Search Results (5,152)

Background/Objectives: Spondylarthritis (SA) is characterized by high clinical heterogeneity, often resulting in a discrepancy between systemic inflammation and patient-reported symptoms. While hematologic indices are emerging as cost-effective biomarkers, their role in phenotypic differentiation remains unclear. We investigated the utility of traditional inflammatory markers, including the novel fibrinogen-to-platelet ratio (FPR), in differentiating SA subtypes and predicting patient-reported disease activity. Methods: This cross-sectional study included 64 patients with spondylarthritis: axial SA (n = 32), peripheral SA (n = 8), and psoriatic SA (n = 24). Clinical assessments included the Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and Functional Index (BASFI). Systemic inflammation was evaluated via C-reactive protein (CRP), fibrinogen, and calculated ratios (NLR, PLR, MLR, and FPR). Principal Component Analysis (PCA) was employed to map the inflammatory architecture, while Receiver Operating Characteristic (ROC) curves evaluated the predictive power for high disease activity (BASDAI ≥ 4). Results: Significant phenotypic differences were observed with the FPR demonstrating superior discriminative capacity (p = 0.003). Patients with peripheral SA exhibited significantly higher FPR (median 1.88) compared to axial (1.33) and psoriatic (1.32) subtypes, and the dedicated ROC analysis for phenotypic discrimination yielded an AUC of 0.866 (95% CI: 0.745–0.987) (1.36, p = 0.039). HLA-B27 prevalence was low overall (31.3%) and in psoriatic SA (4.2%, p = 0.012). Correlation analysis revealed strong associations between BASDAI and BASFI (ρ = 0.79), NLR and MLR (ρ = 0.78), and CRP and fibrinogen (ρ = 0.66). PCA identified two independent inflammatory dimensions explaining 74.8% of variance: neutrophil-hypercoagulable axis (41.4%, driven by NLR, PLR, and MLR), and an acute-phase/fibrinogen axis (33.4%, driven by CRP, fibrinogen, and FPR). Notably, FPR clustered with acute-phase reactants rather than leukocyte-derived ratios, supporting its role as a marker of systemic inflammatory burden. Although fibrinogen is involved in the coagulation cascade, the absence of direct coagulation markers precludes definitive characterization of this component as hypercoagulable. ROC analysis revealed that fibrinogen showed the highest discriminative ability for disease activity (BASDAI ≥ 4), with an AUC of 0.690 (95% CI: 0.519–0.861), followed by NLR (0.621), MLR (0.592), and FPR (0.583). However, overall discriminative performance remained modest, with most 95% confidence intervals crossing 0.5. Conclusions: FPR emerges as a robust phenotypic biomarker capable of discriminating against SA subtypes, particularly identifying peripheral SA with high accuracy and excellent negative predictive value. In contrast, its ability to predict patient-reported disease activity remains limited, reinforcing the distinction between trait and state biomarkers. Exploratory PCA revealed that FPR clusters with acute-phase reactants rather than leukocyte-derived ratios, supporting its biological link to systemic inflammatory burden. These findings advocate for a dual-purpose biomarker approach in SA: FPR for phenotypic stratification and fibrinogen for activity assessment, while clinical indices remain indispensable for symptom monitoring. Validation in larger, prospective cohorts is warranted. Full article

