Jinchang Cohort Research Articles

Interaction between non-alcoholic fatty liver disease and obesity on the risk of developing cardiovascular diseases

BackgroundsThis investigation seeks to explore the correlation between nonalcoholic fatty liver disease (NAFLD) and cardiovascular diseases (CVDs), and to to provide evidence for the prevention and treatment of CVDs.MethodsThis study utilized data from the Jinchang cohort platform, including 19,399 participants without pre-existing major CVDs. Based on the general population and gender stratification, Cox models were used to analyze the risk of NAFLD for CVDs. The combined effect of NAFLD and different obesity indicators on CVDs was analyzed by additive and multiplicative interaction models and subgroups.ResultsThere were 3129 NAFLD patients out of 19399 subjects, and 723 (23.11%) of them had the CVD. After adjusting for multiple confounding factors, the Cox model revealed a 1.17-fold increase in the risk of CVDs among patients with NAFLD compared to those without NAFLD. Moreover, there was no notable disparity in CVDs risk among most NAFLD patients at the same level of obesity. The results indicated no additive interaction between NAFLD and obesity concerning CVDs risk, but rather a positive multiplicative interaction. Using the normal population as a reference, it was found that people with both obesity and NAFLD significantly increased the risk of developing CVDs, with HRs and 95% CIs of 1.790 (1.508, 2.126), 1.356 (1.213, 1.517), and 1.807 (1.503, 2.174), respectively, for BMI, WC, and the combination of BMI and WC.ConclusionsNAFLD and obesity are independent risk factors for CVDs. The synergy of obesity and NAFLD implies that NAFLD patients should control weight gain. Larger BMI and WC values may increase the CVDs risk for NAFLD patients, especially women.

Perfluoroalkyl substances exposure and the risk of breast cancer: A nested case-control study in Jinchang Cohort

BackgroundAs persistent organic pollutants (POPs), perfluoroalkyl substances (PFAS) may potentially impact human health. Our study aimed to investigate the prospective association between PFAS exposure and the incidence risk of breast cancer in females. MethodsBy fully following the Jinchang Cohort after a decade, we conducted this nested case-control study with 135 incidence cases of breast cancer (BC) and 540 bias-paired controls. The PFAS levels were tested by baseline serum samples. Conditional logistic regression and a restricted cubic spline model were employed to investigate the BC incidence risks and the dose-response associated with single PFAS component exposure. Furthermore, the Quantile g-computation model (Qgc), random forest model (RFM), and bayesian kernel machine regression models (BKMR) were integrated to estimate the mixed effects of PFAS exposure on the incidence risk of BC. ResultsExposures to specific PFAS components were positively associated with an increased incidence risk of breast cancer. By grouping the study population into different baseline menopausal statuses, PFHxS, PFNA, PFBA, PFUdA, PFOS, and PFDA demonstrated a similarly positive correlation with BC incidence risks. However, the increased incidence risks of BC associated with PFOA, PFOS, PFUdA, and 9CL-PF3ONS exposure were exclusively found in the premenopausal population. Both BKMR and Qgc revealed that exposure to mixed PFAS was associated with an increased risk of breast cancer, with Qgc specifically indicating an odds ratio (OR) of 2.21 (95% CI: 1.53, 3.19). Random forests showed that PFBA, PFOS, PFHxS, and PFDA emerged as predominant factors potentially influencing breast cancer incidence. ConclusionOur findings suggest a strong association between PFAS exposure and the incidence of breast cancer. Premenopausal women should exercise more caution regarding PFAS exposure.

Association between per- and poly-fluoroalkyl substances and nonalcoholic fatty liver disease: A nested case-control study in northwest China

