A MUC5B Gene Polymorphism, rs35705950-T, Confers Protective Effects Against COVID-19 Hospitalization but Not Severe Disease or Mortality.

Rationale: A common MUC5B gene polymorphism, rs35705950-T, is associated with idiopathic pulmonary fibrosis (IPF), but its role in severe acute respiratory syndrome coronavirus 2 infection and disease severity is unclear. Objectives: To assess whether rs35705950-T confers differential risk for clinical outcomes associated with coronavirus disease (COVID-19) infection among participants in the Million Veteran Program (MVP). Methods: The MUC5B rs35705950-T allele was directly genotyped among MVP participants; clinical events and comorbidities were extracted from the electronic health records. Associations between the incidence or severity of COVID-19 and rs35705950-T were analyzed within each ancestry group in the MVP followed by transancestry meta-analysis. Replication and joint meta-analysis were conducted using summary statistics from the COVID-19 Host Genetics Initiative (HGI). Sensitivity analyses with adjustment for additional covariates (body mass index, Charlson comorbidity index, smoking, asbestosis, rheumatoid arthritis with interstitial lung disease, and IPF) and associations with post-COVID-19 pneumonia were performed in MVP subjects. Measurements and Main Results: The rs35705950-T allele was associated with fewer COVID-19 hospitalizations in transancestry meta-analyses within the MVP (Ncases = 4,325; Ncontrols = 507,640; OR = 0.89 [0.82-0.97]; P = 6.86 × 10-3) and joint meta-analyses with the HGI (Ncases = 13,320; Ncontrols = 1,508,841; OR, 0.90 [0.86-0.95]; P = 8.99 × 10-5). The rs35705950-T allele was not associated with reduced COVID-19 positivity in transancestry meta-analysis within the MVP (Ncases = 19,168/Ncontrols = 492,854; OR, 0.98 [0.95-1.01]; P = 0.06) but was nominally significant (P < 0.05) in the joint meta-analysis with the HGI (Ncases = 44,820; Ncontrols = 1,775,827; OR, 0.97 [0.95-1.00]; P = 0.03). Associations were not observed with severe outcomes or mortality. Among individuals of European ancestry in the MVP, rs35705950-T was associated with fewer post-COVID-19 pneumonia events (OR, 0.82 [0.72-0.93]; P = 0.001). Conclusions: The MUC5B variant rs35705950-T may confer protection in COVID-19 hospitalizations.

Proteomic analysis of cardiometabolic biomarkers and predictive modeling of severe outcomes in patients hospitalized with COVID-19.

BACKGROUND: The high heterogeneity in the symptoms and severity of COVID-19 makes it challenging to identify high-risk patients early in the disease. Cardiometabolic comorbidities have shown strong associations with COVID-19 severity in epidemiologic studies. Cardiometabolic protein biomarkers, therefore, may provide predictive insight regarding which patients are most susceptible to severe illness from COVID-19. METHODS: In plasma samples collected from 343 patients hospitalized with COVID-19 during the first wave of the pandemic, we measured 92 circulating protein biomarkers previously implicated in cardiometabolic disease. We performed proteomic analysis and developed predictive models for severe outcomes. We then used these models to predict the outcomes of out-of-sample patients hospitalized with COVID-19 later in the surge (N = 194). RESULTS: We identified a set of seven protein biomarkers predictive of admission to the intensive care unit and/or death (ICU/death) within 28 days of presentation to care. Two of the biomarkers, ADAMTS13 and VEGFD, were associated with a lower risk of ICU/death. The remaining biomarkers, ACE2, IL-1RA, IL6, KIM1, and CTSL1, were associated with higher risk. When used to predict the outcomes of the future, out-of-sample patients, the predictive models built with these protein biomarkers outperformed all models built from standard clinical data, including known COVID-19 risk factors. CONCLUSIONS: These findings suggest that proteomic profiling can inform the early clinical impression of a patient's likelihood of developing severe COVID-19 outcomes and, ultimately, accelerate the recognition and treatment of high-risk patients.

Association of Kidney Comorbidities and Acute Kidney Failure With Unfavorable Outcomes After COVID-19 in Individuals With the Sickle Cell Trait.

