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The incidence of the most prevalent chronic diseases, risk of death (the mortality rate) and healthspan for UKB participants. The disease incidence increases approximately exponentially with age at approximately the same rates. Disease incidence rates are calculated independently, participants that have more than one condition during follow-up period are counted for every disease they have, except for healthspan which is defined as the first event occurred. Shaded area represents 95% confidence interval

The incidence of the most prevalent chronic diseases, risk of death (the mortality rate) and healthspan for UKB participants. The disease incidence increases approximately exponentially with age at approximately the same rates. Disease incidence rates are calculated independently, participants that have more than one condition during follow-up period are counted for every disease they have, except for healthspan which is defined as the first event occurred. Shaded area represents 95% confidence interval

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Aging populations face diminishing quality of life due to increased disease and morbidity. These challenges call for longevity research to focus on understanding the pathways controlling healthspan. We use the data from the UK Biobank (UKB) cohort and observe that the risks of major chronic diseases increased exponentially and double every eight ye...

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... An example of studies on large well-structured cohorts is the work using the resource of the UK Biobank. Several GWAS used biobank data to search for genotypes associated with heritable life span, as both the life span of parents and the duration of healthy life (defined as a number of years lived in the absence of serious chronic disease) were considered [76,78]. These studies confirmed already known genetic variants (at the loci APOE, ABO, ZC3HC1, IGF2R, CDKN2B-AS1, 5q33.3/EBF1, and FOXO3) and discovered new variants (ATXN2/BRAP, FURIN/FES, ZW10, PSORS1C3, and 13q21.31). ...
... That is, certain genetic variants that directly affect the rate of human aging were not found. It is possible that the effects of such genetic variants were too small to be detected in this study [54,78]. ...
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Unlabelled: Aging is a natural process of extinction of the body and the main aspect that determines the life expectancy for individuals who have survived to the post-reproductive period. The process of aging is accompanied by certain physiological, immune, and metabolic changes in the body, as well as the development of age-related diseases. The contribution of genetic factors to human life expectancy is estimated at about 25-30%. Despite the success in identifying genes and metabolic pathways that may be involved in the life extension process in model organisms, the key question remains to what extent these data can be extrapolated to humans, for example, because of the complexity of its biological and sociocultural systems, as well as possible species differences in life expectancy and causes of mortality. New molecular genetic methods have significantly expanded the possibilities for searching for genetic factors of human life expectancy and identifying metabolic pathways of aging, the interaction of genes and transcription factors, the regulation of gene expression at the level of transcription, and epigenetic modifications. The review presents the latest research and current strategies for studying the genetic basis of human aging and longevity: the study of individual candidate genes in genetic population studies, variations identified by the GWAS method, immunogenetic differences in aging, and genomic studies to identify factors of "healthy aging." Understanding the mechanisms of the interaction between factors affecting the life expectancy and the possibility of their regulation can become the basis for developing comprehensive measures to achieve healthy longevity. Supplementary information: The online version contains supplementary material available at 10.1134/S1022795422120067.
... La mayoría de las enfermedades crónicas no transmisibles constituyen un problema de salud pública, tanto en países desarrollados como en aquellos en vías de desarrollo (Bazalar Palacios, 2017;Muktabhant et al., 2019;Roth et al., 2018). Su origen es multifactorial e involucra, al menos, componentes genéticos/epigenéticos, ambientales y de estilos de vida (Cahuana-Berrocal et al., 2019;Orozco Muñoz et al., 2018;Zenin et al., 2019). Precisamente en este último punto, la inactividad física se destaca como uno de los factores de riesgo más importantes y modificables (Morales Arandojo et al., 2016;Oja et al., 2017;Schwingshackl et al., 2017;Shlisky et al., 2017). ...
