Abstract
Frailty indices (FIs) based on continuous valued health data, such as obtained from blood and urine tests, have been shown to be predictive of adverse health outcomes. However, creating FIs from such biomarker data requires a binarization treatment that is difficult to standardize across studies. In this work, we explore a “quantile” methodology for the generic treatment of biomarker data that allows us to construct an FI without preexisting medical knowledge (i.e. risk thresholds) of the included biomarkers. We show that our quantile approach performs as well as, or even slightly better than, established methods for the National Health and Nutrition Examination Survey and the Canadian Study of Health and Aging data sets. Furthermore, we show that our approach is robust to cohort effects within studies as compared to other data-based methods. The success of our binarization approaches provides insight into the robustness of the FI as a health measure, and the upper limits of the FI observed in various data sets, and also highlights general difficulties in obtaining absolute scales for comparing FIs between studies.
Similar content being viewed by others
References
Armstrong JJ, Mitnitski A, Launer LJ, White LR, Rockwood K (2015) Frailty in the Honolulu-Asia Aging Study: deficit accumulation in a male cohort followed to 90% mortality. J Gerontol A 70(1):125–131
Bennett S, Song X, Mitnitski A, Rockwood K (2013) A limit to frailty in very old, community-dwelling people: a secondary analysis of the Chinese longitudinal health and longevity study. Age Ageing 42(3):372–377
Blodgett JM, Theou O, Howlett SE, Wu FCW, Rockwood K (2016) A frailty index based on laboratory deficits in community-dwelling men predicted their risk of adverse health outcomes. Age Ageing 45(4):463–468
Blodgett JM, Theou O, Howlett SE, Rockwood K (2017) A frailty index from common clinical and laboratory tests predicts increased risk of death across the life course. GeroScience 39(4):447–455
Canadian Study of Health and Aging Working Group (1994) Canadian study of health and aging: study methods and prevalence of dementia. Can Med Assoc J 150(6):899
Centers for Disease Control and Prevention National Center for Health Statistics (2014) National health and nutrition examination survey data. http://www.cdc.gov/nchs/nhanes.htm
Clegg A, Bates C, Young J, Ryan R, Nichols L, Teale EA, Mohammed MA, Parry J, Marshall T (2016) Development and validation of an electronic frailty index using routine primary care electronic health record data. Age Ageing 45:353–360
Davidson-Pilon C, Kalderstam J, Zivich P, Kuhn B, Fiore-Gartland A, Abdeali JK, Moneda L, Gabriel WI, lson D, Parij A, Stark K, Anton S, Besson L, Jona Gadgil H, Golland D, Hussey S, Ravin K, Noorbakhsh J, Klintberg A, Albrecht D, Dhuynh Medvinsky, D, Zgonjanin D, Katz DS, Chen D, Ahern C, Fournier C, Arturo F, Rendeiro A (2019) Camdavidsonpilon/lifelines: v0.22.8. https://zenodo.org/record/805993
Drubbel I, de Wit NJ, Bleijenberg N, Eijkemans RJC, Schuurmans MJ, Numans ME (2013) Prediction of adverse health outcomes in older people using a frailty index based on routine primary care data. J Gerontol A 68(3):301–308
Evans SJ, Sayers M, Mitnitski A, Rockwood K (2014) The risk of adverse outcomes in hospitalized older patients in relation to a frailty index based on a comprehensive geriatric assessment. Age Ageing 43(1):127–132
Farrell SG, Mitnitski AB, Rockwood K, Rutenberg AD (2016) Network model of human aging: frailty limits and information measures. Phys Rev E 94(5):052409
Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, McBurnie MA (2001) Frailty in older adults: evidence for a phenotype. J Gerontol A 56(3):M146–M56
Gu D, Dupre ME, Sautter J, Zhu H, Liu Y, Yi Z (2009) Frailty and mortality among chinese at advanced ages. J Gerontol Soc Sci 64B(2):279–289
Harttgen K, Kowal P, Strulik H, Chatterji S, Vollmer S (2013) Patterns of frailty in older adults: comparing results from higher and lower income countries using the Survey of Health, Ageing and Retirement in Europe (SHARE) and the Study on Global AGEing and Adult Health (SAGE). PLoS ONE 8(10):e75847
Hatheway OL, Mitnitski A, Rockwood K (2017) Frailty affects the initial treatment response and time to recovery of mobility in acutely ill older adults admitted to hospital. Age Ageing 46:920–925
Howlett SE, Rockwood M, Mitnitski A, Rockwood K (2014) Standard laboratory tests to identify older adults at increased risk of death. BMC Med 12(1):171
Hubbard RE, Peel NM, Samanta M, Gray LC, Fries BE, Mitnitski A, Rockwood K (2015) Derivation of a frailty index from the interRAI acute care instrument. BMC Geriatr 15(1):27
Juster R-P, McEwen BS, Lupien SJ (2010) Allostatic load biomarkers of chronic stress and impact on health and cognition. Neurosci Biobehav Rev 35(1):2–16
Kane AE, Keller KM, Heinze-Milne S, Grandy SA, Howlett SE (2019) A murine frailty index based on clinical and laboratory measurements: links between frailty and pro-inflammatory cytokines differ in an sex-specific manner. J Gerontol A 74(3):275–282
Kojima G, Iliffe S, Walters K (2018) Frailty index as a predictor of mortality: a systematic review and meta-analysis. Age Ageing 47(2):193–200
Mantel N (1966) Evaluation of survival data and two new rank order statistics arising in its consideration. Cancer Chemother Rep 50(3):163–170
McPherson R (2017) Henry’s clinical diagnosis and management by laboratory methods. Elsevier, St. Louis
Mitnitski AB, Mogilner AJ, Rockwood K (2001) Accumulation of deficits as a proxy measure of aging. Sci World 1:323–36
Mitnitski A, Collerton J, Martin-Ruiz C, Jagger C, von Zglinicki T, Rockwood K, Kirkwood TBL (2015) Age-related frailty and its association with biological markers of ageing. BMC Med 13(1):161
Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, Mitnitski A (2005) A global clinical measure of fitness and frailty in elderly people. Can Med Assoc J 173(5):489–495
Rockwood K, Mitnitski A, Song X, Steen B, Skoog I (2006) Long-term risks of death and institutionalization of elderly people in relation to deficit accumulation at age 70. J Am Geriatr Soc 54(6):975–979
Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K (2008) A standard procedure for creating a frailty index. BMC Geriatr 8(1):24
Seplaki C, Goldman N, Glei D, Weinstein M (2005) A comparative analysis of measurement approaches for physiological dysregulation in an older population. Exp Gerontol 40(5):438–449
Song X, Mitnitski A, Rockwood K (2014) Age-related deficit accumulation and the risk of late-life dementia. Alzheimer’s Res Therapy 6(5–8):54
Velanovich V, Antoine H, Swartz A, Peters D, Rubinfeld I (2013) Accumulating deficits model of frailty and postoperative mortality and morbidity: its application to a national database. J Surg Res 183(1):104–110
Acknowledgements
We thank Olga Theou for stimulating discussions.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Stubbings, G., Farrell, S., Mitnitski, A. et al. Informative frailty indices from binarized biomarkers. Biogerontology 21, 345–355 (2020). https://doi.org/10.1007/s10522-020-09863-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10522-020-09863-1