Associations between plasma metal/metalloid mixtures and the risk of central obesity: A prospective cohort study of Chinese adults.

Central obesity has increased rapidly over the past decade and posed a substantial disease burden worldwide. Exposure to metals/metalloids has been acknowledged to be involved in the development of central obesity through regulation of cortisol, insulin resistance, and glucocorticoid receptor reduction. Despite the importance, it is lack of prospective study which comprehensively evaluate the relations between multiple metals exposure and central obesity. We explored the prospective associations of plasma metal concentrations with central obesity in a prospective study of the Dongfeng-Tongji cohort. The present study included 2127 participants with a 6.87-year mean follow-up duration. We measured 23 plasma metal/metalloid concentrations at baseline. The associations between metals and incident central obesity were examined utilizing the Cox proportional hazard regression in single and multiple metals models. Additionally, we applied elastic net (ENET), Bayesian kernel machine regression (BKMR), plasma metal score (PMS), and quantile-based g-computation (Qgcomp) models to explore the joint associations of metal mixtures with central obesity. After adjusting potential confounders, we found significant associations of plasma manganese (Mn) and thallium (Tl) concentrations with a higher risk of central obesity, whereas plasma rubidium (Rb) concentration was associated with a lower risk of central obesity both in single and multiple metals models (all FDR <0.05). The ENET and Qqcomp models verified similar metals (Mn, Rb, and Tl) as important predictors for central obesity. The results of both BKMR model and PMS suggested cumulative exposure to metal mixtures was associated with a higher risk of central obesity. Our findings suggested that co-exposure to metals was associated with a higher risk of central obesity. This study expands our knowledge that the management of metals/metalloids exposure may be beneficial for the prevention of new-onset central obesity, which may subsequently alleviate the disease burden of late-life health outcomes.

Nontargeted Metabolomics Revealed Novel Association Between Serum Metabolites and Incident Acute Coronary Syndrome: A Mendelian Randomization Study.

Background This study was performed to identify metabolites associated with incident acute coronary syndrome (ACS) and explore causality of the associations. Methods and Results We performed nontargeted metabolomics in a nested case-control study in the Dongfeng-Tongji cohort, including 500 incident ACS cases and 500 age- and sex-matched controls. Three metabolites, including a novel one (aspartylphenylalanine), and 1,5-anhydro-d-glucitol (1,5-AG) and tetracosanoic acid, were identified as associated with ACS risk, among which aspartylphenylalanine is a degradation product of the gut-brain peptide cholecystokinin-8 rather than angiotensin by the angiotensin-converting enzyme (odds ratio [OR] per SD increase [95% CI], 1.29 [1.13-1.48]; false discovery rate-adjusted P=0.025), 1,5-AG is a marker of short-term glycemic excursions (OR per SD increase [95% CI], 0.75 [0.64-to 0.87]; false discovery rate-adjusted P=0.025), and tetracosanoic acid is a very-long-chain saturated fatty acid (OR per SD increase [95% CI], 1.26 [1.10-1.45]; false discovery rate-adjusted P=0.091). Similar associations of 1,5-AG (OR per SD increase [95% CI], 0.77 [0.61-0.97]) and tetracosanoic acid (OR per SD increase [95% CI], 1.32 [1.06-1.67]) with coronary artery disease risk were observed in a subsample from an independent cohort (152 and 96 incident cases, respectively). Associations of aspartylphenylalanine and tetracosanoic acid were independent of traditional cardiovascular risk factors (P-trend=0.015 and 0.034, respectively). Furthermore, the association of aspartylphenylalanine was mediated by 13.92% from hypertension and 27.39% from dyslipidemia (P<0.05), supported by its causal links with hypertension (P<0.05) and hypertriglyceridemia (P=0.077) in Mendelian randomization analysis. The association of 1,5-AG with ACS risk was 37.99% mediated from fasting glucose, and genetically predicted 1,5-AG level was negatively associated with ACS risk (OR per SD increase [95% CI], 0.57 [0.33-0.96], P=0.036), yet the association was nonsignificant when further adjusting for fasting glucose. Conclusions These findings highlighted novel angiotensin-independent involvement of the angiotensin-converting enzyme in ACS cause, and the importance of glycemic excursions and very-long-chain saturated fatty acid metabolism.

Transitions in Metabolic Health Status and Obesity Over Time and Risk of Diabetes: The Dongfeng-Tongji Cohort Study.