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El objetivo de este estudio fue describir la cantidad de actividad física realizada por la población urbana costarricense y determinar su asociación con variables sociodemográficas y antropométricas. Se analizó una muestra representativa de la población urbana costarricense, constituida por 798 individuos. Los datos de actividad física (desplazamiento, tiempo libre y total) se tomaron mediante el Cuestionario Internacional de Actividad Física (IPAQ, por sus siglas en inglés). Los participantes fueron clasificados como activos o inactivos conforme la recomendación internacional de actividad física. El 37.1% de los participantes no cumplió con estas recomendaciones. Se observó, además, que el tiempo de actividad física fue significativamente mayor (p< .001) en los hombres (535.3 vs 371.3 min/sem), en las personas de menor edad (584.3 min/sem en el grupo de 15 a 19 años vs 309.2 min/sem en el grupo de 50 a 65 años) y en los que no presentaron exceso de peso (521.3 vs 411.7min/sem). La circunferencia de cintura fue significativamente menor entre las personas activas (90.6 vs 94.1 cm, p < .011) y, los hombres activos también mostraron una menor circunferencia de cuello (38.2 vs 39.6 cm, p < .001), menor peso (75.7 vs 79.3 kg, p = .025) y menor índice de masa corporal (26.1 vs 27.9 kg/m2, p = .004). Estos resultados podrían usarse para concientizar sobre la necesidad de mejoras en la implementación de políticas y prácticas globales que promuevan la actividad física.
... The underlying hypothesis in gerontology posits that these devastating diseases share some common pathophysiological mechanisms with intrinsic processes of aging (Probst-Hensch 2010; Kaeberlein et al. 2015). On the other hand, increased lifespan is associated with longer healthspan in humans with exceptional longevity (Fries et al. 2011;Andersen et al. 2012;Sun et al. 2013;Zenin et al. 2019). Extreme longevity in human cohorts is associated with a delayed incidence of diseases: Kaplan-Meyer curves of disease-free survival, stratified by age, demonstrate a consistent delay in the onset of age-related diseases with increasing age of survival (Andersen et al. 2012;Zenin et al. 2019). ...
... On the other hand, increased lifespan is associated with longer healthspan in humans with exceptional longevity (Fries et al. 2011;Andersen et al. 2012;Sun et al. 2013;Zenin et al. 2019). Extreme longevity in human cohorts is associated with a delayed incidence of diseases: Kaplan-Meyer curves of disease-free survival, stratified by age, demonstrate a consistent delay in the onset of age-related diseases with increasing age of survival (Andersen et al. 2012;Zenin et al. 2019). Aging is not only the predominant risk factor for age-related diseases, but aging and age-related diseases also share in their (patho)physiology a variety of biomarkers and common molecular pathways (Cardoso et al. 2018). ...
... The GWAS data of healthspan consists of 300,477 British-ancestry individuals from the UK Biobank (UKB) [19]. Healthspan is defined as the age at onset or death of the first disease (including cancer, stroke, dementia, diabetes, Congestive Heart Failure (CHF), Myocardial Infarction (MI), and Chronic Obstructive Pulmonary Disease (COPD). ...
... Summary statistics retained 11,309,218 single nucleotide polymorphisms (SNPs). More details can be seen in the published article [19]. ...
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Background: Longevity is one of the most complex phenotypes, and its genetic basis remains unclear. This study aimed to explore the genetic correlation and potential causal association between gut microbiota and longevity. Results: Linkage disequilibrium score (LDSC) regression analysis and a bi-directional two-sample Mendelian Randomization (MR) analysis were performed to analyze gut microbiota and longevity-related traits. LDSC analysis detected four candidate genetic correlations, including Veillonella (genetic correlation = 0.5578, P = 4.67 × 10- 2) and Roseburia (genetic correlation = 0.4491, P = 2.67 × 10- 2) for longevity, Collinsella (genetic correlation = 0.3144, P = 4.07 × 10- 2) for parental lifespan and Sporobacter (genetic correlation = 0.2092, P = 3.53 × 10- 2) for healthspan. Further MR analysis observed suggestive causation between Collinsella and parental longevity (father's age at death) (weighted median: b = 1.79 × 10- 3, P = 3.52 × 10- 2). Reverse MR analysis also detected several causal effects of longevity-related traits on gut microbiota, such as longevity and Sporobacter (IVW: b = 7.02 × 10- 1, P = 4.21 × 10- 25). Statistical insignificance of the heterogeneity test and pleiotropy test supported the validity of the MR study. Conclusion: Our study found evidence that gut microbiota is causally associated with longevity, or vice versa, providing novel clues for understanding the roles of gut microbiota in aging development.
