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Type 2 diabetes (T2D) is a heterogeneous disease for which (1) disease-causing pathways are incompletely understood and (2) subclassification may improve patient management. Unlike other biomarkers, germline genetic markers do not change with disease progression or treatment. In this paper, we test whether a germline genetic approach informed by physiology can be used to deconstruct T2D heterogeneity. First, we aimed to categorize genetic loci into groups representing likely disease mechanistic pathways. Second, we asked whether the novel clusters of genetic loci we identified have any broad clinical consequence, as assessed in four separate subsets of individuals with T2D. In an effort to identify mechanistic pathways driven by established T2D genetic loci, we applied Bayesian nonnegative matrix factorization (bNMF) clustering to genome-wide association study (GWAS) results for 94 independent T2D genetic variants and 47 diabetes-related traits. We identified five robust clusters of T2D loci and traits, each with distinct tissue-specific enhancer enrichment based on analysis of epigenomic data from 28 cell types. Two clusters contained variant-trait associations indicative of reduced beta cell function, differing from each other by high versus low proinsulin levels. The three other clusters displayed features of insulin resistance: obesity mediated (high body mass index [BMI] and waist circumference [WC]), \"lipodystrophy-like\" fat distribution (low BMI, adiponectin, and high-density lipoprotein [HDL] cholesterol, and high triglycerides), and disrupted liver lipid metabolism (low triglycerides). Increased cluster genetic risk scores were associated with distinct clinical outcomes, including increased blood pressure, coronary artery disease (CAD), and stroke. We evaluated the potential for clinical impact of these clusters in four studies containing individuals with T2D (Metabolic Syndrome in Men Study [METSIM], N = 487; Ashkenazi, N = 509; Partners Biobank, N = 2,065; UK Biobank [UKBB], N = 14,813). Individuals with T2D in the top genetic risk score decile for each cluster reproducibly exhibited the predicted cluster-associated phenotypes, with approximately 30% of all individuals assigned to just one cluster top decile. Limitations of this study include that the genetic variants used in the cluster analysis were restricted to those associated with T2D in populations of European ancestry. Our approach identifies salient T2D genetically anchored and physiologically informed pathways, and supports the use of genetics to deconstruct T2D heterogeneity. Classification of patients by these genetic pathways may offer a step toward genetically informed T2D patient management.

Aging is a complex biological process marked by a progressive decline in cellular function, leading to age-related diseases such as neurodegenerative disorders, cancer, and cardiovascular diseases. Despite significant advancements in aging research, finding effective interventions to decelerate aging remains a challenge. This review explores microgravity as a novel therapeutic approach to combat aging and promote healthy longevity. The hallmarks of aging, including genomic instability, telomere shortening, and cellular senescence, form the basis for understanding the molecular mechanisms behind aging. Interestingly, microgravity has been shown to accelerate aging-like processes in model organisms and human tissues, making it an ideal environment for studying aging mechanisms in an accelerated manner. Spaceflight studies, such as NASA’s Twins Study and experiments aboard the International Space Station (ISS), reveal striking parallels between the physiological changes induced by microgravity and those observed in aging populations, including muscle atrophy, bone density loss, cardiovascular deconditioning, and immune system decline in a microgravity environment. However, upon microgravity recovery, cellular behavior, gene expression, and tissue regeneration were seen, providing vital insights into aging mechanisms and prospective therapeutic approaches. This review examines the potential of microgravity-based technologies to pioneer novel strategies for decelerating aging and enhancing healthspan under natural gravity, paving the way for breakthroughs in longevity therapies.

About 10% of people older than 70 years of age carry one or more mutations in their hematopoietic cells, and these persons have a higher relative risk of a hematologic cancer (by a factor of 11) and of death from cardiovascular disease (by a factor of 2.0 to 2.6). Cancer is thought to arise through the stepwise acquisition of genetic or epigenetic changes that transform a normal cell. 1 Hence, the existence of a premalignant state bearing only the initiating lesions may be detectable in some persons who have no other signs of disease. For example, multiple myeloma is frequently preceded by monoclonal gammopathy of unknown significance, 2 and chronic lymphocytic leukemia is commonly preceded by monoclonal B-cell lymphocytosis. 3 Several lines of evidence have suggested that clonal hematopoiesis resulting from an expansion of cells that harbor an initiating driver mutation might be an aspect of the aging hematopoietic system. Clonal hematopoiesis . . .