CONTEXT: Evidence regarding the association between metabolically healthy overweight or obesity (MHOO) and diabetes is controversial, and mostly ignores the dynamic change of metabolic health status and obesity. OBJECTIVE: To explore the association between transitions of metabolic health status and obesity over 5 years and diabetes incidence. METHODS: We examined 17 309 participants derived from the Dongfeng-Tongji cohort and followed from 2008 to 2018 (median follow-up 9.9 years). All participants were categorized into 4 phenotypes based on body mass index (BMI) and metabolic health status: metabolically healthy normal weight (MHNW), metabolically unhealthy normal weight (MUNW), MHOO, and metabolically unhealthy overweight or obesity (MUOO). The associations of changes in BMI-metabolic health status (2008-2013) with diabetes incidence (2018) were performed among 12 206 individuals with 2 follow-up examinations. RESULTS: Compared with stable MHNW, stable MHOO (hazard ratio [HR] 1.76; 95% CI 1.26, 2.45) and transition from MHOO to metabolically unhealthy phenotypes were associated with higher risk for diabetes (HR 2.97; 95% CI 1.79, 4.93 in MHOO to MUNW group and HR 3.38; 95% CI 2.54, 4.49 in MHOO to MUOO group). Instead, improvements to metabolic healthy phenotypes or weight loss occurring in MUOO reduced the risk of diabetes compared with stable MUOO, changing from MUOO to MHNW, MUNW, and MHOO resulted in HRs of 0.57 (95% CI 0.37, 0.87), 0.68 (95% CI 0.50, 0.93), and 0.45 (95% CI 0.34, 0.60), respectively. CONCLUSION: People with MHOO, even stable MHOO, or its transition to metabolically unhealthy phenotypes were at increased risk of diabetes. Metabolic improvements and weight control may reduce the risk of diabetes.

Metabolome-Wide Association Study of Multiple Plasma Metals with Serum Metabolomic Profile among Middle-to-Older-Aged Chinese Adults.

Metal exposure has been associated with risk of various cardio-metabolic disorders, and investigation on the association between exposure to multiple metals and metabolic responses may reveal novel clues to the underlying mechanisms. Based on a metabolome-wide association study of 17 plasma metals with untargeted metabolomic profiling of 189 serum metabolites among 1992 participants within the Dongfeng-Tongji cohort, we replicated two metal-associated pathways, linoleic acid metabolism and aminoacyl-tRNA biosynthesis, with novel metal associations (false discovery rate, FDR < 0.05), and we also identified two novel pathways, including biosynthesis of unsaturated fatty acids and alpha-linolenic acid metabolism, as associated with metal exposure (FDR < 0.05). Moreover, two-way orthogonal partial least-squares analysis showed that five metabolites, including aspartylphenylalanine, free fatty acid 14:1, uridine, carnitine C14:2, and LPC 18:2, contributed most to the joint covariation between the two data matrices (12.3%, 8.3%, 8.0%, 7.4%, and 7.3%, respectively). Further BKMR analysis showed significant positive joint associations of plasma Al, As, Ba, and Zn with aspartylphenylalanine and of plasma Ba, Co, Mn, and Pb with carnitine C14:2, when all the metals were at the 55th percentiles or above, compared with the median. We also found significant interactions between As and Ba in the association with aspartylphenylalanine (P for interaction = 0.048) and between Ba and Pb in the association with carnitine C14:2 (P for interaction < 0.001). Together, these findings may provide new insights into the mechanisms underlying the adverse health effects induced by metal exposure.

Analysis of Changes in Weight, Waist Circumference, or Both, and All-Cause Mortality in Chinese Adults.