Per- and poly-fluoroalkyl substances (PFAS) have been reported to have hepatotoxic effects. However, it is unclear whether they are linked to non-alcoholic fatty liver disease (NAFLD). This nested case-control study focused on the epidemiological links between PFAS and the prevalence of NAFLD. We selected 476 new cases of NAFLD and 952 age- and sex-matched controls from the Jinchang cohort population between 2014 and 2019. Serum concentrations of PFAS were measured using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS). Only PFAS with a detection rate of ≥90 % were included for analysis, which included PFPeA, PFOA, PFNA, PFHxS, PFOS, and 9Cl-PF3ONS. The relationship between single and co-exposure to PFAS and the occurrence of NAFLD was evaluated using conditional logistic regression, Quantile g-computation (QgC), and Bayesian kernel machine regression (BKMR) model. Logistic regression indicated that PFPeA, PFOA, and 9Cl-PF3ONS were positive correlation with the incidence of NAFLD after adjusting for confounders, with odds ratios (OR) and 95 % confidence interval (CI) of 3.13 (95 % CI: 2.53, 3.86), 1.39 (95 % CI: 1.12, 1.73), and 1.41 (95 % CI: 1.20, 1.66), respectively. PFNA, PFHxS, and PFOS were nonlinearly and negatively associated with the incidence of NAFLD, with OR (95 % CI) of 0.53 (0.46, 0.62), 0.83 (0.73, 0.95), and 0.52 (0.44, 0.61), respectively. QgC showed a significant joint effect of PFAS mixture on NAFLD onset (OR: 1.52, 95 % CI: 1.24, 1.88). BKMR showed a weak positive trend between PFAS mixtures and NAFLD incidence. Positive correlations were primarily driven by PFPeA and 9Cl-PF3ONS, while negative correlations were mainly influenced by PFNA and PFOS. The BKMR model also suggested that there was an interaction between PFOS and PFNA and other four PFAS compounds. In conclusion, our findings suggest that individual and co-exposure to PFAS is associated with a risk of NAFLD onset.

Exposure to PM2.5 and its five constituents is associated with the incidence of type 2 diabetes mellitus: a prospective cohort study in northwest China.

Studies have demonstrated that fine particulate matter (PM2.5) is an underlying risk factor for type 2 diabetes mellitus (T2DM), but evidence exploring the relationship between PM2.5 chemical components and T2DM was extremely limited, to investigate the effects of long-term exposure to PM2.5 and its five constituents (sulfate [SO42-], nitrate [NO3-], ammonium [NH4+]), organic matter [OM] and black carbon [BC]) on incidence of T2DM. Based on the "Jinchang Cohort" platform, a total of 19,884 participants were selected for analysis. Daily average concentrations of pollutants were gained from Tracking Air Pollution in China (TAP). Cox proportional hazards regression models were utilized to estimate the hazard ratios (HR) and 95% confidence interval (CI) in single-pollutant models, restricted cubic splines functions were used to examine the dose-response relationships, and quantile g-computation (QgC) was applied to evaluate the combined effect of PM2.5 compositions on T2DM. Stratification analysis was also considered. A total of 791 developed new cases of T2DM were observed during a follow-up period of 45254.16 person-years. The concentrations of PM2.5, NO3-, NH4+, OM and BC were significantly associated with incidence of T2DM (P-trend < 0.05), with the HRs in the highest quartiles of 2.16 (95% CI 1.79, 2.62), 1.43 (95% CI 1.16, 1.75), 1.75 (95% CI 1.45, 2.11), 1.31 (95% CI 1.08, 1.59) and 1.79 (95% CI 1.46, 2.21), respectively. Findings of QgC model showed a noticeably positive joint effect of one quartile increase in PM2.5 constituents on increased T2DM morbidity (HR 1.27, 95% CI 1.09, 1.49), and BC (32.7%) contributed the most to the overall effect. The drinkers, workers and subjects with hypertension, obesity, higher physical activity, and lower education and income were generally more susceptible to PM2.5 components hazards. Long-term exposure to PM2.5 and its components (i.e., NO3-, NH4+, OM, BC) was positively correlated with T2DM incidence. Moreover, BC may be the most responsible for the association between PM2.5 constituents and T2DM. In the future, more epidemiological and experimental studies are needed to identify the link and potential biological mechanisms.

Association between metabolic syndrome and the risk of colorectal cancer: a prospective study in China.

