Improvement of cancellous bone microstructure in patients on teriparatide following alendronate pretreatment

An increase in procollagen type I amino-terminal propeptide (PINP) early after teriparatide initiation was shown to correlate with increased lumbar spine areal BMD and is a good predictor of the anabolic response to teriparatide. Few data exist correlating PINP and bone microstructure, and no data exist in patients on teriparatide following prior potent antiresorptive treatment. This exploratory analysis aimed to investigate the effects of teriparatide on cancellous bone microstructure and correlations of bone markers with microstructure in alendronate-pretreated patients. This was a post hoc analysis of changes in bone markers and three-dimensional indices of bone microstructure in paired iliac crest biopsies from a prospective teriparatide treatment study in postmenopausal women with osteoporosis who were either treatment-naïve (TN, n = 16) or alendronate-pretreated (ALN, n = 29) at teriparatide initiation. Teriparatide (20 μg/day) was given for 24 months; biopsies were taken at baseline and endpoint, and serum concentrations of PINP and type 1 collagen cross-linked C-telopeptide (βCTX) were measured at intervals up to 24 months. In the TN and ALN groups, respectively, mean (SD) increases in three-dimensional bone volume/tissue volume were 105 (356)% (P = 0.039) and 55 (139)% (P < 0.005) and trabecular thickness 30.4 (30)% (P < 0.001) and 30.8 (53)% (P < 0.001). No significant changes were observed in trabecular number or separation. In the ALN patients, 3-month change of neither PINP nor βCTX correlated with indices of cancellous bone microstructure. However, 12-month changes in biochemical bone markers correlated significantly with improvements in bone volume/tissue volume, r = 0.502 (P < 0.01) and r = 0.378 (P < 0.05), trabecular number, r = 0.559 (P < 0.01) and r = 0.515 (P < 0.01), and reduction of trabecular separation, r = −0.432 (P < 0.05) and r = −0.530 (P < 0.01), for PINP and βCTX, respectively. We conclude that cancellous bone microstructure improved with teriparatide therapy irrespective of prior antiresorptive use

Reduction of prevalence of patients meeting the criteria for metabolic syndrome with tirzepatide: a post hoc analysis from the SURPASS Clinical Trial Program

Background: Metabolic syndrome is characterized as the co-occurrence of interrelated cardiovascular risk factors, including insulin resistance, hyperinsulinemia, abdominal obesity, dyslipidemia and hypertension. Once weekly tirzepatide is approved in the US and EU for the treatment of type 2 diabetes (T2D) and obesity. In the SURPASS clinical trial program for T2D, tirzepatide demonstrated greater improvements in glycemic control, body weight reduction and other cardiometabolic risk factors versus placebo, subcutaneous semaglutide 1 mg, insulin degludec, and insulin glargine. This post hoc analysis assessed the effect of tirzepatide use on the prevalence of patients meeting the criteria for metabolic syndrome across SURPASS 1–5. Methods: Metabolic syndrome was defined as having ≥ 3 of 5 criteria according to the US National Cholesterol Education Program: Adult Treatment Panel III. Analyses were based on on-treatment data at the primary endpoint from patients adherent to treatment (taking ≥ 75% study drug). A logistic regression model with metabolic syndrome status as the response variable, metabolic syndrome status at the baseline visit as an adjustment, and randomized treatment as fixed explanatory effect was used. The effect of tirzepatide use on the prevalence of patients meeting the criteria for metabolic syndrome by categorical weight loss, background medication and gender were assessed. Results: In SURPASS, the prevalence of patients meeting the criteria for metabolic syndrome at baseline was 67–88% across treatment groups with reductions at the primary endpoint to 38–64% with tirzepatide versus 64–82% with comparators. Reductions in the prevalence of patients meeting the criteria for metabolic syndrome was significantly greater with all tirzepatide doses versus placebo, semaglutide 1 mg, insulin glargine, and insulin degludec (p &lt; 0.001). Individual components of metabolic syndrome were also reduced to a greater extent with tirzepatide vs comparators. Greater reductions in body weight were associated with greater reductions in the prevalence of patients meeting the criteria for metabolic syndrome and its individual components. Background SGLT2i or sulfonylurea use or gender did not impact the change in prevalence of patients meeting the criteria for metabolic syndrome. Conclusions: In this post hoc analysis, tirzepatide at all doses studied was associated with a greater reduction in the prevalence of patients meeting the criteria for metabolic syndrome compared to placebo, semaglutide 1 mg, insulin degludec, and insulin glargine. Although more evidence is needed, these data would support greater potential improvement in cardiovascular risk factor profile with tirzepatide treatment in people across the continuum of T2D

Effects of Tirzepatide Versus Insulin Glargine on Cystatin C–Based Kidney Function:A SURPASS-4 Post Hoc Analysis

