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Background: Tight junction proteins contribute to maintenance of epithelial and endothelial barriers such as the intestinal barrier and the blood brain barrier (BBB). Increased permeability of these barriers has been linked to disease activity in MS and there is currently a lack of easily accessible biomarkers predicting disease activity in MS. Aim: To investigate whether levels of circulating tight junction proteins occludin and zonula occludens-1 (ZO-1) are associated with biomarkers of inflammation and disease activity; and to determine whether they could serve as clinical biomarkers. Methods: We prospectively included 72 newly diagnosed patients with relapsing remitting MS or clinically isolated syndrome with no prior disease modifying therapy (DMT) use and 50 healthy controls (HCs). Patients were followed with blood samples, 3 tesla MRI, and clinical evaluation for 12 months. Occludin, ZO-1, calprotectin and soluble urokinase-type plasminogen activator receptor (suPAR) were measured by ELISA; serum neurofilament light (NfL) and IL-6 by single-molecule array (SIMOA). The mRNA expression of IFNG, IL1R1, IL10, IL1B, ARG1 and TNF was measured by quantitative real time polymerase chain reaction (qPCR) in whole blood. Results: Plasma occludin levels were higher in MS patients compared with HCs. After 12 months on DMT, occludin levels were reduced by approximately 25% irrespective of 1st or 2nd line DMT (p<0.001). Furthermore, NfL and calprotectin levels were significantly reduced by 31% and 29%, respectively. Occludin and ZO-1 did not correlate with biomarkers of inflammation and did not predict disease activity at baseline or after 12 months. Conclusions: Higher levels of occludin suggest an increased permeability of the BBB and/or the intestinal barrier in MS patients. The reduction of occludin after 12 months on DMTs might reflect repair of these barriers upon treatment. However, plasma levels of ZO-1 and occludin could not predict clinical or MRI disease ac

Aim To examine the hypothesis that, based on their glucose curves during a seven-point oral glucose tolerance test, people at elevated type 2 diabetes risk can be divided into subgroups with different clinical profiles at baseline and different degrees of subsequent glycaemic deterioration.Methods We included 2126 participants at elevated type 2 diabetes risk from the Diabetes Research on Patient Stratification (IMI-DIRECT) study. Latent class trajectory analysis was used to identify subgroups from a seven-point oral glucose tolerance test at baseline and follow-up. Linear models quantified the associations between the subgroups with glycaemic traits at baseline and 18 months.Results At baseline, we identified four glucose curve subgroups, labelled in order of increasing peak levels as 1-4. Participants in Subgroups 2-4, were more likely to have higher insulin resistance (homeostatic model assessment) and a lower Matsuda index, than those in Subgroup 1. Overall, participants in Subgroups 3 and 4, had higher glycaemic trait values, with the exception of the Matsuda and insulinogenic indices. At 18 months, change in homeostatic model assessment of insulin resistance was higher in Subgroup 4 (beta = 0.36, 95% CI 0.13-0.58), Subgroup 3 (beta = 0.30; 95% CI 0.10-0.50) and Subgroup 2 (beta = 0.18; 95% CI 0.04-0.32), compared to Subgroup 1. The same was observed for C-peptide and insulin. Five subgroups were identified at follow-up, and the majority of participants remained in the same subgroup or progressed to higher peak subgroups after 18 months.Conclusions Using data from a frequently sampled oral glucose tolerance test, glucose curve patterns associated with different clinical characteristics and different rates of subsequent glycaemic deterioration can be identified.

Aims/hypothesis: It is well established that physical activity, abdominal ectopic fat and glycaemic regulation are related but the underlying structure of these relationships is unclear. The previously proposed twin-cycle hypothesis (TC) provides a mechanistic basis for impairment in glycaemic control through the interactions of substrate availability, substrate metabolism and abdominal ectopic fat accumulation. Here, we hypothesise that the effect of physical activity in glucose regulation is mediated by the twin-cycle. We aimed to examine this notion in the Innovative Medicines Initiative Diabetes Research on Patient Stratification (IMI DIRECT) Consortium cohorts comprised of participants with normal or impaired glucose regulation (cohort 1: N ≤ 920) or with recently diagnosed type 2 diabetes (cohort 2: N ≤ 435). Methods: We defined a structural equation model that describes the TC and fitted this within the IMI DIRECT dataset. A second model, twin-cycle plus physical activity (TC-PA), to assess the extent to which the effects of physical activity in glycaemic regulation are mediated by components in the twin-cycle, was also fitted. Beta cell function, insulin sensitivity and glycaemic control were modelled from frequently sampled 75 g OGTTs (fsOGTTs) and mixed-meal tolerance tests (MMTTs) in participants without and with diabetes, respectively. Abdominal fat distribution was assessed using MRI, and physical activity through wrist-worn triaxial accelerometry. Results are presented as standardised beta coefficients, SE and p values, respectively. Results: The TC and TC-PA models showed better fit than null models (TC: χ2 = 242, p = 0.004 and χ2 = 63, p = 0.001 in cohort 1 and 2, respectively; TC-PA: χ2 = 180, p = 0.041 and χ2 = 60, p = 0.008 in cohort 1 and 2, respectively). The association of physical activity with glycaemic control was primarily mediated by variables in the liver fat cycle. Conclusions/interpretation: These analyses partially support the mechanis, QC 20200626

The TCF7L2 rs7903146 T-allele shows the strongest association with type 2 diabetes (T2D) among common gene variants. The aim of this study was to assess circulating levels of metabolites following a meal test in individuals carrying the high risk rs790346 TT genotype (cases) and low-risk CC genotype (controls). Sixty-two men were recruited based on TCF7L2 genotype, 31 were TT carriers and 31 were age- and BMI-matched CC carriers. All participants consumed a test meal after 12 hours of fasting. Metabolites were measured using proton nuclear magnetic resonance (NMR) spectroscopy. Metabolomic profiling of TCF7L2 carriers were performed for 141 lipid estimates. TT carriers had lower fasting levels of L-VLDL-L (total lipids in large very low density lipoproteins, p = 0.045), L-VLDL-CE (cholesterol esters in large VLDL, p = 0.03), and L-VLDL-C (total cholesterol in large VLDL, p = 0.045) compared to CC carriers. Additionally, TT carriers had lower postprandial levels of total triglycerides (TG) (q = 0.03), VLDL-TG (q = 0.05, including medium, small and extra small, q = 0.048, q = 0.0009, q = 0.04, respectively), HDL-TG (triglycerides in high density lipoproteins q = 0.037) and S-HDL-TG (q = 0.00003). In conclusion, TT carriers show altered postprandial triglyceride response, mainly influencing VLDL and HDL subclasses suggesting a genotype-mediated effect on hepatic lipid regulation.

Trace elements are unevenly distributed and speciated throughout the cereal grain. The germ and the outer layers of the grain have the highest concentrations of trace elements. A large fraction of the trace elements is therefore lost during the milling process. The bioavailability of the remaining trace elements is very low. This is usually ascribed to the formation of poorly soluble complexes with the phosphorus storage compound phytic acid. Hence, analysis of the total concentration of trace elements in grain tissues must be combined with a speciation analysis in order to assess their contribution to human nutrition. This chapter deals with the fractionation of anatomically very different cereal tissues. Procedures for microscaling of digestion procedures are outlined together with requirements for the use of certified reference materials in elemental profiling of grain tissue fractions. Methods for extraction and analysis of complexes containing trace elements in the grain tissue fractions are described. Finally, the chapter concludes with criteria for choice of chromatographic methods and setting of ICP-MS instrument parameters.