Association of the neutrophil percentage-to-albumin ratio with sarcopenia in U.S. adults: Evidence from NHANES 2011-2018 and machine learning-based analyses.

ObjectiveTo evaluate the utility of the preoperative neutrophil percentage-to-albumin ratio (NPAR) for predicting perioperative major adverse cardiovascular events (MACE) in patients with stable coronary artery disease (SCAD) undergoing non-cardiac surgery.MethodsIn this retrospective cohort study, we included all adult SCAD patients who underwent non-cardiac surgery at the Fourth Affiliated Hospital of Zhejiang University School of Medicine between October 2020 and October 2024. The primary endpoint was the occurrence of MACE during the perioperative period, defined as a composite of all-cause mortality, cardiac arrest, myocardial infarction, heart failure, or stroke occurring intraoperatively or during the postoperative hospital stay. We used multivariable logistic regression to assess the independent association between NPAR and MACE risk. To explore potential nonlinearity, we fitted smooth curves and performed threshold-effect analysis. Mediation analysis quantified the proportion of the NPAR–MACE relationship explained by estimated glomerular filtration rate (eGFR). Incremental predictive value was evaluated by comparing the area under the receiver operating characteristic curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) before and after adding NPAR to established risk models. Feature selection was conducted using the Boruta algorithm, and predictive performance was further validated with an XGBoost model interpreted via Shapley Additive Explanations (SHAP).ResultsOf 1,771 patients, 90 (5.1%) experienced MACE. The MACE subgroup had a higher mean NPAR than those without events (19.4 ± 5.3 vs. 15.9 ± 3.5; P < 0.001). Each 1-unit increase in NPAR was associated with a 20% higher risk of MACE (OR 1.20; 95% CI 1.10–1.30). A J-shaped relationship emerged, with an inflection point at NPAR 13.7 (P_threshold = 0.005). eGFR mediated 8.4% of the NPAR–MACE association. NPAR alone yielded an AUC of 0.721. Adding NPAR to the Revised Cardiac Risk Index raised the AUC from 0.679–0.755 (NRI 0.599; IDI 0.035; all P < 0.01). The XGBoost model achieved an AUC of 0.773, and SHAP analysis identified NPAR as the most influential predictor.ConclusionsPreoperative NPAR is an independent, readily available predictor of perioperative MACE in SCAD patients. Incorporation of NPAR into existing risk models significantly enhances predictive accuracy and may inform targeted perioperative management strategies.

Associations of metabolic status with all-cause mortality among individuals with osteoarthritis: A prospective cohort study

Remnant cholesterol and low-grade inflammation are key contributors to acute coronary syndrome (ACS) in patients with diabetes mellitus. The novel remnant cholesterol inflammatory index (NRCII), combining remnant cholesterol (RC) and the neutrophil-to-lymphocyte ratio (NLR), may enhance risk stratification, but its clinical relevance in diabetic inpatients remains unclear. This multicenter retrospective study included 3664 diabetic inpatients hospitalized between August 2019 and June 2025. Logistic regression, restricted cubic spline (RCS), subgroup, and sensitivity analyses were conducted to assess the association between NRCII and ACS. Incremental predictive value was evaluated using C-statistic, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and likelihood-ratio (LR) tests. ACS occurred in 1056 participants (28.8%). Each 1-unit increase in NRCII was associated with a 21% higher risk of ACS in the fully adjusted model (OR = 1.21, 95% CI 1.13–1.29, P < 0.001). Quartile analyses showed a clear dose–response (P for trend < 0.001), with the highest quartile showing nearly three-fold greater risk (OR = 2.78, 95% CI 1.86–4.04) compared to the lowest. RCS confirmed a significant nonlinear positive association. Results remained robust after excluding patients on lipid-lowering therapy. Subgroup analyses revealed interactions with age and alcohol use. NRCII addition to the clinical model provided the greatest performance gain (C-statistic 0.751; NRI 0.271; IDI 0.020; LR 30.63; all P < 0.001) over RC or NLR alone. Higher NRCII is independently and strongly associated with ACS risk among hospitalized patients with diabetes, offering superior predictive value compared to RC or NLR alone. NRCII may serve as a simple, effective tool for ACS risk stratification in this high-risk population.

29P Plasma tumor-derived small extracellular vesicles microRNAs plus CA-125 objectively detect residual disease risk after surgical debulking in advanced ovarian cancer

Background:Individuals with osteoarthritis (OA) often experience significant changes in their metabolic status and have a higher risk of mortality compared to the general population. However, no study has quantified the...