Importance: Although numerous studies have separately investigated the associations of changes in weight or waist circumference with mortality risk, few studies have examined the associations of concurrent changes in these 2 anthropometric parameters with all-cause mortality. Objective: To assess the associations of changes in body weight, waist circumference, or both, combined with all-cause mortality. Design, Setting, and Participants: This cohort study used data from 2 longitudinal cohort studies in Dongfeng-Tongji and Kailuan, China. Participants included 58 132 adults (aged 40 years and older) with measures of weight and waist circumference at baseline and follow-up visit. Statistical analysis was performed from June 2020 to September 2021. Exposures: Changes in weight and waist circumference between 2 visits (2008-2010 to 2013 in the Dongfeng-Tongji cohort, and 2006-2007 to 2010-2011 in the Kailuan study). Stable weight was defined as change in weight within 2.5 kg between the 2 visits and stable waist circumference was defined as changes within 3.0 cm. Changes were categorized as loss, stable, or gain for weight and waist circumference separately, and created a 9-category variable to represent the joint changes. Main Outcomes and Measures: All-cause mortality from follow-up visit (2013 in Dongfeng-Tongji cohort and 2010-2011 in Kailuan study) until December 31, 2018. Cox proportional hazard regression models were used to estimate the associations with adjustment for potential confounders. Results were obtained in the 2 cohorts separately and pooled via fixed-effect meta-analysis. Results: A total of 10 951 participants in the Dongfeng-Tongji cohort (median [IQR] age, 62 [56-66] years; 4203 [38.4%] men) and 47 181 participants in the Kailuan study (median [IQR] age, 51 [46-58] years; 36 663 [77.7%] men) were included in the analysis. During 426 072 person-years of follow-up, 4028 deaths (523 in the Dongfeng-Tongji cohort and 3505 in the Kailuan study) were documented. When changes in weight and waist circumference were examined separately, U-shape associations were found: both gain and loss in weight (weight loss: pooled hazard ratio [HR], 1.33; 95% CI, 1.23-1.43; weight gain: HR, 1.10; 95% CI, 1.02-1.19) or waist circumference (waist circumference loss: HR, 1.14; 95% CI, 1.05-1.24; waist circumference gain: HR, 1.11; 95% CI, 1.03-1.21) were associated with higher mortality risk compared with stable weight or waist group. When changes in weight and waist circumference were jointly assessed, compared with participants with stable weight and waist circumference (16.9% of the total population [9828 of 58 132] with 508 deaths), participants with different combinations of weight and waist circumference change all had higher mortality risks except for those with stable weight but significant loss in waist. Notably, those who lost weight but gained waist circumference (6.4% of the total population [3698 of 58 132] with 308 deaths) had the highest risk of all-cause mortality (HR, 1.69; 95% CI, 1.46-1.96; absolute rate difference per 100 000 person-years in the Dongfeng-Tongji cohort: 414; 95% CI, 116-819; and in the Kailuan study: 333; 95% CI, 195-492) among the joint subgroups. Conclusions and Relevance: In this cohort study, weight loss with concurrent waist circumference gain was associated with a higher mortality risk in middle-aged and older Chinese adults. This study's findings suggest the importance of evaluating the changes in both body weight and waist circumference when assessing their associations with mortality.

Alternations in the gut microbiota and metabolome with newly diagnosed unstable angina.

Gut microbiota plays an important role in coronary heart disease, but its compositional and functional changes in unstable angina (UA) remain unexplored. We performed metagenomic sequencing of 133 newly diagnosed UA patients and 133 sex- and age-matched controls, and profiled the fecal and plasma metabolomes in 30 case-control pairs. The alpha diversity of gut microbiota was increased in UA patients: the adjusted odds ratios (ORs) per standard deviation increase in Shannon and Simpson indices were 1.30 (95% confidence interval, 1.01-1.70) and 1.36 (1.05-1.81), respectively. Two common species (depleted Klebsiella pneumoniae and enriched Streptococcus parasanguinis; P ≤ 0.002) and three rare species (depleted Weissella confusa, enriched Granulicatella adiacens and Erysipelotrichaceae bacterium 6_1_45; P ≤ 0.005) were associated with UA. The UA-associated gut microbiota was depleted in the pathway of L-phenylalanine degradation (P = 0.001), primarily contributed by Klebsiella pneumoniae. Consistently, we found increased circulating phenylalanine in UA patients (OR = 2.76 [1.17-8.16]). Moreover, Streptococcusparasanguinis was negatively correlated with fecal citrulline (Spearman's rs = -0.470, P = 0.009), a metabolite depleted in UA patients (OR = 0.26 [0.08-0.63]). These findings are informative to help understand the metabolic connection between gut microbiota and UA.

Associations of plasma metal concentrations with the risks of all-cause and cardiovascular disease mortality in Chinese adults.

