Emerging role of insulin with incretin therapies for management of type 2 diabetes

Type 2 diabetes mellitus (T2DM) is a progressive disease warranting intensification of treatment, as beta-cell function declines over time. Current treatment algorithms recommend metformin as the first-line agent, while advocating the addition of either basal-bolus or premixed insulin as the final level of intervention. Incretin therapy, including incretin mimetics or enhancers, are the latest group of drugs available for treatment of T2DM. These agents act through the incretin axis, are currently recommended as add-on agents either as second-or third-line treatment, without concurrent use of insulin. Given the novel role of incretin therapy in terms of reducing postprandial hyperglycemia, and favorable effects on weight with reduced incidence of hypoglycemia, we explore alternative options for incretin therapy in T2DM management. Furthermore, as some evidence alludes to incretins potentially increasing betacell mass and altering disease progression, we propose introducing these agents earlier in the treatment algorithm. In addition, we suggest the concurrent use of incretins with insulin, given the favorable effects especially in relation to weight gain

Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome.

Comparing transcript levels between healthy and diseased individuals allows the identification of differentially expressed genes, which may be causes, consequences or mere correlates of the disease under scrutiny. We propose a method to decompose the observational correlation between gene expression and phenotypes driven by confounders, forward- and reverse causal effects. The bi-directional causal effects between gene expression and complex traits are obtained by Mendelian Randomization integrating summary-level data from GWAS and whole-blood eQTLs. Applying this approach to complex traits reveals that forward effects have negligible contribution. For example, BMI- and triglycerides-gene expression correlation coefficients robustly correlate with trait-to-expression causal effects (r <sub>BMI </sub> = 0.11, P <sub>BMI </sub> = 2.0 × 10 <sup>-51</sup> and r <sub>TG </sub> = 0.13, P <sub>TG </sub> = 1.1 × 10 <sup>-68</sup> ), but not detectably with expression-to-trait effects. Our results demonstrate that studies comparing the transcriptome of diseased and healthy subjects are more prone to reveal disease-induced gene expression changes rather than disease causing ones

Glycated hemoglobin, body weight and blood pressure in type 2 diabetes patients initiating dapagliflozin treatment in primary care:a retrospective study

Introduction - The present study aimed to describe characteristics of patients with type 2 diabetes (T2D) in UK primary care initiated on dapagliflozin, post-dapagliflozin changes in glycated hemoglobin (HbA1c), body weight and blood pressure, and reasons for adding dapagliflozin to insulin. Methods - Retrospective study of patients with T2D in the Clinical Practice Research Datalink with first prescription for dapagliflozin. Patients were included in the study if they: (1) had a first prescription for dapagliflozin between November 2012 and September 2014; (2) had a Read code for T2D; (3) were registered with a practice for at least 6 months before starting dapagliflozin; and (4) remained registered for at least 3 months after initiation. A questionnaire ascertained reason(s) for adding dapagliflozin to insulin. Results - Dapagliflozin was most often used as triple therapy (27.7%), dual therapy with metformin (25.1%) or added to insulin (19.2%). Median therapy duration was 329 days [95% confidence interval (CI) 302–361]. Poor glycemic control was the reason for dapagliflozin initiation for 93.1% of insulin-treated patients. Avoiding increases in weight/body mass index and insulin resistance were the commonest reasons for selecting dapagliflozin versus intensifying insulin. HbA1c declined by mean of 9.7 mmol/mol (95% CI 8.5–10.9) (0.89%) 14–90 days after starting dapagliflozin, 10.2 mmol/mol (95% CI 8.9–11.5) (0.93%) after 91–180 days and 12.6 mmol/mol (95% CI 11.0–14.3) (1.16%) beyond 180 days. Weight declined by mean of 2.6 kg (95% CI 2.3–2.9) after 14–90 days, 4.3 kg (95% CI 3.8–4.7) after 91–180 days and 4.6 kg (95% CI 4.0–5.2) beyond 180 days. In patients with measurements between 14 and 90 days after starting dapagliflozin, systolic and diastolic blood pressure decreased by means of 4.5 (95% CI −5.8 to −3.2) and 2.0 (95% CI −2.9 to −1.2) mmHg, respectively from baseline. Similar reductions in systolic and diastolic blood pressure were observed after 91–180 days and when follow-up extended beyond 180 days. Results were consistent across subgroups. Conclusion - HbA1c, body weight and blood pressure were reduced after initiation of dapagliflozin in patients with T2D in UK primary care and the changes were consistent with randomized clinical trials

Synthesis of new DPP-4 inhibitors based on a novel tricyclic scaffold

A novel molecular scaffold has been synthesized and its synthesis and incorporation into new analogues of biologically active molecules will be discussed. A comparison of the inhibitory activity of these compounds to the known type-2 diabetes compound (sitagliptin) against dipeptidyl peptidase-4 (DPP-4) will be shown

Genome-Wide Association Study with Targeted and Non-targeted NMR Metabolomics Identifies 15 Novel Loci of Urinary Human Metabolic Individuality

