BACKGROUND:Type 2 diabetes (T2D) is a disease caused by a combination of genetic and environmental exposures. In addition, there is limited epidemiological evidence on the interaction between metal exposure and genetic risk for T2D. Therefore, we analyzed the interaction effect of serum concentrations of multiple metals and genetic variants on T2D incidence in the general population. METHODS:A prospective cohort study with 14 years of follow-up was performed with 4507 participants without noncommunicable diseases at baseline. Twenty-one metals were measured using inductively coupled plasma-mass spectrometry (ICP-MS). T2D-associated single nucleotide polymorphisms (SNPs) identified by Genome-wide association study (GWAS) were also genotyped. The polygenic risk score (PRS) models based on 46 SNPs identified from East Asians was constructed. A Cox regression model was used to explore the associations of serum metals and their interaction effects with PRS on T2D. RESULTS:Of the 4507 participants, 350 were newly diagnosed with T2D during the 14-year follow-up. After adjusting for covariates, serum vanadium, chromium, manganese, molybdenum, barium and lead levels were significantly associated with decreased T2D risk, while zinc levels were correlated with elevated T2D risk (P < 0.05). Furthermore, zinc, molybdenum, barium and lead showed additive interaction with the PRS derived from 46 SNPs specific for east Asians on T2D. CONCLUSIONS:Significant interaction effects were observed for zinc、molybdenum、barium、lead exposure and PRS. Our findings suggest that people with a high genetic risk of diabetes should pay attention to maintaining appropriate levels of metal.

BACKGROUND AND AIMS:We aimed to explore the association between coronavirus disease-19 (COVID-19) vaccination and long COVID according to the status of chronic multimobidity. METHODS:A total of 1913 participants were recruited in the cross-sectional study on the basis of the Survey of Health and Retirement in Europe. COVID-19 vaccination was defined as vaccination within the last 12 months. Chronic multimorbidity was defined as history of 2 + chronic disease. The study outcome was long COVID during the 12-month follow-up. Multivariable logistic models were performed to estimate the influence of chronic multimorbidity on the association of vaccination with long COVID. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) were calculated. RESULTS:Chronic multimorbidity significantly modified the association of COVID-19 vaccination with long COVID (Pinteraction = 0.024). The rates of study outcome were significantly lower among vaccinated participants in the chronic multimorbidity subgroup, but not in the other subgroup. Multivariable odds ratios (95 % confidence intervals) of study outcome for unvaccination vs. vaccination were 1.494 (1.013-2.203) in those with multimorbidity and 0.915 (0.654-1.280) in those without multimorbidity, respectively. Adding COVID-19 vaccination to a model containing conventional risk factors significantly improved risk reclassification for study outcome among those with chronic multimobidity (continuous NRI was 25.39 % [P = 0.002] and IDI was 0.42 % [P = 0.075]) CONCLUSION: An inverse association of COVID-19 vaccination with long COVID was found among participants with chronic multimorbidity, but not among those without chronic multimorbidity. Chronic multimorbidity might expand the influence of unvaccination on developing long COVID among European aged ≥50 years.

BACKGROUND:Several previous cross-sectional studies suggested that body roundness index (BRI) may be associated with cardiovascular disease (CVD). However, the association should be further validated. Our study aimed to assess the association of the BRI trajectories with CVD among middle-aged and older Chinese people in a longitudinal cohort. METHODS AND RESULTS:A total of 9935 participants from the CHARLS (China Health and Retirement Longitudinal Study) with repeated BRI measurements from 2011 to 2016 were included. The BRI trajectories were identified by group-based trajectory modeling. The primary outcome was incident CVD (stroke or cardiac events), which occurred in 2017 to 2020. Cox proportional hazards regression models were used to examine the association of BRI trajectories with CVD risk. Participants were divided into 3 BRI trajectories, named the low-stable BRI trajectory, moderate-stable BRI trajectory and high-stable BRI trajectory, accounting for 49.81%, 42.35%, and 7.84% of the study population, respectively. Compared with participants in the low-stable BRI trajectory group, those in the moderate-stable and high-stable BRI trajectory groups had an increased risk of CVD, with multivariable adjusted hazard ratios of 1.22 (95% CI, 1.09-1.37) and 1.55 (95% CI, 1.26-1.90), respectively. Furthermore, simultaneously adding the BRI trajectory to the conventional risk model improved CVD risk reclassification (all P<0.05). CONCLUSIONS:A higher BRI trajectory was associated with an increased risk of CVD. The BRI can be included as a predictive factor for CVD incidence.

