Search Results (917)

Objective—To identify effective data analytics and machine learning solutions that can help in the decision-making process in the medical domain and contribute to the understanding of COVID-19 disease. In this study, we analyze data from anonymized electronic medical records of 4711 patients with COVID-19 disease admitted to hospital in Atlanta. Methods—We used random forest, LightGBM, XGBoost, CatBoost, KNN, SVM, logistic regression, and MLP neural network models in this work. The models are evaluated depending on the type of prediction by relevant metrics, especially accuracy, F1-score, and ROC AUC score. Another aim of the work was to find out which factors most affected severity and mortality risk among the patients. To identify the important features, different statistical methods were used, as well as LASSO regression, and explainable artificial intelligence (XAI) method SHAP values for model explainability. The best models were implemented in a web application and tested by medical experts. The model for prediction of mortality risk was tested on a validation cohort of 45 patients from the Department of Infectiology and Travel Medicine, L. Pasteur University Hospital in Košice (UNLP). Results—Our study shows that the best model for predicting COVID-19 disease severity was the LightGBM model with accuracy of 88.4% using all features and 89.5% using the eight most important features. The best model for predicting mortality risk was also the LightGBM model, which achieved a ROC AUC score of 83.7% and a classification accuracy of 81.2% using all features. Using a simplified model trained on the 15 most important features, the ROC AUC score was 83.6% and the classification accuracy was 80.5%. We deployed the simplified models for predicting COVID-19 disease severity and for predicting the risk of COVID-19-related death in a web-based application and tested them with medical experts. This test resulted in a ROC AUC score of 83.6% and an overall prediction accuracy of 73.3%. Full article

Background/Objectives: Heart rate variability (HRV) has been widely investigated as a predictor of disease and mortality across diverse patient populations; however, there remains no consensus on the optimal set or combination of time and frequency domain nor on nonlinear features for reliable prediction across clinical contexts. Given the relevance of the COVID-19 pandemic and the unique clinical profiles of these patients, this retrospective observational study explored the potential of HRV analysis for early prediction of in-hospital mortality using ECG signals recorded during the initial moments of ICU admission in COVID-19 patients. Methods: HRV indices were extracted from four ECG leads (I, II, III, and aVF) using sliding windows of 2, 5, and 7 min across observation intervals of 15, 30, and 60 min. The raw data posed significant challenges in terms of structure, synchronization, and signal quality; thus, from an original set of 381 records from 321 patients, after data pre-processing steps, a final dataset of 82 patients was selected for analysis. To manage data complexity and evaluate predictive performance, two feature selection methods, four feature reduction techniques, and five classification models were applied to identify the optimal approach. Results: Among the feature aggregation methods, compiling feature means across patient windows (Method D) yielded the best results, particularly for longer observation intervals (e.g., using LDA, the best AUC of $0.82\pm 0.13$ was obtained with Method D versus $0.63\pm 0.09$ with Method C using 5 min windows). Linear Discriminant Analysis (LDA) was the most consistent classification algorithm, demonstrating robust performance across various time windows and further improvement with dimensionality reduction. Although Gradient Boosting and Random Forest also achieved high AUCs and F1-scores, their performance outcomes varied across time intervals. Conclusions: These findings support the feasibility and clinical relevance of using short-term HRV as a noninvasive, data-driven tool for early risk stratification in critical care, potentially guiding timely therapeutic decisions in high-risk ICU patients and thereby reducing in-hospital mortality. Full article

Prognostic scores that help allocate resources and time to the most critical patients could have potentially improved the response to the SARS-CoV-2 pandemic. We assessed the performance of five risk scores in predicting death or transfer to the intensive care unit (ICU) or sub-intensive care unit (SICU) in hospitalised patients with SARS-CoV-2 infection, with the three aims of retrospectively analysing the effectiveness of these tools, identifying frail patients at risk of death or complications due to infection, and applying these tools in the event of future pandemics. A retrospective observational study was conducted by evaluating data from patients hospitalised with SARS-CoV-2 infection. Among 134 patients considered, 119 were enrolled. All patients were adults, with a mean age of 64 years, and were hospitalised in the Infectious Diseases Division. We compared the five scores using receiver operating characteristic curves and calculation of the areas under the curve (AUCs) to determine their predictive performance. Four of the five scores demonstrated a high accuracy in predicting mortality among COVID-19-positive patients, with AUCs between 0.749 and 0.885. However, only two of the five scores showed good performance in predicting transfer to the ICU or SICU, with AUCs ranging from 0.740 to 0.802. The 4C Mortality Score and COVID-GRAM presented the highest performance for both outcomes. These two scores are easy to apply and low cost. They could still be used in clinical practice as predictive tools for frail and elderly patients with SARS-CoV-2 infection, as well as in the event of future pandemics. Full article

