@article {12357, title = {Genetic variation in ALDH4A1 is associated with muscle health over the lifespan and across species.}, journal = {Elife}, volume = {11}, year = {2022}, pages = {e74308}, abstract = {

The influence of genetic variation on the aging process, including the incidence and severity of age-related diseases, is complex. Here we define the evolutionarily conserved mitochondrial enzyme ALH-6/ALDH4A1 as a predictive biomarker for age-related changes in muscle health by combining genetics and a gene-wide association scanning (GeneWAS) from older human participants of the US Health and Retirement Study (HRS). In a screen for mutations that activate oxidative stress responses, specifically in the muscle of , we identified 96 independent genetic mutants harboring loss-of-function alleles of , exclusively. Each of these genetic mutations mapped to the ALH-6 polypeptide and led to the age-dependent loss of muscle health. Intriguingly, genetic variants in show associations with age-related muscle-related function in humans. Taken together, our work uncovers mitochondrial as a critical component to impact normal muscle aging across species and a predictive biomarker for muscle health over the lifespan.

}, keywords = {C. elegans, evolutionary biology, Genetics, Genomics}, issn = {2050-084X}, doi = {10.7554/eLife.74308}, author = {Villa, Osvaldo and Stuhr, Nicole L and Yen, Chia-An and Eileen M. Crimmins and Arpawong, Thalida Em and Curran, Sean P} } @article {10.1093/geroni/igz038.1177, title = {ASSOCIATIONS OF GENETICS AND LIFE COURSE CIRCUMSTANCES WITH A NOVEL AGING MEASURE THAT CAPTURES MORTALITY RISK}, journal = {Innovation in Aging}, volume = {3}, year = {2019}, month = {11}, pages = {S322-S323}, abstract = {We aimed to evaluate associations between a comprehensive set of factors, including genetics and childhood and adulthood circumstances, and a novel aging measure, Phenotypic Age (PhenoAge), which has been shown to capture mortality and morbidity risk in the U.S. population. Using data from 2339 adults (aged 51+) from the U.S. Health and Retirement Study, we found that together all 11 study domains (4 childhood and adulthood circumstances domains, 5 polygenic scores [PGSs] domains, and 1 demographics, and 1 behaviors domains) accounted for about 30\% of variance in PhenoAge after accounting for chronological age. Among the 4 circumstances domains, adulthood adversity was the largest contributor (9\%), while adulthood socioeconomic status (SES), childhood adversity, and childhood SES accounted for 2.8\%, 2.1\%, 0.7\%, respectively. All PGSs contributed 3.8\% of variance in PhenoAge (after accounting for chronological age). Further, using Hierarchical Clustering, we identified 6 distinct subpopulations/clusters based on the 4 circumstances domains, and 3 subpopulations/clusters of them that appear to represent disadvantaged circumstances were associated with higher PhenoAge. Finally, there was a significant gene-by-environment interaction between a previously validated PGS for coronary artery disease and the most apparently disadvantaged subpopulation/cluster, suggesting a multiplicative effect of adverse life course circumstances coupled with genetic risk on phenotypic aging. We concluded that socioenvironmental circumstances during childhood and adulthood account for a sizable proportion of differences in phenotypic aging among U.S. older adults. The disadvantaged subpopulations exhibited accelerated aging and the detrimental effects may be further exacerbated among persons with genetic predisposition to coronary artery disease.}, keywords = {Genetics, Mortality, mortality risk}, issn = {2399-5300}, doi = {10.1093/geroni/igz038.1177}, url = {https://doi.org/10.1093/geroni/igz038.1177}, author = {Liu, Zuyun and Chen, Xi and Thomas M Gill and Ma, Chao and Eileen M. Crimmins and Morgan E. Levine} } @article {10119, title = {Associations of genetics, behaviors, and life course circumstances with a novel aging and healthspan measure: Evidence from the Health and Retirement Study.}, journal = {PLoS Medicine}, volume = {16}, year = {2019}, pages = {e1002827}, abstract = {

BACKGROUND: An individual{\textquoteright}s rate of aging directly influences his/her susceptibility to morbidity and mortality. Thus, quantifying aging and disentangling how various factors coalesce to produce between-person differences in the rate of aging, have important implications for potential interventions. We recently developed and validated a novel multi-system-based aging measure, Phenotypic Age (PhenoAge), which has been shown to capture mortality and morbidity risk in the full US population and diverse subpopulations. The aim of this study was to evaluate associations between PhenoAge and a comprehensive set of factors, including genetic scores, childhood and adulthood circumstances, and health behaviors, to determine the relative contributions of these factors to variance in this aging measure.