Background Inflammation and hyperuricemia are closely associated with chronic kidney disease (CKD). The systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), monocyte-to-lymphocyte ratio (MLR), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) are emerging as novel biomarkers. While, the synergistic effects of these biomarkers with hyperuricemia on CKD remain unclear. Method We analyzed 10,226 participants from 2015–2020 National Health and Nutrition Examination Survey (NHANES). The relationships among inflammatory biomarkers (SIRI, SII, MLR, NLR, and PLR), hyperuricemia and CKD were assessed by multivariate logistic regression models. Restricted cubic splines (RCS) and segmented regression models were used to evaluate the nonlinear relationships. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve, and incremental predictive value was further calculated by Net Reclassification Improvement (NRI) and Integrated Discrimination Improvement (IDI). The interaction analysis was performed to explore the combined effects. Results SIRI, SII, MLR, and NLR were significantly linked with CKD. MLR exhibited a threshold effect at 0.22 (p-non-linear < 0.05), with significantly stronger association with CKD above this cutoff. SIRI demonstrated the best diagnostic accuracy among these biomarkers. Significant interactions were observed between hyperuricemia and inflammatory biomarkers (SIRI, SII, MLR, NLR), indicating that the association between inflammatory biomarkers and CKD is more pronounced in the presence of hyperuricemia. Conclusion There were significant associations between inflammatory biomarkers (SII, SIRI, NLR, MLR) and CKD, with particularly stronger correlations observed in patients with hyperuricemia.

Background Although previous studies have demonstrated that neutrophil and albumin are biomarkers of inflammation and malnutrition, which are highly related with contrast-associated acute kidney injury (CA-AKI). However, there has been no study investigated the combined evaluation of neutrophil and albumin in predicting CA-AKI. Purpose To explore the predictive value of neutrophil percentage-to-albumin ratio (NPAR) for CA-AKI in patients undergoing elective percutaneous coronary intervention (PCI). Methods We prospectively observed 5083 consenting patients without chronic kidney disease (CKD) undergoing elective PCI from January 2012 to December 2018. NPAR was calculated as neutrophil percentage numerator divided by serum albumin concentration. CA-AKI was defined as an increase in serum creatinine (SCr) ≥50% or 0.3 mg/dL within 48 hours after contrast medium exposure. The association between NPAR and CA-AKI was investigated by logistic regression analysis. The area under the receiver-operating characteristic curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to make comparison for CA-AKI prediction. Result The incidence of CA-AKI was 5.6% (n=286). The median NPAR was 14.9 (13.0–17.1). According to the receiver operating characteristic curves (ROC), the best cut-off value of NPAR for predicting CA-AKI was 15.7 with 66.8% sensitivity and 61.9% specificity (C statistic=0.679; 95% CI, 0.666–0.691). NPAR displayed higher AUC value in comparison to neutrophil percentage (p&amp;lt;0.001), but not albumin (P=0.063), as a predictor of CA-AKI. However, NPAR significantly improved the prediction of CA-AKI in the continuous NRI and IDI over neutrophil percentage (NRI: 0.353, 95% CI: 0.234–0.472, P&amp;lt;0.001; IDI: 0.017, 95% CI: 0.010–0.024, p&amp;lt;0.001) and albumin (NRI: 0.141, 95% CI: 0.022–0.260, P=0.020; IDI: 0.009, 95% CI: 0.003–0.015, p=0.003) alone. After adjusting for potential confounding risk factors of CA-AKI, multivariable logistic analysis showed that NPAR &amp;gt;15.7 was a strong independent predictor of CA-AKI (OR=1.998, 95% CI, 1.511–2.643, p&amp;lt;0.001). Conclusion NPAR is an independent predictor of CA-AKI, which significantly improved the prediction of CA-AKI over neutrophil and albumin alone in patients without CKD undergoing elective PCI. Funding Acknowledgement Type of funding sources: None. ROC for NPAR to predict CA-AKIPredictors of CA-AKI

