Objective: Current literature is deficient in robust evidence delineating the correlation between the triglyceride glucose-body mass index (TyG-BMI) and the incidence of stroke. Consequently, this investigation seeks to elucidate the potential link between TyG-BMI and stroke risk in a cohort of middle-aged and senior Chinese individuals. Methods: This study employs longitudinal data from four waves of the China Health and Retirement Longitudinal Study (CHARLS) conducted in 2011, 2013, 2015, and 2018, encompassing 8,698 participants. The CHARLS cohort was assembled using a multistage probability sampling technique. Participants underwent comprehensive evaluations through standardized questionnaires administered via face-to-face interviews. Our analytic strategy involved the application of Cox proportional hazards regression models to investigate the association between TyG-BMI and the risk of stroke. To discern potential non-linear relationships, we incorporated Cox proportional hazards regression with smooth curve fitting. Additionally, we executed a battery of sensitivity and subgroup analyses to validate the robustness of our findings. Results: Our study utilized a multivariate Cox proportional hazards regression model and found a significant correlation between the TyG-BMI and the risk of stroke. Specifically, a 10-unit increase in TyG-BMI corresponded to a 4.9% heightened risk of stroke (HR = 1.049, 95% CI 1.029–1.069). The analysis also uncovered a non-linear pattern in this relationship, pinpointed by an inflection point at a TyG-BMI value of 174.63. To the left of this inflection point—meaning at lower TyG-BMI values—a 10-unit hike in TyG-BMI was linked to a more substantial 14.4% rise in stroke risk (HR 1.144; 95% CI 1.044–1.253). Conversely, to the right of the inflection point—at higher TyG-BMI values—each 10-unit increment was associated with a smaller, 3.8% increase in the risk of stroke (HR 1.038; 95% CI 1.016–1.061). Conclusions: In the middle-aged and elderly Chinese population, elevated TyG-BMI was significantly and positively associated with stroke risk. In addition, there was also a specific non-linear association between TyG-BMI and stroke (inflection point 174.63). Further reduction of TyG-BMI below 174.63 through lifestyle changes and dietary control can significantly reduce the risk of stroke.

Objective: Evidence regarding the relationship between the triglyceride glucose-body mass index (TyG-BMI) and the risk of progression from prediabetes to diabetes remains limited. Our study aimed to investigate the relationship between them in patients with prediabetes.Methods: In this retrospective cohort study, data were collected from 25,279 patients with prediabetes who received health checks between 2010 and 2016. We used a Cox proportional-hazards regression model to examine the relationship between TyG-BMI and diabetes risk. We used Cox proportional hazards regression with cubic spline functions and smooth curve fitting to identify the nonlinear relationship between them. In addition, A series of sensitivity and subgroup analyses were also conducted.Results: The mean age of the included participants was 49.29 ± 13.82 years old, and 1,6734 (66.2%) were male. The mean TyG-BMI was 219.47. The median follow-up time was 2.89 years, and 2,687 (10.63%) individuals had a final diagnosis of diabetes. After adjusting for covariates, TyG-BMI was positively linked with incident diabetes in patients with prediabetes (HR = 1.011, 95%CI 1.010–1.012). TyG-BMI had a non-linear connection with diabetes risk, and its inflection point was 231.66. Right and left effects sizes (HR) at the inflection point were 1.017 (95%CI:1.014–1.019) and 1.007 (95%CI:1.005–1.009), respectively. The sensitivity analysis demonstrated the robustness of these results.Conclusion: This study demonstrated a positive, non-linear relationship between the TyG-BMI and diabetes risk in Chinese patients with prediabetes. When the TyG-BMI was <231.66, there was a significant positive association between TyG-BMI and the risk of progression from prediabetes to diabetes. This study serves as a reference to promote clinical consultation and optimize diabetes prevention decisions for patients with prediabetes.

