Increased Estimated GFR Is Negatively Associated With the Risk of SARS-CoV-2 Infection and Severe COVID-19 Within Normal to Mildly Decreased Levels: Nested Case-Control Study.

BACKGROUND: While accumulating evidence indicates chronic kidney disease as a risk factor for coronavirus disease 2019 (COVID-19), the association between normal or mildly decreased kidney function and COVID-19 is unaddressed. Here, we have examined the association of an increase in estimated glomerular filtration rate (eGFR) with the incidence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severe COVID-19 outcomes among patients within normal to mildly decreased kidney function. METHODS: The patients who participated in both health screenings from period I (2017-2018) to II (2019-2020) were enrolled to our study. All participants were categorized into four groups according to the changes in eGFR stage from period I to II: 1) persistently stage G1, 2) from stage G2 to G1, 3) from stage G1 to G2, 4) persistently stage G2. In addition, the changes in eGFR value were defined by subtracting its value of period I from II. Patients were followed up for SARS-CoV-2 infection from January 1, 2021 to any diagnosis of COVID-19 or December 31, 2021, whichever happened first. In addition, those with SARS-CoV-2 infection were followed-up for one month after diagnosis to analyze severe COVID-19. Adjusted odds ratio (aOR) was calculated using multivariable-adjusted logistic regression. RESULTS: We identified 159,427 patients with and 1,804,798 patients without SARS-CoV-2 infection. The risk of SARS-CoV-2 infection decreased when eGFR stage changed from G2 to G1 (aOR, 0.957; 95% confidence interval [CI], 0.938-0.977) and persistently maintained at G1 (aOR, 0.966; 95% CI, 0.943-0.990), compared with the persistently stage G2 group. In addition, the risk showed an inverse relationship with changes in eGFR value, which was depicted by restricted cubic spline curves. For the overall risk of severe COVID-19, the persistently stage G1 showed the lowest risk (aOR, 0.897; 95% CI, 0.827-0.972), followed by those from stage G1 to G2 (aOR, 0.900; 95% CI, 0.828-0.978) and those from stage G2 to G1 (aOR, 0.931; 95% CI, 0.871-0.995), compared with the persistently stage G2 group. CONCLUSION: An increase in eGFR was negatively associated with the risk of SARS-CoV-2 infection and severe COVID-19 among normal or mildly decreased kidney function. For severe COVID-19, maintaining higher baseline eGFR may act as a protective factor against its risk.

Exercise Frequency Reduction Is Associated With Higher Risk of Infection in Newly Diagnosed Diabetes: A Nationally Representative Cohort Study.

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.

Effects of meteorological factors and air pollutants on the incidence of COVID-19 in South Korea.

Air pollution and meteorological factors can exacerbate susceptibility to respiratory viral infections. To establish appropriate prevention and intervention strategies, it is important to determine whether these factors affect the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Therefore, this study examined the effects of sunshine, temperature, wind, and air pollutants including sulfur dioxide (SO2), carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), particulate matter ≤2.5 µm (PM2.5), and particulate matter ≤10 µm (PM10) on the age-standardized incidence ratio of coronavirus disease (COVID-19) in South Korea between January 2020 and April 2020. Propensity score weighting was used to randomly select observations into groups according to whether the case was cluster-related, to reduce selection bias. Multivariable logistic regression analyses were used to identify factors associated with COVID-19 incidence. Age 60 years or over (odds ratio [OR], 1.29; 95% CI, 1.24-1.35), exposure to ambient air pollutants, especially SO2 (OR, 5.19; 95% CI, 1.13-23.9) and CO (OR, 1.17; 95% CI, 1.07-1.27), and non-cluster infection (OR, 1.28; 95% CI, 1.24-1.32) were associated with SARS-CoV-2 infection. To manage and control COVID-19 effectively, further studies are warranted to confirm these findings and to develop appropriate guidelines to minimize SARS-CoV-2 transmission.

Maternal disease factors associated with neonatal jaundice: a case-control study.

