Objective: To analyze the relationship between serum vitamin D level and the risk of type 2 diabetes in Harbin residents. Methods: On April 2010, 24 communities in 7 districts of Harbin were selected as research sites using multi-stage stratified cluster random sampling method. A total of 9 734 residents aged 20-74 years was investigated using general questionnaire survey, dietary survey and biochemical indicators test and followed up from January 2015 to July 2016. A total of 4 721 subjects with serum vitamin D were included in the study. According to the quartile of baseline serum vitamin D, the subjects were divided into four groups, from Q1 to Q4 group. The multivariate logistic regression model was used to analyze the relationship between serum vitamin D and the risk of type 2 diabetes. A mediation analysis model was used to analyze the mediating role of insulin resistance in this risk relationship. Results: At the time of follow-up, 432 patients with type 2 diabetes were screened. The median (P₂₅, P₇₅) age of the diabetic group and the non-diabetic group were 54 (49, 61) and 51 (43, 57) years, respectively, and males accounted for 40.5% (175 cases) and 35.5% (1 513 cases), respectively. The median (P₂₅, P₇₅) serum vitamin D was 16.0 (13.5, 18.5) and 17.4 (14.3, 20.5) ng/ml, respectively. After relevant confounders and insulin resistance index (HOMA-IR) were adjusted, compared to the serum vitamin D level Q₁ group, the risk of diabetes was reduced by 40% in the Q₃ group with RR (95%CI) about 0.60 (0.44-0.82), while the risk of diabetes was reduced by 59% in Q₄ group with RR (95%CI) about 0.41 (0.29-0.57). Through mediation analysis, the Gutt index mediating effect representing peripheral insulin resistance was 53.8%, and the mediating effect of HOMA-IR representing hepatic insulin resistance was 6.6%. Conclusion The risk of type 2 diabetes is low in Harbin residents with higher serum vitamin D level. Insulin resistance has a mediating effect on the relationship of vitamin D and the risk of type 2 diabetes.

Objective: To investigate the relationship between the magnesium intake and patterns of diary and the risk of type 2 diabetes in Harbin residents. Methods: On April 2010, 24 communities in 7 districts of Harbin were selected as research sites using multi-stage stratified random cluster sampling method. A total of 9 734 residents aged 20-74 years was investigated using general questionnaire survey, dietary survey and biochemical indicators test. A total of 9 376 subjects were included in the study. Factor analysis was used to analyze dietary patterns. According to the quartile of dietary magnesium intake, the subjects were divided into four groups, from Q₁ to Q₄ group. The multivariate logistic regression model was used to analyze the relationship between dietary magnesium intake and the risk of type 2 diabetes within different dietary patterns. Results: A total of 998 subjects with type 2 diabetes were screened. The median age of the diabetic group and the non-diabetic group were 54.8 and 50.8, respectively, and the males accounted for 43.4% (2 896 cases) and 34.6% (433 cases), respectively. The magnesium intake median (P₂₅, P₇₅) of two groups was 336.36 (257.31, 440.65) and 339.50 (264.51, 443.78) mg/d. Four dietary patterns were identified as western dietary mode, savvy dietary mode, traditional dietary mode, and staple food mode. In the western dietary model, the Q₄ group had a higher risk of type 2 diabetes than Q₁ group, with an OR (95%CI) value of 1.56 (1.06 to 2.32). However, in the savvy diet mode, compared with the Q₁ group, the risk of diabetes in the Q₄ group was lower, and the OR (95%CI) value was 0.61 (0.37 to 0.96). There was no statistically significant association between dietary magnesium intake and the risk of type 2 diabetes without considering dietary patterns (P>0.05). Conclusion Dietary magnesium intake has a different relationship with the risk of type 2 diabetes within different dietary patterns.