Aging is a complex and systematic biological process that involves multiple genes and biological pathways across different tissues. While existing studies focus on tissue-specific aging factors, the inter-tissue interplay between molecular pathways during aging remains insufficiently explored. To bridge this gap, we propose a novel computational framework to identify the effect of aging on the coordinated patterns of gene-expression across multiple tissues. Our framework includes (1) an adjusted multi-tissue weighted gene co-expression network analysis, (2) differential network connectivity analysis between age groups and (3) machine learning models, XGBoost and Random Forest (RF) fed by gene expression levels and lower-dimensional pathway score space, to identify unique key inter-tissue genes and biological pathways for classifying aging. We applied our approach to three representative tissues: Adipose-Subcutaneous, Muscle-Skeletal and Brain-Cortex. The RF model demonstrated the best performance in predicting age group (AUC < 88%) highlighting key genes involved in inter-tissue coordination processes in aging. We also identified the inter-tissue involvement of lipid metabolism, immune system, and cell communication pathways during aging and detected distinct aging pathways manifested between tissues. The proposed framework highlights the importance of inter-tissue coordination processes underlying aging and provides valuable insights into aging mechanisms which can further assist in the development of therapeutic strategies promoting healthy aging.

We developed a new statistical framework to find genetic variants associated with extreme longevity. The method, informed GWAS (iGWAS), takes advantage of knowledge from large studies of age-related disease in order to narrow the search for SNPs associated with longevity. To gain support for our approach, we first show there is an overlap between loci involved in disease and loci associated with extreme longevity. These results indicate that several disease variants may be depleted in centenarians versus the general population. Next, we used iGWAS to harness information from 14 meta-analyses of disease and trait GWAS to identify longevity loci in two studies of long-lived humans. In a standard GWAS analysis, only one locus in these studies is significant (APOE/TOMM40) when controlling the false discovery rate (FDR) at 10%. With iGWAS, we identify eight genetic loci to associate significantly with exceptional human longevity at FDR < 10%. We followed up the eight lead SNPs in independent cohorts, and found replication evidence of four loci and suggestive evidence for one more with exceptional longevity. The loci that replicated (FDR < 5%) included APOE/TOMM40 (associated with Alzheimer's disease), CDKN2B/ANRIL (implicated in the regulation of cellular senescence), ABO (tags the O blood group), and SH2B3/ATXN2 (a signaling gene that extends lifespan in Drosophila and a gene involved in neurological disease). Our results implicate new loci in longevity and reveal a genetic overlap between longevity and age-related diseases and traits, including coronary artery disease and Alzheimer's disease. iGWAS provides a new analytical strategy for uncovering SNPs that influence extreme longevity, and can be applied more broadly to boost power in other studies of complex phenotypes.

Rather than being a passive, haphazard process of wear and tear, lifespan can be modulated actively by components of the insulin/insulin-like growth factor I (IGFI) pathway in laboratory animals. Complete or partial loss-of-function mutations in genes encoding components of the insulin/IGFI pathway result in extension of life span in yeasts, worms, flies, and mice. This remarkable conservation throughout evolution suggests that altered signaling in this pathway may also influence human lifespan. On the other hand, evolutionary tradeoffs predict that the laboratory findings may not be relevant to human populations, because of the high fitness cost during early life. Here, we studied the biochemical, phenotypic, and genetic variations in a cohort of Ashkenazi Jewish centenarians, their offspring, and offspring-matched controls and demonstrated a gender-specific increase in serum IGFI associated with a smaller stature in female offspring of centenarians. Sequence analysis of the IGF1 and IGF1 receptor (IGF1R) genes of female centenarians showed overrepresentation of heterozygous mutations in the IGF1R gene among centenarians relative to controls that are associated with high serum IGFI levels and reduced activity of the IGFIR as measured in transformed lymphocytes. Thus, genetic alterations in the human IGF1R that result in altered IGF signaling pathway confer an increase in susceptibility to human longevity, suggesting a role of this pathway in modulation of human lifespan.

