INTRODUCTION:Residential natural environments are increasingly recognized as important determinants of human health. However, few studies focus on their associations with risk of esophageal squamous cell carcinoma (ESCC) and gastric cancer (GC). We examined the associations of exposure to green and blue space with gastroesophageal cancer (GEC) risks, and explored the interaction and mediating roles of ambient PM2.5 and NO2. METHODS:This population-based case-control study in Taixing included 2200 GEC cases (1332 ESCC and 868 GC cases) and 1711 controls. The 30 m resolution Normalized Difference Vegetation Index (NDVI) and China Land Cover Dataset were used to estimate NDVI values and the proportions of green space (vegetation) and blue space (water bodies) within 300-, 1000-, and 3000-m buffers around residential addresses. PM2.5 and NO2 concentrations were derived from satellite-based estimates. Long-term exposure was defined as the 3-year average before recruitment. Logistic regression models were used to estimate the associations. The interaction and causal mediation analyses were further conducted. RESULTS:Within the 3000-m buffer, higher NDVI (odds ratio (OR) = 0.81 per standard deviation increase; 95% confidence intervals (CIs), 0.75-0.87) and green space proportion (OR = 0.89; 95% CI, 0.83-0.96) were associated with lower GEC risks, while higher blue space proportion was associated with increased risks, particularly for GC (OR = 1.20; 95% CI, 1.11-1.31). Long-term exposure to PM2.5 and NO2 was positively associated with GEC risks, and mediated 24.9%-33.8% of the protective associations of green space, and 26.9%-42.0% of the adverse associations of blue space. CONCLUSIONS:Green and blue spaces were heterogeneously associated with GEC risk, partially mediated by PM2.5 and NO2, highlighting the importance of integrating space planning with quality management to promote public health.

BACKGROUND:Cardiorenal syndrome (CRS), characterized by cardiac-renal interplay, imposes a substantial clinical burden. Biological aging (BA), a comprehensive indicator for evaluating the overall aging status of an organism, is linked to individual cardiac or renal diseases, while its association with mortality in the CRS population remains unclear. METHODS:We utilized data from the National Health and Nutrition Examination Survey (NHANES) from 1999 to 2018. CRS was defined as chronic cardiorenal comorbidities, specifically the coexistence of self-reported cardiovascular disease and calculated chronic kidney disease. BA was calculated using 12 clinical biomarkers, including the Klemera-Doubal method BA (KDM-BA) and Phenotypic Age (PhenoAge). BA acceleration (BAA), quantified as residuals from linear regression of BA on chronological age, includes KDM-BAA and PhenoAge acceleration (PhenoAgeAccel). Cox proportional hazards model and restricted cubic spline were performed to investigate these associations. Results were presented as hazard ratios (HRs) with 95% confidence intervals (CIs). RESULTS:1,100 CRS participants were included in the final analysis. A 10-year increase in KDM-BA and PhenoAge in CRS participants was associated with 1.14-fold and 1.63-fold higher all-cause mortality risk, respectively. CRS participants with KDM-BAA (HR: 1.40, 95% CI: 1.13-1.73) or PhenoAgeAccel (HR: 1.54, 95% CI: 1.20-1.96) had elevated all-cause mortality risk. Meanwhile, these positive associations were more pronounced in males. Moreover, KDM-BA was associated with mortality from heart disease and cerebrovascular disease in the CRS population, with a non-linear relationship observed in cerebrovascular disease. CONCLUSIONS:As composite indicators for aging, KDM-BA, PhenoAge, KDM-BAA, and PhenoAgeAccel are significantly associated with all-cause mortality among the CRS population. These measures may serve as potential prognostic biomarkers in this population.

