Development of an algorithm for assessing biological age based on an explainable artificial intelligence system using laboratory biomarkers and bioimpedance indicators
https://doi.org/10.33667/2078-5631-2026-3-110-115
Abstract
As research in longevity medicine rapidly develops, biological age is increasingly viewed as the most promising integrated indicator combining biomarker signatures and possessing clinical significance. This paper describes the development of an artificial intelligence system for estimating biological age based on multimodal biomedical data. Eight categories of biomarkers were analyzed: bioimpedance analysis, complete blood count and biochemistry, coagulation profile, lipid profile, endocrine status, inflammatory markers, and prostate-specific antigen. Deep neural network architectures (MLP, DANet, FT-Transformer, GANDALF) and explainable AI methods (SHAP) were applied to achieve a more accurate interpretation. The aggregated model, using multiple categories of indicators, achieved a mean absolute error of 6.7 years and a Pearson correlation coefficient of 0,83 with chronological age. The system enables the interpretation of the contribution of individual biomarkers to agerelated acceleration and patient risk stratification, opening up opportunities for personalized medicine and preventive strategies. The results have been validated in the Russian population.
About the Authors
K. S. Belan
Atlas Medical Center LLC
Russian Federation
Russian Federation
Belan Kirill S., head of Preventive Medicine
Moscow
K. A. Lemberg
Atlas Medical Center LLC
Russian Federation
Russian Federation
Lemberg Ksenia A., general director
Moscow
I. R. Fatkhutdinov
Atlas Medical Center LLC
Russian Federation
Russian Federation
Moscow
K. K. Antonov
Atlas Medical Center LLC
Russian Federation
Russian Federation
Antonov Konstantin K., PhD Med Sci, head of Atlas Clinics
Moscow
O. E. Tikhonova
Atlas Medical Center LLC
Russian Federation
Russian Federation
Tikhonova Olesya E., systems analys
Moscow
N. V. Gryazeva
Central State Medical Academy of the Administrative Department of the President of Russian Federation
Russian Federation
Russian Federation
Gryazeva Natalya V., Dr Med Sci (habil.), professor at Dept of Dermatovenereology and Cosmetology
Moscow
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Review
For citations:
Belan K.S., Lemberg K.A., Fatkhutdinov I.R., Antonov K.K., Tikhonova O.E., Gryazeva N.V. Development of an algorithm for assessing biological age based on an explainable artificial intelligence system using laboratory biomarkers and bioimpedance indicators. Medical alphabet. 2026;(3):110-115. (In Russ.) https://doi.org/10.33667/2078-5631-2026-3-110-115
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