Background: Renal dysfunction is common in patients hospitalized with acute decompensated heart failure (ADHF) and represents a key component of cardiorenal syndrome. However, the relationships between renal impairment, cardiorenal biomarkers, and echocardiographic markers of myocardial function remain incompletely characterized in ADHF populations. Methods: We conducted a cross-sectional analysis of 144 consecutive patients hospitalized with ADHF. Renal dysfunction was defined as an estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m². Clinical, laboratory, and echocardiographic parameters were compared according to renal function. Correlation analyses, multivariable logistic regression, and receiver operating characteristic (ROC) curve analyses were performed to evaluate associations between renal dysfunction, cardiorenal biomarkers, and myocardial functional indices. Results: Patients with renal dysfunction were older (p = 0.002) and more frequently had diabetes mellitus (p = 0.006). Echocardiographic evaluation demonstrated significantly lower systolic mitral annular velocity (S′) (p < 0.001) and higher E/e′ ratios (p < 0.001) in patients with renal dysfunction, whereas left ventricular ejection fraction (p = 0.133) and global longitudinal strain (GLS) (p = 0.121) were similar between groups. Log-transformed NT-proBNP and albuminuria were significantly correlated with S′, GLS, and E/e′ (all p < 0.001). In multivariable analysis adjusted for clinically relevant confounders, chronic kidney disease (OR 8.16, 95% CI 2.13–31.34; p = 0.002) and the E/e′ ratio (OR 2.01, 95% CI 1.52–2.66; p < 0.001) remained independently associated with renal dysfunction. ROC analysis showed that E/e′ had the strongest ability to distinguish between patients with and without renal dysfunction (AUC 0.887, 95% CI 0.834–0.941; p < 0.001). Conclusions: Renal dysfunction in ADHF is associated with echocardiographic markers reflecting impaired longitudinal myocardial function and elevated filling pressure, with E/e′ emerging as the strongest echocardiographic correlate. The integration of echocardiographic parameters with cardiorenal biomarkers may improve the characterization of the cardiorenal profile in patients hospitalized with ADHF. Full article

Background: Growth differentiation factor-15 (GDF-15) is a stress-responsive cytokine associated with inflammation, metabolic dysfunction, and cardiovascular disease. Its role as a biomarker of microvascular complications in type 2 diabetes (T2D) remains incompletely defined. Objective: To evaluate circulating GDF-15 levels and their association with microvascular complications in patients with T2D. Methods: This cross-sectional study included 160 participants divided into three groups: T2D (n = 93), obesity without carbohydrate disorders (n = 36), and healthy controls (n = 31). Microvascular complications (neuropathy, nephropathy, retinopathy) were assessed. Multivariable logistic regression and receiver operating characteristic (ROC) analysis were performed. Results: GDF-15 levels were significantly higher in T2D compared with non-diabetic individuals (267.5 ± 168.9 vs. 118.3 ± 55.5 pg/mL, p < 0.001). Higher GDF-15 was associated with neuropathy (odds ratio (OR) 1.985; 95% confidence interval (CI) 1.431–2.753) and nephropathy (OR 1.673; 95% CI 1.243–2.254) in unadjusted models. After adjustment, only nephropathy remained independently associated (OR 1.405; 95% CI 1.026–1.923). ROC analysis showed moderate discriminative ability for nephropathy (area under the curve (AUC) = 0.763). Conclusions: Circulating GDF-15 levels are significantly elevated in patients with T2D and are associated with microvascular complications, with the strongest independent association observed for diabetic nephropathy. These findings suggest that GDF-15 may represent a promising biomarker reflecting metabolic stress and risk of diabetic complications. Full article

Background/Objectives: This study examined whether dynamic systemic inflammation- and nutrition-based scores measured at baseline (T0) and during follow-up (T1: days 7–10) are associated with treatment response and surgical requirement in first-episode primary spontaneous pneumothorax (PSP). Methods: A total of 216 consecutive patients with first-episode PSP, treated between January 2020 and December 2024, were retrospectively analyzed. All patients initially underwent tube thoracostomy. During follow-up, 117 patients recovered with drainage therapy, whereas 99 required VATS because of a prolonged air leak. The CAR, SIII, SIRI, PIII, NLR, PLR, and PNI, measured at T0 and T1, were analyzed. Δ-values (T1–T0 differences) were evaluated, and diagnostic performance was assessed using ROC curve analysis. Results: At T0, inflammation- and nutrition-based indices did not differ significantly between groups. In contrast, at T1, CAR, SIII, SIRI, PIII, NLR, and PLR values were significantly higher in the VATS group than in the drainage group (all p < 0.05). Over time, inflammatory indices increased markedly in the VATS group, whereas changes in the drainage group remained limited. PNI decreased significantly at T1 in both groups. ROC analysis demonstrated that CAR, SIII, and NLR showed moderate discriminative performance for identifying patients who required VATS (area under the curve ≈ 0.65). Conclusions: Dynamic assessment of systemic inflammation-based biomarkers provides clinically relevant insight for surgical risk stratification in first-episode PSP. While baseline measurements alone are insufficient, follow-up values and temporal changes—particularly in CAR, SIII, and NLR—may reflect progression toward a surgical phenotype and could serve as adjunctive, non-directive decision-support indicators in PSP management. Full article

