Search Results (4)

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. Full article

Damage to porous materials in heritage buildings caused by salt mixture crystallization is driven by the surrounding environmental conditions. To understand the crystallization behavior of a mixed salt solution as a function of changing climatic conditions (i.e., relative humidity and temperature), excluding factors such as the internal pore structure, the thermodynamic model ECOS/RUNSALT is the only freeware available that requires simple input and includes the most relevant ions for heritage buildings and solids. We suggest the use of specific terminology and describe how to use the model and how to interpret the output, with emphasis on key limitations for which solutions are provided. When used correctly, the model output can be trusted, specifically when it is used to inform preventive conservation (e.g., environmental conditions in which salt crystallization cycles should not occur). However, salt mixture kinetics and the internal pore structure remain crucial parameters that are not considered in the model. These aspects need further attention to develop a better understanding and correctly model salt damage in relation to climatic changes. Full article

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 Full article

Mesoporous carbons were synthesized via both soft and hard template methods and compared to a commercial powder activated carbon (PAC) for the adsorption ability of bisphenol-A (BPA) from an aqueous solution. The commercial PAC had a BET-surface of 1027 m²/g with fine pores of 3 nm and less. The hard templated carbon (CMK-3) material had an even higher BET-surface of 1420 m²/g with an average pore size of 4 nm. The soft templated carbon (SMC) reached a BET-surface of 476 m²/g and a pore size of 7 nm. The maximum observed adsorption capacity (q_max) of CMK-3 was the highest with 474 mg/g, compared to 290 mg/g for PAC and 154 mg/g for SMC. The difference in adsorption capacities was attributed to the specific surface area and hydrophobicity of the adsorbent. The microporous PAC showed the slowest adsorption, while the ordered mesopores of SMC and CMK-3 enhanced the BPA diffusion into the adsorbent. This difference in adsorption kinetics is caused by the increase in pore diameter. However, CMK-3 with an open geometry consisting of interlinked nanorods allows for even faster intraparticle diffusion. Full article