Obesity is a significant global health concern; the prevalence continues to rise in developing and developed countries. According to World Health Organization (WHO) data, 39% of the world population over the age of 18 is overweight, including 13% being obese. Numerous studies have identified an association between obesity and cardiovascular diseases (CVD), including heart failure (HF). Obesity is additionally related to an increase in the occurrence of cardiovascular (CV) disorders, including hypertension (HTN), diabetes mellitus (DM), dyslipidemias, and sleep apnea syndrome. This review investigates the various mechanisms and pathogenesis of obesity-related HF and the obesity paradox phenomena, aiming to shed light on futuristic directive approaches in treating HF-related obesity.

Background: Type 2 diabetes mellitus (T2DM) is a chronic disease with significant cardiovascular (CV) implications. The management of diabetes plays a crucial role in reducing the risk of cardiovascular disease (CVD) and improving patient outcomes. This review examines the impact of various therapeutic strategies and their relevance to specific demographic groups, particularly the elderly and those with chronic kidney disease (CKD). Methods: A comprehensive literature review was conducted focusing on randomized controlled trials (RCTs), meta-analyses, systematic reviews, and large observational studies from PubMed, Cochrane Library, and Google Scholar. Results: Metformin and sodium-glucose cotransporter-2 (SGLT2) inhibitors consistently reduced CV mortality by 20–38% compared to other treatments. Incretin-based therapies like liraglutide and semaglutide reduced major adverse cardiovascular events (MACE) by 13–26%. Lifestyle interventions lowered CV risk when combined with medications. SGLT2 inhibitors conferred renal and CV protection in patients with CKD. Bariatric surgery (BS) has been associated with significant improvements in CV outcomes. Conclusion: Metformin, SGLT2 inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1RAs), BS, and lifestyle modification conferred CV benefits. Management of type 2 diabetes with a view to reducing CV risk requires an individualized approach, taking into account patient characteristics, comorbidities, and the CV risk profiles of various treatment options.

Background: Risk stratification for acute decompensated heart failure (ADHF) at admission remains clinically challenging, particularly in patients with concomitant renal dysfunction. Although B-type natriuretic peptide (BNP) is a well-established biomarker in ADHF, its prognostic dominance in prespecified multivariable analyses of cohorts with frequent cardiorenal dysfunction remains incompletely characterized. Methods: We conducted a single-center retrospective cohort study of 220 index admissions for ADHF. A prespecified multivariable logistic regression model was developed to estimate in-hospital mortality using admission variables, including age, sex, heart failure phenotype, systolic blood pressure, estimated glomerular filtration rate, serum albumin, and log-transformed BNP. Model performance was evaluated using discrimination (AUC), bootstrap optimism correction (200 iterations), calibration assessment, and decision curve analysis, in accordance with the TRIPOD guidelines. Results: In-hospital mortality occurred in 7.7% of patients (n = 17). In multivariable analysis, ln(BNP) was the only variable that reached statistical significance as an independent predictor of in-hospital mortality (OR 2.39 per unit increase; 95% CI 1.25–4.59; p = 0.009), representing the dominant prognostic signal in this dataset. Albumin and eGFR showed consistent but non-significant associations. The exploratory model demonstrated apparent discrimination (AUC 0.81), which decreased to 0.73 after optimism correction. A prespecified simplified model including albumin and ln(BNP) yielded an AUC of 0.77. Conclusions: In this exploratory multivariable analysis, admission BNP emerged as the primary prognostic factor for in-hospital mortality in an ADHF cohort with a high prevalence of renal dysfunction. The small number of outcome events limits confidence in model stability and precludes clinical deployment. These findings are hypothesis-generating and support BNP as the strongest prognostic signal observed in this cohort. External validation in larger cohorts is required before any clinical application.

Objective: Exploring the prevalence of alteration in left ventricular (LV) geometry by echocardiography and their correlation with cardiovascular (CV) risk factors and cardiovascular diseases (CVD). Background: Alterations in LV geometry, by echocardiography, is an important prognostic determinant of CVD morbidity and mortality. Yet it is an underutilized tool and has yet to be established in laboratories. Methods: Left ventricular mass index (LVMI) and relative wall thickness (RWT) were calculated by M-mode echocardiography. Patients were classified into four groups based on RWT and LVMI: normal geometry, concentric remodeling (CR), eccentric left ventricular hypertrophy (eLVH), and concentric left ventricular hypertrophy (cLVH). Results: LV geometry patterns revealed normal geometry prevalence (43.46%), followed by CR (30.76%). The commonest CV risk factor was hypertension (HTN) (85%), followed by diabetes mellitus (DM) (72%). A strong association was identified between CVD and LV geometry changes (p-value < 0.005). CVD prevalence among total patients was 66.5% within this group; coronary artery disease (CAD) prevalence was 55.5% (p-value < 0.029), and heart failure (HF) prevalence was 29.6% (p-value < 0.001). Conclusion: We strongly reinforce routine echocardiograms in determining LVM, RWT, and LV geometry patterns. It is an important, underutilized diagnostic and prognostic tool in risk stratification of patients with CVD and altered LV geometry and mass. Future studies for incorporating LVM and RWT in clinical decision algorithms are warranted.

1. Cardiovascular–kidney–metabolic (CKM) syndrome encompasses interconnected pathways linking heart failure, atherosclerotic cardiovascular disease, chronic kidney disease, and metabolic disorders ...

Background and Objectives: Selection for implantable cardioverter-defibrillator (ICD) therapy anchored in left ventricular ejection fraction (LVEF) may miss at-risk patients with LVEF > 35%. We tested whether a simple electrocardiogram (ECG) burden adds discrimination for appropriate ICD therapy beyond clinical factors and LVEF. Methods Single-center retrospective cohort of 237 ICD recipients. Six routinely reported ECG abnormalities were prespecified and summed as an ECG burden (0–6), then categorized as 0–1, 2–3, or ≥ 4 abnormalities. Logistic regression estimated odds ratios (OR) with 95% CIs for appropriate ICD therapy. Multivariable models included LVEF category (≤ 35% vs > 35%), ischemic vs non-ischemic cardiomyopathy, ICD indication (secondary vs primary prevention), and ECG burden category. Discrimination of the clinical + LVEF model with and without ECG burden was assessed by the C-statistic and DeLong test. Results Mean age was 62.1 ± 16.2 years; 71% male; 61% ischemic; 72% primary prevention; follow-up 7.6 ± 2.9 years. High ECG burden (≥ 4) independently predicted appropriate therapy (OR 3.6, 95% CI 1.8–7.2; p < 0.001). Adding ECG burden to a clinical + LVEF model improved discrimination, increasing the C-statistic from 0.67 to 0.75 (DeLong p = 0.01). Kaplan–Meier curves showed graded separation across burden categories (log-rank p < 0.001). Among patients with LVEF > 35%, observed therapy was higher with high burden (~ 26.3%) than with low burden (~ 6.2%). Conclusions An ECG burden based on six routine abnormalities provides independent and incremental prognostic value beyond LVEF, including in patients with preserved LVEF. This approach uses routinely available measures and may help refine risk-aligned ICD decisions. Prospective multicenter validation is warranted.