Background and Objectives: COVID-19 may have long-term adverse effects on bone health, particularly in individuals aged ≥50 years with obesity or diabetes, who are predisposed to impaired bone quality. Materials and Methods: This retrospective cohort study used TriNetX data from 141 healthcare organizations across North America and Western Europe. Patients aged ≥50 years with overweight (body mass index 25–30 kg/m²), obesity (body mass index ≥ 30 kg/m²), or type 2 diabetes (T2DM) and COVID-19 (2019–2024) were propensity score-matched to non-COVID-19 controls. Exclusion criteria included prior overweight, obesity, diabetes, osteoporosis, T-score ≤ −2.5, Z score ≤ −2.0, fractures, pneumonia, tuberculosis, and cancer. Outcomes included new-onset osteoporosis, fragility fractures, and low T-scores (≤−2.5). Cox regression estimated hazard ratios (HRs); sensitivity analyses assessed lag effects (1–4 years). Results: Among 327,933 matched pairs, COVID-19 was linked to increased osteoporosis risk at 3 years (HR, 1.039; 95% CI, 1.003–1.077) and 6 years (HR, 1.095; 95% CI, 1.059–1.133). Sensitivity analysis showed rising risk with longer lag times: HRs were 1.212, 1.379, 1.563, and 1.884 at 1 to 4 years, respectively. Subgroup analyses confirmed consistent trends. Conclusions: COVID-19 is independently associated with elevated long-term osteoporosis risk in older adults with new-onset overweight, obesity, or T2DM, peaking at 4 years post-infection and persisting through 6 years. Full article

Background: Medical education is considered one of the most academically and emotionally demanding training programs. Throughout their education, medical students are exposed to various factors that can lead to psychological distress, depression, and anxiety. The aim of this longitudinal study was to examine the changes in psychological distress, depression, anxiety levels and internet addiction among medical students throughout their six-year education and to identify the contributing factors. Methods: The study cohort consisted of 282 students who enrolled in the medical faculty in the 2017–2018 academic year. A questionnaire including sociodemographic characteristics, the General Health Questionnaire-12 (GHQ-12), Beck Depression Inventory (BDI), State–Trait Anxiety Inventory (STAI), and Young Internet Addiction Test (IAT) was administered to the students during the first week of their education. The same questionnaire was readministered at the end of the third and sixth years. Friedman’s variance analysis was used to compare measurement data across the three time points, while Cochran’s Q Test was employed for categorical variables. Results: The median scores of the GHQ-12, BDI, S-Anxiety, and IAT significantly increased from the first to the sixth year (p < 0.05). The prevalence of depressive symptoms, S-Anxiety, and risky internet use significantly increased from the first to the final year, particularly between the third and sixth years. According to logistic regression analysis based on sixth-year data, students whose fathers were university graduates, who had been diagnosed with COVID-19, and who were dissatisfied with their social lives were found to be at increased risk for psychological distress and depression. Students with high parental expectations were found to be at risk of depression and S-anxiety. Those dissatisfied with their occupational choice were at risk for both psychological distress and S-anxiety. Conclusions: It was found that the mental health of medical students deteriorated during their education, especially during the clinical years. Given that these students will be responsible for protecting and improving public health in the future, it is essential to prioritize their own mental well-being. Interventions aimed at preserving the mental health of medical students should be planned. Full article

