Biomarkers in medicine最新文献

Aim: This study aimed to evaluate the impact of preprocedural neutrophil-to-albumin ratio (NAR) on survival among patients undergoing tunneled hemodialysis catheter (THC) placement.

Methods: We retrospectively reviewed 60 consecutive patients who underwent right-internal-jugular THC placement between July 2021 and October 2023. Demographics, laboratory variables and survival were collected. NAR and neutrophil-percentage-to-albumin ratio (NPAR) were calculated. Cox regression and Kaplan - Meier analyses explored associations with mortality; receiver-operating-characteristic (ROC) curves defined optimal cutoffs.

Results: Mean age was 70 ± 12 years; 24 patients (40%) died, yielding one-year survival of 62.5%. On multivariate Cox regression analysis, after adjustment for all covariates, higher NAR remained an independent predictor of mortality (p <0.001), together with older age and coronary artery disease. The ROC-derived NAR threshold of 175.4 produced an AUC of 0.813 (95% CI: 0.683-0.942). Patients with NAR >175.4 had markedly lower one-year survival than those below the threshold (30.2% vs 88.2%, p <0.001). Catheter patency at one year was 84.8%.

Conclusion: Among patients receiving a tunneled hemodialysis catheter, elevated preprocedural NAR levels were associated with increased mortality. NAR, which is an easily and routinely measurable parameter, may serve as a prognostic biomarker in this patient population.

Aims: To evaluate the predictive value of the C-reactive protein to albumin ratio (CAR) for 6-month volume overload in peritoneal dialysis (PD) patients.

Methods: Retrospective cohort study of 167 stable PD patients without baseline volume overload. CAR was measured at enrollment; volume status was reassessed via bioelectrical impedance analysis (BIA) at 6 months. Multivariate logistic regression, ROC, and restricted cubic spline (RCS) analyses were used.

Results: During follow-up, 64 patients (38.3%) developed volume overload. These patients had significantly higher baseline CAR levels (p < 0.001). Multivariate analysis identified diabetes (OR = 11.85) and baseline CAR (OR = 9.72) as independent predictors. The AUC for CAR predicting volume overload was 0.73, which is superior to that of CRP or albumin alone. An optimal CAR cutoff of 1.09 yielded 73.2% sensitivity and 60.7% specificity. RCS analysis indicated a linear dose-response relationship between CAR and volume overload risk (P for non-linearity = 0.501).

Conclusion: Baseline CAR is a simple, low-cost biomarker for predicting 6-month volume overload in PD patients, valuable for risk stratification in resource-limited settings.

Background: Gastric cancer (GC) is a globally prevalent malignancy with high mortality. Long non-coding RNA CCDC18-AS1 is implicated in GC progression, yet its interaction with miR-214-3p and functional mechanisms remain unclear.

Methods: Using samples from 128 GC patients, CCDC18-AS1 and miR-214-3p levels were quantified via quantitative real-time polymerase chain reaction. Kaplan-Meier and Cox regression analyzed prognosis. Dual-luciferase reporter assay and RNA immunoprecipitation assays validated their binding. Functional impacts of CCDC18-AS1 knockdown on proliferation, migration, and invasion were evaluated using Cell Counting Kit-8 and Transwell assays. Western blotting analyzed apoptosis and cell cycle-related protein changes.

Results: CCDC18-AS1 was upregulated, while miR-214-3p was downregulated in GC. High CCDC18-AS1 correlated with poor survival and was an independent prognostic risk factor. CCDC18-AS1 was directly bound to miR-214-3p and inhibited miR-214-3p. Knockdown of CCDC18-AS1 increased miR-214-3p and suppressed proliferation, migration, and invasion. It also upregulated Bax and P21 while downregulating Bcl-2 and Cyclin B1, effects reversed by inhibiting miR-214-3p.

Conclusion: CCDC18-AS1 is overexpressed in GC and independently predicts poor prognosis. It promotes GC progression by targeting miR-214-3p, thereby regulating key oncogenic processes.

