Longitudinal Analysis of Placental IRS1 DNA Methylation and Childhood Obesity
Accumulating evidence suggests that the predisposition to metabolic diseases is established in utero through epigenomic modifications. However, it remains unclear whether childhood obesity results from preexisting epigenomic alterations or whether obesity itself induces changes in the epigenome. Thi...
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| Published in | International journal of molecular sciences Vol. 26; no. 7; p. 3141 |
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| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
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28.03.2025
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| ISSN | 1422-0067 1661-6596 1422-0067 |
| DOI | 10.3390/ijms26073141 |
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| Abstract | Accumulating evidence suggests that the predisposition to metabolic diseases is established in utero through epigenomic modifications. However, it remains unclear whether childhood obesity results from preexisting epigenomic alterations or whether obesity itself induces changes in the epigenome. This study aimed to identify DNA methylation marks in the placenta associated with obesity-related outcomes in children at age 6 and to assess these marks in blood samples at age 6 and whether they correlate with obesity-related outcomes at that time. Using an epigenome-wide DNA methylation microarray on 24 placental samples, we identified differentially methylated CpGs (DMCs) associated with offspring BMI-SDS at 6 years. Individual DMCs were validated in 147 additional placental and leukocyte samples from children at 6 years of age. The methylation and/or gene expression of IRS1 in both placenta and offspring leukocytes were significantly associated with various metabolic risk parameters at age 6 (all p ≤ 0.05). Logistic regression models (LRM) and machine learning (ML) models indicated that IRS1 methylation in the placenta could strongly predict offspring obesity. Our results suggest that IRS1 may serve as a potential biomarker for the prediction of obesity and metabolic risk in children. |
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| AbstractList | Accumulating evidence suggests that the predisposition to metabolic diseases is established in utero through epigenomic modifications. However, it remains unclear whether childhood obesity results from preexisting epigenomic alterations or whether obesity itself induces changes in the epigenome. This study aimed to identify DNA methylation marks in the placenta associated with obesity-related outcomes in children at age 6 and to assess these marks in blood samples at age 6 and whether they correlate with obesity-related outcomes at that time. Using an epigenome-wide DNA methylation microarray on 24 placental samples, we identified differentially methylated CpGs (DMCs) associated with offspring BMI-SDS at 6 years. Individual DMCs were validated in 147 additional placental and leukocyte samples from children at 6 years of age. The methylation and/or gene expression of
IRS1
in both placenta and offspring leukocytes were significantly associated with various metabolic risk parameters at age 6 (all
p
≤ 0.05). Logistic regression models (LRM) and machine learning (ML) models indicated that
IRS1
methylation in the placenta could strongly predict offspring obesity. Our results suggest that
IRS1
may serve as a potential biomarker for the prediction of obesity and metabolic risk in children. Accumulating evidence suggests that the predisposition to metabolic diseases is established in utero through epigenomic modifications. However, it remains unclear whether childhood obesity results from preexisting epigenomic alterations or whether obesity itself induces changes in the epigenome. This study aimed to identify DNA methylation marks in the placenta associated with obesity-related outcomes in children at age 6 and to assess these marks in blood samples at age 6 and whether they correlate with obesity-related outcomes at that time. Using an epigenome-wide DNA methylation microarray on 24 placental samples, we identified differentially methylated CpGs (DMCs) associated with offspring BMI-SDS at 6 years. Individual DMCs were validated in 