Causal analysis identifies small HDL particles and physical activity as key determinants of longevity of older adults

Virginia Byers Kraus, Sisi Ma, Roshan Tourani, Gerda G. Fillenbaum, Bruce M. Burchett, Daniel C. Parker, William E. Kraus, Margery A. Connelly, James D. Otvos, Harvey Jay Cohen, Melissa C. Orenduff, Carl F. Pieper, Xin Zhang, Constantin F. Aliferis

Research output: Contribution to journalArticlepeer-review

Abstract

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: 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: 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: 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: NIH/NIA R01AG054840, R01AG12765, and P30-AG028716, NIH/NIA Contract N01-AG-12102 and NCRR 1UL1TR002494-01.

Original languageEnglish (US)
Article number104292
JournalEBioMedicine
Volume85
DOIs
StatePublished - Nov 2022

Bibliographical note

Funding Information:
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.

Funding Information:
This was work was supported by NIH/NIA R01AG054840 (to VBK, SM, RT, GGF, BMB, DCP, WEK, HJC, MCO, CFP, and CFA), R01AG12765 (to GGF, and CFP), the Duke Claude D. Pepper Older Americans Independence Center NIH/NIA P30-AG028716 (to VBK, GGF, WEK, HJC, and CFP), NIH/NIA Contract N01-AG12102 (to GGF), and NIH/NCATS UL1TR002494 (to SM, RT, and CFA).

Publisher Copyright:
© 2022 The Author(s)

Keywords

  • Aging
  • Causal analysis
  • High-density lipoprotein
  • Inflammation
  • Longevity
  • Markov boundary
  • Physical activity

PubMed: MeSH publication types

  • Journal Article

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