Numerous studies have investigated the associations between serum vitamin D or testosterone and diabetes; however, inconsistencies are observed. Whether there is an interaction between vitamin D and testosterone and whether the lipid profile (total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C)) mediates the association between vitamin D and diabetes is unclear. To investigate the effect of vitamin D and testosterone on impaired fasting glucose (IFG) or type 2 diabetes mellitus (T2DM), 2659 participants from the Henan Rural Cohort were included in the case-control study. Generalized linear models were utilized to estimate associations of vitamin D with IFG or T2DM and interactive effects of vitamin D and testosterone on IFG or T2DM. Principal component analysis (PCA) and mediation analysis were used to estimate whether the lipid profile mediated the association of vitamin D with IFG or T2DM. Serum 25(OH)D₃, 25(OH)D₂, and total 25(OH)D levels were negatively correlated with IFG (odds ratios (ORs) (95% confidence intervals (CIs)): 0.99 (0.97, 1.00), 0.85 (0.82, 0.88), and 0.97 (0.96, 0.98), respectively). Similarity results for associations between serum 25(OH)D₂ and total 25(OH)D with T2DM (ORs (95%CIs): 0.84 (0.81, 0.88) and 0.97 (0.96, 0.99)) were observed, whereas serum 25(OH)D₃ was negatively correlated to T2DM only in the quartile 2 (Q2) and Q3 groups (both p < 0.05). The lipid profile, mainly TC and TG, partly mediated the relationship between 25(OH)D₂ or total 25(OH)D and IFG or T2DM and the proportion explained was from 2.74 to 17.46%. Furthermore, interactive effects of serum 25(OH)D₂, total 25(OH)D, and testosterone on T2DM were observed in females (both p for interactive <0.05), implying that the positive association between serum testosterone and T2DM was vanished when 25(OH)D₂ was higher than 10.04 ng/mL or total 25(OH)D was higher than 40.04 ng/mL. Therefore, ensuring adequate vitamin D levels could reduce the prevalence of IFG and T2DM, especially in females with high levels of testosterone.

Background: Previous studies have constructed prediction models for type 2 diabetes mellitus (T2DM), but machine learning was rarely used and few focused on genetic prediction. This study aimed to establish an effective T2DM prediction tool and to further explore the potential of genetic risk scores (GRS) via various classifiers among rural adults.Methods: In this prospective study, the GRS for a total of 5,712 participants from the Henan Rural Cohort Study was calculated. Cox proportional hazards (CPH) regression was used to analyze the associations between GRS and T2DM. CPH, artificial neural network (ANN), random forest (RF), and gradient boosting machine (GBM) were used to establish prediction models, respectively. The area under the receiver operating characteristic curve (AUC) and net reclassification index (NRI) were used to assess the discrimination ability of the models. The decision curve was plotted to determine the clinical-utility for prediction models.Results: Compared with the individuals in the lowest quintile of the GRS, the HR (95% CI) was 2.06 (1.40 to 3.03) for those with the highest quintile of GRS (P_trend < 0.05). Based on conventional predictors, the AUCs of the prediction model were 0.815, 0.816, 0.843, and 0.851 via CPH, ANN, RF, and GBM, respectively. Changes with the integration of GRS for CPH, ANN, RF, and GBM were 0.001, 0.002, 0.018, and 0.033, respectively. The reclassifications were significantly improved for all classifiers when adding GRS (NRI: 41.2% for CPH; 41.0% for ANN; 46.4% for ANN; 45.1% for GBM). Decision curve analysis indicated the clinical benefits of model combined GRS.Conclusion: The prediction model combined with GRS may provide incremental predictions of performance beyond conventional factors for T2DM, which demonstrated the potential clinical use of genetic markers to screen vulnerable populations.Clinical Trial Registration: The Henan Rural Cohort Study is registered in the Chinese Clinical Trial Register (Registration number: ChiCTR-OOC-15006699). http://www.chictr.org.cn/showproj.aspx?proj=11375.

Both ambient ozone exposure and residential greenness are linked to the aging process. However, their interactive effect on molecular biomarkers of aging (telomere length (TL) and mitochondrial DNA copy number (mtDNA-CN)) remains unclear. This study was conducted among 6418 rural Chinese adults. The concentration of ambient ozone was assessed using a random forest model. Residential greenness was represented by the normalized difference vegetation index (NDVI). Molecular biomarkers of aging (relative TL and relative mtDNA-CN) were determined by quantitative real-time polymerase chain reaction. Generalized linear regression models were applied to investigate the independent and combined effects of ambient ozone and residential greenness on relative TL and relative mtDNA-CN. The estimated percent changes and 95 % confidence intervals (CIs) of relative TL in response to per-unit increase in ambient ozone were -22.43 % (-23.74 %, -21.18 %), -14.19 % (-15.63 %, -12.72 %) and -4.50 % (-6.57 %, -2.27 %) for participants with low (NDVI ≤ 0.53), moderate (0.54-0.55) and high (≥0.56) residential greenness exposure, respectively, while the corresponding figures of relative mtDNA-CN were -12.63 % (-13.84 %, -11.31 %), -9.52 % (-10.60 %, -8.33 %) and 2.12 % (0.20 %, 4.19 %). Furthermore, negative interactive effects between ambient ozone and residential greenness exposure on molecular biomarkers of aging were observed (P_{for interaction} < 0.001 for relative TL, and 0.098 for relative mtDNA-CN). Long-term exposure to high concentrations of ambient ozone and low residential greenness was associated with decreased mtDNA-CN and shortened TL. The adverse effect of ambient ozone exposure on molecular biomarkers of aging may be attenuated by increased residential greenness.