C-reactive protein-triglyceride-glucose index (CTI) has been proposed as a novel biomarker for insulin resistance and inflammation. However, the association between CTI and the risk of stroke, particularly in individuals with different glycemic status, remains unclear. A total of 10,443 middle-aged and elderly participants were enrolled from the China Health and Retirement Longitudinal Study (CHARLS). The primary endpoint was the occurrence of stroke events. The CTI was calculated using the formula 0.412* Ln (CRP [mg/L]) + Ln (TG [mg/dl] × FPG [mg/dl])/2. The Kaplan–Meier curves, Cox proportional hazard models, and restricted cubic spline analysis were applied to explore the association between CTI and the risk of stroke according to gender, age and glycemic status. During a median follow-up of 9 years, 960 (9.2%) participants experienced a stroke. Our findings revealed a significant positive linear relationship between CTI and the occurrence of stroke. The association was similar between male and female, despite the HR tended to be higher in females (HR 1.22, 95% CI 1.09, 1.36) than males (HR 1.15, 95% CI 1.02, 1.29), and similar in middle-aged (HR 1.25, 95% CI 1.11, 1.41) and elderly participants (HR 1.12, 95% CI 1.00, 1.26). In different glycemic status, high levels of CTI were found to be linked to an increased risk of stroke in individuals with normal glucose regulation (NGR) (HR 1.33, 95% CI 1.11, 1.59) and prediabetes mellitus (Pre-DM) (HR 1.20, 95% CI 1.04, 1.39). However, this association was not observed in individuals with diabetes mellitus (DM). Our findings revealed a significant positive linear relationship between CTI and the occurrence of stroke. The association between CTI and stroke was similar between male and female, and similar in middle-aged and elderly participants. In different glycemic status, the association was significant in individuals with NGR and Pre-DM. CTI has been proposed as a novel biomarker for insulin resistance and inflammation. What is the relationship between CTI and stroke risk under different genders, ages, and glycemic status? This is the first large-scale study to demonstrate this relationship between the CTI and stroke risk, and assessed this relationship according to gender, ages and glycemic status. Our research findings demonstrate a significant positive linear relationship between CTI and the incidence of stroke. To reduce the risk of stroke, different strategies must be adopted to manage CTI levels based on gender, age, and glycemic status.

Early detection of vulnerable carotid plaques is critical for stroke prevention. This study aimed to develop a machine learning model based on routine blood tests and derived indices to predict plaque vulnerability and assess sex-specific risk patterns across biomarker value ranges. We retrospectively included 1701 hospitalized patients from Suzhou Municipal Hospital (2019-2020), selected from an initial cohort of 10,028 individuals. All patients underwent carotid ultrasound, with vulnerable plaques identified using predefined imaging criteria. A total of 30 laboratory variables-including blood count, coagulation, and biochemistry-were extracted, alongside derived indices such as triglyceride-glucose index (TyG), atherogenic index of plasma (AIP), neutrophil-to-lymphocyte ratio (NLR) and others. Features were standardized and selected based on statistical and clinical relevance. Five machine learning models were trained using a 7:3 train-test split and evaluated by cross-validation. Model performance was assessed using AUC, sensitivity, and specificity. The best model was interpreted using SHapley Additive exPlanations (SHAP) analysis. Sex differences were explored using Mann-Whitney U tests and restricted cubic spline (RCS) modeling across value intervals. The Random Forest model showed the highest predictive performance (AUC = 0.847; 95% CI 0.791-0.895; specificity = 89.4%; sensitivity = 64.2%). SHAP analysis identified gender, age, fibrinogen, NLR, creatinine, fasting blood glucose, uric acid to high-density lipoprotein ratio (UHR), TyG, systemic inflammation response index (SIRI), and lymphocyte count as top predictors. Significant sex-specific differences in SHAP values were observed for key biomarkers, including age, UHR, TyG, SIRI, and others. RCS modeling further revealed distinct sex-related patterns in plaque vulnerability across biomarker value ranges. A Random Forest model integrating routine blood markers and derived indices accurately predicted vulnerable carotid plaques. The results underscore the importance of sex-specific risk assessment, highlighting differential effects of key biomarkers across genders and value intervals.

