Associations between epigenetic aging and diabetes mellitus in a Swedish longitudinal study.

Autor: Wikström Shemer D; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77, Stockholm, Sweden.; Molecular Epidemiology, Department of Medical Sciences, and Science for Life Laboratory, Uppsala University, Uppsala, Sweden., Mostafaei S; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77, Stockholm, Sweden., Tang B; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77, Stockholm, Sweden., Pedersen NL; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77, Stockholm, Sweden., Karlsson IK; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77, Stockholm, Sweden., Fall T; Molecular Epidemiology, Department of Medical Sciences, and Science for Life Laboratory, Uppsala University, Uppsala, Sweden., Hägg S; Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 77, Stockholm, Sweden. sara.hagg@ki.se.
Jazyk: angličtina
Zdroj: GeroScience [Geroscience] 2024 Oct; Vol. 46 (5), pp. 5003-5014. Date of Electronic Publication: 2024 Jun 27.
DOI: 10.1007/s11357-024-01252-7
Abstrakt: Diabetes mellitus type 2 (T2D) is associated with accelerated biological aging and the increased risk of onset of other age-related diseases. Epigenetic changes in DNA methylation levels have been found to serve as reliable biomarkers for biological aging. This study explores the relationship between various epigenetic biomarkers of aging and diabetes risk using longitudinal data. Data from the Swedish Adoption/Twin Study of Aging (SATSA) was collected from 1984 to 2014 and included 536 individuals with at least one epigenetic measurement. The following epigenetic biomarkers of aging were employed: DNAm PAI-1, DNAmTL, DunedinPACE, PCHorvath1, PCHorvath2, PCHannum, PCPhenoAge, and PCGrimAge. Firstly, longitudinal analysis of biomarker trajectories was done. Secondly, linear correlations between the biomarkers and time to diabetes were studied within individuals developing diabetes. Thirdly, Cox proportional hazards (PH) models were used to assess the associations between these biomarkers and time of diabetes diagnosis, with adjustments for chronological age, sex, education, smoking, blood glucose, and BMI. The longitudinal trajectories of the biomarkers revealed differences between individuals with and without diabetes. Smoothened average curves for DunedinPACE and DNAm PAI-1 were higher for individuals with diabetes around the age 60-70, compared to controls. Likewise, DunedinPACE and DNAm PAI-1 were higher closer to diabetes onset. However, no significant associations were found between the epigenetic biomarkers of aging and risk of diabetes in Cox PH models. Our findings suggest the potential value of developing epigenetic biomarkers specifically tailored to T2D, should we wish to model and explore the potential for predicting the disease.
(© 2024. The Author(s).)
Databáze: MEDLINE
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