147 additional placental and leukocyte samples from children at 6 years of age. The methylation and/or gene expression of IRS1 in both placenta and offspring leukocytes were significantly associated with various metabolic risk parameters at age 6 (all p ≤ 0.05). Logistic regression models (LRM) and machine learning (ML) models indicated that IRS1 methylation in the placenta could strongly predict offspring obesity. Our results suggest that IRS1 may serve as a potential biomarker for the prediction of obesity and metabolic risk in children. Accumulating evidence suggests that the predisposition to metabolic diseases is established in utero through epigenomic modifications. However, it remains unclear whether childhood obesity results from preexisting epigenomic alterations or whether obesity itself induces changes in the epigenome. This study aimed to identify DNA methylation marks in the placenta associated with obesity-related outcomes in children at age 6 and to assess these marks in blood samples at age 6 and whether they correlate with obesity-related outcomes at that time. Using an epigenome-wide DNA methylation microarray on 24 placental samples, we identified differentially methylated CpGs (DMCs) associated with offspring BMI-SDS at 6 years. Individual DMCs were validated in 147 additional placental and leukocyte samples from children at 6 years of age. The methylation and/or gene expression of IRS1 in both placenta and offspring leukocytes were significantly associated with various metabolic risk parameters at age 6 (all p ≤ 0.05). Logistic regression models (LRM) and machine learning (ML) models indicated that IRS1 methylation in the placenta could strongly predict offspring obesity. Our results suggest that IRS1 may serve as a potential biomarker for the prediction of obesity and metabolic risk in children.Accumulating evidence suggests that the predisposition to metabolic diseases is established in utero through epigenomic modifications. However, it remains unclear whether childhood obesity results from preexisting epigenomic alterations or whether obesity itself induces changes in the epigenome. This study aimed to identify DNA methylation marks in the placenta associated with obesity-related outcomes in children at age 6 and to assess these marks in blood samples at age 6 and whether they correlate with obesity-related outcomes at that time. Using an epigenome-wide DNA methylation microarray on 24 placental samples, we identified differentially methylated CpGs (DMCs) associated with offspring BMI-SDS at 6 years. Individual DMCs were validated in 147 additional placental and leukocyte samples from children at 6 years of age. The methylation and/or gene expression of IRS1 in both placenta and offspring leukocytes were significantly associated with various metabolic risk parameters at age 6 (all p ≤ 0.05). Logistic regression models (LRM) and machine learning (ML) models indicated that IRS1 methylation in the placenta could strongly predict offspring obesity. Our results suggest that IRS1 may serve as a potential biomarker for the prediction of obesity and metabolic risk in children. Accumulating evidence suggests that the predisposition to metabolic diseases is established in utero through epigenomic modifications. However, it remains unclear whether childhood obesity results from preexisting epigenomic alterations or whether obesity itself induces changes in the epigenome. This study aimed to identify DNA methylation marks in the placenta associated with obesity-related outcomes in children at age 6 and to assess these marks in blood samples at age 6 and whether they correlate with obesity-related outcomes at that time. Using an epigenome-wide DNA methylation microarray on 24 placental samples, we identified differentially methylated CpGs (DMCs) associated with offspring BMI-SDS at 6 years. Individual DMCs were validated in 147 additional placental and leukocyte samples from children at 6 years of age. The methylation and/or gene expression of in both placenta and offspring leukocytes were significantly associated with various metabolic risk parameters at age 6 (all ≤ 0.05). Logistic regression models (LRM) and machine learning (ML) models indicated that methylation in the placenta could strongly predict offspring obesity. Our results suggest that may serve as a potential biomarker for the prediction of obesity and metabolic risk in children. |
