Developmental pathways to adiposity begin before birth and are influenced by genotype, prenatal environment and epigenome

Xinyi Lin, Ives Yubin Lim, Yonghui Wu, Ai Ling Teh, Li Chen, Izzuddin M Aris, Shu E Soh, Mya Thway Tint, Julia L MacIsaac, Alexander M Morin, Fabian Yap, Kok Hian Tan, Seang Mei Saw, Michael S Kobor, Michael J Meaney, Keith M Godfrey, Yap Seng Chong, Joanna D Holbrook, Yung Seng Lee, Peter D Gluckman, Neerja Karnani, GUSTO study group, Pratibha Agarwal, Arijit Biswas, Choon Looi Bong, Birit F P Broekman, Shirong Cai, Jerry Kok Yen Chan, Yiong Huak Chan, Cornelia Yin Ing Chee, Helen Chen, Yin Bun Cheung, Amutha Chinnadurai, Chai Kiat Chng, Mary Foong-Fong Chong, Yap-Seng Chong, Shang Chee Chong, Mei Chien Chua, Doris Fok, Marielle V Fortier, Peter D Gluckman, Keith M Godfrey, Anne Eng Neo Goh, Yam Thiam Daniel Goh, Joshua J Gooley, Wee Meng Han, Mark Hanson, Christiani Jeyakumar Henry, Joanna D Holbrook, Chin-Ying Hsu, Neerja Karnani, Jeevesh Kapur, Kenneth Kwek, Ivy Yee-Man Lau, Bee Wah Lee, Yung Seng Lee, Ngee Lek, Sok Bee Lim, Iliana Magiati, Lourdes Mary Daniel, Michael Meaney, Cheryl Ngo, Krishnamoorthy Niduvaje, Wei Wei Pang, Anqi Qiu, Boon Long Quah, Victor Samuel Rajadurai, Mary Rauff, Salome A Rebello, Jenny L Richmond, Anne Rifkin-Graboi, Seang-Mei Saw, Lynette Pei-Chi Shek, Allan Sheppard, Borys Shuter, Leher Singh, Shu-E Soh, Walter Stunkel, Lin Lin Su, Kok Hian Tan, Oon Hoe Teoh, Mya Thway Tint, Hugo P S van Bever, Rob M van Dam, Inez Bik Yun Wong, P C Wong, Fabian Yap, George Seow Heong Yeo, Xinyi Lin, Ives Yubin Lim, Yonghui Wu, Ai Ling Teh, Li Chen, Izzuddin M Aris, Shu E Soh, Mya Thway Tint, Julia L MacIsaac, Alexander M Morin, Fabian Yap, Kok Hian Tan, Seang Mei Saw, Michael S Kobor, Michael J Meaney, Keith M Godfrey, Yap Seng Chong, Joanna D Holbrook, Yung Seng Lee, Peter D Gluckman, Neerja Karnani, GUSTO study group, Pratibha Agarwal, Arijit Biswas, Choon Looi Bong, Birit F P Broekman, Shirong Cai, Jerry Kok Yen Chan, Yiong Huak Chan, Cornelia Yin Ing Chee, Helen Chen, Yin Bun Cheung, Amutha Chinnadurai, Chai Kiat Chng, Mary Foong-Fong Chong, Yap-Seng Chong, Shang Chee Chong, Mei Chien Chua, Doris Fok, Marielle V Fortier, Peter D Gluckman, Keith M Godfrey, Anne Eng Neo Goh, Yam Thiam Daniel Goh, Joshua J Gooley, Wee Meng Han, Mark Hanson, Christiani Jeyakumar Henry, Joanna D Holbrook, Chin-Ying Hsu, Neerja Karnani, Jeevesh Kapur, Kenneth Kwek, Ivy Yee-Man Lau, Bee Wah Lee, Yung Seng Lee, Ngee Lek, Sok Bee Lim, Iliana Magiati, Lourdes Mary Daniel, Michael Meaney, Cheryl Ngo, Krishnamoorthy Niduvaje, Wei Wei Pang, Anqi Qiu, Boon Long Quah, Victor Samuel Rajadurai, Mary Rauff, Salome A Rebello, Jenny L Richmond, Anne Rifkin-Graboi, Seang-Mei Saw, Lynette Pei-Chi Shek, Allan Sheppard, Borys Shuter, Leher Singh, Shu-E Soh, Walter Stunkel, Lin Lin Su, Kok Hian Tan, Oon Hoe Teoh, Mya Thway Tint, Hugo P S van Bever, Rob M van Dam, Inez Bik Yun Wong, P C Wong, Fabian Yap, George Seow Heong Yeo

Abstract

Background: Obesity is an escalating health problem worldwide, and hence the causes underlying its development are of primary importance to public health. There is growing evidence that suboptimal intrauterine environment can perturb the metabolic programing of the growing fetus, thereby increasing the risk of developing obesity in later life. However, the link between early exposures in the womb, genetic susceptibility, and perturbed epigenome on metabolic health is not well understood. In this study, we shed more light on this aspect by performing a comprehensive analysis on the effects of variation in prenatal environment, neonatal methylome, and genotype on birth weight and adiposity in early childhood.

Methods: In a prospective mother-offspring cohort (N = 987), we interrogated the effects of 30 variables that influence the prenatal environment, umbilical cord DNA methylation, and genotype on offspring weight and adiposity, over the period from birth to 48 months. This is an interim analysis on an ongoing cohort study.