Journal Article Cohort Profile: The Taihu Biobank of Tumour Biomarkers (TBTB) study in Wuxi, China Get access Lu Wang, Lu Wang Department of Chronic Non-Communicable Disease Control, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China Search for other works by this author on: Oxford Academic PubMed Google Scholar Jia Liu, Jia Liu Department of Chronic Non-Communicable Disease Control, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, ChinaDepartment of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China Search for other works by this author on: Oxford Academic PubMed Google Scholar Meng Zhu, Meng Zhu Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaJiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China Search for other works by this author on: Oxford Academic PubMed Google Scholar Qian Shen, Qian Shen Department of Chronic Non-Communicable Disease Control, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China Search for other works by this author on: Oxford Academic PubMed Google Scholar Yongchao Liu, Yongchao Liu Department of Chronic Non-Communicable Disease Control, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China Search for other works by this author on: Oxford Academic PubMed Google Scholar Hai Chen, Hai Chen Department of Chronic Non-Communicable Disease Control, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China Search for other works by this author on: Oxford Academic PubMed Google Scholar Yunqiu Dong, Yunqiu Dong Department of Chronic Non-Communicable Disease Control, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China Search for other works by this author on: Oxford Academic PubMed Google Scholar Man Yang, Man Yang Department of Chronic Non-Communicable Disease Control, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China Search for other works by this author on: Oxford Academic PubMed Google Scholar Caiwang Yan, Caiwang Yan Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaJiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China Search for other works by this author on: Oxford Academic PubMed Google Scholar Zhijie Yang, Zhijie Yang Department of Chronic Non-Communicable Disease Control, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China Search for other works by this author on: Oxford Academic PubMed Google Scholar ... Show more Yaqi Liu, Yaqi Liu Department of Chronic Non-Communicable Disease Control, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China Search for other works by this author on: Oxford Academic PubMed Google Scholar Hongxia Ma, Hongxia Ma Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaJiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China https://orcid.org/0000-0002-2462-9693 Search for other works by this author on: Oxford Academic PubMed Google Scholar Zhibin Hu, Zhibin Hu Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaJiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China https://orcid.org/0000-0002-8277-5234 Search for other works by this author on: Oxford Academic PubMed Google Scholar Hongbing Shen, Hongbing Shen Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaJiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China https://orcid.org/0000-0002-2581-5906 Search for other works by this author on: Oxford Academic PubMed Google Scholar Yun Qian, Yun Qian Department of Chronic Non-Communicable Disease Control, Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University, Wuxi Center for Disease Control and Prevention, Wuxi, China https://orcid.org/0000-0002-7921-6407 Search for other works by this author on: Oxford Academic PubMed Google Scholar Guangfu Jin Guangfu Jin Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, ChinaJiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Medicine, Nanjing Medical University, Nanjing, China Corresponding author. Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing 211166, China. E-mail: guangfujin@njmu.edu.cn Search for other works by this author on: Oxford Academic PubMed Google Scholar International Journal of Epidemiology, Volume 53, Issue 1, February 2024, dyad173, https://doi.org/10.1093/ije/dyad173 Published: 18 December 2023 Article history Received: 25 March 2023 Editorial decision: 30 October 2023 Accepted: 01 December 2023 Published: 18 December 2023

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.

Background and aims: High sensitivity C-reactive protein (hsCRP) and triglyceride glucose (TyG) index were proved to be independent risk factors of cardiovascular disease (CVD). However, individual hsCRP or TyG index might not provide sufficient predictive value on CVD risk. The current study aimed to evaluate the cumulative effect of hsCRP and TyG index on CVD risk prospectively.Methods and results: A total of 9626 participants were enrolled in the analysis. The TyG index was calculated as ln(triglyceride [mg/dL] x fasting glucose [mg/dL]/ 2). The primary outcome was new-onset CVD events (cardiac events or stroke), and the secondary outcomes were new-onset cardiac events and stroke, separately. Participants were divided into 4 groups through the median of hsCRP and TyG index. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using multivariable Cox proportion hazard models. From 2013 to 2018, 1730 partici-pants experienced CVD (570 stroke and 1306 cardiac events). Linear associations were found be-tween hsCRP, TyG index, hsCRP/TyG ratio and CVD (all p < 0.05). Compared to participants with low hsCRP/low TyG index, multivariable adjusted HRs (95% CIs) for those with high hsCRP/high TyG index were 1.17 (1.03-1.37) for CVD. No interaction of hsCRP and TyG index was found on CVD (p-interacti on >= 0.05). Furthermore, adding hsCRP and TyG index simultaneously to conven-tional risk model improved risk reclassification for CVD, stroke and cardiac events (all p < 0.05). Conclusion: The present study suggested combination of hsCRP and TyG index might better improved the ability for risk stratification of CVD among middle-aged and older Chinese.(c) 2023 The Italian Diabetes Society, the Italian Society for the Study of Atherosclerosis, the Ital-ian Society of Human Nutrition and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.

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.

