What this means
A positive difference between biological age (BA) and chronological age indicates accelerated aging. The application of existing BA models is limited by high technical demands, a lack of alignment across diverse populations, and difficulty in updating them to accommodate expanding cohorts.
A plain-English summary is on its way. Until then, this is the start of the authors' own summary.
- Who it was tested in
- People
- Kind of research
- A study
The science
- Original title
- 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.
- Authors
- Liu H, Amabi Perez Aranda A, Au A et al.
- Journal
- JMIR formative research
- Published
- Oct 29, 2026
- Study type
- Study · in humans
- PMID
- 42954807 ↗
Abstract
Background
A positive difference between biological age (BA) and chronological age indicates accelerated aging. The application of existing BA models is limited by high technical demands, a lack of alignment across diverse populations, and difficulty in updating them to accommodate expanding cohorts. To address the issues in existing tools, this project aims to develop and validate an automated toolkit for adaptive BA estimation.
What they did
The proposed toolkit, named BACalc, facilitates the processing of raw clinical datasets stored in CSV format. Within the graphical user interface, users can select (1) the chronological age column, (2) binary variables (eg, sex, smoking, and alcohol use), and (3) continuous variables (eg, creatinine levels) for BA estimation, using the principal component analysis (PCA) method with the Dubina correction or the Klemera-Doubal method (KDM). All numerical operations are implemented using the NumPy and Pandas libraries, and linear modeling components are provided by scikit-learn and SciPy. The program generates three main output files: (1) the row-wise predictions, (2) the regression statistics, and (3) the explicit linear equations and PCA loadings. Example outputs were generated using the National Health and Nutrition Examination Survey (NHANES, 1999-2010), UK Biobank (2006-2010), and Dalian Heart Failure (2015-2023). The results were compared with the reference BioAge R package.
What they found
Using the KDM model, the BACalc-derived and BioAge-derived estimates exhibit a near-perfect correlation (r=0.9937, mean absolute error 2.02 y). The PCA-Dubina model also shows a strong correlation with BioAge (r=0.8871), within a ±20-year window of accelerated aging residuals. In the UK Biobank, a 1-year increase in PCA-Dubina-derived age acceleration was associated with a 1.77% higher hazard of all-cause mortality (hazard ratio [HR] 1.018, 95% CI 1.017-1.018; P<.001). Similarly, KDM-derived age acceleration was associated with a 1.59% higher hazard of mortality per year of accelerated aging (HR 1.016, 95% CI 1.016-1.016; P<.001). In the Dalian cohort, a 1-year increase in age acceleration derived from the PCA-Dubina model (BAc) independently predicted mortality with a 2.84% increased hazard per year (HR 1.028, 95% CI 1.022-1.035; P<.001). Similarly, KDM-derived age acceleration (BA_EC) was associated with a 1.26% increased hazard of mortality per year of accelerated aging (HR 1.013, 95% CI 1.007-1.018; P<.001). BACalc provides a reliable platform for BA estimation, consistent with previously validated computational frameworks and convenient for real-time updating. BA acceleration predicted mortality in 2 independent cohorts.
The authors' abstract, grouped under plain headings from its own section labels.
Liu H, Amabi Perez Aranda A, Au A et al. 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. 2026. doi:10.2196/95321. PMID: 42954807. https://pubmed.ncbi.nlm.nih.gov/42954807/