BACKGROUND: Exposure to metals/metalloids from both the natural environment and anthropogenic sources have a complex influence on human health. However, relatively few studies have explored the relations of exposure to multiple metals/metalloids with mortality. Therefore, this prospective study aims to examine the relations of multiple metal/metalloids exposures with all-cause and cardiovascular disease (CVD) mortality. METHODS: A total of 6155 participants within the Dongfeng-Tongji (DF-TJ) cohort were involved in this analysis, which were followed for mortality until December 31, 2018. We applied inductively coupled plasma mass spectrometry (ICP-MS) to measure baseline plasma concentrations of 23 metals. We utilized Cox regression models to calculate the hazard ratios (HRs) for all-cause and CVD mortality associated with metal concentrations. We proposed plasma metal score to assess the simultaneous exposure to multiple metals through summing each metal concentration weighted by the regression coefficients with all-cause mortality. RESULTS: During the follow-up (mean duration, 9.8 years), we ascertained 876 deaths, including 416 deaths of CVD (157 deaths of coronary heart disease and 259 deaths of stroke). In the multiple-metals model, after adjusting for potential confounders, plasma copper, molybdenum, and vanadium were positively associated with all-cause mortality, whereas manganese, selenium, and thallium were negatively associated with the risk of all-cause mortality, with adjusted HRs (95% Confidence Interval, CI) of the fourth quartiles were 1.73 (1.42-2.11, P-trend < 0.001) for copper, 1.33 (1.09-1.63, P-trend = 0.005) for molybdenum, 1.43 (1.16-1.77, P-trend < 0.001) for vanadium, 0.74 (0.58-0.94, P-trend = 0.005) for manganese, 0.68 (0.56-0.83, P-trend < 0.001) for selenium, and 0.74 (0.59-0.92, P-trend = 0.002) for thallium, respectively. Positive associations were observed between plasma copper, molybdenum, vanadium concentrations and CVD mortality, whereas negative associations were found for plasma selenium and thallium concentrations with CVD mortality in the multiple-metals model. Compared with the first quartiles, the HRs of fourth quartiles were 1.94 (1.45-2.58, P-trend < 0.001) for copper, 1.72 (1.26-2.35, P-trend < 0.001) for molybdenum, 1.81 (1.32-2.47, P-trend < 0.001) for vanadium, 0.67 (0.50-0.89, P-trend = 0.003) for selenium, and 0.58 (0.41-0.81, P-trend < 0.001) for thallium, respectively. The plasma metal score was significantly associated with higher risks of all-cause and CVD death in dose-response fashions. When compared with the first quartiles of plasma metal score, the HRs of fourth quartiles were 2.16 (1.76-2.64; P-trend < 0.001) for all-cause mortality and 3.00 (2.24-4.02; P-trend < 0.001) for CVD mortality. CONCLUSIONS: The study indicated that several plasma metals/metalloids were key determinants and predictors of all-cause and CVD death in the Chinese population. Our findings highlighted the importance to comprehensively assess and monitor multiple metals/metalloids exposures.

Associations of plasma metal concentrations with incident dyslipidemia: Prospective findings from the Dongfeng-Tongji cohort.

Metal exposures are ubiquitous around the world, while it is lack of prospective studies to evaluate the associations of exposure to multiple metal/metalloids with incident dyslipidemia. A total of 2947 participants without dyslipidemia at baseline were included in the analyses. We utilized inductively coupled plasma mass spectrometry to measure the baseline plasma metal concentrations. Unconditional logistic regression models were applied to estimate the relations between plasma metals and risk of incident dyslipidemia, and principal component analysis was performed to extract principal components of metals. During 5.01 ± 0.31 years of follow-up, 521 subjects were diagnosed with incident dyslipidemia. After multivariable adjustment, the odds ratios (ORs) of dyslipidemia comparing the highest quartiles to the lowest were 1.58 (95% CI: 1.20, 2.08; Ptrend = 0.001) for aluminum, 1.34 (95% CI: 1.03, 1.75; Ptrend = 0.03) for arsenic, 1.44 (1.09, 1.91; Ptrend = 0.03) for strontium, and 1.47 (95% CI: 1.09, 2.00; Ptrend = 0.005) for vanadium. The four metals also showed significant associations with the subtypes of dyslipidemia, including low HDL-C and high LDL-C. The first principal component, which mainly represented aluminum, arsenic, barium, lead, vanadium, and zinc, was associated with increased risk of incident dyslipidemia, and the adjusted OR was 1.40 (95% CI: 1.07, 1.84; Ptrend = 0.02) comparing extreme quartiles. The study indicated that elevated plasma aluminum, arsenic, strontium, and vanadium concentrations were associated with a higher incidence of dyslipidemia. These findings highlight the importance of controlling metal exposures for dyslipidemia prevention.

Association between weight status, metabolic syndrome, and chronic kidney disease among middle-aged and elderly Chinese.

