A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study

Abstract

Background: A person's rate of aging has important implications for his/her risk of death and disease; thus, quantifying aging using observable characteristics has important applications for clinical, basic, and observational research. Based on routine clinical chemistry biomarkers, we previously developed a novel aging measure, Phenotypic Age, representing the expected age within the population that corresponds to a person's estimated mortality risk. The aim of this study was to assess its applicability for differentiating risk for a variety of health outcomes within diverse subpopulations that include healthy and unhealthy groups, distinct age groups, and persons with various race/ethnic, socioeconomic, and health behavior characteristics.

Methods and findings: Phenotypic Age was calculated based on a linear combination of chronological age and 9 multi-system clinical chemistry biomarkers in accordance with our previously established method. We also estimated Phenotypic Age Acceleration (PhenoAgeAccel), which represents Phenotypic Age after accounting for chronological age (i.e., whether a person appears older [positive value] or younger [negative value] than expected, physiologically). All analyses were conducted using NHANES IV (1999-2010, an independent sample from that originally used to develop the measure). Our analytic sample consisted of 11,432 adults aged 20-84 years and 185 oldest-old adults top-coded at age 85 years. We observed a total of 1,012 deaths, ascertained over 12.6 years of follow-up (based on National Death Index data through December 31, 2011). Proportional hazard models and receiver operating characteristic curves were used to evaluate all-cause and cause-specific mortality predictions. Overall, participants with more diseases had older Phenotypic Age. For instance, among young adults, those with 1 disease were 0.2 years older phenotypically than disease-free persons, and those with 2 or 3 diseases were about 0.6 years older phenotypically. After adjusting for chronological age and sex, Phenotypic Age was significantly associated with all-cause mortality and cause-specific mortality (with the exception of cerebrovascular disease mortality). Results for all-cause mortality were robust to stratifications by age, race/ethnicity, education, disease count, and health behaviors. Further, Phenotypic Age was associated with mortality among seemingly healthy participants-defined as those who reported being disease-free and who had normal BMI-as well as among oldest-old adults, even after adjustment for disease prevalence. The main limitation of this study was the lack of longitudinal data on Phenotypic Age and disease incidence.

Conclusions: In a nationally representative US adult population, Phenotypic Age was associated with mortality even after adjusting for chronological age. Overall, this association was robust across different stratifications, particularly by age, disease count, health behaviors, and cause of death. We also observed a strong association between Phenotypic Age and the disease count an individual had. These findings suggest that this new aging measure may serve as a useful tool to facilitate identification of at-risk individuals and evaluation of the efficacy of interventions, and may also facilitate investigation into potential biological mechanisms of aging. Nevertheless, further evaluation in other cohorts is needed.

Fig 1. The analytic plan for this study.

NHANES III and IV refer to the third and fourth National Health and Nutrition Examination Survey. *We adjusted for chronological age and sex in all models except those in the oldest-old adults. As mentioned in the Methods, we ran 2 parametric proportional hazard models (Gompertz distribution) in this age group, one unadjusted and another with adjustment for disease count, rather than chronological age (unknown).BMI, body mass index.

Fig 2. Frequency of disease counts overall and by age category.

The y-axis depicts the various age groups. The x-axis represents the relative proportions of persons in each disease count category (designated by colors).

Fig 3. Relationship between Phenotypic Age, chronological age, and PhenoAgeAccel.

(A) As expected, Phenotypic Age was highly correlated with chronological age, partially due to the fact that it includes chronological age. The red line depicts the expected Phenotypic Age for each chronological age, with points above the line depicting people who were phenotypically older than expected, and points below the line depicting those who were phenotypically younger than expected. (B) PhenoAgeAccel was fairly normally distributed, with a mean of 0 (blue line), a standard deviation of 1, and a median of −0.13.

Fig 4. Predicted increase in PhenoAgeAccel for each disease count by age category.

The y-axis depicts the increase in PhenoAgeAccel compared to persons who were disease-free. The x-axis shows groups categorized based on chronological age and the number of diseases each participant had. For all age categories, we observed that PhenoAgeAccel was positive among persons who were diagnosed with 1 or more chronic diseases.

Fig 5. Kaplan–Meier curves for persons in the highest 20% versus the lowest 20% of PhenoAgeAccel.

The y-axis indicates the survival rate, and the x-axis indicates follow-up time (in years).

Fig 6. Predicted median life expectancy at age 65 years by sex and the 5 quintiles for PhenoAgeAccel.

Q1–Q5 indicate the 5 quintiles of PhenoAgeAccel. Results are based on parametric survival models (Gompertz distribution) that include quintiles of PhenoAgeAccel, chronological age, and sex. Estimates represent the predicted age by which 50% of the population is expected to have died for each sex by quintile group, assuming a baseline age of 65 years.

Fig 7. Receiver operating characteristic curves for 10-year mortality.

AUC, area under the curve; BMI, body mass index; BP, blood pressure; SE, standard error.