Background/Objectives: Pregnancy has been considered a risk factor for severe SARS-CoV-2 infection, as well as for adverse maternal and neonatal outcomes. This study aimed to assess the clinical impact of COVID-19 on pregnant women managed at a Spanish tertiary care hospital across different phases of the pandemic. Methods: A retrospective observational study was conducted at Dr. Balmis General University Hospital (Alicante, Spain) between March 2020 and May 2023. All pregnant women who received hospital care with confirmed SARS-CoV-2 infection were included. Maternal and neonatal outcomes were analyzed and compared with the 6120 total births recorded during the same period. Results: A total of 249 pregnant women with COVID-19 were included, with 30.8%, 25.0%, and 7.9% hospitalized during each respective pandemic phase. The overall incidence of infection was 41 cases per 1000 births. Hospitalized pregnant women showed significantly higher rates of preterm birth, labor induction (70.4% vs. 47.0%; OR: 2.67; 95% CI: 1.12–6.43), and cesarean delivery (46.9% vs. 24.9%, OR: 2.60; 95% CI: 1.27–5.50). Neonatal outcomes included lower Apgar scores, increased admission to the neonatal unit (25.8% vs. 8.2%, p = 0.007), and a higher rate of neonatal complications (23.3% vs. 7.7%, p = 0.015). Maternal obesity and non-Spanish nationality were associated with more severe maternal disease. Vaccination against SARS-CoV-2 significantly reduced the risk of hospitalization due to the infection (OR: 0.30; 95% CI: 0.13–0.69). Conclusions: Pregnant women admitted with COVID-19 had increased risks of adverse obstetric and neonatal outcomes, underscoring the importance of preventive strategies, such as vaccination. Full article

Background: Women with systemic autoimmune rheumatic disease (SARD) are at higher risk of developing infection-related complications, anxiety, and depression. Using the example of the COVID-19 pandemic, we aimed to explore the impact of this external stressor on symptoms of depression, anxiety, and stress in a sample of women with SARD in a cross-sectional study design. Methods: Females aged 18–50 with SARD were enrolled from 04/2021 to 04/2022 at the Medical University of Vienna or through an online self-help group, while snowball sampling was used to recruit an age-matched healthy control group. Participants completed questionnaires including: (1) demographic information, medical history, and access to healthcare; (2) the Depression, Anxiety, and Stress Scale (DASS-21); and (3) the Coronavirus Anxiety Scale (CAS). Parameters were compared between groups using Chi-squared, Fisher’s exact, and Mann–Whitney U tests. Linear regression analysis was used to investigate which individual factors predicted the DASS-21 in women with SARD. Results: The study sample consisted of 226 women (n = 99 with SARD and n = 127 healthy controls). Women with SARD reported lower DASS-21 stress (p = 0.008) and CAS scores (p = 0.057) than the control group. There were no significant differences in DASS-21 anxiety or depression scores. Among women with SARD, a linear regression model identified the most important predictors of DASS-21 as access to rheumatological care (p = 0.002) and recent disease activity (p = 0.028). Conclusions: Despite the pandemic, women with SARD reported mental health outcomes equal to or better than those of the healthy control group. Continued access to rheumatological care may serve as an important protective factor for their mental health during large-scale crises like pandemics. Full article

Neonatal calves possess an immature and naïve immune system and are reliant on the intake of maternal colostrum for the passive transfer of immunoglobulins. Maternal antibodies delivered to the calf via colostrum, are crucial to prevent calfhood diseases and death. Failure of transfer of passive immunity (FTPI) is a condition in which calves do not acquire enough maternal antibodies, mostly in the form of IgG, due to inadequate colostrum quality or delayed colostrum feeding. The diagnosis and risk factors for FTPI have been widely studied in dairy cattle; however, in beef calves, the research interest in the topic is relatively recent, and the most adequate diagnostic and preventative methods are still in development, making it difficult to define recommendations for the assessment and prevention of FTPI in cow–calf operations. The objective of this scoping review is to identify the published literature on best practices for colostrum management and transfer of passive immunity (TPI) in neonatal beef calves. The literature was searched using three electronic databases (CAB Direct, Scopus, and PubMed) for publications from 2003 to 2025. The search process was performed during the period from May to July 2023, and was repeated in January 2025. All screening processes were performed using Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia). A total of 800 studies were initially identified through database searches. After removing duplicates, 346 studies were screened based on their titles and abstracts, leading to the exclusion of 260 studies. The remaining 86 studies underwent full-text screening, and 58 studies were considered eligible for data extraction. Hand-searching the references from published review papers on the subject yielded an additional five studies, bringing the total to 63 included articles. The prevalence of FTPI has been estimated to be between 5.8% and 34.5% in beef calves. Factors studied related to colostrum management include quality and quantity of colostrum intake, the timing and method of colostrum feeding, and the microbial content of the colostrum. Studies on risk factors related to the calf include the topics calf sex, twin status, calf vigor, weight, month of birth, cortisol and epinephrine concentrations, and the administration of nonsteroidal anti-inflammatory drugs to calves after difficult calving. The dam-related risk factors studied include dam body condition score and udder conformation, breed, parity, genetics, prepartum vaccinations and nutrition, calving area and difficulty, and the administration of nonsteroidal anti-inflammatory drugs at C-section. Most importantly for beef systems, calves with low vigor and a weak suckling reflex are at high risk for FTPI; therefore, these calves should be given extra attention to ensure an adequate consumption of colostrum. While serum IgG levels of < 8 g/L or < 10 g/L have been suggested as cutoffs for the diagnosis of FTPI, 16 g/L and 24 g/L have emerged as cutoffs for adequate and optimal serum IgG levels in beef calves. Several field-ready diagnostics have been compared in various studies to the reference standards for measuring indicators of TPI in beef calves, where results often differ between models or manufacturers. Therefore, care must be taken when interpreting these results. Full article

