BackgroundLung cancer is the leading malignancy worldwide and a major public health challenge in China. Driven by population aging, tobacco use and air pollution, its disease burden remains severe. Zibo is a densely populated industrial city with a heavy lung cancer burden. Understanding the incidence and mortality trends and quantifying age-period-cohort effects are critical for targeted prevention and control. This study analyzed data from 2014 to 2024 to identify trends and high-risk populations to inform evidence-based intervention strategies.ObjectiveThis study aimed to examine trends in lung cancer incidence and mortality among residents of Zibo from 2014 to 2024, evaluate the independent effects of age, period and birth cohort, identify high-risk populations and thereby provide evidence for developing targeted lung cancer prevention and intervention strategies.MethodsIndividual registration data on lung cancer incidence and mortality among registered residents in Zibo from 2014 to 2024 were obtained. The joinpoint regression was used to analyze temporal trends in incidence and mortality rates, and the average annual percentage change (AAPC) with 95% confidence interval (CI) was calculated. The age-period-cohort model was applied to quantify the effects of age, period and cohort on lung cancer incidence and mortality risk among residents aged ≥20 years.ResultsFrom 2014 to 2024, the age-standardized incidence rates of lung cancer among males, females and the overall population in Zibo all showed a significant upward trend, while the age-standardized mortality rates showed a downward trend. The AAPC for the overall standardized incidence rate was 4.34% (95% CI: 2.14–6.59, p< 0.05), and the AAPC for the overall standardized mortality rate was -2.13% (95% CI: -3.53 to -0.72, p< 0.05). The standardized incidence and mortality rates among males were consistently higher than those in females (p< 0.05 for both). The age effect on lung cancer incidence first increased and then decreased with age, the period effect increased over time, and the cohort effect decreased with successive birth cohorts. The age effect on lung cancer mortality increased with age, the period effect increased over time among male, and decreased over time among females. The cohort effect also generally decreased across successive birth cohorts.ConclusionFrom 2014 to 2024, the incidence rate of lung cancer in Zibo continued to increase, while the mortality rate showed a downward trend. Prevention and control of lung cancer should be continuously strengthened in the elderly and males.

INTRODUCTION:Stroke remains a leading cause of death and disability worldwide, with abdominal obesity (AO) and insulin resistance (IR) recognized as modifiable risk factors. However, their joint effects and potential mediating relationships with stroke risk remain unclear. METHODS:This study enrolled 5,537 eligible middle-aged and older Chinese adults from the China Health and Retirement Longitudinal Study (CHARLS). AO was defined on the basis of waist circumference, and cumulative average nontraditional IR parameters were calculated from blood samples collected in 2011 and 2015. Cox proportional hazards models were used to assess adjusted associations, whereas the Kaplan-Meier analysis was used to estimate cumulative hazards. Restricted cubic splines evaluated nonlinear relationships between nontraditional IR parameters and stroke risk among AO participants. Subgroup analyses stratified by age, gender, body mass index, smoking, drinking, and hypertension were used to assess the interaction effects. Sensitivity analyses were used to examine the robustness of the results. Exploratory mediation analyses were performed, with emphasis on indices not directly incorporating waist circumference to reduce potential mathematical overlap with AO. RESULTS:During a median follow-up of 57.2 months, 490 (8.85%) participants experienced stroke. Participants with both AO and elevated IR indices presented the highest stroke risk (p trend <0.05). Among AO individuals, CVAI showed the highest AUC for stroke among the nontraditional IR indices (0.591, 95% confidence interval, 0.560-0.622), indicating modest discriminative ability. Nonlinear associations between lipid accumulation product (LAP), TyG-WC, and stroke risk were observed in non-AO individuals, with significant risk increases above thresholds (LAP 0.64; TyG-WC 594.09). Exploratory mediation analyses suggested potential bidirectional pathways, particularly for indices not directly incorporating waist circumference. CONCLUSION:AO and nontraditional IR parameters jointly contribute to stroke risk. The incorporation of these indices into clinical assessments may increase the accuracy of early stroke prevention strategies and improve risk stratification in middle-aged and older populations.

