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BACKGROUND: Frailty status has been sparsely studied in some groups including Native Hawaiians and Asian Americans. METHODS: We developed a questionnaire-based deficit accumulation frailty index (FI) in the Multiethnic Cohort (MEC) and examined frailty status (robust, FI 0 to

Racial/ethnic minorities have experienced higher COVID-19 infection rates than whites, but it is unclear how individual-level housing, occupational, behavioral, and socioeconomic conditions contribute to these disparities in a nationally representative sample. In this study, we assess the extent to which social determinants of health contribute to racial/ethnic differences in COVID-19 infection. Data are from the Understanding America Study's Understanding Coronavirus in America survey (UAS COVID-19 waves 7-29). UAS COVID-19 is one of the only nationally representative longitudinal data sources that collects information on household, work, and social behavioral context during the pandemic. We analyze onset of COVID-19 cases, defined as a positive test or a diagnosis of COVID-19 from a healthcare provider since the previous survey wave, over a year of follow-up (June 2020-July 2021). We consider educational attainment, economic resources, work arrangements, household size, and social distancing as key social factors that may be structured by racism. Cox hazard models indicate that Hispanic people have 48% higher risk of experiencing a COVID-19 infection than whites after adjustment for age, sex, local infection rate, and comorbidities, but we do not observe a higher risk of COVID-19 among Black respondents. Controlling for engagement in any large or small social gathering increases the hazard ratio for Hispanics by 9%, suggesting that had Hispanics had the same social engagement patterns as whites, they may have had even higher risk of COVID-19. Other social determinants-lower educational attainment, working away from home, and number of coresidents-all independently predict higher risk of COVID-19, but do not explain why Hispanic Americans have higher COVID-19 infection risk than whites.

Epigenetic clocks are widely used aging biomarkers calculated from DNA methylation data, but this data can be surprisingly unreliable. Here we show technical noise produces deviations up to 9 years between replicates for six prominent epigenetic clocks, limiting their utility. We present a computational solution to bolster reliability, calculating principal components from CpG-level data as input for biological age prediction. Our retrained principal-component versions of six clocks show agreement between most replicates within 1.5 years, improved detection of clock associations and intervention effects, and reliable longitudinal trajectories in vivo and in vitro. This method entails only one additional step compared to traditional clocks, requires no replicates or prior knowledge of CpG reliabilities for training, and can be applied to any existing or future epigenetic biomarker. The high reliability of principal component-based clocks is critical for applications to personalized medicine, longitudinal tracking, in vitro studies, and clinical trials of aging interventions.

BackgroundThere are large differences in adult mortality across schooling groups in many high-income countries (HICs). An important open question is whether there are similar gradients in adult mortality in middle-income countries (MICs), where schooling and healthcare quality tends to be lower and health-related behaviors are often not strongly patterned by schooling.MethodsWe present one of the first international-comparative studies on schooling differences in adult mortality across MICs using harmonizedlongitudinal data on adults ages 50+ from China, Costa Rica, Indonesia, Mexico, South Africa, and South Korea. We use Cox proportional hazards models to estimate differences in the hazard of mortality across schooling groups overall and separately by sex and broad age groups. We also estimate schooling gradients in smoking and body mass index to determine whether risk factor gradients potentially explain mortality patterns.ResultsOnly adults with tertiary schooling have a consistent adult mortality advantage compared to those with no schooling. We do not find evidence that individuals with primary schooling have a lower hazard of mortality compared to individuals with no schooling in five of the six countries. The mortality advantage for individuals with secondary schooling is mixed, with evidence of lower mortality relative to those with no schooling in Mexico, South Africa, and South Korea. Gradients in BMI and smoking are inconsistent across countries and unlikely to explain mortality differences.ConclusionsWe find that adult mortality and risk factor gradients in MICs can be much different than the established patterns seen in modern HICs. Our results highlight that adult mortality gradients are not an inevitability and are not found in all populations. Understanding what factors give rise to inequalities in adult mortality and what can be done to minimize gradients while still ensuring continued mortality improvements in MICs is a crucial focus for research and policy.