Importance: Sickle cell trait (SCT), defined as the presence of 1 hemoglobin beta sickle allele (rs334-T) and 1 normal beta allele, is prevalent in millions of people in the US, particularly in individuals of African and Hispanic ancestry. However, the association of SCT with COVID-19 is unclear. Objective: To assess the association of SCT with the prepandemic health conditions in participants of the Million Veteran Program (MVP) and to assess the severity and sequelae of COVID-19. Design, Setting, and Participants: COVID-19 clinical data include 2729 persons with SCT, of whom 353 had COVID-19, and 129 848 SCT-negative individuals, of whom 13 488 had COVID-19. Associations between SCT and COVID-19 outcomes were examined using firth regression. Analyses were performed by ancestry and adjusted for sex, age, age squared, and ancestral principal components to account for population stratification. Data for the study were collected between March 2020 and February 2021. Exposures: The hemoglobin beta S (HbS) allele (rs334-T). Main Outcomes and Measures: This study evaluated 4 COVID-19 outcomes derived from the World Health Organization severity scale and phenotypes derived from International Classification of Diseases codes in the electronic health records. Results: Of the 132 577 MVP participants with COVID-19 data, mean (SD) age at the index date was 64.8 (13.1) years. Sickle cell trait was present in 7.8% of individuals of African ancestry and associated with a history of chronic kidney disease, diabetic kidney disease, hypertensive kidney disease, pulmonary embolism, and cerebrovascular disease. Among the 4 clinical outcomes of COVID-19, SCT was associated with an increased COVID-19 mortality in individuals of African ancestry (n = 3749; odds ratio, 1.77; 95% CI, 1.13 to 2.77; P = .01). In the 60 days following COVID-19, SCT was associated with an increased incidence of acute kidney failure. A counterfactual mediation framework estimated that on average, 20.7% (95% CI, -3.8% to 56.0%) of the total effect of SCT on COVID-19 fatalities was due to acute kidney failure. Conclusions and Relevance: In this genetic association study, SCT was associated with preexisting kidney comorbidities, increased COVID-19 mortality, and kidney morbidity.

Understanding the Link Between Obesity and Severe COVID-19 Outcomes: Causal Mediation by Systemic Inflammatory Response.

BACKGROUND: Obesity is an established risk factor for severe COVID-19 outcomes. The mechanistic underpinnings of this association are not well-understood. OBJECTIVE: To evaluate the mediating role of systemic inflammation in obesity-associated COVID-19 outcomes. METHODS: This hospital-based, observational study included 3828 SARS-CoV-2-infected patients who were hospitalized February to May 2020 at Massachusetts General Hospital (MGH) or Columbia University Irving Medical Center/New York Presbyterian Hospital (CUIMC/NYP). We use mediation analysis to evaluate whether peak inflammatory biomarkers (C-reactive protein [CRP], erythrocyte sedimentation rate [ESR], D-dimer, ferritin, white blood cell count and interleukin-6) are in the causal pathway between obesity (BMI ≥ 30) and mechanical ventilation or death within 28 days of presentation to care. RESULTS: In the MGH cohort (n = 1202), obesity was associated with greater likelihood of ventilation or death (OR = 1.73; 95% CI = [1.25, 2.41]; P = 0.001) and higher peak CRP (P < 0.001) compared with nonobese patients. The estimated proportion of the association between obesity and ventilation or death mediated by CRP was 0.49 (P < 0.001). Evidence of mediation was more pronounced in patients < 65 years (proportion mediated = 0.52 [P < 0.001] vs 0.44 [P = 0.180]). Findings were more moderate but consistent for peak ESR. Mediation by other inflammatory markers was not supported. Results were replicated in CUIMC/NYP cohort (n = 2626). CONCLUSION: Findings support systemic inflammatory pathways in obesity-associated severe COVID-19 disease, particularly in patients < 65 years, captured by CRP and ESR. Contextualized in clinical trial findings, these results reveal therapeutic opportunity to target systemic inflammatory pathways and monitor interventions in high-risk subgroups and particularly obese patients.

Diabetes mellitus as a risk factor for SARS-CoV-2 test positivity in Mexico: A propensity score matched study.