Higher exposure to organophosphate flame retardants (OPFRs) may contribute to type 2 diabetes mellitus (T2DM), but prospective evidence, lifestyle modification, and biological plausibility remain insufficiently characterized. We conducted a nested case-control study within the Henan Rural Cohort to evaluate associations between OPFRs exposure and incident T2DM. Cox models, WQS, QGC, BKMR, and an adaptive elastic-net-based environmental risk score were used to assess single and mixed OPFRs exposure. Baseline and trajectory-based healthy lifestyle scores were applied to evaluate joint effects, and signed Wald χ² decomposition quantified contributions of OPFRs and lifestyle domains. Mechanistic evidence was integrated from in vitro experiments and toxicogenomic analyses, including RNA-sequencing, network toxicology, molecular docking, and GEO re-analysis, to evaluate EGFR-related signaling and build an integrative adverse outcome pathway. Higher urinary OPFRs, particularly TPHP, were associated with increased T2DM risk, and OPFRs mixtures showed a linear dose-response relationship with T2DM risk. High OPFRs exposure combined with unhealthy or deteriorating lifestyle trajectories conferred the greatest risk, with OPFRs plus lifestyle explaining 55.36% of total model χ². In HepG2 cells, TPHP impaired glucose consumption and downregulated EGFR, whereas EGFR overexpression alleviated the TPHP-associated abnormal glucose consumption. EGFR overexpression was confirmed at both mRNA and protein levels. The p-AKT/total AKT results provided supportive evidence of AKT-related signaling changes, whereas total GLUT2 protein abundance was not significantly altered. Molecular docking supported potential TPHP-EGFR binding affinity of -8.5 kcal/mol, and GEO analyses provided external supportive evidence that EGFR expression tended to be higher in healthier lifestyle-related conditions. These findings suggest that mixed OPFRs exposure, dominated by TPHP, was associated with higher incident T2DM risk, partly attenuated by healthier lifestyle trajectories, supporting combined exposure-reduction and lifestyle-based prevention strategies.

The effects of cooking duration and the combined effects of cooking fuel, cooking duration, and ventilation remain unclear, particularly in relation to evidence from measured kitchen particulate matter (PM) exposure. Data were sourced from the Henan Rural Cohort Study and Panel study. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). Cooking fuel, cooking duration, and kitchen ventilation were obtained, and kitchen PM was monitored using U-MINI208. In qualitative analysis, 9403 participants were enrolled. Individuals with long cooking durations scored 0.36 points lower than those with short ones. Those using solid fuels, particularly with long cooking durations and poor ventilation, had the lowest cognitive scores (β = −2.12) and the highest cognitive dysfunction (CD) risk (OR = 1.88). In quantitative analysis, 135 households and 52 individuals were enrolled. Households utilizing solid fuels, longer cooking durations, or natural ventilation showed significantly increased PM concentrations, and elevated kitchen particulate levels are associated with a decline in MMSE scores. Solid fuel, long cooking duration, and poor ventilation are associated with lower cognitive function, highlighting the importance of transitioning to cleaner energy sources, reducing cooking duration, and improving kitchen environments to protect cognition.