To evaluate the correlation between metabolic syndrome (MetS) and its components on the incidence of colorectal cancer (CRC) based on data from Jinchang Cohort. This is a large prospective cohort study. Between 2011 and 2020, a total of 43 516 individuals from Jinchang Cohort were included for this study. Hazard ratios (HRs) with 95% confidence intervals (CIs) for CRC according to MetS were calculated with the Cox proportional hazard models. The restricted cubic spine models with four knots were conducted to fit the dose-response relationships. MetS was associated with increased risk of CRC (n = 141; HR: 1.64, 95% CI: 1.15-2.33) after adjusting for confounding factors (age, sex, education level, family history of CRC, smoking index and alcohol index). Participants with hyperglycemia had a significantly higher risk of developing incident CRC (HR: 1.70; 95% CI: 1.19-2.43). The positive association between MetS and CRC was observed in males (HR: 1.76; 95% CI: 1.17-2.63), but not in females (HR: 1.24; 95% CI: 0.59-2.64). Furthermore, linear dose-response relationship was found between fasting plasma glucose (FPG) and CRC risk in males ( Poverall < 0.05, Pnon-linear = 0.35). When stratified by smoke and drink, MetS was found to increase the incidence of CRC only in the smoke (HR: 2.07, 95% CI: 1.35-3.18) and drink (HR: 2.93, 95% CI: 1.51-5.69) groups. MetS was associated with a higher risk of CRC incidence. Hyperglycemia lended strong support to the role of MetS in new-onset CRC, especially in males. Other components of MetS were not found to be associated with increased risk of CRC.

Effect of CVAI on the incidence of MASLD compared to BMI in populations with different body types: A prospective cohort study in China

Background and aimsTo explore the relationship between body mass index (BMI), chinese visceral adiposity index (CVAI) and the risk of metabolic dysfunction-associated steatotic liver disease (MASLD) in populations with different body types defined by BMI. Methods and results24 191 participants from the Jinchang cohort were involved in the prospective cohort study with a 2.3-year follow-up. Information from epidemiological investigations, comprehensive health examinations and biochemical examinations was collected. MASLD was assessed by abdominal ultrasonography. BMI and CVAI were calculated using recognized formulas. Cox regressions, Restricted cubic spline (RCS) and Receiver operating characteristic (ROC) analysis were performed. The risk of MASLD increased with the increase in BMI and CVAI (Ptrend <0.001), and there was a nonlinear dose-response relationship. In the total population, BMI and CVAI increased the risk of MASLD with adjusted HR (95%CI) of 1.097 (1.091–1.104) and 1.024 (1.023–1.026), respectively. The results were similar in the lean and overweight/obese groups. There was also a nonlinear relationship between CVAI and MASLD (Pnon-linearity<0.001), no matter in which group. The area under the curve of CVAI was significantly higher than that of BMI in females with different body types, and the areas in the whole females were 0.802 (95%CI: 0.787–0.818) and 0.764 (95%CI: 0.747–0.780), respectively. There was no significant difference in the ability of BMI and CVAI to predict MASLD in all-sex and males, either in lean or overweight/obese groups. ConclusionsCVAI and BMI were independently associated with the risk of MASLD regardless of body types defined by BMI, and CVAI showed better diagnostic ability for MASLD in females.

Progression from different blood glucose states to cardiovascular diseases: a prospective study based on multi-state model.

To quantify the trajectories from normoglycaemia to prediabetes, subsequently to type 2 diabetes (T2DM), cardiovascular diseases (CVD), and cardiovascular death, and the effects of risk factors on the rates of transition. We used data from the Jinchang cohort of 42,585 adults aged 20-88 free of coronary heart disease (CHD) and stroke at baseline. A multistate model was applied for analyzing the progression of CVD and its relation to various risk factors. During a median follow-up of 7 years, 7,498 participants developed prediabetes, 2,307 developed T2DM, 2,499 developed CVD, and 324 died from CVD. Among 15 postulated transitions, transition from comorbid CHD and stroke to cardiovascular death had the highest rate (157.21/1000 person-years), followed by transition from stroke alone to cardiovascular death (69.31/1000 person-years.), and transition from prediabetes to normoglycaemia (46.51/1000 person-years). Prediabetes had a sojourn time of 6.77 years, and controlling weight, blood lipids, blood pressure, and uric acid within normal limits may promote reversion to normoglycaemia. Among transitions to CHD alone and stroke alone, transition from T2DM had the highest rate (12.21/1000 and 12.16/1000 person-years), followed by transition from prediabetes (6.81/1000 and 4.93/1000 person-years), and normoglycaemia (3.28/1000 and 2.39/1000 person-years). Age and hypertension were associated with an accelerated rate for most transitions. Overweight/obesity, smoking, dyslipidemia, and hyperuricemia played crucial but different roles in transitions. Prediabetes was the optimal intervention stage in the disease trajectory. The derived transition rates, sojourn time, and influence factors could provide scientific support for the primary prevention of both T2DM and CVD.

