Background: By identifying individuals at high risk for non-alcoholic fatty liver disease (NAFLD), interventional programs could be targeted more effectively. Some studies have demonstrated that triglyceride glucose-body mass index (TyG-BMI) showed an independent positive association with NAFLD. However, research on its diagnostic value in patients with suspected NAFLD is limited. In this study, we aimed to evaluate whether TyG-BMI was accurate in detecting NAFLD in the general Japanese population.Methods: A cross-sectional study of 14,280 individuals who underwent a comprehensive health examination was conducted. Standard protocols were followed to collect anthropometric measurements, lab data, and ultrasonography features. All participants were randomly stratified into the development group (n = 7118) and validation group (n = 7162). The TyG-BMI was calculated. Following this, the diagnostic value of the TyG-BMI was evaluated based on the area under the receiver-operating characteristic curve (AUROC). Two cutoff points were selected and used to rule out or rule in the NALFD, and the specificity, sensitivity, negative predictive value, and positive predictive value were explored, respectively. In order to verify the stability of the results, external verification was performed.Results: There were 1272 and 1243 NAFLD participants in the development and validation groups, respectively.The area under the ROC curve (AUC) of TyG-BMI was 0.888 (95% CI 0.876–0.896) and 0.884 (95% CI 0.875–0.894) for the training and validation group, respectively. Using the low TyG-BMI (182.2) cutoff, NAFLD could be excluded with high accuracy (negative predictive value: 96.9% in estimation and 96.9% in validation). The presence of NAFLD could effectively be determined by applying the high cutoff of TyG-BMI (224.0), as the positive predictive value of the estimation and validation groups is 70.7% and 70.1%, respectively. As a result of applying this model, 9996 (70%) of the 14,280 participants would not have undergone ultrasonography, with an accurate prediction of 9308 (93.1%). AUC was 0.874 for external validation using 183,730 Chinese non-obese participants. TyG-BMI was demonstrated to be an excellent diagnostic tool by both internal and external validation.Conclusions: In conclusion, the present study developed and validated a simple, non-invasive, and cost-effective tool to accurately separate participants with and without NAFLD in the Japanese population, rendering ultrasonography for identifying NAFLD unnecessary in a substantial proportion of people.

Previous evidence revealed that central obesity played a vital role in the development of diabetes mellitus (DM). However, because of imbalanced confounding variables, some studies have not wholly established the association between central obesity and diabetes. Propensity score matching (PSM) analysis can minimize the impact of potential confounding variables. Therefore, the aim of the present study was to explore the relationship between central obesity and diabetes in the Japanese population by using PSM analysis. This retrospective cohort study included 15,453 Japanese adults who were free of diabetes at baseline between 2004 and 2015, which provided all medical records for individuals participating in the physical exam. Central obesity at baseline was an independent variable, and incident diabetes during follow-up was an outcome variable. Using a 1:1 PSM analysis, the present retrospective cohort study included 1639 adults with and without central obesity. Additionally, we employed a doubly robust estimation method to identify the association between central obesity and diabetes. Subjects with central obesity were 92% more likely to develop DM (HR = 1.65, 95%CI 1.12, 2.41). After adjusting for covariates, subjects with central obesity had a 72% increased risk of developing DM compared with subjects with non-central obesity in the PSM cohort (HR = 1.72, 95% CI 1.16, 2.56). Central obesity individuals had a 91% higher risk of DM than non-central obesity individuals, after adjustment for propensity score (HR = 1.91, 95% CI 1.29, 2.81). In sensitivity analysis, the central obesity group had a 44% (HR = 1.44, 95% CI 1.09, 1.90) and 59% (HR = 1.59, 95% CI1.35, 1.88) higher risk of DM than the non-central obesity group in the original and weighted cohorts after adjusting for confounding variables, respectively. Central obesity was independently associated with an increased risk of developing diabetes. After adjustment for confounding covariates, central obesity participants had a 72% higher risk of development of diabetes than non-central obesity individuals in the PSM cohort.

