Background: Brown adipose tissue (BAT) is abundant in small mammals and in newborns and helps them to survive cold temperatures. In adults, it had long been considered to be absent or at least of no relevance. Recent investigations, however, have fuelled interest in adult BAT. Objective: We aimed at (1) summarizing structural and physiological characteristics of BAT versus white adipose tissue (WAT); (2) discussing the development of the two adipose tissue types; (3) reviewing the data available from human studies on BAT, and (4) discussing the impact of aging. Methods: We summarize recent descriptions of BAT and WAT based on the original literature and reviews in the field, with emphasis on human BAT. Results: WAT and BAT have essentially antagonistic functions: WAT stores excess energy as triglycerides and BAT is specialized in the dissipation of energy through the production of heat. Considerable amounts of BAT are present in a substantial proportion of adult humans and relatively high quantities of BAT are associated with lower body weight. With increasing age, BAT decreases and body weight increases. Conclusions: Although the available cross-sectional data do not allow definite conclusions to be drawn concerning a causal relationship between loss of BAT and increasing body weight with advancing age or obesity-related metabolic disorders of older age, stimulation of BAT appears to be an attractive novel candidate target for the treatment of age-related obesity.

In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (ClinicalTrials.gov number, NCT00000620) reported on by Ginsberg et al. (April 29 issue),¹ the use of fenofibrate in patients with type 2 diabetes mellitus who were receiving simvastatin and who were at high risk for cardiovascular disease greatly reduced triglyceride levels but hardly increased levels of high-density lipoprotein (HDL) cholesterol and did not reduce cardiovascular risk. To put these findings into context, it is important to know which lipid variables most strongly predict risk among high-risk patients who receive statins.

The aim of this collaborative document is to provide an update for clinicians on best antithrombotic strategies in patients with aortic and/or peripheral arterial diseases. Antithrombotic therapy is a pillar of optimal medical treatment for these patients at very high cardiovascular risk. While the number of trials on antithrombotic therapies in patients with aortic or peripheral arterial diseases is substantially smaller than for those with coronary artery disease, recent evidence deserves to be incorporated into clinical practice. In the absence of specific indications for chronic oral anticoagulation due to concomitant cardiovascular disease, a single antiplatelet agent is the basis for long-term antithrombotic treatment in patients with aortic or peripheral arterial diseases. Its association with another antiplatelet agent or low-dose anticoagulants will be discussed, based on patient’s ischaemic and bleeding risk as well therapeutic paths (e.g. endovascular therapy). This consensus document aims to provide a guidance for antithrombotic therapy according to arterial disease localizations and clinical presentation. However, it cannot substitute multidisciplinary team discussions, which are particularly important in patients with uncertain ischaemic/bleeding balance. Importantly, since this balance evolves over time in an individual patient, a regular reassessment of the antithrombotic therapy is of paramount importance.

OBJECTIVE—To evaluate the atherogenicity of lipids in coronary patients with normal fasting glucose (NFG), impaired fasting glucose (IFG), and type 2 diabetes. RESEARCH DESIGN AND METHODS—Serum lipid values, the presence of angiographic coronary artery disease (CAD) at baseline, and the incidence of vascular events over 2.3 years were recorded in 750 consecutive patients undergoing coronary angiography. RESULTS—Triglycerides significantly (P < 0.001) increased and HDL cholesterol (P < 0.001) as well as LDL particle diameter (P < 0.001) significantly decreased from subjects with NFG <5.6 mmol/l (n = 272) over patients with IFG ≥5.6 mmol/l (n = 314) to patients with type 2 diabetes (n = 164). Factor analysis revealed two factors in the lipid profiles of our patients: triglycerides, HDL cholesterol, apolipoprotein A1, and LDL particle diameter loaded high on an HDL-related factor, and total cholesterol, LDL cholesterol, and apolipoprotein B loaded high on an LDL-related factor. In patients with type 2 diabetes, the HDL-related factor (odds ratio 0.648 [95% CI 0.464–0.904]; P = 0.011), but not the LDL-related factor (0.921 [0.677–1.251]; P = 0.597), was associated with significant coronary stenoses ≥50%. Consistently, in the prospective study, the HDL-related factor (0.708 [0.506–0.990]; P = 0.044), but not the LDL-related factor (1.362 [0.985–1.883]; P = 0.061), proved significantly predictive for vascular events in patients with type 2 diabetes. CONCLUSIONS—The low HDL cholesterol/high triglyceride pattern is associated with the degree of hyperglycemia. In coronary patients with type 2 diabetes, this pattern correlates with the prevalence of CAD and significantly predicts the incidence of vascular events.

Metabolomics, with its wealth of data, offers a valuable avenue for enhancing predictions and decision-making in diabetes. This observational study aimed to leverage machine learning (ML) algorithms to predict the 4-year risk of developing type 2 diabetes mellitus (T2DM) using targeted quantitative metabolomics data. A cohort of 279 cardiovascular risk patients who underwent coronary angiography and who were initially free of T2DM according to American Diabetes Association (ADA) criteria was analyzed at baseline, including anthropometric data and targeted metabolomics, using liquid chromatography (LC)–mass spectroscopy (MS) and flow injection analysis (FIA)–MS, respectively. All patients were followed for four years. During this time, 11.5% of the patients developed T2DM. After data preprocessing, 362 variables were used for ML, employing the Caret package in R. The dataset was divided into training and test sets (75:25 ratio) and we used an oversampling approach to address the classifier imbalance of T2DM incidence. After an additional recursive feature elimination step, identifying a set of 77 variables that were the most valuable for model generation, a Support Vector Machine (SVM) model with a linear kernel demonstrated the most promising predictive capabilities, exhibiting an F1 score of 50%, a specificity of 93%, and balanced and unbalanced accuracies of 72% and 88%, respectively. The top-ranked features were bile acids, ceramides, amino acids, and hexoses, whereas anthropometric features such as age, sex, waist circumference, or body mass index had no contribution. In conclusion, ML analysis of metabolomics data is a promising tool for identifying individuals at risk of developing T2DM and opens avenues for personalized and early intervention strategies.