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.

Background: The atherogenic index of plasma (AIP) can reflect the burden of atherosclerosis. Hyperglycemia is one of the leading causes of atherosclerosis. However, the relationship between AIP and prediabetes is rarely studied. Therefore, we aimed to explore the relationship between AIP and prediabetes. Methods: This retrospective cohort study recruited 100,069 Chinese adults at the Rich Healthcare Group from 2010 to 2016. AIP was calculated according to Log10 (triglyceride/high-density lipoprotein cholesterol) formula. Cox regression method, sensitivity analyses and subgroup analyses were used to examine the relationship between AIP and prediabetes. Cox proportional hazards regression with cubic spline functions and smooth curve fitting was performed to explore the non-linearity between AIP and prediabetes. The two-piece Cox proportional hazards regression model was used to determine the inflection point of AIP on the risk of prediabetes. Results: After adjusting for confounding covariates, AIP was positively associated with prediabetes (HR: 1.41, 95%CI: 1.31–1.52, P < 0.0001). The two-piecewise Cox proportional hazards regression model discovered that the AIP’s inflection point was 0.03 (P for log-likelihood ratio test < 0.001). AIP was positively associated with the risk of prediabetes when AIP ≤ 0.03 (HR: 1.90, 95%CI: 1.66–2.16, P < 0.0001). In contrast, When AIP > 0.03, their association was not significant (HR: 1.04, 95%CI: 0.91–1.19, P = 0.5528). Conclusion: This study shows that AIP was positively and non-linearly associated with the risk of prediabetes after adjusting for other confounding factors. When AIP ≤ 0.03, AIP was positively associated with the risk of prediabetes.

Background: Although there is strong evidence linking triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio to insulin resistance and diabetes mellitus, its clinical importance in pregnant women has not been well determined. This study sought to determine the connection between the TG/HDL-C ratio in the first trimester and the eventual onset of gestational diabetes mellitus (GDM).Methods: We performed a secondary analysis of open-access data from a prospective cohort study. This present study included 590 singleton pregnant women at 10-14 weeks who visited the outpatient clinics for prenatal checks and were recorded at Incheon Seoul Women’s Hospital and Seoul Metropolitan Government Seoul National University Boramae Medical Center in Korea. A binary logistic regression model, a series of sensitivity analyses, and subgroup analysis were used to examine the relationship between TG/HDL-C ratio and incident GDM. A receiver operating characteristic (ROC) analysis was also conducted to assess the ability of the TG/HDL-C ratio to predict GDM.Results: The mean age of the included individuals was 32.06 ± 3.80 years old. The mean TG/HDL-C ratio was 1.96 ± 1.09. The incidence rate of GDM was 6.27%. After adjustment for potentially confounding variables, TG/HDL-C ratio was positively associated with incident GDM (OR=1.77, 95%CI: 1.32-2.38, P=0.0001). Sensitivity analyses and subgroup analysis demonstrated the validity of the relationship between the TG/HDL-C ratio and GDM. The TG/HDL-C ratio was a good predictor of GDM, with an area under the ROC curve of 0.7863 (95% CI: 0.7090-0.8637). The optimal TG/HDL-C ratio cut-off value for detecting GDM was 2.2684, with a sensitivity of 72.97% and specificity of 75.05%.Conclusion: Our results demonstrate that the elevated TG/HDL-C ratio is related to incident GDM. The TG/HDL-C ratio at 10-14 weeks could help identify pregnant women at risk for GDM and may make it possible for them to receive early and effective treatment to improve their prognosis.