Early identification is crucial to effectively intervene in individuals at high risk of developing pre-diabetes. This study aimed to create a personalized nomogram to determine the 5-year risk of pre-diabetes among Chinese adults. This retrospective cohort study included 184,188 participants without prediabetes at baseline. Training cohorts (92,177) and validation cohorts (92,011) were randomly assigned (92,011). We compared five prediction models on the training cohorts: full cox proportional hazards model, stepwise cox proportional hazards model, multivariable fractional polynomials (MFP), machine learning, and least absolute shrinkage and selection operator (LASSO) models. At the same time, we validated the above five models on the validation set. And we chose the LASSO model as the final risk prediction model for prediabetes. We presented the model with a nomogram. The model's performance was evaluated in terms of its discriminative ability, clinical utility, and calibration using the area under the receiver operating characteristic (ROC) curve, decision curve analysis, and calibration analysis on the training cohorts. Simultaneously, we also evaluated the above nomogram on the validation set. The 5-year incidence of prediabetes was 10.70% and 10.69% in the training and validation cohort, respectively. We developed a simple nomogram that predicted the risk of prediabetes by using the parameters of age, body mass index (BMI), fasting plasma glucose (FBG), triglycerides (TG), systolic blood pressure (SBP), and serum creatinine (Scr). The nomogram's area under the receiver operating characteristic curve (AUC) was 0.7341 (95% CI 0.7290–0.7392) for the training cohort and 0.7336 (95% CI 0.7285–0.7387) for the validation cohort, indicating good discriminative ability. The calibration curve showed a perfect fit between the predicted prediabetes risk and the observed prediabetes risk. An analysis of the decision curve presented the clinical application of the nomogram, with alternative threshold probability spectrums being presented as well. A personalized prediabetes prediction nomogram was developed and validated among Chinese adults, identifying high-risk individuals. Doctors and others can easily and efficiently use our prediabetes prediction model when assessing prediabetes risk.

Background: The connection between the triglyceride-glucose index (TyG index) and gestational diabetes mellitus (GDM) is currently debated. Our study aimed to investigate the connection between the TyG index and GDM within the Korean population.Methods: Using publically accessible data in Korea, we performed a secondary study on a sample of 589 pregnant women who were carrying a single fetus. The analysis employed a binary logistic regression model, some sensitivity analyses, and subgroup analysis to investigate the association between the TyG index and the occurrence of GDM. To assess the TyG index’s potential to predict GDM, a receiver operating characteristic (ROC) study was also carried out.Results: The mean age of the pregnant women was 32.065 ± 3.798 years old, while the mean TyG index was 8.352 ± 0.400. The prevalence rate of GDM was found to be 6.112%. Upon adjusting for potential confounding variables, a positive association was detected between the TyG index and incident GDM (OR = 12.923, 95%CI: 3.581–46.632, p = 0.00009). The validity of this connection was further confirmed by subgroup analysis and sensitivity analyses. With an area under the ROC curve of 0.807 (95%CI: 0.734–0.879), the TyG index showed strong predictive power for GDM. The TyG index’s ideal cutoff value for detecting GDM was found to be 8.632, with a sensitivity of 78.7% and a specificity of 72.2%.Conclusion: The findings of our study provide evidence that an increased TyG index is significantly associated with the occurrence of GDM. Utilizing the TyG index during the 10–14 week gestational period may be a valuable tool in identifying pregnant individuals at a heightened risk for developing GDM. Early detection enables timely and efficacious interventions, thereby enhancing the prognosis of affected individuals.