Genome-wide association studies with metabolic traits (mGWAS) uncovered many genetic variants that influence human metabolism. These genetically influenced metabotypes (GIMs) contribute to our metabolic individuality, our capacity to respond to environmental challenges, and our susceptibility to specific diseases. While metabolic homeostasis in blood is a well investigated topic in large mGWAS with over 150 known loci, metabolic detoxification through urinary excretion has only been addressed by few small mGWAS with only 11 associated loci so far. Here we report the largest mGWAS to date, combining targeted and non-targeted 1H NMR analysis of urine samples from 3,861 participants of the SHIP-0 cohort and 1,691 subjects of the KORA F4 cohort. We identified and replicated 22 loci with significant associations with urinary traits, 15 of which are new (HIBCH, CPS1, AGXT, XYLB, TKT, ETNPPL, SLC6A19, DMGDH, SLC36A2, GLDC, SLC6A13, ACSM3, SLC5A11, PNMT, SLC13A3). Two-thirds of the urinary loci also have a metabolite association in blood. For all but one of the 6 loci where significant associations target the same metabolite in blood and urine, the genetic effects have the same direction in both fluids. In contrast, for the SLC5A11 locus, we found increased levels of myo-inositol in urine whereas mGWAS in blood reported decreased levels for the same genetic variant. This might indicate less effective re-absorption of myo-inositol in the kidneys of carriers. In summary, our study more than doubles the number of known loci that influence urinary phenotypes. It thus allows novel insights into the relationship between blood homeostasis and its regulation through excretion. The newly discovered loci also include variants previously linked to chronic kidney disease (CPS1, SLC6A13), pulmonary hypertension (CPS1), and ischemic stroke (XYLB). By establishing connections from gene to disease via metabolic traits our results provide novel hypotheses about molecular mechanisms involved in the etiology of diseases

Linagliptin Improves Insulin Sensitivity and Hepatic Steatosis in Diet-Induced Obesity

Linagliptin (tradjenta™) is a selective dipeptidyl peptidase-4 (DPP-4) inhibitor. DPP-4 inhibition attenuates insulin resistance and improves peripheral glucose utilization in humans. However, the effects of chronic DPP-4 inhibition on insulin sensitivity are not known. The effects of long-term treatment (3–4 weeks) with 3 mg/kg/day or 30 mg/kg/day linagliptin on insulin sensitivity and liver fat content were determined in diet-induced obese C57BL/6 mice. Chow-fed animals served as controls. DPP-4 activity was significantly inhibited (67–89%) by linagliptin (P<0.001). Following an oral glucose tolerance test, blood glucose concentrations (measured as area under the curve) were significantly suppressed after treatment with 3 mg/kg/day (–16.5% to –20.3%; P<0.01) or 30 mg/kg/day (–14.5% to –26.4%; P<0.05) linagliptin (both P<0.01). Liver fat content was significantly reduced by linagliptin in a dose-dependent manner (both doses P<0.001). Diet-induced obese mice treated for 4 weeks with 3 mg/kg/day or 30 mg/kg/day linagliptin had significantly improved glycated hemoglobin compared with vehicle (both P<0.001). Significant dose-dependent improvements in glucose disposal rates were observed during the steady state of the euglycemic–hyperinsulinemic clamp: 27.3 mg/kg/minute and 32.2 mg/kg/minute in the 3 mg/kg/day and 30 mg/kg/day linagliptin groups, respectively; compared with 20.9 mg/kg/minute with vehicle (P<0.001). Hepatic glucose production was significantly suppressed during the clamp: 4.7 mg/kg/minute and 2.1 mg/kg/minute in the 3 mg/kg/day and 30 mg/kg/day linagliptin groups, respectively; compared with 12.5 mg/kg/minute with vehicle (P<0.001). In addition, 30 mg/kg/day linagliptin treatment resulted in a significantly reduced number of macrophages infiltrating adipose tissue (P<0.05). Linagliptin treatment also decreased liver expression of PTP1B, SOCS3, SREBP1c, SCD-1 and FAS (P<0.05). Other tissues like muscle, heart and kidney were not significantly affected by the insulin sensitizing effect of linagliptin. Long-term linagliptin treatment reduced liver fat content in animals with diet-induced hepatic steatosis and insulin resistance, and may account for improved insulin sensitivity

Troponin I and cardiovascular risk prediction in the general population: the BiomarCaRE consortium

Our aims were to evaluate the distribution of troponin I concentrations in population cohorts across Europe, to characterize the association with cardiovascular outcomes, to determine the predictive value beyond the variables used in the ESC SCORE, to test a potentially clinically relevant cut-off value, and to evaluate the improved eligibility for statin therapy based on elevated troponin I concentrations retrospectively

Accelerating Drug Development Using Biomarkers: A Case Study with Sitagliptin, A Novel DPP4 Inhibitor for Type 2 Diabetes

The leveraged use of biomarkers presents an opportunity in understanding target engagement and disease impact while accelerating drug development. For effective integration in drug development, it is essential for biomarkers to aid in the elucidation of mechanisms of action and disease progression. The recent years have witnessed significant progress in biomarker selection, validation, and qualification, while enabling surrogate and clinical endpoint qualification and application. Biomarkers play a central role in target validation for novel mechanisms. They also play a central role in the learning/confirming paradigm, particularly when utilized in concert with pharmacokinetic/pharmacodynamic modeling. Clearly, these attributes make biomarker integration attractive for scientific and regulatory applications to new drug development. In this review, applications of proximal, or target engagement, and distal, or disease-related, biomarkers are highlighted using the example of the recent development of sitagliptin for type 2 diabetes, wherein elucidation of target engagement and disease-related biomarkers significantly accelerated sitagliptin drug development. Importantly, use of biomarkers as tools facilitated design of clinical efficacy trials while streamlining dose focus and optimization, the net impact of which reduced overall cycle time to filing as compared to the industry average