PURPOSE:This study aimed to investigate whether exposure to them increases the risk of hypertension (HTN) and evaluated the mediating role of sex hormones and the modifying effect of lifestyle. METHODS:This study recruited 560 pairs of HTN cases and matched controls. After applying inclusion and exclusion criteria, 444 pairs were included. Plasma pyrethroid and serum sex hormone levels were measured. Cox regression models were used to assess the association between pyrethroid exposure and HTN risk. Quantile G-computation (QGC), Bayesian Kernel Machine Regression (BKMR),and adaptive elastic-net to (AENET) were employed to evaluate the effects of pyrethroid mixture exposure on HTN. Mediation analyses were performed to examine the role of sex hormones, and interaction analyses were conducted to assess the modifying effect of lifestyle. RESULTS:In model 3, six pyrethroids were positively associated with HTN in males and females (hazard ratio (HR): 1.216-1.930), and pyrethroid mixture exposure was positively associated with HTN in males(HR: 1.513, 95% confidence interval (CI): 1.299-1.762), with fenvalerate identified as the primary contributor. Meanwhile, the increased HTN risk associated with pyrethroid exposure was exacerbated by lower socioeconomic status, current or former smoking, current or former drinking, and a BMI ≥ 24 kg/m2. Progesterone exhibited a partial mediating effect on the association between pyrethroid exposure and HTN in males and females, with a mediation proportion of 2.9%-27.6% (P < 0.05). CONCLUSION:Pyrethroid exposure was associated with an increased risk of HTN. This association was influenced by specific lifestyle and partially mediated by progesterone.

Background: Diet plays an important role in preventing and managing the progression from prediabetes to type 2 diabetes mellitus (T2DM). This study aims to develop prediction models incorporating specific dietary indicators and explore the performance in T2DM patients and non-T2DM patients. Methods: This retrospective study was conducted on 2215 patients from the Henan Rural Cohort. The key variables were selected using univariate analysis and the least absolute shrinkage and selection operator (LASSO). Multiple predictive models were constructed separately based on dietary and clinical factors. The performance of different models was compared and the impact of integrating dietary factors on prediction accuracy was evaluated. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the predictive performance. Meanwhile, group and spatial validation sets were used to further assess the models. SHapley Additive exPlanations (SHAP) analysis was applied to identify key factors influencing the progression of T2DM. Results: Nine dietary indicators were quantitatively collected through standardized questionnaires to construct dietary models. The extreme gradient boosting (XGBoost) model outperformed the other three models in T2DM prediction. The area under the curve (AUC) and F1 score of the dietary model in the validation cohort were 0.929 [95% confidence interval (CI) 0.916–0.942] and 0.865 (95%CI 0.845–0.884), respectively. Both were higher than the traditional model (AUC and F1 score were 0.854 and 0.779, respectively, p < 0.001). SHAP analysis showed that fasting plasma glucose, eggs, whole grains, income level, red meat, nuts, high-density lipoprotein cholesterol, and age were key predictors of the progression. Additionally, the calibration curves displayed a favorable agreement between the dietary model and actual observations. DCA revealed that employing the XGBoost model to predict the risk of T2DM occurrence would be advantageous if the threshold were beyond 9%. Conclusions: The XGBoost model constructed by dietary indicators has shown good performance in predicting T2DM. Emphasizing the role of diet is crucial in personalized patient care and management.

The impact of organophosphate pesticide (OPP) exposure on osteoporosis in adult population remains unclear. Thus, it is necessary to explore the association between the exposure to a mixture of OPPs and the prevalence of osteoporosis as well as to identify the major contributor of OPPs in this association. Participants were selected from the 2005-2008 cycle of the NHANES cross-sectional study. OPP exposure was estimated using six different metabolites found in urine. Dual-energy X-ray absorptiometry (DXA) was used to measure bone mineral density (BMD). Survey-weighted generalized linear regression models (SWGLMs) were used to estimate the association between individual OPP exposure and osteoporosis/BMD. Weighted quantile sum (WQS) regression and quantile g-computation (Qgcomp) models were used to assess the mixture of OPPs and identify the key pollutants. SWGLMs indicated that higher concentrations of dimethyl dithiophosphate (DMDTP) and diethyl dithiophosphate (DEDTP) were associated with increased osteoporosis risk in the upper quartiles. WQS models revealed a significant combined effect of six OPP metabolites on osteoporosis (OR = 1.35, 95% CI: 1.06-1.73, P = 0.015), femoral neck BMD (β = -0.012, 95% CI: -0.020, -0.004, P = 0.003) and lumbar spine BMD (β = -0.015, 95% CI: -0.025, -0.006, P = 0.001), with DMDTP and DEDTP identified as key pollutants. Results from the Qgcomp models showed no substantial changes. This study indicated that exposure to both individual OPPs and their mixtures were associated with decreased BMD and increased osteoporosis risk, with DMDTP and DEDTP identified as major contributors to these associations. This underscores the need to prioritize control of these two pollutants to limit their exposure for osteoporosis prevention.