AIMS: We sought to investigate whether individuals with diabetes have a higher likelihood of testing positive for SARS-CoV-2, as a proxy for infection risk, than individuals without diabetes. METHODS: We conducted a cross-sectional study of publicly available data among a Mexican population, totaling 2,314,022 adults ≥ 18 years who underwent SARS-CoV-2 testing between March 1 and December 20, 2020. We used 1:1 nearest neighborhood propensity score matching by diabetes status to account for confounding among those with and without diabetes. RESULTS: In the overall study population, 1,057,779 (45.7%) individuals tested positive for SARS-CoV-2 and 270,486 (11.7%) self-reported diabetes. After propensity score matching, patient characteristics were well-balanced, with 150,487 patients in the diabetes group (mean [SD] age 55.9 [12.7] years; 51.3% women) and 150,487 patients in the no diabetes group (55.5 [13.3] years; 50.3% women). The strictest matching algorithm (1:1 nearest neighbor) showed that compared to individuals without diabetes, having diabetes was associated with 9.0% higher odds of having a positive SARS-CoV-2 test (OR 1.09 [95% CI: 1.08-1.10]). CONCLUSIONS: Presence of diabetes was associated with higher odds of testing positive for SARS-CoV-2, which could have important implications for risk mitigation efforts for people with diabetes at risk of SARS-CoV-2 infection.

Visceral Adiposity and Severe COVID-19 Disease: Application of an Artificial Intelligence Algorithm to Improve Clinical Risk Prediction.

BACKGROUND: Obesity has been linked to severe clinical outcomes among people who are hospitalized with coronavirus disease 2019 (COVID-19). We tested the hypothesis that visceral adipose tissue (VAT) is associated with severe outcomes in patients hospitalized with COVID-19, independent of body mass index (BMI). METHODS: We analyzed data from the Massachusetts General Hospital COVID-19 Data Registry, which included patients admitted with polymerase chain reaction-confirmed severe acute respiratory syndrome coronavirus 2 infection from March 11 to May 4, 2020. We used a validated, fully automated artificial intelligence (AI) algorithm to quantify VAT from computed tomography (CT) scans during or before the hospital admission. VAT quantification took an average of 2 ± 0.5 seconds per patient. We dichotomized VAT as high and low at a threshold of ≥100 cm2 and used Kaplan-Meier curves and Cox proportional hazards regression to assess the relationship between VAT and death or intubation over 28 days, adjusting for age, sex, race, BMI, and diabetes status. RESULTS: A total of 378 participants had CT imaging. Kaplan-Meier curves showed that participants with high VAT had a greater risk of the outcome compared with those with low VAT (P < .005), especially in those with BMI <30 kg/m2 (P < .005). In multivariable models, the adjusted hazard ratio (aHR) for high vs low VAT was unchanged (aHR, 1.97; 95% CI, 1.24-3.09), whereas BMI was no longer significant (aHR for obese vs normal BMI, 1.14; 95% CI, 0.71-1.82). CONCLUSIONS: High VAT is associated with a greater risk of severe disease or death in COVID-19 and can offer more precise information to risk-stratify individuals beyond BMI. AI offers a promising approach to routinely ascertain VAT and improve clinical risk prediction in COVID-19.

Cardiometabolic risk factors for COVID-19 susceptibility and severity: A Mendelian randomization analysis.

