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Theory predicts that biological processes of aging may contribute to poor mental health in late life. To test this hypothesis, we evaluated prospective associations between biological age and incident depression and anxiety in 424,299 UK Biobank participants. We measured biological age from clinical traits using the KDM-BA and PhenoAge algorithms. At baseline, participants who were biologically older more often experienced depression/anxiety. During a median of 8.7 years of follow-up, participants with older biological age were at increased risk of incident depression/anxiety (5.9% increase per standard deviation [SD] of KDM-BA acceleration, 95% confidence intervals [CI]: 3.3%–8.5%; 11.3% increase per SD of PhenoAge acceleration, 95% CI: 9.%–13.0%). Biological-aging-associated risk of depression/anxiety was independent of and additive to genetic risk measured by genome-wide-association-study-based polygenic scores. Advanced biological aging may represent a potential risk factor for incident depression/anxiety in midlife and older adults and a potential target for risk assessment and intervention. Theory indicates that biological aging may contribute to poor mental health in late life. Here, authors show advanced biological aging may represent a potential risk factor for incident depression/anxiety in midlife and older adults and a potential target for risk assessment and intervention.

The lifespan extension induced by 40% caloric restriction (CR) in rodents is accompanied by postponement of disease, preservation of function, and increased stress resistance. Whether CR elicits the same physiological and molecular responses in humans remains mostly unexplored. In the CALERIE study, 12% CR for 2 years in healthy humans induced minor losses of muscle mass (leg lean mass) without changes of muscle strength, but mechanisms for muscle quality preservation remained unclear. We performed high‐depth RNA‐Seq (387–618 million paired reads) on human vastus lateralis muscle biopsies collected from the CALERIE participants at baseline, 12‐ and 24‐month follow‐up from the 90 CALERIE participants randomized to CR and “ad libitum” control. Using linear mixed effect model, we identified protein‐coding genes and splicing variants whose expression was significantly changed in the CR group compared to controls, including genes related to proteostasis, circadian rhythm regulation, DNA repair, mitochondrial biogenesis, mRNA processing/splicing, FOXO3 metabolism, apoptosis, and inflammation. Changes in some of these biological pathways mediated part of the positive effect of CR on muscle quality. Differentially expressed splicing variants were associated with change in pathways shown to be affected by CR in model organisms. Two years of sustained CR in humans positively affected skeletal muscle quality, and impacted gene expression and splicing profiles of biological pathways affected by CR in model organisms, suggesting that attainable levels of CR in a lifestyle intervention can benefit muscle health in humans. Two years of sustained CR in humans positively affected skeletal muscle quality, and impacted gene expression and splicing profiles of biological pathways known to affected by CR in model organisms. CR involved genes related in proteostasis, circadian rhythm regulation, DNA repair, mitochondrial biogenesis, mRNA processing/splicing, FOXO3 metabolism, apoptosis, and inflammation. Alternative splicing plays a key role in regulating key biological mechanisms targeted by CR.

Variation in DNA methylation is being increasingly associated with health and disease outcomes. Although DNA methylation is hypothesized to be a mechanism by which both genetic and non-genetic factors can influence the regulation of gene expression, little is known about the extent to which DNA methylation at specific sites is influenced by heritable as well as environmental factors. We quantified DNA methylation in whole blood at age 18 in a birth cohort of 1,464 individuals comprising 426 monozygotic (MZ) and 306 same-sex dizygotic (DZ) twin pairs. Site-specific levels of DNA methylation were more strongly correlated across the genome between MZ than DZ twins. Structural equation models revealed that although the average contribution of additive genetic influences on DNA methylation across the genome was relatively low, it was notably elevated at the highly variable sites characterized by intermediate levels of DNAm that are most relevant for epigenetic epidemiology. Sites at which variable DNA methylation was most influenced by genetic factors were significantly enriched for DNA methylation quantitative trait loci (mQTL) effects, and overlapped with sites where inter-individual variation correlates across tissues. Finally, we show that DNA methylation at sites robustly associated with environmental exposures such as tobacco smoking and obesity is also influenced by additive genetic effects, highlighting the need to control for genetic background in analyses of exposure-associated DNA methylation differences. Estimates of the contribution of genetic and environmental influences to DNA methylation at all sites profiled in this study are available as a resource for the research community (http://www.epigenomicslab.com/online-data-resources).