Background This study aims to identify risk factors associated with tuberculosis-specific mortality (TSM) in older adult patients with pulmonary tuberculosis (TB) and to develop a competing risk nomogram for TSM prediction.Methods We conducted a retrospective cohort study and randomly selected 528 older adult pulmonary TB patients hospitalized in designated hospitals in Henan Province between January 2015 and December 2020. The cumulative incidence function (CIF) was calculated for both TSM and non-tuberculosis-specific mortality (non-TSM). A Fine and Gray proportional subdistribution hazards model and a competing risk nomogram were developed to predict TSM in older adult patients.Results The 5-year cumulative incidence functions (CIFs) for TSM and non-TSM were 9.7 and 9.4%, respectively. The Fine and Gray model identified advanced age, retreatment status, chest X-rays (CXR) cavities, and hypoalbuminemia as independent risk factors for TSM. The competing risk nomogram for TSM showed good calibration and excellent discriminative ability, achieving a concordance index (c-index) of 0.844 (95% confidence interval [CI]: 0.830-0.857).Conclusion The Fine and Gray model provided an accurate evaluation of risk factors associated with TSM. The competing risk nomogram, developed using the Fine and Gray model, provided accurate and personalized predictions of TSM.

BackgroundMild cognitive impairment (MCI), as an early manifestation of Alzheimer's disease and dementia, not only diminishes quality of life for the elderly but also imposes a substantial disease burden on society.ObjectiveThis study aims to investigate the epidemiological characteristics and influencing factors of MCI among rural elderly, and to utilize life expectancy (LE) and healthy life expectancy (HLE) as metrics to assess quality of life.MethodsThis study involved 14,549 participants aged 60 years and older from the Henan Rural Cohort Study. Cognitive function was assessed using the Mini-Mental State Examination. LE and HLE were calculated using the Sullivan method. A meta-analysis, which included 16 published studies, was conducted to validate the findings from the cross-sectional survey.ResultsThe crude and age-standardized prevalence of MCI were 32.96% and 34.14%, respectively. The prevalence of MCI was increased with age and was significantly higher among women than men. The results of the meta-analysis support the cross-sectional findings. Older age, being women, living alone, low income, low-level physical activity, insufficient fruit and vegetable intake, night sleep duration ≥8 h, hypertension, dyslipidemia, T2DM, cardiovascular diseases, depression, anxiety, and underweight are associated with an increased risk of MCI. The HLE/LE ratio declined with increasing age, and the HLE/LE ratio of women in each age group is lower than men.ConclusionsMCI is highly prevalent with multiple influencing factors. The HLE/LE ratio of elderly could increase from the reduction of MCI. Future research should focus on targeted screening and intervention approaches for MCI.

