Population aging presents a serious public health burden, as age is one of the strongest risk factors for chronic-illnesses and deaths. Prevention of aging-related diseases and promotion of healthy aging interventions are thus of paramount importance. It has long been observed that there is person-to-person variation in terms of the pace of aging. This brought in a key concept that due to underlying biological mechanisms, biological age at an individual level can be separated from chronological age (1-3). Estimated using clinical and molecular biomarkers, biological age indeed predicts overall mortality and age-related diseases, which provides important insight on developing surrogate endpoints of health-span extension. Biological age investigations can thus help identify individuals at higher risk of disease and death before they develop clinical manifestation of disease. Biomarkers of biological age have important application potentials, such as evaluation of healthy-aging intervention programs, patient stratification on the basis of biological age in clinical trials, as well as usage as personal health management tools. These applications lead to extension of not just lifespan but also health span to cope with the burden of aging populations worldwide.
We have recently developed and cross-validated biological age algorithms using routine clinical blood biomarkers in advanced age cohorts from the Rotterdam Study (4) (n=1930). Exploring the application of Gompertz proportional hazard regression (5-6) as an integrative platform to integrate multiple biological system data elements from diversified systems, developed and cross-validated two new “Bio-System Age” algorithms in the Rotterdam Study. We have shown within the Rotterdam Study that biological age, the expected age corresponding to a person’s estimated 10-yr mortality risk within a population, predicts elevated risks of all major age-related diseases such as coronary heart disease, diabetes, cancer, stroke, COPD and dementia. These surrogates of biological age are valuable indicators for evaluating preventive strategies. With these pilot data, our next step is to implement and further validate these “Bio-System Age” algorithms in large cohorts.
Nederlandse samenvatting:
Preventie van ouderdomsgerelateerde ziekten en bevordering van interventies voor gezond ouder worden zijn van het grootste belang. Het is al lang waargenomen dat er een variatie is van persoon tot persoon in het tempo van veroudering. Biologische leeftijdsonderzoeken kunnen helpen bij het identificeren van individuen met een hoger risico op ziekte vóór klinische manifestatie. We hebben onlangs cross-gevalideerde biologische leeftijdsalgoritmen met behulp van klinische bloed biomarkers in geavanceerde leeftijdscohorten. Lifelines biedt belangrijke kansen om ons begrip van biologische leeftijd en toepassing voor ziektepreventie te vergroten. We stellen voor om onze algoritmen te implementeren voor alle deelnemers aan Lifelines en om hun associaties met risico op sterfte/ morbiditeiten en leefstijlfactoren te bestuderen.
Engelse samenvatting:
Prevention of aging-related diseases and promotion of healthy aging interventions are of paramount importance. It has long been observed that there is person-to-person variation in pace of aging. Biological age investigations can help identify individuals at higher risk of disease before clinical manifestation. We have recently cross-validated biological age algorithms using clinical blood biomarkers in advanced age cohorts. Lifelines provides important opportunities to further our understanding of biological age and application for disease prevention. We propose to implement our algorithms to all the participants in Lifelines, and to study their associations with risk of mortality/morbidities and lifestyle factors.