The rapid integration of artificial intelligence (AI) into educational systems is transforming how student performance is analysed and how educational policies are informed by large-scale data. Within this context, machine learning techniques are increasingly used to identify patterns associated with academic success and educational inequality. However, the use of predictive algorithms in education also raises important questions regarding transparency, fairness, and potential algorithmic bias. This study examines the predictive performance and fairness implications of machine learning models used to identify academically resilient students using data from the Programme for International Student Assessment (PISA) 2022. The analysis is based on a dataset containing more than 600,000 student observations across multiple national education systems. Academic resilience is operationalised following the OECD framework, identifying students who belong to the lowest quartile of the socioeconomic status index (ESCS) within their country while simultaneously achieving mathematics performance in the top quartile (PV1MATH). A predictive framework incorporating six supervised learning algorithms—Logistic Regression, Random Forest, Gradient Boosting, XGBoost, LightGBM, and CatBoost—was implemented. The modelling pipeline includes data preprocessing, missing value imputation, class imbalance correction using SMOTE, and model evaluation through multiple classification metrics, including accuracy, F1-score, and the area under the ROC curve (AUC). In addition, fairness diagnostics are conducted to examine potential disparities in prediction outcomes across gender groups, while feature importance analysis and SHAP-based explanations are used to interpret the contribution of key predictors. The results indicate that ensemble-based models achieve the highest predictive performance, particularly those based on gradient boosting techniques. At the same time, the analysis reveals that socioeconomic status, migration background, and school repetition constitute the most influential predictors of academic resilience. Although gender displays relatively low predictive importance, measurable differences in positive prediction rates across gender groups suggest the presence of potential algorithmic disparities. These findings highlight the importance of integrating fairness evaluation, transparency, and interpretability into educational data science workflows. The study contributes to ongoing discussions on the responsible use of artificial intelligence in education by emphasising the need for governance frameworks capable of ensuring that algorithmic systems support equity-oriented educational policies. Full article

Background and Objectives: Acute exacerbation of COPD (AECOPD) poses a major burden on healthcare systems, with critically ill AECOPD patients having increased morbidity and mortality. Since adverse outcomes are due both to respiratory failure and the systemic inflammatory response, prognostic markers accounting for these patterns are needed. Our aim was to investigate the predictive power of derived hematological indices for intensive care unit (ICU) mortality in patients with non-infectious versus infectious AECOPD. Materials and Methods: This is a retrospective, observational, monocentric cohort study on 88 AECOPD patients admitted to the ICU between 2018 and 2023. Descriptive statistics were performed for the entire cohort, and for predefined subgroups (non-infectious, infectious and bacterial AECOPD). Receiver Operating Characteristics (ROC) analysis was performed to test the predictive power of the studied indices. Cut-off values were identified using the Youden index. Kaplan–Meier analysis was conducted to test the association with ICU mortality. Results: Overall ICU mortality was 44%. For the whole cohort, neutrophil-to-lymphocyte ratio (NLR), neutrophil-to-platelets ratio (NPR) and systemic inflammation response index (SIRI) showed moderate predictive power for ICU mortality (areas under the curve (AUCs) of 0.71–0.73). Non-infectious and infectious subgroups were comparable in terms of size, demographics, comorbidities and baseline COPD characteristics (p > 0.05). Mortality was significantly higher in infectious AECOPD (64.6% versus 20%, p < 0.001). For non-infectious AECOPD, monocyte-to-lymphocyte ratio (MLR) and SIRI had very good predictive power (AUCs between 0.82 and 0.855), while NPR and systemic inflammation index (SII) showed moderate AUC values (between 0.7 and 0.79). In infectious AECOPD, only NPR retained fair predictive power (AUC 0.691), which improved in bacterial AECOPD (AUC 0.781). Conclusions: Derived hematological indices have different predictive values for ICU mortality. MLR and SIRI exhibited very good predictive power in non-infectious AECOPD, while NPR was the best discriminator in bacterial AECOPD. These stress the importance of individualized prognostication in AECOPD. Full article