BACKGROUND: Neonatal jaundice is common, and despite the considerable medical costs associated with it, there are still few studies on the maternal factors associated with it. Identification of maternal factors associated with neonatal jaundice is very important in terms of prevention, screening and management of neonatal jaundice. The current study aimed to identify maternal disease factors associated with neonatal jaundice. METHODS: We compared the maternal disease diagnostic codes during pregnancy (study A) and 1 year before conception (study B) in mothers whose insurance claims data included newborns treated for neonatal jaundice before birth registration via the National Health Insurance Service-National Sample Cohort (control group). To decrease the effect of confounding variables, the neonatal jaundice and control groups were matched at a ratio of 1:10 via propensity score matching using covariates including age and income. RESULTS: The matched samples for studies A and B included 4,026 and 3,278 (jaundice group: 366 and 298) delivery cases, respectively. In both studies, the jaundice group had a higher proportion of patients who underwent cesarean section than the control group. In study A, other diseases of the digestive system had the highest odds ratio (OR) (K92; adjusted OR: 14.12, 95% confidence interval [CI]: 2.70-82.26). Meanwhile, gastritis and duodenitis had the lowest OR (K29; adjusted OR: 0.39, 95% CI: 0.22-0.69). In study B, salpingitis and oophoritis had the highest OR (N70; adjusted OR: 3.33, 95% CI: 1.59-6.94). Heartburn had the lowest OR (R12; adjusted OR: 0.29, 95% CI:0.12-0.71). CONCLUSIONS: This study identified maternal disease factors correlated with neonatal jaundice during pregnancy and 1 year before conception. Maternal risk factors for neonatal jaundice included syphilis and leiomyoma during pregnancy, and salpingo-oophoritis before pregnancy. The protective factors included infection, inflammatory diseases, and dyspepsia.

Discovery of Depression-Associated Factors From a Nationwide Population-Based Survey: Epidemiological Study Using Machine Learning and Network Analysis.

BACKGROUND: In epidemiological studies, finding the best subset of factors is challenging when the number of explanatory variables is large. OBJECTIVE: Our study had two aims. First, we aimed to identify essential depression-associated factors using the extreme gradient boosting (XGBoost) machine learning algorithm from big survey data (the Korea National Health and Nutrition Examination Survey, 2012-2016). Second, we aimed to achieve a comprehensive understanding of multifactorial features in depression using network analysis. METHODS: An XGBoost model was trained and tested to classify "current depression" and "no lifetime depression" for a data set of 120 variables for 12,596 cases. The optimal XGBoost hyperparameters were set by an automated machine learning tool (TPOT), and a high-performance sparse model was obtained by feature selection using the feature importance value of XGBoost. We performed statistical tests on the model and nonmodel factors using survey-weighted multiple logistic regression and drew a correlation network among factors. We also adopted statistical tests for the confounder or interaction effect of selected risk factors when it was suspected on the network. RESULTS: The XGBoost-derived depression model consisted of 18 factors with an area under the weighted receiver operating characteristic curve of 0.86. Two nonmodel factors could be found using the model factors, and the factors were classified into direct (P<.05) and indirect (P≥.05), according to the statistical significance of the association with depression. Perceived stress and asthma were the most remarkable risk factors, and urine specific gravity was a novel protective factor. The depression-factor network showed clusters of socioeconomic status and quality of life factors and suggested that educational level and sex might be predisposing factors. Indirect factors (eg, diabetes, hypercholesterolemia, and smoking) were involved in confounding or interaction effects of direct factors. Triglyceride level was a confounder of hypercholesterolemia and diabetes, smoking had a significant risk in females, and weight gain was associated with depression involving diabetes. CONCLUSIONS: XGBoost and network analysis were useful to discover depression-related factors and their relationships and can be applied to epidemiological studies using big survey data.

Epidemiological Characterization of a Directed and Weighted Disease Network Using Data From a Cohort of One Million Patients: Network Analysis.

