BACalc, an automated GUI-based toolkit for adaptive biological age estimation using PCA-Dubina or Klemera-Doubal methods, produced estimates consistent with the reference BioAge R package, and BA acceleration predicted all-cause mortality in 2 independent cohorts (UK Biobank and Dalian heart failure).
BACalc's KDM-derived biological age estimates were nearly identical to those from the reference BioAge R package.
The BACalc PCA-Dubina model correlated strongly with BioAge, though less tightly than KDM.
In the UK Biobank, PCA-Dubina-derived age acceleration was associated with higher all-cause mortality hazard.
In the UK Biobank, KDM-derived age acceleration was associated with higher all-cause mortality hazard.
In the Dalian heart failure cohort, PCA-Dubina-derived age acceleration (BAc) independently predicted mortality.
In the Dalian cohort, KDM-derived age acceleration (BA_EC) was also associated with increased mortality hazard, with a smaller effect than PCA-Dubina.
BACalc is a CSV-based toolkit with a graphical interface that lets users choose variables and method without programming and outputs prediction and model files.
The authors conclude BACalc is a reliable platform for BA estimation that is consistent with validated frameworks and convenient for real-time updating.
Biological age is an estimate of how old a person's body seems based on blood tests and other health measures, compared with the number of years they have lived. Existing tools for calculating it often require programming skills and are hard to adapt to new populations or update as more data arrive. The researchers built BACalc, a point-and-click program that takes a spreadsheet of clinical data and calculates biological age using two established methods. When the team compared BACalc with an existing reference tool (the BioAge R package), the Klemera-Doubal method results were nearly identical (correlation 0.99), and the PCA-based method also agreed closely (0.89). In the UK Biobank and in a cohort of heart failure patients from Dalian, people whose biological age was further ahead of their actual age had a higher risk of death. Each extra year of age acceleration was linked to roughly a 1.3% to 2.8% higher mortality hazard, depending on the cohort and method. This research suggests that biological age estimates can be produced by an accessible tool that matches an established one and can be applied to different datasets. The findings show association with mortality, not that any intervention based on these scores would help, and the individual per-year effect sizes are modest.
Liu H, Amabi Perez Aranda A, Au A, Wu D, Zhou H, Wong W, et al.. (2026). Dataset-Agnostic Biological Age Calculator and Predictions of Mortality in NHANES (1999-2010), UK Biobank (2006-2010), and Dalian Heart Failure (2015-2023) Cohorts: Development and Validation Study.. JMIR formative research. https://doi.org/10.2196/95321