Antiaging therapies show promise in model organism research. Translation to humans is needed to address the challenges of an aging global population. Interventions to slow human aging will need to be applied to still-young individuals. However, most human aging research examines older adults, many with chronic disease. As a result, little is known about aging in young humans. We studied aging in 954 young humans, the Dunedin Study birth cohort, tracking multiple biomarkers across three time points spanning their third and fourth decades of life. We developed and validated two methods by which aging can be measured in young adults, one cross-sectional and one longitudinal. Our longitudinal measure allows quantification of the pace of coordinated physiological deterioration across multiple organ systems (e.g., pulmonary, periodontal, cardiovascular, renal, hepatic, and immune function). We applied these methods to assess biological aging in young humans who had not yet developed age-related diseases. Young individuals of the same chronological age varied in their “biological aging” (declining integrity of multiple organ systems). Already, before midlife, individuals who were aging more rapidly were less physically able, showed cognitive decline and brain aging, self-reported worse health, and looked older. Measured biological aging in young adults can be used to identify causes of aging and evaluate rejuvenation therapies. The global population is aging, driving up age-related disease morbidity. Antiaging interventions are needed to reduce the burden of disease and protect population productivity. Young people are the most attractive targets for therapies to extend healthspan (because it is still possible to prevent disease in the young). However, there is skepticism about whether aging processes can be detected in young adults who do not yet have chronic diseases. Our findings indicate that aging processes can be quantified in people still young enough for prevention of age-related disease, opening a new door for antiaging therapies. The science of healthspan extension may be focused on the wrong end of the lifespan; rather than only studying old humans, geroscience should also study the young.

Biological aging is the gradual, progressive decline in system integrity that occurs with advancing chronological age, causing morbidity and disability. Measurements of the pace of aging are needed as surrogate endpoints in trials of therapies designed to prevent disease by slowing biological aging. We report a blood-DNA-methylation measure that is sensitive to variation in pace of biological aging among individuals born the same year. We first modeled change-over-time in 18 biomarkers tracking organ-system integrity across 12 years of follow-up in n = 954 members of the Dunedin Study born in 1972–1973. Rates of change in each biomarker over ages 26–38 years were composited to form a measure of aging-related decline, termed Pace-of-Aging. Elastic-net regression was used to develop a DNA-methylation predictor of Pace-of-Aging, called DunedinPoAm for Dunedin(P)ace(o)f(A)ging(m)ethylation. Validation analysis in cohort studies and the CALERIE trial provide proof-of-principle for DunedinPoAm as a single-time-point measure of a person’s pace of biological aging. People’s bodies age at different rates. Age-related biological changes that increase the risk of disease and disability progress rapidly in some people. In others, these processes occur at a slower pace, allowing those individuals to live longer, healthier lives. This observation has led scientists to try to develop therapies that slow aging. The hope is that such treatments could prevent or delay diseases like heart disease or dementia, for which older age is the leading risk factor. Studies in animals have identified treatments that extend the creatures’ lives and slow age-related disease. But testing these treatments in humans is challenging. Our lives are much longer than the worms, flies or mice used in the experiments. Scientists would have to follow human study participants for decades to detect delays in disease onset or an extension of their lives. An alternative approach is to try to develop a test that measures the pace of aging, or essentially “a speedometer for aging”. This would allow scientists to more quickly determine if treatments slow the aging process. Now, Belsky et al. show a blood test designed to measure the pace of aging predicts which people are at increased risk of poor health, chronic disease and an earlier death. First, data about chemical changes to an individual’s DNA, called DNA methylation, were analyzed from white blood cell samples collected from 954 people in a long-term health study known as “The Dunedin Study”. Using the data, Belsky et al. then developed an algorithm – named “DunedinPoAm” – that identified people with an accelerated or slowed pace of aging based on a single blood test. Next, they used the algorithm on samples from participants in three other long-term studies. This verified that those people the algorithm identified as aging faster had a greater risk of poor health, developing chronic diseases or dying earlier. Similarly, those identified as aging more slowly performed better on tests of balance, strength, walking speed and mental ability, and they also looked younger to trained raters. Additionally, Belsky et al. used the test on participants in a randomized trial testing whether restricting calories had potential to extend healthy lifespan. The results suggested that the calorie restriction could counter the effects of an accelerated pace of aging. The test developed by Belsky et al. may provide an alternate way of measuring whether age-slowing treatments work. This would allow faster testing of treatments that can extend the healthy lifespan of humans. The test may also help identify individuals with accelerated aging. This might help public health officials test whether policies or programs can help people lead longer, healthier lives.