BACKGROUND: In the past 20 years, various methods have been introduced to construct disease networks. However, established disease networks have not been clinically useful to date because of differences among demographic factors, as well as the temporal order and intensity among disease-disease associations. OBJECTIVE: This study sought to investigate the overall patterns of the associations among diseases; network properties, such as clustering, degree, and strength; and the relationship between the structure of disease networks and demographic factors. METHODS: We used National Health Insurance Service-National Sample Cohort (NHIS-NSC) data from the Republic of Korea, which included the time series insurance information of 1 million out of 50 million Korean (approximately 2%) patients obtained between 2002 and 2013. After setting the observation and outcome periods, we selected only 520 common Korean Classification of Disease, sixth revision codes that were the most prevalent diagnoses, making up approximately 80% of the cases, for statistical validity. Using these data, we constructed a directional and weighted temporal network that considered both demographic factors and network properties. RESULTS: Our disease network contained 294 nodes and 3085 edges, a relative risk value of more than 4, and a false discovery rate-adjusted P value of <.001. Interestingly, our network presented four large clusters. Analysis of the network topology revealed a stronger correlation between in-strength and out-strength than between in-degree and out-degree. Further, the mean age of each disease population was related to the position along the regression line of the out/in-strength plot. Conversely, clustering analysis suggested that our network boasted four large clusters with different sex, age, and disease categories. CONCLUSIONS: We constructed a directional and weighted disease network visualizing demographic factors. Our proposed disease network model is expected to be a valuable tool for use by early clinical researchers seeking to explore the relationships among diseases in the future.

Yin and Yang of disease genes and death genes between reciprocally scale-free biological networks.

Biological networks often show a scale-free topology with node degree following a power-law distribution. Lethal genes tend to form functional hubs, whereas non-lethal disease genes are located at the periphery. Uni-dimensional analyses, however, are flawed. We created and investigated two distinct scale-free networks; a protein-protein interaction (PPI) and a perturbation sensitivity network (PSN). The hubs of both networks exhibit a low molecular evolutionary rate (P < 8 × 10(-12), P < 2 × 10(-4)) and a high codon adaptation index (P < 2 × 10(-16), P < 2 × 10(-8)), indicating that both hubs have been shaped under high evolutionary selective pressure. Moreover, the topologies of PPI and PSN are inversely proportional: hubs of PPI tend to be located at the periphery of PSN and vice versa. PPI hubs are highly enriched with lethal genes but not with disease genes, whereas PSN hubs are highly enriched with disease genes and drug targets but not with lethal genes. PPI hub genes are enriched with essential cellular processes, but PSN hub genes are enriched with environmental interaction processes, having more TATA boxes and transcription factor binding sites. It is concluded that biological systems may balance internal growth signaling and external stress signaling by unifying the two opposite scale-free networks that are seemingly opposite to each other but work in concert between death and disease.

COVID-19-related cardiovascular disease risk due to weight gain: a nationwide cohort study.

BACKGROUND: Cardiovascular disease (CVD) is a significant contributor to morbidity and mortality worldwide, with CVD and post-acute COVID-19 associated CVD increasing. It remains unknown whether COVID-19 patients with weight gain are at a high risk for CVD events. Therefore, the primary objective of this study is to investigate the association between weight control and the risk of CVD following COVID-19. METHODS: The study included 2,024,728 adults who participated in two rounds of health screening between 2017 and 2020. The final cohort, which included 70,996 participants in the COVID-19 group and 212,869 participants in the control group. The adjusted hazard ratio of BMI change to CVD risk was calculated using Cox proportional hazards regression. RESULTS: We identified a total of 2869 cases of CVD (861 events for COVID-19 group and 2,008 events for the control group). Compared to individuals with a stable BMI, COVID-19 patients without obesity had an increased risk of CVD (adjusted hazard ratio [aHR] = 2.28; 95% confidence interval [CI], 1.15-4.53; p-value = 0.018). Additionally, non-COVID-19 patients with obesity also exhibited a higher risk of CVD (aHR = 1.58; 95% CI, 1.01-2.47; p-value = 0.046). CONCLUSION: In conclusion, people who gained weight during the pandemic, regardless of their weight category, had a significantly higher risk of CVD associated with COVID-19 compared to those who maintained their weight before the pandemic.

Predicted Body Composition Against COVID-19: A Potential Digital Health Strategy.

This study focused on the associations between predicted lean body mass index (LBMI), appendicular skeletal muscle mass index (ASMI), and body fat mass index (BFMI) with the 2019 coronavirus disease (COVID-19). A nationwide population-based non-underweight cohort of 2,037,714 participants underwent two consecutive biennial health screening examinations, with changes in predicted body composition indices estimated using a multivariable-adjusted logistic regression model. Increased LBMI and ASMI were associated with a lower COVID-19 risk among men who became obese. In COVID-19 patients, increased LBMI, ASMI, and BFMI were associated with a higher risk of extracorporeal membrane oxygenation among obese men.