Background: Hyperparathyroidism is a common endocrine disorder, and its diagnosis can be complex. Various indices based on blood biomarkers have been proposed to improve diagnostic accuracy. The objective of this systematic review was to analyze the diagnostic utility of different indices in primary hyperparathyroidism. Methods: A systematic review was performed with searches up to January 2026. Risk of bias was assessed, and a meta-analysis was conducted for indices with two or more studies, calculating sensitivity, specificity, and other accuracy measures. The certainty of the evidence was evaluated using the GRADE system. Results: Twelve studies were included. The calcium–phosphorus ratio demonstrated a sensitivity of 91.6%, specificity of 89.3%, and an area under the curve of 0.957. The parathyroid function index showed a sensitivity of 94.4% and specificity of 94.2%; however, this finding is based on only two studies and requires validation in larger cohorts. The Wisconsin index also showed good performance. Other indices, including the Ca × Cl/P ratio (evaluated in a single study), yielded promising results but with very limited evidence that precludes firm conclusions. All indices performed poorly in cases with normal calcium. Certainty assessment indicated moderate evidence for the main indices and low or very low evidence for the others. Conclusions: The calcium–phosphorus ratio and the parathyroid function index are valid and useful tools for the diagnosis of primary hyperparathyroidism, with excellent performance. The calcium–phosphorus ratio is especially valuable due to its simplicity and accessibility for screening. No index should be used in isolation; integration with clinical evaluation remains essential. Full article

Background: The prognostic assessment of patients with spinal metastases is primarily based on clinical and radiological parameters. Biological tumor characteristics such as the proliferation marker Ki-67 have prognostic relevance in various metastatic settings. This study aimed to evaluate the prognostic impact of the Ki-67 proliferation index on survival outcomes in patients undergoing surgery for spinal metastases. Methods: We included 166 patients who underwent surgical treatment for spinal metastases at our university clinic between 2015 and 2024. Clinical, functional, tumor-related, and perioperative variables were collected. Receiver operating characteristic (ROC) analysis was performed to evaluate the discriminatory ability of Ki-67, and comparisons were made between patient groups according to Ki-67 expression (≤20% vs. >20%). Results: Based on ROC analysis, Ki-67 demonstrated a moderate but significant predictive ability for 1-year mortality (area under the curve [AUC]: 0.69, p = 0.001). Patients with a Ki-67 index of >20% showed a significantly shorter overall survival than those with a lower Ki-67 index of ≤20% (median overall survival: 5.0 vs. 14.5 months, p < 0.001). One-year mortality was significantly higher in the high Ki-67 group (78.9% vs. 41.8%, p = 0.001). High Ki-67 expression was associated with more aggressive tumor characteristics but was not associated with increased perioperative morbidity. Conclusions: The Ki-67 proliferation index is a significant prognostic biomarker in surgically treated patients with spinal metastases. A Ki-67 index threshold of 20% identifies patients at increased risk of early mortality and significantly reduced overall survival. Full article

