Biological Age Study in Lifelines

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.