A prospective cohort study to develop multi-biomarkers panel to define biological ageing in six different cohorts from newborn to oldest adult: a study protocol.

Age-associated disease management depends significantly on chronological age and macro-level clinical data sets. However, the biological age captures bio-physiological deterioration more precisely than the chronological age. Biological ageing is the accumulation of successive damage to various cells, tissues, and individual organs over the ageing period. It is the explicit reflection of functional decline. Therefore, quantifying biological age can be highly valuable for improving clinical management of age-related changes. Various epigenetic clocks have been used to quantify biological age. However, epigenetics alone cannot fully account for the complex ageing process, which involves ageing hallmarks, signalling pathways, clinical phenotypes, physiological functions, environmental exposures, and lifestyle habits. Therefore, the primary purpose of this pilot study is the feasibility testing and trajectory mapping of the ageing biomarkers across diverse age-based subgroups. This study will help to find reliable, reproducible, robust, and integrative ageing biomarkers to quantify biological age. This community-based prospective cohort study will be conducted at the National Centre of Ageing, All India Institute of Medical Sciences, New Delhi. This study will include 250 participants from six cohorts, i.e. newborns, adolescents (10-19 years), young adults (20-39 years), middle-aged individuals (40-59 years), young olds (60-79 years), and the oldest old (above 80 years). Forty individuals from each cohort will be recruited to study blood and stool biomarkers along with a comprehensive assessment of cognitive behaviour, psychological well-being, functional capacity, gut health, nutritional behaviour, and physiological measures. Participants will also be monitored in real time through wearable devices. After five years, participants will be followed up with the same biomarkers to gain insights about the speed of ageing, predicting disease and mortality. Multi-domain data will be integrated to develop a deep learning-based multi-model algorithm for biological age estimation. This first-of-its-kind study would provide an exhaustive understanding of the ageing process throughout life, 0-100 years. Integrative biomarkers would make a precise determination of biological age. Additionally, studying change in these parameters after five years would elucidate the pace of biological ageing and predict life expectancy and disability.