Evidence map›Paper›PMID 35869169›Full record

ArticleNPJ digital medicine2022

Age estimation from sleep studies using deep learning predicts life expectancy.

Andreas Brink-Kjaer, Eileen B Leary, Haoqi Sun, M Brandon Westover, Katie L Stone, Paul E Peppard, Nancy E Lane, Peggy M Cawthon, Susan Redline, Poul Jennum and 2 more

Abstract read
In one paragraph

Article in NPJ digital medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

23 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Observational
  10. [Research progress in electroencephalogram-based brain age prediction].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025
    Review
  11. Article
  12. Article
  13. Article
  14. Review
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  16. Strengths, weaknesses, opportunities, and threats of using AI-enabled technology in sleep medicine: a commentary.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024
    Review
  17. Review
  18. Review
  19. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Andreas Brink-KjaerDepartment of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark. andbri@dtu.dk.
Eileen B LearyStanford Center for Sleep Sciences and Medicine, Stanford University, Palo Alto, CA, USA.
Haoqi SunDepartment of Neurology, Massachusetts General Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0002-5041-8312
M Brandon WestoverDepartment of Neurology, Massachusetts General Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0003-4803-312X
Katie L StoneResearch Institute, California Pacific Medical Center, San Francisco, CA, USA.
Paul E PeppardDepartment of Population Health Sciences, University of Wisconsin-Madison, Madison, WI, USA.
Nancy E LaneDepartment of Medicine, University of Davis School of Medicine, Sacramento, CA, USA.
Peggy M CawthonResearch Institute, California Pacific Medical Center, San Francisco, CA, USA.
Susan RedlineDepartment of Medicine, Harvard Medical School, Boston, MA, USA.ORCID http://orcid.org/0000-0002-6585-1610
Poul Jennum *Danish Center for Sleep Medicine, Department of Clinical Neurophysiology, Rigshospitalet, Denmark.
Helge B D Sorensen *Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark.
Emmanuel Mignot *Stanford Center for Sleep Sciences and Medicine, Stanford University, Palo Alto, CA, USA. mignot@stanford.edu.ORCID http://orcid.org/0000-0002-6928-5310

Funding

University of Wisconsin Institute for Clinical and Translational ResearchUL1TR002373 · NCATS · UNIVERSITY OF WISCONSIN-MADISON · PI ELIZABETH S BURNSIDE, Allan R. Brasier · 2017 to 2026
$75.9M
INSTITUTIONAL CTSA (UW-MADISON): CLINICAL TRIALSUL1RR025011 · NCRR · UNIVERSITY OF WISCONSIN-MADISON · PI DREZNER, MARC KENNETH · 2007 to 2011
$29.5M
Oregon Clinical and Translational Research InstituteUL1TR000128 · NCATS · OREGON HEALTH & SCIENCE UNIVERSITY · PI ELLISON, DAVID H · 2012 to 2015
$28.7M
Zosuquidar trihydrochloride for acute myeloid leukemia &refractory anemiaM01RR000080 · NCRR · UNIVERSITY HOSPITALS OF CLEVELAND · PI DEARBORN, DORR GELLATLY · 1985 to 2007
$22.2M
EPIDEMIOLOGY OF SLEEP-DISORDERED BREATHING IN ADULTSR01HL062252 · NHLBI · UNIVERSITY OF WISCONSIN-MADISON · PI PEPPARD, PAUL E · 1999 to 2013
$14.1M
Outcomes of Sleep Disorders in Older MenR01HL071194 · NHLBI · UNIVERSITY OF CALIFORNIA SAN FRANCISCO · PI STONE, KATIE L · 2003 to 2013
$12.2M
Osteoporotic Fractures in Men (MrOS) - San Francisco Coordinating CenterU01AR066160 · NIAMS · CALIFORNIA PACIFIC MED CTR RES INSTITUTE · PI CUMMINGS, STEVEN RON · 2013 to 2017
$9.3M
National Sleep Research Resource (NSRR)R24HL114473 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI REDLINE, SUSAN S., ZHANG, GUO-QIANG · 2013 to 2017
$7.5M
Phenotypic and Molecular Signatures for Sleep Apnea and Related MorbiditiesR35HL135818 · NHLBI · BRIGHAM AND WOMEN'S HOSPITAL · PI REDLINE, SUSAN S. · 2017 to 2023
$7.2M
BIOMETRIC-GENETIC ANALYSIS OF CARDIOVASCULAR DISEASET32HL007567 · NHLBI · LOUISIANA STATE UNIV HSC NEW ORLEANS · PI ZHU, XIAOFENG · 1985 to 2021
$6.0M
Osteoporotic Fractures in Men (MrOS) - MinneapolisU01AG042145 · NIA · UNIVERSITY OF MINNESOTA · PI ENSRUD, KRISTINE · 2013 to 2019
$5.3M
Osteporotic Fractures in Men (MrOS)- Admin CenterU01AG027810 · NIA · OREGON HEALTH & SCIENCE UNIVERSITY · PI ORWOLL, ERIC S. · 2006 to 2017
$5.1M
NCATS NIH HHS UL1 TR000128NCATS NIH HHS UL1 TR002373NCRR NIH HHS M01 RR000080NCRR NIH HHS UL1 RR025011NHLBI NIH HHS R01 HL046380NHLBI NIH HHS R01 HL062252NHLBI NIH HHS R01 HL070837NHLBI NIH HHS R01 HL070838NHLBI NIH HHS R01 HL070839NHLBI NIH HHS R01 HL070841NHLBI NIH HHS R01 HL070842NHLBI NIH HHS R01 HL070847NHLBI NIH HHS R01 HL070848NHLBI NIH HHS R01 HL071194NHLBI NIH HHS R24 HL114473NHLBI NIH HHS R35 HL135818NHLBI NIH HHS T32 HL007567NHLBI NIH HHS U01 HL053916NHLBI NIH HHS U01 HL053931NHLBI NIH HHS U01 HL053934NHLBI NIH HHS U01 HL053937NHLBI NIH HHS U01 HL053938NHLBI NIH HHS U01 HL053941NHLBI NIH HHS U01 HL063463NHLBI NIH HHS U01 HL064360NIAMS NIH HHS U01 AR066160NIA NIH HHS R01 AG036838NIA NIH HHS R01 AG058680NIA NIH HHS U01 AG027810NIA NIH HHS U01 AG042124NIA NIH HHS U01 AG042139NIA NIH HHS U01 AG042140NIA NIH HHS U01 AG042143NIA NIH HHS U01 AG042145NIA NIH HHS U01 AG042168
6 · The paper itself

Abstract

Sleep disturbances increase with age and are predictors of mortality. Here, we present deep neural networks that estimate age and mortality risk through polysomnograms (PSGs). Aging was modeled using 2500 PSGs and tested in 10,699 PSGs from men and women in seven different cohorts aged between 20 and 90. Ages were estimated with a mean absolute error of 5.8 ± 1.6 years, while basic sleep scoring measures had an error of 14.9 ± 6.29 years. After controlling for demographics, sleep, and health covariates, each 10-year increment in age estimate error (AEE) was associated with increased all-cause mortality rate of 29% (95% confidence interval: 20-39%). An increase from -10 to +10 years in AEE translates to an estimated decreased life expectancy of 8.7 years (95% confidence interval: 6.1-11.4 years). Greater AEE was mostly reflected in increased sleep fragmentation, suggesting this is an important biomarker of future health independent of sleep apnea.

Identifiers

PMID35869169
PMCPMC9307657

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.