The nonHDLc/HDLc ratio (in which nonHDLc is defined as total cholesterol minus HDLc) is positively associated with multiple dyslipidemia-related disorders. This study aimed to determine whether the nonHDLc/HDLc ratio is an independent predictor of new-onset NAFLD (non-alcoholic fatty liver disease) in Chinese population. A perspective cohort study consisting of 3374 Chinese adults without liver diseases or metabolic disturbances was performed. Anthropometric parameters and data of metabolic and plasma lipid profile were collected. Univariate and multivariate Cox proportional analyses were carried out to evaluate the association of the nonHDLc/HDLc ratio with incident NAFLD. ROC curve analysis was preformed to compare the predictive value between the nonHDLc/HDLc and the nonHDLc for NAFLD. Two thousand seven hundred seventeen participants were included in the final analysis. During a median follow-up period of 1.6 years, 264 participants (9.71%) developed NAFLD. After adjustment for potential confounders, a high nonHDLc/HDLc ratio (highest tertile) was associated with elevated risk of NAFLD (HR = 2.66; 95% CI, 1.13–6.24; P = 0.025 in female and HR = 2.11; 95% CI, 1.15–3.90; P = 0.016 in male). A nonlinear relationship was observed when the nonHDLc/HDLc ratio was ≤3.5. AUC values for nonHDLc/HDLc ratios (0.717 in female and 0.682 in male) were significantly higher than nonHDLc (0.675 in female and 0.653 in male) (P = 0.049 in female and P = 0.037 in male). In addition, the optimal cut-off value of nonHDLc/HDLc ratio for detection of NAFLD was 2.4 in female and 2.3 in male. The nonHDLc/HDLc ratio is an independent predictor of NAFLD and a stronger predictor than nonHDLc in Chinese population, which might be expected to better guide early identification of individuals at risk of NAFLD.

Aims To develop a precise personalized type 2 diabetes mellitus (T2DM) prediction model by cost-effective and readily available parameters in a Central China population. Methods A 3-year cohort study was performed on 5557 nondiabetic individuals who underwent annual physical examination as the training cohort, and a subsequent validation cohort of 1870 individuals was conducted using the same procedures. Multiple logistic regression analysis was performed, and a simple nomogram was constructed via the stepwise method. Receiver operating characteristic (ROC) curve and decision curve analyses were performed by 500 bootstrap resamplings to assess the determination and clinical value of the nomogram, respectively. We also estimated the optimal cutoff values of each risk factor for T2DM prediction. Results The 3-year cumulative incidence of T2DM was 10.71%. We developed simple nomograms that predict the risk of T2DM for females and males by using the parameters of age, BMI, fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDLc), high-density lipoprotein cholesterol (HDLc), and triglycerides (TG). In the training cohort, the area under the ROC curve (AUC) showed statistical accuracy (AUC = 0.863 for female, AUC = 0.751 for male), and similar results were shown in the subsequent validation cohort (AUC = 0.847 for female, AUC = 0.755 for male). Decision curve analysis demonstrated the clinical value of this nomogram. To optimally predict the risk of T2DM, the cutoff values of age, BMI, FBG, systolic blood pressure, diastolic blood pressure, total cholesterol, LDLc, HDLc, and TG were 47.5 and 46.5 years, 22.9 and 23.7 kg/m², 5.1 and 5.4 mmol/L, 118 and 123 mmHg, 71 and 85 mmHg, 5.06 and 4.94 mmol/L, 2.63 and 2.54 mmol/L, 1.53 and 1.34 mmol/L, and 1.07 and 1.65 mmol/L for females and males, respectively. Conclusion Our nomogram can be used as a simple, plausible, affordable, and widely implementable tool to predict a personalized risk of T2DM for Central Chinese residents. The successful identification of at-risk individuals and intervention at an early stage can provide advanced strategies from a predictive, preventive, and personalized medicine perspective.

Background: The associations between maternal exposure to ambient PM_2.5 during pregnancy and the risk of premature rupture of membranes (PROM) and preterm premature rupture of membranes (PPROM) are controversial. And no relevant study has been conducted in Asia. This study aimed to determine the association between maternal exposure to ambient PM_2.5 during pregnancy and the risk of (P)PROM. Methods: A cohort study including all singleton births in a hospital located in Central China from January 2015 through December 2017 was conducted. Multivariable logistic regression models, stratified analysis, generalized additive model, and two-piece-wise linear regression were conducted to evaluate how exposure to ambient PM_2.5 during pregnancy is associated with the risks of PROM and PPROM. Results: A total of 4364 participants were included in the final analysis, where 11.71 and 2.34% of births were complicated by PROM and PPROM, respectively. The level of PM_2.5 exhibited a degree of seasonal variation, and its median concentrations were 63.7, 59.3, 55.8, and 61.8 μg/m³ for the first trimester, second trimester, third trimester, and the whole duration of pregnancy, respectively. After adjustment for potential confounders, PROM was positively associated with PM_2.5 exposure (per 10 μg/m³) [Odds Ratio (OR) = 1.14, 95% Confidence Interval (CI), 1.02–1.26 for the first trimester; OR = 1.09, 95% CI, 1.00–1.18 for the second trimester; OR = 1.13, 95% CI, 1.03–1.24 for the third trimester; OR = 1.35, 95% CI, 1.12–1.63 for the whole pregnancy]. PPROM had positive relationship with PM_2.5 exposure (per 10 μg/m³) (OR = 1.17, 95% CI, 0.94–1.45 for first trimester; OR = 1.11, 95% CI, 0.92–1.33 for second trimester; OR = 1.19, 95% CI, 0.99–1.44 for third trimester; OR = 1.53, 95% CI, 1.03–2.27 for the whole pregnancy) Positive trends between the acute exposure window (mean concentration of PM_2.5 in the last week and day of pregnancy) and risks of PROM and PPROM were also observed. Conclusions: Exposure to ambient PM_2.5 during pregnancy was associated with the risk of PROM and PPROM.