Objectives: This study aims to examine the relationship between dietary trace elements and Type 2 diabetes mellitus (T2DM), as well as to assess the influence of body mass index (BMI) on this relationship. Methods: A total of 38,384 participants participated in this study. Dietary intakes of iron, copper, zinc, heme iron, and non-heme iron were assessed using validated food frequency questionnaires. The odds ratio (OR) and 95% confidence interval (CI) were calculated using the logistic regression model to evaluate the association of dietary intake of iron, copper, zinc, heme iron, and non-heme iron with T2DM. Restrictive cubic splines (RCS) were used to explore the dose-response relationship. In addition, causal mediation analysis was used to explore the role of BMI. Results: After adjusting for the relevant covariates, the highest quartile (Q4) compared with the lowest quartile (Q1) the odds ratios and 95% confidence intervals of iron, heme iron, non-heme iron, copper, and zinc between T2DM were 0.81 (0.70-0.92), 0.81 (0.70-0.92), 0.79 (0.70-0.90), 0.64 (0.77-0.72), and 0.65 (0.55-0.78), respectively. The RCS results showed that the hazards of copper and heme iron in T2DM decreased with the increase in dose (p-non < 0.05). The results of the mediation analysis showed that BMI mediated the association between dietary trace elements and T2DM. Furthermore, subgroup analysis showed the same results. Conclusions: This study indicates that moderate intake of dietary trace elements may help reduce the incidence of T2DM in rural areas. BMI can mediate the association between the two.

There is no evidence on the associations between persistent organic pollutants (POPs) and the incidence of chronic kidney disease (CKD) in the Chinese rural population. We aimed to investigate the individual and mixed effects of 22 POPs on the prevalence and incidence of CKD, and the joint effects of POPs and abnormal glucose metabolism as well as the modification effects of healthy lifestyle on these associations. A total of 2775 subjects, including 925 subjects with normal plasma glucose (NPG) and 925 subjects with prediabetes (PDM) and type 2 diabetes mellitus (T2DM), were enrolled from the Henan Rural Cohort Study. Logistic regression and quantile gcomputation were performed to assess the individual and mixed effects of POPs on the risk of CKD. Joint effects of POPs and abnormal glucose metabolism status, as well as the modification effects of lifestyle on CKD were assessed. After 3-year follow-up, an increment of ln-o,p'-DDT was related to an elevated risk of CKD prevalence. Positive associations of p,p'-DDE and (3-BHC with CKD incidence were observed (P < 0.05). In addition, participants with high levels of & sum;POPs were associated elevated incidence risk of CKD (OR: 1.217, 95%CI: 1.008-1.469). One quartile increase in POPs mixture was associated with the increased incidence of CKD among T2DM patients (P < 0.05). Further, a higher risk of CKD was observed among PDM and T2DM patients with high levels of o,p'-DDT, p,p'-DDE, (3-BHC, and & sum;POPs than NPG subjects with low levels of pollutants. In addition, interactive effects of & sum;POPs and lifestyle score on CKD incidence were found. Individual and mixed exposure to POPs increased the prevalence and incidence of CKD, and glucose metabolic status exacerbated the risk of CKD resulting from such exposures. Further, the modifying effects of lifestyle were observed, highlighting the importance of precision prevention for high-risk CKD population and healthy lifestyle intervention measures.

To investigate the association between low-carbohydrate diet scores (LCDs) and the risk of type 2 diabetes in rural China. A total of 38,100 adults were included in the Henan Rural Cohort Study. Macronutrient intake was assessed via a validated food-frequency questionnaire to create low-carbohydrate diet (LCD) scores. Multivariate logistic regression models and subgroup analysis were performed to estimate the odds ratio (OR) and 95

BACKGROUND:Ozone (O3) exposure and telomere shortening are associated with insulin resistance (IR). However, the role of telomere shortening in ambient O3 exposure-related IR is largely unclear. METHODS:The Henan Rural Cohort recruited participants and performed a random forest method to estimate residential O3 concentration. IR was reflected by homeostasis model assessment-IR, quantitative insulin sensitivity check index, triglyceride and glucose index, etc. Generalized linear model, quantile regression model, and mediation effects analysis were utilized to assess the associations of O3 exposure and relative telomere length (RTL) with longitudinal IR markers and their change rates. Furthermore, the role of telomere homeostasis in O3-exposure-induced IR in vivo and in vitro experiments was verified. RESULTS:O3 exposure was positively associated with longitudinal IR. The proportions of RTL mediated associations between O3 exposure and longitudinal IR markers ranged from 11.92 % to 60.36 %. O3-exposed mice exhibited a higher glucose load, upregulation of GSK-3β and G-6-Pase expression at mRNA levels, glycogen accumulation reduction, telomere shortening, and decreased telomerase reverse transcriptase activity relative to air-exposed mice. In vitro experiments reveal that overexpression of TERT in HepG2 cells up-regulated G-6-Pase mRNA expression level. CONCLUSIONS:Impaired telomere homeostasis may be involved in O3 exposure-related IR via inhibition of glycogen synthesis and acceleration of gluconeogenesis and the specific mechanisms are still further elucidated.