Genetic Risk Prediction for CKD: A Journey of a Thousand Miles

Sarcopenia, a growing public health concern lacking targeted therapies, highlights the need to investigate modifiable factors like physical activity (PA) and sedentary behavior, which influence muscle health. However, most research focuses on older adults, with limited data on young and middle-aged populations. This study leverages the National Health and Nutrition Examination Survey (NHANES) data to investigate this topic in the US population aged 18 to 59 to address this critical gap. This cross-sectional study utilized data from 7869 participants in the NHANES (2011-2018) to assess associations between PA patterns, sedentary behavior, and sarcopenia risk. PA and sedentary behavior were measured via self-report, "weekend warriors" was defined as individuals who meet weekly physical activity guidelines (≥150 minutes) but exercise infrequently (<2 sessions/week), and sarcopenia was defined using the Foundation for the National Institutes of Health (FNIH) guidelines. Weighted multivariate logistic regression was used, with results presented as odds ratios (ORs) with 95% confidence intervals (CIs). Nonlinear associations were explored using restricted cubic splines. In the final analysis, 689 participants (8.76%) were classified as having sarcopenia. After adjusting the covariates, sedentary time (h/day) increased the risk of sarcopenia (OR = 1.05, 95% CI: 1.01-1.10), with a linear dose-response relationship. However, for every 1-hour increment in PA, there was a 6% reduction in the risk of sarcopenia (a linear relationship was also observed), and this negative association was more pronounced for vigorous PA (OR = 0.39, 95% CI: 0.29-0.53). Meanwhile, compared to inactive individuals, both "weekend warriors" (OR = 0.41, 95% CI: 0.23-0.72) and those with regular PA patterns (OR = 0.71, 95% CI: 0.54-0.92) were less susceptible to sarcopenia. These associations showed a potentially more significant trend in older (45-59 years) and male participants. This study identifies that PA can decrease the potential risk of sarcopenia in adults aged 18 to 59, whereas prolonged sedentary behavior increases it. Promoting population-level PA participation could serve as a preventive strategy for sarcopenia, however, additional research is necessary to confirm these findings.

Sarcopenia, characterized by the progressive loss of skeletal muscle mass and strength, poses a significant public health challenge. This study explores the sex-specific associations between prebiotic intake and the risk of sarcopenia, utilizing data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2011 and 2014. Adult participants provided information on their prebiotic consumption and sarcopenia status, which was defined according to the Foundation for the National Institutes of Health (FNIH) criteria, focusing on grip strength and appendicular skeletal muscle mass adjusted for body mass index (BMI). The analysis identified 4306 individuals as nonconsumers of prebiotics, whereas 157 were identified as consumers. The results showed an inverse association between prebiotic intake and the risk of sarcopenia among females, with an odds ratio of 0.11 (95% CI: 0.05-0.32, p = 0.01). In contrast, no significant association was observed in males. These findings suggest that prebiotic consumption may be particularly beneficial in reducing the risk of sarcopenia among adult women, highlighting the need for further research to investigate the underlying mechanisms.

Incremental Predictive Value of Sarcopenia for Incident Fracture in an Elderly Chinese Cohort: Results From the Osteoporotic Fractures in Men (MrOs) Study

BackgroundPlasma apolipoproteins are linked to cardiometabolic dysfunctions, but their potential as biomarkers for metabolic dysfunction-associated steatohepatitis (MASH) remains underexplored.MethodsPlasma levels of 14 apolipoproteins (apoA-I, A-II, A-IV, B100, C-I, C-II, C-III, D, E, F, H, J, L1, M) were quantified using liquid chromatography–tandem mass spectrometry in a cross-sectional study of 148 individuals with obesity undergoing bariatric surgery. Based on liver histology, participants were categorized as non-MASH (n = 94; no liver alterations or simple steatosis, ≥ 5% intrahepatic fat) or MASH (n = 54; steatosis with ballooning and lobular inflammation, with or without fibrosis). Correlations with clinical and biochemical parameters were assessed via Spearman’s rank correlation, and associations with MASH were evaluated using logistic regression. Incremental predictive value beyond established risk factors was assessed through likelihood ratio tests (LRT), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).ResultsApoC-III and apoL1 were significantly higher in MASH compared with non-MASH participants, while other apolipoproteins showed no group differences. Higher apoE, apoL1 and apoJ levels were associated with increased odds of MASH, independently of age and sex. Associations for apoL1 and apoJ remained significant after adjustment for diabetes, dyslipidemia, and hypertension, or for established MASH risk factors including insulin resistance, triglycerides, waist circumference, and the AST/ALT ratio. LRT analyses showed that apoJ (ΔDeviance = 4.085, p = 0.043) and apoL1 (ΔDeviance = 3.954, p = 0.047) each improved model fit, with their combination providing additional improvement (ΔDeviance = 7.534, p = 0.023). NRI analysis indicated that the combination of apoJ and apoL1 provided the largest improvement (NRI total = 0.39, p = 0.026), mainly by correctly reclassifying non-MASH individuals (NRI non-event = 0.31, p = 0.0023). IDI was also greatest for the combination (IDI = 0.04, p = 0.034), indicating enhanced discrimination between MASH and non-MASH individuals. In an external cohort, the elevation of plasma apoJ in MASH was consistently replicated, whereas apoL1, apoC-III, and apoE showed no such pattern.ConclusionsPlasma apoJ and apoL1 may serve as potential biomarkers for diagnosing MASH in individuals with obesity, independent of traditional risk factors. Further validation in larger cohorts and mechanistic studies is warranted.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12944-025-02733-0.