BACKGROUND AND AIMS: Obesity often initiates or coexists with certain metabolic abnormalities. This study sought to examine the independent and joint relations of weight and metabolic syndrome (MetS) with incident chronic kidney disease (CKD) among Chinese elderly people. METHODS AND RESULTS: A total of 15,229 participants (mean age: 62.8 years) from the Dongfeng-Tongji cohort with complete baseline questionnaire and medical examination data were followed from 2008 to 2010 to 2013. All participants were categorized into four phenotypes: metabolically healthy non-overweight/obesity (MHNO), metabolically healthy overweight/obesity (MHO), metabolically unhealthy non-overweight/obesity (MUNO), metabolically unhealthy overweight/obesity (MUO). Multivariable-adjusted logistic regression models were applied to estimate the odds ratios (ORs) and confidence intervals (CIs) of four phenotypes with the risk of incident CKD, which was defined as estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73 m2. A total of 1151 CKD cases were identified during a mean of 4.6-year follow-up. After adjusting for potential confounders, both overweight/obesity and MetS were associated with higher risk of CKD, and the ORs (95% CI) were 1.32 (1.15-1.52) and 1.50 (1.31-1.73), respectively. The risk of CKD was progressively higher in MHO (1.31, 1.09-1.57), MUNO (1.54, 1.22-1.93), and MUO (2.05, 1.73-2.42) as compared with MHNO phenotype, without significant multiplicative interaction between overweight/obesity and MetS (Pinteraction = 0.906). These associations were slightly stronger among those aged >60 years or with baseline diabetes. CONCLUSION: Both overweight/obesity and MetS were associated with an increased risk of CKD. It is worth noting that MHO and MUNO also have an elevated risk. Maintaining both normal weight and healthy metabolic profile is recommended.

Multiple plasma metals, genetic risk and serum C-reactive protein: A metal-metal and gene-metal interaction study.

BACKGROUND: C-reactive protein (CRP) is a well-recognized biomarker of inflammation, which can be used as a predictor of cardiovascular disease. Evidence have suggested exposure to multiple metals/metalloids may affect immune system and give rise to cardiovascular disease. However, it is lack of study to comprehensively evaluate the association of multiple metals and CRP, the interactions between metals, and the gene-metal interaction in relation to CRP levels. AIMS: To explore the associations of multiple plasma metals with serum CRP, and to test the interactions between metals, and gene-metal interactions on the levels of serum CRP. METHODS: We included 2882 participants from the Dongfeng-Tongji cohort, China, and measured 23 plasma metals and serum CRP concentrations. The genetic risk score (GRS) was calculated based on 7 established CRP-associated variants. For metals which were associated with the levels of CRP, we further tested the interactions between metals on CRP, and analyzed the gene-metal interactions on CRP. RESULTS: The median level for CRP in the total population was 1.17 mg/L. After multivariable adjustment, plasma copper was positively associated with serum CRP (FDR < 0.001), whereas selenium was negatively associated with serum CRP (FDR = 0.01). Moreover, selenium and zinc attenuated the positive association between high plasma copper and CRP (P for interaction < 0.001). Participants with a higher GRS had a higher CRP level, with the increase in ln-transformed CRP per increment of 5 risk alleles were 0.64 for weighted GRS, and 0.54 for unweighted GRS (both P < 0.001). Furthermore, the genetic association with CRP was modified by copper concentration (P for interaction < 0.001). CONCLUSIONS: Our results suggest that serum CRP is positively associated with plasma concentration of copper, and inversely associated with selenium. Plasma zinc, selenium and CRP genetic predisposition would modify the associations between plasma copper and serum CRP.

Associations of plasma metal concentrations with the decline in kidney function: A longitudinal study of Chinese adults.

Metals are widespread pollutants in the environment which have been reported to be associated with kidney dysfunction in many existing epidemiological studies. However, most of the studies are cross-sectional design and mainly focus on several toxic metals including arsenic, lead and cadmium. Therefore, we conducted this prospective study within the Dongfeng-Tongji cohort to evaluate the associations of plasma multiple metals with the decline in kidney function among Chinese middle-aged and elderly. In total, 1434 participants free of chronic diseases at baseline were included in analysis. We measured baseline plasma concentrations of 23 metals and calculated estimated glomerular filtration rate (eGFR) using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation based on serum creatinine, age, sex and ethnicity. Bonferroni correction was used for multiple testing to reduce the probability of a type I error. Principal component analysis was conducted to evaluate the combined effect of multiple metal co-exposure. Most of the plasma metal concentrations were within the literature reported reference values, whereas the concentration of lead and nickel exceeded the guideline value. We found that plasma concentrations of aluminum, arsenic, barium, lead, molybdenum, rubidium, strontium, vanadium and zinc were significantly associated with the decline in kidney function measured by annual eGFR decline, rapid renal function decline (defined as an annual decline in eGFR ≥ 5 mL/min/1.73 m2) or incident eGFR < 60 mL/min/1.73 m2, with the adjusted beta coefficients (95% CI) for annual eGFR decline 0.50 (0.30, 0.69), 0.98 (0.74, 1.23), 0.56 (0.32, 0.79), 0.21 (0.03, 0.39), 0.35 (0.16, 0.54), 0.94 (0.71, 1.17), 0.37 (0.15, 0.60), 0.78 (0.54, 1.02), and 0.74 (0.57, 0.91), respectively. The metals exposures were linked with increased risks of impaired kidney function. Associations of principal components representing these metals with the decline in kidney function were significant and suggest a possible additional health risk by co-exposure. Participants engaged in manufacturing had higher plasma levels of several metals compared with those who had been involved in management- or administration-related work. Our findings suggest that exposure to multiple metals contribute to the decline in kidney function among the middle-aged and elderly. Co-exposure to multiple metals may have synergetic effect on the kidney function. Further studies are warranted to confirm our findings and clarify the potential mechanisms.

