This study aimed to explore the relationships between cumulative C-reactive protein-triglyceride-glucose index (cuCTI) and CTI changes and the risk of new-onset cardiovascular disease (CVD) in middle-aged and older Chinese individuals. This longitudinal study employed data from the China Health and Retirement Longitudinal Study. To evaluate the association between the cuCTI index and CVD risk, we conducted Cox regression analyses alongside restricted cubic spline (RCS) regression models. The participants were categorized into three groups on the basis of the dynamic changes in the CTI through K-means clustering. Subgroup analysis and sensitivity analysis were performed to confirm the robustness of the results. A total of 4157 participants were included in the study, of whom 609 (13.5%) were diagnosed with CVD. The incidence risk of CVD increased with increasing cuCTI. In fully adjusted Model 3, the Cox regression results indicated that, compared with those in Q1, participants in Q2 had a significantly greater risk of developing CVD (HR, 1.53 [1.22, 1.92], Padj < 0.001); similarly, participants in Q3 had a 1.36 [1.07, 1.74] increased risk (Padj = 0.014). RCS analysis revealed a linear association between cuCTI and the risk of CVD (Padj < 0.05). K-means clustering identified three groups of CTI trajectory changes. Compared with participants with consistently low CTI levels, those with persistently high and increasing CTI levels had a significantly elevated risk of developing CVD (HR, 1.31 [95% CI 1.01–1.70], Padj = 0.041). Subgroup and interaction analyses revealed no statistically significant differences across all subgroups (all p > 0.05). This study revealed that cuCTI and changes in CTI are independently associated with the risk of CVD among middle-aged and elderly individuals with elevated cuCTI levels, particularly those with sustained high CTI levels, exhibited a significantly higher risk of CVD. As a cost-effective and accessible tool, cuCTI may provide supplementary information for assessing CVD risk. Monitoring long-term changes in the CTI and maintaining it at relatively low levels may aid early identification of individuals at high risk for CVD.

Background: This study aimed to evaluate the relationship between CBC-derived inflammatory markers (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality (ACM) risk in arthritis (AR) patients with hypertensive (HTN) using data from the NHANES.Methods: We employed weighted multivariable logistic regression and WQS regression to explore the relationship between inflammatory markers and ACM in AR patients, as well as to determine the weights of different markers. Kaplan–Meier curves, restricted cubic splines (RCS) and ROC curves were utilized to monitor cumulative survival differences, non-linear relationships and diagnostic utility of the markers for ACM risk, respectively. Key markers were selected using XGBoost and LASSO regression machine learning methods, and a nomogram prognostic model was constructed and evaluated through calibration curves and decision curve analysis (DCA).Results: The study included 4,058 AR patients with HTN, with 1,064 deaths over a median 89-month follow-up. All six inflammatory markers were significantly higher in the deceased group (p < 0.001). Weighted multivariable logistic regression showed these markers’ elevated levels significantly correlated with increased ACM risk in hypertensive AR patients across all models (p < 0.001). Kaplan–Meier analysis linked higher marker scores to lower survival rates in AR patients with HTN (p < 0.001). WQS models found a positive correlation between the markers and hypertensive AR patients (p < 0.001), with NPAR having the greatest impact (70.02%) and SIRI next (29.01%). ROC analysis showed SIRI had the highest AUC (0.624) for ACM risk prediction, closely followed by NPAR (AUC = 0.618). XGBoost and LASSO regression identified NPAR and SIRI as the most influential markers, with higher LASSO-based risk scores correlating to increased mortality risk (HR, 2.07; 95% CI, 1.83–2.35; p < 0.01). RCS models revealed non-linear correlations between NPAR (Pnon-linear<0.01) and SIRI (Pnon-linear148.56 and SIRI >1.51. A prognostic model using NPAR and SIRI optimally predicted overall survival.Conclusion: These results underscore the necessity of monitoring and managing NPAR and SIRI indicators in clinical settings for AR patients with HTN, potentially improving patient survival outcomes.

