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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]

Background: CT is thought to play a key role in coronavirus disease 2019 (COVID-19) diagnostic workup. The possibility of comparing data across different settings depends on the systematic and reproducible manner in which the scans are analyzed and reported. The COVID-19 Reporting and Data System (CO-RADS) and the corresponding CT severity score (CTSS) introduced by the Radiological Society of the Netherlands (NVvR) attempt to do so. However, this system has not been externally validated.Research Question: We aimed to prospectively validate the CO-RADS as a COVID-19 diagnostic tool at the ED and to evaluate whether the CTSS is associated with prognosis.Study Design and Methods: We conducted a prospective, observational study in two tertiary centers in The Netherlands, between March 19 and May 28, 2020. We consecutively included 741 adult patients at the ED with suspected COVID-19, who received a chest CT and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR (PCR). Diagnostic accuracy measures were calculated for CO-RADS, using PCR as reference. Logistic regression was performed for CTSS in relation to hospital admission, ICU admission, and 30-day mortality.Results: Seven hundred forty-one patients were included. We found an area under the curve (AUC) of 0.91 (CI, 0.89-0.94) for CO-RADS using PCR as reference. The optimal CO-RADS cutoff was 4, with a sensitivity of 89.4% (CI, 84.7-93.0) and specificity of 87.2% (CI, 83.9-89.9). We found a significant association between CTSS and hospital admission, ICU admission, and 30-day mortality; adjusted ORs per point increase in CTSS were 1.19 (CI, 1.09-1.28), 1.23 (1.15-1.32), 1.14 (1.07-1.22), respectively. Intraclass correlation coefficients for CO-RADS and CTSS were 0.94 (0.91-0.96) and 0.82 (0.70-0.90).Interpretation: Our findings support the use of CO-RADS and CTSS in triage, diagnosis, and management decisions for patients presenting with possible COVID-19 at the ED. [ABSTRACT FROM AUTHOR]

A growing body of evidence suggests that the human gut microbiota plays a role in the development of obesity and related metabolic diseases. However, there is little consensus between studies, which could be due to biological as well as technical variation. In addition, little human data are available to investigate whether tissue-specific insulin sensitivity is related to specific microbial patterns. We examined this relation in two independent cohorts of overweight and obese pre-diabetic men, using phylogenetic microarray data and hepatic, peripheral and adipose tissue insulin sensitivity that were determined by a two-step hyperinsulinemic-euglycemic clamp with [6,6-2H2]-glucose tracer infusion. Despite a prominent subject-specific microbiota, we found significant associations of microbial taxa with tissue-specific insulin sensitivity using regression analysis. Using random forests we found moderate associations with other measures of glucose homeostasis in only one of the cohorts (fasting glucose concentrations AUC = 0.66 and HbA1c AUC = 0.65). However, all findings were cohort-specific due to pronounced variation in microbiota between cohorts, suggesting the existence of alternative states for dysbiosis in metabolic syndrome patients. Our findings suggest individual or group related dynamics, instead of universal microbiota signals, related to the host when the overweight or obese state has already developed and argue that care should be taken with extrapolating significant correlations from single cohorts, into generalized biological relevance. [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]