The bidirectional relationship between NAFLD and type 2 diabetes: A prospective population-based cohort study

Background and aimsTo explore the bidirectional relationship between NAFLD and type 2 diabetes and the possible directions of the main effect. Methods and results30 633 participants from the Jinchang cohort were enrolled. Firstly, cox proportional hazards regression model was used to assess the unidirectional causality between NAFLD and prediabetes and type 2 diabetes. Secondly, cross-lag path analysis model was conducted to estimate the bidirectional relationship between NAFLD and prediabetes and type 2 diabetes, and to determine the direction of the main effects. Finally, potential effect modifications were also considered by age, sex, hyperlipidemia, and overweight/obesity. We found that NAFLD increased the risk of prediabetes and type 2 diabetes with adjusted HR (95%CI) of 1.355(95%CI: 1.255–1.462) and 1.898(95%CI: 1.415–2.545), respectively. Prediabetes and type 2 diabetes also increased the risk of NAFLD, with adjusted HR (95%CI) of 1.245(95%CI: 1.115–1.392) and 1.592(95%CI: 1.373–1.846), respectively. Cross-lag path analysis showed that NAFLD significantly affected the incidence of prediabetes (β = 0.285, P < 0.001), while the effect on type 2 diabetes was not statistically significant. The effect of prediabetes and type 2 diabetes on the risk of NAFLD was weak, and the path coefficients were 0.076 and 0.037, respectively. Stratified analyses showed similar results. ConclusionThis study provides evidence that there was a bidirectional causal association between NAFLD and type 2 diabetes, and the progression from NAFLD through prediabetes to type 2 diabetes may be the main pathway.

Mediating effect of metabolic diseases on the relationship between hyperuricemia and coronary heart disease

Background and aimsStudies have shown that elevated serum uric acid (SUA) may increase the risk of coronary heart disease (CHD). However, it is still disputable how mediate effects between metabolic diseases and hyperuricemia affect the incidence of CHD. This study aimed to explore whether metabolic diseases may mediate the connection from hyperuricemia at baseline to the elevated incidence risk of CHD during follow-ups. Methods and ResultsBased on the Jinchang cohort, 48 001 subjects were followed for 9 years between June 2011 and December 2019. Multivariate-adjusted Cox regression models were applied to estimate hazard ratios (HRs) of CHD with 95% confidence intervals (CIs). Significantly increased risks of CHD were observed in hyperuricemia (HR:1.46, 95%CI:1.28, 1.67) when compared with normouricemia population. The mediating effect model further demonstrated that metabolic diseases could mediate the association between hyperuricemia and CHD pathogenesis, partially for the combined metabolic diseases with mediation effects of 45.12%, 25.24% for hypertension, 28.58% for overweight or obese status, 29.05% for hypertriglyceridemia, 6.70% for hypercholesterolemia, 3.52% for low high density lipoprotein cholesterol (HDL-C), and 6.51% for high low density lipoprotein cholesterol (LDL-C), respectively. ConclusionsHyperuricemia significantly increased the risk of incident CHD, and this association was partly mediated by metabolic diseases.

Hidden intrapancreatic portal cavernoma by pancreatic cephalic carcinoma Ц an indication for portal vein resection Ц Case Report

Studies have shown that elevated serum uric acid (SUA) may increase the risk of coronary heart disease (CHD). However, it was still disputable how mediate effects between metabolic diseases and hyperuricemia affect the incidence of CHD. This study aimed to explore whether metabolic diseases may mediate the connection from hyperuricemia at baseline to the elevated incidence risk of CHD during follow-ups.Based on the Jinchang cohort, 48 001 subjects were followed for 9 years between June 2011 and December 2019. Multivariate-adjusted Cox regression models were applied to estimate hazard ratios (HRs) of CHD with 95% confidence intervals (CIs). Significantly increased risks of CHD were observed in hyperuricemia (HR:1.46, 95%CI:1.28, 1.67) when compared with normouricemia population. The mediating effect model further demonstrated that metabolic diseases could mediate the association between hyperuricemia and CHD pathogenesis, partially for the combined metabolic diseases with mediation effects of 45.12%, 25.24% for hypertension, 28.58% for overweight or obese status, 29.05% for hypertriglyceridemia, 6.70% for hypercholesterolemia, 3.52% for low high density lipoprotein cholesterol (HDL-C), and 6.51% for high low density lipoprotein cholesterol (LDL-C), respectively.Hyperuricemia significantly increased the risk of incident CHD, and this association was partly mediated by metabolic diseases.

