Mendelian randomization of genetically independent aging phenotypes identifies LPA and VCAM1 as biological targets for human aging

Mendelian randomization evidence for the causal effect of mental well-being on healthy aging

Mental well-being relates to multitudinous lifestyle behaviours and morbidities and underpins healthy aging. Thus far, causal evidence on whether and in what pattern mental well-being impacts healthy aging and the underlying mediating pathways is unknown. Applying genetic instruments of the well-being spectrum and its four dimensions including life satisfaction, positive affect, neuroticism and depressive symptoms (n = 80,852 to 2,370,390), we performed two-sample Mendelian randomization analyses to estimate the causal effect of mental well-being on the genetically independent phenotype of aging (aging-GIP), a robust and representative aging phenotype, and its components including resilience, self-rated health, healthspan, parental lifespan and longevity (n = 36,745 to 1,012,240). Analyses were adjusted for income, education and occupation. All the data were from the largest available genome-wide association studies in populations of European descent. Better mental well-being spectrum (each one Z-score higher) was causally associated with a higher aging-GIP (β [95% confidence interval (CI)] in different models ranging from 1.00 [0.82-1.18] to 1.07 [0.91-1.24] standard deviations (s.d.)) independent of socioeconomic indicators. Similar association patterns were seen for resilience (β [95% CI] ranging from 0.97 [0.82-1.12] to 1.04 [0.91-1.17] s.d.), self-rated health (0.61 [0.43-0.79] to 0.76 [0.59-0.93] points), healthspan (odds ratio [95% CI] ranging from 1.23 [1.02-1.48] to 1.35 [1.11-1.65]) and parental lifespan (1.77 [0.010-3.54] to 2.95 [1.13-4.76] years). Two-step Mendelian randomization mediation analyses identified 33 out of 106 candidates as mediators between the well-being spectrum and the aging-GIP: mainly lifestyles (for example, TV watching and smoking), behaviours (for example, medication use) and diseases (for example, heart failure, attention-deficit hyperactivity disorder, stroke, coronary atherosclerosis and ischaemic heart disease), each exhibiting a mediation proportion of >5%. These findings underscore the importance of mental well-being in promoting healthy aging and inform preventive targets for bridging aging disparities attributable to suboptimal mental health.

[Genetics, epigenetics, and environmental factors in life expectancy-What role does nature-versus-nurture play in aging?]

In Germany and worldwide, the average age of the population is continuously rising. With this general increase in chronological age, the focus on biological age, meaning the actual health and fitness status, is becoming more and more important. The key question is to what extent the age-related decline in fitness is genetically predetermined or malleable by environmental factors and lifestyle.Many epigenetic studies in aging research have provided interesting insights in this nature-versus-nurture debate. In most model organisms, aging is associated with specific epigenetic changes, which can be countered by certain interventions like moderate caloric restriction or increased physical activity. Since these interventions also have positive effects on lifespan and health, epigenetics appears to be the interface between environmental factors and the aging process. This notion is supported by the fact that an epigenetic drift occurs through the life course of identical twins, which is related to the different manifestations of aging symptoms. Furthermore, biological age can be determined with high precision based on DNA methylation patterns, further emphasizing the importance of epigenetics in aging.This article provides an overview of the importance of genetic and epigenetic parameters for life expectancy. A major focus will be on the possibilities of maintaining a young epigenome through lifestyle and environmental factors, thereby slowing down biological aging.

Genome-wide transcriptome profiling and development of age prediction models in the human brain

Aging-related transcriptome changes in various regions of the healthy human brain have been explored in previous works, however, a study to develop prediction models for age based on the expression levels of specific panels of transcripts is lacking. Moreover, studies that have assessed sexually dimorphic gene activities in the aging brain have reported discrepant results, suggesting that additional studies would be advantageous. The prefrontal cortex (PFC) region was previously shown to have a particularly large number of significant transcriptome alterations during healthy aging in a study that compared different regions in the human brain. We harmonized neuropathologically normal PFC transcriptome datasets obtained from the Gene Expression Omnibus (GEO) repository, ranging in age from 21 to 105 years, and found a large number of differentially regulated transcripts in the old and elderly, compared to young samples overall, and compared female and male-specific expression alterations. We assessed the genes that were associated with age by employing ontology, pathway, and network analyses. Furthermore, we applied various established (least absolute shrinkage and selection operator (Lasso) and Elastic Net (EN)) and recent (eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM)) machine learning algorithms to develop accurate prediction models for chronological age and validated them. Studies to further validate these models in other large populations and molecular studies to elucidate the potential mechanisms by which the transcripts identified may be related to aging phenotypes would be advantageous.

