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 years, i.e., at a rate compatible with the Gompertz mortality law. Assuming that aging drives the acceleration in morbidity rates, we build a risk model to predict the age at the end of healthspan depending on age, gender, and genetic background. Using the sub-population of 300,447 British individuals as a discovery cohort, we identify 12 loci associated with healthspan at the whole-genome significance level. We find strong genetic correlations between healthspan and all-cause mortality, life-history, and lifestyle traits. We thereby conclude that the healthspan offers a promising new way to interrogate the genetics of human longevity.
Introduction
Age is the most important single risk factor for multiple diseases, see, e.g., ref. 1. Likewise, 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 survival2. Therefore, the emerging premise is that aging itself is the common driver of chronic diseases and conditions that limit the functional and disease-free survival3. Healthy and morbidity-free lifespan, often termed “healthspan”, is thus a promising phenotype for longevity research4 and possibly a target for future anti-aging interventions3,5. The thorough delineation between the healthspan and lifespan is more than of academic interest: the last century saw a dramatic increase in lifespan, not necessarily followed by a matching improvement in the healthspan6.
Genomics provide a hypothesis-free approach to study the biology of complex traits, including aging5. The increasing number of available genomes of very old people7,8,9, though representing a rather specific and a relatively small sub-group of exceptionally successfully aging individuals, can provide an insight into the genetic architecture of exceptional life-spans and health-spans by use of Genome-Wide Association Studies (GWAS). While such studies suggested a fair number of loci, the APOE locus is probably among the few consistently implicated in multiple studies, see ref. 10 for a review. GWAS of the disease-free survival has been performed in relatively large cohorts (n = 25,007), however, without producing genome-wide significant associations11, highlighting the complexity of healthspan phenotype. Further gains can be naturally achieved by increasing the population size with the help of proxy phenotypes, such as a search for genetic variants that predispose one to age-related disease and hence are depleted in long-lived persons compared to controls8. Another promising alternative involves GWAS of parental lifespans12,13,14.
In this paper, we focused on aging and morbidity in mid-life using clinical histories for over 300,000 people, aged 37 to 73, and participating in the UK Biobank (UKB) cohort. We checked the for incidence of chronic diseases and identified a cluster of the top eight morbidities strongly associated with age after the age of 40 and ranked by the number of occurrences. We observed that the risk of the selected diseases increases exponentially at similar rates. The corresponding doubling time is approximately eight years, close to the mortality risk doubling time from Gompertz law of mortality15. The close association between disease and mortality risk dynamics suggests the possibility of a single underlying mechanism, that is aging. We hypothesize that the incidence of the selected diseases is therefore a natural measure of the organism resilience and hence of aging process progression. Accordingly, the disease-free survival, the healthspan, is expected to be a useful phenotype, directly associated with the rate of aging. To reveal the genetic determinants of the healthspan, we built a proportional hazards model to predict the age corresponding to the incidence of the first disease from the “Gompertzian cluster” depending on an individual’s age, gender, genetic variation, and a number of more “technical” covariates. We used the sub-population of 300,447 genetically confirmed white British ancestry individuals (hereafter referred to as GCW-British) as a discovery cohort for a GWAS and identified 12 loci associated with healthspan at the whole-genome level of significance. The genetic signature of healthspan has high and significant genetic correlations with GWAS of obesity, type 2 diabetes, coronary heart disease, traits related to metabolic syndrome, and all-cause mortality (as derived from parental survival). We conclude by noting that the healthspan phenotype offers a promising new way to investigate human aging by exploiting the data from large cohorts of living individuals with rich clinical information.
Source:
https://www.nature.com/articles/s42003-019-0290-0