Metal Exposure, Smoking, and the Risk of COPD: A Nested Case-Control Study in a Chinese Occupational Population.

Chronic obstructive pulmonary disease (COPD) was the third leading cause of death worldwide in 2019, with a significant disease burden. We conducted a nested case–control study using data from the China Metal-Exposed Workers Cohort Study (Jinchang Cohort) and assessed the associations of exposure to metals and tobacco smoking with the risk of COPD. We used the logistic regression model and the interaction multiplication model to assess the independent and combined effects of heavy metal and smoke exposure on COPD. The cumulative incidence of COPD was 1.04% in 21,560 participants during a median of two years of follow-up. The risk of COPD was significantly elevated with an increase in the amount of tobacco smoked daily (p < 0.05), the number of years of smoking (ptrend < 0.05), and the number of packs of cigarettes smoked per year (ptrend < 0.01). Compared with the low metal exposure group, the adjusted OR was 1.22 (95% CI: 0.85–1.76) in the medium exposure group (mining/production workers) and 1.50 (95% CI: 1.03–2.18) in the high exposure group; smoking and metal exposure had a combined effect on the incidence of COPD (pinteraction < 0.01), with an OR of 4.60 for those with >40 pack-years of smoking who also had the highest metal exposures. Both exposures to metals and smoking were associated with the risk of COPD, and there was an interaction between the two exposures for the risk of COPD.

Interaction effect of blood glucose and pressure on the risk of chronic kidney disease: a population-based prospective cohort study.

To evaluate the interaction effect of blood glucose and blood pressure on the risk of chronic kidney disease (CKD). 31,165 subjects were selected without CKD at baseline and had completed the first follow-up from "Jinchang cohort". Cox regression model and restricted cubic splines functions were used to evaluate the effects of blood glucose or pressure on the incidence of CKD and dose-response relationship after adjusting for confounding covariates. Synergic effect was assessed by the multiplicative or additive interaction scale. Among 31,165 subjects, 1307 new-onset CKD were observed during 68905.78 person-years follow-up, and the incidence density was 18.97 per 1000 person-years. The risk of CKD gradually increased with the increase of blood pressure in diabetes, pre-diabetes and normal groups (Ptrend < 0.05). And, the risk was greatest when SBP/DBP reached ≥150/≥110 mmHg in three groups, and HRs (95% CI) were 1.610 (1.070-2.422), 2.142 (1.396-3.288) and 2.455 (1.941-3.106), respectively. Additionally, among hypertension, pre-hypertension and normal groups, the risk of CKD increased by 16.0%, 14.3% and 25.2% for each 1 mmol/L of FPG. When FPG level was more than 9.0 mmol/L, the risk was greatest and adjusted HRs (95% CI) were 2.856 (2.176-3.748), 2.979 (1.828-4.854) and 7.520 (4.517-12.519). Furthermore, the risk was highest when hypertension was accompanied by diabetes (HR = 4.915, 95% CI: 3.923-6.157). This analysis supported a less than multiplicative effect (HR = 0.634, 95% CI: 0.417-0.964) for the interaction term of diabetes and hypertension, while there was no additive interaction towards CKD in all interaction term. Blood glucose and pressure were independent risk factors in incidence of CKD, but there was only a negative multiplicative interaction between hypertension and diabetes, but no additive interaction effect between them.

Cohort Profile Update: the China Metal-Exposed workers Cohort Study (Jinchang Cohort).