Abstract Purpose Growing evidence from observational studies reveals that gut microbiota is associated with type 2 diabetes (T2D), type 1 diabetes (T1D) and glycemic traits. Aiming to comprehensively explore these causal relationships, we conducted a two-sample bidirectional Mendelian randomization (MR) analysis. Method We conducted a bidirectional two-sample Mendelian randomization (MR) analysis using publicly available genome-wide association study (GWAS) summary data. The gut microbiota-related GWAS data were obtained from the MiBioGen consortium, and the summary statistics for T2D and T1D from the GWAS database. Besides, the 3 glycemic traits (2h-glucose, fasting glucose, fasting insulin) summary statistics were all obtained from Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC). The selection of instrumental variables strictly conformed to a set of predefined inclusion and exclusion criteria. Inverse variance weighted (IVW), weighted median, MR-Egger, weighted mode and simple mode were used to access the causal association. Several sensitivity analyses are used to ensure the robustness of the results. Results According to causal effect models with MR analysis, we identified 7 significant causal relationships between gut microbiota and diabetes (T2D/T1D) and glycemic traits, including phylum Verrucomicrobia, genus Actinomyces, family Veillonellaceae, class Melainabacteria, order Gastranaerophilales, family unknownfamily.id.1000001214 and phylum Proteobacteria. Evidence from multiple sensitivity analyses further supports these associations. Conclusions Our research revealed that gut microbiota was causally associated with diabetes (T2D/T1D) and glycemic traits and may provide fresh ideas for early detection and treatment.

Evidence of the relationship between fecal short-chain fatty acids (SCFA) levels, dietary quality and type 2 diabetes mellitus (T2DM) in rural populations is limited. Here, we aimed to investigate the association between fecal SCFA levels and T2DM and the combined effects of dietar quality on T2DM in rural China. In total, 100 adults were included in the case-control study. Dietary quality was assessed by the Alternate Healthy Eating Index 2010 (AHEI-2010), and SCFA levels were analysed using the GC-MS system. Generalised linear regression was conducted to calculate the OR and 95 % CI to evaluate the effect of SCFA level and dietary quality on the risk of T2DM. Finally, an interaction was used to study the combined effect of SCFA levels and AHEI-2010 scores on T2DM. T2DM participants had lower levels of acetic and butyric acid. Generalised linear regression analysis revealed that the OR (95 % CI) of the highest acetic and butyric acid levels were 0·099 (0·022, 0·441) and 0·210 (0·057, 0·774), respectively, compared with the subjects with the lowest tertile of level. We also observed a significantly lower risk of T2DM with acetic acid levels > 1330·106 μg/g or butyric acid levels > 585·031 μg/g. Moreover, the risks of higher acetic and butyric acid levels of T2DM were 0·007 (95 % CI: 0·001, 0·148), 0·005 (95 % CI: 0·001, 0·120) compared with participants with lower AHEI-2010 scores (all P < 0·05). Acetate and butyrate levels may be important modifiable beneficial factors affecting T2DM in rural China. Improving dietary quality for body metabolism balance should be encouraged to promote good health.

