Anat Yaskolka Meir1, Maria Keller2,3, Stephan H Bernhart4,5,6, Ehud Rinott1, Gal Tsaban1, Hila Zelicha1, Alon Kaplan1, Dan Schwarzfuchs7, Ilan Shelef7, Yftach Gepner8, Jun Li9, Yifei Lin10, Matthias Blüher2, Uta Ceglarek11, Michael Stumvoll2,3,12, Peter F Stadler5,13,14,15,16,17,18, Meir J Stampfer9,10,19, Peter Kovacs3,12, Liming Liang20,21, Iris Shai22,23. 1. Department of Public Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, 84105, Beer-Sheva, Israel. 2. Helmholtz Institute for Metabolic, Obesity and Vascular Research (HI-MAG) of the Helmholtz Center Munich at the University of Leipzig and University Hospital Leipzig, Leipzig, 04103, Germany. 3. Medical Department III - Endocrinology, Nephrology, Rheumatology, University of Leipzig Medical Center, 04103, Leipzig, Germany. 4. Interdisciplinary Center for Bioinformatics, University of Leipzig, 04107, Leipzig, Germany. 5. Bioinformatics Group, Department of Computer Science, University of Leipzig, 04107, Leipzig, Germany. 6. Transcriptome Bioinformatics, LIFE Research Center for Civilization Diseases, University of Leipzig, 04107, Leipzig, Germany. 7. Soroka University Medical Center, Beer-Sheva, 84101, Israel. 8. Department of Epidemiology and Preventive Medicine, School of Public Health, Sackler Faculty of Medicine and Sylvan Adams Sports Institute, Tel Aviv University, Tel Aviv, 6997801, Israel. 9. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA. 10. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. 11. Institute for Laboratory Medicine, University of Leipzig Medical Center, Leipzig, 04103, Germany. 12. Deutsches Zentrum Für Diabetesforschung, Neuherberg, 85764, Germany. 13. Competence Center for Scalable Data Services and Solutions Dresden/Leipzig, German Centre for Integrative Biodiversity Research (iDiv), and Leipzig Research Center for Civilization Diseases, University of Leipzig, 04109, Leipzig, Germany. 14. Max Planck Institute for Mathematics in the Sciences, 04103, Leipzig, Germany. 15. Fraunhofer Institute for Cell Therapy and Immunology, 04103, Leipzig, Germany. 16. Department of Theoretical Chemistry, University of Vienna, 1090, Vienna, Austria. 17. Center for RNA in Technology and Health, University of Copenhagen, 1871, Frederiksberg, Denmark. 18. Santa Fe Institute, Santa Fe, NM, 87501, USA. 19. Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, 02115, MA, USA. 20. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA. lliang@hsph.harvard.edu. 21. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA. lliang@hsph.harvard.edu. 22. Department of Public Health, Faculty of Health Sciences, Ben-Gurion University of the Negev, 84105, Beer-Sheva, Israel. irish@bgu.ac.il. 23. Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, 02115, MA, USA. irish@bgu.ac.il.