| Audience | Academic |
| Author | Mas-Parés, Berta López-Bermejo, Abel Bonmatí-Santané, Alexandra Niubó-Pallàs, Maria Bassols, Judit Gómez-Vilarrubla, Ariadna de Zegher, Francis Ibáñez, Lourdes Martínez-Calcerrada, Jose-Maria Peñas-Cruz, Aaron López, Beatriz |
| AuthorAffiliation | 7 Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain 9 Department of Medical Sciences, University of Girona, 17003 Girona, Spain 1 Maternal-Fetal Metabolic Research Group, Girona Institute for Biomedical Research (IDIBGI), 17190 Salt, Spain; agomez@idibgi.org (A.G.-V.); mniubo@idibgi.org (M.N.-P.) 2 Pediatric Endocrinology Research Group, Girona Institute for Biomedical Research (IDIBGI), 17190 Salt, Spain 3 Department of Gynecology, Dr. Josep Trueta Hospital, 17007 Girona, Spain 8 Department of Pediatrics, Dr. Josep Trueta Hospital, 17007 Girona, Spain 5 Department of Development and Regeneration, University of Leuven, 3000 Leuven, Belgium 4 Control Engineering and Intelligent Systems (eXiT), University of Girona, 17003 Girona, Spain 6 Endocrinology, Pediatric Research Institute, Sant Joan de Déu Children’s Hospital, 08950 Esplugues de Llobregat, Spain |
| AuthorAffiliation_xml | – name: 5 Department of Development and Regeneration, University of Leuven, 3000 Leuven, Belgium – name: 6 Endocrinology, Pediatric Research Institute, Sant Joan de Déu Children’s Hospital, 08950 Esplugues de Llobregat, Spain – name: 2 Pediatric Endocrinology Research Group, Girona Institute for Biomedical Research (IDIBGI), 17190 Salt, Spain – name: 4 Control Engineering and Intelligent Systems (eXiT), University of Girona, 17003 Girona, Spain – name: 7 Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain – name: 8 Department of Pediatrics, Dr. Josep Trueta Hospital, 17007 Girona, Spain – name: 9 Department of Medical Sciences, University of Girona, 17003 Girona, Spain – name: 1 Maternal-Fetal Metabolic Research Group, Girona Institute for Biomedical Research (IDIBGI), 17190 Salt, Spain; agomez@idibgi.org (A.G.-V.); mniubo@idibgi.org (M.N.-P.) – name: 3 Department of Gynecology, Dr. Josep Trueta Hospital, 17007 Girona, Spain |
| Author_xml | – sequence: 1 givenname: Ariadna orcidid: 0000-0001-8183-5218 surname: Gómez-Vilarrubla fullname: Gómez-Vilarrubla, Ariadna – sequence: 2 givenname: Maria orcidid: 0009-0001-2331-0218 surname: Niubó-Pallàs fullname: Niubó-Pallàs, Maria – sequence: 3 givenname: Berta orcidid: 0000-0001-8283-3089 surname: Mas-Parés fullname: Mas-Parés, Berta – sequence: 4 givenname: Alexandra orcidid: 0000-0002-4354-7773 surname: Bonmatí-Santané fullname: Bonmatí-Santané, Alexandra – sequence: 5 givenname: Jose-Maria surname: Martínez-Calcerrada fullname: Martínez-Calcerrada, Jose-Maria – sequence: 6 givenname: Beatriz orcidid: 0000-0001-9210-0073 surname: López fullname: López, Beatriz – sequence: 7 givenname: Aaron surname: Peñas-Cruz fullname: Peñas-Cruz, Aaron – sequence: 8 givenname: Francis surname: de Zegher fullname: de Zegher, Francis – sequence: 9 givenname: Lourdes orcidid: 0000-0003-4595-7191 surname: Ibáñez fullname: Ibáñez, Lourdes – sequence: 10 givenname: Abel surname: López-Bermejo fullname: López-Bermejo, Abel – sequence: 11 givenname: Judit orcidid: 0000-0001-8821-6655 surname: Bassols fullname: Bassols, Judit |
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| Keywords | DNA methylation leukocytes placenta fetal programming metabolic risk childhood obesity |
| Language | English |
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| SubjectTerms | Analysis Carbohydrates Child Children CpG Islands DNA DNA Methylation DNA microarrays Epigenesis, Genetic Epigenetics Female Gene expression Genes Health aspects Humans Insulin Receptor Substrate Proteins - genetics Insulin Receptor Substrate Proteins - metabolism Insulin resistance Kinases Longitudinal Studies Machine learning Male Methylation Obesity Obesity in children Pediatric Obesity - genetics Pediatric Obesity - metabolism Placenta - metabolism Pregnancy Type 2 diabetes |
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| Title | Longitudinal Analysis of Placental IRS1 DNA Methylation and Childhood Obesity |
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