Gezond ouder worden en and Langlevendheid
Ons doel is mechanismen te identificeren die bijdragen aan gezonder veroudering en langlevendheid. Hiervoor zijn wij gestart met de Leiden LangLeven Studie, waarin 421 families betrokken zijn en verrijkt zijn voor familiare langlevendheid.
In summary, we have enrolled 944 nonagenarian siblings, 1671 of their offspring and 744 partners thereof representing the general population. The middle-aged offspring of the nonagenarians display lower incidence of Type 2 Diabetes, Myocardial infarction and Hypertension than the population controls. Hence, mechanisms involved in the escape, delay of onset of disease and/or the severity of disease may potentially be discovered by studying familial longevity.
To further investigate the underlying mechanism of the metabolic health of these families, we collect omics data (GWAS, next-generation sequencing, lipidomics, metabolomics, transcriptomics) and apply state-of-the-art data-analysis approaches to identify genomic regions and metabolic patterns associated with health parameters.
We recently reported that the nonagenarians of the Leiden Longevity carry as many risk alleles for cardiovascular disease, cancer and type 2 diabetes as the younger population controls. This suggests that other genetic and environmental factors present among individuals surviving into old age may counteract the detrimental effects of disease susceptibility alleles.
In search for genetic loci contributing to healthy ageing and longevity, we initiated a metaGWAS for the longevity phenotype amongst European cohorts. In total, 8,000 long lived cases aged 85 years and over will be compared to European population controls younger than 65 years of age.
Wide scales of genetic approaches are applied to the data of the Leiden Longevity Study, varying from genetic analyses like affected sibpair linkage analysis, genome-wide association analysis and candidate gene based analysis, to bioinformatic approaches for the integration of genetic, transcriptomic and other omics data. Studies integrating genetic data with omics data may eventually reveal genomic risk factors that are more powerful in predicting health and disease than solely based on DNA sequence only.