METHODS AND FINDINGS: Based on data from 2,339 adults (aged 51+ years, mean age 69.4 years, 56\% female, and 93.9\% non-Hispanic white) from the US Health and Retirement Study, we calculated PhenoAge and evaluated the multivariable associations for a comprehensive set of factors using 2 innovative approaches-Shapley value decomposition (the Shapley approach hereafter) and hierarchical clustering. The Shapley approach revealed that together all 11 study domains (4 childhood and adulthood circumstances domains, 5 polygenic score [PGS] domains, and 1 behavior domain, and 1 demographic domain) accounted for 29.2\% (bootstrap standard error = 0.003) of variance in PhenoAge after adjustment for chronological age. Behaviors exhibited the greatest contribution to PhenoAge (9.2\%), closely followed by adulthood adversity, which was suggested to contribute 9.0\% of the variance in PhenoAge. Collectively, the PGSs contributed 3.8\% of the variance in PhenoAge (after accounting for chronological age). Next, using hierarchical clustering, we identified 6 distinct subpopulations based on the 4 childhood and adulthood circumstances domains. Two of these subpopulations stood out as disadvantaged, exhibiting significantly higher PhenoAges on average. Finally, we observed a significant gene-by-environment interaction between a previously validated PGS for coronary artery disease and the seemingly most disadvantaged subpopulation, suggesting a multiplicative effect of adverse life course circumstances coupled with genetic risk on phenotypic aging. The main limitations of this study were the retrospective nature of self-reported circumstances, leading to possible recall biases, and the unrepresentative racial/ethnic makeup of the population.

CONCLUSIONS: In a sample of US older adults, genetic, behavioral, and socioenvironmental circumstances during childhood and adulthood account for about 30\% of differences in phenotypic aging. Our results also suggest that the detrimental effects of disadvantaged life course circumstances for health and aging may be further exacerbated among persons with genetic predisposition to coronary artery disease. Finally, our finding that behaviors had the largest contribution to PhenoAge highlights a potential policy target. Nevertheless, further validation of these findings and identification of causal links are greatly needed.

