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Context/Objective: In order to better understand which metabolic differences are related to insulin resistance in metabolic syndrome (MetSyn), we used hyperinsulinemic–euglycemic (HE) clamps in individuals with MetSyn and related peripheral insulin resistance to circulating biomarkers. Design/Methods: In this cross-sectional study, HE-clamps were performed in treatment-naive men (n = 97) with MetSyn. Subjects were defined as insulin-resistant based on the rate of disappearance (Rd). Machine learning models and conventional statistics were used to identify biomarkers of insulin resistance. Findings were replicated in a cohort with n = 282 obese men and women with (n = 156) and without (n = 126) MetSyn. In addition to this, the relation between biomarkers and adipose tissue was assessed by nuclear magnetic resonance imaging. Results: Peripheral insulin resistance is marked by changes in proteins related to inflammatory processes such as IL-1 and TNF-receptor and superfamily members. These proteins can distinguish between insulin-resistant and insulin-sensitive individuals (AUC = 0.72 ± 0.10) with MetSyn. These proteins were also associated with IFG, liver fat (rho 0.36, p = 1.79 × 10−9) and visceral adipose tissue (rho = 0.35, p = 6.80 × 10−9). Interestingly, these proteins had the strongest association in the MetSyn subgroup compared to individuals without MetSyn. Conclusions: MetSyn associated with insulin resistance is characterized by protein changes related to body fat content, insulin signaling and pro-inflammatory processes. These findings provide novel targets for intervention studies and should be the focus of future in vitro and in vivo studies. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the association between overweight and obesity on the clinical course and outcomes in patients hospitalized with COVID-19. Design: Retrospective, observational cohort study. Methods: We performed a multicenter, retrospective, observational cohort study of hospitalized COVID-19 patients to evaluate the associations between overweight and obesity on the clinical course and outcomes. Results: Out of 1634 hospitalized COVID-19 patients, 473 (28.9%) had normal weight, 669 (40.9%) were overweight, and 492 (30.1%) were obese. Patients who were overweight or had obesity were younger, and there were more women in the obese group. Normal-weight patients more often had pre-existing conditions such as malignancy, or were organ recipients. During admission, patients who were overweight or had obesity had an increased probability of acute respiratory distress syndrome [OR 1.70 (1.26-2.30) and 1.40 (1.01-1.96)], respectively and acute kidney failure [OR 2.29 (1.28-3.76) and 1.92 (1.06-3.48)], respectively. Length of hospital stay was similar between groups. The overall in-hospital mortality rate was 27.7%, and multivariate logistic regression analyses showed that overweight and obesity were not associated with increased mortality compared to normal-weight patients. Conclusion: In this study, overweight and obesity were associated with acute respiratory distress syndrome and acute kidney injury, but not with in-hospital mortality nor length of hospital stay. [ABSTRACT FROM AUTHOR]

Metabolic syndrome (MetSyn) is an important risk factor for type 2 diabetes and cardiovascular diseases (CVD). This study aimed to find distinct plasma metabolite profiles between insulin-resistant and non-insulin resistant subjects with MetSyn and evaluate if MetSyn metabolite profiles are related to CVD risk and lipid fluxes. In a cross-sectional study, untargeted metabolomics of treatment-naive males with MetSyn (n = 132) were analyzed together with clinical parameters. In a subset of MetSyn participants, CVD risk was calculated using the Framingham score (n = 111), and lipolysis (n = 39) was measured by a two-step hyperinsulinemic euglycemic clamp using [1,1,2,3,3-2H5] glycerol to calculate lipolysis suppression rates. Peripheral insulin resistance was related to fatty acid metabolism and glycerolphosphorylcholine. Interestingly, although insulin resistance is considered to be a risk factor for CVD, we observed that there was little correspondence between metabolites associated with insulin resistance and metabolites associated with CVD risk. The latter mainly belonged to the androgenic steroid, fatty acid, phosphatidylethanolamine, and phophatidylcholine pathways. These data provide new insights into metabolic changes in mild MetSyn pathophysiology and MetSyn CVD risk related to lipid metabolism. Prospective studies may focus on the pathophysiological role of the here-identified biomarkers. [ABSTRACT FROM AUTHOR]