Anemia has been reported as a risk factor for chronic kidney disease (CKD) progression. However, there are still few studies examining the relationship between specific hemoglobin (Hb) levels and renal prognosis and renal function decline simultaneously. Meanwhile, the possible non-linear relationship between Hb and CKD progression also deserves further exploration. On that account, our primary goal is to explore the link of Hb on renal prognosis and renal function decline in patients with CKD. This study was a secondary analysis of a prospective cohort study, which consecutively and non-selectively collected 962 participants from the research of CKD-ROUTE in Japan from November 2010 to December 2011. We used the Cox proportional-hazards and linear regression models to evaluate the independent association between baseline Hb and renal prognosis (renal composite endpoint, initiation of dialysis during follow-up or 50% decline in eGFR from baseline) and renal function decline(annual eGFR decline), respectively. A multivariate Cox proportional hazards regression analysis with cubic spline functions model and smooth curve fitting (penalized spline method) were conducted to address Hb and CKD prognosis's non-linearity. At the same time, a generalized additive model (GAM) and smooth curve fitting (penalized spline method) was conducted to explore the exact shape of the curve between Hb and renal function decline. Additionally, we did a series of sensitivity analyses to ensure the robustness of the results. Moreover, we conducted subgroup analyses. The mean age of the included patients was 67.35 ± 13.56 years old, and 69.65% were male. The mean baseline Hb and estimated glomerular filtration rate (eGFR) was 12.06 ± 2.21 g/dL and 33.04 ± 18.01 ml/min per 1.73 m2. The annual decline in eGFR was 2.09 mL/min/1.73 m2/year. During a median follow-up time of 33.5 months, 252(26.2%) people experienced renal composite endpoint. After adjusting covariates, the results showed that Hb was negatively associated with renal composite endpoint (HR = 0.836, 95%CI: 0.770, 0.907) and renal function decline (β = -0.436, 95%CI: -0.778, -0.093). There was also a non-linear relationship between Hb and renal composite endpoint, and the inflection point of Hb was 8.6 g/dL. The effect sizes(HR) on the left and right sides of the inflection point were 1.257 (0.841, 1.878) and 0.789 (0.715, 0.870), respectively. And the sensitive analysis demonstrated the robustness of the results. Subgroup analysis showed that Hb was more strongly associated with the renal composite endpoint in non-hypertensive, SBP < 140 mmHg, urine protein-to-creatinine ratio (UPCR) < 0.5 g/gCr, and diuretic use patients. In contrast, the weaker association was probed in hypertensive and non-diuretic use patients and the patients with SBP ≥ 140 mmHg, and UPCR ≥ 0.5 g/gCr. This study demonstrates a negative and non-linear relationship between Hb and renal prognosis and renal function decline in Japanese CKD patients. Hb is strongly related to renal prognosis when Hb is above 8.6 g/dL.

Background and objective: High-density lipoprotein cholesterol (HDL-C) may be directly involved in glucose metabolism by enhancing insulin sensitivity and insulin secretion. This current study aimed to explore the association between HDL-C and the risk of diabetes mellitus (DM) in Japanese population. Methods: This retrospective cohort study was based on a publicly available DRYAD dataset. We enrolled 15,388 Japanese participants who received medical examinations from 2004 to 2015 at Murakami Memorial Hospital. Our study selected HDL-C at baseline and incident DM during follow-up as the target independent variable and the dependent variable, respectively. Cox proportional-hazards regression was used to investigate the association between HDL-C and DM, generalized additive models to identify non-linear relationships. Results: After adjusting for the demographic and clinical covariates, the result showed low HDL-C levels were associated with increased risk for diabetes (HR = 0.54, 95%CI (0.35, 0.82)). The results remained robust in a series of sensitive analysis. A non-linear relationship was detected between HDL-C and incident DM with an inflection point of HDL-C at 1.72 mmol/L (Log-likelihood ratio test P = 0.005). Subgroup analysis showed that a stronger association could be found in ex-smokers and current-smokers. The same trend was also seen in the community with hypertension (P for interaction = 0.010, HR = 1.324). Conclusion: This study demonstrates a negative and non-linear relationship between HDL-C and diabetes in the Japanese population. There is a threshold effect between HDL-C and diabetes. When HDL-C is lower than 1.72 mmol/L, the decreased HDL-C levels were associated with an increased risk for diabetes.