Sleep duration, midday napping, and sleep quality and incident stroke: The Dongfeng-Tongji cohort.

OBJECTIVE: To investigate the associations of sleep duration, midday napping, sleep quality, and change in sleep duration with risk of incident stroke and stroke subtypes. METHODS: Among 31,750 participants aged 61.7 years on average at baseline from the Dongfeng-Tongji cohort, we used Cox regression models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for incident stroke. RESULTS: Compared with sleeping 7 to <8 hours/night, those reporting longer sleep duration (≥9 hours/night) had a greater risk of total stroke (hazard ratio [HR] 1.23; 95% confidence interval [CI] 1.07-1.41), while shorter sleep (<6 hours/night) had no significant effect on stroke risk. The HR (95% CI) of total stroke was 1.25 (1.03-1.53) for midday napping >90 minutes vs 1-30 minutes. The results were similar for ischemic stroke. Compared with good sleep quality, those with poor sleep quality showed a 29%, 28%, and 56% higher risk of total, ischemic, and hemorrhagic stroke, respectively. Moreover, we observed significant joint effects of sleeping ≥9 hours/night and midday napping >90 minutes (HR 1.85; 95% CI 1.28-2.66), and sleeping ≥9 hours/night and poor sleep quality (HR 1.82; 95% CI 1.33-2.48) on risk of total stroke. Furthermore, compared with persistently sleeping 7-9 hours/night, those who persistently slept ≥9 hours/night or switched from 7 to 9 hours to ≥9 hours/night had a higher risk of total stroke. CONCLUSIONS: Long sleep duration, long midday napping, and poor sleep quality were independently and jointly associated with higher risks of incident stroke. Persistently long sleep duration or switch from average to long sleep duration increased the risk of stroke.

Circulating Multiple Metals and Incident Stroke in Chinese Adults.

Background and Purpose- Circulating metals synchronously reflect multiple metal exposures from both natural and anthropogenic sources, which may be linked with the risk of stroke. However, there is a lack of prospective studies investigating the associations of multiple metal exposures with incident stroke. Methods- We performed a nested case-control study within the ongoing Dongfeng-Tongji cohort launched in 2008. A total of 1304 incident stroke cases (1035 ischemic strokes and 269 hemorrhagic strokes) were prospectively identified by December 31, 2016, and matched to incident identity sampled controls according to age (within 1 year), sex, and blood sampling date (within 1 month). We determined the concentrations of 24 plasma metals and assessed the associations of plasma multiple metal concentrations with incident stroke using conditional logistic regression and elastic net model. Results- The average follow-up was 6.1 years. After adjusting for established risk confounders, copper, molybdenum, and titanium were significantly associated with higher risk of ischemic stroke (odds ratios according to per interquartile range increase, 1.29 [95% CI, 1.13-1.46], 1.19 [95% CI, 1.05-1.35], and 1.30 [95% CI, 1.07-1.59]), whereas rubidium and selenium were associated with lower risk of hemorrhagic stroke (odds ratios according to per interquartile range increase, 0.66 [95% CI, 0.50-0.87] and 0.68 [95% CI, 0.51-0.91]). The predictive plasma metal scores based on multiple metal exposures were significantly associated with higher risk of ischemic and hemorrhagic stroke (adjusted odds ratios according to per interquartile range increase, 1.37 [95% CI, 1.20-1.56] and 1.53 [95% CI, 1.16-2.01]). Conclusions- Plasma copper, molybdenum, and titanium were associated with higher risk of ischemic stroke, whereas plasma rubidium and selenium were associated with lower risk of hemorrhagic stroke. These findings may have important public health implications given the ever-increasing burden of stroke worldwide.

Serum alanine transaminase levels predict type 2 diabetes risk among a middle-aged and elderly Chinese population.

