Background Age is the most important risk factor for the majority of human diseases. Addressing the impact of age-related diseases has become a priority in healthcare practice, leading to the exploration of innovative approaches, including the development of predictors to estimate biological age (so-called “ageing clocks”). These predictors offer promising insights into the ageing process and age-related diseases. This study aims to showcase the significance of ageing clocks within a unique, deeply phenotyped longitudinal cohort. By utilising omics-based approaches alongside gold-standard clinical risk predictors, we elucidate the potential of these novel predictors in revolutionising personalised healthcare and better understanding the ageing process.

Methods We analysed data from the IAM Frontier longitudinal study that collected extensive data from 30 healthy individuals over the timespan of 13 months: DNA methylation data, clinical biochemistry, proteomics and metabolomics measurements as well as data from physical health examinations. For each individual, biological age (BA) and health traits predictions were computed from 29 epigenetic clocks, 4 clinical-biochemistry clocks, 2 proteomics clocks, and 3 metabolomics clocks.

Findings Within the BA prediction framework, comprehensive analyses can discover deviations in biological ageing. Our study shows that the within-person BA predictions at different time points are more similar to each other than the between-person predictions at the same time point, indicating that the ageing process is different between individuals but relatively stable within individuals. Individual-based analyses show interesting findings for three study participants, including observed hematological problems, that further supported and complemented by the current gold standard clinical laboratory profiles.

Interpretation Our analyses indicate that BA predictions can serve as instruments for explaining many biological phenomena and should be considered crucial biomarkers that can complement routine medical tests. With omics becoming routinely measured in regular clinical settings, omics-based BA predictions can be added to the lab results to give a supplementary outlook assisting decision-making in doctors’ assessments.