Machine Learning Models for Low Back Pain Detection and Factor Identification: Insights From a 6-Year Nationwide Survey.

This study aimed to enhance performance, identify additional predictors, and improve the interpretability of biopsychosocial machine learning models for low back pain (LBP). Using survey data from a 6-year nationwide study involving 17,609 adults aged ≥50 years (Korea National Health and Nutrition Examination Survey), we explored 119 factors to detect LBP in individuals who reported experiencing LBP for at least 30 days within the previous 3 months. Our primary model, model 1, employed eXtreme Gradient Boosting (XGBoost) and selected primary factors (PFs) based on their feature importance scores. To extend this, we introduced additional factors, such as lumbar X-ray findings, physical activity, sitting time, and nutrient intake levels, which were available only during specific survey periods, into models 2 to 4. Model performance was evaluated using the area under the curve, with predicted probabilities explained by SHapley Additive exPlanations. Eleven PFs were identified, and model 1 exhibited an enhanced area under the curve .8 (.77-.84, 95% confidence interval). The factors had varying impacts across individuals, underscoring the need for personalized assessment. Hip and knee joint pain were the most significant PFs. High levels of physical activity were found to have a negative association with LBP, whereas a high intake of omega-6 was found to have a positive association. Notably, we identified factor clusters, including hip joint pain and female sex, potentially linked to osteoarthritis. In summary, this study successfully developed effective XGBoost models for LBP detection, thereby providing valuable insight into LBP-related factors. Comprehensive LBP management, particularly in women with osteoarthritis, is crucial given the presence of multiple factors. PERSPECTIVE: This article introduces XGBoost models designed to detect LBP and explores the multifactorial aspects of LBP through the application of SHapley Additive exPlanations and network analysis on the 4 developed models. The utilization of this analytical system has the potential to aid in devising personalized management strategies to address LBP.

Association between short- and medium-term exposure to air pollutants and depressive episode using comprehensive air quality index among the population in South Korea.

BACKGROUND: 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. METHODS: 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, PM2.5, 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. RESULTS: 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, PM2.5, 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. CONCLUSIONS: Short- and medium-term exposure of air pollution may be associated with an increased risk of depressive episodes, especially for cold season.

Moderate-to-Vigorous Physical Activity Changes with the Risk of SARS-CoV-2 Infection: A Nested Case-Control Study.

We identified the association of changes in moderate-to-vigorous physical activity (MVPA) with SARS-CoV-2 infection. From 6,396,500 patients, we performed a nested case-control study who participated in both biennial check-ups. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were calculated using multivariable logistic regression. From physically inactive patients at period I, the odds increased when engaged in 1-2, 3-4, or ≥5 times of MVPA/week at period II. This study found that MVPA was directly associated with SARS-CoV-2 infection.

Empowering Healthcare through Comprehensive Informatics Education: The Status and Future of Biomedical and Health Informatics Education.

OBJECTIVES: Education in biomedical and health informatics is essential for managing complex healthcare systems, bridging the gap between healthcare and information technology, and adapting to the digital requirements of the healthcare industry. This review presents the current status of biomedical and health informatics education domestically and internationally and proposes recommendations for future development. METHODS: We analyzed evidence from reports and papers to explore global trends and international and domestic examples of education. The challenges and future strategies in Korea were also discussed based on the experts' opinions. RESULTS: This review presents international recommendations for establishing education in biomedical and health informatics, as well as global examples at the undergraduate and graduate levels in medical and nursing education. It provides a thorough examination of the best practices, strategies, and competencies in informatics education. The review also assesses the current state of medical informatics and nursing informatics education in Korea. We highlight the challenges faced by academic institutions and conclude with a call to action for educators to enhance the preparation of professionals to effectively utilize technology in any healthcare setting. CONCLUSIONS: To adapt to the digitalization of healthcare, systematic and continuous workforce development is essential. Future education should prioritize curriculum innovations and the establishment of integrated education programs, focusing not only on students but also on educators and all healthcare personnel in the field. Addressing these challenges requires collaboration among educational institutions, academic societies, government agencies, and international bodies dedicated to systematic and continuous workforce development.

