A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study.
Level 3 - non-randomized controlled study
Prospective cohort study analyzing mortality outcomes over follow-up
PubMed 30596641 · doi:10.1371/journal.pmed.1002718
What was done
The authors evaluated Phenotypic Age and Phenotypic Age Acceleration (PhenoAgeAccel)—calculated from chronological age and 9 routine clinical chemistry biomarkers—in an independent cohort of NHANES IV participants (1999–2010). The analytic sample comprised 11,432 adults aged 20–84 years and 185 oldest-old adults aged 85 years and older. Proportional hazard models and receiver operating characteristic curves were used to analyze 1,012 deaths ascertained over up to 12.6 years of follow-up via the National Death Index across diverse demographic, socioeconomic, behavioral, and health strata.
What was found
Participants with more diseases exhibited older Phenotypic Age; among young adults, those with 1 disease were 0.2 years older phenotypically, and those with 2 or 3 diseases were approximately 0.6 years older than disease-free individuals. After adjusting for chronological age and sex, Phenotypic Age significantly predicted all-cause mortality and cause-specific mortality, with the exception of cerebrovascular disease mortality. Associations with all-cause mortality remained robust across subgroups stratified by age, race/ethnicity, education, disease count, and health behaviors, and persisted among disease-free participants with normal BMI as well as the oldest-old.
Why it matters
This study shows that a biological age measure constructed from standard, accessible laboratory tests reliably stratifies mortality and morbidity risk beyond chronological age across diverse population groups.
Limits
The study lacked longitudinal repeat measures of Phenotypic Age and longitudinal data on incident non-fatal disease. Biomarker performance was not significantly predictive for cerebrovascular mortality, and the cohort was limited to US adults.
Cited by
- supports Morgan Levine at Yale has identified a set of clinical biomarkers derived from large population datasets that is predictive of biological age.