目的 了解无锡市社区居民理想心血管健康因素分布情况及影响因素.方法 从2019年无锡市慢性病防控社会因素调查数据中,筛查符合本次研究要求的研究对象,最终纳入分析7 804人.采用美国心脏协会提出的"理想心血管健康"标准,纳入7项理想心血管健康因素(吸烟、体质指数、体力活动、膳食、总胆固醇、血压和空腹血糖),每项因素分为理想、一般、差3个等级,分析其分布情况及影响因素.结果 7 804人中,保持7项因素均为理想水平仅占2.9％,男性为0.6％,女性为5.1％.7项心血管健康因素为理想状态比例,依次为总胆固醇68.6％、空腹血糖68.4％、吸烟76.6％、体力活动76.6％、BMI 51.1％、血压27.3％、膳食21.3％.除总胆固醇外(x2=5.10,P＞0.05),女性的各项因素情况均好于男性,差异均有统计学意义(x2值为19.70～2 500.00,P值均＜0.05);不同年龄中除体力活动外,其他因素20～44岁组情况均好于其他年龄组,差异均有统计学意义(x2值为81.92～1 300.00,P值均＜0.05).保持6～7项理想因素的数量,女性是男性的58.13(38.63～87.47)倍,20～44岁、45～59岁组分别是≥60岁组的8.00(5.07～12.62)、1.73(1.14～2.62)倍,初高中、大学及以上者分别是小学及以下者的2.47(1.62～3.75)、9.30(5.34～16.18)倍(P值均＜0.05).结论 无锡市社区居民保持理想心血管健康水平比例较低,应采取针对性干预策略,提升居民理想心血管健康水平.

目的 了解无锡市城区老年人群糖尿病患病、知晓和控制情况,为制订老年人糖尿病健康促进策略和措施提供依据.方法 对2020年江苏省无锡市6城区205123例65岁及以上老年人健康体检数据进行分析,采用SPSS 22.0软件分析比较分性别、分年龄组人群的糖尿病患病、知晓和控制情况及影响因素.结果 无锡市城区65岁及以上老年人群糖尿病患病率、知晓率及控制率分别为24.61％、64.16％和20.06％.年龄大、文化程度低、BMI高、有高血压、总胆固醇高、甘油三酯高、低密度脂蛋白胆固醇高、高密度脂蛋白胆固醇低是影响老年人群糖尿病患病、知晓和控制的因素(P均＜0.05).结论 老年人群糖尿病控制率有待提升,须开发适宜老年人群、通俗易懂的糖尿病健康教育工具,加强对老年人群降糖、降压、调脂等综合管理.

目的 分析江苏省无锡市2008-2019年跌倒死亡导致的疾病负担及对期望寿命的影响,为制定干预措施提供依据.方法 利用2008-2019年无锡市全人群死因监测数据,计算跌倒死亡率和早死所致寿命损失年(years of life lost,YLL),以平均年度变化百分比(average annual percent change,AAPC)分析变化趋势及对期望寿命增量的贡献.结果 2008-2019年无锡市居民跌倒粗死亡率AAPC为7.0％(95％CI:4.8～9.4)(P＜0.05)和标化死亡率AAPC为3.7％(95％CI:1.4～6.0)均呈上升趋势(P＜0.05).2008-2019年因跌倒死亡中,女性占比达到57.62％,略高于男性,且60岁及以上老年人占比达到89.98％.2008-2019年因跌倒死亡导致的期望寿命平均下降了 0.11岁(6.43％),且对女性影响大于男性.2008-2019年全人群和60岁及以上老年人因跌倒死亡导致的YLL率均呈上升趋势,AAPC分别为3.3％(95％CI:1.2～5.5)(P＜0.05)和 4.5％(95％CI:2.0～7.0)(P＜0.05),其中 2019 年的 60 岁及以上老年人的 YLL 率已达到13.10/千人年.结论 无锡市居民因跌倒导致的死亡和疾病负担呈上升趋势,对期望寿命增长呈负向效应,以60岁及以上老年人为主,应加强对老年人群预防跌倒的宣传和干预.

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.

目的 探究江苏省无锡市社区人群中不同肥胖和代谢类型的现患情况及其影响因素.方法 以整群随机抽样方法抽取无锡市18周岁及以上、居住满6个月及以上常住居民为研究对象,进行问卷调查、体格检查和血生化检测,分析4种不同肥胖和代谢类型患病情况,采用Logistic回归分析不同肥胖和代谢类型患病的影响因素.结果 本研究共纳入研究对象8102人,体重正常代谢正常(MHNW)占37.33％,体重正常代谢肥胖(MONW)、代谢正常性超重/肥胖(MHO)和代谢异常性超重/肥胖(MAO)的年龄标化患病率分别为8.43％、23.78％、23.93％.在体重正常人群中,MONW的患病与年龄增大、水果摄入不足(OR=1.44,95％CI:1.14～1.82)以及豆制品摄入不足(OR=1.28,95％CI:1.08～1.51)有关.在超重/肥胖人群中,年龄增大、过度饮酒(OR=1.25,95％CI:1.05～1.48)、吸烟(OR=1.31,95％CI:1.09～1.58)是MAO人群的患病的影响因素.结论 加强对戒烟、限酒、增加豆制品和水果摄入的健康促进,体重正常者应加强豆制品和水果摄入以防向MONW转变;超重/肥胖者更要戒烟、限酒以防转变为MAO.