The rate of ageing in humans is not uniform, due to genetic heterogeneity and the influence of environmental factors. Age-related changes in body function or composition that could serve as a measure of “biological” age and predict the onset of age-related diseases and/or residual lifetime are termed “biomarkers of ageing”. Many candidate biomarkers have been proposed but in all cases their variability in cross-sectional studies is considerable, and therefore no single measurement has so far proven to yield a useful biomarker of ageing on its own, probably due to the multi-causal and multi-system nature of ageing. We propose to conduct a population study (3,700 probands) to identify a set biomarkers of ageing which, as a combination of parameters with appropriate weighting, would measure biological age better than any marker in isolation. Two large groups of subjects will be recruited, i.e. (1) randomly recruited age-stratified individuals from the general population covering the age range 35-74 years and (2) subjects born from a long-living parent belonging to a family with long living sibling(s) already recruited in the framework of the GEHA project. For genetic reasons such individuals (“GEHA offspring”) are expected to age at a slower rate. They will be recruited together with their spouses as controls, thus allowing initial validation of the biomarkers identified. (3) A small number of patients with progeroid syndromes will also be included in the study. A wide range of candidate biomarkers will be tested, including (a) “classical” ones for which data from several smaller studies have been published; (b) “new” ones, based on recent preliminary data, as well as (c) “novel” ones, based on recent research on mechanistic aspects of ageing, conducted by project participants. Bioinformatics will be used in order to extract a robust set of biomarkers of human ageing from the large amounts of data to be generated and to derive a model for healthy ageing.
The recruitment of probands will generate demographic and clinical data to be entered into the central phenotypic database as well as biological samples to be stored in and distributed from the central biobank to Beneficiaries in work packages 2-7. The data arising from their measurements of the large set of candidate biomarkers will be entered into the phenotypic database. Data analysis and bioinformatics work (WP8) on the RASIG probands will yield a “biological age score”. Its initial validation will be possible by performing comparisons between GO and SGO probands and progeroid syndromes (small numbers only) vs RASIG probands, respectively. It is expected that the biological age score is lower in the GO compared to SGO, and higher in the progeroid syndromes compared to RASIG.
Work Package No
Work package title
|1||Recruitment of probands and physiological markers|
|3||Markers based on proteins and their modifications|
|5||Clinical chemistry, hormones and markers of metabolism|
|6||Oxidative stress markers Clinical chemistry, hormones and markers of metabolism|
|7||Emergent biomarkers of ageing from model systems and novel methodological approaches|
|8||Data analysis and bioinformatics|
|9||Dissemination and training|
|10||Project management and ethical issues|