Objective: Body mass index (BMI) and stroke risk have been linked, but these findings are still debated. This study investigated the relationship between BMI and stroke risk in a middle-aged and elderly Chinese population.Methods: This study used four waves of CHARLS data (2011, 2013, 2015, and 2018), including 12,161 participants. The CHARLS sample was obtained by multi-stage probability sampling and all participants were assessed by one-to-one interviews using a standardized questionnaire. We used a Cox proportional-hazards regression model to examine the relationship between BMI and stroke risk. We used Cox proportional hazards regression with cubic spline functions and smooth curve fitting to identify the non-linear relationship between them. A series of sensitivity analyses were also conducted.Results: The multivariate Cox proportional hazards regression model identified a positive association between BMI and stroke risk (HR=1.025, 95% CI: 1.010-1.040). We also found a non-linear relationship between BMI and stroke incidence, with an inflection point at 26.63 kg/m² for BMI. Each 1 kg/m² increase in BMI to the left of the inflection point was related to a 4.4% increase in stroke risk (HR=1.044, 95% CI: 1.019-1.069). We stratified individuals by gender to further investigate their association and found a particular non-linear relationship and saturation effect between BMI and stroke risk in men, with the inflection point at 25.94 kg/m². Each 1 kg/m² increase in BMI to the left of the inflection point was related to a 7.6% increase in stroke risk (HR=1.076, 95% CI 1.034-1.119). The association was linear in women, with each 1 kg/m² increase in BMI associated with a 2.1% increase in stroke risk (HR=1.021, 95% CI 1.002, 1.040).Conclusion: In men, there was a specific non-linear association and saturation effect of BMI with stroke (inflection point of 25.94 kg/m²), while in women, there was none. When males had a BMI below 25.94 kg/m², the risk of stroke was significantly and positively associated with BMI. By controlling BMI below 25.94 kg/m² in men, a further decrease in BMI may promote a significant reduction in stroke risk.

Objective: Evidence regarding the association between the total cholesterol, high-density lipoprotein, and glucose (CHG) index and diabetic retinopathy (DR) remains limited. This study aimed to explore the relationship between CHG and the prevalence of DR and evaluate its discriminative ability for DR. Methods: This cross-sectional study analyzed data from 1,909 individuals with diabetes mellitus (DM), aged 45–90 years, whose information was collected between August and December 2011. To determine the association between CHG and DR, binary logistic regression models were employed. The discriminative ability of CHG for DR was assessed using receiver operating characteristic (ROC) analysis. Additionally, a series of sensitivity analyses and subgroup analyses were conducted. Results: After multivariable adjustment, binary logistic regression analysis showed that each 0.1−unit increase in CHG was associated with a 14.2% higher prevalence of DR (OR = 1.142; 95% CI: 1.081–1.206). Additionally, CHG demonstrated the highest area under the curve (AUC) for discriminating DR (0.6673, 95% CI: 0.6287–0.7059), outperforming triglyceride-glucose body mass index (TyG-BMI, 0.4958, 95% CI: 0.4548–0.5368), triglyceride to high-density lipoprotein cholesterol ratio (THR, 0.5118, 95% CI: 0.4704–0.5532), and triglyceride-glucose index (TyG, 0.5720, 95% CI: 0.5314–0.6125). Sensitivity analysis and subgroup analyses further confirmed the reliability of these results. Conclusion: This study demonstrates that elevated CHG is independently and positively associated with DR in adults with DM. Moreover, CHG shows a certain discriminative ability for DR and may have potential utility in DR assessment.

Background: Relative fat mass (RFM) represents a newly developed sex-specific anthropometric formula to estimate total body fat percentage. Nonetheless, research examining the correlation between RFM and the risk of diabetes remains scarce. This research assessed the link between RFM and DM risk within the Japanese demographic. Methods: From 2004 to 2015, 15,462 Japanese individuals without diabetes underwent physical evaluations at Murakami Memorial Hospital. The relationship between RFM and the onset of diabetes was analyzed separately using Cox proportional-hazards regression models. This study employed Cox proportional hazards regression incorporating cubic spline functions and smooth curve fitting to detect non-linear associations between RFM and new cases of diabetes, categorized by sex. Sensitivity analyses were performed to confirm the robustness of the link between RFM and incident diabetes. Results: After controlling for confounding factors, a significant positive correlation between RFM and diabetes risk was found in women (HR: 1.13, 95%CI: 1.04–1.24, P = 0.0061), while the association in men was not statistically significant (HR: 1.05, 95%CI: 0.98–1.13, P = 0.1511). Additionally, a non-linear relationship between RFM and the incidence of diabetes was detected in both genders. The RFM threshold was identified at 39.23 for women and 23.08 for men. For women, HR was 1.11 (95%CI: 1.01–1.21) below the threshold and 1.39 (95%CI: 1.17–1.65) above it. In men, an RFM above 23.08 was positively related to diabetes risk (HR: 1.16, 95%CI: 1.06–1.28, P = 0.0012), whereas an RFM below this point did not show a significant association (HR: 0.98, 95%CI: 0.91–1.06, P = 0.5899). Conclusion: Our findings suggest a non-linear relationship and threshold effect between RFM and the risk of diabetes. These findings imply that maintaining RFM at lower levels may be beneficial in mitigating the onset of DM.