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Yet, many researchers continue to seek a single \u201cfrailty biomarker\u201d to facilitate clinical screening. We investigated the prognostic accuracy of 70 individual biomarkers in predicting mortality, comparing each with a composite FI. A total of 29,341 individuals from the comprehensive cohort of the Canadian Longitudinal Study on Aging were included (mean, 59.4\u2009\u00b1\u20099.9\u00a0years; 50.3% female). Twenty-three blood-based biomarkers and 47 test-based biomarkers (e.g., physical, cardiac, cardiology) were examined. Two composite FIs were derived: FI-Blood and FI-Examination. Mortality status was ascertained using provincial vital statistics linkages and contact with next of kin. Areas under the curve were calculated to compare prognostic accuracy across models (i.e., age, sex, biomarker, FI) in predicting mortality. Compared to an age-sex only model, the addition of individual biomarkers demonstrated improved model fit for 24\/70 biomarkers (11 blood, 13 test-based). Inclusion of FI-Blood or FI-Examination improved mortality prediction when compared to any of the 70 biomarker-age-sex models. Individual addition of seven biomarkers (walking speed, chair rise, time up and go, pulse, red blood cell distribution width, C-reactive protein, white blood cells) demonstrated an improved fit when added to the age-sex-FI model. FI scores had better mortality risk prediction than any biomarker. Although seven biomarkers demonstrated improved prognostic accuracy when considered alongside an FI score, all biomarkers had worse prognostic accuracy on their own. Rather than a single biomarker test, implementation of routine FI assessment in clinical settings may provide a more accurate and reliable screening tool to identify those at increased risk of adverse outcomes.<\/jats:p>","DOI":"10.1007\/s11357-023-01055-2","type":"journal-article","created":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T23:22:06Z","timestamp":1704496926000},"page":"3061-3069","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Prognostic accuracy of 70 individual frailty biomarkers in predicting mortality in the Canadian Longitudinal Study on Aging"],"prefix":"10.1007","volume":"46","author":[{"given":"Joanna M.","family":"Blodgett","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario Ulisses","family":"P\u00e9rez-Zepeda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Judith","family":"Godin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dustin Scott","family":"Kehler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Melissa K.","family":"Andrew","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan","family":"Kirkland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kenneth","family":"Rockwood","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olga","family":"Theou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,6]]},"reference":[{"issue":"6","key":"1055_CR1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1003138","volume":"17","author":"X Cheng","year":"2020","unstructured":"Cheng X, Yang Y, Schwebel DC, Liu Z, Li L, Cheng P, Ning P, Hu G. Population ageing and mortality during 1990\u20132017: a global decomposition analysis. PLoS Med. 2020;17(6): e1003138.","journal-title":"PLoS Med"},{"key":"1055_CR2","unstructured":"Canada Statistics. Population projections for Canada (2018 to 2068), provinces and territories (2018\u20132043). Statistics Canada. 2019. https:\/\/www150.statcan.gc.ca\/n1\/pub\/91-520-x\/91-520-x2019001-eng.htm. Accessed 20 Nov 2023."},{"issue":"9868","key":"1055_CR3","doi-asserted-by":"publisher","first-page":"752","DOI":"10.1016\/S0140-6736(12)62167-9","volume":"381","author":"A Clegg","year":"2013","unstructured":"Clegg A, et al. Frailty in elderly people. Lancet. 2013;381(9868):752\u201362.","journal-title":"Lancet"},{"issue":"5","key":"1055_CR4","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1503\/cmaj.050051","volume":"173","author":"K Rockwood","year":"2005","unstructured":"Rockwood K, et al. A global clinical measure of fitness and frailty in elderly people. 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MKA reports grant funding from GSK, Sanofi, Pfizer, and the Canadian Frailty Network, and honoraria from Sanofi, Pfizer, and Seqirus, for work unrelated to the present study. KR asserts copyright of the Clinical Frailty Scale through Dalhousie University\u2019s Industry, Liaison, and Innovation Office; is a co-founder of Ardea Outcomes (formerly DGI Clinical), which has had contracts with pharmaceutical and device manufacturers (Biogen, Shire, Hollister, Novartis, Nutricia, Roche, and Takeda) on individualized outcome measurement in the last 5\u00a0years; attended an advisory board meeting on dementia with Lundbeck in 2017; chaired a Scientific Workshop and Technical Review Panel on frailty for the Singapore National Research Foundation in 2020; received personal fees directly from event organizers for invited guest lectures, rounds, academic symposia, and presentations on frailty; and is an Associate Director of the Canadian Consortium on Neurodegeneration in Aging, which is funded by the Canadian Institutes for Health Research, the Alzheimer Society of Canada, and several other charities.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}