Objective: Evidence regarding the relationship between body mass index (BMI) and reversion to normoglycemia from prediabetes is still limited. The purpose of our study is to survey the link of BMI on reversion to normoglycemia among patients with impaired fasting glucose (IFG).Methods: This study, a retrospective cohort, covered 32 regions and 11 cities in China and collected 258,74 IFG patients who underwent a health check from 2010 to 2016. We investigated the association between baseline BMI and reversion to normoglycemia in patients with IFG using the Cox proportional-hazards regression model. The nonlinear relationship between BMI and reversion to normoglycemia was determined using a Cox proportional hazards regression with cubic spline functions and smooth curve fitting. In addition, we also performed a series of sensitivity analyses and subgroup analyses. A competing risk multivariate Cox regression was performed using progression to diabetes as a competing risk for reversal of normoglycemic events.Results: After adjusting covariates, the results showed that BMI was negatively related to the probability of reversion to normoglycemia (HR=0.977, 95%CI:0.971-0.984). Compared with participants with normal BMI(<24kg/m²), overweight (BMI:24-28kg/m²) participants with IFG had a 9.9% lower probability of returning to normoglycemia (HR=0.901,95%CI:0.863-0.939), while obese patients (BMI ≥ 28kg/m²) had a 16.9% decreased probability of reverting from IFG to normoglycemia (HR=0.831,95%CI:0.780-0.886). There was also a nonlinear relationship between them, and the inflection point of BMI was 21.7kg/m². The effect sizes (HR) on the left sides of the inflection point were 0.972(95%CI:0.964-0.980). The competing risks multivariate Cox’s regression and sensitivity analysis demonstrated the robustness of our results.Conclusion: This study demonstrates a negative and nonlinear relationship between BMI and reversion to normoglycemia in Chinese patients with IFG. Minimizing BMI to 21.7 kg/m² in patients with IFG through aggressive intervention may significantly increase the probability of returning to normoglycemia.

Aim: To develop a personalized nomogram and risk score to predict the 5‐year risk of diabetes among Chinese adults with prediabetes. Methods: There were 26 018 participants with prediabetes at baseline in this retrospective cohort study. We randomly stratified participants into two cohorts for training (n = 12 947) and validation (n = 13 071). The least absolute shrinkage and selection operator (LASSO) model was applied to select the most significant variables among candidate variables. And we further established a stepwise Cox proportional hazards model to screen out the risk factors based on the predictors chosen by the LASSO model. We presented the model with a nomogram. The model's discrimination, clinical use and calibration were assessed using the area under the receiver operating characteristic (ROC) curve, decision curve and calibration analysis. The associated risk factors were also categorized according to clinical cut‐points or tertials to create the diabetes risk score model. Based on the total score, we divided it into four risk categories: low, middle, high and extremely high. We also evaluated our diabetes risk score model's performance. Results: We developed a simple nomogram and risk score that predicts the risk of prediabetes by using the variables age, triglyceride, fasting blood glucose, body mass index, alanine aminotransferase, high‐density lipoprotein cholesterol and family history of diabetes. The area under the ROC curve of the nomogram was 0.8146 (95% CI 0.8035‐0.8258) and 0.8147 (95% CI 0.8035‐0.8259) for the training and validation cohort, respectively. The calibration curve showed a perfect fit between predicted and observed diabetes risks at 5 years. Decision curve analysis presented the clinical use of the nomogram, and there was a wide range of alternative threshold probability spectrums. A total risk score of 0 to 2.5, 3 to 4.5, 5 to 7.5 and 8 to 13.5 is associated with low, middle, high and extremely high diabetes risk status, respectively. Conclusions: We developed and validated a personalized prediction nomogram and risk score for 5‐year diabetes risk among Chinese adults with prediabetes, identifying individuals at a high risk of developing diabetes. Doctors and other healthcare professionals can easily and quickly use our diabetes score model to assess the diabetes risk status in patients with prediabetes. In addition, the nomogram model and risk score we developed need to be validated in a prospective cohort study.

