OBJECTIVETo explore whether the dental restoration of nickel-chromium (Ni-Cr) based alloys will lead to extra excretions of urinary Ni and Cr.

METHODSUrinary Ni and Cr were repeatedly measured in 33 patients before and 2 months after the dental restoration of Ni-Cr alloys. The associations between alloy restoration and urinary Ni or Cr were analyzed by paired t test and general linear model of repeated measures.

RESULTSA slightly higher urinary Ni was found in patients after 2 month of the alloy restoration, but the difference was not statistically significant (before: 46.4 microg x mol(-1) crea; after: 67.6 microg x mol(-1) crea; P=0.063). This difference was only in female subjects (before: 44.8 microg x mol(-1) crea; after: 73.7 microg x mol(-1) crea; P=0.068). A significant higher urinary Cr was found in patients after 2 month of the alloy restoration (before: 57.0 microg x mol(-1) crea; after: 99.4 microg x mol(-1) crea; P=0.024). This significant difference was only in female subjects (before: 59.8 microg x mol(-1) crea; after: 124.4 microg x mol(-1) crea; P=0.023). General linear models of repeated measurements showed that urinary excretions of Ni and Cr were associated with the number of restoration and the area of metal basis uncovered with porcelain.

CONCLUSIONDental restoration of Ni-Cr alloy might lead to the enhanced excretions of urinary Ni and Cr.

Chromium ; Chromium Alloys ; Dental Porcelain ; Female ; Follow-Up Studies ; Humans ; Male ; Nickel ; Prospective Studies

Background: The influencing factors of diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus (T2DM) were explored to develop and validate a DKD diagnostic tool based on nomogram approach for patients with T2DM. Methods: A total of 2,163 in-hospital patients with diabetes diagnosed from March 2015 to March 2017 were enrolled. Specified logistic regression models were used to screen the factors and establish four different diagnostic tools based on nomogram according to the final included variables. Discrimination and calibration were used to assess the performance of screening tools. Results: Among the 2,163 participants with diabetes (1,227 men and 949 women), 313 patients (194 men and 120 women) were diagnosed with DKD. Four different screening equations (full model, laboratory-based model 1 [LBM1], laboratory-based model 2 [LBM2], and simplified model) showed good discriminations and calibrations. The C-indexes were 0.8450 (95% confidence interval [CI], 0.8202 to 0.8690) for full model, 0.8149 (95% CI, 0.7892 to 0.8405) for LBM1, 0.8171 (95% CI, 0.7912 to 0.8430) for LBM2, and 0.8083 (95% CI, 0.7824 to 0.8342) for simplified model. According to Hosmer-Lemeshow goodness-of-fit test, good agreement between the predicted and observed DKD events in patients with diabetes was observed for full model (χ2=3.2756, P=0.9159), LBM1 (χ2=7.749, P=0.4584), LBM2 (χ2=10.023, P=0.2634), and simplified model (χ2=12.294, P=0.1387). Conclusion LBM1, LBM2, and simplified model exhibited excellent predictive performance and availability and could be recommended for screening DKD cases among Chinese patients with diabetes.

Background: The influencing factors of diabetic kidney disease (DKD) in Chinese patients with type 2 diabetes mellitus (T2DM) were explored to develop and validate a DKD diagnostic tool based on nomogram approach for patients with T2DM. Methods: A total of 2,163 in-hospital patients with diabetes diagnosed from March 2015 to March 2017 were enrolled. Specified logistic regression models were used to screen the factors and establish four different diagnostic tools based on nomogram according to the final included variables. Discrimination and calibration were used to assess the performance of screening tools. Results: Among the 2,163 participants with diabetes (1,227 men and 949 women), 313 patients (194 men and 120 women) were diagnosed with DKD. Four different screening equations (full model, laboratory-based model 1 [LBM1], laboratory-based model 2 [LBM2], and simplified model) showed good discriminations and calibrations. The C-indexes were 0.8450 (95% confidence interval [CI], 0.8202 to 0.8690) for full model, 0.8149 (95% CI, 0.7892 to 0.8405) for LBM1, 0.8171 (95% CI, 0.7912 to 0.8430) for LBM2, and 0.8083 (95% CI, 0.7824 to 0.8342) for simplified model. According to Hosmer-Lemeshow goodness-of-fit test, good agreement between the predicted and observed DKD events in patients with diabetes was observed for full model (χ2=3.2756, P=0.9159), LBM1 (χ2=7.749, P=0.4584), LBM2 (χ2=10.023, P=0.2634), and simplified model (χ2=12.294, P=0.1387). Conclusion LBM1, LBM2, and simplified model exhibited excellent predictive performance and availability and could be recommended for screening DKD cases among Chinese patients with diabetes.

ObjectiveTo conduct comprehensive assessment of internal and external cadmium exposure and health risks for Shanghai residents. MethodsCadmium levels in food samples were calculated by employing two dietary exposure assessment methods， total diet study （TDS） and food frequency questionnaire （FFQ）， to estimate the daily dietary cadmium exposure of Shanghai residents. The provisional tolerable monthly intake （PTMI） of cadmium set by joint food and agriculture organization/WHO expert committee on food additives （JECFA） was applied to evaluate the health risk. Differences in dietary and urinary cadmium were compared by rank-sum test among different regions， age， gender， smoking status， and BMI groups， and the association between internal and external cadmium exposure was investigated by correlation analysis. ResultsThe mean value of urinary cadmium for 1 300 respondents was 0.542 μg·L^-1. Urinary cadmium was higher in the population in central urban and urban-rural fringe areas than in the suburban area， higher in the older age group than in the younger age group， and higher in the smoking group than in the non-smoking group （all P<0.01）. The two assessment methods showed that the mean values of daily dietary cadmium exposure for Shanghai residents were 0.306 and 0.090 μg·kg^-1， with 3.69% and 0.85% of Shanghai residents exceeding the PTMI， respectively. Correlation analyses showed that dietary exposure to cadmium based on the FFQ method was positively correlated with the urinary cadmium level when smoking status， age， gender， and BMI were adjusted. ConclusionDietary exposure to cadmium of Shanghai residents is mainly derived from vegetables， aquatic products， cereals and potatoes， and is overall at a low-risk level. Dietary exposure assessment based on FFQ and risk monitoring data can effectively estimate long-term cadmium exposure.