Background: In this study, we aimed to describe the impact of MBs on atherosclerosis and survival, in patients with coronary artery disease (CAD). Methods: We retrospectively studied 1920 consecutive patients who underwent conventional coronary angiography for suspected CAD. Atherosclerotic load (AL), defined as the sum of degrees of stenosis, and general atherosclerotic load (GAL), representing the sum of AL, were compared between patients with MB and a control group without MB; patients in these groups were similar in age and sex. We assessed survival at 10 years after the last enrolled patient. Results: Prevalence of MB was 3.96%, predominantly in the mid-segment of left anterior descendent artery (LAD). In the presence of MB, GAL was lower (158.1 ± 93.7 vs. 205.3 ± 117.9, p = 0.004) with a lesser AL in the proximal (30.3 ± 39.9 vs. 42.9 ± 41.1, p = 0.038) and mid-segments (8.1 ± 20.0 vs. 25.3 ± 35.9, p < 0.001) of LAD. Based on a Multinominal Logistic Regression, we found that the presence of MB on LAD (regardless of its location on this artery) is a protective factor against atherosclerotic lesions, decreasing the probability of significant stenosis, especially of those ≥70%, on the entire artery (B −1.539, OR 4660; 95% CI = 1.873–11.595, p = 0.001) and on each of its segments as well: proximal LAD (B −1.275, OR 0.280; 95% CI = 0.015–5.073; p = 0.038), mid-LAD (B −1.879, OR 6.545; 95% CI = 1.492–28.712; p = 0.013) and distal LAD (B −0.900, OR 2.459, 95% CI = 2.459–2.459, p = 0.032). However, 10-year survival was similar between groups (76.70% vs. 74.30%, p = 0.740). Conclusion: The presence of MB on LAD proved to be a protective factor against atherosclerosis for the entire artery and for each of its segments, but it does not influence long-term survival in patients with CAD.

High-density lipoprotein (HDL) and small dense low-density lipoprotein (sdLDL) represent two critical yet contrasting components in lipid metabolism and cardiovascular risk modulation. While HDL has traditionally been viewed as cardioprotective due to its role in reverse cholesterol transport and anti-inflammatory effects, emerging evidence emphasizes that HDL functionality—rather than concentration alone—is pivotal in atheroprotection. Conversely, sdLDL particles are increasingly recognized as highly atherogenic due to their enhanced arterial penetration, oxidative susceptibility, and prolonged plasma residence time. This review critically examined the physiological roles, pathological implications, and therapeutic interventions targeting HDL function and sdLDL burden. Lifestyle modifications, pharmacologic agents including statins, fibrates, PCSK9 inhibitors, and novel therapies such as icosapent ethyl were discussed in the context of their effects on HDL quality and sdLDL reduction. Additionally, current clinical guidelines were analyzed, highlighting a paradigm shift away from targeting HDL-C levels toward apoB-driven risk reduction. Although HDL-targeted therapies remain under investigation, the consensus supports focusing on lowering apoB-containing lipoproteins while leveraging lifestyle strategies to improve HDL functionality. In the setting of heart failure, particularly with preserved ejection fraction (HFpEF), alterations in HDL composition and elevated sdLDL levels have been linked to endothelial dysfunction and systemic inflammation, further underscoring their relevance beyond atherosclerosis. A comprehensive understanding of HDL and sdLDL dynamics is essential for optimizing cardiovascular prevention strategies.

Background and Objectives: The management of type 2 diabetes (T2D) extends beyond glycemic control, requiring a more global strategy that includes optimization of body composition, even more so in the context of sarcopenia and visceral adiposity, as they contribute to poor outcomes. Past reviews have typically been focused on weight reduction or glycemic effectiveness, with limited inclusion of new therapies’ effects on muscle and fat distribution. In addition, the emergence of incretin-based therapies and dual agonists such as tirzepatide requires an updated synthesis of their impacts on body composition. This review attempts to bridge the gap by taking a systematic approach to how current blood-glucose lowering therapies affect lean body mass, fat mass, and the risk of sarcopenia in T2D patients. Materials and Methods: Between January 2015 and March 2025, we conducted a narrative review by searching the PubMed, Scopus, and Web of Science databases for English-language articles. The keywords were combinations of the following: “type 2 diabetes,” “lean body mass,” “fat mass,” “body composition,” “sarcopenia,” “GLP-1 receptor agonists,” “SGLT2 inhibitors,” “tirzepatide,” and “antidiabetic pharmacotherapy.” Reference lists were searched manually as well. The highest precedence was assigned to studies that aimed at adult type 2 diabetic subjects and reported body composition results. Inclusion criteria for studies were: (1) type 2 diabetic mellitus adult patients and (2) reporting measures of body composition (e.g., lean body mass, fat mass, or muscle function). We prioritized randomized controlled trials and large observational studies and excluded mixed diabetic populations, non-pharmacological interventions only, and poor reporting of body composition. Results: Metformin was widely found to be weight-neutral with minimal effects on muscle mass. Insulin therapy, being an anabolic hormone, often leads to fat mass accumulation and increases the risk of sarcopenic obesity. Incretin-based therapies induced substantial weight loss, mostly from fat mass. Notable results were observed in studies with tirzepatide, demonstrating superior reduction not only in fat mass, but also in visceral fat. Sodium-glucose cotransporter 2 inhibitors (SGLT2 inhibitors) promote fat loss but are associated with a small yet significant decrease in lean muscle mass. Conclusions: Blood-glucose lowering therapies demonstrated clinically relevant effects on body composition. Treatment should be personalized, balancing glycemic control, cardiovascular, and renal benefits, together with optimal impact on muscle mass along with glycemic, cardiovascular, and renal benefits.