Aim: To investigate the dynamics of serum amyloid A (SAA) and procalcitonin (PCT) in sepsis-associated acute kidney injury (SAKI) and to evaluate their prognostic value for renal outcomes.

Methods: 159 Emergency Intensive Care Unit (EICU) patients with SAKI were enrolled. Serum SAA and PCT levels were measured on days 1, 3, 5, and 7 after enrollment. Patients were stratified by 28d renal recovery status. Associations with renal outcomes and predictive performance were examined using Pearson correlation, multivariable logistic regression and receiver operating characteristic (ROC) curve analysis.

Results: Non-recovery was associated with higher baseline levels of SAA, PCT, lactate, serum creatinine, and APACHE II scores (all p < 0.05). Both biomarkers declined progressively, reaching their lowest levels by day 7. Baseline SAA and PCT showed strong negative correlations with 28d eGFR (r = -0.414, -0.491; p < 0.05). Multivariable logistic regression identified APACHE II score (OR = 1.260), SAA (OR = 1.025), and PCT (OR = 1.275) as independent predictors of non-recovery. ROC curve analysis demonstrated good discriminative ability, with AUCs of 0.813 for SAA, 0.819 for PCT, and 0.827 for APACHE II (all p < 0.05).

Conclusion: SAA and PCT are robust short-term predictors of renal outcomes in sepsis-associated AKI. Their dynamic trajectories reflect disease severity and treatment response.

Aims: To evaluate the prognostic significance of the preprocedural albumin - bilirubin (ALBI) score on short- and long-term outcomes after transcatheter aortic valve implantation (TAVI).

Methods: We retrospectively analyzed 737 consecutive patients who underwent TAVI. Patients were stratified into tertiles according to ALBI score. Baseline clinical, echocardiographic, laboratory features were compared across tertiles. The primary endpoint was long-term all-cause mortality; secondary endpoints included in-hospital mortality and major complications. Kaplan-Meier analysis assessed survival, and Cox-regression models evaluated predictors of long-term mortality after adjustment for conventional risk factors.

Results: Higher ALBI tertiles were associated with a greater comorbidity burden, worse ventricular function, and elevated biomarkers of inflammation and cardiac stress. In-hospital mortality increased across tertiles (5.3%, 7.3%, 11.8%). Long-term mortality also rose progressively (7.3%, 9.8%, 11.8%), with 60-month survival rates of approximately 90% (T1), 88% (T2), and 73% (T3). ALBI score independently predicted long-term mortality (HR 1.86, 95% CI 1.23-2.82), along with chronic kidney disease and lower TAPSE. Restricted cubic spline analysis demonstrated a near-linear association between ALBI and mortality risk.

Conclusion: Preprocedural ALBI score is independently associated with both short and long-term mortality after TAVI and may reflect systemic vulnerability not captured by conventional risk models. Incorporating ALBI into preprocedural assessment could enhance risk stratification.

Background: Glioblastoma (GBM) is a highly aggressive form of brain tumor with poor prognosis. This study aimed to identify genes critical to glioma development and assess their potential diagnostic and therapeutic value through an integrated analysis of microarray and RNA-Seq datasets.

Methods: We used data integration to improve statistical robustness and reliability. LASSO regression, along with seven machine learning classifiers, was utilized to find the most important features. The classifier performance was validated on unseen datasets, where SVM and kNN showed the highest accuracy. Functional enrichment analysis was performed on the selected features to assess their biological relevance, followed by validation of biomarkers through qPCR.

Results: We identified three key genes, PLOD1, COL4A2, and COL5A2, that emerged as consistently top-ranked across models. Survival analysis revealed PLOD1 is the only gene significantly associated with poor prognosis in GBM. Further validation in patient samples confirmed a strong association between high PLOD1 expression and GBM, highlighting its potential as a diagnostic biomarker and therapeutic target.

Conclusion: This robust integrative machine learning approach for biomarker discovery in GBM supports the development of more personalized treatment strategies.

Background: The Castelli Risk Index (CRI) is an atherogenic index to assess cardiovascular disease risk. But the predictive value of CRI in assessing the risk of cardiovascular and cerebrovascular disease (CCVD)-related death in elderly patients with type 2 diabetes mellitus (T2DM) remains unclear.