Background and aimsHuman studies about short-chain fatty acids (SCFAs), the gut microbiome, and Type 2 diabetes (T2DM) are limited. Here we explored the association between SCFAs and T2DM and the effects of gut microbial diversity on glucose status in rural populations.Methods and resultsWe performed a cross-sectional study from the Henan Rural Cohort and collected stool samples. Gut microbiota composition and faecal SCFA concentrations were measured by 16S rRNA and GC-MS. The population was divided based on the tertiles of SCFAs, and logistic regression models assessed the relationship between SCFAs and T2DM. Generalized linear models tested the interactions between SCFAs and gut microbial diversity on glucose indicators (glucose, HbAlc and insulin). Compared to the lowest tertile of total SCFA, acetate and butyrate, the highest tertile exhibited lower T2DM prevalence, with ORs and 95% CIs of 0.291 (0.085-0.991), 0.160 (0.044-0.574) and 0.171 (0.047-0.620), respectively. Restricted cubic spline demonstrated an approximately inverse S-shaped association. We also noted interactions of the ACE index with the highest tertile of valerate on glucose levels (P-interaction = 0.022) and the Shannon index with the middle tertile of butyrate on insulin levels (P-interaction = 0.034). Genus Prevotella_9 and Odoribacter were inversely correlated with T2DM, and the genus Blautia was positively associated with T2DM. These bacteria are common SCFA-producing members.ConclusionsInverse S-shaped associations between SCFAs (total SCFA, acetate, and butyrate) and T2DM were observed. Valerate and butyrate modify glucose status with increasing gut microbial diversity.

Background and aims: There is no evidence on the longitudinal and causal associations between multiple pesticides and the incidence of type 2 diabetes mellitus (T2DM) in the Chinese rural population, and whether physical activity (PA) modified these associations remains unclear. Here, we aimed to investigate the longitudinal and causal associations between pesticides mixture and T2DM, and determine whether PA modified these associations. Methods: A total of 925 subjects with normal glucose and 925 subjects with impaired fasting glucose (IFG) were enrolled in this case-cohort study. A total of 51 targeted pesticides were quantified at baseline. Logistic regression, quantile g-computation, and Bayesian kernel machine regression (BKMR) were used to assess the individual and combined effects of pesticides on IFG and T2DM. Mendelian randomization (MR) analysis was employed to obtain the causal association between pesticides and T2DM. Results: After 3-year follow-up, one-unit increment in ln-isofenphos, ln-malathion, and ln-deltamethrin were associated with an increase conversion of IFG to T2DM (FDR-P<0.05). One quartile increment in organochlorine pesticides (OCPs), organophosphorus pesticides (OPs), herbicides and pyrethroids mixtures were related to a higher incidence of T2DM among IFG patients (P<0.05). The BKMR results showed a positive trend between exposure to pesticides mixture and T2DM. The MR analysis indicated a positive association between exposure to pesticides and T2DM risk (P<0.05). No any significant association was found between pesticides and IFG. In addition, compared to subjects with high levels of PA, those with low levels of PA were related to increased risk of T2DM with the increased levels of pesticides among IFG patients. Conclusions: Individual and combined exposure to pesticides increased the incidence of T2DM among IFG patients. MR analysis further supported the causal association of pesticides exposure with T2DM risk. Our study furtherly indicated that high levels of PA attenuated the diabetogenic effect of pesticides exposure.