... In addition to lifespan, healthy ageing is a complex outcome that reflects on the overall quality of life as well as cognitive and physical functions among the elderly population. Recent genetic studies for healthy ageing have primarily evaluated the incidence of major chronic diseases and defined healthy ageing as diseasefree survival to old age [5][6][7] . Fewer studies have evaluated the multiple facets of healthy ageing in a complete manner, but nevertheless have described the important role of genetic factors in the process of ageing. ...
... A recent example is the identification of the LRP1B locus in long-lived individuals associated with both good cognitive function and the absence of major chronic diseases 8 . Nevertheless, holistic evaluations for healthy ageing have also been largely limited to studies performed in European ancestry populations 5,6,8 and related data is sparse among participants of East-Asian ancestry. ...
... This might be due to the relatively modest sample size of the replication dataset. Additionally, given the differences in definitions for healthy ageing, it would be challenging to evaluate the transferability of variants identified for related phenotypes, such as human healthspan focused on disease-free survival to old age, which have been recently reported in European ancestry samples 6 . Furthermore, given that the variants identified in the present study were East-Asian specific, our data also highlights on the importance of potential ethnic-specific mechanisms that may influence healthy ageing in the population. ...
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The genetic basis of overall healthy ageing, especially among the East-Asian population is understudied. We conducted a genome-wide association study among 1618 Singapore Chinese elderly participants (65 years or older) ascertained to have aged healthily and compared their genome-wide genotypes to 6221 participants who did not age healthily, after a 20-year follow-up. Two genetic variants were identified ( P Meta < 2.59 × 10 ⁻⁸ ) to be associated with healthy aging, including the LRP1B locus previously associated in long-lived individuals without cognitive decline. Our study sheds additional insights on the genetic basis of healthy ageing.
... Most of the disease profiles belonging to clusters interpreted as being 'Diagnoses of senior life stage onwards' or 'Diagnoses of senior and (especially) super senior life stage' fit well to a 2-parameter exponential growth model, showing that these diagnoses increase exponentially with age. This is consistent with research for human diseases (24) and suggests that healthspan, on average, extends to the onset of the 'Senior' life stage. For 'exponential' disease clusters in humans, the corresponding doubling rate has been seen to be numerically close to the mortality rate doubling time from the Gompertz mortality law (24). ...
... This is consistent with research for human diseases (24) and suggests that healthspan, on average, extends to the onset of the 'Senior' life stage. For 'exponential' disease clusters in humans, the corresponding doubling rate has been seen to be numerically close to the mortality rate doubling time from the Gompertz mortality law (24). The exponential model used here is equivalent in form to the Gompertz equation, so that the maximum growth rate in our model would be equivalent to the 'actuarial aging rate' of the Gompertz equation if applied to mortality data and inversely proportional to the doubling time (29). ...
... shows that there are groups of diseases in humans that increase exponentially with age(24). Most of the disease profiles in this analysis belonging to clustersinterpreted as being 'Diagnoses of senior life stage onwards' or 'Diagnoses of senior and (especially) super senior life stage' are fitted well by a 2-parameter exponential growth model out of the 38 ailments thus classified, 20 have an R 2 of at least 90% in all species / breed size where they feature and a further 8 have an R 2 of at least 80%. ...
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Aging is a complex, multifactorial process, where different life stages reflect changes in metabolic processes, immune capacities and genetic/epigenetic repertoires. With accumulating exposure to environmental stresses and deterioration of physiological functions, body systems become more prone to low-grade chronic inflammation and an increasing range of pathologies. We hypothesised that differential susceptibility to diseases across lifespan reflect phased changes in an organism’s physiological capacity that may highlight when interventions may be appropriately used. Furthermore, the number of life stages may vary between species and be impacted by signalment such as breed. We tested this hypothesis using disease diagnoses data from veterinary electronic medical records containing almost two million cats and over four million dogs. Bi-clustering (on rates of disease diagnoses) and adaptive branch pruning were used to identify age clusters that could be used to define adult life stages. Clustering amongst diagnoses were then interpreted within the context of each defined life stage. The analyses identified five age clusters in cats and four age clusters within each of the four canine breed size categories used. This study, using population scale data for two species, one with differential size and life expectancies, is the first to our knowledge to use disease diagnosis data to define adult life stages. The life stages presented here are a result of a data-driven approach to age and disease stratification and are intended to support conversations between clinicians and clients about appropriate healthcare recommendations.
