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Weight loss induced by glucagon‐like peptide‐1 receptor agonists (GLP‐1RAs) and dual glucagon‐like peptide‐1 receptor (GLP‐1R)/glucose‐dependent insulinotropic polypeptide receptor agonists is coming closer to the magnitudes achieved with surgery. However, with greater weight loss there is concern about potential side effects on muscle quantity (mass), health and function. There is heterogeneity in the reported effects of GLP‐1‐based therapies on lean mass changes in clinical trials: in some studies, reductions in lean mass range between 40% and 60% as a proportion of total weight lost, while other studies show lean mass reductions of approximately 15% or less of total weight lost. There are several potential reasons underlying this heterogeneity, including population, drug‐specific/molecular, and comorbidity effects. Furthermore, changes in lean mass may not always reflect changes in muscle mass as the former measure includes not only muscle but also organs, bone, fluids, and water in fat tissue. Based on contemporary evidence with the addition of magnetic resonance imaging‐based studies, skeletal muscle changes with GLP‐1RA treatments appear to be adaptive: reductions in muscle volume seem to be commensurate with what is expected given ageing, disease status, and weight loss achieved, and the improvement in insulin sensitivity and muscle fat infiltration likely contributes to an adaptive process with improved muscle quality, lowering the probability for loss in strength and function. Nevertheless, factors such as older age and severity of disease may influence the selection of appropriate candidates for these therapies due to risk of sarcopenia. To further improve muscle health during weight loss, several pharmacological treatments to maintain or improve muscle mass designed in combination with GLP‐1‐based therapies are under development. Future research on GLP‐1‐based and other therapies designed for weight loss should focus on more accurate and meaningful assessments of muscle mass, composition, as well as function, mobility or strength, to better define their impact on muscle health for the substantial number of patients who will likely be taking these medications well into the future. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the impact of the Dexcom G6 continuous glucose monitoring (CGM) device on glycaemic control and cardiometabolic risk in patients with type 2 diabetes mellitus (T2DM) at high cardiovascular risk who are not on insulin therapy. Materials and Methods: Adults with T2DM with glycated haemoglobin (HbA1c) >7% and body mass index (BMI) ≥30 kg/m2 not using insulin were enrolled in a two‐phase cross‐over study. In phase 1, CGM data were blinded, and participants performed standard glucose self‐monitoring. In phase 2, the CGM data were unblinded, and CGM, demographic and cardiovascular risk factor data were collected through 90 days of follow‐up and compared using paired tests. Results: Forty‐seven participants were included (44% women; 34% Black; mean age 63 years; BMI 37 kg/m2; HbA1c 8.4%; 10‐year predicted atherosclerotic cardiovascular disease risk 24.0%). CGM use was associated with a reduction in average glucose (184.0 to 147.2 mg/dl, p <.001), an increase in time in range (57.8 to 82.8%, p <.001) and a trend towards lower glucose variability (26.2 to 23.8%). There were significant reductions in HbA1c, BMI, triglycerides, blood pressure, total cholesterol, diabetes distress and 10‐year predicted risk for atherosclerotic cardiovascular disease (p <.05 for all) and an increase in prescriptions for sodium‐glucose cotransporter 2 inhibitors (36.2 to 83.0%) and glucagon‐like peptide‐1 receptor agonists (42.5 to 87.2%, p <.001 for both). Conclusions: Dexcom G6 CGM was associated with improved glycaemic control and cardiometabolic risk in patients with T2DM who were not on insulin. CGM can be a safe and effective tool to improve diabetes management in patients at high risk for adverse cardiovascular outcomes. [ABSTRACT FROM AUTHOR]

Aims: To describe the overall fat distribution patterns independent of body mass index (BMI) in participants with type 2 diabetes (T2D) in the SURPASS‐3 MRI substudy by comparison with sex‐ and BMI‐matched virtual control groups (VCGs) derived from the UK Biobank imaging study at baseline and Week 52. Methods: For each study participant at baseline and Week 52 (N = 296), a VCG of ≥150 participants with the same sex and similar BMI was identified from the UK Biobank imaging study (N = 40 172). Average visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (aSAT) and liver fat (LF) levels and the observed standard deviations (SDs; standardized normal z‐scores: z‐VAT, z‐aSAT and z‐LF) were calculated based on the matched VCGs. Differences in z‐scores between baseline and Week 52 were calculated to describe potential shifts in fat distribution pattern independent of weight change. Results: Baseline fat distribution patterns were similar across pooled tirzepatide (5, 10 and 15 mg) and insulin degludec (IDeg) arms. Compared with matched VCGs, SURPASS‐3 participants had higher baseline VAT (mean [SD] z‐VAT +0.42 [1.23]; p < 0.001) and LF (z‐LF +1.24 [0.92]; p < 0.001) but similar aSAT (z‐aSAT −0.13 [1.11]; p = 0.083). Tirzepatide‐treated participants had significant decreases in z‐VAT (−0.18 [0.58]; p < 0.001) and z‐LF (−0.54 [0.84]; p < 0.001) but increased z‐aSAT (+0.11 [0.50]; p = 0.012). Participants treated with IDeg had a significant change in z‐LF only (−0.46 [0.90]; p = 0.001), while no significant changes were observed for z‐VAT (+0.13 [0.52]; p = 0.096) and z‐aSAT (+0.09 [0.61]; p = 0.303). Conclusion: In this exploratory analysis, treatment with tirzepatide in people with T2D resulted in a significant reduction of z‐VAT and z‐LF, while z‐aSAT was increased from an initially negative value, suggesting a possible treatment‐related shift towards a more balanced fat distribution pattern with prominent VAT and LF loss. [ABSTRACT FROM AUTHOR]

