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Carnitine supplementation improves insulin sensitivity and skeletal muscle acetylcarnitine formation in patients with type 2 diabetes

Abstract
Aim/Hypothesis
Recently, we reported that increasing free carnitine availability resulted in elevated skeletal muscle acetylcarnitine concentrations and restored metabolic flexibility in individuals who have impaired glucose tolerance. Metabolic flexibility is defined as the capacity to switch from predominantly fat oxidation while fasted to carbohydrate oxidation while insulin stimulated. Here we investigated if carnitine supplementation enhances the capacity of skeletal muscle to form acetylcarnitine and thereby improves insulin sensitivity and glucose homeostasis in patients with type 2 diabetes (T2DM).
Methods
Thirty-two patients followed a 12-week L-carnitine treatment (2970 mg/day, orally). Insulin sensitivity was assessed by a two-step hyperinsulinemic-euglycemic clamp. In vivo skeletal muscle acetylcarnitine concentrations at rest and post-exercise (30 min, 70% W
max) and intrahepatic lipid content (IHL) were determined by proton magnetic resonance spectroscopy (1H-MRS). All measurements were performed before and after 12 weeks of carnitine supplementation.
Results
Compliance with the carnitine supplementation was good (as indicated by increased plasma-free carnitine levels (p < 0.01) and pill count (97.1 ± 0.7%)). Insulin-induced suppression of endogenous glucose production (31.9 ± 2.9 vs. 39.9 ± 3.2%, p = 0.020) and peripheral insulin sensitivity (Δ rate of glucose disappearance (ΔRd): 10.53 ± 1.85 vs. 13.83 ± 2.02 μmol/kg/min, p = 0.005) improved after supplementation. Resting (1.18 ± 0.13 vs. 1.54 ± 0.17 mmol/kgww, p = 0.008) and post-exercise (3.70 ± 0.22 vs. 4.53 ± 0.30 mmol/kgww, p < 0.001) skeletal muscle acetylcarnitine concentrations were both elevated after carnitine supplementation. Plasma glucose (p = 0.083) and IHL (p = 0.098) tended to be reduced after carnitine supplementation.
Conclusion
Carnitine supplementation improved insulin sensitivity and tended to lower IHL and fasting plasma glucose levels in patients with type 2 diabetes. Furthermore, carnitine supplementation increased acetylcarnitine concentration in muscle, which may underlie the beneficial effect on insulin sensitivity.

Disparities in heart failure deaths among people with diabetes in the United States: 1999–2020

Abstract
Aims
Heart failure is a leading cause of mortality in the United States, with significant disparities in its burden, particularly among underserved populations. A similar pattern exists for diabetes, but less is known about the mortality impact of these two comorbid conditions. This study aims to examine the risk of death from heart failure among people with diabetes, focusing on socio-demographic disparities.
Materials and Methods
We analysed data from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research Multiple Cause of Death Database, examining patterns of heart failure deaths in which diabetes was a contributing cause. Our analysis was stratified by socio-demographic variables, including race, ethnicity and geography, and we also explored trends over time.
Results
Between 1999 and 2020, there were 82 617 deaths from heart failure in which diabetes was a contributing cause, with an age-adjusted mortality rate of 32.04 deaths per 1 000 000 individuals. The death rate increased by 2.18% during the study period. Death rates were higher among Black Americans compared with White Americans (age-adjusted mortality rate ratio = 1.51, 95% confidence interval: 1.49–1.53), with disparities growing over time (a 10.75% increase for Black Americans vs. a 1.11% increase for White Americans).
Conclusions
Deaths from comorbid heart failure and diabetes are increasing in the United States, with significant and worsening disparities, particularly among minorities. Urgent action is needed to reduce heart failure mortality among people with diabetes, especially in underserved populations.

No Suicide Risk Associated With GLP-1 Drugs

FRIDAY, Feb. 28, 2025 — GLP-1 drugs like Ozempic/Wegovy don’t increase a person’s risk of suicidal actions or thoughts, a new study has concluded.
Earlier research had potentially linked these drugs — which are used to treat type 2…

iGlarLixi provides improved early glycaemic control after 12 weeks of treatment compared with basal insulin in Asian people with type 2 diabetes: A post hoc analysis of the LixiLan‐O‐AP and LixiLan‐L‐CN studies

