نبذة مختصرة : 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.
Morgan Levine and colleagues describe a phenotypic aging model which can predict health outcomes in individuals from different populations taking into account age, health, chronological age and behaviours.
Author summary Why was this study done? Aging is one of the leading risk factors for most chronic diseases; therefore, measuring aging has important applications for clinical, basic, and observational research. Persons of the same chronological age may vary in their rate of aging, suggesting that chronological age is an imperfect proxy of biological aging. Based on traditional clinical chemistry biomarkers, we recently developed a novel aging measure, Phenotypic Age, which can differentiate mortality risk among persons at the same chronological age. However, little is known about the applicability of this new aging measure for differentiating morbidity and mortality risk across diverse subpopulations such as healthy and unhealthy groups, distinct age groups, and persons with various race/ethnic, socioeconomic, and health behavior characteristics. What did the researchers do and find? We calculated Phenotypic Age for 11,432 adults aged 20–84 years and 185 oldest-old adults top-coded at age 85 years from NHANES IV and assessed its association with morbidity and mortality. We found that, overall, Phenotypic Age was highly predictive of mortality even after adjusting for chronological age. This mortality prediction was valid across different subpopulations, stratified by age, race/ethnicity, education, disease count, health behaviors, and cause of death. We also observed a strong association between Phenotypic Age and the disease count a person had, after adjusting for chronological age. What do these findings mean? Phenotypic Age can facilitate identification of at-risk individuals for a number of diverse conditions and causes of death. Further, it captures risk stratification in both the healthiest and the unhealthiest populations. In clinical research, it may serve as a useful tool for evaluating intervention efficacy, avoiding the need for decades of follow-up. Phenotypic Age may also be applicable to basic and observational research, shedding light on factors that alter the pace of aging, and facilitating investigation into potential biological mechanisms and environmental stressors.
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