Invasive pneumococcal disease (IPD) is a serious infection caused by the bacterium Streptococcus pneumoniae (pneumococcus) that can produce a wide spectrum of clinical manifestations. The aim of this study was to analyze the comorbidity factors that influenced the mortality in patients with diabetes (D) according to IPD. A retrospective study to analyze patients with D and IPD was carried out. Based on the discharge reports from the Spanish Minimum Basic Data Set (MBDS) from 1997 to 2022, the Elixhauser Comorbidity Index (ECI) and the Charlson Comorbidity Index (CCI) were calculated to predict in-hospital mortality (IHM) in Spain. A total of 12,994,304 patients with D were included, and 84,601 cases of IPD were identified. The average age for men was 70.23 years and for women 73.94 years. In all years, ECI and CCI were larger for type 2 D than for type 1 D, with men having a higher mean than women. An association was found between risk factors ECI, age, type 1 D, COVID-19, IPD (OR = 1.31; 95% CI: 1.29–1.35; p < 0.001); CCI, age, type 1 D, COVID-19, IPD (OR = 1.45; 95% CI: 1.42–1.49; p < 0.001), and increased mortality. The IHM increased steadily with the number of comorbidities and index scores from 1997 to 2022. D remains a relevant cause of hospitalization in Spain. Comorbidities reflected a great impact on patients with D and IPD, which would mean a higher risk of mortality. Predicting mortality events and length of stay by comparing indices showed that CCI outperforms ECI in predicting inpatient death after IPD. Full article

Background: Solid Organ Transplant Recipients (SOTRs) face an elevated risk of Sars-CoV-2 infection and poor outcomes if they contract the infection. This can induce or exacerbate anxiety and depressive symptoms. We used the Patient-Reported Outcomes Measurement Information System (PROMIS) Anxiety (A) and Depression (D) scores to conduct a repeated cross-sectional (“pseudo-longitudinal”) comparison of SOTRs’ anxiety and depressive symptoms before and after the COVID-19 pandemic onset. Methods: This secondary analysis used cross-sectional data from a convenience sample of adult SOTRs (kidney, kidney–pancreas, and liver) recruited between 2016 and 2024. The exposure was categorized as follows: “Pandemic Experience” was categorized as PRE (pre-pandemic reference; transplanted and anxiety and depressive symptoms assessed pre-pandemic onset), POST-1 (transplanted before and assessed after onset), and POST-2 (transplanted and assessed after onset). The outcomes were PROMIS-A and PROMIS-D scores. The differences were assessed using multivariable linear regression-estimated means. Results: Of the 816 participants, 588 (72%) were PRE, 135 (17%) were POST-1, and 93 (11%) were POST-2. In the fully adjusted model, the POST-2 group had significantly higher PROMIS-A scores (more severe symptoms) compared with PRE (adjusted mean [95% CI]: 54.2 [52.3; 56.1] vs. 51.7 [50.9; 52.4], p = 0.02). The proportion of patients with potentially clinically significant anxiety was also higher in the POST-2 group, compared with PRE (OR [95%CI] 1.59 [1.0; 2.5]). The PROMIS-A scores were similar between PRE and POST-1, and between POST-1 and POST-2. The PROMIS-D scores were not different across the exposure groups. Conclusions: SOTRs transplanted after the pandemic onset experienced more anxiety but similar depression symptoms compared with pre-pandemic levels. Future research should explore mental health support for SOTRs during crisis situations involving infectious risk. Full article

