Weight management intervention identifies association of decreased DNA methylation age with improved functional age measures in older adults with obesity

Curtis L Petersen, Brock C Christensen, John A Batsis, Curtis L Petersen, Brock C Christensen, John A Batsis

Abstract

Background: Assessing functional ability is an important component of understanding healthy aging. Objective measures of functional ability include grip strength, gait speed, sit-to-stand time, and 6-min walk distance. Using samples from a weight loss clinical trial in older adults with obesity, we examined the association between changes in physical function and DNA-methylation-based biological age at baseline and 12 weeks in 16 individuals. Peripheral blood DNA methylation was measured (pre/post) with the Illumina HumanMethylationEPIC array and the Hannum, Horvath, and PhenoAge DNA methylation age clocks were used. Linear regression models adjusted for chronological age and sex tested the relationship between DNA methylation age and grip strength, gait speed, sit-to-stand, and 6-min walk.

Results: Participant mean weight loss was 4.6 kg, and DNA methylation age decreased 0.8, 1.1, and 0.5 years using the Hannum, Horvath, and PhenoAge DNA methylation clocks respectively. Mean grip strength increased 3.2 kg. Decreased Hannum methylation age was significantly associated with increased grip strength (β = -0.30, p = 0.04), and increased gait speed (β = 0.02, p = 0.05), in adjusted models. Similarly, decreased methylation age using the PhenoAge clock was associated with significantly increased gait speed (β = 0.02, p = 0.04). A decrease in Horvath DNA methylation age and increase in physical functional ability did not demonstrate a significant association.

Conclusions: The observed relationship between increased physical functional ability and decreased biological age using DNA methylation clocks demonstrate the potential utility of DNA methylation clocks to assess interventional approaches to improve health in older obese adults.

Trial registration: National Institute on Aging (NIA), NCT03104192. Posted April 7, 2017, https://ichgcp.net/clinical-trials-registry/NCT03104192.

Keywords: Aging; Anti-aging; DNA methylation; Functional ability; Healthy aging; Methylation age; Methylation clock.

Conflict of interest statement

The authors declare that they have no competing interests.

