Identification of 12 genetic loci associated with human healthspan

Abstract

The mounting challenge of preserving the quality of life in an aging population directs the focus of longevity science to the regulatory pathways controlling healthspan. To understand the nature of the relationship between the healthspan and lifespan and uncover the genetic architecture of the two phenotypes, we studied the incidence of major age-related diseases in the UK Biobank (UKB) cohort. We observed that the incidence rates of major chronic diseases increase exponentially. The risk of disease acquisition doubled approximately every eight years, i.e., at a rate compatible with the doubling time of the Gompertz mortality law. Assuming that aging is the single underlying factor behind the morbidity rates dynamics, we built a proportional hazards model to predict the risks of the diseases and therefore the age corresponding to the end of healthspan of an individual depending on their age, gender, and the genetic background. We suggested a computationally efficient procedure for the determination of the effect size and statistical significance of individual gene variants associations with healthspan in a form suitable for a Genome-Wide Association Studies (GWAS). Using the UKB sub-population of 300, 447 genetically Caucasian, British individuals as a discovery cohort, we identified 12 loci associated with healthspan and reaching the whole-genome level of significance. We observed strong (|ρg| > 0.3) genetic correlations between healthspan and the incidence of specific age-related disease present in our healthspan definition (with the notable exception of dementia). Other examples included all-cause mortality (as derived from parental survival, with ρg = −0.76), life-history traits (metrics of obesity, age at first birth), levels of different metabolites (lipids, amino acids, glycemic traits), and psychological traits (smoking behaviour, cognitive performance, depressive symptoms, insomnia). We conclude by noting that the healthspan phenotype, suggested and characterized here, offers a promising new way to investigate human longevity by exploiting the data from genetic and clinical data on living individuals.

Quantitative Characterization of Biological Age and Frailty Based on Locomotor Activity Records

“…The latter was supported by a significant association between P C 1 and the log-linear proportional incidence of chronic age-related diseases risk estimate in the UKB dataset. These findings corroborate the findings investigating the GWAS of healthspan [18], where at least some of the genetic variants associated with longer healthspans were found to predict both lifespan and the incidence of specific age-related diseases.…”

“…BAA also turned out to be a significant risk factor for the prospective incidence of chronic related diseases. Following [18], we observed that there is a large cluster of age-related diseases, such as cardiovascular diseases (coronary heart disease, heart attack, heart failure, stroke), diabetes, hypertension, and cancer. All the diseases from the list are characterized by an exponentially increasing probability of incidence, with a doubling time similar to that of the mortality rate of eight years identified in the Gompertz mortality law in human population.…”

Biological age is a universal marker of aging, stress, and frailty

“…Aging in most species, including humans, manifests itself as a progressive functional decline leading to increasing prevalence (Mitnitski andRockwood 2016, Yu et al 2017) and incidence of the chronic age-related diseases, such as cancers, diabetes, cardiovascular diseases (Niccoli and Partridge 2012, Podolskiy et al 2016, Zenin et al 2019 or disease-specific mortality (Barzilai and Rennert 2012), all accelerating exponentially at a rate compatible with that of the Gompertz mortality law (Gompertz 1820, Makeham 1860). The physiological indices or physiological state variables, such as blood biochemistry or cell count markers change with age in a highly coordinated manner.…”

Genetically independent phenotype analysis identifies LPA and VCAM1 as drug targets for human ageing

“…The majority of lead SNPs are strongly associated with selfrated health and resilience, in line with the large loadings of these traits in the construction of GIP1 (Figure 3; Supplementary Table 3). Loci near APOE, HLA-DRB1/DQA1, LPA, and CDKN2B/-AS1 have previously been validated using the same trait in an external cohort [2][3][4] . For the remaining 23 loci, we measured lead SNP effects on FinnGen participant survival (Release 5; N = 203,244; 6.94% deceased) and BioBank Japan participant survival (N = 135,983; 24.1% deceased), to provide additional evidence of their association with human ageing traits in independent samples.…”