A predictive model for determining the probability of age-related diseases

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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 significant (p<0.001). It was shown that the chances of prediabetes decrease with age, but increase 16.923 times in the presence of excess body weight, 19.924 times in the presence of obesity, and 2.574 times in the presence of diabetes mellitus. The probability estimate “p” has statistically significant predictive power (AUC=0.885; p<0.001). The optimal threshold value is p=0.089 (sensitivity – 96.2%, specificity – 74.7%).

Conclusion. A predictive model for age-related diseases is important in personalized medicine and aging prevention. Developing predictive models requires analyzing big data, identifying key biomarkers and risk factors that correlate with disease development.

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作者简介

I. Kochetkova

N.N. Burdenko Voronezh State Medical University, Ministry of Health of Russia

编辑信件的主要联系方式.
Email: iri4217@yandex.ru
ORCID iD: 0000-0002-7546-6679
SPIN 代码: 9933-5015

Associate Professor, Candidate of Medical Sciences

俄罗斯联邦, Voronezh

参考

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2. Fig. 1. Assessment of odds ratios with a 95% confidence interval for the considered prognostic factors of prediabetes

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3. Fig. 2. ROC curve demonstrating the capabilities of the regression model in identifying cases of prediabetes in prediction

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4. Fig. 3. Influence of threshold values for prediabetes probability on the sensitivity and specificity indicators of the developed model

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5. Fig. 4. Assessment of odds ratios with a 95% confidence interval for the considered prognostic factors of diabetes

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6. Fig. 5. ROC curve demonstrating the effectiveness of the regression model in identifying cases of diabetes in prediction

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7. Fig. 6. Influence of threshold values for diabetes probability on the sensitivity and specificity indicators of the developed model

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