The Jinchang Cohort was an ongoing 20-year ambispective cohort with unique metal exposures to an occupational population. From January 2014 to December 2019, the Jinchang Cohort has completed three phases of follow-up. The baseline cohort was completed from June 2011 to December 2013, and a total of 48 001 people were included. Three phases of follow-ups included 46 713, 41 888, and 40 530 participants, respectively. The death data were collected from 2001 to 2020. The epidemiological, physical examination, physiological, and biochemical data of the cohort were collected at baseline and during follow-up. Biological specimens were collected on the baseline to establish a biological specimen bank. The concentrations of metals in urine and serum were detected by inductively coupled plasma mass spectrometry (ICP-MS). The new areas of research aim to study the all-cases mortality, the burden of diseases, heavy metals and diseases, and the course of the chain from disease to high-risk outcomes using a combination of macro and micro means, which provided a scientific basis to explore the pathogenesis of multi-etiology and multi-disease and to evaluate the effects of the intervention measures in the population.

Long-term association of serum selenium levels and the diabetes risk: Findings from a case-control study nested in the prospective Jinchang Cohort

An increasing body of evidence implicates high levels of selenium intake in the development of diabetes, although prospective studies remain sparse. We conducted a nested case-control study of 622 diabetes incident cases and 622-age, sex, and follow-up time-matched controls in the prospective Jinchang cohort of 48,001 participants with a median of 5.8 years of follow-up. Inductively coupled plasma mass spectrometry (ICP-MS) was used to measure all 622 case-control pairs' baseline serum levels of selenium (Se), which were then categorized into quartiles based on the frequency distribution among the controls. Multivariable adjusted conditional logistic regression and restricted cubic splines (RCS) models were applied to evaluate independent odds ratios (OR) as estimates for relative risks (RR) of diabetes according to quartiles (Q) of selenium levels. Compared to the lowest quartile (Q1 as reference), significantly greater diabetes risks (with 95% confidence interval) were observed in Q3 (OR = 1.62, 1.17–2.35) and Q4 (OR = 1.79, 1.21–2.64). Sub-analyses showed these increased risks of diabetes by serum levels of Se. appeared to differ by sex, age, BMI status, history of hypertension, and dyslipidemia. Further, application of RSC models showed that serum Se levels between 95 and 120 μg/L were significantly and positively associated with diabetes risk whereas no apparent relation exists when Se levels were under 95 μg/L in this cohort population.

Serum Uric Acid Levels and Their Changes and Risk of Stroke: A 7-Year Prospective Cohort Study in Northwest China

Introduction: It is not clear whether serum uric acid (SUA) levels and their changes over time are associated with the risk of stroke. A 7-year prospective cohort study in northwest China was conducted to analyze effects of SUA and their changes on the risk of stroke. Methods: A total of 23,262 individuals without cardiovascular disease in the Jinchang cohort were followed up for an average of 5.26 years. The Cox proportional hazard model was used to estimate the hazard ratios (HRs) and 95% confidence interval (95% CI) of stroke incidence to SUA and relative changes in SUA. Sensitivity analysis was performed after controlling the effect of renal insufficiency. Results: Baseline SUA and relative changes in SUA were positively correlated with the incidence of stroke in both males and females (p for overall association <0.0001). Stroke risk increased by 4.6% per 10% increase in the relative change of SUA (HR = 1.046, 95% CI, 1.007–1.086). The fully adjusted regression analysis demonstrated that only the large gain (>30%) in SUA was associated with an increased risk of stroke by 36.5% (95% CI, 1.8–83.0%), compared with the reference group (participants within ±10% changes in SUA). The same trend was observed in people with normal baseline SUA. In the unadjusted model, the risk of stroke associated with elevated SUA was significantly higher in the hyperuricemia group than in the normal SUA group. Conclusion: High initial SUA concentration and an increase in SUA concentration over time would increase the risk of stroke, and this means that there is no safe increase in SUA.

Effects of ambient particulate matter (PM10) on prevalence of diabetes and fasting plasma glucose