Achieving early diagnosis of esophageal squamous cell carcinoma (ESCC) is crucial for reducing the disease burden. This study aims to identify potential serum protein biomarkers associated with the early diagnosis of ESCC in two independent populations and further develop diagnostic models. This case-control study identified and verified ESCC-shared biomarkers in two independent populations from Taixing and Jinan (China). Utilize differential analysis and functional enrichment analysis, combined with bulk RNA-seq and single-cell RNA-seq data, were used to validate the diagnostic proteins at the tissue and cellular levels. Multiple machine learning algorithms were employed to screen core diagnostic proteins and develop models for ESCC diagnosis. Indicators such as area under the curve (AUC) were used to evaluate the models’ predictive capabilities for ESCC diagnosis. Lastly, survival data were employed to further expand the ability of genes encoding core diagnostic proteins to predict the prognosis of ESCC patients. Differential analysis validated across two independent populations revealed 13 proteins with diagnostic value for ESCC, with most proteins showing consistent expression patterns in mRNA and single-cell RNA compared to serum samples. The diagnostic model, developed using the gradient-boosted decision tree algorithm based on the variables retained after Lasso regression (CDKN1A, ADAMTS15, KLK13, RSPO3, ANXA1, MetAP2), demonstrated best discriminative effects, calibration, and clinical utility in both the derivation dataset [AUC = 0.932, 95

Parkinson's disease (PD) is often perceived as an elderly condition, overshadowing its impact on younger populations. This study assesses the global, regional, and national burden of early-onset PD (EOPD) in individuals aged 20-49 years from 1990 to 2021 using data from the Global Burden of Disease Study 2021. The estimated annual percentage change (EAPC) quantified temporal trends, while the Socio-Demographic Index (SDI) evaluated socioeconomic development. Decomposition analysis identified factors driving burden changes, while health inequality analysis quantified cross-national disparities. Findings reveal that global incident cases, prevalent cases, and years lived with disability (YLDs) for EOPD more than doubled over three decades. Age-standardized rates per 100,000 increased for incidence (1.46-2.35), prevalence (10.03-14.00), and YLDs (1.65-2.27), with corresponding EAPCs of 1.42%, 1.09%, and 1.05%, respectively. Men had approximately 1.5 times higher burden than women. Health inequalities have worsened, with middle and high-middle SDI regions, especially East Asia and Andean Latin America, exhibiting the fastest growth rates. EOPD burden positively correlated with pesticide use. Rising YLDs were primarily driven by increased prevalence in higher SDI regions and population growth in lower SDI regions. These trends highlight the need for targeted strategies to reduce risk and promote early diagnosis in younger populations globally.

BACKGROUND:Evidence linking residential environments to Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains limited. METHODS:We followed 426,220 dementia-free UK Biobank participants for a median 13.6 years. Cox models evaluated associations between green, blue spaces, and natural environment (300 and 1000 m buffers) and AD/MCI risk, alongside exploratory mediation and gene-environment interactions. RESULTS:Incident cases included 3513 AD and 864 MCI. For AD, higher green space, blue space, and natural environment within 300 m lowered risk (decreases of 5.8, 0.8, and 5.7% per IQR, respectively). At 1000 m, only green space (6.7%) and natural environment (5.7%) remained inversely associated. Blue space exhibited a non-linear AD association. For MCI, green and natural spaces consistently lowered risk across both buffers (14.9-22.2% reductions); blue space was non-significant. Analyses showed limited mediation by air pollutants and selective additive gene-environment synergy. CONCLUSION:Higher residential green space and natural environment are observationally associated with reduced AD and MCI risks, whereas blue space associations with MCI remain inconclusive. Underlying mechanisms and exploratory gene-environment synergies require further validation.

