to bottom). **(E)** Scatter plots of birth weight (horizontal axis) against % methylation at cg16672562 (vertical axis), each panel displays data for binned (four bins of equal number of unique values) birth length, ranges for each bin are displayed in panel headers.

<b>Figure 2.</b> — **Figure 2.**. **Association between percentage methylation at cg16672562 and birth weight, stratified by rs3826795 genotype.**
**(A)** Scatter plots of birth weight (horizontal axis) against % methylation at cg16672562 (vertical axis), each panel displays data for each genotype. **(B)** Box plot of rs3826795 genotype (horizontal axis) against % methylation at cg16672562 (vertical axis). Number of individual data points, median and mean % methylation of each bin is displayed in the table below the horizontal axis.

<b>Figure 3.</b> — **Figure 3.**. **Three of the simplest possible scenarios to explain the relationship between *HIF3A* methylation and adiposity.**

<b>Figure 4.</b> — **Figure 4.**. **Association between umbilical cord methylation at three sites in *HIF3A* and child weight measured at 0, 6, 12, 18 and 24 months.**
**(A)** Without adjusting for birth weight. **(B)** Adjusted for log-transformed birth weight. Regression coefficients (Est.) and 95% CI are reported as percentage change in child weight for 10% increase in methylation level. p-values are two-sided. Using linear regression models estimated using generalized estimating equations to account for repeated measures, we regressed log-transformed weight against methylation at each CpG site, adjusting for fixed effects of time, child sex, ethnicity, cell type proportions and interactions between ethnicity and proportions. Time was coded using a binary variable for each distinct time point and interaction terms of time with all variables (methylation, child sex, ethnicity, cell type proportions and interactions between ethnicity and proportions) were included.

See this image and copyright information in PMC

References

1. Holbrook JD. An epigenetic escape route. Trends Genet. 2015;31(1):2–4. - PubMed
2. • Review explaining the potential utility of epigenetic biomarkers for developmental trajectories.
1. Dick KJ, Nelson CP, Tsaprouni L, et al. DNA methylation and body-mass index: a genome-wide analysis. Lancet. 2014;383(9933):1990–1998. - PubMed
2. •• Original epigenome-wide association study that linked HIF3A methylation to adult BMI.
1. 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 Epub ahead of print. - PMC - PubMed
1. Hanson M, Godfrey KM, Lillycrop KA, Burdge GC, Gluckman PD. Developmental plasticity and developmental origins of non-communicable disease: theoretical considerations and epigenetic mechanisms. Prog. Biophys. Mol. Biol. 2011;106(1):272–280. - PubMed
1. Low FM, Gluckman PD, Hanson MA. Developmental plasticity and epigenetic mechanisms underpinning metabolic and cardiovascular diseases. Epigenomics. 2011;3(3):279–294. - PubMed

Publication types

Actions

MeSH terms

Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions
Actions

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

HIF3A association with adiposity: the story begins before birth

Collaborators

Affiliations

HIF3A association with adiposity: the story begins before birth

Authors

Collaborators

Affiliations

Abstract

Conflict of interest statement

Figures

References

Publication types

MeSH terms

Substances

Grants and funding

LinkOut - more resources

Full Text Sources

Other Literature Sources