Objective: To explore the effect of long-term exposure to ambient particulate matter (PM10) on the prevalence of diabetes and fasting plasma glucose (FPG). Methods: The subjects of the study were from the baseline population of "Jinchang Cohort", and 24 285 subjects were finally included after excluding incomplete home address information and diabetic diagnosis information. The demographic characteristics, lifestyle and health status of the survey subjects were collected through questionnaire, physical examination and laboratory tests. ArcGIS software was used to match the nearest environmental monitoring stations for each subject according to residential address. Two-year average concentrations of PM10 were calculated to estimate exposure level. The logistic regression and the multiple linear regression were conducted to assess the effects of ambient PM10 on the prevalence of diabetes and FPG. The restricted cubic spline was used to quantify the dose-response relationship. Stratified analysis and effect modification analysis were also performed. Results: The age of 24 285 participants was (49.32±8.60) years, and the BMI was (24.22±6.09) kg/m2. There were 13 950 (57.44%) males and 2 066 (8.51%) diabetic patients. After adjusting for confounders, for every 10 μg/m3 increase in the average PM10 concentration in the first two years of the survey, the prevalence of diabetes increased [OR (95%CI) =1.05 (1.01-1.09)]and the FPG level elevated [β (95%CI) = 0.061 (0.047-0.076) mmol/L]. The results of the restricted cubic spline analysis showed a nonlinear relationship between PM10 concentration and FPG level (P<0.001). Further subgroup analysis showed that female [OR (95%CI) =1.10 (1.03-1.18)], people over 50 years old [OR (95%CI) =1.06 (1.02-1.11) ], subjects with family history of diabetes [OR (95%CI) = 1.13 (1.04-1.23) ], and with hypertension [OR (95%CI) = 1.07 (1.02-1.12) ] had a stronger association between the prevalence of diabetes and PM10 exposure (all P interaction values were<0.05). The effects of PM10 on FPG were more significant in people older than 50 years[β (95%CI) = 0.080 (0.050-0.109) mmol/L], with family history of diabetes [β (95%CI) = 0.087 (0.036-0.137) mmol/L], and hypertension [β (95%CI) = 0.077 (0.046-0.108) mmol/L] (all P interaction values were<0.05). Conclusions: Long-term exposure to ambient PM10 increases the diabetes prevalence and FPG. People older than 50 years old, with family history of diabetes and hypertension could be more sensitive to the effects of PM10 exposure.

Comparing the Diagnostic Criteria of MAFLD and NAFLD in the Chinese Population: A Population-based Prospective Cohort Study.

Background and AimsMetabolic dysfunction-associated fatty liver disease (MAFLD) is a new concept, proposed in 2020; however, its applicability in Asia populations has yet to be evaluated. Therefore, we aimed to compare the difference in epidemiological and clinical characteristics between MAFLD and non-alcoholic fatty liver disease (NAFLD) among Asian populations.MethodsBased on the Jinchang cohort, 30,633 participants were collected. The prevalence and incidence of MAFLD and NAFLD were used to analyze the epidemic characteristics and its overlapping effects. In addition, the corresponding clinical characteristics of the two diagnostic criteria populations were compared.ResultsThe prevalence rates of MAFLD and NAFLD were 21.03% and 18.83%, respectively. After an average 2.28-year follow-up, the incidence densities of MAFLD and NAFLD were 41.58 per 1,000 person-years and 37.69 per 1,000 person-years, respectively. With the increase of baseline age, body mass index (BMI), and waist circumference (WC) levels, the prevalence and incidence of MAFLD and NAFLD were on the rise (all ptrend<0.05). Among the total patients diagnosed at baseline or follow-up, most patients had both MAFLD and NAFLD, accounting for 78.84% and 82.88%, respectively. Compared with NAFLD, MAFLD patients had greater proportions of males and metabolic diseases (diabetes, dyslipidemia), and had higher BMI, WC, liver enzymes, blood glucose, and lipid levels in the baseline diagnosis patients (p<0.05). Additionally, lean MAFLD patients had higher metabolic disorders than lean NAFLD patients (p<0.05).ConclusionsCompared with NAFLD, the newly proposed definition of MAFLD is more practical and accurate, and it can help identify more fatty liver patients with high-risk diseases.

Mediating effects of insulin resistance on the development of hypertension associated with elevated serum uric acid: a prospective cohort study.