This study aims to assess the current status of self-management ability in patients with neurogenic bladder resulting from traumatic spinal cord injury (TSCI) and to analyze the factors influencing this ability. A cross-sectional study was conducted from November 2023 to July 2024, a total of 305 patients with neurogenic bladder due to TSCI were recruited from over 30 medical institutions, including rehabilitation centers within tertiary general hospitals, secondary rehabilitation hospitals, and private rehabilitation facilities across Shandong Province, China. Face-to-face interviews were conducted using a general information questionnaire, the patient positive measurement scale, the social support rating scale, and the self-management ability scale. Pearson correlation analysis and multiple linear regression were employed for statistical analysis. Among 305 patients with neurogenic bladder caused by TSCI, the score of positivemeasurement scale was 40.95 ± 8.26, the score of the social support scale was 38.12 ± 8.35, and the score of self-management ability scale was 118.19 ± 37.16. In univariate analysis, social support (β = 0.364, P < 0.001), positive degree (β = 0.505, P < 0.001) and bladder training (β = -0.349, P < 0.001) positively predicted self-management ability. Age was a negative predictor of self-management ability (β = -0.204, P < 0.001). In multiple linear regression analysis, social support (β = 0.194, P < 0.001), bladder training (β = -0.262, P < 0.001) and positive degree (β = 0.376, P < 0.001) were still positively correlated with self-management ability. Self-management ability in patients with neurogenic bladder secondary to TSCI is moderate, and patient activation is low. The establishment of an effective social support system, the implementation of bladder training and the improvement of patients positive degree can be used as an entry point to improve their self-management behavior ability, so as to reduce the patients’ dependence on others care and prepare for discharge and return to society. Not applicable.

Lung cancer is the most common cancer and the leading cause of cancer-related deaths. Developing therapies for lung cancer is challenging, and new targets are urgently required. TFIIB-related factor 2 (BRF2) plays a crucial role in the development and progression of various tumors. However, the potential role of BRF2 in lung squamous carcinoma (LUSC) is unclear. Therefore, the aim of this study was to elucidate the mechanism of BRF2 regulation in LUSC development. Flow cytometry, protein blotting, and in vivo experiments were performed to assess the function of BRF2 in LUSC. Transmission electron microscopy imaging and mitochondrial membrane potential (MMP) measurements were used to determine the effect of BRF2 on mitochondria in LUSC. The impact of the downstream molecule SLC8A3 was predicted using bioinformatics analysis, and the mechanism was investigated by analyzing quantitative reverse transcription-polymerase chain reaction and immunoprecipitation (IP) assays, which were confirmed through rescue experiments. BRF2 expression was upregulated in squamous carcinoma cells, which increased SLC8A3 protein expression, promoted mitochondrial autophagy, stabilized MMP, and reduced apoptosis. In addition, SLC8A3 overexpression inhibited PTEN-induced putative kinase 1 (PINK1) binding to TIMM23 to promote mitochondrial autophagy and stabilize the MMP, which counteracted BRF2 knockdown-induced apoptosis. BRF2 mediated SLC8A3 expression to reduce apoptosis in LUSC cells by maintaining mitochondrial homeostasis. These findings provide novel selective therapeutic targets and ideas for the treatment of LUSC.

Previous studies have demonstrated that reproductive factors are associated with future frailty or frailty-related health issues in women. In view of the limited evidence, investigating the association between different reproductive factors and frailty in older women requires further exploration. The purpose of this study is to explore whether reproductive factors contribute to an increased risk of frailty in older women. This study included 238,555 female participants from the UK Biobank. Reproductive factors and several frailty-related indicators were obtained through self-reported data. A binomial logistic regression analysis was used to assess the association between reproductive factors and frailty in older women, while adjusting for age, race, smoking and drinking status, body mass index (BMI), Townsend deprivation index, education, occupation, income level, history of oral contraceptive use, and history of hormone replacement therapy (HRT) use. Finally, stratified analysis and sensitivity analysis were performed to verify the robustness of our results. Compared with the menarche age of 12–13 years old, the risk of frailty was higher in those < 12 years old (OR: 1.118, 95

