Multidimensional Sleep and Mortality in Older Adults: A Machine-Learning Comparison With Other Risk Factors

Meredith L. Wallace, Daniel J. Buysse, Susan Redline, Katie L. Stone, Kristine Ensrud, Yue Leng, Sonia Ancoli-Israel, Martica H. Hall

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Background: Sleep characteristics related to duration, timing, continuity, and sleepiness are associated with mortality in older adults, but rarely considered in health recommendations. We applied machine learning to: (i) establish the predictive ability of a multidimensional self-reported sleep domain for all-cause and cardiovascular mortality in older adults relative to other established risk factors and (ii) to identify which sleep characteristics are most predictive. Methods: The analytic sample includes N = 8,668 older adults (54% female) aged 65-99 years with self-reported sleep characterization and longitudinal follow-up (≤15.5 years), aggregated from three epidemiological cohorts. We used variable importance (VIMP) metrics from a random survival forest to rank the predictive abilities of 47 measures and domains to which they belong. VIMPs > 0 indicate predictive variables/domains. Results: Multidimensional sleep was a significant predictor of all-cause (VIMP [99.9% confidence interval {CI}] = 0.94 [0.60, 1.29]) and cardiovascular (1.98 [1.31, 2.64]) mortality. For all-cause mortality, it ranked below that of the sociodemographic (3.94 [3.02, 4.87]), physical health (3.79 [3.01, 4.57]), and medication (1.33 [0.94, 1.73]) domains but above that of the health behaviors domain (0.22 [0.06, 0.38]). The domains were ranked similarly for cardiovascular mortality. The most predictive individual sleep characteristics across outcomes were time in bed, hours spent napping, and wake-up time. Conclusion: Multidimensional sleep is an important predictor of mortality that should be considered among other more routinely used predictors. Future research should develop tools for measuring multidimensional sleep - especially those incorporating time in bed, napping, and timing - and test mechanistic pathways through which these characteristics relate to mortality.

Original languageEnglish (US)
Pages (from-to)1903-1909
Number of pages7
JournalJournals of Gerontology - Series A Biological Sciences and Medical Sciences
Volume74
Issue number12
DOIs
StatePublished - Nov 13 2019

Bibliographical note

Funding Information:
K.E.  and M.L.W.  receive grant support from the NIH (and supporting agencies) as listed under Funding Sources. K.S. receives grant funding from Merck and NIH (and supporting agencies) as listed under Funding Sources. D.J.B. reports receiving consulting fees from American Academy of Physician Assistants, Bayer HealthCare, BeHealth Solutions, CME Institute, Emmi Solutions, and grants from NIH, outside the submitted work. In addition, D.J.B. receives licensing fees (royalties) for the Pittsburgh Sleep Quality Index (PSQI), which is copyrighted by the University of Pittsburgh. S.R.  reports grant support from JAZZ Pharma, ASMF, and NIH, outside the submitted work. In addition, S.R. reports consulting for Jazz Pharma, outside the submitted work. S.A.-I. is a consultant for Eisai, Purdue, Merck, Acadia, and Jazz Pharmaceuticals. M.H.H. and Y.L. report no conflicts of interest.

Funding Information:
The Osteoporotic Fractures in Men (MrOS) Study is supported by National Institutes of Health funding. The following institutes provide support: the National Institute on Aging (NIA), the National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), the National Center for Advancing Translational Sciences (NCATS), and NIH Roadmap for Medical Research under the following grant numbers: U01 AG027810, U01 AG042124, U01 AG042139, U01 AG042140, U01 AG042143, U01 AG042145, U01 AG042168, U01 AR066160, and UL1 TR000128. The National Heart, Lung, and Blood Institute (NHLBI) provides funding for the MrOS Sleep ancillary study “Outcomes of Sleep Disorders in Older Men” under the following grant numbers: R01 HL071194, R01 HL070848, R01 HL070847, R01 HL070842, R01 HL070841, R01 HL070837, R01 HL070838, and R01 HL070839. The Study of Osteoporotic Fractures (SOF) is supported by National Institutes of Health funding. The National Institute on Aging (NIA) provides support under the following grant numbers: R01 AG005407, R01 AR35582, R01 AR35583, R01 AR35584, R01 AG005394, R01 AG027574, R01 AG027576, and R01 AG026720. The Sleep Heart Health Study (SHHS) is supported by grants U01HL53916, U01HL53931, U01HL53934, U01HL53937, U01HL53938, U01HL53940, U01HL53941, and U01HL64360. This work was also supported by R01 AG056331 (Wallace), R35 HL135818 (Redline), R01 AG047139 (Buysse/Hall), and the National Sleep Research Resource (NSRR) funded by NHLBI grant HL114473.

Publisher Copyright:
© 2019 The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved.

Keywords

  • Elderly
  • Machine learning
  • Mortality
  • Random forest
  • Sleep health

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