Randomized open-label trial of semaglutide and dapagliflozin in patients with type 2 diabetes of different pathophysiology.

The limited understanding of the heterogeneity in the treatment response to antidiabetic drugs contributes to metabolic deterioration and cardiovascular complications1,2, stressing the need for more personalized treatment1. Although recent attempts have been made to classify diabetes into subgroups, the utility of such stratification in predicting treatment response is unknown3. We enrolled participants with type 2 diabetes (n = 239, 74 women and 165 men) and features of severe insulin-deficient diabetes (SIDD) or severe insulin-resistant diabetes (SIRD). Participants were randomly assigned to treatment with the glucagon-like peptide 1 receptor agonist semaglutide or the sodium-glucose cotransporter 2 inhibitor dapagliflozin for 6 months (open label). The primary endpoint was the change in glycated haemoglobin (HbA1c). Semaglutide induced a larger reduction in HbA1c levels than dapagliflozin (mean difference, 8.2 mmol mol-1; 95% confidence interval, -10.0 to -6.3 mmol mol-1), with a pronounced effect in those with SIDD. No difference in adverse events was observed between participants with SIDD and those with SIRD. Analysis of secondary endpoints showed greater reductions in fasting and postprandial glucose concentrations in response to semaglutide in participants with SIDD than in those with SIRD and a more pronounced effect on postprandial glucose by dapagliflozin in participants with SIDD than in those with SIRD. However, no significant interaction was found between drug assignment and the SIDD or SIRD subgroup. In contrast, continuous measures of body mass index, blood pressure, insulin secretion and insulin resistance were useful in identifying those likely to have the largest improvements in glycaemic control and cardiovascular risk factors by adding semaglutide or dapagliflozin. Thus, systematic evaluation of continuous pathophysiological variables can guide the prediction of the treatment response to these drugs and provide more information than stratified subgroups ( NCT04451837 ).

Increasing circulating levels of Tenascin C in response to the Wingate anaerobic test.

AIM: Tenascin C (TNC) is a large extracellular matrix glycoprotein. It is involved in development and upregulated both during tissue repair and in several pathological conditions, including cardiovascular disease. Extracellular matrix proteins play a role in promoting exercise responses, leading to adaptation, regeneration, and repair. The main goal of this study was to investigate whether a short anaerobic effort leads to increased levels of TNC in serum. METHODS: Thirty-nine healthy men performed a Wingate test followed by a muscle biopsy. Myoblasts were isolated from the muscle biopsies and differentiated to myotubes ex vivo. TNC RNA was quantified in the biopsies, myotubes and myoblasts using RNA sequencing. Blood samples were drawn before and 5 min after the Wingate test. Serum TNC levels were measured using enzyme-linked immunosorbent assay. RESULTS: After the Wingate test, serum TNC increased on average by 23% [15-33], median [interquartile range]; PWilcoxon < 0.0001. This increase is correlated with peak power output and power drop, but not with VO2max . TNC RNA expression is higher in myoblasts and myotubes compared to skeletal muscle tissue. CONCLUSION: TNC is secreted systemically as a response to the Wingate anaerobic test in healthy males. The response was positively correlated with peak power and power drop, but not with VO2max which implicates a relation to mechanical strain and/or blood flow. With higher expression in undifferentiated myoblast cells than muscle tissue, it is likely that TNC plays a role in muscle tissue remodelling in humans. Our findings open for research on how TNC contributes to exercise adaptation.

Examining the causal effect of type 2 diabetes on ischemic heart disease - A longitudinal study with four measurements (1980-2017).

OBJECTIVE: This longitudinal study examines a possible causal effect between type 2 diabetes and ischemic heart disease (IHD) by using measurements on four occasions from the Swedish Statistics on Income and Living Conditions (SILC) together with nationwide healthcare registers. METHODS: This was a longitudinal study based on a random sample of men and women (n = 2014) from the Swedish population with four measurements in the SILC every eight years. Baseline was 1980/81 and the participants were followed for up to 37 years. The mean age and age range at baseline were 36.5 and 20-59 years, respectively. The study used Marginal Structural Modeling (MSM-Cox) to account for time-varying exposures by implementing inverse probability weighting (IPTW). MSM-Cox with IPTW was compared with Cox proportional hazard modelling. RESULTS: The hazard ratio (HR) for IHD (369 cases) with 95% confidence interval (CI) in participants with type 2 diabetes (11.1%) compared to participants without type 2 diabetes (88.9%) was significantly higher (1.99; CI = 1.15 - 3.44) when using MSM-Cox with IPTW after adjustments for clinical and sociodemographic risk factors. When applying Cox proportional hazard models adjusted for the same variables, the HR was lower and non-significant at 1.34 (CI = 0.94 - 1.98). CONCLUSIONS: This longitudinal study with four measurements assessed a possible causal association between type 2 diabetes and IHD by applying MSM-Cox with IPTW. Although causality cannot be determined due to the remaining risk of residual bias, the results may help to elucidate a potential causal relationship between type 2 diabetes and IHD. Further causal studies on possible underlying mechanisms are, however, needed.

High-throughput muscle fiber typing from RNA sequencing data.