BACKGROUND: Epidemiological studies report associations of diverse cardiometabolic conditions including obesity with COVID-19 illness, but causality has not been established. We sought to evaluate the associations of 17 cardiometabolic traits with COVID-19 susceptibility and severity using 2-sample Mendelian randomization (MR) analyses. METHODS AND FINDINGS: We selected genetic variants associated with each exposure, including body mass index (BMI), at p < 5 × 10-8 from genome-wide association studies (GWASs). We then calculated inverse-variance-weighted averages of variant-specific estimates using summary statistics for susceptibility and severity from the COVID-19 Host Genetics Initiative GWAS meta-analyses of population-based cohorts and hospital registries comprising individuals with self-reported or genetically inferred European ancestry. Susceptibility was defined as testing positive for COVID-19 and severity was defined as hospitalization with COVID-19 versus population controls (anyone not a case in contributing cohorts). We repeated the analysis for BMI with effect estimates from the UK Biobank and performed pairwise multivariable MR to estimate the direct effects and indirect effects of BMI through obesity-related cardiometabolic diseases. Using p < 0.05/34 tests = 0.0015 to declare statistical significance, we found a nonsignificant association of genetically higher BMI with testing positive for COVID-19 (14,134 COVID-19 cases/1,284,876 controls, p = 0.002; UK Biobank: odds ratio 1.06 [95% CI 1.02, 1.10] per kg/m2; p = 0.004]) and a statistically significant association with higher risk of COVID-19 hospitalization (6,406 hospitalized COVID-19 cases/902,088 controls, p = 4.3 × 10-5; UK Biobank: odds ratio 1.14 [95% CI 1.07, 1.21] per kg/m2, p = 2.1 × 10-5). The implied direct effect of BMI was abolished upon conditioning on the effect on type 2 diabetes, coronary artery disease, stroke, and chronic kidney disease. No other cardiometabolic exposures tested were associated with a higher risk of poorer COVID-19 outcomes. Small study samples and weak genetic instruments could have limited the detection of modest associations, and pleiotropy may have biased effect estimates away from the null. CONCLUSIONS: In this study, we found genetic evidence to support higher BMI as a causal risk factor for COVID-19 susceptibility and severity. These results raise the possibility that obesity could amplify COVID-19 disease burden independently or through its cardiometabolic consequences and suggest that targeting obesity may be a strategy to reduce the risk of severe COVID-19 outcomes.

CoVA: An Acuity Score for Outpatient Screening that Predicts Coronavirus Disease 2019 Prognosis.

BACKGROUND: We sought to develop an automatable score to predict hospitalization, critical illness, or death for patients at risk for coronavirus disease 2019 (COVID-19) presenting for urgent care. METHODS: We developed the COVID-19 Acuity Score (CoVA) based on a single-center study of adult outpatients seen in respiratory illness clinics or the emergency department. Data were extracted from the Partners Enterprise Data Warehouse, and split into development (n = 9381, 7 March-2 May) and prospective (n = 2205, 3-14 May) cohorts. Outcomes were hospitalization, critical illness (intensive care unit or ventilation), or death within 7 days. Calibration was assessed using the expected-to-observed event ratio (E/O). Discrimination was assessed by area under the receiver operating curve (AUC). RESULTS: In the prospective cohort, 26.1%, 6.3%, and 0.5% of patients experienced hospitalization, critical illness, or death, respectively. CoVA showed excellent performance in prospective validation for hospitalization (expected-to-observed ratio [E/O]: 1.01; AUC: 0.76), for critical illness (E/O: 1.03; AUC: 0.79), and for death (E/O: 1.63; AUC: 0.93). Among 30 predictors, the top 5 were age, diastolic blood pressure, blood oxygen saturation, COVID-19 testing status, and respiratory rate. CONCLUSIONS: CoVA is a prospectively validated automatable score for the outpatient setting to predict adverse events related to COVID-19 infection.

Massachusetts general hospital Covid-19 registry reveals two distinct populations of hospitalized patients by race and ethnicity.

OBJECTIVE: To evaluate differences by race/ethnicity in clinical characteristics and outcomes among hospitalized patients with Covid-19 at Massachusetts General Hospital (MGH). METHODS: The MGH Covid-19 Registry includes confirmed SARS-CoV-2-infected patients hospitalized at MGH and is based on manual chart reviews and data extraction from electronic health records (EHRs). We evaluated differences between White/Non-Hispanic and Hispanic patients in demographics, complications and 14-day outcomes among the N = 866 patients hospitalized with Covid-19 from March 11, 2020-May 4, 2020. RESULTS: Overall, 43% of patients hospitalized with Covid-19 were women, median age was 60.4 [IQR = (48.2, 75)], 11.3% were Black/non-Hispanic and 35.2% were Hispanic. Hispanic patients, representing 35.2% of patients, were younger than White/non-Hispanic patients [median age 51y; IQR = (40.6, 61.6) versus 72y; (58.0, 81.7) (p<0.001)]. Hispanic patients were symptomatic longer before presenting to care (median 5 vs 3d, p = 0.039) but were more likely to be sent home with self-quarantine than be admitted to hospital (29% vs 16%, p<0.001). Hispanic patients had fewer comorbidities yet comparable rates of ICU or death (34% vs 36%). Nonetheless, a greater proportion of Hispanic patients recovered by 14 days after presentation (62% vs 45%, p<0.001; OR = 1.99, p = 0.011 in multivariable adjusted model) and fewer died (2% versus 18%, p<0.001). CONCLUSIONS: Hospitalized Hispanic patients were younger and had fewer comorbidities compared to White/non-Hispanic patients; despite comparable rates of ICU care or death, a greater proportion recovered. These results have implications for public health policy and the design and conduct of clinical trials.

