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. 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. 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.

As the number of older people globally increases, health systems need to be reformed to meet the growing need for medical resources. A few previous studies reported varying health impacts of population ageing, but they focused only on limited countries and diseases. We comprehensively quantify the impact of population ageing on mortality for 195 countries/territories and 169 causes of death. Using data from the Global Burden of Disease Study 2017 (GBD 2017), this study derived the total number of deaths and population size for each year from 1990 to 2017. A decomposition method was used to attribute changes in total deaths to population growth, population ageing, and mortality change between 1990 and each subsequent year from 1991 through 2017, for 195 countries/territories and for countries grouped by World Bank economic development level. For countries with increases in deaths related to population ageing, we calculated the ratio of deaths attributed to mortality change to those attributed to population ageing. The proportion of people aged 65 years and older increased globally from 6.1% to 8.8%, and the number of global deaths increased by 9 million, between 1990 and 2017. Compared to 1990, 12 million additional global deaths in 2017 were associated with population ageing, corresponding to 27.9% of total global deaths. Population ageing was associated with increases in deaths in high-, upper-middle-, and lower-middle-income countries but not in low-income countries. The proportions of deaths attributed to population ageing in 195 countries/territories ranged from −43.9% to 117.4% for males and −30.1% to 153.5% for females. The 2 largest contributions of population ageing to disease-specific deaths globally between 1990 and 2017 were for ischemic heart disease (3.2 million) and stroke (2.2 million). Population ageing was related to increases in deaths in 152 countries for males and 159 countries for females, and decreases in deaths in 43 countries for males and 36 countries for females, between 1990 and 2017. The decreases in deaths attributed to mortality change from 1990 to 2017 were more than the increases in deaths related to population ageing for the whole world, as well as in 55.3% (84/152) of countries for males and 47.8% (76/159) of countries for females where population ageing was associated with increased death burden. As the GBD 2017 does not provide variances in the estimated death numbers, we were not able to quantify uncertainty in our attribution estimates. In this study, we found that population ageing was associated with substantial changes in numbers of deaths between 1990 and 2017, but the attributed proportion of deaths varied widely across country income levels, countries, and causes of death. Specific preventive and therapeutic techniques should be implemented in different countries and territories to address the growing health needs related to population ageing, especially targeting the diseases associated with the largest increase in number of deaths in the elderly.

Epigenetic clocks, developed using DNA methylation data, have been widely used to quantify biological aging in multiple tissues/cells. However, many existing epigenetic clocks are weakly correlated with each other, suggesting they may capture different biological processes. We utilize multi‐omics data from diverse human tissue/cells to identify shared features across eleven existing epigenetic clocks. Despite the striking lack of overlap in CpGs, multi‐omics analysis suggested five clocks (Horvath1, Horvath2, Levine, Hannum, and Lin) share transcriptional associations conserved across purified CD14+ monocytes and dorsolateral prefrontal cortex. The pathways enriched in the shared transcriptional association suggested links between epigenetic aging and metabolism, immunity, and autophagy. Results from in vitro experiments showed that two clocks (Levine and Lin) were accelerated in accordance with two hallmarks of aging—cellular senescence and mitochondrial dysfunction. Finally, using multi‐tissue data to deconstruct the epigenetic clock signals, we developed a meta‐clock that demonstrated improved prediction for mortality and robustly related to hallmarks of aging in vitro than single clocks.

