Johnny Assaf, Ishant Khurana, Ram Abou Zaki, Claudia H.T. Tam, Ilana Correa, Scott Maxwell, Julie Kinnberg, Malou Christiansen, Caroline Frørup, Heung Man Lee, Harikrishnan Kaipananickal, Jun Okabe, Safiya Naina Marikar, Kwun Kiu Wong, Cadmon K.P. Lim, Lai Yuk Yuen, Xilin Yang, Chi Chiu Wang, Juliana C.N. Chan, Kevin Y.L. Yip, William L. Lowe, Wing Hung Tam, Ronald C.W. Ma, Assam El-Osta
{"title":"DNA甲基化生物标志物预测妊娠期高血糖母亲的后代代谢风险","authors":"Johnny Assaf, Ishant Khurana, Ram Abou Zaki, Claudia H.T. Tam, Ilana Correa, Scott Maxwell, Julie Kinnberg, Malou Christiansen, Caroline Frørup, Heung Man Lee, Harikrishnan Kaipananickal, Jun Okabe, Safiya Naina Marikar, Kwun Kiu Wong, Cadmon K.P. Lim, Lai Yuk Yuen, Xilin Yang, Chi Chiu Wang, Juliana C.N. Chan, Kevin Y.L. Yip, William L. Lowe, Wing Hung Tam, Ronald C.W. Ma, Assam El-Osta","doi":"10.2337/db25-0105","DOIUrl":null,"url":null,"abstract":"Gestational diabetes mellitus affects almost 18 million pregnancies worldwide, increasing by >70% in the past 20 years. DNA methylation has been associated with maternal hyperglycemia and type 2 diabetes risk in offspring. This study hypothesized that hyperglycemia during pregnancy influences DNA methylation changes at birth that mediate metabolic risk in offspring. Cord blood samples (n = 112) were obtained from women with normal (n = 43), impaired (n = 31), and low (n = 38) glucose tolerance enrolled in the Hong Kong field center of the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study. Differentially methylated regions (DMRs) were identified using methylation sequencing and evaluated for their association with offspring metabolic dysfunction. Receiver operating characteristic curve analysis assessed the predictive value of DMRs for the classification of maternal glycemic status. These DMRs were assessed in human β-cells and pancreatic ductal epithelial cells in response to hyperglycemic stimuli. Methylation sequencing identified 19 methylation biomarkers in cord blood associated with maternal hyperglycemia, which correlated with offspring metabolic abnormalities. Incorporating the 19 DMRs improved the prediction of offspring β-cell dysfunction at 7, 11, and 18 years of age from area under the curve (AUC) scores ranging from 0.53 to 0.68 using clinical factors alone to AUC scores ranging from 0.71 to 0.95. Validation in human cell models confirmed that hyperglycemia influences methylation-dependent gene expression. This study demonstrates that DNA methylation biomarkers in cord blood predict offspring metabolic dysfunction, highlighting their potential as early indicators of diabetes risk. The findings align with methylation-mediated regulation in human pancreatic cells. ARTICLE HIGHLIGHTS Maternal hyperglycemia is linked to 19 cord blood DNA methylation biomarkers that predict offspring metabolic dysfunction. These methylation changes, associated with maternal glycemic status, improved the prediction of β-cell dysfunction at 7, 11, and 18 years of age compared with clinical factors alone. Validation in human β-cells and pancreatic ductal epithelial cells confirmed that hyperglycemia influences methylation-dependent gene expression. These findings highlight the role of epigenetic modifications at birth as early indicators of diabetes risk, suggesting that in utero hyperglycemic exposure may mediate long-term metabolic outcomes in offspring.","PeriodicalId":11376,"journal":{"name":"Diabetes","volume":"70 1","pages":"1695-1707"},"PeriodicalIF":7.5000,"publicationDate":"2025-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DNA Methylation Biomarkers Predict Offspring Metabolic Risk From Mothers With Hyperglycemia in Pregnancy\",\"authors\":\"Johnny Assaf, Ishant Khurana, Ram Abou Zaki, Claudia H.T. Tam, Ilana Correa, Scott Maxwell, Julie Kinnberg, Malou Christiansen, Caroline Frørup, Heung Man Lee, Harikrishnan Kaipananickal, Jun Okabe, Safiya Naina Marikar, Kwun Kiu Wong, Cadmon K.P. Lim, Lai Yuk Yuen, Xilin Yang, Chi Chiu Wang, Juliana C.N. Chan, Kevin Y.L. Yip, William L. Lowe, Wing Hung Tam, Ronald C.W. Ma, Assam El-Osta\",\"doi\":\"10.2337/db25-0105\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Gestational diabetes mellitus affects almost 18 million pregnancies worldwide, increasing by >70% in the past 20 years. DNA methylation has been associated with maternal hyperglycemia and type 2 diabetes risk in offspring. This study hypothesized that hyperglycemia during pregnancy influences DNA methylation changes at birth that mediate metabolic risk in offspring. Cord blood samples (n = 112) were obtained from women with normal (n = 43), impaired (n = 31), and low (n = 38) glucose tolerance enrolled in the Hong Kong field center of the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study. Differentially methylated regions (DMRs) were identified using methylation sequencing and evaluated for their association with offspring metabolic dysfunction. Receiver operating characteristic curve analysis assessed the predictive value of DMRs for the classification of maternal glycemic status. These DMRs were assessed in human β-cells and pancreatic