Methods: This retrospective cohort study analyzed clinical data from the SDTM Study, which began in 2005, including 888 patients aged ≥60 years with T2DM. Baseline lipid parameters and deaths related to CCVD during follow-up were recorded in hospital ward or outpatient clinic. The association between CRI and CCVD mortality was assessed by the Cox proportional hazards model. A competing-risk model was additionally employed to evaluate the association.

Results: After a median follow-up of 7.62 years, 40 CCVD deaths occurred. The -CRI-I predicted CCVD deaths in elderly T2DM patients (HR: 1.35, 95% CI: 1.02 - 1.78, p = 0.038) after fully adjusting for age, gender, smoking, drinking, AST, ALT, creatinine, Fasting Blood Glucose, and use of statin. -Subgroup analysis showed that CCVD deaths were more strongly associated with CRI-I among females, those not taking statins, and those without coronary heart disease or hypertension.

Conclusions: CRI -can independently predict CCVD mortality in elderly T2DM patients.

Background: Interventional therapies for tricuspid regurgitation are rapidly evolving, yet optimal patient selection remains challenging. Conventional biomarkers such as NT-proBNP may inadequately reflect the complex pathophysiology and clinical risk in this population. Emerging biomarkers-including soluble suppression of tumorigenicity 2 (sST2), soluble urokinase-type plasminogen activator receptor (suPAR), heart-type fatty acid-binding protein (H-FABP), and growth differentiation factor-15 (GDF-15)-remain insufficiently studied in patients undergoing transcatheter tricuspid valve interventions.

Methods: This prospective, single-center observational cohort study enrolled consecutive patients undergoing transcatheter treatment for severe tricuspid regurgitation, including transcatheter edge-to-edge repair and heterotopic caval valve implantation. Pre-procedural blood samples were collected and analyzed for GDF-15, suPAR, H-FABP, and sST2 using standardized assays. Prognostic performance for the composite endpoint of all-cause mortality or heart-failure rehospitalization within three months was assessed using receiver-operating-characteristic analysis and multivariable Cox regression.

Results: Sixty patients (mean age 80.3 ± 7.3 years; 48.3% male) were included. At three months, 12 patients (20%) were rehospitalized and 6 (10%) had died. GDF-15 (AUC 0.867) and suPAR (AUC 0.885) demonstrated strong predictive accuracy and outperformed NT-proBNP. GDF-15 >1,400 pg/mL independently predicted adverse outcomes (HR 2.03, p = 0.046).

Conclusion: GDF-15 and suPAR provide incremental prognostic value for short-term risk stratification after transcatheter tricuspid valve intervention.

Aims: This meta-analysis evaluates the diagnostic and prognostic performance of severe pneumonia through a comprehensive meta-analysis.

Patients and methods: A systematic literature search was conducted across PubMed, Embase, Cochrane Library, Web of Science, and Scopus. Studies were included if they focused on severe pneumonia patients, assessed relevant inflammatory markers, and provided sufficient data for meta-analysis.

Results: The diagnostic meta-analysis found a sensitivity of 0.72 (95% CI: 0.64-0.79) and a specificity of 0.75 (95% CI: 0.70-0.80). Area under the summary receiver operating characteristic curve was 0.80 (95% CI: 0.76-0.83). However, these pooled estimates showed high heterogeneity (I² > 80%), indicating substantial variation across studies and requiring cautious interpretation. Subgroup analyses revealed that procalcitonin (PCT) had the highest specificity. For predicting short-term mortality, a preliminary analysis of five studies (n = 463) showed a pooled sensitivity of 0.72 (95% CI: 0.64-0.79) and a specificity of 0.65 (95% CI: 0.59-0.70), suggesting a potential prognostic signal that needs validation in larger, prospective cohorts.

Conclusions: Inflammatory markers are valuable auxiliary tools to support clinical judgment in assessing severity and predicting outcomes in patients with severe pneumonia. They offer moderate accuracy in distinguishing severe from non-severe cases and in predicting short-term mortality.