Background: Previous studies indicated that obesity would accelerate frailty progression. However, obesity is heterogeneous by different metabolic status. The associations of metabolic heterogeneity of obesity with frailty progression remain unclear.Methods: A total of 6730 participants from the China Health and Retirement Longitudinal Study (CHARLS) and 4713 from the English Longitudinal Study of Ageing (ELSA) were included at baseline. Metabolic heterogeneity of obesity was evaluated based on four obesity and metabolic phenotypes as metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), metabolically healthy overweight/obesity (MHOO), and metabolically unhealthy overweight/obesity (MUOO). Frailty status was assessed by the frailty index (FI) ranging from 0 to 100 and frailty was defined as FI ≥ 25. Linear mixed‐effect models were used to analyse the associations of metabolic heterogeneity of obesity with frailty progression.Results: In the CHARLS, MUOO and MUNW presented the accelerated FI progression with additional annual increases of 0.284 (95% CI: 0.155 to 0.413, P < 0.001) and 0.169 (95% CI: 0.035 to 0.303, P = 0.013) as compared with MHNW. MHOO presented no accelerated FI progression (β: ‐0.011, 95% CI: −0.196 to 0.173, P = 0.904) as compared with MHNW. In the ELSA, the accelerated FI progression was marginally significant for MUOO (β: 0.103, 95% CI: −0.005 to 0.210, P = 0.061) and MUNW (β: 0.157, 95% CI: −0.011 to 0.324, P = 0.066), but not for MHOO (β: ‐0.047, 95% CI: −0.157 to 0.062, P = 0.396) in comparison with MHNW. The associations of MUOO and MUNW with the accelerated FI progression were stronger after excluding the baseline frail participants in both cohorts. The metabolic status changed over time. When compared with stable MHNW, participants who changed from MHNW to MUNW presented the accelerated FI progression with additional annual increases of 0.356 (95% CI: 0.113 to 0.599, P = 0.004) and 0.255 (95% CI: 0.033 to 0.477, P = 0.024) in the CHARLS and ELSA, respectively. The accelerated FI progression was also found in MHOO participants who transitioned to MUOO (CHARLS, β: 0.358, 95% CI: 0.053 to 0.663, P = 0.022; ELSA, β: 0.210, 95% CI: 0.049 to 0.370, P = 0.011).Conclusions: Metabolically unhealthy overweight/obesity and normal weight, but not metabolically healthy overweight/obesity, accelerated frailty progression as compared with metabolically healthy normal weight. Regardless of obesity status, transitions from healthy metabolic status to unhealthy metabolic status accelerated frailty progression as compared with stable metabolically healthy normal weight. Our findings highlight the important role of metabolic status in frailty progression and recommend the stratified management of obesity based on metabolic status.

Aging is a complex process of multisystem physiological dysregulation, and accelerated aging makes adults more vulnerable to chronic diseases and death.¹ Delaying aging has been a goal of human beings over a long history. Understanding the factors associated with aging may help us to work toward addressing racial and ethnic disparities in aging and creating lifespan equality. Meanwhile, how to estimate the outcomes of interventions to delay aging remains a challenge given the lack of comprehensive aging measures. Chronological aging serves as the major factor associated with risk of many chronic diseases and death. However, persons with the same chronological age may differ in their rates of aging. We have developed a novel phenotypic aging measure—phenotypic age acceleration—using 9 clinical biomarkers chosen for their ability to estimate mortality and morbidity.^2,3 This aging measure provides a useful indicator for evaluating the outcomes of geroprotective interventions, identification of risk factors, and elucidation of mechanisms of aging.⁴

This study aimed to: (i) develop 2 composite aging measures in the Chinese population using 2 recent advanced algorithms (the Klemera and Doubal method and Mahalanobis distance); and (ii) validate the 2 measures by examining their associations with mortality and disease counts. Based on data from the China Nutrition and Health Survey (CHNS) 2009 wave (N = 8119, aged 20–79 years, 53.5% women), a nationwide prospective cohort study of the Chinese population, we developed Klemera and Doubal method-biological age (KDM-BA) and physiological dysregulation (PD, derived from Mahalanobis distance) using 12 biomarkers. For the validation analysis, we used Cox proportional hazard regression models (for mortality) and linear, Poisson, and logistic regression models (for disease counts) to examine the associations. We replicated the validation analysis in the China Health and Retirement Longitudinal Study (CHARLS, N = 9304, aged 45–99 years, 53.4% women). Both aging measures were predictive of mortality after accounting for age and gender (KDM-BA, per 1-year, hazard ratio [HR] = 1.14, 95% confidence interval [CI] = 1.08, 1.19; PD, per 1-SD, HR = 1.50, 95% CI = 1.33, 1.69). With few exceptions, these mortality predictions were robust across stratifications by age, gender, education, and health behaviors. The 2 aging measures were associated with disease counts both cross-sectionally and longitudinally. These results were generally replicable in CHARLS although 4 biomarkers were not available. We successfully developed and validated 2 composite aging measures—KDM-BA and PD, which have great potentials for applications in early identifications and preventions of aging and aging-related diseases in China.