Prognostic Implication of Bone Marrow Abnormality of Appendicular Skeletons Detected By Low-Dose Multidetector Computed Tomography in Patients with Myelodysplastic Syndrome

Mortality in metabolic dysfunction-associated steatotic liver disease (MASLD) is primarily driven by the synergy between insulin resistance (IR) and systemic inflammation. However, practical tools for integrated risk assessment remain scarce. This study aimed to evaluate the individual and joint prognostic value of the estimated Glucose Disposal Rate (eGDR) and Systemic Inflammation Response Index (SIRI)-validated surrogates for IR and inflammatory status-to refine risk stratification and elucidate their reciprocal associations on MASLD survival. This observational analysis included 7520 MASLD adults from the continuous National Health and Nutrition Examination Survey (NHANES) (1999-2018) and an independent external validation cohort of 1182 ultrasound-confirmed patients from NHANES III (1988-1994), with mortality linked through 2019. Multivariable Cox models and restricted cubic splines were employed to evaluate the associations between biomarkers and mortality. The combined model's predictive performance and clinical benefit were quantified via Receiver Operating Characteristic curves, Net Reclassification Improvement (NRI), Integrated Discrimination Improvement (IDI), and Decision Curve Analysis. Furthermore, exploratory bidirectional mediation analysis was conducted to assess the potential statistical interplay between the two markers. Over a median follow-up of 138months, 1375 all-cause and 442 cardiovascular deaths occurred. Lower eGDR and higher SIRI were independently and linearly associated with increased mortality. A low eGDR/high SIRI phenotype was identified as the highest-risk category, exhibiting a 1.860-fold risk of all-cause mortality (95% CI 1.439-2.405) and a 2.395-fold risk of cardiovascular mortality (95% CI 1.379-4.159). The combined model demonstrated superior predictive accuracy (Area Under the Curve (AUC) 0.686-0.848) and significant reclassification improvement (NRI 0.161; IDI 0.017), and higher clinical net benefit than either indicator alone (p < 0.001). Furthermore, mediation analysis suggested that SIRI statistically accounted for 10.31% of the association between eGDR and all-cause mortality, highlighting a potential reciprocal statistical interplay. Crucially, this high-risk 'low eGDR/high SIRI' phenotype was successfully validated in the imaging-confirmed external cohort (ACM: HR = 2.354; CVM: HR = 3.153; both p < 0.001). Integrating eGDR and SIRI identifies a high-risk MASLD phenotype with the poorest prognosis, reflecting a synergistic metabolic-inflammatory burden. This joint assessment significantly improves predictive accuracy and offers superior net clinical benefits for long-term mortality prediction.

Metabolic syndrome (MetS) is a cluster of risk factors including increased triglycerides, insulin resistance, and hypertension, posing increasing public health challenges. Both cardiorespiratory fitness (CRF) and the Triglyceride-Glucose (TyG) index have been associated with MetS risk independently. However, their combined predictive value remains unclear. This study aims to assess the combined influence of CRF and TyG index on MetS risk in a survey sample. Data from 3742 participants in the National Health and Nutrition Examination Survey (NHANES) in year cycle of 1999-2004 were analyzed. Logistic regression and restricted cubic spline (RCS) analyses were used to evaluate the associations of CRF and TyG index with MetS risk. Subgroup analyses by different CRF, TyG, and disease conditions were conducted to explore interaction effects across different populations. Sensitivity analysis was implemented to verify the robustness of the results. Predictive value was assessed using net reclassification improvement (NRI), integrated discrimination improvement (IDI), and area under the curve (AUC) of receiver operating characteristic (ROC) curve. Logistic regression showed that impaired CRF was associated with a 73% higher risk of MetS (Odds Ratio (OR) 1.73; 95% Confidence Interval (CI), 1.23-2.42), while elevated TyG index was associated with a 6.84-fold increased risk (OR 6.84; 95% CI, 2.71-17.29). The combination of impaired CRF and high TyG index showed the highest risk of MetS (OR 11.99; 95% CI, 3.79-37.98). In sensitivity analysis, the results remained similar. Subgroup and interaction analyses further confirmed these findings, showing consistent results across demographic groups and under various analytical conditions. The combined use of CRF and TyG index significantly enhanced the predictive performance (AUC 0.871; 95% CI, 0.856-0.886) and improved model classification capabilities (NRI 0.393; 95% CI, 0.309-0.476; IDI 0.020; 95% CI, 0.014-0.025). This study reveals that CRF and TyG index independently predict MetS risk, while their combination demonstrates superior predictive accuracy compared to using either parameter alone. These findings indicate that integrating both CRF and TyG into clinical practice may improve early detection and preventive strategies for MetS.