Background: Fetuin-A, a hepatokine implicated in metabolic regulation, has been associated with both metabolic syndrome and cardiovascular disease. However, its specific role in type 2 diabetes mellitus (T2DM) remains incompletely understood. Objective: This study aimed to investigate the relationship between fetuin-A levels and key components of metabolic syndrome (abdominal obesity, arterial hypertension, hyperglycemia, hypertriglyceridemia and low high-density lipoprotein cholesterol) as well as other cardiovascular risk markers, including metabolic dysfunction-associated fatty liver disease (MAFLD), carotid intima-media thickness (CIMT), and the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR). Methods: A total of 51 patients with T2DM not receiving insulin therapy were enrolled. Participants underwent clinical, biochemical, and imaging evaluations. Hepatic steatosis was assessed via abdominal ultrasonography, and subclinical atherosclerosis was evaluated using CIMT measured with Doppler ultrasonography. Serum fetuin-A was quantified by ELISA. Results: Hepatic steatosis was significantly associated with metabolic syndrome, increased CIMT, and dyslipidemia (elevated total cholesterol, triglycerides, and reduced HDL cholesterol). Although no direct correlation was found between fetuin-A levels and hepatic steatosis, multivariate analysis revealed that fetuin-A concentrations were significantly influenced by total cholesterol and LDL cholesterol levels. Conclusions: Fetuin-A appears to be linked to lipid abnormalities in T2DM and may contribute to cardiovascular risk in this population. These findings support the potential utility of fetuin-A as a biomarker and possible therapeutic target for dyslipidemia management in diabetic patients.

Functional capacity (FC), ideally determined by a cardiopulmonary exercise test (CPET), is a valuable prognostic marker in chronic coronary syndrome (CCS). As CPET has limited availability, biomarkers of inflammation and/or fibrosis could help predict diminished FC. Our objective was to assess the value of galectin-3 (gal-3) and that of three inflammatory markers easily obtained from a complete blood count (NLR (neutrophil-to-lymphocyte ratio), PLR (platelet-to-lymphocyte ratio) and MLR (monocyte-to-lymphocyte ratio) in predicting diminished FC in males with recent elective percutaneous coronary intervention (PCI) for CCS. Our prospective study enrolled 90 males who had undergone elective PCI in the previous 3 months (mean age 60.39 ± 10.39 years) referred to a cardiovascular rehabilitation (CR) clinic between February 2023 and December 2024. All subjects received clinical examination, a cardiopulmonary stress test, transthoracic echocardiography and bloodwork. Based on percentage of predicted oxygen uptake (%VO2max), patients were classified in two subgroups—impaired FC (≤70%, n = 50) and preserved FC (>70%, n = 40). NLR, PLR and gal-3 were elevated in patients with poor FC and were significant predictors of diminished FC in multivariate analysis. PLR, NLR and gal-3 could guide referrals for CR for high-risk males with recent elective PCI.

Hepatic diseases pose a significant public health concern. Regardless of the severity of hepatic fibrosis, treatment is recommended for all chronic hepatitis C virus (HCV) subjects. However, fibrosis and steatosis assessment remains crucial for evaluating the prognosis, progression, and hepatic disease monitoring, particularly following the treatment with direct-acting antivirals (DAAs). The aim of our study was to evaluate the impact of metabolic factors and the extent of hepatic fibrosis and fat accumulation in chronic HCV infection subjects. Additionally, another objective was to investigate modifications regarding fibrosis and steatosis three months after a successful sustained viral response (SVR). A total of 100 patients with compensated cirrhosis and chronic hepatitis C (CHC) were included in our study. These patients received treatment with DAA and underwent Fibromax assessment before and three months post SVR. After DAA treatment, a significant decrease was observed in the degree of hepatic fibrosis and hepatic steatosis. This regression was evident three months following the achievement of SVR. Chronic viral hepatitis C may trigger risk factors for metabolic syndromes, such as obesity and type 2 diabetes mellitus. Conclusions: It is crucial to monitor metabolic factors and take timely measures to prevent or treat metabolic syndrome in patients with chronic viral hepatitis C.