BACKGROUND:Plasma polybrominated diphenyl ethers (PBDE) were used as flame retardants widely, however, epidemiological evidence for the association between PBDEs and type 2 diabetes mellitus (T2DM) is inconsistent. Moreover, the combined effects of PBDEs and blood lipid indicators on impaired fasting glucose (IFG) and T2DM remains largely unknown in rural areas lacking good waste recycling infrastructure. METHODS:In this study, a total of 2607 subjects aged 18-79 years were included from the Henan Rural Cohort. Generalized linear and logistic regression models were applied to evaluate the associations of various PBDE pollutants on IFG and T2DM. Quantile g-computation regression and PBDE pollution score created by the adaptive elastic net were applied to evaluate the impact of PBDEs mixtures on IFG and T2DM. Interaction effects of individual PBDE pollutants and blood lipid indicators on IFG and T2DM were assessed by using Interaction plots. RESULTS:The geometric mean concentrations (detection rates) were 0.09 ng/mL (100.0%), 0.12 ng/mL (97.8%), 0.22 ng/mL (94.7%), 0.16 ng/mL (99.2%) and 0.28 ng/mL (100.0%) for PBDE-28, PBDE-47, PBDE-99, and PBDE-153 respectively. However, PBDE-28, PBDE-99, PBDE-100, and ΣPBDEs were positively associated with IFG (odds ratios (ORs) (95% confidence intervals (CIs)): 1.14 (1.06, 1.23), 1.16 (1.04, 1.29), 1.25 (1.14, 1.37), and 1.27 (1.08, 1.50)). Similarly, PBDE-28, PBDE-47, PBDE-99, PBDE-100, and ΣPBDEs were positively associated with T2DM (ORs (95% CIs): 1.30 (1.10, 1.54), 1.13 (1.06, 1.22), 1.27 (1.13, 1.43), 1.27 (1.15, 1.40), and 1.30 (1.10, 1.54)). Moreover, five PBDE mixtures or jointly as PBDE pollution score, were significantly associated with an increased risk of T2DM (P < 0.05 for all). In addition, the harmful effect of PBDE exposure on T2DM was decreased with accompanying high-density lipoprotein cholesterol (HDL-C) levels increased. CONCLUSIONS:Our findings highlight the importance of managing PBDEs contamination and suggest that HDL-C may be a novel way to prevent T2DM.

Background: Although it has been reported that herbicides exposure is related to adverse outcomes, available evidence on the associations of quantitatively measured herbicides with type 2 diabetes mellitus (T2DM) and prediabetes is still scant. Furthermore, the effects of herbicides mixtures on T2DM and prediabetes remain unclear among the Chinese rural population. Aims: To assess the associations of plasma herbicides with T2DM and prediabetes among the Chinese rural population. Methods: A total of 2626 participants were enrolled from the Henan Rural Cohort Study. Plasma herbicides were measured with gas chromatography coupled to triple quadrupole tandem mass spectrometry. Generalized linear regression analysis was employed to assess the associations of a single herbicide with T2DM, prediabetes, as well as indicators of glucose metabolism. In addition, the quantile g-computation and environmental risk score (ERS) structured by adaptive elastic net (AENET), and Bayesian kernel machine regression (BKMR) were used to estimate the effects of herbicides mixtures on T2DM and prediabetes. Results: After adjusting for covariates, positive associations of atrazine, ametryn, and oxadiazon with the increased odds of T2DM were obtained. As for prediabetes, each 1-fold increase in ln-transformed oxadiazon was related to 8.4% (95% confidence interval (CI): 1.033, 1.138) higher odds of prediabetes. In addition, several herbicides were significantly related to fasting plasma glucose, fasting insulin, and HOMA2-IR (false discovery rates adjusted P value < 0.05). Furthermore, the quantile g-computation analysis showed that one quartile increase in multiple herbicides was associated with T2DM (OR (odds ratio): 1.099, 95%CI: 1.043, 1.158), and oxadiazon was assigned the largest positive weight, followed by atrazine. In addition, the ERS calculated by the selected herbicides from AENET were found to be associated with T2DM and prediabetes, and the corresponding ORs and 95%CIs were 1.133 (1.108, 1.159) and 1.065 (1.016, 1.116), respectively. The BKMR analysis indicated a positive association between mixtures of herbicides exposure and the risk of T2DM. Conclusions: Exposure to mixtures of herbicides was associated with an increased risk of T2DM among Chinese rural population, indicating that the impact of herbicides exposure on diabetes should be paid attention to and measures should be taken to avoid herbicides mixtures exposure.