Abstract
Aims
To evaluate early glycaemic control (glycated haemoglobin [HbA1c] < 7.0% [<53.0 mmol/mol], fasting plasma glucose [FPG] ≤ 7.0 mmol/L or postprandial glucose [PPG] ≤ 10.0 mmol/L) with iGlarLixi versus insulin glargine 100 U/mL (Gla-100) in Asian people with suboptimally controlled type 2 diabetes (T2D) on oral antidiabetic drugs (OADs) in LixiLan-O-AP or basal insulin (BI) ± OADs in LixiLan-L-CN.
Materials and Methods
This post hoc analysis evaluated changes from baseline to Week 12 in HbA1c, FPG and PPG, hypoglycaemia incidence and the rates of target HbA1c achievement at Weeks 8 and 12. Median time to glycaemic control (i.e., time to 50% achieving target HbA1c, FPG or PPG) was also assessed.
Results
At Week 12, mean HbA1c reductions were greater with iGlarLixi versus Gla-100 in LixiLan-O-AP (−1.6% vs. −1.1% [−17.0 vs. −12.0 mmol/mol]) and LixiLan-L-CN (−1.3% vs. −0.5% [−13.9 vs. −5.4 mmol/mol]). PPG reductions were greater with iGlarLixi, while FPG reductions and hypoglycaemia incidence were similar. At Weeks 8 and 12, more participants had achieved target HbA1c or PPG with iGlarLixi versus Gla-100 in both studies. Median time to achieve HbA1c and PPG targets was shorter with iGlarLixi versus Gla-100 in LixiLan-O-AP (85 vs. 126 days and 84 vs. 167 days) and LixiLan-L-CN (85 vs. 239 days and 85 days vs. not estimable); median time to achieve FPG target was similar in LixiLan-O-AP (57 vs. 57 days) and LixiLan-L-CN (29 vs. 30 days).
Conclusions
In Asian people with T2D suboptimally controlled on OADs or BI, iGlarLixi provided comprehensive earlier glycaemic control than Gla-100.

Impact of a 12‐week personalized dietary intervention on vascular function and cardiovascular risk factors

Abstract
Aims
Individuals with liver insulin-resistant (LIR) or muscle insulin-resistant (MIR) phenotypes may respond differently to dietary interventions. Given the interaction between insulin resistance and cardiovascular risk, this sub-analysis of the PERSON study examined whether a personalized diet according to MIR or LIR phenotypes improves vascular function and cardiovascular disease risk factors.
Materials and Methods
We randomized 119 participants to a 12-week low-fat, high-protein, high-fibre diet (LFHP; may be optimal for LIR) or Mediterranean diet (high in monounsaturated fat, HMUFA; may be optimal for MIR). Randomization linked the insulin-resistant (IR) phenotype to the proposed optimal diet, leading to PhenoDiet A (MIR-HMUFA and LIR-LFHP) and PhenoDiet B (MIR-LFHP and LIR-HMUFA). Before and after the intervention, vascular function (carotid artery reactivity) and cardiovascular risk factors (blood pressure, total cholesterol, HDL-cholesterol and Framingham risk score) were examined. A 7-point oral glucose tolerance test was performed to determine insulin resistance (Matsuda index and HOMA-IR) and disposition index.
Results
Following drop-out (n = 18), 101 participants finished the intervention (54 women, 61 ± 7 years, 27.6 [26.4;30.0] kg/m2), with n = 80 available for the primary outcome of vascular function. Overall, the dietary interventions significantly decreased blood pressure, total cholesterol, HDL-cholesterol and the Framingham risk score (all p < 0.05), while vascular function was not affected (p = 0.485). Insulin resistance (p ≤ 0.001), but not disposition index (p = 0.362), was significantly improved after intervention. The Matsuda index (p = 0.078) tended to increase more and total cholesterol (p = 0.052) tended to decrease more in PhenoDiet group B than A, but other changes in outcome parameters were not significantly different between PhenoDiet groups. The LFHP diet resulted in more pronounced improvements in cholesterol, diastolic blood pressure (DBP) and insulin resistance compared with the HMUFA diet (all p < 0.05).
Conclusion
A 12-week diet improves metabolic and cardiovascular outcomes, but not vascular function in insulin-resistant adults with overweight or obesity. Whilst the LFHP diet resulted in greater improvements in cardiometabolic risk markers than the HMUFA diet, we found no significant differences between the PhenoDiet groups.

What do the guidelines say about use of biosimilar insulin therapy? Simple practical considerations to guide clinicians in different patient subgroups—Sharing Canadian perspectives