References

    1. Fakhouri TH, Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity among older adults in the United States, 2007–2010. NCHS Data Brief. 2012;106:1–8.
    1. Brown JD, Buscemi J, Milsom V, Malcolm R, O’Neil PM. Effects on cardiovascular risk factors of weight losses limited to 5–10% Transl Behav Med. 2016;6(3):339–346. doi: 10.1007/s13142-015-0353-9.
    1. Santanasto AJ, Glynn NW, Newman MA, Taylor CA, Brooks MM, Goodpaster BH, Newman AB. Impact of weight loss on physical function with changes in strength, muscle mass, and muscle fat infiltration in overweight to moderately obese older adults: a randomized clinical trial. J Obes. 2011 doi: 10.1155/2011/516576.
    1. Kritchevsky SB, Beavers KM, Miller ME, Shea MK, Houston DK, Kitzman DW, et al. Intentional weight loss and all-cause mortality: a meta-analysis of randomized clinical trials. PLoS ONE. 2015;10(3):e0121993. doi: 10.1371/journal.pone.0121993.
    1. Ashutosh K, Methrotra K, Fragale-Jackson J. Effects of sustained weight loss and exercise on aerobic fitness in obese women. J Sports Med Phys Fitness. 1997;37(4):252–257.
    1. Racette SB, Rochon J, Uhrich ML, Villareal DT, Das SK, Fontana L, et al. Effects of two years of calorie restriction on aerobic capacity and muscle strength. Med Sci Sports Exerc. 2017;49(11):2240. doi: 10.1249/MSS.0000000000001353.
    1. Batsis JA, Villareal DT. Sarcopenic obesity in older adults: aetiology, epidemiology and treatment strategies. Nat Rev Endocrinol. 2018;14(9):513–537. doi: 10.1038/s41574-018-0062-9.
    1. Bell CG, Lowe R, Adams PD, Baccarelli AA, Beck S, Bell JT, et al. DNA methylation aging clocks: challenges and recommendations. Genome Biol. 2019;20(1):1–24. doi: 10.1186/s13059-019-1824-y.
    1. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14(10):R115-R. doi: 10.1186/gb-2013-14-10-r115.
    1. Cavalli G, Heard E. Advances in epigenetics link genetics to the environment and disease. Nature. 2019;571(7766):489–499. doi: 10.1038/s41586-019-1411-0.
    1. Christensen BC, Houseman EA, Marsit CJ, Zheng S, Wrensch MR, Wiemels JL, et al. Aging and environmental exposures alter tissue-specific DNA methylation dependent upon CpG Island context. PLoS Genet. 2009;5(8):e1000602. doi: 10.1371/journal.pgen.1000602.
    1. Field AE, Robertson NA, Wang T, Havas A, Ideker T, Adams PD. DNA Methylation clocks in aging: categories, causes, and consequences. Mol Cell. 2018;71(6):882–895. doi: 10.1016/j.molcel.2018.08.008.
    1. Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018;19(6):371–384. doi: 10.1038/s41576-018-0004-3.
    1. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Mol Cell. 2013;49(2):359–367. doi: 10.1016/j.molcel.2012.10.016.
    1. Levine ME, Lu AT, Quach A, Chen BH, Assimes TL, Bandinelli S, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging (Albany NY) 2018;10(4):573–591. doi: 10.18632/aging.101414.
    1. Chen BH, Marioni RE, Colicino E, Peters MJ, Ward-Caviness CK, Tsai PC, et al. DNA methylation-based measures of biological age: meta-analysis predicting time to death. Aging. 2016;8(9):1844. doi: 10.18632/aging.101020.
    1. Lu AT, Quach A, Wilson JG, Reiner AP, Aviv A, Raj K, et al. DNA methylation grimage strongly predicts lifespan and healthspan. Aging. 2019;11(2):303. doi: 10.18632/aging.101684.
    1. Kresovich JK, Xu Z, O'Brien KM, Weinberg CR, Sandler DP, Taylor JA. Methylation-based biological age and breast cancer risk. J Natl Cancer Inst. 2019;111(10):1051–1058. doi: 10.1093/jnci/djz020.
    1. Simpkin AJ, Cooper R, Howe LD, Relton CL, Davey Smith G, Teschendorff A, Widschwendter M, Wong A, Kuh D, Hardy R. Are objective measures of physical capability related to accelerated epigenetic age? Findings from a British birth cohort. BMJ Open. 2017;7(10):e016708. doi: 10.1136/bmjopen-2017-016708.
    1. Sillanpää E, Laakkonen EK, Vaara E, Rantanen T, Kovanen V, Sipilä S, Kaprio J, Ollikainen M. Biological clocks and physical functioning in monozygotic female twins. BMC Geriatr. 2018;18(1):83. doi: 10.1186/s12877-018-0775-6.
    1. Marioni RE, Shah S, McRae AF, Ritchie SJ, Muniz-Terrera G, Harris SE, et al. The epigenetic clock is correlated with physical and cognitive fitness in the Lothian Birth Cohort 1936. Int J Epidemiol. 2015;44:1388–1396. doi: 10.1093/ije/dyu277.
    1. McCrory C, Fiorito G, McLoughlin S, Polidoro S, Cheallaigh C, Bourke N, et al. Epigenetic clocks and allostatic load reveal potential sex-specific drivers of biological aging. J GerontolSer A BiolSci Med Sci. 2020;75(3):495–503.