BackgroundA 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 findingsPhenotypic 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.ConclusionsIn 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.

Identifying genes regulating the pace of epigenetic ageing represents a new frontier in genome-wide association studies (GWASs). Here using 1,796 brain samples from 1,163 individuals, we carry out a GWAS of two DNA methylation-based biomarkers of brain age: the epigenetic ageing rate and estimated proportion of neurons. Locus 17q11.2 is significantly associated (P=4.5 × 10-9) with the ageing rate across five brain regions and harbours a cis-expression quantitative trait locus for EFCAB5 (P=3.4 × 10-20). Locus 1p36.12 is significantly associated (P=2.2 × 10-8) with epigenetic ageing of the prefrontal cortex, independent of the proportion of neurons. Our GWAS of the proportion of neurons identified two genome-wide significant loci (10q26 and 12p13.31) and resulted in a gene set that overlaps significantly with sets found by GWAS of age-related macular degeneration (P=1.4 × 10-12), ulcerative colitis (P

Background Cardiometabolic risk (CMR) is associated with cognitive health, but the association can be affected by broader social, economic, and medical contexts. The United States and China have very different developmental and epidemiological histories, and thus CMR among older people could be linked to cognitive function differently in the 2 countries. Methods Cross-sectional and longitudinal ordinary least squares regression models were estimated for each country using nationally representative samples of populations over age 50: 7 430/4 474 Americans and 6 108/3 655 Chinese in the cross-sectional/longitudinal samples. Results In the United States, higher CMR is associated with worse cognitive function (b  = −0.08, p  < .016). Longitudinally, CMR increase is associated with worse cognitive function at a marginally significant level (b  = −0.10, p  = .055). No relationship between CMR level or change and cognitive function is observed in China. Higher education levels are linked to better cognitive function and slower cognitive decline in both countries. Unlike older Americans, relative to those with very low education levels, among older Chinese with the highest education level, a higher CMR links to better cognitive function (b  = 0.63, p  = .013) and slower cognitive decline (b  = 0.35, p  = .062); Nevertheless, a rapid increase in CMR is additionally harmful (b  = −0.54, p  = .050) for cognitive function and may lead to faster cognitive decline (b  = −0.35, p  = .079). Conclusions The significant relationship between CMR and cognitive function in the United States suggests the importance of monitoring and controlling CMR factors at older ages. The insignificant relationship in China may be explained by the high CMR among those with high education levels, highlighting the need for improving cardiometabolic health through education and promoting healthy lifestyles. [ABSTRACT FROM AUTHOR]

Introduction: A growing number of international population surveys have included measurement of biomarkers, but differ in the type of specimens collected, sample processing procedures, shipment protocols, and laboratory assay platforms. The purpose of this study is to harmonize biomarker data from nine nationally representative studies of people 50 years of age and over by adjusting for assay platforms and type of specimens for total cholesterol (total‐C), high‐density lipoprotein cholesterol (HDL‐C), glycosylated hemoglobin (HbA1c), and C‐reactive protein (CRP). Methods: Sets of 24 identical serum, plasma, whole blood, and dried blood spot harmonization samples with known analyte levels were generated at a reference laboratory, shipped at −80°C to the respective study laboratories, and subsequently assayed following the study laboratory's protocol. Both original and harmonized study data were used to calculate mean values and at‐risk prevalence. Results: The correlation coefficients between the biomarker values of the harmonization samples obtained by the study laboratories and the reference laboratory were 0.99 or above for all analytes and laboratories, indicating the high quality of assays at all laboratories. However, using the harmonized data from each study, there were significant differences in the mean values and country ranking of the prevalence of at‐risk levels of these four biomarkers. Conclusions: While the biomarker data from the different study laboratories were highly correlated, indicating very high correlation of rank order of specimens, absolute values did vary significantly. This can have a major impact on assessment of international differences in estimates of risks for chronic morbidity and mortality. [ABSTRACT FROM AUTHOR]