OBJECTIVE Tirzepatide reduces HbA1c and body weight, and creatinine-based estimated glomerular filtration rate (eGFR) decline. Unlike creatine-derived eGFR (eGFR-creatinine), cystatin C–derived eGFR (eGFR-cystatin C) is unaffected by muscle mass changes. We assessed effects of tirzepatide on eGFR-creatinine and eGFR-cystatin C. RESEARCH DESIGN AND METHODS Our primary outcome was eGFR change from baseline at 52 weeks with pooled tirzepatide (5, 10, and 15 mg) and titrated insulin glargine in adults with type 2 diabetes and high cardiovascular risk (SURPASS-4). RESULTS Least squares mean (SE) eGFR-creatinine (mL/min/1.73 m2) changes from baseline with tirzepatide and insulin glargine were 22.5 (0.38) and 23.9 (0.38) (between-group difference, 1.4 [95% CI 0.3–2.4]) and 23.5 (0.37) and 25.3 (0.37) (between-group difference, 1.8 [95% CI 0.8–2.8]) for eGFR-cystatin C. Baseline, 1-year, and 1-year change from baseline values significantly correlated between eGFR-cystatin C and eGFR-creatinine. Measures of eGFR changes did not correlate with body weight changes. CONCLUSIONS Tirzepatide slows the eGFR decline rate, supporting a kidney-protective effect.</p

Pharmacogenomics of GLP-1 Receptor Agonists:a genome-wide analysis of observational data and large randomised controlled trials

Background: In the treatment of type 2 diabetes, GLP-1 receptor agonists lower blood glucose concentrations, body weight, and have cardiovascular benefits. The efficacy and side effects of GLP-1 receptor agonists vary between people. Human pharmacogenomic studies of this inter-individual variation can provide both biological insight into drug action and provide biomarkers to inform clinical decision making. We therefore aimed to identify genetic variants associated with glycaemic response to GLP-1 receptor agonist treatment. Methods: In this genome-wide analysis we included adults (aged &gt;= 18 years) with type 2 diabetes treated with GLP-1 receptor agonists with baseline HbA1c of 7% or more (53 mmol/mol) from four prospective observational cohorts (DIRECT, PRIBA, PROMASTER, and GoDARTS) and two randomised clinical trials (HARMONY phase 3 and AWARD). The primary endpoint was HbA1c reduction at 6 months after starting GLP-1 receptor agonists. We evaluated variants in GLP1R, then did a genome-wide association study and gene-based burden tests. Findings: 4571 adults were included in our analysis, of these, 3339 (73%) were White European, 449 (10%) Hispanic, 312 (7%) American Indian or Alaskan Native, and 471 (10%) were other, and around 2140 (47%) of the participants were women. Variation in HbA1c reduction with GLP-1 receptor agonists treatment was associated with rs6923761G -&gt; A (Gly168Ser) in the GLP1R (0.08% [95% CI 0.04-0.12] or 0.9 mmol/mol lower reduction in HbA1c per serine, p=6.0 x 10(-5)) and low frequency variants in ARRB1 (optimal sequence kernel association test p=6.7 x 10(-8)), largely driven by rs140226575G -&gt; A (Thr370Met; 0.25% [SE 0.06] or 2.7 mmol/mol [SE 0.7] greater HbA1c reduction per methionine, p=5.2 x 10(-6)). A similar effect size for the ARRB1 Thr370Met was seen in Hispanic and American Indian or Alaska Native populations who have a higher frequency of this variant (6-11%) than in White European populations. Combining these two genes identified 4% of the population who had a 30% greater reduction in HbA1c than the 9% of the population with the worse response. Interpretation: This genome-wide pharmacogenomic study of GLP-1 receptor agonists provides novel biological and clinical insights. Clinically, when genotype is routinely available at the point of prescribing, individuals with ARRB1 variants might benefit from earlier initiation of GLP-1 receptor agonists

Distinct molecular signatures of clinical clusters in people with type 2 diabetes:an IMI-RHAPSODY study

Type 2 diabetes is a multifactorial disease with multiple underlying aetiologies. To address this heterogeneity a previous study clustered people with diabetes into five diabetes subtypes. The aim of the current study is to investigate the aetiology of these clusters by comparing their molecular signatures. In three independent cohorts, in total 15,940 individuals were clustered based on five clinical characteristics. In a subset, genetic- (N=12828), metabolomic- (N=2945), lipidomic- (N=2593) and proteomic (N=1170) data were obtained in plasma. In each datatype each cluster was compared with the other four clusters as the reference. The insulin resistant cluster showed the most distinct molecular signature, with higher BCAAs, DAG and TAG levels and aberrant protein levels in plasma enriched for proteins in the intracellular PI3K/Akt pathway. The obese cluster showed higher cytokines. A subset of the mild diabetes cluster with high HDL showed the most beneficial molecular profile with opposite effects to those seen in the insulin resistant cluster. This study showed that clustering people with type 2 diabetes can identify underlying molecular mechanisms related to pancreatic islets, liver, and adipose tissue metabolism. This provides novel biological insights into the diverse aetiological processes that would not be evident when type 2 diabetes is viewed as a homogeneous diseas