... Each disease included in the ARD group is caused by different causes affecting various organs, such as mutations, dysregulated homeostasis, brosis, and degenerative processes [9]. However, previous studies have shown that diseases belonging to ARD tend to increase approximately exponentially with age and then decrease in very old age; additionally, the slope of the rising portion of the incidence curve is similar at 6-8% per year [19,20]. This similarity suggests that a general biological aging process dominates the pathogenesis of various diseases, which can be explained by the accumulation of senescent cells and differences in individual susceptibility to diseases [15]. ...
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Background: Age-related diseases (ARDs) do not have a defined category by consensus opinion. This study aims to redefine ARDs in Korea, which is about to have a super-aged society, and to examine the incidence rate of ARDs and identify their characteristics. Methods: Using a National Health Insurance Service-Sample Cohort (NHIS-NSC), which sampled 1 million individuals who maintained health insurance and medical benefit beneficiaries in Korea for one year in 2006 and followed up from 2002 to 2019, we selected 14 diseases with high disease burden and prevalence among Koreans from 92 ARDs diseases defined in the GBD study as ARDs. The annual incidence rate is the number of subjects newly diagnosed with each ARD each year for a total of 14 years from 2006 to 2019, after excluding subjects with a history of being diagnosed with ARDs from 2002 to 2005. The incidence rate by age was divided into units of 10 years by age as of 2019, the number of subjects with aging-related diseases for each age group was used as the numerator, and the incidence rate of each age group was calculated by age group as the denominator. Results: From 2006 to 2019, the diseases that showed a decrease in the annual incidence were chronic obstructive pulmonary disease, congestive heart failure, and ischemic heart disease, and diseases that showed significant increase were dyslipidemia, chronic kidney disease, cataract, deafness, and Parkinson's disease. Notably, hypertension, diabetes, cerebrovascular disease, osteoporosis, osteoarthritis, and age-related macular degeneration showed a gradual decrease in the incidence and tendency to increase after 2015. However, almost all diseases showed a difference in degree when the incidence rate of each disease was examined, regardless of the difference in the incidence rate by year; however, the incidence increased exponentially as the age increased, and then at a very high age demonstrate a characteristic form of decrease. Conclusions: The incidence of diseases belonging to the newly defined ARDs increased exponentially with age and had a common characteristic showing that incidence decreased at a very high age.
... Until recently, estimates of heritability were mostly in the range 15%-30% (summarized in Table 1 of Murabito et al., 2012; see also Shor et al., 2019), but significant familial nongenetic effects and assortative mating may have led to inflated estimates of heritability (Ruby et al., 2018). Genome-wide association studies (GWAS) have identified a number of significant loci for lifespan or related phenotypes (Deelen et al., 2019;Deelen et al., 2016;Joshi et al., 2017;Timmers et al., 2020;Timmers et al., 2019;Wright et al., 2019;Zenin et al., 2019), and the challenge is to expand the number of substantiated loci to improve our understanding of the pathways that are associated with variation in lifespan. ...
... This can use the same individuals for both genotyping and phenotyping. So far, such endophenotypes have included cognition, physical activity, lung function, blood pressure, muscle strength and age at onset of major disease ('healthspan'; Deelen et al., 2016;Marioni et al., 2016;Sanders et al., 2014;Singh et al., 2015;Zenin et al., 2019). Inclusion of obesity-related metabolic phenotypes has also been suggested (Marron et al., 2019). ...
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Biomarkers may be useful endophenotypes for genetic studies if they share genetic sources of variation with the outcome, for example, with all-cause mortality. Australian adult study participants who had reported their parental survival information were included in the study: 14,169 participants had polygenic risk scores (PRS) from genotyping and up to 13,365 had biomarker results. We assessed associations between participants’ biomarker results and parental survival, and between biomarker results and eight parental survival PRS at varying p -value cut-offs. Survival in parents was associated with participants’ serum bilirubin, C-reactive protein, HDL cholesterol, triglycerides and uric acid, and with LDL cholesterol for participants’ fathers but not for their mothers. PRS for all-cause mortality were associated with liver function tests (alkaline phosphatase, butyrylcholinesterase, gamma-glutamyl transferase), metabolic tests (LDL and HDL cholesterol, triglycerides, uric acid), and acute-phase reactants (C-reactive protein, globulins). Association between offspring biomarker results and parental survival demonstrates the existence of familial effects common to both, while associations between biomarker results and PRS for mortality favor at least a partial genetic cause of this covariation. Identification of genetic loci affecting mortality-associated biomarkers offers a route to the identification of additional loci affecting mortality.