Previous genetic studies have suggested a history of sub-Saharan African gene flow into some West Eurasian populations after the initial dispersal out of Africa that occurred at least 45,000 years ago. However, there has been no accurate characterization of the proportion of mixture, or of its date. We analyze genome-wide polymorphism data from about 40 West Eurasian groups to show that almost all Southern Europeans have inherited 1%-3% African ancestry with an average mixture date of around 55 generations ago, consistent with North African gene flow at the end of the Roman Empire and subsequent Arab migrations. Levantine groups harbor 4%-15% African ancestry with an average mixture date of about 32 generations ago, consistent with close political, economic, and cultural links with Egypt in the late middle ages. We also detect 3%-5% sub-Saharan African ancestry in all eight of the diverse Jewish populations that we analyzed. For the Jewish admixture, we obtain an average estimated date of about 72 generations. This may reflect descent of these groups from a common ancestral population that already had some African ancestry prior to the Jewish Diasporas.

Human genetic diversity in southern Europe is higher than in other regions of the continent. This difference has been attributed to postglacial expansions, the demic diffusion of agriculture from the Near East, and gene flow from Africa. Using SNP data from 2,099 individuals in 43 populations, we show that estimates of recent shared ancestry between Europe and Africa are substantially increased when gene flow from North Africans, rather than Sub-Saharan Africans, is considered. The gradient of North African ancestry accounts for previous observations of low levels of sharing with Sub-Saharan Africa and is independent of recent gene flow from the Near East. The source of genetic diversity in southern Europe has important biomedical implications; we find that most disease risk alleles from genome-wide association studies follow expected patterns of divergence between Europe and North Africa, with the principal exception of multiple sclerosis.

The Druze are a distinct group known for their close community, traditions, and consanguineous marriages, dating back to the eleventh century. This practice has led to unique genetic variations, impacting both pathology and gene-associated phenotypes. Some Druze clans, particularly those with exceptional long-lived family heads (ELLI), attracted attention. Given that the bulk of these ELLI were men, the d3GHR polymorphism was the first obvious possibility. Among the 73 clan members, 8.2% carried the d3GHR isoform, with nearly 11% being males. There was a significant age-related increase ( p  = 0.04) in this isoform among males, leading to examination of potential environmental mediators affecting gene regulation among these carriers during life (namely epigenetic). We focused on DNA methylation due to its crucial role in gene regulation, development, and disease progression. We analyzed DNA samples from 14 clan members with different GHR genotypes, finding a significant ( p  < 0.05) negative correlation between DNA methylation levels and age. Employing a biological age clock, we observed a significant + 4.229 years favoring the d3GHR group over the WT and heterozygous groups. In conclusion, this study highlights the advantage of d3GHR carriers among this unique Druze clan and underscores the importance of genotype-environment interaction in epigenetic regulation and its impact on health.

The highly polygenic nature of human longevity renders pleiotropy an indispensable feature of its genetic architecture. Leveraging the genetic correlation between aging-related traits (ARTs), we aimed to model the additive variance in lifespan as a function of the cumulative liability from pleiotropic segregating variants. We tracked allele frequency changes as a function of viability across different age bins and prioritized 34 variants with an immediate implication on lipid metabolism, body mass index (BMI), and cognitive performance, among other traits, revealed by PheWAS analysis in the UK Biobank. Given the highly complex and non-linear interactions between the genetic determinants of longevity, we reasoned that a composite polygenic score would approximate a substantial portion of the variance in lifespan and developed the integrated longevity genetic scores ( iLGSs ) for distinguishing exceptional survival. We showed that coefficients derived from our ensemble model could potentially reveal an interesting pattern of genomic pleiotropy specific to lifespan. We assessed the predictive performance of our model for distinguishing the enrichment of exceptional longevity among long-lived individuals in two replication cohorts (the Scripps Wellderly cohort and the Medical Genome Reference Bank (MRGB)) and showed that the median lifespan in the highest decile of our composite prognostic index is up to 4.8 years longer. Finally, using the proteomic correlates of iLGS , we identified protein markers associated with exceptional longevity irrespective of chronological age and prioritized drugs with repurposing potentials for gerotherapeutics. Together, our approach demonstrates a promising framework for polygenic modeling of additive liability conferred by ARTs in defining exceptional longevity and assisting the identification of individuals at a higher risk of mortality for targeted lifestyle modifications earlier in life. Furthermore, the proteomic signature associated with iLGS highlights the functional pathway upstream of the PI3K-Akt that can be effectively targeted to slow down aging and extend lifespan.