A Prospective Cohort Study to Develop Multi-Biomarkers Panel to Define Biological Ageing in Five Different Cohorts from Newborn to Oldest Adult: A Study Protocol

Background

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 calendar age. Biological ageing is the accumulation of successive damage to various cells, tissues, and individual organs over the ageing period. Quantifying biological ageing could be of great value for better clinical decision-making. Various epigenetic clocks, including the Hannum clock, GrimAge clock, Horvath clock, PhenoAge clock, and DunedinPACE, have been used to quantify biological age. However, epigenetics alone cannot explain all other critical processes, ranging from ageing hallmarks, signalling pathways, clinical phenotypes, physiological functioning, and environmental exposure to lifestyle habits that participate in the ageing process.

Therefore, our primary objective is to define reliable, reproducible, robust, and integrative biomarkers that can manifest all ageing hallmarks and other associated factors to quantify biological age.

Methods/Design

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 200 participants from five cohorts, i . e . newborns, adolescents (10-19 years), middle-aged individuals (20-59 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 behavior, 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.

Conclusion

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, the study of change in these parameters after five years would elucidate the pace of biological ageing and predict life expectancy and disability.