There are more than 300 theories of aging, but none has generated scientific consensus that captures the real aging process. This is a big gap in the field as aging is the most important risk factor for many diseases (cancer, neurodegenerative, infections, cardiovascular, metabolic). Despite many claimed biomarkers of aging, none fulfill standard definition criteria for biomarkers. Most of the time these biomarkers reflect a visible consequence of an unknown mechanism. If we want to influence the process of aging for a longer healthspan we should identify biomarkers that represent through genetics a real surrogate of the healthy aging process genders and understand how it is integrated in physiological processes such as aging. The aging process can be conceptualised as an intertwined hierarchal dysregulation at every level of the organism, from molecules to the organism. Accordingly, we will cover multiple domains using an “omics” approach and treated under a complex bio-informatics pipeline (systems biology approach). Without such an effort we will not be able to understand the healthy aging process which will ultimately lead to identification of ways to slow aging or reverse some of its symptoms. We believe that without such an effort we would not be able to find the real biomarkers (or their combinations) making it possible to influence efficiently the aging process in a beneficial way.
We propose a deep analysis by omics (proteomics, epigenomics, metabolomics, transcriptomics, immunomics and physiologics) generating data on multiple domains of biological aging to identify a restricted series of clinically usable biomarkers some of which representing hubs/nodes being purposeful targets for interventions. These will be related to physiologically relevant clinical dynamic biomarkers determined for each organ (e.g. DEXA, Brain and heart fMRI, vaccination, TUG). This will be a pilot, unbiased hypothesis free study.
We will use advanced integrative bioinformatics and biostatistics methods to discover testable biomarkers for interventions (advanced regression analysis, principal component analysis, module identification) (e.g. Horvath 2013, Hannum 2013, Enroth 2015, Cohen 2013, 2016, Yashin 2015) in order to identify biomarkers’ connectivity and not just statistically significant modulation of the biomarkers.
We will enrol 440 healthy individuals from 11 countries representing a wide genetic diversity in humans (Canada, USA, Mexico, Tunisia, Singapore, India, Italy, Australia, Senegal, Sweden and Russia). Each country will have a total number of 40 participants, 1 male and 1 female from each decade of life: first decade with cord blood representing first decade and then every decade until 60, 2 males and 2 females at decades 60, 70 and 80 and 4 males and 4 females for decades 90 and 100. They will be genetically from the same ethnicity inside of each country. Participants will be free of diseases (diabetes, cancer, hypertension, cognitive impairment, frailty) and from the same socio-economic status. To control for the possibility of cohort effects, meaningful clinically targetable biomarkers will be validated at a 2-year follow-up visit.
Samples: blood (plasma/serum, PBMC, RBC), stool, saliva and urine. Samples will be collected using standard SOP and centralized in one facility. Sample processing for the different omics will be performed using centralized platforms to ensure comparable data between the different partners.
Novelty: few ethnic, gender diversified healthy persons, different ages, different countries, same number of gender, deep omics, innovative computational approach, different sources for omics.
Outcome: We expect with this pilot study to discover real, meaningful, surrogate, targetable biomarkers of healthy aging based on complex omics and treated with a systems biology approach serving as a base for prolonging healthspan.