Search

There is no diabetes risk model that includes dietary predictors in Asia. We sought to develop a diet-containing noninvasive diabetes risk model in Northern China and to evaluate whether dietary predictors can improve model performance and predictive ability. Cross-sectional data for 9,734 adults aged 20–74 years old were used as the derivation data, and results obtained for a cohort of 4,515 adults with 4.2 years of follow-up were used as the validation data. We used a logistic regression model to develop a diet-containing noninvasive risk model. Akaike’s information criterion (AIC), area under curve (AUC), integrated discrimination improvements (IDI), net classification improvement (NRI) and calibration statistics were calculated to explicitly assess the effect of dietary predictors on a diabetes risk model. A diet-containing type 2 diabetes risk model was developed. The significant dietary predictors including the consumption of staple foods, livestock, eggs, potato, dairy products, fresh fruit and vegetables were included in the risk model. Dietary predictors improved the noninvasive diabetes risk model with a significant increase in the AUC (delta AUC = 0.03, P<0.001), an increase in relative IDI (24.6%, P-value for IDI <0.001), an increase in NRI (category-free NRI = 0.155, P<0.001), an increase in sensitivity of the model with 7.3% and a decrease in AIC (delta AIC = 199.5). The results of the validation data were similar to the derivation data. The calibration of the diet-containing diabetes risk model was better than that of the risk model without dietary predictors in the validation data. Dietary information improves model performance and predictive ability of noninvasive type 2 diabetes risk model based on classic risk factors. Dietary information may be useful for developing a noninvasive diabetes risk model. [ABSTRACT FROM AUTHOR]

Objectives: To assess the relationships between the risk factors and the incidence of nonfatal occupational injury of coal mine workers of Shanxi Province. Methods: A cross-sectional study was conducted from July 2013 to December 2013, and 4319 workers were recruited from more than 200,000 coal mine employees who are exposed to continuous potential risk of occupational injuries by using a two-stage stratified cluster sampling method. Trained interviewers having necessary medical knowledge conducted face-to-face interviews with the participants. Univariate and multivariable logistic regression models were used to estimate the odds ratio (OR) and the 95% confidence interval (CI). Results: A total number of 3618 effective respondents were got from 4319 participants (83.77%) and the mean age of the participants was 41.5 years with the standard deviation of 8.65. Significant crude odds ratios were observed for all factors considered except for marital status, education, work duration, BMI, EPQ-RSC(P) scale and EPQ-RSC(L) scale. Results from multivariable logistic regression model showed significant adjusted odds ratios for risk factors including gender (female vs male 0.275, 0.094–0.800), age (≥55 vs ≤25yr 0.169, 0.032–0.900), work type (light physical labor vs heavy physical labor 0.504, 0.328–0.774), workplace (underground auxiliary vs underground front-line 0.595, 0.385–0.919), length of shiftwork experience (0~5yr vs no shift 2.075, 1.287–3.344 and ≥15yr vs no shift 2.076, 1.230–3.504) and EPQ-RSC(E) score (extraversion vs introversion 0.538, 0.334–0.867). Conclusions: Several risk factors of nonfatal occupational injury were identified including male, age, heavy physical labor, underground front-line, length of shiftwork experience and introversion. The coal mining enterprises should pay attention to controlling the hazards associated with frontline physical work. Workers’ behaviors, life styles and personality traits should also be considered, so that the enterprises could set achievable targets for workers and lessen the exposed period to the risky underground workstation. [ABSTRACT FROM AUTHOR]

Background: A key limitation of conventional ultrasound (US) includes poor differentiation of benign from malignant thyroid nodules. Contrast-enhanced US (CEUS) and acoustic radiation force impulse (ARFI) could provide better characterization of focal thyroid nodules; however, no studies have compared their efficacies. Objective: To evaluate the diagnostic efficacy of conventional US,CEUS, ARFI, and their combined use in differentiating focal solid thyroid nodules. Methods: One-hundred-forty-six Chinese patients with 175 thyroid nodules (119 benign and 56 malignant) were prospectively enrolled. Each patients underwent conventional US, CEUS and ARFI, respectively. The diagnostic performance of the conventional US, CEUS, ARFI, combined use of either CEUS or ARFI and combined use of the three modalities were assessed and compared using Pathological diagnosis (histological/cytological) as the reference method. Results: There were no significant differences between individual groups (CEUS vs US, P = 0.279, ARFI vs US, P = 0.372, CEUS vs ARFI, P = 0.849), combined use of US and CEUS or combined use of US and ARFI yielded significant difference compared to US. (combination of US & CEUS vs US, P = 0.021; combination of US & ARFI vs US, P = 0.036). The combination of three modalities significantly improved the diagnostic accuracy compared with either combination of conventional US and CEUS or combination of conventional US and ARFI (P = 0.045 and P  = 0.027, respectively). Conclusions: CEUS and ARFI can be used as an additional tool in the diagnostic work up of thyroid nodules. The combination of CEUS with ARFI can significantly improve the diagnostic accuracy. [ABSTRACT FROM AUTHOR]