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Aims CVD is the main cause of morbidity and mortality in individuals with diabetes. It is currently unclear whether daily glucose variability contributes to CVD. Therefore, we investigated whether glucose variability is associated with arterial measures that are considered important in CVD pathogenesis. Methods We included participants of The Maastricht Study, an observational population-based cohort, who underwent at least 48 h of continuous glucose monitoring (CGM) (n = 853; age: 59.9 ± 8.6 years; 49% women, 23% type 2 diabetes). We studied the cross-sectional associations of two glucose variability indices (CGM-assessed SD [SDCGM] and CGM-assessed CV [CVCGM]) and time in range (TIRCGM) with carotid–femoral pulse wave velocity (cf-PWV), carotid distensibility coefficient, carotid intima–media thickness, ankle–brachial index and circumferential wall stress via multiple linear regression. Results Higher SDCGM was associated with higher cf-PWV after adjusting for demographics, cardiovascular risk factors and lifestyle factors (regression coefficient [B] per 1 mmol/l SDCGM [and corresponding 95% CI]: 0.413 m/s [0.147, 0.679], p = 0.002). In the model additionally adjusted for CGM-assessed mean sensor glucose (MSGCGM), SDCGM and MSGCGM contributed similarly to cf-PWV (respective standardised regression coefficients [st.βs] and 95% CIs of 0.065 [−0.018, 0.167], p = 0.160; and 0.059 [−0.043, 0.164], p = 0.272). In the fully adjusted models, both higher CVCGM (B [95% CI] per 10% CVCGM: 0.303 m/s [0.046, 0.559], p = 0.021) and lower TIRCGM (B [95% CI] per 10% TIRCGM: −0.145 m/s [−0.252, −0.038] p = 0.008) were statistically significantly associated with higher cf-PWV. Such consistent associations were not observed for the other arterial measures. Conclusions Our findings show that greater daily glucose variability and lower TIRCGM are associated with greater aortic stiffness (cf-PWV) but not with other arterial measures. If corroborated in prospective studies, these results support the development of therapeutic agents that target both daily glucose variability and TIRCGM to prevent CVD. Graphical abstract

B-cells contribute to MS pathogenesis. The association of circulating B-cell phenotypes with combined unique active lesions (CUA) on MRI at 48 weeks follow-up was investigated in 50 interferon beta-treated MS patients. Transitional B-cell proportions were lower in participants with CUA at week 0 and 48 [p = 0.004, p = 0.002]. A decrease in circulating anti-EBNA-1 IgG levels between week 0 and 48 associated with absence of CUA [p = 0.047], but not with B-cell profiles. In a multi-factor model for CUA-risk, transitional B-cell proportions contributed independent from NK/T-cell ratio, change in anti-EBNA-1 IgG, and vitamin D supplementation. Transitional B-cells may predict treatment response in MS.

Cardiac troponin T (cTnT) is a sensitive and specific biomarker for detecting cardiac muscle injury. Its concentration in blood can be significantly elevated outside the normal reference range under several pathophysiological conditions. The classical analytical method in routine clinical analysis to detect cTnT in serum or plasma is a single commercial immunoassay, which is designed to quantify the intact cTnT molecule. The targeted epitopes are located in the central region of the cTnT molecule. However, in blood cTnT exists in different biomolecular complexes and proteoforms: bound (to cardiac troponin subunits or to immunoglobulins) or unbound (as intact protein or as proteolytic proteoforms). While proteolysis is a principal posttranslational modification (PTM), other confirmed PTMs of the proteoforms include N-terminal initiator methionine removal, N-acetylation, O-phosphorylation, O-(N-acetyl)-glucosaminylation, N(ɛ)-(carboxymethyl)lysine modification and citrullination. The immunoassay probably detects several of those cTnT biomolecular complexes and proteoforms, as long as they have the centrally targeted epitopes in common. While analytical cTnT immunoreactivity has been studied predominantly in blood, it can also be detected in urine, although it is unclear in which proteoform cTnT immunoreactivity is present in urine. This review presents an overview of the current knowledge on the pathophysiological lifecycle of cTnT. It provides insight into the impact of PTMs, not only on the analytical immunoreactivity, but also on the excretion of cTnT in urine as one of the waste routes in that lifecycle. Accordingly, and after isolating the proteoforms from urine of patients suffering from proteinuria and acute myocardial infarction, the structures of some possible cTnT proteoforms are reconstructed using mass spectrometry and presented.

Background Closed-loop insulin delivery systems, which integrate continuous glucose monitoring (CGM) and algorithms that continuously guide insulin dosing, have been shown to improve glycaemic control. The ability to predict future glucose values can further optimize such devices. In this study, we used machine learning to train models in predicting future glucose levels based on prior CGM and accelerometry data. Methods We used data from The Maastricht Study, an observational population‐based cohort that comprises individuals with normal glucose metabolism, prediabetes, or type 2 diabetes. We included individuals who underwent >48h of CGM (n = 851), most of whom (n = 540) simultaneously wore an accelerometer to assess physical activity. A random subset of individuals was used to train models in predicting glucose levels at 15- and 60-minute intervals based on either CGM data or both CGM and accelerometer data. In the remaining individuals, model performance was evaluated with root-mean-square error (RMSE), Spearman’s correlation coefficient (rho) and surveillance error grid. For a proof-of-concept translation, CGM-based prediction models were optimized and validated with the use of data from individuals with type 1 diabetes (OhioT1DM Dataset, n = 6). Results Models trained with CGM data were able to accurately predict glucose values at 15 (RMSE: 0.19mmol/L; rho: 0.96) and 60 minutes (RMSE: 0.59mmol/L, rho: 0.72). Model performance was comparable in individuals with type 2 diabetes. Incorporation of accelerometer data only slightly improved prediction. The error grid results indicated that model predictions were clinically safe (15 min: >99%, 60 min >98%). Our prediction models translated well to individuals with type 1 diabetes, which is reflected by high accuracy (RMSEs for 15 and 60 minutes of 0.43 and 1.73 mmol/L, respectively) and clinical safety (15 min: >99%, 60 min: >91%). Conclusions Machine learning-based models are able to accurately and safely predict glucose values at 15- and 60-minute intervals based on CGM data only. Future research should further optimize the models for implementation in closed-loop insulin delivery systems.

