Obesity is a risk factor for COVID-19. However, it is unknown whether weight changes can alter this risk. We investigated the association of weight changes with SARS-CoV-2 infection and acute severe COVID-19 outcomes occurring within two months of the infection. We used 6.3 million nationwide cohort. The body weight was classified as follows: (1) underweight, body mass index (BMI) < 18.5 kg/m²; (2) normal, BMI 18.5-22.9 kg/m²; (3) overweight, BMI 23-24.9 kg/m²; (4) obese, BMI≥ 25 kg/m². Weight changes were defined by comparing the classification of body weight during the health screening period I and II. The outcomes were SARS-CoV-2 infection and severe COVID-19 outcomes within two months after the infection. The association was evaluated using multivariable-adjusted logistic regression. The following covariates were adjusted: age, sex, household income, cigarette smoking, alcohol consumption, physical activity, hypertension, diabetes mellitus, dyslipidemia, Charlson comorbidity index score, and dose of all COVID-19 vaccinations prior to SARS-CoV-2 infection. Of the 2119,460 study participants, 184,204 were infected with SARS-CoV-2. Weight gain showed a higher risk of SARS-CoV-2 infection in underweight to normal and normal to overweight groups. Conversely, weight loss showed a lower risk of SARS-CoV-2 infection in normal to underweight, overweight to underweight, overweight to normal, obese to normal, and obese to overweight groups. In addition, weight gain revealed a higher risk of severe COVID-19 outcomes, whereas weight loss showed a lower risk of severe COVID-19 outcomes. This study found that weight loss and gain are associated with a lower and higher risk of both SARS-CoV-2 infection and severe COVID-19 outcomes, respectively. Healthy weight management may be beneficial against the risk of COVID-19.

Currently, air pollution is suggested as a risk factor for depressive episodes. Our study aimed to consider multiple air pollutants simultaneously, and continuously evaluate air pollutants using comprehensive air quality index (CAI) with depressive episode risk. Using a nationally representative sample survey from South Korea between 2014 and 2020, 20,796 participants who underwent health examination and Patient Depression Questionnaire-9 were included in the study. Six air pollutants (PM₁₀, PM_2.5, O₃, CO, SO₂, NO₂) were measured for the analysis. Every air pollutant was standardized by air quality index (AQI) and CAI was calculated for universal representation. Using logistic regression, short- and medium-term exposure by AQI and CAI with the risk of depressive episode was calculated by odds ratio and 95 % confidence interval (CI). Furthermore, consecutive measurements of CAI over 1-month time intervals were evaluated with the risk of depressive episodes. Every analysis was conducted seasonally. There were 950 depressive episodes occurred during the survey. An increase in AQI for short-term exposure (0-30 days) showed higher risk of depressive episode in CO, while medium-term exposure (0-120 days) showed higher risk of depressive episode in CO, SO₂, PM_2.5, and PM₁₀. During the cold season, the exposure to at least one abnormal CAI within 1-month intervals over 120 days was associated with a 68 % (95 % CI 1.11-2.54) increase in the risk of depressive episode. Short- and medium-term exposure of air pollution may be associated with an increased risk of depressive episodes, especially for cold season.

Lim Y, et al. J Korean Med Sci. 2023 May;38(23):e176. https://doi.org/10.3346/jkms.2023.38.e176

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.

Lim Y, et al. J Korean Med Sci. 2023 Dec;38:e415. https://doi.org/10.3346/jkms.2023.38.e415