Plasma metabolites affect a range of metabolic functions in humans, including insulin sensitivity (IS). A subset of these plasma metabolites is modified by the gut microbiota. To identify potential microbial–metabolite pathways involved in IS, we investigated the link between plasma metabolites, gut microbiota composition, and IS, using the gold-standard for peripheral and hepatic IS measurement in a group of participants with metabolic syndrome (MetSyn). In a cross-sectional study with 115 MetSyn participants, fasting plasma samples were collected for untargeted metabolomics analysis and fecal samples for 16S rRNA gene amplicon sequencing. A two-step hyperinsulinemic euglycemic clamp was performed to assess peripheral and hepatic IS. Collected data were integrated and potential interdependence between metabolites, gut microbiota, and IS was analyzed using machine learning prediction models. Plasma metabolites explained 13.2% and 16.7% of variance in peripheral and hepatic IS, respectively. Fecal microbiota composition explained 4.2% of variance in peripheral IS and was not related to hepatic IS. Although metabolites could partially explain the variances in IS, the top metabolites related to peripheral and hepatic IS did not significantly correlate with gut microbiota composition (both on taxonomical level and alpha-diversity). However, all plasma metabolites could explain 18.5% of the variance in microbial alpha-diversity (Shannon); the top 20 metabolites could even explain 44.5% of gut microbial alpha-diversity. In conclusion, plasma metabolites could partially explain the variance in peripheral and hepatic IS; however, these metabolites were not directly linked to the gut microbiota composition, underscoring the intricate relation between plasma metabolites, the gut microbiota, and IS in MetSyn [ABSTRACT FROM AUTHOR]

Background: In this coronavirus disease 2019 (COVID-19) pandemic, fast and accurate testing is needed to profile patients at the emergency department (ED) and efficiently allocate resources. Chest imaging has been considered in COVID-19 workup, but evidence on lung ultrasound (LUS) is sparse. We therefore aimed to assess and compare the diagnostic accuracy of LUS and computed tomography (CT) in suspected COVID-19 patients., Methods: This multicentre, prospective, observational study included adult patients with suspected COVID-19 referred to internal medicine at the ED. We calculated diagnostic accuracy measures for LUS and CT using both PCR and multidisciplinary team (MDT) diagnosis as reference. We also assessed agreement between LUS and CT, and between sonographers., Results: One hundred and eighty-seven patients were recruited between March 19 and May 4, 2020. Area under the receiver operating characteristic (AUROC) was 0.81 (95% CI 0.75-0.88) for LUS and 0.89 (95% CI 0.84-0.94) for CT. Sensitivity and specificity for LUS were 91.9% (95% CI 84.0-96.7) and 71.0% (95% CI 61.1-79.6), respectively, versus 88.4% (95% CI 79.7-94.3) and 82.0% (95% CI 73.1-89.0) for CT. Negative likelihood ratio was 0.1 (95% CI 0.06-0.24) for LUS and 0.14 (95% CI 0.08-0.3) for CT. No patient with a false negative LUS required supplemental oxygen or admission. LUS specificity increased to 80% (95% CI 69.9-87.9) compared to MDT diagnosis, with an AUROC of 0.85 (95% CI 0.79-0.91). Agreement between LUS and CT was 0.65. Interobserver agreement for LUS was good: 0.89 (95% CI 0.83-0.93)., Conclusion: LUS and CT have comparable diagnostic accuracy for COVID-19 pneumonia. LUS can safely exclude clinically relevant COVID-19 pneumonia and may aid COVID-19 diagnosis in high prevalence situations., Competing Interests: Conflict of interest: A.W.E. Lieveld has nothing to disclose. Conflict of interest: B. Kok has nothing to disclose. Conflict of interest: F.H. Schuit has nothing to disclose. Conflict of interest: K. Azijli has nothing to disclose. Conflict of interest: J. Heijmans has nothing to disclose. Conflict of interest: A. van Laarhoven has nothing to disclose. Conflict of interest: N.L. Assman has nothing to disclose. Conflict of interest: R.S. Kootte has nothing to disclose. Conflict of interest: T.J. Olgers has nothing to disclose. Conflict of interest: P.W.B. Nanayakkara has nothing to disclose. Conflict of interest: F.H. Bosch has nothing to disclose., (Copyright ©ERS 2020.)

Hyperthyroidism is associated with several changes in the haemostatic system resulting in a hypercoagulable state. It is uncertain at this stage whether this leads to an increased risk of venous thromboembolism (VTE). The aim of this retrospective cohort study was to determine the risk of VTE in all patients with overt hyperthyroidism and to compare this to the risk of VTE in the general population. In three hospitals in the Netherlands, patients with biochemically confirmed hyperthyroidism caused by Graves' disease, multinodular goiter or toxic adenoma were included. All available electronic and handwritten records were examined. Primary outcome was the occurrence of VTE within six months before and until six months after the diagnosis of hyperthyroidism. We included a total of 587 patients. Five patients experienced a VTE during the study period, resulting in an incidence rate of 8.7 (95% CI 2.8 - 20.2) per 1,000 person-years. Three of these five patients had a first VTE (incidence rate for first VTE was 5.3 [95% CI 1.1 - 15.6] per 1,000 person-years). Incidence rates of VTE in the general population are between 0.6 and 1.6 per 1,000 person-years for first VTE and 0.7 and 1.8 per 1,000 person-years for all VTE. In conclusion, the incidence rate of VTE in patients with hyperthyroidism appears to be high. Future prospective studies are needed to further explore this possible association and to address its clinical implications.