... In this study, healthspan was defined based on eight health terminating events (congestive heart failure, myocardial infarction, chronic obstructive pulmonary disease, stroke, dementia, diabetes, cancer, and death) that are strongly associated with longevity [31]. Only the participants who were diagnosed with any of these conditions first during the study period were considered to have terminated healthspan. ...
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Objectives To examine the associations between four sleep behaviors and the risk of healthspan termination. Methods This study included 323,373 participants, free of terminated healthspan at baseline, from the UK-Biobank (UKB). We applied multivariable-adjusted Cox regression models to estimate the risk of terminated healthspan based on four sleep behaviors (insomnia/sleeplessness, napping, daytime sleepiness, and difficulty getting up from bed), which were self-reported and measured on Likert scales from “usually” to “never/rarely” experiences. In this study, healthspan was defined based on eight events that are strongly associated with longevity (congestive heart failure, myocardial infarction, chronic obstructive pulmonary disease, stroke, dementia, diabetes, cancer, and death). Results Participants who reported the following unhealthy sleep behaviors had a significantly higher risk of terminated healthspan: “usually experience sleeplessness/insomnia” (HR = 1.05, 95% CI: 1.03–1.07; P < 0.001); “usually nap” (HR = 1.22, 95% CI: 1.18–1.26; P < 0.01); “excessive daytime sleepiness” (HR = 1.25, 95% CI: 1.19–1.32; P < 0.001); and “difficult getting up from bed” (HR = 1.08, 95% CI: 1.05–1.10; P < 0.001). The corresponding population attributable risk percentage (PAR%) indicated that about 7% of healthspan termination in this cohort would have been eliminated if all participants had healthy sleep behaviors. Conclusion Participants who reported “usually experience sleeplessness/insomnia,” “usually nap,” “excessive daytime sleepiness,” and “difficult getting up from bed” had increased risk of shortened healthspan. Therefore, adherence to healthy sleep behavior is significant for the extension of healthspan.
... Genomic instability and epigenetic alterations frequently contribute to development of cancers of, for example, the breast and bowel (Hanahan & Weinberg, 2011). The role of genes in individual human ARDs and ARD multimorbidity has been studied extensively (Amell et al., 2018;Johnson et al., 2015;Zenin et al., 2019), as has the link between individual aging hallmarks and ARDs (Andreassen et al., 2019;Johnson et al., 2015). For example, previous studies have demonstrated that multiple, individual human ARDs share gene ontology (GO) terms linked to aging hallmarks (Johnson et al., 2015). ...
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Genetic, environmental, and pharmacological interventions into the aging process can confer resistance to multiple age‐related diseases in laboratory animals, including rhesus monkeys. These findings imply that individual mechanisms of aging might contribute to the co‐occurrence of age‐related diseases in humans and could be targeted to prevent these conditions simultaneously. To address this question, we text mined 917,645 literature abstracts followed by manual curation and found strong, non‐random associations between age‐related diseases and aging mechanisms in humans, confirmed by gene set enrichment analysis of GWAS data. Integration of these associations with clinical data from 3.01 million patients showed that age‐related diseases associated with each of five aging mechanisms were more likely than chance to be present together in patients. Genetic evidence revealed that innate and adaptive immunity, the intrinsic apoptotic signaling pathway and activity of the ERK1/2 pathway were associated with multiple aging mechanisms and diverse age‐related diseases. Mechanisms of aging hence contribute both together and individually to age‐related disease co‐occurrence in humans and could potentially be targeted accordingly to prevent multimorbidity. Using text‐mining, gene set enrichment and network analysis, we found that five aging hallmarks (deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion and altered intercellular communication) were associated with co‐occurrence of age‐related diseases. Innate and adaptive immunity, Ras‐ERK and intrinsic apoptotic signalling pathways were enriched for all nine aging hallmarks, indicating that multiple diseases may be prevented by targeting these pathways.