INTRODUCTION AND AIM: It is indicated that high levels of serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) are associated with increased incident type 2 diabetes risk. However, whether serum ALT levels could improve the discrimination of type 2 diabetes remains unclear. METHODS: The data was derived from the Dongfeng-Tongji cohort study, which was established in 2008 and followed until October 2013. A total of 17,173 participants free of type 2 diabetes at baseline were included and 1159 participants developed diabetes after 4.51 (0.61) years of follow-up. Cox proportional hazard regression model was used to calculate the hazard ratios (HRs) for the association between ALT and AST levels with incident diabetes risk. Receiver-operating characteristic (ROC) curves analysis was used to evaluate the predictive accuracy of models incorporating traditional risk factors with and without ALT. RESULTS: Compared with the lowest quartile of ALT and AST levels, the highest quartile had a significantly higher risk of developing type 2 diabetes (HR: 2.17 [95% CI: 1.78-2.65] and 1.29 [1.08-1.54], respectively) after adjustment for potential confounders. The addition of ALT levels into the traditional risk factors did not improve the predictive ability of type 2 diabetes, with AUC increase from 0.772 to 0.774; P=0.86. CONCLUSIONS: Although elevated ALT or AST levels increased incident type 2diabetes risk, addition of ALT levels into the prediction model did not improve the discrimination of type 2 diabetes.

A multi-stage association study of plasma cytokines identifies osteopontin as a biomarker for acute coronary syndrome risk and severity.

Cytokines play a critical role in the pathogenesis and development of cardiovascular diseases. However, data linking cytokines to risk and severity of acute coronary syndrome (ACS) are still limited. We measured plasma profile of 280 cytokines using a quantitative protein microarray in 12 ACS patients and 16 healthy controls, and identified 15 differentially expressed cytokines for ACS. Osteopontin, chemokine ligand 23, brain derived neurotrophic factor and C-reactive protein (CRP) were further validated using immunoassay in two independent case-control studies with a total of 210 ACS patients and 210 controls. We further examined their relations with incident ACS among 318 case-control pairs nested within the Dongfeng-Tongji cohort, and found plasma osteopontin and CRP concentrations were associated with incident ACS, and the multivariable-adjusted odds ratio (95% confidence interval) was 1.29 (1.06-1.57) per 1-SD increase for osteopontin and 1.30 (1.02-1.66) for CRP, respectively. Higher levels of circulating osteopontin were also correlated with higher severity of ACS, and earlier ACS onset time. Adding osteopontin alone or in combination with CRP modestly improved the predictive ability of ACS beyond the Framingham risk scores. Our findings suggested that osteopontin might be a biomarker for incident ACS, using osteopontin adds moderately to traditional cardiovascular risk factors.

Serum uric acid levels and decreased estimated glomerular filtration rate in patients with type 2 diabetes: A cohort study and meta-analysis.

BACKGROUND: Epidemiological studies suggest that elevated serum uric acid (SUA) is associated with heightened incident kidney disease in both the general population and the type 2 diabetes (T2D) cases, although the results were not entirely consistent. METHODS: We investigated prospective association between SUA levels and estimated glomerular filtration (eGFR) decline risk (eGFR <60 mL min-1 1.73 m-2 ) among 3123 T2D in the Dongfeng-Tongji cohort and further examined this association with a meta-analysis. Generalize linear model was used to assess the associations of SUA with eGFR decline in the cohort. In the meta-analysis, we used both fix-effects and random-effects models to calculate the overall effect estimate. RESULTS: During 5-year follow-up, 303 (9.7%) patients developed eGFR decline. After multiple adjustments, the relative risk (RR) (95% CI) of eGFR decline was 1.55 (1.07, 2.26) when comparing the highest with the lowest sex-specific uric acid quartile. A 100 µmol/L increment of SUA level was significantly associated with 21% increased risk of eGFR decline. The SUA-eGFR decline association was more evident in men, but not in women. In meta-analysis, the pooled RR (95% CI) was 2.33 (1.66, 3.25) for developing eGFR decline when comparing the highest with the lowest levels of uric acid. A 100 µmol/L increment of SUA level was significantly associated with a 33% increased risk of eGFR decline. CONCLUSIONS: Our results indicate an independent and significant positive association between higher SUA and increased risk of developing eGFR decline among T2D cases.

Associations of multiple plasma metals with incident type 2 diabetes in Chinese adults: The Dongfeng-Tongji Cohort.

