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DNA methylation age calculators reveal association with diabetic neuropathy in type 1 diabetes

BACKGROUND: Many CpGs become hyper or hypo-methylated with age. Multiple methods have been developed by Horvath et al. to estimate DNA methylation (DNAm) age including Pan-tissue, Skin & Blood, PhenoAge, and GrimAge. Pan-tissue and Skin & Blood try to estimate chronological age in the normal...

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Autores principales: Roshandel, Delnaz, Chen, Zhuo, Canty, Angelo J., Bull, Shelley B., Natarajan, Rama, Paterson, Andrew D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7132894/
https://www.ncbi.nlm.nih.gov/pubmed/32248841
http://dx.doi.org/10.1186/s13148-020-00840-6
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author Roshandel, Delnaz
Chen, Zhuo
Canty, Angelo J.
Bull, Shelley B.
Natarajan, Rama
Paterson, Andrew D.
author_facet Roshandel, Delnaz
Chen, Zhuo
Canty, Angelo J.
Bull, Shelley B.
Natarajan, Rama
Paterson, Andrew D.
author_sort Roshandel, Delnaz
collection PubMed
description BACKGROUND: Many CpGs become hyper or hypo-methylated with age. Multiple methods have been developed by Horvath et al. to estimate DNA methylation (DNAm) age including Pan-tissue, Skin & Blood, PhenoAge, and GrimAge. Pan-tissue and Skin & Blood try to estimate chronological age in the normal population whereas PhenoAge and GrimAge use surrogate markers associated with mortality to estimate biological age and its departure from chronological age. Here, we applied Horvath’s four methods to calculate and compare DNAm age in 499 subjects with type 1 diabetes (T1D) from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study using DNAm data measured by Illumina EPIC array in the whole blood. Association of the four DNAm ages with development of diabetic complications including cardiovascular diseases (CVD), nephropathy, retinopathy, and neuropathy, and their risk factors were investigated. RESULTS: Pan-tissue and GrimAge were higher whereas Skin & Blood and PhenoAge were lower than chronological age (p < 0.0001). DNAm age was not associated with the risk of CVD or retinopathy over 18–20 years after DNAm measurement. However, higher PhenoAge (β = 0.023, p = 0.007) and GrimAge (β = 0.029, p = 0.002) were associated with higher albumin excretion rate (AER), an indicator of diabetic renal disease, measured over time. GrimAge was also associated with development of both diabetic peripheral neuropathy (OR = 1.07, p = 9.24E−3) and cardiovascular autonomic neuropathy (OR = 1.06, p = 0.011). Both HbA1c (β = 0.38, p = 0.026) and T1D duration (β = 0.01, p = 0.043) were associated with higher PhenoAge. Employment (β = − 1.99, p = 0.045) and leisure time (β = − 0.81, p = 0.022) physical activity were associated with lower Pan-tissue and Skin & Blood, respectively. BMI (β = 0.09, p = 0.048) and current smoking (β = 7.13, p = 9.03E−50) were positively associated with Skin & Blood and GrimAge, respectively. Blood pressure, lipid levels, pulse rate, and alcohol consumption were not associated with DNAm age regardless of the method used. CONCLUSIONS: Various methods of measuring DNAm age are sub-optimal in detecting people at higher risk of developing diabetic complications although some work better than the others.
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spelling pubmed-71328942020-04-11 DNA methylation age calculators reveal association with diabetic neuropathy in type 1 diabetes Roshandel, Delnaz Chen, Zhuo Canty, Angelo J. Bull, Shelley B. Natarajan, Rama Paterson, Andrew D. Clin Epigenetics Research BACKGROUND: Many CpGs become hyper or hypo-methylated with age. Multiple methods have been developed by Horvath et al. to estimate DNA methylation (DNAm) age including Pan-tissue, Skin & Blood, PhenoAge, and GrimAge. Pan-tissue and Skin & Blood try to estimate chronological age in the normal population whereas PhenoAge and GrimAge use surrogate markers associated with mortality to estimate biological age and its departure from chronological age. Here, we applied Horvath’s four methods to calculate and compare DNAm age in 499 subjects with type 1 diabetes (T1D) from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study using DNAm data measured by Illumina EPIC array in the whole blood. Association of the four DNAm ages with development of diabetic complications including cardiovascular diseases (CVD), nephropathy, retinopathy, and neuropathy, and their risk factors were investigated. RESULTS: Pan-tissue and GrimAge were higher whereas Skin & Blood and PhenoAge were lower than chronological age (p < 0.0001). DNAm age was not associated with the risk of CVD or retinopathy over 18–20 years after DNAm measurement. However, higher PhenoAge (β = 0.023, p = 0.007) and GrimAge (β = 0.029, p = 0.002) were associated with higher albumin excretion rate (AER), an indicator of diabetic renal disease, measured over time. GrimAge was also associated with development of both diabetic peripheral neuropathy (OR = 1.07, p = 9.24E−3) and cardiovascular autonomic neuropathy (OR = 1.06, p = 0.011). Both HbA1c (β = 0.38, p = 0.026) and T1D duration (β = 0.01, p = 0.043) were associated with higher PhenoAge. Employment (β = − 1.99, p = 0.045) and leisure time (β = − 0.81, p = 0.022) physical activity were associated with lower Pan-tissue and Skin & Blood, respectively. BMI (β = 0.09, p = 0.048) and current smoking (β = 7.13, p = 9.03E−50) were positively associated with Skin & Blood and GrimAge, respectively. Blood pressure, lipid levels, pulse rate, and alcohol consumption were not associated with DNAm age regardless of the method used. CONCLUSIONS: Various methods of measuring DNAm age are sub-optimal in detecting people at higher risk of developing diabetic complications although some work better than the others. BioMed Central 2020-04-05 /pmc/articles/PMC7132894/ /pubmed/32248841 http://dx.doi.org/10.1186/s13148-020-00840-6 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Research
Roshandel, Delnaz
Chen, Zhuo
Canty, Angelo J.
Bull, Shelley B.
Natarajan, Rama
Paterson, Andrew D.
DNA methylation age calculators reveal association with diabetic neuropathy in type 1 diabetes
title DNA methylation age calculators reveal association with diabetic neuropathy in type 1 diabetes
title_full DNA methylation age calculators reveal association with diabetic neuropathy in type 1 diabetes
title_fullStr DNA methylation age calculators reveal association with diabetic neuropathy in type 1 diabetes
title_full_unstemmed DNA methylation age calculators reveal association with diabetic neuropathy in type 1 diabetes
title_short DNA methylation age calculators reveal association with diabetic neuropathy in type 1 diabetes
title_sort dna methylation age calculators reveal association with diabetic neuropathy in type 1 diabetes
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7132894/
https://www.ncbi.nlm.nih.gov/pubmed/32248841
http://dx.doi.org/10.1186/s13148-020-00840-6
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