Abstract
BACKGROUND: DNA methylation age (mAge), a methylation biomarker for the aging process, might serve as a more accurate predictor of morbidity and aging status than chronological age. We evaluated the role of multiple factors, including fat deposition, cardiometabolic risk factors and lifestyle weight-loss intervention, on the deviation of mAge from chronological age (mAge deviation) or 18-month change in mAge (∆mAge). In this sub-study of the CENTRAL magnetic resonance imaging weight-loss trial, we evaluated mAge by a validated 240-CpG-based prediction formula at baseline and after 18-month intervention of either low fat (LF) or mediterranean/low carbohydrate (MED/LC) diets. RESULTS: Among 120 CENTRAL participants with abdominal obesity or dyslipidemia, mAge (mean ± SD: 60.3 ± 7.5 years) was higher than the chronological age (48.6 ± 9.3 years) but strongly correlated (r = 0.93; p = 3.1 × 10-53). Participants in the lowest tertile of mAge deviation from their chronological age had significantly lower waist-circumference, visceral adipose tissue, intrahepatic fat (IHF) content, fasting-glucose and HOMA-IR, as compared with participants in the highest sex-specific residual tertile (p < 0.05 for all). IHF% remained associated with greater mAge deviation after further adjustments (β = 0.23; p = 0.02). After 18-month weight-loss lifestyle intervention, mAge remained significantly correlated with chronological age (r = 0.94, p = 1.5 × 10-55). mAging occurred, with no difference between lifestyle intervention groups (∆ = 0.9 ± 1.9 years in MED/LC vs. ∆ = 1.3 ± 1.9 years in LF; p = 0.2); however, we observed a mAging attenuation in successful weight losers (> 5% weight loss) vs. weight-loss failures ( ∆ = 0.6 years vs. ∆ = 1.1 years; p = 0.04), and in participants who completed the trial with healthy liver fat content (< 5% IHF) vs. participants with fatty liver (∆ = 0.6 years vs. ∆ = 1.8 years; p = 0.003). Overall, 18 months of weight-loss lifestyle intervention attenuated the mAging of the men, mainly the older, by 7.1 months than the expected (p < 0.05). CONCLUSIONS: Lifestyle weight-loss intervention may attenuate mAging. Deviation of mAge from chronological age might be related to body fat distribution and glycemic control and could indicate biological age, health status and the risk for premature cardiometabolic diseases. TRIAL REGISTRATION: ClinicalTrials.gov NCT01530724. Registered 10 February 2012, https://clinicaltrials.gov/ct2/show/study/NCT01530724 .
BACKGROUND: DNA methylation age (mAge), a methylation biomarker for the aging process, might serve as a more accurate predictor of morbidity and aging status than chronological age. We evaluated the role of multiple factors, including fat deposition, cardiometabolic risk factors and lifestyle weight-loss intervention, on the deviation of mAge from chronological age (mAge deviation) or 18-month change in mAge (∆mAge). In this sub-study of the CENTRAL magnetic resonance imaging weight-loss trial, we evaluated mAge by a validated 240-CpG-based prediction formula at baseline and after 18-month intervention of either low fat (LF) or mediterranean/low carbohydrate (MED/LC) diets. RESULTS: Among 120 CENTRAL participants with abdominal obesity or dyslipidemia, mAge (mean ± SD: 60.3 ± 7.5 years) was higher than the chronological age (48.6 ± 9.3 years) but strongly correlated (r = 0.93; p = 3.1 × 10-53). Participants in the lowest tertile of mAge deviation from their chronological age had significantly lower waist-circumference, visceral adipose tissue, intrahepatic fat (IHF) content, fasting-glucose and HOMA-IR, as compared with participants in the highest sex-specific residual tertile (p < 0.05 for all). IHF% remained associated with greater mAge deviation after further adjustments (β = 0.23; p = 0.02). After 18-month weight-loss lifestyle intervention, mAge remained significantly correlated with chronological age (r = 0.94, p = 1.5 × 10-55). mAging occurred, with no difference between lifestyle intervention groups (∆ = 0.9 ± 1.9 years in MED/LC vs. ∆ = 1.3 ± 1.9 years in LF; p = 0.2); however, we observed a mAging attenuation in successful weight losers (> 5% weight loss) vs. weight-loss failures ( ∆ = 0.6 years vs. ∆ = 1.1 years; p = 0.04), and in participants who completed the trial with healthy liver fat content (< 5% IHF) vs. participants with fatty liver (∆ = 0.6 years vs. ∆ = 1.8 years; p = 0.003). Overall, 18 months of weight-loss lifestyle intervention attenuated the mAging of the men, mainly the older, by 7.1 months than the expected (p < 0.05). CONCLUSIONS: Lifestyle weight-loss intervention may attenuate mAging. Deviation of mAge from chronological age might be related to body fat distribution and glycemic control and could indicate biological age, health status and the risk for premature cardiometabolic diseases. TRIAL REGISTRATION: ClinicalTrials.gov NCT01530724. Registered 10 February 2012, https://clinicaltrials.gov/ct2/show/study/NCT01530724 .
Entities:
Keywords:
Age prediction; Aging; DNA methylation; Intrahepatic fat; Weight loss
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