Background: The triglyceride–glucose (TyG) index has been proposed as a reliable marker of insulin resistance. However, its value in patients with carotid plaque stability remains unclear. This study investigated the association between the TyG index and unstable carotid plaque. Methods: A total of 12 068 participants were enrolled. Carotid ultrasound was used to determine the stability of carotid plaque. Logistic regression was used to analyze the relationship between the TyG index and unstable carotid plaque. The relationship between the TyG index and unstable carotid plaque was evaluated according to sex, age, and glucose metabolism states. Further, the dose–response relationship between the TyG index and unstable carotid plaque was also determined by restrictive cubic splines. Results: Of the 12 068 participants, 11 601 had stable carotid plaque and 467 had unstable carotid plaque. In several different adjustment models, the TyG index is significantly related to the risk of unstable carotid plaque. The association between the TyG index and an unstable carotid plaque was similar between men and women, despite the fact that the odds ratio (OR) tended to be higher in men (OR, 2.80 [95% CI, 2.04–3.83]) than women (OR, 2.07 [95% CI, 1.51–2.82]), and higher in older patients (aged >60 years; (OR, 3.59 [95% CI, 2.74–4.70]) than middle‐aged patients (aged ≤60 years) (OR, 2.00 [95% CI, 1.36–2.95]). The TyG index of patients with different glycemic status was significantly correlated with the risk of unstable carotid plaque, among which the OR value of diabetes (OR, 2.51 [95% CI, 1.87–3.36]) was the highest. The restrictive cubic spline analysis indicated a nonlinear relationship between the TyG index and unstable carotid plaque, with TyG index >8.63 identified as an independent risk factor for unstable carotid plaque. Conclusions: The TyG index has a significant association with unstable carotid plaque. The association between the TyG index and unstable carotid plaque is similar between men and women, and the association in older patients is higher than that in middle‐aged patients. In different glycemic status, the association between the TyG index and unstable carotid plaque is highest in patients with diabetes.

Background: The atherogenic index of plasma (AIP) is a marker of lipids and atherosclerosis. However, its associations with stroke risk in a population with cardiovascular‐kidney‐metabolic (CKM) syndrome stages 0 to 3 remain unclear. Methods: The data for this study were obtained from CHARLS (China Health and Retirement Longitudinal Study). The AIP was calculated using the formula: AIP=log10 (triglycerides/high‐density lipoprotein cholesterol). The outcome of this study was stroke events. Cox regression models and restricted cubic spline were applied to investigate the relationship between AIP and stroke risk in populations with CKM syndrome stages 0 to 3. Results: A total of 7664 participants were included in this study, with a mean age of 59.30±9.58 years, and 53.08% were women. A total of 683 (8.91%) stroke events were recorded. After fully adjusting for covariates, each 1‐unit increase in AIP was associated with a 22% increase in stroke risk (hazard ratio [HR], 1.22 [95% CI, 1.10–1.34]). AIP was further categorized into quartiles. The adjusted HRs for quartile 2, quartile 3, and quartile 4 compared with quartile 1 were 1.48 (95% CI, 1.18–1.86), 1.70 (95% CI, 1.36–2.14), and 1.58 (95% CI, 1.25–2.00), respectively. Results from restricted cubic spline revealed a significant nonlinear relationship within a population with CKM syndrome stages 0 to 3 between AIP and stroke risk (threshold=1.516). Conclusions: This study found a nonlinear association between AIP and increased stroke incidence in a population with CKM syndrome stages 0 to 3, with a threshold of 1.516. These findings indicate that AIP could serve as a potential predictor for the risk of stroke.

Background: The estimated glucose disposal rate (eGDR) is recognized as a reliable marker of insulin resistance. However, the association between eGDR and the risk of stroke remains unclear. Methods and Results: A total of 13 706 middle‐aged and older participants were enrolled from CHARLS (China Health and Retirement Longitudinal Study). The primary end point was the occurrence of stroke events. The Kaplan–Meier curves, Cox proportional hazard models, and restricted cubic spline analysis were applied to explore the association between eGDR and the risk of stroke according to sex, age, and glycemic status. A total of 1101 stroke events were recorded. Our findings revealed a significant nonlinear relationship between eGDR and the occurrence of stroke. The association was similar between men (hazard ratio [HR], 0.83 [95% CI, 0.80–0.87]) and women (HR, 0.86 [95% CI, 0.80–0.87]), as well as among participants with normal glucose tolerance (HR, 0.83 [95% CI, 0.79–0.87]), prediabetes (HR, 0.85 [95% CI, 0.82–0.89]), and diabetes (HR, 0.87 [95% CI, 0.82–0.92]). However, the association was stronger in middle‐aged participants (HR, 0.82 [95% CI, 0.78–0.86]) compared with older individuals (HR, 0.87 [95% CI, 0.83–0.90]; P for interaction=0.019). Conclusions: This study demonstrates that lower eGDR levels are significantly linked to increased stroke risk. The relationship between eGDR and stroke risk was similar across different sexes and glycemic statuses and was stronger in middle‐aged participants compared with older participants.

The atherogenic index of plasma (AIP) is a marker of atherosclerosis, while frailty reflects cumulative physiological decline. However, the combined impact