Association of serum metal levels with type 2 diabetes: A prospective cohort and mediating effects of metabolites analysis in Chinese population.

Several studies have suggested an association between exposure to various metals and the onset of type 2 diabetes (T2D). However, the results vary across different studies. We aimed to investigate the associations between serum metal concentrations and the risk of developing T2D among 8734 participants using a prospective cohort study design. We utilized inductively coupled plasmamass spectrometry (ICP-MS) to assess the serum concentrations of 27 metals. Cox regression was applied to calculate the hazard ratios (HRs) for the associations between serum metal concentrations on the risk of developing T2D. Additionally, 196 incident T2D cases and 208 healthy control participants were randomly selected for serum metabolite measurement using an untargeted metabolomics approach to evaluate the mediating role of serum metabolite in the relationship between serum metal concentrations and the risk of developing T2D with a nested casecontrol study design. In the cohort study, after Bonferroni correction, the serum concentrations of zinc (Zn), mercury (Hg), and thallium (Tl) were positively associated with the risk of developing T2D, whereas the serum concentrations of manganese (Mn), molybdenum (Mo), barium (Ba), lutetium (Lu), and lead (Pb) were negatively associated with the risk of developing T2D. After adding these eight metals, the predictive ability increased significantly compared with that of the traditional clinical model (AUC: 0.791 vs. 0.772, P=8.85×10-5). In the nested casecontrol study, a machine learning analysis revealed that the serum concentrations of 14 out of 1579 detected metabolites were associated with the risk of developing T2D. According to generalized linear regression models, 7 of these metabolites were significantly associated with the serum concentrations of the identified metals. The mediation analysis showed that two metabolites (2-methyl-1,2-dihydrophthalazin-1-one and mestranol) mediated 46.81% and 58.70%, respectively, of the association between the serum Pb concentration and the risk of developing T2D. Our study suggested that serum Mn, Zn, Mo, Ba, Lu, Hg, Tl, and Pb were associated with T2D risk. Two metabolites mediated the associations between the serum Pb concentration and the risk of developing T2D.

Polygenic Risk Score, Lifestyles, and Type 2 Diabetes Risk: A Prospective Chinese Cohort Study.

The aim of this study was to generate a polygenic risk score (PRS) for type 2 diabetes (T2D) and test whether it could be used in identifying high-risk individuals for lifestyle intervention in a Chinese cohort. We genotyped 80 genetic variants among 5024 participants without non-communicable diseases at baseline in the Wuxi Non-Communicable Diseases cohort (Wuxi NCDs cohort). During the follow-up period of 14 years, 440 cases of T2D were newly diagnosed. Using Cox regression, we found that the PRS of 46 SNPs identified by the East Asians was relevant to the future T2D. Participants with a high PRS (top quintile) had a two-fold higher risk of T2D than the bottom quintile (hazard ratio: 2.06, 95% confidence interval: 1.42-2.97). Lifestyle factors were considered, including cigarette smoking, alcohol consumption, physical exercise, diet, body mass index (BMI), and waist circumference (WC). Among high-PRS individuals, the 10-year incidence of T2D slumped from 6.77% to 3.28% for participants having ideal lifestyles (4-6 healthy lifestyle factors) compared with poor lifestyles (0-2 healthy lifestyle factors). When integrating the high PRS, the 10-year T2D risk of low-clinical-risk individuals exceeded that of high-clinical-risk individuals with a low PRS (3.34% vs. 2.91%). These findings suggest that the PRS of 46 SNPs could be used in identifying high-risk individuals and improve the risk stratification defined by traditional clinical risk factors for T2D. Healthy lifestyles can reduce the risk of a high PRS, which indicates the potential utility in early screening and precise prevention.

A three-plasma miRNA panel predicts the risk of colorectal cancer: a community-based nested case‒control study.

Circulating microRNAs (miRNAs) have been considered potential markers for the early detection of malignant colorectal cancer (CRC). We aimed to identify a group of miRNAs for the early detection of CRC and assess their predictive ability in a community-based population in China. A nested case‒control study consisting of 97 incident colorectal cancer cases and 103 frequency-matched healthy controls was conducted. The data were randomly assigned into a training set (60%) and a test set (40%). We selected and detected 10 kinds of miRNAs in plasma samples. Multivariate logistic regression analysis was used to identify miRNAs associated with colorectal cancer risk in the training set and test set. Then, we evaluated the predictive ability of the identified miRNAs by the receiver operating characteristic curve (ROC). In this study, three miRNAs (miRNA-29a, miRNA-125b, miRNA-145) were significantly associated with colorectal cancer risk in both the training set and test set. The sensitivity of the identified miRNAs ranged from 0.854 to 0.961. After adding the identified miRNAs, the AUC (area under the curve) value significantly increased from 0.61 to 0.71 compared with the basic model consisting of only basic demographic information. We identified a three-plasma miRNA signature that may serve as a novel non-invasive biomarker in early CRC detection and in predicting individual CRC risk in the generation population.