Background and Objectives: Investigating the adherence to antihypertensive medication and identifying patients with low adherence allows targeted interventions to improve therapeutic outcomes. Artificial intelligence (AI) offers advanced tools for analyzing medication adherence data. This study aimed to develop and validate several predictive models for non-adherence, using patient-reported data collected via a structured questionnaire. Materials and Methods: A cross-sectional, multi-center study was conducted on 3095 hypertensive patients from community pharmacies. A structured questionnaire was administered, collecting data on sociodemographic factors, medical history, self-monitoring behaviors, and informational exposure, alongside medication adherence measured using the Romanian-translated and validated ARMS (Adherence to Refills and Medications Scale). Five machine learning models were developed to predict non-adherence, defined by ARMS quartile-based thresholds. The models included Logistic Regression, Random Forest, and boosting algorithms (CatBoost, LightGBM, and XGBoost). Models were evaluated based on their ability to stratify patients according to adherence risk. Results: A total of 79.13% of respondents had an ARMS Score ≥ 15, indicating a high prevalence of suboptimal adherence. Better adherence was statistically associated (adjusted for age and sex) with more frequent blood pressure self-monitoring, a reduced salt intake, fewer daily supplements, more frequent reading of medication leaflets, and the receipt of specific information from pharmacists. Among the ML models, CatBoost achieved the highest ROC AUC Scores across the non-adherence classifications, although none exceeded 0.75. Conclusions: Several machine learning models were developed and validated to estimate levels of medication non-adherence. While the performance was moderate, the results demonstrate the potential of AI in identifying and stratifying patients by adherence profiles. Notably, to our knowledge, this study represents the first application of permutation and SHapley Additive exPlanations feature importance in combination with probability-based adherence stratification, offering a novel framework for predictive adherence modelling.

Obesity remains a major global health challenge, with glucagon-like peptide-1 receptor agonists (GLP-1RAs) providing substantial yet sensitive benefits in weight reduction, glycemic control, and cardiovascular protection. Despite robust trial data, real-world persistence is limited by cost, tolerability, and hedonic adaptation. Intermittent fasting and time-restricted eating offer physiologically complementary, low-cost strategies that enhance fat oxidation, insulin sensitivity, and metabolic flexibility while engaging behavioral mechanisms of self-control and dietary regularity. This narrative review synthesizes current evidence and proposes a pragmatic, phased framework integrating GLP-1RA therapy with structured intermittent fasting and protein-optimized nutrition. The model emphasizes sequential initiation, transition, and maintenance phases designed to align pharmacologic appetite suppression with lifestyle-driven metabolic remodeling. Mechanistically, GLP-1RAs target vascular and neuroendocrine pathways, whereas fasting activates nutrient-sensing networks (AMPK, mTOR, sirtuins) associated with autophagy and longevity. Combined application may preserve lean mass, improve psychological autonomy, and reduce healthcare costs. Future research should validate this hybrid strategy in randomized trials assessing long-term weight durability, functional outcomes, and cost-effectiveness. By uniting pharmacologic potency with behavioral sustainability, phased GLP-1–fasting integration may represent an effective, affordable, and longevity-oriented paradigm for metabolic health.

Heart failure (HF) remains a leading cause of morbidity and mortality globally, with increasing prevalence driven by aging populations and comorbidities such as diabetes mellitus. Recent advances have highlighted sodium-glucose cotransporter-2 (SGLT2) inhibitors, particularly empagliflozin and dapagliflozin, as effective agents in HF management across a broad spectrum of ejection fractions. Initially developed for glycemic control in type 2 diabetes, both drugs have demonstrated significant cardiovascular benefits, including reductions in HF hospitalizations and improvements in symptoms and quality of life. Their mechanisms extend beyond glucose lowering, involving natriuresis, osmotic diuresis, improved myocardial energetics, reduced sympathetic activation, and anti-inflammatory effects. While empagliflozin and dapagliflozin share a core renal mechanism via selective SGLT2 inhibition, subtle differences in pharmacokinetics, potency, and tissue selectivity may influence their clinical profiles. Emerging evidence suggests empagliflozin may confer stronger benefits in heart failure with reduced ejection fraction (HFrEF), while dapagliflozin could offer enhanced efficacy in heart failure with preserved ejection franction (HFpEF), although head-to-head comparisons are lacking. This review synthesizes current evidence comparing the mechanisms of action and clinical performance of empagliflozin and dapagliflozin in HF, providing insight into agent selection and future directions in therapy personalization.