Development of a deep learning model for predicting recurrence of hepatocellular carcinoma after liver transplantation.

Background: Liver transplantation (LT) is one of the main curative treatments for hepatocellular carcinoma (HCC). Milan criteria has long been applied to candidate LT patients with HCC. However, the application of Milan criteria failed to precisely predict patients at risk of recurrence. As a result, we aimed to establish and validate a deep learning model comparing with Milan criteria and better guide post-LT treatment. Methods: A total of 356 HCC patients who received LT with complete follow-up data were evaluated. The entire cohort was randomly divided into training set (n = 286) and validation set (n = 70). Multi-layer-perceptron model provided by pycox library was first used to construct the recurrence prediction model. Then tabular neural network (TabNet) that combines elements of deep learning and tabular data processing techniques was utilized to compare with Milan criteria and verify the performance of the model we proposed. Results: Patients with larger tumor size over 7 cm, poorer differentiation of tumor grade and multiple tumor numbers were first classified as high risk of recurrence. We trained a classification model with TabNet and our proposed model performed better than the Milan criteria in terms of accuracy (0.95 vs. 0.86, p < 0.05). In addition, our model showed better performance results with improved AUC, NRI and hazard ratio, proving the robustness of the model. Conclusion: A prognostic model had been proposed based on the use of TabNet on various parameters from HCC patients. The model performed well in post-LT recurrence prediction and the identification of high-risk subgroups.

A blockchain-based healthcare data marketplace: prototype and demonstration.

Objectives: This study aimed to develop healthcare data marketplace using blockchain-based B2C model that ensures the transaction of healthcare data among individuals, companies, and marketplaces. Materials and methods: We designed an architecture for the healthcare data marketplace using blockchain. A healthcare data marketplace was developed using Panacea, MySQL 8.0, JavaScript library, and Node.js. We evaluated the performance of the data marketplace system in 3 scenarios. Results: We developed mobile and web applications for healthcare data marketplace. The transaction data queries were executed fully within about 1-2 s, and approximately 9.5 healthcare data queries were processed per minute in each demonstration scenario. Discussion: Blockchain-based healthcare data marketplaces have shown compliance performance in the process of data collection and will provide a meaningful role in analyzing healthcare data. Conclusion: The healthcare data marketplace developed in this project can iron out time and place limitations and create a framework for gathering and analyzing fragmented healthcare data.

COVID-19 Vaccination Is Not Associated with Psychiatric Adverse Events: A Meta-Analysis.

Coronavirus disease 2019 (COVID-19) has been a global health problem since December 2019. Vaccination has been widely considered the best way to prevent COVID-19 pandemic, but public concerns about the safety of vaccines remain. There have been many studies reporting adverse events in the vaccinated. However, to date, no meta-analysis of the association of COVID-19 vaccination with psychiatric adverse events has been conducted yet. In this meta-analysis, studies on depression, anxiety and distress after COVID-19 vaccination were searched in the PubMed, Cochrane and Embase from January 2020 to April 2022. The OR of depression in four studies with a total sample size of 462,406 is obtained as 0.88 (95% CI; 0.75, 1.03), and the OR of anxiety as 0.86 (95% CI; 0.71, 1.05). However, there were no statistically significant differences between the groups. The mean difference of distress in two studies was −0.04 (95%CI; −0.05, −0.02; p < 0.0001). As a result of the moderator analysis, married people experienced less depression and anxiety after vaccination, and in White people, depression after vaccination was lower than others. We also found that people with a history of COVID-19 infection were more depressed and anxious after vaccination. We suggest that COVID-19 vaccination was not associated with a worsening of depression and anxiety.

Association of Physical Activity With SARS-CoV-2 Infection and Severe Clinical Outcomes Among Patients in South Korea.

