This predictive model determines risk probabilities for age-related diseases using clinical and genetic data, indicating possible preventive measures.
Objective. To develop and validate a predictive model for determining the probability of developing age-related diseases based on a comprehensive analysis of clinical, genetic, and metabolic data. Materials and methods. The characteristics of somatic health (anthropometry, blood pressure, pulse, complete blood count, ratio of neutrophils to lymphocytes, platelets to lymphocytes, blood biochemistry, glomerular filtration rate, echocardiography, electrocardiography, drug therapy), musculoskeletal parameters (FRAIL, Katz scale, SARC-F questionnaire, handgrip strength, bioimpedance measurement), sensory (ophthalmological examination), cognitive (MMSE test), psychological (Beck scale, asthenic state scale), and nutritional (MNA scale) parameters. Results. A predictive model for the risk of developing prediabetes was created. The model was statistically significan
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Irina Kochetkova (2025) studied this question.
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