Which sleep health characteristics predict all-Cause mortality in older men? An application of flexible multivariable approaches

Meredith L. Wallace, Katie Stone, Stephen F. Smagula, Martica H. Hall, Burcin Simsek, Deborah M. Kado, Susan Redline, Tien N. Vo, Daniel J. Buysse, Osteoporotic Fractures in Men (MrOS) Study Research Group

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

Study Objectives: Sleep is multidimensional, with domains including duration, timing, continuity, regularity, rhythmicity, quality, and sleepiness/alertness. Individual sleep characteristics representing these domains are known to predict health outcomes. However, most studies consider sleep characteristics in isolation, resulting in an incomplete understanding of which sleep characteristics are the strongest predictors of health outcomes. We applied three multivariable approaches to robustly determine which sleep characteristics increase mortality risk in the osteoporotic fractures in men sleep study. Methods: In total, 2,887 men (mean 76.3 years) completed relevant assessments and were followed for up to 11 years. One actigraphy or self-reported sleep characteristic was selected to represent each of seven sleep domains. Multivariable Cox models, survival trees, and random survival forests were applied to determine which sleep characteristics increase mortality risk. Results: Rhythmicity (actigraphy pseudo-F statistic) and continuity (actigraphy minutes awake after sleep onset) were the most robust sleep predictors across models. In a multivariable Cox model, lower rhythmicity (hazard ratio, HR [95%CI] =1.12 [1.04, 1.22]) and lower continuity (1.16 [1.08, 1.24]) were the strongest sleep predictors. In the random survival forest, rhythmicity and continuity were the most important individual sleep characteristics (ranked as the sixth and eighth most important among 43 possible sleep and non-sleep predictors); moreover, the predictive importance of all sleep information considered simultaneously followed only age, cognition, and cardiovascular disease. Conclusions: Research within a multidimensional sleep health framework can jumpstart future research on causal pathways linking sleep and health, new interventions that target specific sleep health profiles, and improved sleep screening for adverse health outcomes.

Original languageEnglish (US)
Article numberY
JournalSleep
Volume41
Issue number1
DOIs
StatePublished - Jan 1 2018

Bibliographical note

Funding Information:
SR reports receiving a research contract from Jazz Pharmaceuticals. DJB reports receiving consultation fees from Bayer HealthCare, BeHealth Solutions, Ebb Therapuetics/Cereve, Inc., CME Insitute, CME Outfitters, Emmi Solutions, Medscape, and Merck; and grants from NIH, outside the submitted work. In addition, DJB receives licensing fees (royalties) for the Pittsburgh Sleep Quality Index (PSQI), which is copyrighted by the University of Pittsburgh.

Funding Information:
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 Osteoporotic Fractures in Men (MrOS) Study is supported by National Institutes of Health funding. The following institutes provided their 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 National Institutes of Health 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. MLW has been supported by National Institutes of Health grants K01 MH096944 and R01 AG056331. SFS has been supported by National Institutes of Health grants T32 MH019986 and T32 HL082610.

Keywords

  • Circadian rhythm
  • Late-life
  • Men
  • Mortality
  • Multivariable analyses
  • Random survival forest
  • Sleep health
  • Survival tree

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