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Genetic studies reveal the role of the endocrine and metabolic systems in aging.

ABSTRACT: Aging is a natural process that involves a general decline in many physiological functions, resulting in loss of function and eventually death. Extensive research is being performed in order to elucidate the biology of aging, especially with the advent of newer molecular and genetic methodologies. The endocrine system plays a major role in orchestrating cellular interactions, metabolism, growth, and senescence. Thus, researchers traditionally used hormones as tools to induce and examine specific biological effects that are associated with aging. Furthermore, because our recent knowledge on hormonal action expanded significantly, downstream pathways and genetic determinants currently prevail in aging research. In this review, we will summarize the effects of several hormones on human aging and longevity and present recent data from the Longevity Genes Study performed at Albert Einstein College of Medicine, looking at the phenotype and genotype of centenarians and their offspring. We will demonstrate that genetic factors that are associated with human longevity are heritable and may contribute not only to quantitative longevity but also to protection from age-dependent disease and exceptional good health.

SUBMITTER: Barzilai N

PROVIDER: S-EPMC3050096 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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Genetic studies reveal the role of the endocrine and metabolic systems in aging.

Barzilai Nir N Gabriely Ilan I Atzmon Gil G Suh Yousin Y Rothenberg Devorah D Bergman Aviv A

The Journal of clinical endocrinology and metabolism 20101001 10

Aging is a natural process that involves a general decline in many physiological functions, resulting in loss of function and eventually death. Extensive research is being performed in order to elucidate the biology of aging, especially with the advent of newer molecular and genetic methodologies. The endocrine system plays a major role in orchestrating cellular interactions, metabolism, growth, and senescence. Thus, researchers traditionally used hormones as tools to induce and examine specific ...[more]

PMID: 20926537

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Project description:An unrealized potential to understand the genetic basis of aging in humans, is to consider the immense survival advantage of the rare individuals who live 100 years or more. The Longevity Gene Study was initiated in 1998 at the Albert Einstein College of Medicine to investigate longevity genes in a selected population: the "oldest old" Ashkenazi Jews, 95 years of age and older, and their children. The study proved the principle that some of these subjects are endowed with longevity-promoting genotypes. Here we reason that some of the favorable genotypes act as mechanisms that buffer the deleterious effect of age-related disease genes. As a result, the frequency of deleterious genotypes may increase among individuals with extreme lifespan because their protective genotype allows disease-related genes to accumulate. Thus, studies of genotypic frequencies among different age groups can elucidate the genetic determinants and pathways responsible for longevity. Borrowing from evolutionary theory, we present arguments regarding the differential survival via buffering mechanisms and their target age-related disease genes in searching for aging and longevity genes. Using more than 1,200 subjects between the sixth and eleventh decades of life (at least 140 subjects in each group), we corroborate our hypotheses experimentally. We study 66 common allelic site polymorphism in 36 candidate genes on the basis of their phenotype. Among them we have identified a candidate-buffering mechanism and its candidate age-related disease gene target. Previously, the beneficial effect of an advantageous cholesteryl ester transfer protein (CETP-VV) genotype on lipoprotein particle size in association with decreased metabolic and cardiovascular diseases, as well as with better cognitive function, have been demonstrated. We report an additional advantageous effect of the CETP-VV (favorable) genotype in neutralizing the deleterious effects of the lipoprotein(a) (LPA) gene. Finally, using literature-based interaction discovery methods, we use the set of longevity genes, buffering genes, and their age-related target disease genes to construct the underlying subnetwork of interacting genes that is expected to be responsible for longevity. Genome wide, high-throughput hypothesis-free analyses are currently being utilized to elucidate unknown genetic pathways in many model organisms, linking observed phenotypes to their underlying genetic mechanisms. The longevity phenotype and its genetic mechanisms, such as our buffering hypothesis, are similar; thus, the experimental corroboration of our hypothesis provides a proof of concept for the utility of high-throughput methods for elucidating such mechanisms. It also provides a framework for developing strategies to prevent some age-related diseases by intervention at the appropriate level.

Project description:BACKGROUND:Common metabolic diseases, including type 2 diabetes, coronary artery disease, and hypertension, arise from disruptions of the body's metabolic homeostasis, with relatively strong contributions from genetic risk factors and substantial comorbidity with obesity. Although genome-wide association studies have revealed many genomic loci robustly associated with these diseases, biological interpretation of such association is challenging because of the difficulty in mapping single-nucleotide polymorphisms (SNPs) onto the underlying causal genes and pathways. Furthermore, common diseases are typically highly polygenic, and conventional single variant-based association testing does not adequately capture potentially important large-scale interaction effects between multiple genetic factors. METHODS:We analyzed moderately sized case-control data sets for type 2 diabetes, coronary artery disease, and hypertension to characterize the genetic risk factors arising from non-additive, collective interaction effects, using a recently developed algorithm (discrete discriminant analysis). We tested associations of genes and pathways with the disease status while including the cumulative sum of interaction effects between all variants contained in each group. RESULTS:In contrast to non-interacting SNP mapping, which produced few genome-wide significant loci, our analysis revealed extensive arrays of pathways, many of which are involved in the pathogenesis of these metabolic diseases but have not been directly identified in genetic association studies. They comprised cell stress and apoptotic pathways for insulin-producing ?-cells in type 2 diabetes, processes covering different atherosclerotic stages in coronary artery disease, and elements of both type 2 diabetes and coronary artery disease risk factors (cell cycle, apoptosis, and hemostasis) associated with hypertension. CONCLUSIONS:Our results support the view that non-additive interaction effects significantly enhance the level of common metabolic disease associations and modify their genetic architectures and that many of the expected genetic factors behind metabolic disease risks reside in smaller genotyping samples in the form of interacting groups of SNPs.

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Genetic studies reveal the role of the endocrine and metabolic systems in aging.

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Genetic studies reveal the role of the endocrine and metabolic systems in aging.

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