Incremental Risk of Developing Severe COVID-19 Among Mexican Patients With Diabetes Attributed to Social and Health Care Access Disadvantages.

OBJECTIVE: Diabetes is an important risk factor for severe coronavirus disease 2019 (COVID-19), but little is known about the marginal effect of additional risk factors for severe COVID-19 among individuals with diabetes. We tested the hypothesis that sociodemographic, access to health care, and presentation to care characteristics among individuals with diabetes in Mexico confer an additional risk of hospitalization with COVID-19. RESEARCH DESIGN AND METHODS: We conducted a cross-sectional study using public data from the General Directorate of Epidemiology of the Mexican Ministry of Health. We included individuals with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 between 1 March and 31 July 2020. The primary outcome was the predicted probability of hospitalization, inclusive of 8.5% of patients who required intensive care unit admission. RESULTS: Among 373,963 adults with COVID-19, 16.1% (95% CI 16.0-16.3) self-reported diabetes. The predicted probability of hospitalization was 38.4% (37.6-39.2) for patients with diabetes only and 42.9% (42.2-43.7) for patients with diabetes and one or more comorbidities (obesity, hypertension, cardiovascular disease, and chronic kidney disease). High municipality-level of social deprivation and low state-level health care resources were associated with a 9.5% (6.3-12.7) and 17.5% (14.5-20.4) increased probability of hospitalization among patients with diabetes, respectively. In age-, sex-, and comorbidity-adjusted models, living in a context of high social vulnerability and low health care resources was associated with the highest predicted probability of hospitalization. CONCLUSIONS: Social vulnerability contributes considerably to the probability of hospitalization among individuals with COVID-19 and diabetes with associated comorbidities. These findings can inform mitigation strategies for populations at the highest risk of severe COVID-19.

Diabetes as a Risk Factor for Poor Early Outcomes in Patients Hospitalized With COVID-19.

OBJECTIVE: Diabetes and obesity are highly prevalent among hospitalized patients with coronavirus disease 2019 (COVID-19), but little is known about their contributions to early COVID-19 outcomes. We tested the hypothesis that diabetes is a risk factor for poor early outcomes, after adjustment for obesity, among a cohort of patients hospitalized with COVID-19. RESEARCH DESIGN AND METHODS: We used data from the Massachusetts General Hospital (MGH) COVID-19 Data Registry of patients hospitalized with COVID-19 between 11 March 2020 and 30 April 2020. Primary outcomes were admission to the intensive care unit (ICU), need for mechanical ventilation, and death within 14 days of presentation to care. Logistic regression models were adjusted for demographic characteristics, obesity, and relevant comorbidities. RESULTS: Among 450 patients, 178 (39.6%) had diabetes-mostly type 2 diabetes. Among patients with diabetes versus patients without diabetes, a higher proportion was admitted to the ICU (42.1% vs. 29.8%, respectively, P = 0.007), required mechanical ventilation (37.1% vs. 23.2%, P = 0.001), and died (15.9% vs. 7.9%, P = 0.009). In multivariable logistic regression models, diabetes was associated with greater odds of ICU admission (odds ratio 1.59 [95% CI 1.01-2.52]), mechanical ventilation (1.97 [1.21-3.20]), and death (2.02 [1.01-4.03]) at 14 days. Obesity was associated with greater odds of ICU admission (2.16 [1.20-3.88]) and mechanical ventilation (2.13 [1.14-4.00]) but not with death. CONCLUSIONS: Among hospitalized patients with COVID-19, diabetes was associated with poor early outcomes, after adjustment for obesity. These findings can help inform patient-centered care decision making for people with diabetes at risk for COVID-19.