Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China

ObjectiveInsulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the “common soil” of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings.MethodsWe analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models.ResultsThe LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc.ConclusionThe ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings

Dostępność opieki zdrowotnej oraz ostatnie zmiany w systemie ochrony zdrowia w Republice Białorusi

The accessibility of health care to the population in Belarus is illustrated in terms of indicators of health care provision: medical services personnel density, hospital bed / population ratio and development of medical education. Through the prism of WHO-defined “financial profile” of health care, several aspects of state policy are analyzed. Particular attention is given to the quality of medical education, employment and financial motivation of health care workers. The areas for further health care development in the Republic are specified.Dostępność opieki zdrowotnej na Białorusi zilustrowano za pomocą wskaźników świadczeń zdrowotnych dla ludności: gęstości personelu medycznego, wskaźnika liczby łóżek szpitalnych do liczby ludności oraz rozwoju kształcenia medycznego. Przez pryzmat zdefiniowanego przez WHO „profilu finansowego” opieki zdrowotnej dokonano analizy szeregu aspektów polityki państwa. Szczególną uwagę poświęcono jakości kształcenia medycznego, zatrudnieniu i motywacji finansowej pracowników służby zdrowia, a także wskazano obszary dalszego rozwoju opieki zdrowotnej w kraju

Nutritional and lifestyle factors determining the problem of overweight and obesity among teenagers and youth during the COVID-19 pandemic : a comparative survey study among Poles and Belarusians

Introduction: The prevalence of obesity is increasing worldwide, especially among youth. The main cause of obesity in young people is a combination of excessive caloric intake and reduced physical activity. In addition to individual dietary and physical activity behaviors, genetic predisposition, socioeconomic and environmental factors, and comorbidities may contribute to obesity. Aim: The study aims to examine and compare the factors determining food preferences and habits as well as the occurrence of overweight and obesity among teenagers and youth in Poland and Belarus. Material and methods: An anonymous questionnaire consisting of 58 questions based on the Eating Behavior Questionnaire (QEB) was used. Responses were received from 700 young people from Poland and 690 from Belarus, some of whom were rejected. The data was collected in May and June 2021 and analyzed in the Statistica program (statistically significant results at p<0.05). The research was financed by a grant from the Polish National Agency for Academic Exchange. Results: The study group was divided into four groups in terms of age and nationality: Poles <20 years old (428 people), Belarusians <20 years old (222 people), Poles 20-29 years old (210 people), and Belarusians 20-29 years old (295 people ). The correct BMI had 66.7%, 74.3%, 70.7%, and 71.2% of the respondents, respectively (p=0.057; p=0.888). In both age groups, in the Mann-Whitney U test, there are significant differences in the number of meals per day (p<0.001) - Poles eat 4 or 5 meals a day more often, and Belarusians 3. In the chi-square test, both age groups showed a significant statistical difference (p<0.001) in the question about eating breakfast every day - Poles eat breakfast more often than Belarusians. The Mann-Whitney U test showed no differences in sleep duration between the respondents from both countries (p=0.453; p=0.905). Conclusions: The results of the research indicate a strong need to implement educational programs to raise the awareness among young people about healthy eating. Obesity, skipping breakfast, too few meals during the day, and their incorrect composition indicate the direction of pro-health policy, which will be conducive to making the right food choices for youth

Table_1_Development and validation of machine learning-augmented algorithm for insulin sensitivity assessment in the community and primary care settings: a population-based study in China.docx

ObjectiveInsulin plays a central role in the regulation of energy and glucose homeostasis, and insulin resistance (IR) is widely considered as the “common soil” of a cluster of cardiometabolic disorders. Assessment of insulin sensitivity is very important in preventing and treating IR-related disease. This study aims to develop and validate machine learning (ML)-augmented algorithms for insulin sensitivity assessment in the community and primary care settings.MethodsWe analyzed the data of 9358 participants over 40 years old who participated in the population-based cohort of the Hubei center of the REACTION study (Risk Evaluation of Cancers in Chinese Diabetic Individuals). Three non-ensemble algorithms and four ensemble algorithms were used to develop the models with 70 non-laboratory variables for the community and 87 (70 non-laboratory and 17 laboratory) variables for the primary care settings to screen the classifier of the state-of-the-art. The models with the best performance were further streamlined using top-ranked 5, 8, 10, 13, 15, and 20 features. Performances of these ML models were evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPR), and the Brier score. The Shapley additive explanation (SHAP) analysis was employed to evaluate the importance of features and interpret the models.ResultsThe LightGBM models developed for the community (AUROC 0.794, AUPR 0.575, Brier score 0.145) and primary care settings (AUROC 0.867, AUPR 0.705, Brier score 0.119) achieved higher performance than the models constructed by the other six algorithms. The streamlined LightGBM models for the community (AUROC 0.791, AUPR 0.563, Brier score 0.146) and primary care settings (AUROC 0.863, AUPR 0.692, Brier score 0.124) using the 20 top-ranked variables also showed excellent performance. SHAP analysis indicated that the top-ranked features included fasting plasma glucose (FPG), waist circumference (WC), body mass index (BMI), triglycerides (TG), gender, waist-to-height ratio (WHtR), the number of daughters born, resting pulse rate (RPR), etc.ConclusionThe ML models using the LightGBM algorithm are efficient to predict insulin sensitivity in the community and primary care settings accurately and might potentially become an efficient and practical tool for insulin sensitivity assessment in these settings.</p