Causality-enriched epigenetic age uncouples damage and adaptation

Machine learning models based on DNA methylation data can predict biological age but often lack causal insights. By harnessing large-scale genetic data through epigenome-wide Mendelian randomization, we identified CpG sites potentially causal for aging-related traits. Neither the existing epigenetic clocks nor age-related differential DNA methylation are enriched in these sites. These CpGs include sites that contribute to aging and protect against it, yet their combined contribution negatively affects age-related traits. We established a new framework to introduce causal information into epigenetic clocks, resulting in DamAge and AdaptAge-clocks that track detrimental and adaptive methylation changes, respectively. DamAge correlates with adverse outcomes, including mortality, while AdaptAge is associated with beneficial adaptations. These causality-enriched clocks exhibit sensitivity to short-term interventions. Our findings provide a detailed landscape of CpG sites with putative causal links to lifespan and healthspan, facilitating the development of aging biomarkers, assessing interventions, and studying reversibility of age-associated changes.

Global and tissue-specific aging effects on murine proteomes

Maintenance of protein homeostasis degrades with age, contributing to aging-related decline and disease. Previous studies have primarily surveyed transcriptional aging changes. To define the effects of age directly at the protein level, we perform discovery-based proteomics in 10 tissues from 20 C57BL/6J mice, representing both sexes at adult and late midlife ages (8 and 18 months). Consistent with previous studies, age-related changes in protein abundance often have no corresponding transcriptional change. Aging results in increases in immune proteins across all tissues, consistent with a global pattern of immune infiltration with age. Our protein-centric data reveal tissue-specific aging changes with functional consequences, including altered endoplasmic reticulum and protein trafficking in the spleen. We further observe changes in the stoichiometry of protein complexes with important roles in protein homeostasis, including the CCT/TriC complex and large ribosomal subunit. These data provide a foundation for understanding how proteins contribute to systemic aging across tissues.

Effects of putative metformin targets on phenotypic age and leukocyte telomere length: a mendelian randomisation study using data from the UK Biobank

BACKGROUND

Metformin, a first-line medication for type 2 diabetes, might also have a protective effect against ageing-related diseases, but so far little experimental evidence is available. We sought to assess the target-specific effect of metformin on biomarkers of ageing in the UK Biobank.

METHODS

In this drug target mendelian randomisation study, we assessed the target-specific effect of four putative targets of metformin (AMPK, ETFDH, GPD1, and PEN2), involving ten genes. Genetic variants with evidence of causation of gene expression, glycated haemoglobin A1c (HbA1c), and colocalisation were used as instruments mimicking the target-specific effect of metformin via HbA1c lowering. The biomarkers of ageing considered were phenotypic age (PhenoAge) and leukocyte telomere length. To triangulate the evidence, we also assessed the effect of HbA1c on the outcomes using a polygenic mendelian randomisation design and assessed the effect of metformin use on these outcomes using a cross-sectional observational design.

FINDINGS

GPD1-induced HbA1c lowering was associated with younger PhenoAge (β -5·26, 95% CI -6·69 to -3·83) and longer leukocyte telomere length (β 0·28, 0·03 to 0·53), and AMPKγ2 (PRKAG2)-induced HbA1c lowering was associated with younger PhenoAge (β -4·88, -7·14 to -2·62) but not with longer leukocyte telomere length. Genetically predicted HbA1c lowering was associated with younger PhenoAge (β -0·96 per SD lowering of HbA1c, 95% CI -1·19 to -0·74) but not associated with leukocyte telomere length. In the propensity score matched analysis, metformin use was associated with younger PhenoAge (β -0·36, 95% CI -0·59 to -0·13) but not with leukocyte telomere length.

INTERPRETATION

This study provides genetic validation evidence that metformin might promote healthy ageing via targets GPD1 and AMPKγ2 (PRKAG2), and the effect could be in part due to its glycaemic property. Our findings support further clinical research into metformin and longevity.

FUNDING

Healthy Longevity Catalyst Award, National Academy of Medicine, and Seed Fund for Basic Research, The University of Hong Kong.

Locus for severity implicates CNS resilience in progression of multiple sclerosis

Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) that results in significant neurodegeneration in the majority of those affected and is a common cause of chronic neurological disability in young adults1,2. Here, to provide insight into the potential mechanisms involved in progression, we conducted a genome-wide association study of the age-related MS severity score in 12,584 cases and replicated our findings in a further 9,805 cases. We identified a significant association with rs10191329 in the DYSF-ZNF638 locus, the risk allele of which is associated with a shortening in the median time to requiring a walking aid of a median of 3.7 years in homozygous carriers and with increased brainstem and cortical pathology in brain tissue. We also identified suggestive association with rs149097173 in the DNM3-PIGC locus and significant heritability enrichment in CNS tissues. Mendelian randomization analyses suggested a potential protective role for higher educational attainment. In contrast to immune-driven susceptibility3, these findings suggest a key role for CNS resilience and potentially neurocognitive reserve in determining outcome in MS.