ductal epithelial cells in response to hyperglycemic stimuli. Methylation sequencing identified 19 methylation biomarkers in cord blood associated with maternal hyperglycemia, which correlated with offspring metabolic abnormalities. Incorporating the 19 DMRs improved the prediction of offspring β-cell dysfunction at 7, 11, and 18 years of age from area under the curve (AUC) scores ranging from 0.53 to 0.68 using clinical factors alone to AUC scores ranging from 0.71 to 0.95. Validation in human cell models confirmed that hyperglycemia influences methylation-dependent gene expression. This study demonstrates that DNA methylation biomarkers in cord blood predict offspring metabolic dysfunction, highlighting their potential as early indicators of diabetes risk. The findings align with methylation-mediated regulation in human pancreatic cells. ARTICLE HIGHLIGHTS Maternal hyperglycemia is linked to 19 cord blood DNA methylation biomarkers that predict offspring metabolic dysfunction. These methylation changes, associated with maternal glycemic status, improved the prediction of β-cell dysfunction at 7, 11, and 18 years of age compared with clinical factors alone. Validation in human β-cells and pancreatic ductal epithelial cells confirmed that hyperglycemia influences methylation-dependent gene expression. 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DNA Methylation Biomarkers Predict Offspring Metabolic Risk From Mothers With Hyperglycemia in Pregnancy
Gestational diabetes mellitus affects almost 18 million pregnancies worldwide, increasing by >70% in the past 20 years. DNA methylation has been associated with maternal hyperglycemia and type 2 diabetes risk in offspring. This study hypothesized that hyperglycemia during pregnancy influences DNA methylation changes at birth that mediate metabolic risk in offspring. Cord blood samples (n = 112) were obtained from women with normal (n = 43), impaired (n = 31), and low (n = 38) glucose tolerance enrolled in the Hong Kong field center of the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study. Differentially methylated regions (DMRs) were identified using methylation sequencing and evaluated for their association with offspring metabolic dysfunction. Receiver operating characteristic curve analysis assessed the predictive value of DMRs for the classification of maternal glycemic status. These DMRs were assessed in human β-cells and pancreatic ductal epithelial cells in response to hyperglycemic stimuli. Methylation sequencing identified 19 methylation biomarkers in cord blood associated with maternal hyperglycemia, which correlated with offspring metabolic abnormalities. Incorporating the 19 DMRs improved the prediction of offspring β-cell dysfunction at 7, 11, and 18 years of age from area under the curve (AUC) scores ranging from 0.53 to 0.68 using clinical factors alone to AUC scores ranging from 0.71 to 0.95. Validation in human cell models confirmed that hyperglycemia influences methylation-dependent gene expression. This study demonstrates that DNA methylation biomarkers in cord blood predict offspring metabolic dysfunction, highlighting their potential as early indicators of diabetes risk. The findings align with methylation-mediated regulation in human pancreatic cells. ARTICLE HIGHLIGHTS Maternal hyperglycemia is linked to 19 cord blood DNA methylation biomarkers that predict offspring metabolic dysfunction. These methylation changes, associated with maternal glycemic status, improved the prediction of β-cell dysfunction at 7, 11, and 18 years of age compared with clinical factors alone. Validation in human β-cells and pancreatic ductal epithelial cells confirmed that hyperglycemia influences methylation-dependent gene expression. These findings highlight the role of epigenetic modifications at birth as early indicators of diabetes risk, suggesting that in utero hyperglycemic exposure may mediate long-term metabolic outcomes in offspring.
期刊介绍:
Diabetes is a scientific journal that publishes original research exploring the physiological and pathophysiological aspects of diabetes mellitus. We encourage submissions of manuscripts pertaining to laboratory, animal, or human research, covering a wide range of topics. Our primary focus is on investigative reports investigating various aspects such as the development and progression of diabetes, along with its associated complications. We also welcome studies delving into normal and pathological pancreatic islet function and intermediary metabolism, as well as exploring the mechanisms of drug and hormone action from a pharmacological perspective. Additionally, we encourage submissions that delve into the biochemical and molecular aspects of both normal and abnormal biological processes.
However, it is important to note that we do not publish studies relating to diabetes education or the application of accepted therapeutic and diagnostic approaches to patients with diabetes mellitus. Our aim is to provide a platform for research that contributes to advancing our understanding of the underlying mechanisms and processes of diabetes.