Background Exercise is an important method to control the progression of diabetes. Since diabetes compromises immune function and increases the risk of infectious diseases, we hypothesized that exercise may affect the risk of infection by its immunoprotective effects. However, population-based cohort studies regarding the association between exercise and the risk of infection are limited, especially regarding changes in exercise frequency. The aim of this study was to determine the association between the change in exercise frequency and the risk of infection among patients with newly diagnosed diabetes. Methods Data of 10,023 patients with newly diagnosed diabetes were extracted from the Korean National Health Insurance Service-Health Screening Cohort. Self-reported questionnaires for moderate-to-vigorous physical activity (MVPA) were used to classify changes in exercise frequency between two consecutive two-year periods of health screenings (2009–2010 and 2011–2012). The association between changes in exercise frequency and the risk of infection was evaluated using multivariable Cox proportional-hazards regression. Results Compared with engaging in ≥ 5 times of MVPA/week during both periods, a radical decrease in MVPA (from ≥ 5 times of MVPA/week to physical inactivity) was associated with a higher risk of pneumonia (adjusted hazard ratio [aHR], 1.60; 95% confidence interval [CI], 1.03–2.48) and upper respiratory tract infection (aHR, 1.15; 95% CI, 1.01–1.31). In addition, a reduction of MVPA from ≥ 5 to < 5 times of MVPA/week was associated with a higher risk of pneumonia (aHR, 1.52; 95% CI, 1.02–2.27), whereas the risk of upper respiratory tract infection was not higher. Conclusion Among patients with newly diagnosed diabetes, a reduction in exercise frequency was related to an increase in the risk of pneumonia. For patients with diabetes, a modest level of physical activity may need to be maintained to reduce the risk of pneumonia.

Background: Prediction of type 2 diabetes mellitus (DM) has been studied widely. However, a hospital visit was necessary to apply previous prediction models for the evaluation of DM. This study was conducted to develop and validate a hospital visit-free self-diagnosis tool for DM.Methods: Participants who underwent health screening between 2017-2018 (n=7,519; training cohort) and 2019-2020 (n=7,564; validation cohort) were extracted from the Korea National Health and Nutrition Examination Survey (KNHANES). DM was defined as doctor-diagnosed DM in a questionnaire. Logistic regression was used to determine independent predictors for DM, and a multivariable logistic regressionbased nomogram was developed for the prediction of DM, which was validated in a cohort consisting of an independent population. The presence of nonalcoholic fatty liver disease (NAFLD) was operationally defined using the KNHANES-NAFLD score.Results: Age, sex, waist circumference, systolic blood pressure, total cholesterol, triglyceride, aspartate aminotransferase, blood urea nitrogen, urinary protein, urinary glucose, and NAFLD were identified as independent predictors for DM. After excluding laboratory variables that require laboratory tests, a simplified multivariable model was conducted based on hospital visit-free variables, including age, sex, waist circumference, systolic blood pressure, and NAFLD. The full and simplified prediction models for DM were presented as nomograms. In the independent validation cohort, the full and simplified DM prediction models were validated with an area under the curve values of 0.903 and 0.824 from the receiver operating characteristic curves, respectively.Conclusions: Involvement of NAFLD has allowed satisfactory prediction of DM without laboratory tests that require a hospital visit. The developed model may be promising in terms of early diagnosis of DM among individuals without hospital visits and may reduce the socioeconomic burden of DM in the realworld, which awaits future prospective trials to confirm.

Chronic obstructive pulmonary disease (COPD) is considered a major cause of death worldwide, and various studies have been conducted for its early diagnosis. Our work developed a scoring system by predicting and validating COPD and performed predictive model implementations. Participants who underwent a health screening between 2017 and 2020 were extracted from the Korea National Health and Nutrition Examination Survey (KNHANES) database. COPD individuals were defined as aged 40 years or older with prebronchodilator forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC < 0.7). The logistic regression model was performed, and the C-index was used for variable selection. Receiver operating characteristic (ROC) curves with area under the curve (AUC) values were generated for evaluation. Age, sex, waist circumference and diastolic blood pressure were used to predict COPD and to develop a COPD score based on a multivariable model. A simplified model for COPD was validated with an AUC value of 0.780 from the ROC curves. In addition, we evaluated the association of the derived score with cardiovascular disease (CVD). COPD scores showed significant performance in COPD prediction. The developed score also showed a good effect on the diagnostic ability for CVD risk. In the future, studies comparing the diagnostic accuracy of the derived scores with standard diagnostic tests are needed.