IntroductionPatients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important for appropriate triage and early treatment. The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk scores.MethodsA single-center, retrospective cohort study was conducted amongst 1,344 emergency department patients fulfilling sepsis criteria. Laboratory and clinical data that was available in the first two hours of presentation from these patients were randomly partitioned into a development (n = 1,244) and validation dataset (n = 100). Machine learning models were trained and evaluated on the development dataset and compared to internal medicine physicians and risk scores in the independent validation dataset. The primary outcome was 31-day mortality.ResultsA number of 1,344 patients were included of whom 174 (13.0%) died. Machine learning models trained with laboratory or a combination of laboratory + clinical data achieved an area-under-the ROC curve of 0.82 (95% CI: 0.80–0.84) and 0.84 (95% CI: 0.81–0.87) for predicting 31-day mortality, respectively. In the validation set, models outperformed internal medicine physicians and clinical risk scores in sensitivity (92% vs. 72% vs. 78%;p0.02). The model had higher diagnostic accuracy with an area-under-the-ROC curve of 0.85 (95%CI: 0.78–0.92) compared to abbMEDS (0.63,0.54–0.73), mREMS (0.63,0.54–0.72) and internal medicine physicians (0.74,0.65–0.82).ConclusionMachine learning models outperformed internal medicine physicians and clinical risk scores in predicting 31-day mortality. These models are a promising tool to aid in risk stratification of patients presenting to the ED with sepsis.

Objective Due to the high awareness of coeliac disease and improvement of serological tests, the number of requested laboratory tests has increased substantially over the years. In the current study we have evaluated the requesting behaviour of distinct clinical disciplines in relation to the prevalence of positive results and in the context of existing guidelines. Methods Data were retrospectively extracted from the laboratory information system over a time-span of 5 years in a tertiary hospital and compared with the situation in a secondary hospital. Results Data reveal that for initial testing (n=18,183) the percentage positive results for IgA anti-TTG is, Highlights • The prevalence of IgA anti-TTG for diagnosis of coeliac disease is very low. • The prevalence of IgA anti-TTG for diagnosis of paediatric patients is higher. • The request for IgA anti-TTG occurs more often in females than in males (2:1). • The request for IgA anti-TTG occurs as often in girls as in boys (1:1). • Positive IgA anti-TTG are not consistently confirmed in an independent sample.

BACKGROUNDCardiac troponin T (cTnT) is the preferred biomarker for the diagnosis of acute myocardial infarction (AMI). It has been suggested that cTnT is present predominantly in fragmented forms in human serum following AMI. In this study, we have used a targeted mass spectrometry assay and epitope mapping using Western blotting to confirm this hypothesis.METHODScTnT was captured from the serum of 12 patients diagnosed with AMI using an immunoprecipitation technique employing the M11.7 catcher antibody and fractionated with SDS-PAGE. Coomassie-stained bands of 4 patients at 37, 29, and 16 kDa were excised from the gel, digested with trypsin, and analyzed on a Q Exactive instrument set on targeted Selected Ion Monitoring mode with data-dependent tandem mass spectrometry (MS/MS) for identification. Western blotting employing 3 different antibodies was used for epitope mapping.RESULTSTen cTnT peptides of interest were targeted. By using MS/MS, all of these peptides were identified in the 37-kDa, intact, cTnT band. In the 29- and 16-kDa fragment bands, 8 and 4 cTnT-specific peptides were identified, respectively. Some of these peptides were “semitryptic,” meaning that their C-termini were not formed by trypsin cleavage. The C-termini of these semitryptic peptides represent the C-terminal end of the cTnT molecules present in these bands. These results were confirmed independently by epitope mapping.CONCLUSIONSUsing LC-MS, we have succeeded in positively identifying the 29- and 16-kDa fragment bands as cTnT-derived products. The amino acid sequences of the 29- and 16-kDa fragments are Ser79-Trp297 and Ser79-Gln199, respectively.

Cardiac troponin T (cTnT) has been shown to be present in fragmented forms in human serum after acute myocardial infarction (AMI). While calpain-1 and caspase-3 have been identified as intracellular pro teases able to cleave the N-terminus of cTnT, it is still unclear which proteases are responsible for the extensive and progressive cTnT fragmentation observed in serum of AMI-patients. In this pilot study we have investigated the possibility that human thrombin may be involved in this process. Purified human cTnT was spiked in unprocessed and deproteinated serum in the presence or absence of either purified human thrombin or PPACK thrombin inhibitor. After immunoprecipitation, SDS-PAGE and Western blotting we observed an increase in cTnT fragmentation when purified thrombin was added to deproteinated serum. Consequently, the addition of thrombin inhibitor to unprocessed serum resulted in a decrease of cTnT fragmentation. Our results suggest that multiple enzymes are involved in cTnT degradation, and that thrombin plays an important role.

The role of cardiac troponins (cTn) have become increasingly important in diagnosing myocardial infarction (MI), especially in patients without electrocardiogram abnormalities (1).