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.

Newly diagnosed diabetes vs. pre-existing diabetes upon admission for COVID-19: Associated factors, short-term outcomes, and long-term glycemic phenotypes.

AIMS: High rates of newly diagnosed diabetes mellitus (NDDM) have been reported in association with coronavirus disease-2019 (COVID-19). Factors associated with NDDM and long-term glycemic outcomes are not known. METHODS: Retrospective review of individuals admitted with COVID-19 and diabetes mellitus (DM; based on labs, diagnoses, outpatient insulin use, or severe inpatient hyperglycemia) between March and September 2020, with follow-up through July 2021. RESULTS: Of 1902 individuals admitted with COVID-19, 594 (31.2%) had DM; 77 (13.0%) of these had NDDM. Compared to pre-existing DM, NDDM was more common in younger patients and less common in those of non-Hispanic White race/ethnicity. Glycemic parameters were lower and inflammatory markers higher in patients with NDDM. In adjusted models, NDDM was associated with lower insulin requirements, longer length of stay, and intensive care unit admission but not death. Of 64 survivors with NDDM, 36 (56.3%) continued to have DM, 26 (40.6%) regressed to normoglycemia or pre-diabetes, and 2 were unable to be classified at a median follow-up of 323 days. CONCLUSIONS: Diabetes diagnosed at COVID-19 presentation is associated with lower glucose but higher inflammatory markers and ICU admission, suggesting stress hyperglycemia as a major physiologic mechanism. Approximately half of such individuals experience regression of DM.

Challenges and Opportunities for Osteoporosis Care During the COVID-19 Pandemic.

PURPOSE: The coronavirus disease 2019 (COVID-19) has both directly and indirectly affected osteoporosis diagnosis and treatment throughout the world. METHODS: This mini-review summarizes the available evidence regarding the effects of COVID-19, its treatment, and the consequences of the pandemic itself on bone health. Additionally, we review evidence and expert recommendations regarding putative effects of osteoporosis medications on COVID-19 outcomes and vaccine efficacy and summarize recommendations for continuation of osteoporosis treatment during the pandemic. RESULTS: The use of standard screening procedures to assess for osteoporosis and fracture risk declined dramatically early in the pandemic, while rates of fragility fractures were largely unchanged. COVID-19, its treatments, and public health measures to prevent viral spread are each likely to negatively affect bone health. Osteoporosis treatments are not known to increase risk of adverse events from COVID-19, and preclinical data suggest possible beneficial effects of some therapies. Vitamin D deficiency is clearly associated with adverse outcomes from COVID-19, but it remains unclear whether vitamin D supplementation may improve outcomes. Osteoporosis treatment should be continued whenever possible, and recommendations for substituting therapies, if required, are available. CONCLUSION: The COVID-19 pandemic has decreased screening and disrupted treatment for osteoporosis. Osteoporosis medications are safe and effective during the pandemic and should be continued whenever possible. Further studies are needed to fully understand the impact of the COVID-19 pandemic on long-term bone health.

Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes.

BACKGROUND: The SARS-CoV-2 pandemic has disproportionately affected racial and ethnic minority communities across the United States. We sought to disentangle individual and census tract-level sociodemographic and economic factors associated with these disparities. METHODS AND FINDINGS: All adults tested for SARS-CoV-2 between February 1 and June 21, 2020 were geocoded to a census tract based on their address; hospital employees and individuals with invalid addresses were excluded. Individual (age, sex, race/ethnicity, preferred language, insurance) and census tract-level (demographics, insurance, income, education, employment, occupation, household crowding and occupancy, built home environment, and transportation) variables were analyzed using linear mixed models predicting infection, hospitalization, and death from SARS-CoV-2. Among 57,865 individuals, per capita testing rates, individual (older age, male sex, non-White race, non-English preferred language, and non-private insurance), and census tract-level (increased population density, higher household occupancy, and lower education) measures were associated with likelihood of infection. Among those infected, individual age, sex, race, language, and insurance, and census tract-level measures of lower education, more multi-family homes, and extreme household crowding were associated with increased likelihood of hospitalization, while higher per capita testing rates were associated with decreased likelihood. Only individual-level variables (older age, male sex, Medicare insurance) were associated with increased mortality among those hospitalized. CONCLUSIONS: This study of the first wave of the SARS-CoV-2 pandemic in a major U.S. city presents the cascade of outcomes following SARS-CoV-2 infection within a large, multi-ethnic cohort. SARS-CoV-2 infection and hospitalization rates, but not death rates among those hospitalized, are related to census tract-level socioeconomic characteristics including lower educational attainment and higher household crowding and occupancy, but not neighborhood measures of race, independent of individual factors.