Background: Gliomas are the most common primary malignant tumors of the central nervous system. Accurate preoperative grading is essential for individualized surgical planning and treatment selection; however, reliable non-invasive prediction tools integrating multimodal preoperative data remain limited. This study aimed to develop and internally validate an interpretable machine-learning model for non-invasive glioma grading. Methods: Clinical and imaging data from 400 patients with pathologically confirmed gliomas were retrospectively collected. Twenty-four preoperative variables were analyzed. The dataset was randomly divided into training and validation cohorts (7:3). Feature selection was performed using a combination of the Boruta algorithm and logistic regression analyses, followed by correlation filtering. Seventeen machine-learning algorithms were benchmarked using five-fold cross-validation, and the optimal model was evaluated in the independent validation cohort using ROC analysis, calibration assessment, precision–recall curves, and decision curve analysis. Model interpretability was examined using SHAP. Results: Eight key predictors were identified, including age, focal neurological deficits, midline shift, tumor laterality, tumor lobar location, enhancing tumor volume, and MRS-derived Cho/NAA and Cho/Cr ratios. The Random Forest model achieved an area under the ROC curve of 0.946 (95% CI: 0.902–0.989) in the validation cohort. Calibration analysis demonstrated reasonable agreement between predicted and observed outcomes, and the precision–recall curve yielded an average precision of 0.98. Decision curve analysis indicated net clinical benefit across relevant probability thresholds. Conclusions: A multimodal machine-learning model integrating clinical, structural imaging, and MRS-derived metabolic features was developed and internally validated for non-invasive preoperative glioma grading. The model showed good discrimination and calibration and provided individualized probability estimates, suggesting potential value for preoperative risk stratification. However, clinical deployment remains premature, and further external validation is required. Full article

Background: This study evaluated the independent predictive value of preoperative Systemic Immune–Inflammation Index (SII) for postoperative wound infection (WI) in diabetic patients undergoing isolated Coronary Artery Bypass Grafting (CABG). Methods: A retrospective cohort of 300 diabetic patients (2024–2025) was analyzed. The primary outcome was 30-day postoperative WI. Preoperative SII was calculated from blood counts within 24 h before surgery. Multivariable logistic regression was performed using both a primary model (adjusting for age, BMI, and comorbidities) and an extended model including glycemic control (HbA1c), smoking status, operative duration, and transfusion requirements. Model discrimination was evaluated via Area Under the ROC Curve (AUC). Statistical power and sensitivity analyses were conducted to ensure the robustness of the findings. Results: WI occurred in 7% (n = 21). Preoperative SII was significantly lower in the WI group (958.48 ± 493.49 vs. 1293.56 ± 758.15, p = 0.047). SII remained an independent predictor in the adjusted model (Adjusted OR per 100-unit increase: 0.93; 95% CI: 0.86–1.00; p = 0.048). ROC analysis confirmed an inverse predictive pattern (AUC: 0.374, 95% CI: 0.312–0.436). Comparative analysis showed that SII provided superior additional insight compared to NLR and PLR in this population. Conclusions: Preoperative SII is an independent predictor for WI in diabetic CABG patients. However, given the modest discriminative performance (AUC: 0.374), it should be integrated into a broader clinical risk assessment. Contrary to conventional expectations, lower SII values indicated increased susceptibility, suggesting that immune exhaustion rather than hyperinflammation may drive infectious risk in diabetic patients. Full article