Results: Eleven of 30 prenatal environments, including maternal adiposity, smoking, blood glucose and plasma unsaturated fatty acid levels, were associated with birth weight. Polygenic risk scores derived from genetic association studies on adult adiposity were also associated with birth weight and child adiposity, indicating an overlap between the genetic pathways influencing metabolic health in early and later life. Neonatal methylation markers from seven gene loci (ANK3, CDKN2B, CACNA1G, IGDCC4, P4HA3, ZNF423 and MIRLET7BHG) were significantly associated with birth weight, with a subset of these in genes previously implicated in metabolic pathways in humans and in animal models. Methylation levels at three of seven birth weight-linked loci showed significant association with prenatal environment, but none were affected by polygenic risk score. Six of these birth weight-linked loci continued to show a longitudinal association with offspring size and/or adiposity in early childhood.

Conclusions: This study provides further evidence that developmental pathways to adiposity begin before birth and are influenced by environmental, genetic and epigenetic factors. These pathways can have a lasting effect on offspring size, adiposity and future metabolic outcomes, and offer new opportunities for risk stratification and prevention of obesity.

Clinical trial registration: This birth cohort is a prospective observational study, designed to study the developmental origins of health and disease, and was retrospectively registered on 1 July 2010 under the identifier NCT01174875 .

Keywords: Birth weight; DNA methylation; Epigenome-wide association study; Offspring adiposity; Prenatal environment.

Figures

Fig. 1
Fig. 1
Prenatal environment influences on birth weight. a Scatterplots of birth weight (vertical axis) against significantly associated continuous prenatal environment variables (horizontal axis). b Boxplots of birth weight (vertical axis) against significantly associated binary prenatal environment variables (horizontal axis). c Univariate association between birth weight and each significantly associated prenatal environment variable, adjusted for infant sex, ethnicity and gestational age. Point estimates (height of bars) and 95% confidence intervals (top and bottom whiskers), show percentage change in birth weight for two standard deviations increase in continuous prenatal environment variable, or for comparing the two categories of binary prenatal environment variables. d Multivariate association between birth weight and significantly associated prenatal environment variables, adjusted for infant sex, ethnicity, gestational age and for each other. Point estimates (height of bars) and 95% confidence intervals (top and bottom whiskers), show percentage change in birth weight, for two standard deviations increase in a continuous prenatal environment variable, or for comparing the two categories of binary prenatal environment variables
Fig. 2
Fig. 2
Genetic influences on birth weight: Associations of child weight (a and b) and body mass index (c and d) at different time points with best-fit polygenic risk score (PRS). Best-fit PRS for Chinese, Malay and Indian ethnic groups used clumping P value thresholds pT = 0.5, 0.1 and 10–4, respectively. PRS was standardised to mean zero and unit variance within each ethnic group. Left panel (a and c) shows point estimates (height of bars) and 95% confidence intervals (top and bottom whiskers), for percentage change in child outcome, for a 2 SD increase in PRS, adjusted for child sex, gestational age and ethnicity. Analysis was done by linear regression of log-transformed child anthropometric outcome at each time point against PRS, adjusted for child sex, gestational age and ethnicity. Right panel (b and d) shows scatterplot of standardised (mean zero and unit variance) log-transformed child outcome (vertical axis) against PRS (horizontal axis)
Fig. 3
Fig. 3
Influence of prenatal environment on methylome at birth. a Associations of DNA methylation at birth with prenatal environment. Colour in heatmap represents regression coefficients for associations between methylation and each prenatal environment variable. Each row represents a CpG and each column represents a prenatal environment variable. With increasing magnitudes, colour changes from white to red (for negative coefficients) or from white to blue (for positive coefficients). Asterisks within each square represent P values for associations between methylation and each prenatal environment variable (P < 5 × 10–8 is represented with eight asterisks, 5 × 10–8 ≤ P < 5 × 10–7 is represented with seven asterisks, 5 × 10–3 ≤ P < 5 × 10–2 is represented with two asterisks, P ≥ 5 × 10–2 is represented with a blank square). Analysis was done by linear regression of methylation at each CpG site against each prenatal environment variable, adjusted for child sex, gestational age, ethnicity, cellular proportions and interactions between ethnicity and cellular proportions. Regression coefficients and P values are reported as an increase in percent methylation for a 2 SD increase in continuous prenatal environment variable, or for comparing the two categories of binary prenatal environment variables. b Flow chart summarises associations between birth weight, methylation and prenatal environment for three CpGs (three loci) influenced by the prenatal environment. A CpG was defined to be influenced by the prenatal environment if the most significant association between the CpG and prenatal environment attained a P value of < 5 × 10–4 the Bonferroni threshold to maintain a family-wise Type 1 error rate of 0.05 across approximately 100 tests (8 CpGs x 11 prenatal environment variables). Directions in arrows indicate temporal sequence, measurements obtained at the same time are indicated with two-headed arrows
Fig. 4
Fig. 4
Influence of methylome at birth on adiposity outcomes in early childhood: Associations of child weight (a) and body mass index (b) at different time points with DNA methylation at birth. Colour in heatmap represents regression coefficients for associations between child anthropometric outcome and methylation. Each row represents a CpG and each column represents a time point. With increasing magnitudes, colour changes from white to red (for negative coefficients) or from white to grey (for positive coefficients). Asterisks within each square represent P values for associations between child anthropometric outcome and methylation (P < 5 × 10–8 is represented with eight asterisks, 5 × 10–8 ≤ P < 5 × 10–7 is represented with seven asterisks, 5 × 10–3 ≤ P < 5 × 10–2 is represented with two asterisks, P ≥ 5 × 10–2 is represented with a blank square). Analysis was done by linear regression of log-transformed child anthropometric outcome at each time point against methylation at each CpG site, adjusted for child sex, gestational age, ethnicity, cellular proportions and interactions between ethnicity and cellular proportions. Regression coefficients and P values are reported as percentage change in child anthropometric outcome for 10% increase in percent methylation

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Source: PubMed

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