}, keywords = {Genetics, Health Behavior, Life trajectories}, issn = {1549-1676}, doi = {10.1371/journal.pmed.1002827}, author = {Liu, Zuyun and Chen, Xi and Thomas M Gill and Ma, Chao and Eileen M. Crimmins and Morgan E. Levine} } @article {9960, title = {Differential vulnerability to neighbourhood disorder: a gene{\texttimes}environment interaction study}, journal = {Journal of Epidemiology and Community Health}, volume = {73}, year = {2019}, month = {05/2019}, pages = {388-392}, abstract = {Background: Type 2 diabetes (T2D) is preventable, it is increasing in prevalence and it is a major risk factor for morbidity and mortality. Importantly, residents of neighbourhoods with high levels of disorder are more likely to develop T2D than those living in less disordered neighbourhoods and neighbourhood disorder may exacerbate genetic risk for T2D. Method: We use genetic, self-reported neighbourhood, and health data from the Health and Retirement Study. We conducted weighted logistic regression analyses in which neighbourhood disorder, polygenic scores for T2D and their interaction predicted T2D. Results: Greater perceptions of neighbourhood disorder (OR=1.11, p<0.001) and higher polygenic scores for T2D (OR=1.42, p<0.001) were each significantly and independently associated with an increased risk of T2D. Furthermore, living in a neighbourhood perceived as having high levels of disorder exacerbated genetic risk for T2D (OR=1.10, p=0.001). This significant gene{\texttimes}environment interaction was observed after adjusting for years of schooling, age, gender, levels of physical activity and obesity. Conclusion: Findings in the present study suggested that minimising people{\textquoteright}s exposure to vandalism, vacant buildings, trash and circumstances viewed by residents as unsafe may reduce the burden of this prevalent chronic health condition, particularly for subgroups of the population who carry genetic liability for T2D.}, keywords = {Diabetes, Genetics, Neighborhoods}, issn = {0143-005X}, doi = {10.1136/jech-2018-211373}, url = {http://jech.bmj.com/lookup/doi/10.1136/jech-2018-211373https://syndication.highwire.org/content/doi/10.1136/jech-2018-211373}, author = {Jennifer W Robinette and Jason D Boardman and Eileen M. Crimmins} } @article {Robinette388, title = {Differential vulnerability to neighbourhood disorder: a gene{\texttimes}environment interaction study}, journal = {Journal of Epidemiology \& Community Health}, volume = {73}, year = {2019}, pages = {388{\textendash}392}, abstract = {Background Type 2 diabetes (T2D) is preventable, it is increasing in prevalence and it is a major risk factor for morbidity and mortality. Importantly, residents of neighbourhoods with high levels of disorder are more likely to develop T2D than those living in less disordered neighbourhoods and neighbourhood disorder may exacerbate genetic risk for T2D.Method We use genetic, self-reported neighbourhood, and health data from the Health and Retirement Study. We conducted weighted logistic regression analyses in which neighbourhood disorder, polygenic scores for T2D and their interaction predicted T2D.Results Greater perceptions of neighbourhood disorder (OR=1.11, p\<0.001) and higher polygenic scores for T2D (OR=1.42, p\<0.001) were each significantly and independently associated with an increased risk of T2D. Furthermore, living in a neighbourhood perceived as having high levels of disorder exacerbated genetic risk for T2D (OR=1.10}, keywords = {Diabetes, Genetics, neighborhood}, issn = {0143-005X}, doi = {10.1136/jech-2018-211373}, url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6935762/}, author = {Jennifer W Robinette and Jason D Boardman and Eileen M. Crimmins} } @article {9326, title = {Contemporaneous Social Environment and the Architecture of Late-Life Gene Expression Profiles.}, journal = {American Journal of Epidemiology}, volume = {186}, year = {2017}, pages = {503-509}, abstract = {Environmental or social challenges can stimulate a cascade of coordinated physiological changes in stress response systems. Unfortunately, chronic activation of these adaptations under conditions such as low socioeconomic status (SES) can have negative consequences for long-term health. While there is substantial evidence tying low SES to increased disease risk and reduced life expectancy, the underlying biology remains poorly understood. Using pilot data on 120 older adults from the Health and Retirement Study (United States, 2002-2010), we examined the associations between SES and gene expression levels in adulthood, with particular focus on a gene expression program known as the conserved transcriptional response to adversity. We also used a bioinformatics-based approach to assess the activity of specific gene regulation pathways involved in inflammation, antiviral responses, and stress-related neuroendocrine signaling. We found that low SES was related to increased expression of conserved transcriptional response to adversity genes and distinct patterns of proinflammatory, antiviral, and stress signaling (e.g., sympathetic nervous system and hypothalamic-pituitary-adrenal axis) transcription factor activation.}, keywords = {Genetics, Socioeconomic factors}, issn = {1476-6256}, doi = {10.1093/aje/kwx147}, author = {Morgan E. Levine and Eileen M. Crimmins and David R Weir and Steven W. Cole} } @article {9090, title = {Genetic architecture of epigenetic and neuronal ageing rates in human brain regions}, journal = {Nature Communications}, volume = {8}, year = {2017}, month = {Jun-05-2018}, abstract = {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 {\texttimes} 10 -9 ) with the ageing rate across five brain regions and harbours a cis-expression quantitative trait locus for EFCAB5 (P=3.4 {\texttimes} 10-20 ). Locus 1p36.12 is significantly associated (P=2.2 {\texttimes} 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 {\texttimes} 10 -12 ), ulcerative colitis (P<1.0 {\texttimes} 10-20 ), type 2 diabetes (P=2.8 {\texttimes} 10-13 ), hip/waist circumference in men (P=1.1 {\texttimes} 10-9 ), schizophrenia (P=1.6 {\texttimes} 10-9 ), cognitive decline (P=5.3 {\texttimes} 10-4 ) and Parkinson{\textquoteright}s disease (P=8.6 {\texttimes} 10-3 ).}, keywords = {Genetics}, doi = {10.1038/ncomms15353}, url = {http://www.nature.com/doifinder/10.1038/ncomms15353http://www.nature.com/doifinder/10.1038/ncomms15353}, author = {Lu, Ake T and Hannon, Eilis and Morgan E. Levine and Eileen M. Crimmins and Lunnon, Katie and Mill, Jonathan and Daniel H. Geschwind and Horvath, Steve} }