Ischemic heart disease (IHD) remains a leading cause of morbidity and mortality worldwide. Metabolic syndrome (MetS), a cluster of interrelated metabolic abnormalities including central obesity, insulin resistance, hypertension, dyslipidemia, and impaired glucose metabolism, has emerged as a major contributor to the development and progression of IHD. The presence of MetS significantly increases cardiovascular risk and worsens clinical outcomes in patients with established coronary artery disease.
We summarize current evidence on the role of metabolic syndrome in ischemic heart disease, with a focus on underlying pathophysiological mechanisms and clinical implications. Key mechanisms linking MetS to IHD include insulin resistance, endothelial dysfunction, chronic low-grade inflammation, oxidative stress, atherogenic dyslipidemia, and pro-thrombotic states, all of which promote atherosclerosis and plaque instability. In addition, emerging biomarkers such as high-sensitivity C-reactive protein (hs-CRP), lipoprotein-associated phospholipase A₂ (Lp-PLA₂), adipokines, and inflammatory cytokines provide further insight into the complex interaction between metabolic disturbances and ischemic heart disease.
Understanding the role of metabolic syndrome in IHD is essential for early risk stratification and targeted prevention strategies. Comprehensive management of metabolic syndrome components may reduce the incidence, severity, and recurrence of ischemic heart disease. This review highlights the importance of an integrated metabolic and cardiovascular approach in the prevention and management of IHD.

Background: Cardiovascular disease (CVD) remains the leading cause of death worldwide, accounting for approximately 19.8 million deaths in 2022, or 32% of all deaths. Insulin resistance is an important pathophysiological mechanism not only in the development of type 2 diabetes mellitus (T2DM) but also in the development and progression of cardiovascular disease. Evidence remains limited regarding the simultaneous assessment of associations between selected anthropometric measures and insulin resistance in patients with ischemic heart disease (IHD). Aim: To investigate the association between waist-to-height ratio (WHtR) and insulin resistance indices (TG/HDL and HOMA-IR) in patients with chronic ischemic heart disease. Materials and Methods: This hospital-based cross-sectional study included 274 patients aged 40–75 years. Written informed consent was obtained from all participants. Data were collected using a structured questionnaire, anthropometric measurements were performed, and peripheral blood samples were collected. Laboratory measurements included fasting lipid profile, fasting glucose, and fasting insulin levels. WHtR was calculated as waist circumference (cm) divided by height (cm). Insulin resistance was assessed using the TG/HDL ratio, calculated as TG (mmol/L) divided by HDL (mmol/L), and the HOMA-IR index. Metabolic syndrome was defined according to the Joint Interim Statement (JIS-2009) criteria. Results: Among the 274 participants, 209 (76.3%) were male and 65 (23.7%) were female, with a mean age of 61.1±7.9 years. WHtR was significantly and positively correlated with HOMA-IR, with a weak-to-moderate correlation (Spearman’s ρ=0.303, p<0.001). A weak but statistically significant positive correlation was also observed between WHtR and the TG/HDL ratio (ρ=0.136, p=0.039). The discriminative ability of WHtR for insulin resistance was moderate (AUC=0.645, 95% CI: 0.562–0.729; p=0.001). The optimal cutoff value determined using the Youden index was 0.60, with a sensitivity of 63.2% and specificity of 64.2%. Multivariable logistic regression analysis showed that WHtR ≥0.60 was independently associated with insulin resistance. Conclusion WHtR was significantly and positively associated with HOMA-IR and the TG/HDL ratio, and WHtR ≥0.60 was independently associated with insulin resistance.

Background: Serum natriuretic peptide (NT-proBNP) is a critical biomarker for diagnosing left ventricular dysfunction. Heart failure is the leading cause of mortality in chronic kidney disease (CKD), emphasizing the need for its early detection and prognosis. Objective: This study aimed to determine the serum NT-proBNP levels in participants with CKD and establish a cut-off value for predicting heart failure. Methods: A descriptive cross-sectional study was conducted from April 1 to July 1,2024. This study received approval from the Ethics Committee of the Institute of Medical Sciences (Approval No.24/01). A total of 117 CKD patients hospitalized in the Nephrology and Endocrinology Department of the third state hospital were enrolled based on predefined inclusion and exclusion criteria. Data were collected using questionnaires, laboratory and heart ultrasound test results. Serum NT-proBNP levels were measured using a rapid immunofluorescence quantitative analyzer. Data were analyzed with SPSS 26.0. Results: The mean age of the 117 participants was 57.9 ± 14.7 years, with 51.3% being male. The mean serum NT-proBNP level was 7686 ± 12149 pg/mL. Statistically significant differences were observed in serum creatinine, sodium, calcium, CKD stage, and arterial hypertension between genders (p<0.05). NT-proBNP levels in hemodialysis patients differed significantly between heart failure and non-heart failure groups (p<0.05). Significant differences were also found in hemoglobin, serum albumin, NT-proBNP levels, and CKD stages (p<0.05). NT-proBNP correlated significantly with risk factors such as hemodialysis, diabetes, and decreased systolic blood pressure (p<0.0001). A weak inverse relationship was noted between systolic blood pressure and NT-proBNP (R² = 0.16). The NT-proBNP cut-off value for predicting heart failure was 3027 pg/mL, with an AUC of 61.7% (sensitivity: 74.5%, specificity: 55%). Conclusion Serum NT-proBNP levels are elevated in CKD patients regardless of heart failure. The established cut-off value for NT-proBNP in CKD patients to detect heart failure was 3027 pg/mL, with moderate diagnostic utility (AUC = 61.7%).

Chronic kidney disease (CKD) is a global health issue characterized by a gradual loss of kidney function over time. As the disease progresses, it leads to an increased risk of cardiovascular complications, which are the leading cause of morbidity and mortality in CKD patients. B-type natriuretic peptide (BNP) and its inactive fragment, N-terminal pro b-type natriuretic peptide (NT-proBNP), are biomarkers widely used in the diagnosis and management of heart failure. Their role in CKD, however, is complex due to the overlapping pathophysiological mechanisms between cardiac and renal dysfunctions. This literature review aims to explore the diagnostic and prognostic value of BNP and NT-proBNP in patients with CKD, highlighting their clinical relevance, the impact of renal function on their levels, and potential therapeutic implications. The review focuses on studies published in the last decade, examining the clinical applications, outcomes, and challenges associated with using BNP and NT-proBNP as biomarkers in CKD patients.

A lot of factors can cause coronary heart disease and ischemic stroke including external risk factors such as tobacco, alcohol consumption, decreased physical activity, obesity while arterial maintenance, high blood sugar, increased LDL are internal risk factors. We can reduce our external risk factors by changing our lifestyle. Recent studies have shown increased blood Lp(a) levels are independent risk factor for cardiovascular disease. After 1987, the number of publications has increased since the cDNA homology sequence of Lp(a) and plasminogen 2 was identified. Lp(a) is protein complex consisting from apolipoprotein, phospholipid, free cholesterol, cholesterol esters and tryglyceride. Apoliprotein is a lipid that binds with lipoprotein. Lipoproteins have water-soluble and fat-soluble parts, and those parts bind to lipids and are transported in the bloodstream.How is elevated Lp(a) a risk factor for cardiovascular disease? How much does lowering Lp(a) reduce CVD risk factors? If high Lp(a) concentrations are present, mitigation measures are outlined below.