Background: COVID-19 has been linked to prolonged immune dysfunction and long-term health complications. Herpes zoster (HZ), a marker of impaired cell-mediated immunity, may signal increased vulnerability to infections, cardiovascular disease, and potentially cancer. However, its association with colorectal cancer (CRC) after COVID-19 has not been fully explored. Objective: To investigate the long-term risks of cardiovascular events, acute respiratory failure, sepsis, and CRC in COVID-19 survivors who developed HZ compared to those who did not. Methods: We conducted a retrospective cohort study using the TriNetX Global Collaborative Network. Adults diagnosed with COVID-19 between January 2020 and January 2022 were included. Among the full cohort (aged ≥18 years), 27,664 patients with post-COVID HZ were identified. Due to platform limitations, propensity score matching (PSM) was applied to a restricted subgroup of patients aged 55–60 years, yielding a 1:1 matched cohort for controlled comparisons. Outcomes were assessed over a three-year follow-up. Results: In the matched age-restricted cohort, patients with post-COVID HZ had significantly higher risks of cardiovascular events, acute respiratory failure, sepsis, and CRC compared to matched controls. Subgroup analyses identified age ≥ 50, chronic kidney disease, diabetes, and hypertension as strong independent risk factors across outcomes. Despite the low absolute CRC incidence, cancer-free survival significantly favored the non-HZ group. Conclusion: Herpes zoster reactivation after COVID-19 is associated with increased risk of colorectal cancer. Enhanced surveillance and early CRC screening may benefit this high-risk population. Full article

Objective: This study aims to investigate the feasibility of an online music therapy protocol for individuals previously diagnosed with COVID-19, focusing on their perceptions of their respiratory symptoms and the intervention’s impact on psychosocial measures. Methods: A within-subject experimental design was applied to examine an eight-week weekly online music therapy protocol, including singing, wind instrument playing, and music visualizations. All self-report data were collected bi-weekly throughout the 16-weeks study period, including baseline and post-tests. The measures for respiratory symptoms included the Medical Research Council’s Dyspnea Scale (MRC Dyspnea), Chronic Respiratory Questionnaire-Mastery Scores (CRQ Mastery), and Visual Analogue Scale for breathlessness. The measures for the secondary psychosocial outcomes were the Beck Depression Inventory-Short Form, the Generalized Anxiety Disorder 7-item, the Hospital Anxiety and Depression Scale, the Fatigue Severity Scale, the Epworth Sleepiness Scale, the EuroQol 5-Dimension 5-Level, and the Connor-Davidson Resilience Scale. Results: Twenty-four participants were enrolled. The participants perceived a reduction in respiratory symptoms, and shortness of breath (MRC Dyspnea). Planned comparisons showed significant decreases in MRC from baseline to post-treatment (p = 0.008). The mixed-effects model, including pre-baseline and post-treatment, was significant (p < 0.001). Significant changes in Breathing VAS were consistent with improvements in MRC Dyspnea, showing a significant baseline-to-post difference (p = 0.01). The CRQ Mastery showed significant improvements from baseline to Week 12 (p < 0.001). No significant changes were observed in other secondary measures. Conclusions: Our preliminary findings suggest that this protocol is feasible, and as a result, may help individuals previously diagnosed with COVID-19 to cope with lasting respiratory symptoms and improve their perception of shortness of breath. Live music-making, including playing accessible wind instruments and singing, may contribute to an increase sense of control over breathing. As this was a feasibility study, we conducted multiple uncorrected statistical comparisons to explore potential effects. While this approach may increase the risk of Type I error, the findings are intended to inform hypotheses for future confirmatory studies rather than to draw definitive conclusions. Full article

Background/Objectives: The COVID-19 pandemic has caused sickness and death among many health care workers. However, the apparent resistance of health care workers to SARS-CoV-2 infection despite their high-risk work environment remains unclear. To investigate if inflammation and circadian disruption contribute to resistance or diminished susceptibility to the SARS-CoV-2 virus, we retrospectively evaluated a cohort of volunteer hospital nurses (VHNs). Methods: A total of 246 apparently healthy VHNs (mean age 37.4 ± 5.9 years) who had received the BNT162b2 mRNA vaccine were asked to report their sleep quality, according to the Pittsburgh Sleep Quality Index, and number of SARS-CoV-2 infections during the observational study period (from the end of December 2020 to April 2025). The expression of inflammation-associated mediators and circadian transcription factors in peripheral blood mononuclear cells, as well as sleep quality, were examined. Results: Our findings revealed no anthropometric, biochemical, or inflammation-associated parameters but demonstrated significantly greater levels of NFE2L2, also known as nuclear factor erythroid-derived 2-like 2 (NFR2), gene expression in peripheral blood mononuclear cells among VHNs who had never been infected with SARS-CoV-2 (n = 97) than in VHNs with only one (n = 119) or with two or more (n = 35) prior SARS-CoV-2 infections (p < 0.01). This result was confirmed through one-to-one propensity score matching (p < 0.01). Moreover, NRF2 gene expression was not associated with the number of COVID-19 vaccinations (p = 0.598). Finally, NRF2 gene expression was higher among participants who reported better sleep quality (p < 0.01). Conclusions: Our findings suggest possible interactions among NRF2 gene expression, protection against SARS-CoV-2 infection, and the modulation of COVID-19 vaccination efficacy. Full article