Objective: Evidence regarding the relationship between serum triglyceride-to-high density lipoprotein cholesterol (TG/HDL-c) ratio and outcomes in acute ischemic stroke (AIS) patients is still mixed. Therefore, the present study was undertaken to explore the link between the TG/HDL-c ratio and unfavorable outcomes in patients with AIS. Methods: This was a second analysis based on a cohort study. The study population was 1764 patients with AIS collected from January 2010 to December 2016 at a hospital in South Korea. We used a binary logistic regression model to assess the linear association between the TG/HDL-c ratio and unfavorable outcomes for AIS patients. A generalized additive model (GAM) and smooth curve fitting (penalized spline method) was conducted to explore the nonlinear relationship between TG/HDL-c ratio and unfavorable outcomes for AIS patients. Additionally, we compute the inflection point using a recursive algorithm and then build a two-piece binary logistic regression model on both sides of the inflection point. A log-likelihood ratio test was used to determine the most appropriate model describing the association of TG/HDL-c ratio and unfavorable outcomes in patients with AIS. Results: The incidence rate of unfavorable outcomes was 28.2%, and the median TG/HDL-c ratio was 2.130. After adjusting covariates, the results of the binary logistic regression model suggested that the relationship between the TG/HDL-c ratio and the risk of unfavorable outcomes for AIS patients was not statistically significant. However, there was a nonlinear relationship between them, and the inflection point of the TG/HDL-c ratio was 3.515. On the left side of the inflection point, each 1-unit increase in the TG/HDL-c ratio was associated with a 22.6% lower risk of unfavorable outcomes (OR = 0.774, 95%CI:0.656 to 0.914, p = 0.002). On the right side of the inflection point, the effect size (OR) was 1.195 (95%CI:1.004 to1.423, p = 0.003). Conclusion: There is a nonlinear relationship and threshold effect between the TG/HDL-c ratio and 3-month unfavorable outcomes in AIS patients. When the TG/HDL-c ratio is lower than 3.515, the TG/HDL-c ratio is significantly negatively related to the risk of unfavorable outcomes. When the TG/HDL-c ratio is greater than 3.515, the TG/HDL-c ratio was positively associated with the risk of unfavorable outcomes in AIS patients. This provides a reference for optimizing lipidemia intervention and promoting clinical communication in patients with AIS.

Objective: The current body of evidence on the association between the ratio of triglycerides to high-density lipoprotein cholesterol (TG/HDL-c) and the reversal of prediabetes to normoglycemia remains limited. The aim of this study is to investigate the association between TG/HDL-c and the reversion to normoglycemia in patients with prediabetes. Methods: This retrospective cohort study included 15,107 individuals with prediabetes from 32 Chinese districts and 11 cities who completed health checks from 2010 to 2016. The Cox proportional-hazards regression model examined baseline TG/HDL-c and reversion to normoglycemia from prediabetes. Cox proportional hazards regression with cubic spline functions and smooth curve fitting determined the non-linear connection between TG/HDL-c and reversion to normoglycemia. We also ran sensitivity and subgroup analysis. By characterizing progression to diabetes as a competing risk for the reversal of prediabetes to normoglycemic event, a multivariate Cox proportional hazards regression model with competing risks was created. Results: Upon adjusting for covariates, the findings indicate a negative association between TG/HDL-c and the likelihood of returning to normoglycemia (HR = 0.869, 95%CI:0.842–0.897). Additionally, a non-linear relationship between TG/HDL-c and the probability of reversion to normoglycemia was observed, with an inflection point of 1.675. The HR on the left side of the inflection point was 0.748 (95%CI:0.699, 0.801). The robustness of our results was confirmed through competing risks multivariate Cox's regression and a series of sensitivity analyses. Conclusion: The present study reveals a negative and non-linear correlation between TG/HDL-c and the reversion to normoglycemia among Chinese individuals with prediabetes. The findings of this study are anticipated to serve as a valuable resource for clinicians in managing dyslipidemia in prediabetic patients. Interventions aimed at reducing the TG/HDL-c ratio through the reduction of TG or elevation of HDL-c levels may substantially enhance the likelihood of achieving normoglycemia in individuals with prediabetes.