Background: Inflammation is central to myocardial injury and repair after ST-segment elevation myocardial infarction (STEMI). C-reactive protein (CRP) is an established biomarker of systemic inflammation, but its prognostic thresholds across patient subgroups are not well defined. Methods: In this retrospective cohort study, admission CRP was analyzed in 958 consecutive STEMI patients admitted to University Hospital Salzburg 2018–2020 and categorized into four groups (Serum CRP < 5.0, 5.0–9.9, 10.0–15, and >15.0 mg/dL). Mortality was assessed during short- (30, 90, and 180 days) and long-term (1, 3, and 5 years) follow-up. Kaplan–Meier analyses compared survival, Cox regression tested associations, and receiver operating characteristic (ROC) curves determined discriminatory value and optimal cut-offs. Results: Elevated admission CRP was associated with larger infarct size, impaired left ventricular function, and increased mortality. Kaplan–Meier curves showed progressively poorer survival with higher CRP, with worst outcomes at >15 mg/dL. At 30, 90, and 180 days, CRP demonstrated moderate discrimination (AUC 0.628, 0.653, and 0.654; all p < 0.001), with predictive cut-offs 11–15 mg/dL in the overall cohort. Subgroup analyses revealed markedly lower thresholds in vulnerable populations. Diabetic patients showed cut-offs 5–6 mg/dL with the highest AUC values (up to 0.714). Younger patients and smokers exhibited thresholds near 9–10 mg/dL, while subacute STEMI presentations demonstrated lower cut-offs compared with acute infarction. These findings indicate that the prognostic value of CRP is context-dependent rather than uniform. Conclusions: Admission CRP predicts short-term mortality after STEMI, with subgroup-specific cut-offs emerging below conventional thresholds, highlighting profiles where modest inflammatory activation carries disproportionate risk. Full article

Groundwater sustains human activity in arid crystalline terrains where surface water is scarce and hydrogeological data are limited. However, most groundwater potential mapping approaches depend on deterministic weighting methods without quantifying model variability. This study describes an uncertainty-aware Remote Sensing and Geographic Information Systems (RS–GIS) framework to delineate groundwater potential zones in the Wadi Arab Watershed, Northeastern Sudan. Nine thematic factors—geology and lithology, rainfall, slope, drainage density, lineament density, soil, land use/land cover, topographic wetness index, and height above nearest drainage—were integrated using the Analytical Hierarchy Process (AHP), with acceptable consistency (Consistency Ratio (CR) < 0.1). To address subjectivity in weights, a Dirichlet-based Monte Carlo simulation (500 iterations) was implemented to perturb AHP weights whilst preserving compositional constraints. The resulting Groundwater Potential Index (GWPI) classified 32.69% of the watershed as high to very high potential, primarily associated with alluvial deposits and fractured crystalline rocks. Model validation using Receiver Operating Characteristic (ROC) analysis yielded an Area Under the Curve (AUC) of 0.704, indicating acceptable predictive performance. Uncertainty assessment showed low spatial variability (mean standard deviation (SD) = 0.215) and stable exceedance probabilities, verifying the robustness of predicted high-potential zones. The proposed probabilistic AHP framework augments decision reliability and provides a transferable, cost-effective tool for groundwater planning in data-limited arid basement environments. Full article

Background: Early risk assessment in non-variceal upper gastrointestinal bleeding (NVUGIB) is essential for guiding clinical management. The neutrophil percentage-to-albumin ratio (NPAR) has recently been proposed as a marker reflecting both inflammatory response and physiological reserve. This study aimed to evaluate the prognostic value of NPAR for in-hospital mortality and its relationship with established risk scores in patients with NVUGIB. Methods: This retrospective observational study included 94 patients hospitalized with NVUGIB. NPAR was calculated using laboratory parameters obtained at admission. Patients were stratified according to AIMS65 (<2 vs. ≥2) and Rockall (<5 vs. ≥5) scores. In addition, inflammation-based indices, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune-inflammation index (SII), were calculated. Predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis, and associations with clinical outcomes were assessed. Results: The in-hospital mortality rate was 12.8%. NPAR values were significantly higher in patients with AIMS65 ≥ 2 and Rockall ≥ 5 (p < 0.001 for both). NPAR demonstrated good discriminative ability for AIMS65 ≥ 2 (AUC: 0.843) and moderate performance for Rockall ≥ 5 (AUC: 0.714). For mortality prediction, NPAR showed excellent performance (AUC: 0.900). A cut-off value of 27.4 yielded a sensitivity of 91.7% and a specificity of 75.6%. Higher NPAR values were associated with increased mortality risk (OR 31.9, 95% CI: 3.88–102.59, p < 0.001), while the negative predictive value was high (98.4%). In contrast, NLR, PLR, and SII showed limited predictive value for in-hospital mortality. Conclusions: NPAR shows promise as a potential prognostic biomarker for assessing disease severity and in-hospital mortality in NVUGIB. Its high negative predictive value and association with established risk scores suggest that it may complement current risk stratification approaches. However, these findings should be considered preliminary, given the retrospective design and limited sample size, and require validation in larger prospective studies. Full article