Mendelian randomization analyses reveal causal relationships between the human microbiome and longevity

Although recent studies have revealed the association between the human microbiome especially gut microbiota and longevity, their causality remains unclear. Here, we assess the causal relationships between the human microbiome (gut and oral microbiota) and longevity, by leveraging bidirectional two-sample Mendelian randomization (MR) analyses based on genome-wide association studies (GWAS) summary statistics of the gut and oral microbiome from the 4D-SZ cohort and longevity from the CLHLS cohort. We found that some disease-protected gut microbiota such as Coriobacteriaceae and Oxalobacter as well as the probiotic Lactobacillus amylovorus were related to increased odds of longevity, whereas the other gut microbiota such as colorectal cancer pathogen Fusobacterium nucleatum, Coprococcus, Streptococcus, Lactobacillus, and Neisseria were negatively associated with longevity. The reverse MR analysis further revealed genetically longevous individuals tended to have higher abundances of Prevotella and Paraprevotella but lower abundances of Bacteroides and Fusobacterium species. Few overlaps of gut microbiota-longevity interactions were identified across different populations. We also identified abundant links between the oral microbiome and longevity. The additional analysis suggested that centenarians genetically had a lower gut microbial diversity, but no difference in oral microbiota. Our findings strongly implicate these bacteria to play a role in human longevity and underscore the relocation of commensal microbes among different body sites that would need to be monitored for long and healthy life.

Association of PM2.5 Components with Acceleration of Aging: Moderating Role of Sex Hormones

Fine particulate matter (PM_2.5) has been linked to aging risk, and a lack of knowledge about the relationships between PM_2.5 components and aging risk impeded the development of healthy aging. Participants were recruited through a multicenter cross-sectional study in the Beijing-Tianjin-Hebei region in China. Middle-age and older males and menopausal women completed the collection of basic information, blood samples, and clinical examinations. The biological age was estimated by Klemera-Doubal method (KDM) algorithms based on clinical biomarkers. Multiple linear regression models were applied to quantify the associations and interactions while controlling for confounders, and a restricted cubic spline function estimated the corresponding dose-response curves of the relationships. Overall, KDM-biological age acceleration was associated with PM_2.5 component exposure over the preceding year in both males and females, with calcium [females: 0.795 (95% CI: 0.451, 1.138); males: 0.712 (95% CI: 0.389, 1.034)], arsenic [females: 0.770 (95% CI: 0.641, 0.899); males: 0.661 (95% CI: 0.532, 0.791)], and copper [females: 0.401 (95% CI: 0.158, 0.644); males: 0.379 (95% CI: 0.122, 0.636)] having greater estimates of the effect than total PM_2.5 mass. Additionally, we observed that the associations of specific PM_2.5 components with aging were lower in the higher sex hormone scenario. Maintaining high levels of sex hormones may be a crucial barrier against PM_2.5 component-related aging in the middle and older age groups.

New Insights into the Genetics and Epigenetics of Aging Plasticity

Biological aging is characterized by irreversible cell cycle blockade, a decreased capacity for tissue regeneration, and an increased risk of age-related diseases and mortality. A variety of genetic and epigenetic factors regulate aging, including the abnormal expression of aging-related genes, increased DNA methylation levels, altered histone modifications, and unbalanced protein translation homeostasis. The epitranscriptome is also closely associated with aging. Aging is regulated by both genetic and epigenetic factors, with significant variability, heterogeneity, and plasticity. Understanding the complex genetic and epigenetic mechanisms of aging will aid the identification of aging-related markers, which may in turn aid the development of effective interventions against this process. This review summarizes the latest research in the field of aging from a genetic and epigenetic perspective. We analyze the relationships between aging-related genes, examine the possibility of reversing the aging process by altering epigenetic age.

A Novel Framework for Analysis of the Shared Genetic Background of Correlated Traits

We propose a novel effective framework for the analysis of the shared genetic background for a set of genetically correlated traits using SNP-level GWAS summary statistics. This framework called SHAHER is based on the construction of a linear combination of traits by maximizing the proportion of its genetic variance explained by the shared genetic factors. SHAHER requires only full GWAS summary statistics and matrices of genetic and phenotypic correlations between traits as inputs. Our framework allows both shared and unshared genetic factors to be effectively analyzed. We tested our framework using simulation studies, compared it with previous developments, and assessed its performance using three real datasets: anthropometric traits, psychiatric conditions and lipid concentrations. SHAHER is versatile and applicable to summary statistics from GWASs with arbitrary sample sizes and sample overlaps, allows for the incorporation of different GWAS models (Cox, linear and logistic), and is computationally fast.

How Important Are Genes to Achieve Longevity?

Several studies on the genetics of longevity have been reviewed in this paper. The results show that, despite efforts and new technologies, only two genes, APOE and FOXO3A, involved in the protection of cardiovascular diseases, have been shown to be associated with longevity in nearly all studies. This happens because the genetic determinants of longevity are dynamic and depend on the environmental history of a given population. In fact, population-specific genes are thought to play a greater role in the attainment of longevity than those shared between different populations. Hence, it is not surprising that GWAS replicated associations of common variants with longevity have been few, if any, as these studies pool together different populations. An alternative way might be the study of long-life families. This type of approach is proving to be an ideal resource for uncovering protective alleles and associated biological signatures for healthy aging phenotypes and exceptional longevity.