Background: Our study aims to utilize unsupervised machine learning methods to perform inflammation clustering on stroke patients via novel CBC-derived inflammatory indicators (NLR, PLR, NPAR, SII, SIRI, and AISI), evaluate the mortality risk among these different clusters and construct prognostic models to provide reference for clinical management.Methods: A cross-sectional analysis was conducted using data from stroke participants in the U.S. NHANES 1999–2018. Weighted multivariate logistic regression was used to construct different models; consensus clustering methods were employed to subtype stroke patients based on inflammatory marker levels; LASSO regression analysis was used to construct an inflammatory risk score model to analyze the survival risks of different inflammatory subtypes; WQS regression, Cox regression, as well as XGBoost, random forest, and SVMRFE machine learning methods were used to screen hub markers which affected stroke prognosis; finally, a prognostic nomogram model based on hub inflammatory markers was constructed and evaluated using calibration and DCA curves.Results: A total of 918 stroke patients with a median follow-up of 79 months and 369 deaths. Weighted multivariate logistic regression analysis revealed that high SIRI and NPAR levels were significantly positively correlated with increased all-cause mortality risk in stroke patients (p < 0.001), independent of potential confounders; Consensus clustering divided patients into two inflammatory subgroups via SIRI and NPAR, with subgroup 2 having significantly higher markers and mortality risks than subgroup 1 (p < 0.001); LASSO regression analysis showed subgroup 2 had higher risk scores and shorter overall survival than subgroup 1 [HR, 1.99 (1.61–2.45), p < 0.001]; WQS regression, Cox regression, and machine learning methods identified NPAR and SIRI as hub prognostic inflammatory markers; The nomogram prognostic model with NPAR and SIRI demonstrated the best net benefit for predicting 1, 3, 5 and 10-year overall survival in stroke patients.Conclusion: This study shows NPAR and SIRI were key prognostic inflammatory markers and positively correlated with mortality risk (p < 0.001) for stroke patients. Patients would been divided into 2 inflammatory subtypes via them, with subtype 2 having higher values and mortality risks (p < 0.001). It suggests that enhanced monitoring and management for patients with high SIRI and NPAR levels to improve survival outcomes.

Background: Pseudohypertriglyceridemia (pseudo-HTG) is a condition in patients with glycerol kinase deficiency or other disorders of glycerol metabolism, as well as in individuals with alcoholism, severe liver disease, or metabolic disturbances, and those receiving heparin therapy. Exogenous glycerol intake can also trigger this condition. However, the causes of pseudo-HTG are poorly understood, and a clinical algorithm for its diagnosing remains to be developed. Case presentation: We present the case of a 46-year-old man admitted to hospital with hypertriglyceridemia-induced severe acute pancreatitis (HTG-SAP) and type 2 diabetes mellitus. Upon admission, his plasma triglyceride (TG) level was critically high at 43.78 mmol/L (3877 mg/dL). During hospitalization, he developed acute renal insufficiency and diabetic ketoacidosis (DKA). Despite conventional lipid-lowering treatments, including extracorporeal lipoprotein apheresis, his TG levels remained elevated. The unusually clear serum led to suspicion of pseudo-HTG. A glycerol-corrected TG assay confirmed normal TG values, thereby diagnosing pseudo-HTG. Conclusions: This report presents the first confirmed case of pseudo-HTG verified through definitive glycerol kinase (GK) gene testing in a patient without glycerol kinase deficiency. We also include a review of the relevant literature and propose a clinical algorithm. The case report highlights the importance of considering pseudo-HTG in hypertriglyceridemia patients who do not respond well to the standard TG-lowering treatment. Our proposed clinical algorithm for diagnosing pseudo-HTG is potentially invaluable in clinical practice, and helps to prevent unnecessary lipid-lowering treatments for patients with pseudo-HTG.

Depressive symptoms are significant and deleterious complication of cardiovascular and cerebrovascular diseases (CCVD), profoundly impairing patients’