Background: Early vascular aging (EVA) is a common complication of type 2 diabetes mellitus. Early identification is crucial in middle-aged individuals with T2DM, as vascular stiffness can occur gradually for years before cardiovascular disease. However, EVA is rarely considered in routine care. Adropin is a vasoprotective peptide that may counter-regulate endothelin-1 (ET-1). Therefore, this study aims to examine the association between circulating adropin, ET-1, oxLDL, MMP-2, VEGFA, and EVA. Methods: This observational study included 300 adults aged 25–55 years (150 T2DM; 150 age/sex-matched controls). ePWV was calculated from age and mean blood pressure. EVA was classified using a residual-based, age-specific ePWV threshold derived from controls. Associations were tested using correlation and logistic regression. ROC and decision curve analyses were performed to evaluate diagnostic performance and clinical utility. Results: EVA prevalence was 38.6% overall, occurring in 7.3% of controls and increasing across T2DM with good and poor glycemic control (56.1% and 80.95%, respectively, p < 0.001). Compared with normal vascular aging, EVA showed lower adropin and higher ET-1, oxLDL and MMP-2, with lower VEGFA (all p < 0.05). In fully adjusted models, adropin (OR 0.991 per pg/mL; p < 0.001) and ET-1 (OR 1.017 per pg/mL, p = 0.005) remained independently associated with EVA. A combined adropin + ET-1 predictor improved discrimination (AUC 0.901, 95% CI 0.868–0.934), at a predicted-probability cutoff of 0.607, 78.7% sensitivity and 87.0% specificity. Conclusions: In middle-aged T2DM, EVA was associated with lower adropin and higher ET-1 in T2DM. These findings support an association between these biomarkers and the EVA phenotype. Full article

Primary Sjögren’s disease (SjD) is characterized by lymphocyte infiltration into exocrine glands. Mitochondrial dysfunction is a critical pathological mechanism underlying SjD, and mitophagy plays a vital role in clearing damaged mitochondria. This study used bioinformatic analysis to explore the potential roles of mitophagy-related genes in SjD pathogenesis and immune infiltration. Bioinformatic analysis was performed on the SjD microarray datasets to identify differentially expressed genes (DEGs). Mitophagy-related DEGs were selected and analyzed using functional enrichment, protein–protein interaction (PPI) networks, and machine learning (Least Absolute Shrinkage and Selection Operator [LASSO] and Random Forest) to identify hub genes. Their diagnostic value was assessed by receiver operating characteristic (ROC) curves. Immune infiltration and its correlation with hub genes were also evaluated. Hub gene expression in the salivary glands of patients was validated using qRT-PCR. Regulatory networks were also predicted. Three hub genes (GABARAPL1, PINK1, and SQSTM1) were identified. They showed high diagnostic specificity and were downregulated in SjD salivary glands. Immune infiltration analysis revealed increased levels of activated natural killer (NK) cells, memory B cells, plasma cells, CD8+ T cells, Tfh cells, and M1 macrophages, but decreased levels of Tregs and M2 macrophages. Hub gene expression was correlated with specific immune cell subsets. Regulatory network predictions highlighted potential upstream regulators and therapeutic compounds. This study identified three mitophagy-related hub genes linked to immune dysregulation in SjD, providing novel insights into disease mechanisms and potential therapeutic targets. Full article