Profiles of glucose metabolism in different prediabetes phenotypes, classified by fasting glycemia, 2-hour OGTT, glycated hemoglobin, and 1-hour OGTT:An IMI DIRECT study

Differences in glucose metabolism among categories of prediabetes have not been systematically investigated. In this longitudinal study, participants (N = 2,111) underwent a 2-h 75-g oral glucose tolerance test (OGTT) at baseline and 48 months. HbA1c was also measured. We classified participants as having isolated prediabetes defect (impaired fasting glucose [IFG], impaired glucose tolerance [IGT], or HbA1c indicative of prediabetes [IA1c]), two defects (IFG+IGT, IFG+IA1c, or IGT+IA1c), or all defects (IFG+IGT+IA1c). β-Cell function (BCF) and insulin sensitivity were assessed from OGTT. At baseline, in pooling of participants with isolated defects, they showed impairment in both BCF and insulin sensitivity compared with healthy control subjects. Pooled groups with two or three defects showed progressive further deterioration. Among groups with isolated defect, those with IGT showed lower insulin sensitivity, insulin secretion at reference glucose (ISRr), and insulin secretion potentiation (P &lt; 0.002). Conversely, those with IA1c showed higher insulin sensitivity and ISRr (P &lt; 0.0001). Among groups with two defects, we similarly found differences in both BCF and insulin sensitivity. At 48 months, we found higher type 2 diabetes incidence for progressively increasing number of prediabetes defects (odds ratio &gt;2, P &lt; 0.008). In conclusion, the prediabetes groups showed differences in type/degree of glucometabolic impairment. Compared with the pooled group with isolated defects, those with double or triple defect showed progressive differences in diabetes incidence.</p

Post-load glucose subgroups and associated metabolic traits in individuals with type 2 diabetes:An IMI-DIRECT study

AIM: Subclasses of different glycaemic disturbances could explain the variation in characteristics of individuals with type 2 diabetes (T2D). We aimed to examine the association between subgroups based on their glucose curves during a five-point mixed-meal tolerance test (MMT) and metabolic traits at baseline and glycaemic deterioration in individuals with T2D. METHODS: The study included 787 individuals with newly diagnosed T2D from the Diabetes Research on Patient Stratification (IMI-DIRECT) Study. Latent class trajectory analysis (LCTA) was used to identify distinct glucose curve subgroups during a five-point MMT. Using general linear models, these subgroups were associated with metabolic traits at baseline and after 18 months of follow up, adjusted for potential confounders. RESULTS: At baseline, we identified three glucose curve subgroups, labelled in order of increasing glucose peak levels as subgroup 1-3. Individuals in subgroup 2 and 3 were more likely to have higher levels of HbA1c, triglycerides and BMI at baseline, compared to those in subgroup 1. At 18 months (n = 651), the beta coefficients (95% CI) for change in HbA1c (mmol/mol) increased across subgroups with 0.37 (-0.18-1.92) for subgroup 2 and 1.88 (-0.08-3.85) for subgroup 3, relative to subgroup 1. The same trend was observed for change in levels of triglycerides and fasting glucose. CONCLUSIONS: Different glycaemic profiles with different metabolic traits and different degrees of subsequent glycaemic deterioration can be identified using data from a frequently sampled mixed-meal tolerance test in individuals with T2D. Subgroups with the highest peaks had greater metabolic risk

Processes Underlying Glycemic Deterioration in Type 2 Diabetes: An IMI DIRECT Study

Objective We investigated the processes underlying glycemic deterioration in type 2 diabetes (T2D). Research Design and Methods 732 recently diagnosed T2D patients from the IMI-DIRECT study were extensively phenotyped over three years, including measures of insulin sensitivity (OGIS), β-cell glucose sensitivity (GS) and insulin clearance (CLIm) from mixed meal tests, liver enzymes, lipid profiles, and baseline regional fat from MRI. The associations between the longitudinal metabolic patterns and HbA1c deterioration, adjusted for changes in BMI and in diabetes medications, were assessed via stepwise multivariable linear and logistic regression. Results Faster HbA1c progression was independently associated with faster deterioration of OGIS and GS, and increasing CLIm; visceral or liver fat, HDL-cholesterol and triglycerides had further independent, though weaker, roles (R2=0.38). A subgroup of patients with a markedly higher progression rate (fast progressors) was clearly distinguishable considering these variables only (discrimination capacity from AUROC=0.94). The proportion of fast progressors was reduced from 56% to 8-10% in subgroups in which only one trait among OGIS, GS and CLIm was relatively stable (odds ratios 0.07 to 0.09). T2D polygenic risk score and baseline pancreatic fat, GLP-1, glucagon, diet, and physical activity did not show an independent role. Conclusions Deteriorating insulin sensitivity and β-cell function, increasing insulin clearance, high visceral or liver fat, and worsening of the lipid profile are the crucial factors mediating glycemic deterioration of T2D patients in the initial phase of the disease. Stabilization of a single trait among insulin sensitivity, β-cell function, and insulin clearance may be relevant to prevent progression

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models.

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities. [Abstract copyright: © 2023. The Author(s).