The long-term associations between multiple metals and incident diabetes are uncertain. We aimed to examine the relationship between plasma concentrations of 23 metals and the incidence of type 2 diabetes among Chinese senior adults. We quantified fasting plasma concentrations of 23 metals by inductively coupled plasma mass spectrometry among 1039 incident diabetes cases and 1039 controls (age and sex matched) nested in a prospective study, the Dongfeng-Tongji cohort. Both cases and controls were free of diabetes at baseline (2008-2010), incident diabetes were identified using the following criteria: fasting glucose ≥ 7.0 mmoL/l; or hemoglobin A1c (HbA1c) ≥ 6.5%; or self-reported physician diagnosis of diabetes or use of anti-diabetic medication during the follow-up visits in 2013. In the conditional logistic regression models, the multivariable adjusted ORs (95% CIs) of diabetes across quartiles (Q1-Q4) of metal concentrations were as follows: titanium, 1.00, 0.92, 1.31, 1.38 (1.00-1.91, Ptrend = 0.011); selenium, 1.00, 1.08, 1.45, 1.27 (0.93-1.74, Ptrend = 0.05); and antimony, 1.00, 0.79, 0.77, 0.60 (0.44-0.83, Ptrend = 0.002). Arsenic was significantly associated with diabetes in the crude model (ORs comparing extreme quartiles 1.30; 1.02-1.65; Ptrend = 0.006), but was not significant after adjustment for socio-demographic factors. No significant associations were found for other metals. In conclusion, titanium and selenium were positively while antimony was negatively associated with incident diabetes.

Hearing loss is associated with increased CHD risk and unfavorable CHD-related biomarkers in the Dongfeng-Tongji cohort.

BACKGROUND AND AIMS: The association between hearing loss and coronary heart disease (CHD) is inconsistent. We aimed to investigate the association of hearing loss with prevalent CHD and CHD-related biomarkers among a middle aged and elderly Chinese population. METHODS: We included 14,755 Chinese aged 64.6 years from the Dongfeng-Tongji Cohort in 2013. Hearing loss was classified into normal, mild, moderate or greater levels by the pure tone average (PTA) at low frequency and high frequency, respectively. Logistic regression models were used to estimate the odds ratios (ORs) of CHD risk in relation to hearing loss. Linear regression models were used to evaluate the effect of hearing loss on CHD-related biomarkers. RESULTS: The adjusted ORs for prevalent CHD increased gradually with the increasing hearing loss levels. Compared with normal hearing, individuals having mild- and moderate or greater-hearing loss had a higher CHD risk of 19% and 20% at low frequency, and 33% and 41% at high frequency, respectively (all p for trend < 0.05). The associations were more evident among subjects who were females, overweight, exposed to occupational noise and with hyperglycemia, hypertension or dyslipidemia at low frequency, and those with hyperglycemia at high frequency. Meanwhile, moderate or greater hearing loss combined with overweight, hyperglycemia, hypertension or dyslipidemia had joint effects on CHD. In addition, the majority of CHD-related biomarkers worsened with increasing hearing loss levels. CONCLUSIONS: There may be a dose-response relationship between hearing loss and CHD prevalence, and the association could partially be explained by intermediate CHD-related biomarkers.

Development of a new scoring system to predict 5-year incident diabetes risk in middle-aged and older Chinese.

AIMS: The aim of this study was to develop a new risk score system to predict 5-year incident diabetes risk among middle-aged and older Chinese population. METHODS: This prospective study included 17,690 individuals derived from the Dongfeng-Tongji cohort. Participants were recruited in 2008 and were followed until October 2013. Incident diabetes was defined as self-reported clinician diagnosed diabetes, fasting glucose ≥7.0 mmol/l, or the use of insulin or oral hypoglycemic agent. A total of 1390 incident diabetic cases were diagnosed during the follow-up period. ß-Coefficients were derived from Cox proportional hazard regression model and were used to calculate the risk score. RESULTS: The diabetes risk score includes BMI, fasting glucose, hypertension, hyperlipidemia, current smoking status, and family history of diabetes. The ß-coefficients of these variables ranged from 0.139 to 1.914, and the optimal cutoff value was 1.5. The diabetes risk score was calculated by multiplying the ß-coefficients of the significant variables by 10 and rounding to the nearest integer. The score ranges from 0 to 36. The area under the receiver operating curve of the score was 0.751. At the optimal cutoff value of 15, the sensitivity and specificity were 65.6 and 72.9%, respectively. Based upon these risk factors, this model had the highest discrimination compared with several commonly used diabetes prediction models. CONCLUSIONS: The newly established diabetes risk score with six parameters appears to be a reliable screening tool to predict 5-year risk of incident diabetes in a middle-aged and older Chinese population.