Metabolic and Genetic Markers Improve Prediction of Incident Type 2 Diabetes: A Nested Case-Control Study in Chinese.

CONTEXT: It is essential to improve the current predictive ability for type 2 diabetes (T2D) risk. OBJECTIVE: We aimed to identify novel metabolic markers for future T2D in Chinese individuals of Han ethnicity and to determine whether the combined effect of metabolic and genetic markers improves the accuracy of prediction models containing clinical factors. METHODS: A nested case-control study containing 220 incident T2D patients and 220 age- and sex- matched controls from normoglycemic Chinese individuals of Han ethnicity was conducted within the Wuxi Non-Communicable Disease cohort with a 12-year follow-up. Metabolic profiling detection was performed by high-performance liquid chromatography‒mass spectrometry (HPLC-MS) by an untargeted strategy and 20 single nucleotide polymorphisms (SNPs) associated with T2D were genotyped using the Iplex Sequenom MassARRAY platform. Machine learning methods were used to identify metabolites associated with future T2D risk. RESULTS: We found that abnormal levels of 5 metabolites were associated with increased risk of future T2D: riboflavin, cnidioside A, 2-methoxy-5-(1H-1, 2, 4-triazol-5-yl)- 4-(trifluoromethyl) pyridine, 7-methylxanthine, and mestranol. The genetic risk score (GRS) based on 20 SNPs was significantly associated with T2D risk (OR = 1.35; 95% CI, 1.08-1.70 per SD). The area under the receiver operating characteristic curve (AUC) was greater for the model containing metabolites, GRS, and clinical traits than for the model containing clinical traits only (0.960 vs 0.798, P = 7.91 × 10-16). CONCLUSION: In individuals with normal fasting glucose levels, abnormal levels of 5 metabolites were associated with future T2D. The combination of newly discovered metabolic markers and genetic markers could improve the prediction of incident T2D.

Associations of one-carbon metabolism-related gene polymorphisms with breast cancer risk are modulated by diet, being higher when adherence to the Mediterranean dietary pattern is low.

PURPOSE: Breast cancer is more likely attributed to a combination of genetic variations and lifestyle factors. Both one-carbon metabolism and diet-related factors could interfere with the carcinogenesis of breast cancer (BC), but whether diet consumed underlie a specific metabolism pathway could influence the impact of genetic variants on breast cancer risk remains equivocal. METHODS: A case-control study of the Chinese female population (818 cases, 935 controls). 13 SNPs in eight one-carbon metabolism-related genes (MTHFD1, TYMS, MTRR, MAT2B, CDO1, FOLR1, UNG2, ADA) were performed. Diet was assessed by a validated food-frequency questionnaire. We examined the associations of the adherence to the Mediterranean dietary pattern (MDP) and single-nucleotide polymorphisms (SNPs) of one-carbon metabolism with breast cancer risk. We constructed an aggregate polygenic risk score (PRS) to test the additive effects of genetic variants and analyzed the gene-diet interactions. RESULTS: High adherence (highest quartile) to the MDP decreased the risk of breast cancer among post- but not premenopausal women, respectively (OR = 0.54, 95% CI = 0.38 to 0.78 and 0.90, 0.53 to 1.53). Neither of the polymorphisms or haplotypes was associated with breast cancer risk, irrespective of menopause. However, a high PRS (highest quartile) was associated with more than a doubling risk in both post- and premenopausal women, respectively (OR = 1.95, 95% CI = 1.32 to 2.87 and 2.09, 1.54 to 2.85). We found a gene-diet interaction with adherence to the MDP for aggregate PRS (P-interaction = 0.000) among postmenopausal women. When adherence to the MDP was low (< median), carries with high PRS (highest quartile) had higher BC risk (OR = 2.80, 95% CI = 1.55 to 5.07) than low PRS (lowest quartile), while adherence to the MDP was high (≥ median), the association disappeared (OR = 1.57, 95% CI = 0.92 to 2.66). CONCLUSION: High adherence to the MDP may counteract the genetic predisposition associated with one-carbon metabolism on breast cancer risk in postmenopausal women.