Importance: The association of moderate to vigorous physical activity (MVPA) with COVID-19 outcomes is unclear and needs to be investigated. Objective: To identify the association of longitudinal changes in MVPA with SARS-CoV-2 infection and severe COVID-19 outcomes. Design, Setting, and Participants: This nested case-control study used data from 6 396 500 adult patients in South Korean who participated in National Health Insurance Service (NHIS) biennial health screenings from period 1 (2017-2018) to period 2 (2019-2020). Patients were followed from October 8, 2020, until the diagnosis of COVID-19 or December 31, 2021. Exposure: Moderate to vigorous physical activity was measured by self-report on questionnaires during both NHIS health screenings and calculated by adding the frequency (times per week) of each moderate (≥30 minutes per day) and vigorous (≥20 minutes per day) physical activity. Main Outcomes and Measures: The main outcomes were a positive diagnosis of SARS-CoV-2 infection and severe COVID-19 clinical events. Adjusted odds ratio (aORs) and 99% CIs were calculated using multivariable logistic regression analysis. Results: A total of 183 350 patients with COVID-19 (mean [SD] age, 51.9 [13.8] years; female, 89 369 [48.7%]; male, 93 981 [51.3%]) among 2 110 268 participants were identified. For participants with vs without COVID-19, the proportion of MVPA frequency at period 2 was 35.8% vs 35.9% for physically inactive, 18.9% vs 18.9% for 1 to 2 times per week, 17.7% vs 17.7% for 3 to 4 times per week, and 27.5% vs 27.4% for 5 or more times per week. Among unvaccinated, physically inactive patients at period 1, the odds for infection increased when engaged in MVPA 1 to 2 times per week (aOR, 1.08; 99% CI, 1.01-1.15), 3 to 4 times per week (aOR, 1.09; 99% CI, 1.03-1.16), or 5 or more times per week (aOR, 1.10; 99% CI, 1.04-1.17) at period 2. Conversely, among unvaccinated patients with MVPA of 5 or more times per week at period 1, the odds for infection decreased when engaged 1 to 2 times per week (aOR, 0.90; 99% CI, 0.81-0.98) or physically inactive (aOR, 0.80; 99% CI, 0.73-0.87) at period 2. The trend of MVPA and incident infection was mitigated when participants were fully vaccinated. Furthermore, the odds for severe COVID-19 showed significant but limited associations with MVPA. Conclusions and Relevance: The findings of this nested case-control study show a direct association of MVPA with risk of SARS-CoV-2 infection, which was mitigated after completion of the COVID-19 vaccination primary series. In addition, higher levels of MVPA were associated with a lower risk of severe COVID-19 outcomes to limited proportions.

Association of weight changes with SARS-CoV-2 infection and severe COVID-19 outcomes: A nationwide retrospective cohort study.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Poorer outcomes following COVID-19 infection for patients with depression: A cohort analysis in South Korea.

Introduction: It remains unknown whether patients with pre-existing depressive conditions are at high risk of severe COVID-19. Therefore, this study aims to investigate the association between patients with pre-existing depressive conditions and severe COVID-19. Method: This study is part of the Korea Disease Control and Prevention Agency-COVID19-National Health Insurance Service cohort study of an ongoing large-scale health screening survey of adults 18 years and older residing in South Korea. Pre-existing depression status was measured from 552,860 patients who participated in a biennial health screening from 2019 to 2020. Finally, 29,106 confirmed COVID-19 patients were enrolled and followed up to track any severe clinical events within 1 month of their diagnosis date. Adjusted odds ratio (aOR) and 95% confidence interval (CI) were calculated using multivariate-adjusted logistic regression analysis. Results: We identified 2868 COVID-19 patients with severe clinical events and 26,238 COVID-19 patients without severe clinical events. The moderate-to-severe depressive symptoms group showed an elevated odds of severe outcomes of COVID-19 (aOR, 1.46; 95% CI, 1.25-1.72), including those without vaccination (aOR, 1.32; 95% CI, 1.08-1.61) and those with complete vaccination (aOR, 1.76; 95% CI, 1.18-2.63). In addition, those who were diagnosed with depression along with depressive symptoms at the health screening revealed an increased risk of severe outcomes of COVID-19 (aOR, 2.22; 95% CI, 1.22-4.05). Conclusion: Moderate-to-severe depressive symptoms were associated with higher odds of severe COVID-19 events in both no and complete vaccination groups. Participants with depressive symptoms may be at higher risk of severe outcomes of COVID-19.