Causal analysis identifies small HDL particles and physical activity as key determinants of longevity of older adults.
Aging
Causal analysis
High-density lipoprotein
Inflammation
Longevity
Markov boundary
Physical activity
Journal
EBioMedicine
ISSN: 2352-3964
Titre abrégé: EBioMedicine
Pays: Netherlands
ID NLM: 101647039
Informations de publication
Date de publication:
Nov 2022
Nov 2022
Historique:
received:
30
06
2022
revised:
15
08
2022
accepted:
13
09
2022
pubmed:
2
10
2022
medline:
16
11
2022
entrez:
1
10
2022
Statut:
ppublish
Résumé
The hard endpoint of death is one of the most significant outcomes in both clinical practice and research settings. Our goal was to discover direct causes of longevity from medically accessible data. Using a framework that combines local causal discovery algorithms with discovery of maximally predictive and compact feature sets (the "Markov boundaries" of the response) and equivalence classes, we examined 186 variables and their relationships with survival over 27 years in 1507 participants, aged ≥71 years, of the longitudinal, community-based D-EPESE study. As few as 8-15 variables predicted longevity at 2-, 5- and 10-years with predictive performance (area under receiver operator characteristic curve) of 0·76 (95% CIs 0·69, 0·83), 0·76 (0·72, 0·81) and 0·66 (0·61, 0·71), respectively. Numbers of small high-density lipoprotein particles, younger age, and fewer pack years of cigarette smoking were the strongest determinants of longevity at 2-, 5- and 10-years, respectively. Physical function was a prominent predictor of longevity at all time horizons. Age and cognitive function contributed to predictions at 5 and 10 years. Age was not among the local 2-year prediction variables (although significant in univariable analysis), thus establishing that age is not a direct cause of 2-year longevity in the context of measured factors in our data that determine longevity. The discoveries in this study proceed from causal data science analyses of deep clinical and molecular phenotyping data in a community-based cohort of older adults with known lifespan. NIH/NIA R01AG054840, R01AG12765, and P30-AG028716, NIH/NIA Contract N01-AG-12102 and NCRR 1UL1TR002494-01.
Sections du résumé
BACKGROUND
BACKGROUND
The hard endpoint of death is one of the most significant outcomes in both clinical practice and research settings. Our goal was to discover direct causes of longevity from medically accessible data.
METHODS
METHODS
Using a framework that combines local causal discovery algorithms with discovery of maximally predictive and compact feature sets (the "Markov boundaries" of the response) and equivalence classes, we examined 186 variables and their relationships with survival over 27 years in 1507 participants, aged ≥71 years, of the longitudinal, community-based D-EPESE study.
FINDINGS
RESULTS
As few as 8-15 variables predicted longevity at 2-, 5- and 10-years with predictive performance (area under receiver operator characteristic curve) of 0·76 (95% CIs 0·69, 0·83), 0·76 (0·72, 0·81) and 0·66 (0·61, 0·71), respectively. Numbers of small high-density lipoprotein particles, younger age, and fewer pack years of cigarette smoking were the strongest determinants of longevity at 2-, 5- and 10-years, respectively. Physical function was a prominent predictor of longevity at all time horizons. Age and cognitive function contributed to predictions at 5 and 10 years. Age was not among the local 2-year prediction variables (although significant in univariable analysis), thus establishing that age is not a direct cause of 2-year longevity in the context of measured factors in our data that determine longevity.
INTERPRETATION
CONCLUSIONS
The discoveries in this study proceed from causal data science analyses of deep clinical and molecular phenotyping data in a community-based cohort of older adults with known lifespan.
FUNDING
BACKGROUND
NIH/NIA R01AG054840, R01AG12765, and P30-AG028716, NIH/NIA Contract N01-AG-12102 and NCRR 1UL1TR002494-01.
Identifiants
pubmed: 36182774
pii: S2352-3964(22)00474-1
doi: 10.1016/j.ebiom.2022.104292
pmc: PMC9526168
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
104292Subventions
Organisme : NIA NIH HHS
ID : P30 AG028716
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG054840
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG070146
Pays : United States
Informations de copyright
Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of interests Drs. Connelly and Otvos are employees of and own stock in Labcorp, the commercial provider of the NMR LipoProfile blood test. Additional institutional NIH funding is declared for Dr. Zhang (RO1 AG070146) and Dr. Ma (RO1AG070146 and RO1 HL153497) and consulting fees to Dr. Ma related to this work from the Duke Claude D. Pepper Older Americans Independence Center NIH/NIA P30-AG028716 grant. The remaining authors declare no competing interests. The funding sources provided funding only and had no role in writing of the manuscript or the decision to submit it for publication. No author has been paid to produce this manuscript. The authors were not precluded from accessing data in the study, and they accept responsibility to submit for publication.