Association between dietary patterns and risk of breast cancer in Chinese female population: a latent class analysis.

OBJECTIVE: To determine if specific dietary patterns are associated with breast cancer (BC) risk in Chinese women. DESIGN: Latent class analysis (LCA) was performed to identify generic dietary patterns based on daily food-frequency data. SETTING: The Chinese Wuxi Exposure and Breast Cancer Study (2013-2014). PARTICIPANTS: A population-based case-control study (695 cases, 804 controls). RESULTS: Four dietary patterns were identified, Prudent, Chinese traditional, Western and Picky; the proportion in the controls and cases was 0·30/0·32/0·16/0·23 and 0·29/0·26/0·11/0·33, respectively. Women in Picky class were characterised by higher extreme probabilities of non-consumption of specific foods, the highest probabilities of consumption of pickled foods and the lowest probabilities of consumption of cereals, soya foods and nuts. Compared with Prudent class, Picky class was associated with a higher risk (OR = 1·42, 95 % CI 1·06, 1·90), while the relevant association was only in post- (OR = 1·44, 95 % CI 1·01, 2·05) but not in premenopausal women. The Western class characterised by high-protein, high-fat and high-sugar foods, and the Chinese traditional class characterised by typical consumption of soya foods and white meat over red meat, both of them showed no difference in BC risk compared with Prudent class did. CONCLUSIONS: LCA captures the heterogeneity of individuals embedded in the population and could be a useful approach in the study of dietary pattern and disease. Our results indicated that the Picky class might have a positive association with the risk of BC.

Patterns and changes in life expectancy in China, 1990-2016.

OBJECTIVE: To achieve the goal of "healthy China 2030", reasonable health policies must be developed based on the changes of death spectrum. We aim to investigate the temporal patterns of life expectancy (LE) and age/cause-specific contributions from 1990 to 2016. METHODS: Joinpoint regression model was used with Arriaga's decomposition method. RESULTS: LE in China has reached to 76.3 years in 2016 with an increase of 9.44 years from 1990. From 1990 to 2002, a remarkable reduction in infant mortality accounted for an increase of 1.27 years (35.39%) to LE which mainly resulted from diarrhea, lower respiratory, and other common infectious diseases (1.00 years, 27.79%). After 2002, those aged 65+ years contributed most to increased LE and the most prominent causes included cardiovascular diseases (0.67 years, 23.36%), chronic respiratory diseases (0.54 years, 18.76%) and neoplasms (0.39 years, 13.44%). Moreover, the effects of transport injuries changed from negative to positive. After 2007, contributions of transport and unintentional injuries increased especially for males. And for females contributions of cardiovascular diseases sharply increased LE by 1.17 years (32.26%). CONCLUSION: More attention should be paid to cardiovascular diseases, chronic respiratory diseases and neoplasms which were mainly attributed to the increase of LE, especially for males and elderly population.

The burden of ambient air pollution on years of life lost in Wuxi, China, 2012-2015: A time-series study using a distributed lag non-linear model.

Ambient air pollution ranks high among the risk factors that increase the global burden of disease. Previous studies focused on assessing mortality risk and were sparsely performed in populous developing countries with deteriorating environments. We conducted a time-series study to evaluate the air pollution-associated years of life lost (YLL) and mortality risk and to identify potential modifiers relating to the season and demographic characteristics. Using linear (for YLL) and Poisson (for mortality) regression models and controlling for time-varying factors, we found that an interquartile range (IQR) increase in a three-day average cumulative (lag 0-2 day) concentrations of PM2.5, PM10, NO2 and SO2 corresponded to increases in YLL of 12.09 (95% confidence interval [CI]: 2.98-21.20), 13.69 (95% CI: 3.32-24.07), 26.95 (95% CI: 13.99-39.91) and 24.39 (95% CI: 8.62-40.15) years, respectively, and to percent increases in mortality of 1.34% (95% CI: 0.67-2.01%), 1.56% (95% CI: 0.80-2.33%), 3.36% (95% CI: 2.39-4.33%) and 2.39% (95% CI: 1.24-3.55%), respectively. Among the specific causes of death, cardiovascular and respiratory diseases were positively associated with gaseous pollutants (NO2 and SO2), and diabetes was positively correlated with NO2 (in terms of the mortality risk). The effects of air pollutants were more pronounced in the cool season than in the warm season. The elderly (>65 years) and females were more vulnerable to air pollution. Studying effect estimates and their modifications by using YLL to detect premature death should support implementing health risk assessments, identifying susceptible groups and guiding policy-making and resource allocation according to specific local conditions.