BACKGROUND: Skeletal muscle fiber type distribution has implications for human health, muscle function, and performance. This knowledge has been gathered using labor-intensive and costly methodology that limited these studies. Here, we present a method based on muscle tissue RNA sequencing data (totRNAseq) to estimate the distribution of skeletal muscle fiber types from frozen human samples, allowing for a larger number of individuals to be tested. METHODS: By using single-nuclei RNA sequencing (snRNAseq) data as a reference, cluster expression signatures were produced by averaging gene expression of cluster gene markers and then applying these to totRNAseq data and inferring muscle fiber nuclei type via linear matrix decomposition. This estimate was then compared with fiber type distribution measured by ATPase staining or myosin heavy chain protein isoform distribution of 62 muscle samples in two independent cohorts (n = 39 and 22). RESULTS: The correlation between the sequencing-based method and the other two were rATPas = 0.44 [0.13-0.67], [95% CI], and rmyosin = 0.83 [0.61-0.93], with p = 5.70 × 10-3 and 2.00 × 10-6, respectively. The deconvolution inference of fiber type composition was accurate even for very low totRNAseq sequencing depths, i.e., down to an average of ~ 10,000 paired-end reads. CONCLUSIONS: This new method ( https://github.com/OlaHanssonLab/PredictFiberType ) consequently allows for measurement of fiber type distribution of a larger number of samples using totRNAseq in a cost and labor-efficient way. It is now feasible to study the association between fiber type distribution and e.g. health outcomes in large well-powered studies.

Relationship between insulin sensitivity and gene expression in human skeletal muscle.

BACKGROUND: Insulin resistance (IR) in skeletal muscle is a key feature of the pre-diabetic state, hypertension, dyslipidemia, cardiovascular diseases and also predicts type 2 diabetes. However, the underlying molecular mechanisms are still poorly understood. METHODS: To explore these mechanisms, we related global skeletal muscle gene expression profiling of 38 non-diabetic men to a surrogate measure of insulin sensitivity, i.e. homeostatic model assessment of insulin resistance (HOMA-IR). RESULTS: We identified 70 genes positively and 110 genes inversely correlated with insulin sensitivity in human skeletal muscle, identifying autophagy-related genes as positively correlated with insulin sensitivity. Replication in an independent study of 9 non-diabetic men resulted in 10 overlapping genes that strongly correlated with insulin sensitivity, including SIRT2, involved in lipid metabolism, and FBXW5 that regulates mammalian target-of-rapamycin (mTOR) and autophagy. The expressions of SIRT2 and FBXW5 were also positively correlated with the expression of key genes promoting the phenotype of an insulin sensitive myocyte e.g. PPARGC1A. CONCLUSIONS: The muscle expression of 180 genes were correlated with insulin sensitivity. These data suggest that activation of genes involved in lipid metabolism, e.g. SIRT2, and genes regulating autophagy and mTOR signaling, e.g. FBXW5, are associated with increased insulin sensitivity in human skeletal muscle, reflecting a highly flexible nutrient sensing.

Serology assessment of antibody response to SARS-CoV-2 in patients with COVID-19 by rapid IgM/IgG antibody test.

The coronavirus disease 2019 (COVID-19) pandemic has created a global health- and economic crisis. Detection of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which causes COVID-19 by serological methods is important to diagnose a current or resolved infection. In this study, we applied a rapid COVID-19 IgM/IgG antibody test and performed serology assessment of antibody response to SARS-CoV-2. In PCR-confirmed COVID-19 patients (n = 45), the total antibody detection rate is 92% in hospitalized patients and 79% in non-hospitalized patients. The total IgM and IgG detection is 63% in patients with <2 weeks from disease onset; 85% in non-hospitalized patients with >2 weeks disease duration; and 91% in hospitalized patients with >2 weeks disease duration. We also compared different blood sample types and suggest a higher sensitivity by serum/plasma over whole blood. Test specificity was determined to be 97% on 69 sera/plasma samples collected between 2016-2018. Our study provides a comprehensive validation of the rapid COVID-19 IgM/IgG serology test, and mapped antibody detection patterns in association with disease progress and hospitalization. Our results support that the rapid COVID-19 IgM/IgG test may be applied to assess the COVID-19 status both at the individual and at a population level.

N1-methylnicotinamide is a signalling molecule produced in skeletal muscle coordinating energy metabolism.

Obesity is a major health problem, and although caloric restriction and exercise are successful strategies to lose adipose tissue in obese individuals, a simultaneous decrease in skeletal muscle mass, negatively effects metabolism and muscle function. To deeper understand molecular events occurring in muscle during weight-loss, we measured the expressional change in human skeletal muscle following a combination of severe caloric restriction and exercise over 4 days in 15 Swedish men. Key metabolic genes were regulated after the intervention, indicating a shift from carbohydrate to fat metabolism. Nicotinamide N-methyltransferase (NNMT) was the most consistently upregulated gene following the energy-deficit exercise. Circulating levels of N1-methylnicotinamide (MNA), the product of NNMT activity, were doubled after the intervention. The fasting-fed state was an important determinant of plasma MNA levels, peaking at ~18 h of fasting and being lowest ~3 h after a meal. In culture, MNA was secreted by isolated human myotubes and stimulated lipolysis directly, with no effect on glucagon or insulin secretion. We propose that MNA is a novel myokine that enhances the utilization of energy stores in response to low muscle energy availability. Future research should focus on applying MNA as a biomarker to identify individuals with metabolic disturbances at an early stage.