Ischemic heart disease (IHD) remains the leading cause of chronic heart failure (HF) worldwide, yet the biological processes underlying this transition are not fully elucidated. Growing evidence indicates that chronic, low-grade inflammation acts as a pivotal link between ischemic injury and progressive myocardial dysfunction. Our review is the most up-to-date and structured synthesis on the pathophysiological pathways, biomarkers, and therapeutic implications of subclinical inflammation in patients with IHD at risk of developing HF. Following acute or repetitive ischemic episodes, persistent immune activation—mediated through interleukin-6 (IL-6), interleukin-1β (IL-1β), and tumor necrosis factor-α (TNF-α)—promotes endothelial dysfunction, microvascular instability, and extracellular matrix remodeling. These mechanisms culminate in ventricular stiffness, diastolic impairment, and adverse structural remodeling, even when left ventricular ejection fraction is preserved. Biomarkers such as Galectin-3, cancer antigen 125 (CA125), and high-sensitivity C-reactive protein (hsCRP) provide valuable insight into the interplay between fibrosis, congestion, and systemic inflammatory load, supporting early detection of subclinical myocardial injury. Advanced imaging modalities, including strain echocardiography and cardiac magnetic resonance imaging (MRI) mapping, enhance the phenotypic characterization of inflammatory cardiomyopathy. Understanding and targeting these inflammatory pathways may open new avenues for precision-based prevention and treatment, ultimately improving outcomes across the IHD–HF continuum.

Background/Objectives: Advanced heart failure (HF) carries a high symptom burden and mortality, and identifying the right time for palliative and advanced-therapy discussions remains difficult. We systematically reviewed the prognostic value of serological biomarkers, echocardiographic indices, and patient-reported outcomes (PROs) in advanced HF. Methods: PubMed, Scopus, Web of Science, and the Cochrane Library were searched on 1 June 2026 for studies published within the prior 10 years, following PRISMA 2020 (PROSPERO CRD420261505929). Risk of bias was assessed with the Newcastle–Ottawa Scale and certainty of evidence with GRADE. Hazard ratios (HRs) for all-cause mortality were pooled with random-effects models within pathophysiologically defined serological subgroups; markers with opposing prognostic directions were synthesized narratively. Results: Of 3866 records, 37 studies (approximately 31,849 patients) were included; 29 (78%) were at low risk of bias. The pooled HR was 1.35 (95% CI 1.06–1.74; k = 4; I² = 92%) for neurohormonal/congestive markers; exploratory two-study estimates were 1.75 (1.50–2.04; I² = 6%) for renal/cardiorenal and 2.17 (0.85–5.52; I² = 86%) for inflammatory-nutritional markers. GRADE certainty was Low for the renal/cardiorenal estimate and Very Low for the other two, and funnel-plot asymmetry suggested small-study effects. Echocardiographic and PRO markers were individually associated with prognosis but showed opposing directions within outcome categories and were not pooled. Conclusions: Serological, echocardiographic, and patient-reported markers each carry prognostic information in advanced HF, but the pooled estimates are exploratory. Most studies assessed mortality or hospitalization rather than palliative or advanced-therapy decisions; relevance to such decisions is interpretive and requires prospective confirmation.

Background and Objectives: Potentially inappropriate medication (PIM) use is associated with adverse clinical outcomes in older adults. Several explicit screening tools are available for identifying PIMs, but Romanian data regarding the application of updated criteria remain limited. This study aimed to assess PIM identification according to the STOPP v3, Beers, EU(7)-PIM criteria and PRISCUS v2, to compare PIM counts and PIM presence across criteria, and to explore associations between PIM counts and selected patient characteristics. Materials and Methods: A retrospective observational study with a cross-sectional design was conducted in a single family medicine practice in Iasi, Romania, including 100 patients aged ≥ 65 years with at least one chronic condition and receiving at least two medications. Medications documented between January and June 2024 were assessed using the four explicit PIM criteria. One-way ANOVA with Bonferroni correction, repeated-measures ANOVA, agreement analyses, and Poisson regression models were used to examine differences between criteria and associations with patient characteristics, multimorbidity, and medication burden. Results: Mean PIM counts ranged from 0.75 for PRISCUS to 1.31 for STOPP. At least one PIM was identified in 73% of participants according to STOPP, 62% according to Beers, 60% according to EU(7)-PIM, and 53% according to PRISCUS. STOPP identified significantly more PIMs than each of the other three criteria (p < 0.001), while pairwise kappa coefficients ranged from 0.506 to 0.777. Among the 16 one-way ANOVA comparisons, only the association between obesity and EU(7)-PIM counts remained significant after Bonferroni correction (p < 0.001). In adjusted Poisson models, medication burden was associated with PIM counts according to STOPP (p < 0.001), Beers (p = 0.016), and EU(7)-PIM (p = 0.018). Obesity remained associated with EU(7)-PIM counts (p = 0.001), multimorbidity with STOPP PIM counts (p = 0.049), and sex with PRISCUS PIM counts (p = 0.030). Age, type 2 diabetes mellitus, and dyslipidemia were not independently associated with PIM counts. Conclusions: PIM identification varied according to the explicit criteria applied, with STOPP identifying the highest mean PIM count. Medication burden was the factor most consistently associated with PIM counts in adjusted analyses, while other patient characteristics showed criterion-specific associations. Given the small sample, single-practice setting, and non-probability sampling approach, these findings should be considered exploratory and require confirmation in larger, multicenter primary care studies.