Interplay of body mass index and metabolic syndrome: association with physiological age from midlife to late-life.
Biological age
Frailty index
Metabolic health
Metabolic syndrome
Obesity
Journal
GeroScience
ISSN: 2509-2723
Titre abrégé: Geroscience
Pays: Switzerland
ID NLM: 101686284
Informations de publication
Date de publication:
16 Dec 2023
16 Dec 2023
Historique:
received:
26
09
2023
accepted:
01
12
2023
medline:
16
12
2023
pubmed:
16
12
2023
entrez:
15
12
2023
Statut:
aheadofprint
Résumé
Obesity and metabolic syndrome (MetS) share common pathophysiological characteristics with aging. To better understand their interplay, we examined how body mass index (BMI) and MetS jointly associate with physiological age, and if the associations changed from midlife to late-life. We used longitudinal data from 1,825 Swedish twins. Physiological age was measured as frailty index (FI) and functional aging index (FAI) and modeled independently in linear mixed-effects models adjusted for chronological age, sex, education, and smoking. We assessed curvilinear associations of BMI and chronological age with physiological age, and interactions between BMI, MetS, and chronological age. We found a significant three-way interaction between BMI, MetS, and chronological age on FI (p-interaction = 0·006), not FAI. Consequently, we stratified FI analyses by age: < 65, 65-85, and ≥ 85 years, and modeled FAI across ages. Except for FI at ages ≥ 85, BMI had U-shaped associations with FI and FAI, where BMI around 26-28 kg/m
Identifiants
pubmed: 38102440
doi: 10.1007/s11357-023-01032-9
pii: 10.1007/s11357-023-01032-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : NIA NIH HHS
ID : R01 AG060470
Pays : United States
Informations de copyright
© 2023. The Author(s).
Références
GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396(10258):1223–49. https://doi.org/10.1016/S0140-6736(20)30752-2 .
doi: 10.1016/S0140-6736(20)30752-2
Swinburn BA, Kraak VI, Allender S, et al. The global syndemic of obesity, undernutrition, and climate change: the Lancet Commission report. Lancet. 2019;393(10173):791–846. https://doi.org/10.1016/S0140-6736(18)32822-8 .
doi: 10.1016/S0140-6736(18)32822-8
pubmed: 30700377
Alberti KG, Eckel RH, Grundy SM, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–5. https://doi.org/10.1161/CIRCULATIONAHA.109.192644 .
doi: 10.1161/CIRCULATIONAHA.109.192644
pubmed: 19805654
Powell-Wiley TM, Poirier P, Burke LE, et al. Obesity and cardiovascular disease: a scientific statement from the American Heart Association. Circulation. 2021;143(21):e984–1010. https://doi.org/10.1161/CIR.0000000000000973 .
doi: 10.1161/CIR.0000000000000973
pubmed: 33882682
pmcid: 8493650
ElSayed NA, Aleppo G, Aroda VR, et al. 8. Obesity and weight management for the prevention and treatment of type 2 diabetes: standards of care in diabetes-2023. Diabetes Care. 2023;46(Suppl 1):S128–39. https://doi.org/10.2337/dc23-S008 .
doi: 10.2337/dc23-S008
pubmed: 36507637
Expert Panel on Detection Evaluation and Treatment of High Blood Cholesterol in Adults. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). JAMA. 2001;285(19):2486–97. https://doi.org/10.1001/jama.285.19.2486 .
doi: 10.1001/jama.285.19.2486
Lopez-Otin C, Blasco MA, Partridge L, Serrano M, Kroemer G. Hallmarks of aging: An expanding universe. Cell. 2023;186(2):243–78. https://doi.org/10.1016/j.cell.2022.11.001 .
doi: 10.1016/j.cell.2022.11.001
pubmed: 36599349
Jylhava J, Pedersen NL, Hagg S. Biological age predictors. EBioMedicine. 2017;21:29–36. https://doi.org/10.1016/j.ebiom.2017.03.046 .
doi: 10.1016/j.ebiom.2017.03.046
pubmed: 28396265
pmcid: 5514388
Dent E, Martin FC, Bergman H, Woo J, Romero-Ortuno R, Walston JD. Management of frailty: opportunities, challenges, and future directions. Lancet. 2019;394(10206):1376–86. https://doi.org/10.1016/S0140-6736(19)31785-4 .
doi: 10.1016/S0140-6736(19)31785-4
pubmed: 31609229
Kim S, Myers L, Wyckoff J, Cherry KE, Jazwinski SM. The frailty index outperforms DNA methylation age and its derivatives as an indicator of biological age. Geroscience. 2017;39(1):83–92. https://doi.org/10.1007/s11357-017-9960-3 .
doi: 10.1007/s11357-017-9960-3
pubmed: 28299637
pmcid: 5352589
Finkel D, Sternang O, Jylhava J, Bai G, Pedersen NL. Functional aging index complements frailty in prediction of entry into care and mortality. J Gerontol A Biol Sci Med Sci. 2019;74(12):1980–6. https://doi.org/10.1093/gerona/glz155 .
doi: 10.1093/gerona/glz155
pubmed: 31222213
pmcid: 7357456
Li X, Ploner A, Wang Y, et al. Longitudinal trajectories, correlations and mortality associations of nine biological ages across 20-years follow-up. Elife. 2020;9. https://doi.org/10.7554/eLife.51507 .