Background: This study aims to identify predictive factors for pulmonary fibrosis development in COVID-19 patients by analyzing thorax CT (computed tomography) findings, serum prolidase activity, MMP-1, MMP-7, TGF-β values, laboratory findings, and demographic characteristics. Materials and methods: The investigation involved 68 patients, both male and female, aged 18 years and older, who were volunteers and had been diagnosed with confirmed COVID-19. The pulmonologist and the radiologist evaluated the thorax CT by consensus. Patients were evaluated in two categories, group 1 and group 2, based on the status of fibrotic changes, and 3-month fibrosis scores were calculated. Findings in both lungs were calculated and noted for the lobes, considering lobar spread. Correlations between quantitative parameters were assessed with Spearman’s rho correlation coefficient. Comparisons between independent samples were evaluated using either the independent sample t-test or the Mann–Whitney U test. We evaluated the relationship between categorical variables using the Pearson chi-square test and Fisher’s exact test. Results: Serum prolidase activity, MMP-1, MMP-7, and TGF-β biomarkers were not statistically significant among groups. LDH was found to be significantly high in the group with fibrotic changes. Additionally, the group with fibrotic changes also had higher levels of fibrinogen. The percentage of neutrophils, the severity of the disease, muscle–joint pain and fatigue symptoms, and the length of hospitalization stay were correlated with the total scores of fibrosis at the third month. In the group with fibrotic changes, the duration of muscle–joint pain and fatigue symptoms and the length of hospitalization were longer than in the other group. Conclusions: The group with fibrotic changes showed an increase in biomarkers. However, this increase did not reach a statistically significant level, suggesting that the third month may be an early period for these changes. The group with fibrotic changes showed high levels of LDH, one of the most important laboratory parameters of pulmonary fibrosis risk factors, along with fibrinogen, suggesting that these parameters are valuable in predicting pulmonary fibrosis. Patients with fibrotic changes can experience specific symptoms, commonly seen in COVID-19. Full article

The coronavirus disease 2019 (COVID-19) pandemic has led to substantial health and financial burdens worldwide, and vaccines provide hope for reducing the burden of this pandemic. However, vaccinated people remain at risk for SARS-CoV-2 infection. Genome-wide association studies (GWASs) may identify potential genetic factors involved in the development of COVID-19 breakthrough infections (BIs); however, very few or no GWASs have been conducted for COVID-19 BI thus far. We conducted a GWAS and detailed bioinformatics analysis on COVID-19 BIs in a European population via the UK Biobank (UKBB). We conducted a series of analyses at different levels, including SNP-based, gene-based, pathway, and transcriptome-wide association analyses, to investigate genetic factors associated with COVID-19 BIs and hospitalized infections. The polygenic risk score (PRS) and Hoeffding’s test were performed to reveal the genetic relationships between BIs and other medical conditions. Two independent loci (LD-clumped at r² = 0.01) reached genome-wide significance (p < 5 × 10⁻⁸), including rs36170929, which mapped to LOC102725191/VWDE, and rs28645263, which mapped to RETREG1. A pathway enrichment analysis highlighted pathways such as viral myocarditis, Rho-selective guanine exchange factor AKAP13 signaling, and lipid metabolism. The PRS analyses revealed significant genetic overlap between COVID-19 BIs and heart failure and between HbA1c and type 1 diabetes. Genetic dependence was also observed between COVID-19 BIs and asthma, lung abnormalities, schizophrenia, and type 1 diabetes on the basis of Hoeffding’s test. This GWAS revealed two significant loci that may be associated with COVID-19 BIs and a number of genes and pathways that may be involved in BIs. Genetic overlap with other diseases was identified. Further studies are warranted to replicate these findings and elucidate the mechanisms involved. Full article