Background Aluminum activates signal transducer and activator of transcription 3 (STAT3), causing microglial nucleotide-binding and oligomerization domain-like receptors protein 3 (NLRP3) inflammasome activation and inflammatory responses and producing neurotoxicity. Objective To explore the role of STAT3 regulated NLRP3 inflammasomes in the inflammatory response of mouse microglia cell line (BV2) cells induced by maltol aluminum [Al(mal)₃]. Methods BV2 cells were assigned to five groups: one control group, three Al(mal)₃ exposure groups (low, medium, and high doses at 40, 80, and 160 μmol·L⁻¹ Al(mal)₃ respectively), and one C188-9 (STAT3 antagonist) intervention group [10 μmol·L⁻¹ C188-9 +160 μmol·L⁻¹ Al(mal)₃]. Cell viability was detected by CCK8. The expression of M1/M2 type markers, i.e. CD68/CD206, STAT3, p-STAT3, NLRP3, cleaved-casepase-1, and apoptosis-associated speck-like protein (ASC) in BV2 cells were detected by Western blotting, and proinflammatory cytokines interleukin (IL)-1β and IL-18, and anti-inflammatory cytokine IL-10 were determined by ELISA. Results The results of cell viability assay showed that cell viability gradually decreased with the increase of Al(mal)₃ dose. Compared with the control group, the cell viability of the Al(mal)₃ high-dose group was decreased by 18% (P<0.05); compared with the Al(mal)₃ high-dose group, the cell viability of the C188-9 intervention group was significantly elevated by 14% (P<0.05). Compared with the control group, the expression levels of CD68 in the Al(mal)₃ low-, medium-, and high-dose groups were elevated by 19%, 20%, and 21%, respectively (P<0.05); the expression level of CD206 in the Al(mal)₃ high-dose group was decreased by 25% (P<0.05). Compared with the Al(mal)₃ high-dose group, the expression level of CD68 in the C188-9 intervention group was reduced by 9% (P<0.05), whereas the expression level of CD206 was elevated by 22% (P<0.05). Compared with the control group, the p-STAT3 protein expression and the p-STAT3/STAT3 ratio in the Al(mal)₃ high-dose group increased by 129% and 127%, respectively (P<0.05). Compared with the Al(mal)₃ high-dose group, the p-STAT3 protein expression and the p-STAT3/STAT3 ratio in the C188-9 intervention group were decreased by 55% and 54%, respectively (P>0.05). Compared with the control group, the expression level of NLRP3 protein increased by 75% in the Al(mal)₃ high-dose group (P<0.05), the expression levels of cleaved-casepase-1 protein increased by 28% and 35% in the Al(mal)₃ medium- and high-dose groups (P<0.05), and the expression levels of ASC increased by 22%, 25%, and 53% in the Al(mal)₃ low-, medium- and high-dose groups (P<0.05), respectively. Compared with the Al(mal)₃ high-dose group, the expression levels of NLRP3, cleaved-casepase-1, and ASC proteins in the C188-9 intervention group decreased by 30%, 19%, and 32%, respectively (P<0.05). Compared with the control group, the levels of IL-1β in the Al(mal)₃ medium- and high-dose groups increased by 18% and 21%, respectively (P<0.05), and the level of IL-18 in the Al(mal)₃ high-dose group increased by 10% (P<0.05). Compared with the Al(mal)₃ high-dose group, the IL-18 levels were reduced by 23% in the C188-9 intervention group (P<0.05). The content of anti-inflammatory factor IL-10 did not differ significantly between groups (P>0.05). Conclusion Aluminum can induce inflammatory responses in BV2 microglia and is predominantly pro-inflammatory, and the mechanism may involve STAT3 regulation of NLRP3 inflammasome secretion of inflammatory factors.

Objective: To construct a precise model for identifying traditional Chinese medicine (TCM) constitutions, thereby offering optimized guidance for clinical diagnosis and treatment planning, and ultimately enhancing medical efficiency and treatment outcomes. Methods: First, TCM full-body inspection data acquisition equipment was employed to collect full-body standing images of healthy people, from which the constitutions were labelled and defined in accordance with the Constitution in Chinese Medicine Questionnaire (CCMQ), and a dataset encompassing labelled constitutions was constructed. Second, heat-suppression valve (HSV) color space and improved local binary patterns (LBP) algorithm were leveraged for the extraction of features such as facial complexion and body shape. In addition, a dual-branch deep network was employed to collect deep features from the full-body standing images. Last, the random forest (RF) algorithm was utilized to learn the extracted multifeatures, which were subsequently employed to establish a TCM constitution identification model. Accuracy, precision, and F1 score were the three measures selected to assess the performance of the model. Result: It was found that the accuracy, precision, and F1 score of the proposed model based on multifeatures for identifying TCM constitutions were 0.842, 0.868, and 0.790, respectively. In comparison with the identification models that encompass a single feature, either a single facial complexion feature, a body shape feature, or deep features, the accuracy of the model that incorporating all the aforementioned features was elevated by 0.105, 0.105, and 0.079, the precision increased by 0.164, 0.164, and 0.211, and the F1 score rose by 0.071, 0.071, and 0.084, respectively. Conclusion The research findings affirmed the viability of the proposed model, which incorporated multifeatures, including the facial complexion feature, the body shape feature, and the deep feature. In addition, by employing the proposed model, the objectification and intelligence of identifying constitutions in TCM practices could be optimized.