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Visceral adipose tissue (VAT) is a strong prognostic factor for cardiovascular disease and a potential target for cardiovascular risk stratification. Because VAT is difficult to measure in clinical practice, we estimated prediction models with predictors routinely measured in general practice and VAT as outcome using ridge regression in 2,501 middle-aged participants from the Netherlands Epidemiology of Obesity study, 2008-2012. Adding waist circumference and other anthropometric measurements on top of the routinely measured variables improved the optimism-adjusted R2 from 0.50 to 0.58 with a decrease in the root-mean-square error (RMSE) from 45.6 to 41.5 cm2 and with overall good calibration. Further addition of predominantly lipoprotein-related metabolites from the Nightingale platform did not improve the optimism-corrected R2 and RMSE. The models were externally validated in 370 participants from the Prospective Investigation of Vasculature in Uppsala Seniors (PIVUS, 2006-2009) and 1,901 participants from the Multi-Ethnic Study of Atherosclerosis (MESA, 2000-2007). Performance was comparable to the development setting in PIVUS (R2 = 0.63, RMSE = 42.4 cm2, calibration slope = 0.94) but lower in MESA (R2 = 0.44, RMSE = 60.7 cm2, calibration slope = 0.75). Our findings indicate that the estimation of VAT with routine clinical measurements can be substantially improved by incorporating waist circumference but not by metabolite measurements.

Background Identifying associations between serum metabolites and visceral adipose tissue ( VAT ) could provide novel biomarkers of VAT and insights into the pathogenesis of obesity-related diseases. We aimed to discover and replicate metabolites reflecting pathways related to VAT . Methods and Results Associations between fasting serum metabolites and VAT area (by computed tomography or magnetic resonance imaging) were assessed with cross-sectional linear regression of individual-level data from participants in MESA (Multi-Ethnic Study of Atherosclerosis; discovery, N=1103) and the NEO (Netherlands Epidemiology of Obesity) study (replication, N=2537). Untargeted 1H nuclear magnetic resonance metabolomics profiling of serum was performed in MESA, and metabolites were replicated in the NEO study using targeted 1H nuclear magnetic resonance spectroscopy. A total of 30 590 metabolomic spectral variables were evaluated. After adjustment for age, sex, race/ethnicity, socioeconomic status, smoking, physical activity, glucose/lipid-lowering medication, and body mass index, 2104 variables representing 24 nonlipid and 49 lipid/lipoprotein subclass metabolites remained significantly associated with VAT ( P=4.88×10-20-1.16×10-3). These included conventional metabolites, amino acids, acetylglycoproteins, intermediates of glucose and hepatic metabolism, organic acids, and subclasses of apolipoproteins, cholesterol, phospholipids, and triglycerides. Metabolites mapped to 31 biochemical pathways, including amino acid substrate use/metabolism and glycolysis/gluconeogenesis. In the replication cohort, acetylglycoproteins, branched-chain amino acids, lactate, glutamine (inversely), and atherogenic lipids remained associated with VAT ( P=1.90×10-35-8.46×10-7), with most associations remaining after additional adjustment for surrogates of VAT (glucose level, waist circumference, and serum triglycerides), reflecting novel independent associations. Conclusions We identified and replicated a metabolite panel associated with VAT in 2 community-based cohorts. These findings persisted after adjustment for body mass index and appear to define a metabolic signature of visceral adiposity.

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]

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]

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]

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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]