Santos AL, Sinha S. Obesity and aging: Molecular mechanisms and therapeutic approaches. Ageing Res Rev. 2021;67:101268. https://doi.org/10.1016/j.arr.2021.101268 .
doi: 10.1016/j.arr.2021.101268
pubmed: 33556548
Tam BT, Morais JA, Santosa S. Obesity and ageing: Two sides of the same coin. Obes Rev. 2020;21(4):e12991. https://doi.org/10.1111/obr.12991 .
doi: 10.1111/obr.12991
pubmed: 32020741
Yuan L, Chang M, Wang J. Abdominal obesity, body mass index and the risk of frailty in community-dwelling older adults: a systematic review and meta-analysis. Age Ageing. 2021;50(4):1118–28. https://doi.org/10.1093/ageing/afab039 .
doi: 10.1093/ageing/afab039
pubmed: 33693472
Dao HHH, Burns MJ, Kha R, Chow CK, Nguyen TN. The Relationship between Metabolic Syndrome and Frailty in Older People: A Systematic Review and Meta-Analysis. Geriatrics. 2022;7(4). https://doi.org/10.3390/geriatrics7040076 . (Basel).
Jiang X, Xu X, Ding L, et al. The association between metabolic syndrome and presence of frailty: a systematic review and meta-analysis. Eur Geriatr Med. 2022;13(5):1047–56. https://doi.org/10.1007/s41999-022-00688-4 .
doi: 10.1007/s41999-022-00688-4
pubmed: 36036343
Kane AE, Gregson E, Theou O, Rockwood K, Howlett SE. The association between frailty, the metabolic syndrome, and mortality over the lifespan. Geroscience. 2017;39(2):221–9. https://doi.org/10.1007/s11357-017-9967-9 .
doi: 10.1007/s11357-017-9967-9
pubmed: 28281219
pmcid: 5411368
Jayanama K, Theou O, Godin J, Mayo A, Cahill L, Rockwood K. Relationship of body mass index with frailty and all-cause mortality among middle-aged and older adults. BMC Med. 2022;20(1):404. https://doi.org/10.1186/s12916-022-02596-7 .
doi: 10.1186/s12916-022-02596-7
pubmed: 36280863
pmcid: 9594976
Gold CH, Malmberg B, McClearn GE, Pedersen NL, Berg S. Gender and health: a study of older unlike-sex twins. J Gerontol: B. 2002;57(3):S168–76. https://doi.org/10.1093/geronb/57.3.S168 .
doi: 10.1093/geronb/57.3.S168
McClearn GE, Johansson B, Berg S, et al. Substantial genetic influence on cognitive abilities in twins 80 or more years old. Science. 1997;276(5318):1560–3. https://doi.org/10.1126/science.276.5318.1560 .
doi: 10.1126/science.276.5318.1560
pubmed: 9171059
Pedersen NL, McClearn GE, Plomin R, Nesselroade JR, Berg S, DeFaire U. The Swedish adoption twin study of aging: an update. Acta Genet Med Gemellol. 1991;40(1):7–20. https://doi.org/10.1017/s0001566000006681 . (Roma).
doi: 10.1017/s0001566000006681
pubmed: 1950353
Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008;8:24. https://doi.org/10.1186/1471-2318-8-24 .
doi: 10.1186/1471-2318-8-24
pubmed: 18826625
pmcid: 2573877
Bai G, Szwajda A, Wang Y, et al. Frailty trajectories in three longitudinal studies of aging: Is the level or the rate of change more predictive of mortality? Age Ageing. 2021;50(6):2174–82. https://doi.org/10.1093/ageing/afab106 .
doi: 10.1093/ageing/afab106
pubmed: 34120182
pmcid: 8581383
Jiang M, Zou Y, Xin Q, et al. Dose-response relationship between body mass index and risks of all-cause mortality and disability among the elderly: A systematic review and meta-analysis. Clin Nutr. 2019;38(4):1511–23. https://doi.org/10.1016/j.clnu.2018.07.021 .
doi: 10.1016/j.clnu.2018.07.021
pubmed: 30082166
Global BMI Mortality Collaboration, Di Angelantonio E, Bhupathiraju SN, et al. Body-mass index and all-cause mortality: individual-participant-data meta-analysis of 239 prospective studies in four continents. Lancet. 2016;388(10046):776–86. https://doi.org/10.1016/S0140-6736(16)30175-1 .
doi: 10.1016/S0140-6736(16)30175-1
pubmed: 27423262
Shakya S, Bajracharya R, Ledbetter L, Cary MP Jr. The association between cardiometabolic risk factors and frailty in older adults: a systematic review. Innov Aging. 2022;6(5):igac032. https://doi.org/10.1093/geroni/igac032 .
doi: 10.1093/geroni/igac032
pubmed: 35795135
pmcid: 9250659
Lavie CJ, De Schutter A, Milani RV. Healthy obese versus unhealthy lean: the obesity paradox. Nat Rev Endocrinol. 2015;11(1):55–62. https://doi.org/10.1038/nrendo.2014.165 .
doi: 10.1038/nrendo.2014.165
pubmed: 25265977
Perera LAM, Chopra A, Shaw AL. Approach to patients with unintentional weight loss. Med Clin North Am. 2021;105(1):175–86. https://doi.org/10.1016/j.mcna.2020.08.019 .
doi: 10.1016/j.mcna.2020.08.019
pubmed: 33246517
Sattar N, Preiss D. Reverse causality in cardiovascular epidemiological research: More common than imagined? Circulation. 2017;135(24):2369–72. https://doi.org/10.1161/CIRCULATIONAHA.117.028307 .
doi: 10.1161/CIRCULATIONAHA.117.028307
pubmed: 28606949