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Research Paper|Volume 18|pp 102—116

Blood biochemical and gut microbiotic neural network models forecasting human biological age

Anastasia A. Kobelyatskaya1,2, Olga N. Tkacheva1, Alexandra A. Melnitskaia1, Anna K. Ilyushchenko1, Lubov V. Machekhina1, Irina D. Strazhesko1, Alexey Moskalev2
  • 1Russian Clinical Research Center for Gerontology, Pirogov Russian National Research Medical University, Ministry of Healthcare of the Russian Federation, Moscow 129226, Russia
  • 2Institute of Biology of Aging and Healthy Longevity Medicine with Preventive Medicine Clinic, Petrovsky Russian Research Centre of Surgery, Moscow, Russia
Received: August 25, 2025Accepted: January 30, 2026Published: March 12, 2026
https://doi.org/10.18632/aging.206360

Copyright: © 2026 Kobelyatskaya et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Biological age reflects the current state of the body, considering the aspects of lifestyle, environment, and hereditary component. Currently there is no universal formula for determining it, but there are markers that can be used to calculate it. This study aims to develop and compare two models for calculating biological age based on laboratory blood tests and composition of gut microbiota. The biochemical model of biological age uses 7 indicators and is gender-specific (general – cystatin-C, IGF-1, DHEAS, only for females – homocysteine, urea, glucose, zonulin, only for males – HbA1c, NT-proBNP, free testosterone, hs-CRP). The microbial model requires the input of percentages of 45 bacterial species as indicators of the gut microbiota. Both methods demonstrate high predictive accuracy (MAE ~ 6 years, R2 > 0.8) and the degree of agreement of assessments both with each other and with PhenoAge (correlation > 0.89). For enhanced interpretability of the models, we applied the SHAP explanation algorithm, which allowed us to evaluate the contribution of each predictor to the final assessment of the biological age.