In the past 2 decades, health care has witnessed technological and pharmacological advancements leading to innovations in diabetes management. Despite these advances, published guidelines, and treatment algorithms, most people with diabetes remain above glycemic targets. Thus, the authors designed a novel care model aimed at improving several causative factors, including therapeutic inertia, limited access to endocrinology and cardiovascular specialists, time constraints, and complexity in incorporating clinical practice guidelines. The model involves collaboration between the diabetes specialty team and primary care providers (PCPs). The intervention reviewed uncontrolled diabetes data and the patient's electronic medical record (EMR) and sent personalized, evidence-based recommendations to the provider using the task function in the EMR. Other services (eg, diabetes education) were utilized to optimize patient care to achieve optimal glycemic targets and address cardiometabolic risk. The overall mean hemoglobin A1c (HbA1c) decreased pre-post intervention by almost 1%, and 52.1% (347 of 666) of the cohort had ≥−0.5% change in HbA1c post-intervention. All pathways exhibited a decrease in HbA1c. Team-based approaches to managing diabetes patient care were the most effective. The interventions effectively utilized the resources across the health system without placing additional load or burden on primary care or diabetes specialty care teams. In the future, the authors hope to address the limitations of the current gap caused by increasing diabetes numbers, decreasing availability of PCPs and endocrinologists, and fee-for-service models using the innovative specialty consultant–primary care connection and knowledge exchange offered by this novel model, which can only be sustained with payer's support. [ABSTRACT FROM AUTHOR]

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Aim: The effects of weight loss with a partial or total meal replacement programme (MRP) on atherosclerotic cardiovascular disease (ASCVD) risk factors are not fully understood, in particular in people at higher CV risk. In the 52‐week randomized controlled OPTIWIN study in men and women with obesity, meal replacement programme (total for first 26 weeks, partial for the ensuing 26 weeks) with OPTIFAST (OP) resulted in significantly greater weight loss compared with a low‐calorie food‐based (FB) dietary plan, both as part of a comprehensive lifestyle intervention [OP (n = 135)/FB (n = 138) week 26: −12.4%/−6.0%, p <.001; week 52: −10.5%/−5.5%, p <.001]. Here, we examined effects on ASCVD risk factors and 10‐year ASCVD risk. Materials and Methods: Participants with body mass index 30‐55 kg/m2 and age 18‐70 years, and not on anti‐obesity medications, were recruited. The effects on systolic and diastolic blood pressure (SBP, DBP), lipid parameters and 10‐year ASCVD risk were analysed as changes over time using linear mixed models. Subgroup analyses were conducted for changes in SBP, DBP and ASCVD risk by categories of age (<40, 40‐59, ≥60 years), baseline SBP (

Objective: Fat distribution pattern could help determine cardiometabolic risk profile. This study aimed to evaluate the association of balance/ imbalance between visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (aSAT), and liver fat (LF) with incident type 2 diabetes (T2D) and cardiovascular disease (CVD) in the UK Biobank prospective cohort study. Methods: Magnetic resonance images of 40 174 participants were analyzed for VAT, aSAT, and LF using AMRA® Researcher. To assess fat distribution patterns independent of body mass index (BMI), fat z-scores (z-VAT, z-aSAT, z-LF) were calculated. Participants without prevalent T2D/CVD (N = 35 138) were partitioned based on balance between (1) z-VAT and z-LF (z-scores = 0 as cut-points for high/low), (2) z-VAT and z-aSAT, and (3) z-LF and z-aSAT. Associations with T2D/CVD were investigated using Cox regression (crude and adjusted for sex, age, BMI, lifestyle, arterial hypertension, statin treatment). Results: T2D was significantly associated with z-LF (hazard ratio, [95% CI] 1.74 [1.52-1.98], P < .001) and z-VAT (1.70 [1.49-1.95], P < .001). Both remained significant after full adjustment. For z-scores balance, strongest associations with T2D were z-VAT > 0 and z-LF > 0 (4.61 [2.98-7.12]), z-VAT > 0 and z-aSAT < 0 (4.48 [2.85-7.06]), and z-LF > 0 and z-aSAT < 0 (2.69 [1.76-4.12]), all P < .001. CVD was most strongly associated with z-VAT (1.22 [1.16-1.28], P < .001) which remained significant after adjustment for sex, age, BMI, and lifestyle. For z-scores balance, strongest associations with CVD were z-VAT > 0 and z-LF < 0 (1.53 [1.34-1.76], P < .001) and z-VAT > 0 and z-aSAT < 0 (1.54 [1.34-1.76], P < .001). When adjusted for sex, age, and BMI, only z-VAT > 0 and z-LF < 0 remained significant. Conclusion: High VAT in relation to BMI (z-VAT > 0) was consistently linked to both T2D and CVD; z-LF > 0 was linked to T2D only. Skewed fat distribution patterns showed elevated risk for CVD (z-VAT > 0 and z-LF < 0 and z-VAT > 0 and z-aSAT < 0) and T2D (z-VAT > 0 and z-aSAT < 0). [ABSTRACT FROM AUTHOR]

The prevalence of obesity has significantly increased in high-income countries over the past 40 years, while rates of smoking, high blood pressure, and high cholesterol have decreased. This has led to a rise in cardiovascular diseases such as heart failure, myocardial infarction, and stroke. New evidence suggests that obesity plays a larger role in cardiovascular conditions than previously thought, and weight loss interventions, including medications and surgery, have shown promising results in reducing cardiovascular events and improving quality of life. However, more research is needed to fully understand the mechanisms and benefits of weight loss in cardiovascular medicine. The cardiology profession must address the growing problem of obesity to prevent further increases in multimorbidity rates and cardiovascular diseases. [Extracted from the article]