    1. Simpkin AJ, Howe LD, Tilling K, Gaunt TR, Lyttleton O, McArdle WL, et al. The epigenetic clock and physical development during childhood and adolescence: longitudinal analysis from a UK birth cohort. Int J Epidemiol. 2017;46(2):549–558.
    1. Jura M, Kozak L. Obesity and related consequences to ageing. Age (Dordr). 2016;38(1):23. doi: 10.1007/s11357-016-9884-3.
    1. Samblas M, Milagro FI, Martínez A. DNA methylation markers in obesity, metabolic syndrome, and weight loss. Epigenetics. 2019;14(5):421–444. doi: 10.1080/15592294.2019.1595297.
    1. Turner DC, Gorski PP, Maasar MF, Seaborne RA, Baumert P, Brown AD, et al. DNA methylation across the genome in aged human skeletal muscle tissue and muscle-derived cells: the role of HOX genes and physical activity. Sci Rep. 2020;10(1):1–19. doi: 10.1038/s41598-019-56847-4.
    1. Taylor D, Jackson A, Narisu N, Hemani G, Erdos M, Chines P, et al. Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle. ProcNatlAcadSci USA. 2019;116(22):10883–10888. doi: 10.1073/pnas.1814263116.
    1. Roshandel D, Chen Z, Canty AJ, Bull SB, Natarajan R, Paterson AD. DNA methylation age calculators reveal association with diabetic neuropathy in type 1 diabetes. ClinEpigenet. 2020;12(1):1–16.
    1. Batsis JA, Petersen CL, Clark MM, Cook SB, Lopez-Jimenez F, Al-Nimr RI, Pidgeon D, Kotz D, Mackenzie TA, Bartels SJ. A weight loss intervention augmented by a wearable device in rural older adults with obesity: a feasibility study. J Gerontol A Biol Sci Med Sci. 2021;76(1):95–100. doi: 10.1093/gerona/glaa115.
    1. Di Monaco M, Castiglioni C, De Toma E, Gardin L, Giordano S, Di Monaco R, et al. Handgrip strength but not appendicular lean mass is an independent predictor of functional outcome in hip-fracture women: a short-term prospective study. Arch Phys Med Rehabil. 2014;95(9):1719–1724. doi: 10.1016/j.apmr.2014.04.003.
    1. Lauretani F, Russo CR, Bandinelli S, Bartali B, Cavazzini C, Di Iorio A, Corsi AM, Rantanen T, Guralnik JM, Ferrucci L. Age-associated changes in skeletal muscles and their effect on mobility: an operational diagnosis of sarcopenia. J Appl Physiol. 2003;95(5):1851–1860. doi: 10.1152/japplphysiol.00246.2003.
    1. Lee M, Hsu C, Tsai Y, Chen C, Lin C, Wang C. Criterion-referenced values of grip strength and usual gait speed using instrumental activities of daily living disability as the criterion. J GeriatrPhys Therapy. 2018;41(1):14–19. doi: 10.1519/JPT.0000000000000106.
    1. AbellanvanKan G, Rolland Y, Andrieu S, Bauer J, Beauchet O, Bonnefoy M, et al. Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) Task Force. J Nutr Health Aging. 2009;13(10):881–889. doi: 10.1007/s12603-009-0246-z.
    1. Guralnik JM, Ferrucci L, Simonsick EM, Salive ME, Wallace RB. Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. N Engl J Med. 1995;332(9):556–561. doi: 10.1056/NEJM199503023320902.
    1. Bohannon R. Sit-to-stand test for measuring performance of lower extremity muscles. Percept Motor Skills. 1995;80(1):163–166. doi: 10.2466/pms.1995.80.1.163.
    1. McCarthy EK, Horvat MA, Holtsberg PA, Wisenbaker JM. Repeated chair stands as a measure of lower limb strength in Sexagenarian Women. J GerontolSer A. 2004;59(11):1207–1212. doi: 10.1093/gerona/59.11.1207.
    1. Bean JF, Kiely DK, Leveille SG, Herman S, Huynh C, Fielding R, et al. The 6-minute walk test in mobility-limited elderswhat is being measured? J GerontolSer A. 2020;57(11):M751–M756. doi: 10.1093/gerona/57.11.M751.
    1. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen KD, et al. Minfi: a flexible and comprehensive bioconductor package for the analysis of infinium DNA methylation microarrays. Bioinformatics. 2014;30(10):1363–1369. doi: 10.1093/bioinformatics/btu049.
    1. Triche TJ, Weisenberger DJ, Van Den Berg D, Laird PW, Siegmund KD. Low-level processing of illuminainfinium DNA methylation beadarrays. Nucleic Acids Res. 2013;41:e90. doi: 10.1093/nar/gkt090.
    1. Xu Z, Niu L, Li L, Taylor JA. ENmix: a novel background correction method for Illumina HumanMethylation450 BeadChip. Nucleic Acids Res. 2016;44(3):e20. doi: 10.1093/nar/gkv907.
    1. Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinform. 2012;13:86. doi: 10.1186/1471-2105-13-86.
    1. Salas LA, Koestler DC, Butler RA, Hansen HM, Wiencke JK, Kelsey KT, et al. An optimized library for reference-based deconvolution of whole-blood biospecimens assayed using the IlluminaHumanMethylationEPICBeadArray. Genome Biol. 2018;19(1):64. doi: 10.1186/s13059-018-1448-7.
    1. R Core Team . R: a language and environment for statistical computing. 360. Vienna: R Foundation for Statistical Computing; 2019.

Source: PubMed

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