Abstract
Background
The rising cost of insulins are significantly impacting health care expenditure, thereby limiting access to treatment for more people affected by diabetes. Fear and misunderstanding of insulin therapy have worsened with the emergence of biosimilar insulins. Biosimilars are not the same as generic medications. Generic medication contains identical ingredients to the reference, whereas biosimilar medication is highly comparable but not necessarily identical to the reference.
Results
There are five biosimilar insulins currently available in Canada: insulin glargine (U-100) as the biosimilar insulins, Basaglar® and Semglee®, insulin lispro (U-100) as the biosimilar insulin, Admelog® and insulin aspart (U-100) as the biosimilar insulins, Trurapi® and Kirsty ™. Recent clinical trials have demonstrated comparable efficacy, safety and immunogenicity for biosimilar insulins compared with reference insulins. The dosing of biosimilar insulins is also the same as the reference for initiating, switching (1:1) and titrating. Regulatory agencies, payors and clinical practice guideline committees are initiating biosimilar initiatives aimed at reducing costs, impacting more patients worldwide. While few studies have evaluated biosimilar insulin use in a real-world clinical practice setting, the descriptive patterns retrieved from the LMC Diabetes Registry reflect the Ontario Ministry of Health’s changes in biologic drug policy that were implemented to promote the use of biosimilar insulins.
Conclusion
Many health care providers are largely unfamiliar with biosimilar insulins. This limits the acceptance of biosimilar insulins by patients, as it is related to the comfort of health care providers in educating patients. Tailoring effective conversations to patient needs ensures the best possible therapeutic outcomes.
Plain Language Summary
This review article intends to review the efficacy and safety data from pivotal clinical trials with biosimilar insulins, as well as the regulatory and health economic considerations which underpin the safe and cost-effective use of biosimilar insulin therapy. Biosimilars are not the same as generic medications. Generic medication contains identical ingredients to the reference, whereas biosimilar medication is highly comparable but not necessarily identical to the reference. There are five biosimilar insulins currently available in Canada: insulin glargine (U-100) as the biosimilar insulins, Basaglar® and Semglee®, insulin lispro (U-100) as the biosimilar insulin, Admelog® and insulin aspart (U-100) as the biosimilar insulins, Trurapi® and Kirsty™. Data for biosimilars must be submitted in a stepwise approach to demonstrate similarity to the reference biologic under the following categories: structure & function, human clinical trials, comparative studies evaluating efficacy and safety and manufacturing quality control. Recent clinical trials have demonstrated comparable efficacy, safety and immunogenicity for biosimilar insulins compared with reference insulins. The dosing of biosimilar insulins is also the same as the reference for initiating, switching (1:1) and titrating. Health care providers are encouraged to stay up to date on the latest guidelines and recommendations regarding biosimilar insulin interchangeability to ensure safe and cost-effective use of these products. Regulatory agencies, payors and clinical practice guideline committees are initiating biosimilar initiatives aimed at reducing costs, impacting more patients worldwide. While few studies have evaluated biosimilar insulin use in a real-world clinical practice setting, the descriptive patterns retrieved from the LMC Diabetes Registry reflect the Ontario Ministry of Health’s changes in biologic drug policy that were implemented to promote the use of biosimilar insulins. A summary of adults with diabetes from this registry showed 3.8% of individuals with T1D were prescribed Basaglar® before April 2023 compared to 12.0% after January 2024. For the T2D cohort, the use of basal biosimilar insulins, Basaglar® and Semglee®, similarly increased by approximately 10% and 2% after January 2024, respectively. The use of bolus biosimilar insulins also increased after January 2024 by approximately 28% in the T1D cohort and 60% in the T2D cohort using insulin therapy. Many health care providers are largely unfamiliar with biosimilar insulins. This limits the acceptance of biosimilar insulins by patients, as it is related to the comfort of health care providers in educating patients. People living with diabetes must have access to safe and effective treatment options, and they should be able to obtain appropriate medications at an affordable price and in a fair and timely manner.

Machine learning approach on plasma proteomics identifies signatures associated with obesity in the KORA FF4 cohort

Abstract
Aims
This study investigated the role of plasma proteins in obesity to identify predictive biomarkers and explore underlying biological mechanisms.
Methods
In the Cooperative Health Research in the Region of Augsburg (KORA) FF4 study, 809 proteins were measured in 2045 individuals (564 obese and 1481 non-obese). Multivariate logistic regression adjusted for confounders (basic and full models) was used to identify obesity-associated proteins. Priority-Lasso was applied for feature selection, followed by machine learning models (support vector machine [SVM], random forest [RF], k-nearest neighbour [KNN] and adaptive boosting [Adaboost]) for prediction. Correlation and enrichment analyses were performed to elucidate relationships between protein biomarkers, obesity risk factors and perturbed pathways. Mendelian randomisation (MR) assessed causal links between proteins and obesity.
Results
A total of 16 proteins were identified as significantly associated with obesity through multivariable logistic regression in the basic model and subsequent Priority-Lasso analysis. Enrichment analyses highlighted immune response, lipid metabolism and inflammation regulation were linked to obesity. Machine learning models demonstrated robust predictive performance with area under the curves (AUC) of 0.820 (SVM), 0.805 (RF), 0.791 (KNN) and 0.819 (Adaboost). All 16 proteins correlated with obesity-related risk factors such as blood pressure and lipid levels. MR analysis identified AFM, CRP and CFH as causal and potentially modifiable proteins.
Conclusions
The protein signatures identified in our study showed promising predictive potential for obesity. These findings warrant further investigation to evaluate their clinical applicability, offering insights into obesity prevention and treatment strategies.