Little is known about the genetic architecture of traits affecting educational attainment other than cognitive ability. We used genomic structural equation modeling and prior genome-wide association studies (GWASs) of educational attainment ( n  = 1,131,881) and cognitive test performance ( n  = 257,841) to estimate SNP associations with educational attainment variation that is independent of cognitive ability. We identified 157 genome-wide-significant loci and a polygenic architecture accounting for 57% of genetic variance in educational attainment. Noncognitive genetics were enriched in the same brain tissues and cell types as cognitive performance, but showed different associations with gray-matter brain volumes. Noncognitive genetics were further distinguished by associations with personality traits, less risky behavior and increased risk for certain psychiatric disorders. For socioeconomic success and longevity, noncognitive and cognitive-performance genetics demonstrated associations of similar magnitude. By conducting a GWAS of a phenotype that was not directly measured, we offer a view of genetic architecture of noncognitive skills influencing educational success. Genomic structural equation modeling of genome-wide association data for educational attainment and cognitive test performance is used to estimate the genetic component of variation in educational attainment that is independent of cognitive ability. The study finds that noncognitive skills account for 57% of genetic variation in educational attainment.

Epigenetic Clocks have been trained to predict chronological age, healthspan and lifespan. Such clocks are often analysed in relation to disease outcomes – typically using small datasets and a limited number of clocks. Here, we present a large-scale ( n  = 18,859), unbiased comparison of 14 widely used clocks as predictors of 174 incident disease outcomes and all-cause mortality over 10-years of follow up. Second- and third-generation clocks significantly outperform first-generation clocks, which have limited applications in disease settings. Of the 176 Bonferroni significant (P < 0.05/174) associations from fully-adjusted Cox regression models controlling for lifestyle and socioeconomic measures, there are 27 diseases (including primary lung cancer and diabetes) where the hazard ratio for the clock exceeds the clock’s association with all-cause mortality. Furthermore, for 32 of the 176 findings, adding the clock to a null classification model with traditional risk factors significantly increases the classification accuracy by >1%. However, there is minimal evidence for interactions between the clocks and sex or smoking (ever/never) status. Second- and third-generation epigenetic clocks show promise for disease risk prediction, particularly in relation to respiratory and liver-based conditions. Epigenetic clocks estimate biological age and health risks. Here, the authors compare 14 clocks in 18,859 individuals, showing second-generation clocks better predict disease incidence and mortality, particularly for respiratory and liver-related conditions.

Associations of biological aging with the development and mortality of cardiometabolic multimorbidity (CMM) remain unclear. Here we conducted a multistate analysis in 341,159 adults of the UK Biobank. CMM was defined as the coexistence of two or three cardiometabolic diseases (CMDs), including type 2 diabetes, ischemic heart disease and stroke. Biological aging was measured using the Klemera-Doubal Method Biological Age and PhenoAge algorithms. Over a median follow-up of 8.84 years, biologically older participants demonstrated robust higher risks from first CMD to CMM and then to death. In particular, adjusted hazard ratios for first CMD to CMM and for CMM to death were 1.15 (95% confidence interval (CI): 1.12, 1.19) and 1.26 (95% CI: 1.17, 1.35) per 1 s.d. increase in PhenoAge acceleration, respectively. Compared with frailty, Framingham Risk Score and Systematic Coronary Risk Evaluation 2 (SCORE2), biological aging measures yielded consistent substantial associations with CMM development. Accelerated biological aging may help identify individuals with CMM risks, potentially enabling early intervention and subclinical prevention.

Integrating the analysis of molecular data from different sources may improve our understanding of the effects of biological aging.Integrating the analysis of molecular data from different sources may improve our understanding of the effects of biological aging.

This study asks two related questions about the shifting landscape of marriage and reproduction in US society over the course of the last century with respect to a range of health and behavioral phenotypes and their associated genetic architecture: (i) Has assortment on measured genetic factors influencing reproductive and social fitness traits changed over the course of the 20th century? (ii) Has the genetic covariance between fitness (as measured by total fertility) and other traits changed over time? The answers to these questions inform our understanding of how the genetic landscape of American society has changed over the past century and have implications for population trends. We show that husbands and wives carry similar loadings for genetic factors related to education and height. However, the magnitude of this similarity is modest and has been fairly consistent over the course of the 20th century. This consistency is particularly notable in the case of education, for which phenotypic similarity among spouses has increased in recent years. Likewise, changing patterns of the number of children ever born by phenotype are not matched by shifts in genotype–fertility relationships over time. Taken together, these trends provide no evidence that social sorting is becoming increasingly genetic in nature or that dysgenic dynamics have accelerated.