Many studies have demonstrated that elevated serum uric acid independently increases the risk of developing hypertension. However, the role of insulin resistance in the relationship between serum uric acid and hypertension is still unelucidated. Based on a prospective cohort study, we aimed to examine the longitudinal link between serum uric acid and hypertension and whether this relationship was mediated by insulin resistance. Overall, 21,999 participants without hypertension or gout at baseline with a mean age of 46 ± 13 years in the Jinchang Cohort were included in our study. Adjusted Cox-regression analyses and mediation analyses were performed to assess the risk of hypertension by serum uric acid quartile distribution and whether insulin resistance mediated the association between serum uric acid and hypertension. During the first follow-up period, 3080 participants developed hypertension. After controlling for covariates, compared with the lowest quartile of serum uric acid, the risk of hypertension in the highest quartile was 1.21 (1.06, 1.38) in the overall population. The risks for males and females were 1.14 (1.00-1.29) and 1.30 (1.08-1.56), respectively. The correlation between serum uric acid and hypertension was especially observed in younger people (<30 years). The mediating effects of insulin resistance were 0.058 (0.051, 0.065), 0.030 (0.025, 0.036) and 0.056 (0.047, 0.065), and the proportions mediated were 39.73, 36.59 and 38.62% in the overall, male and female populations, respectively. Elevated serum uric acid levels are associated with an increased risk of incident hypertension, and insulin resistance may play a mediating role in the relationship between serum uric acid and hypertension.

The Risk and Influencing Factors of Hypertension in Jinchang Cohort

Abstract Background To investigate the incidence and influencing factors of hypertension in Jinchang cohort. Methods A prospective cohort study was conducted to collect the baseline and follow-up information of 22,826 employees from 2011 to 2015 based on the Jinchang cohort platform. The incidence of hypertension in the cohort was calculated and the influencing factors were analyzed by Cox proportional risk model. Results The subjects were followed up for an average of 22 years, and the incidence density of hypertension was 56.31/1000 person-years. Multivariate Cox regression results showed that the main risk factors for hypertension in the population included age ≥40 years, alcohol consumption, high-salt diet, history of diabetes, family history of hypertension, overweight and obesity, baseline systolic blood pressure ≥110 mm Hg and diastolic blood pressure ≥75 mm Hg, and higher education levels (middle school, high school, and above) were a protective factor. The incidence of grade 1 hypertension was related to age, drinking, history of diabetes, family history of hypertension and baseline blood pressure levels, and the incidence of grades 2 and 3 hypertension was related to high-salt diet, family history of hypertension, overweight or obesity, and baseline blood pressure levels. Conclusions The Jinchang cohort has a higher risk of hypertension. Age, unhealthy lifestyle, history of diabetes, family history of hypertension, obesity, and baseline blood pressure are strongly associated with the onset of hypertension. There are differences in the risk factors for the occurrence of grades 1, 2, and 3 hypertension.

Relationship of body mass index and blood pressure with diabetes: a nested case-control study

Objective: To explore the relationship of body mass index and blood pressure with the incidence of diabetes in Jinchang cohort. Methods: We designed a nested case-control study, a total of 29 572 workers who had no history of diabetes in baseline survey in Jinchang cohort were selected as the study cohort from June 2011 to December 2013. After 2 year follow-up, 1 021 workers with first diagnosed diabetes were selected as the case group, after 1∶1 matching according to the same gender and age ±2 years among those without diabetes, circulatory system, or endocrine system diseases during the same follow-up period, 1 021 controls was selected and 2 042 subjects were finally included. We used multivariate conditional logistic regression model, additive interaction model and multiplicative interaction model to explore the relationship of body mass index and blood pressure with the incidence of diabetes. Results: After adjusting for factors such as occupation, alcohol use, family history of diabetes, hyperuricemia, hypercholesterolemia, hypertriglyceridemia, low-HDL cholesterolemia and high-LDL cholesterolemia, multivariate conditional logistic regression analysis showed that the risk of diabetes increased with body mass index and blood pressure. Hypertension and overweight/obesity had a multiplicative interaction on the incidence of diabetes. The risks of diabetes in men and women with hypertension and overweight/obese were 2.04 times (95%CI: 1.54-2.69) and 3.88 times (95%CI: 2.55-5.91) higher than those in men and women with normal body weight and blood pressure, respectively. In the combination of BMI and blood pressure, obese individuals with SBP≥160 mmHg were 4.57 times (95%CI: 2.50-8.34) more likely to have diabetes than those with normal BMI and SBP, obese individuals with DBP≥90 mmHg were 3.40 times (95%CI: 2.19-5.28) more likely to have diabetes than those with normal BMI and DBP. Conclusions: Overweight/obesity and hypertension can increase the risk of diabetes. Health education about body weight and blood pressure controls should be strengthened to reduce the risk of diabetes.