ABSTRACT
AIM: To explore pituitary-gonadal hormone concentrations and assess their association to inflammation, severe respiratory failure and mortality in hospitalized men and women with coronavirus disease 2019 (COVID-19) and compare these to hormone concentrations in hospitalized patients with bacterial community-acquired pneumonia (CAP), influenza virus CAP, and to concentrations in a reference group of healthy individuals. METHODS: Serum concentrations of testosterone, estrone sulfate, luteinizing hormone (LH), follicle-stimulating hormone (FSH), and interleukin-6 (IL-6) were measured within three days of admission. Associations were assessed by logistic regression analysis in patients with COVID-19, and results were reported as odds ratio with 95% confidence interval per two-fold reduction after adjustment for age, comorbidities, days to sample collection, and IL-6 concentrations. RESULTS: In total 278 with COVID-19, 21 with influenza virus CAP, and 76 with bacterial CAP were included. Testosterone concentrations were suppressed in men hospitalized with COVID-19, bacterial- and influenza virus CAP and moderately suppressed in women. Reductions in testosterone (OR 3.43 [1.14-10.30], p=0.028) and LH (OR 2.51 [1.28-4.92], p=0.008) were associated higher odds of mechanical ventilation (MV) in men with COVID-19. In women with COVID-19, reductions in LH (OR 3.34 [1.02-10-90], p=0.046) and FSH (OR 2.52 [1.01-6.27], p=0.047) were associated with higher odds of MV. CONCLUSION: Low testosterone and LH concentrations were predictive of severe respiratory failure in men with COVID-19, whereas low concentrations of LH and FSH predicted severe respiratory failure in women with COVID-19.
ABSTRACT
Sobczyk and Gaunt genetically predicted circulating zinc, selenium, copper, and vitamin K1 levels-instead of directly measuring nutrients in blood-and hypothesized that these levels would associate with SARS-CoV-2 infection and COVID-19 severity [...].
Subject(s)
COVID-19 , Selenium , Copper , Humans , Mendelian Randomization Analysis , Nutrients , SARS-CoV-2 , Vitamin K 1 , ZincABSTRACT
It has recently been hypothesized that vitamin K could play a role in COVID-19. We aimed to test the hypotheses that low vitamin K status is a common characteristic of patients hospitalized with COVID-19 compared to population controls and that low vitamin K status predicts mortality in COVID-19 patients. In a cohort of 138 COVID-19 patients and 138 population controls, we measured plasma dephosphorylated-uncarboxylated Matrix Gla Protein (dp-ucMGP), which reflects the functional vitamin K status in peripheral tissue. Forty-three patients died within 90 days from admission. In patients, levels of dp-ucMGP differed significantly between survivors (mean 877; 95% CI: 778; 995) and non-survivors (mean 1445; 95% CI: 1148; 1820). Furthermore, levels of dp-ucMGP (pmol/L) were considerably higher in patients (mean 1022; 95% CI: 912; 1151) compared to controls (mean 509; 95% CI: 485; 540). Cox regression survival analysis showed that increasing levels of dp-ucMGP (reflecting low vitamin K status) were associated with higher mortality risk (sex- and age-adjusted hazard ratio per doubling of dp-ucMGP was 1.49, 95% CI: 1.03; 2.24). The association attenuated and became statistically insignificant after adjustment for co-morbidities (sex, age, CVD, diabetes, BMI, and eGFR adjusted hazard ratio per doubling of dp-ucMGP was 1.22, 95% CI: 0.82; 1.80). In conclusion, we found that low vitamin K status was associated with mortality in patients with COVID-19 in sex- and age-adjusted analyses, but not in analyses additionally adjusted for co-morbidities. Randomized clinical trials would be needed to clarify a potential role, if any, of vitamin K in the course of COVID-19.
Subject(s)
COVID-19/mortality , Calcium-Binding Proteins/metabolism , Extracellular Matrix Proteins/metabolism , Hospitalization , Vitamin K Deficiency/mortality , Vitamin K/blood , Adult , Aged , Biomarkers/blood , Blood Coagulation , COVID-19/complications , COVID-19/metabolism , Calcium-Binding Proteins/blood , Cohort Studies , Extracellular Matrix Proteins/blood , Female , Hospital Mortality , Humans , Male , Middle Aged , Proportional Hazards Models , Regression Analysis , SARS-CoV-2 , Thrombosis/metabolism , Vitamin K Deficiency/blood , Vitamin K Deficiency/complications , Young AdultABSTRACT
Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics-Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.