Early identification of high-risk individuals is crucial for optimizing cancer screening, particularly when considering expensive and invasive methods such as multi-omics technologies and endoscopic procedures. However, developing a robust, practical multi-cancer risk prediction model that integrates diverse, multi-scale data and with proper validation remains a significant challenge. We initialized the FuSion study by recruiting 42,666 participants from Taizhou, China, with a discovery cohort (n = 16,340) and an independent validation cohort (n = 26,308) after exclusion criteria. We integrated multi-scale data from 54 blood-derived biomarkers and 26 epidemiological exposures to develop a risk prediction model for five common cancers, including lung, esophageal, liver, gastric, and colorectal cancer. Employing five supervised machine learning approaches, we used a LASSO-based feature selection strategy to identify the most informative predictors. The model was trained and internally validated in the discovery cohort, externally applied in the validation cohort, and further evaluated through a prospective clinical follow-up to assess cancer events via clinical examinations. The final model comprising four key biomarkers along with age, sex, and smoking intensity, achieving an AUROC of 0.767 (95

BACKGROUND:Previous studies reported possible connections between dietary factors and pregnancy complications; however, confounders tend to confound the results. A two-sample Mendelian randomization (MR) study was carried out to explore the impact of dietary intakes on the risk of pregnancy complications. METHODS:Exposure data in this study were derived from the IEU Open GWAS project, and the outcome data were from the FinnGen study. The inverse variance-weighted (IVW) method is the main analytical method used in this study. In addition, we verified the accuracy of the findings by performing sensitivity analyses using other methods. RESULTS:After rigorous False Discovery Rate (FDR) correction, dried fruit intake can reduce the risk of ectopic pregnancy (OR [odds ratio]: 0.36, 95% CI [confidence interval]: 0.21-0.62). Fresh fruit intake was positively associated with pregnancy hypertension (OR: 2.26, 95% CI: 1.32-3.87), and cheese intake was negatively related to pregnancy hypertension (OR: 0.63, 95% CI: 0.47-0.85). In addition, cheese intake was negatively associated with pre-eclampsia (OR: 0.53, 95% CI: 0.38-0.72) and gestational diabetes (OR: 0.48, 95% CI: 0.36-0.64). There was no significant causality in this study for the analyses of other dietary intakes and pregnancy complications, and no heterogeneity or horizontal pleiotropy was found. CONCLUSIONS:Our two-sample MR study explores the causal association between dietary intakes and pregnancy complications, and our results contribute to the primary prevention of pregnancy complications. The mechanism by which dietary intakes affects pregnancy complications can be validated by further basic observational studies.

BACKGROUND:The Life's Essential 8 (LE8) score, developed by the American Heart Association to evaluate cardiovascular health (CVH), was recently updated. Few studies have explored its effect on the incidence, progression, and prognosis of cardiometabolic multimorbidity (CMM). This study examines the association between the LE8 and the progress of CMM. METHODS:This prospective cohort study included 264,597 participants from the UK Biobank. CMM was defined as the presence of at least two of the three cardiometabolic diseases (CMDs): type 2 diabetes (T2D), stroke, and ischemic heart disease (IHD). Multi-state models were employed to investigate the relationship between the LE8 score and its subscales with risk of transitions from healthy to the onset of first cardiometabolic diseases (FCMD), followed by progression to CMM and to death. RESULTS:Over a median follow-up of 13.94 years, 33,868 individuals developed at least one CMD, 4282 were diagnosed with CMM, and 4955 died. The results indicated that for each 1-SD increase in the LE8 score, a notable reduction was observed in the rate of progression from baseline to FCMD, baseline to death, and FCMD to CMM, with hazard ratios (HRs) of 0.64 (95% CI: 0.63, 0.65), 0.83 (95% CI: 0.81, 0.85), and 0.80 (95% CI: 0.78, 0.83), respectively. However, no correlation was found between the LE8 score and the transition from FCMD or CMM to death. By contrast, per 1-SD increment in the behavior scale score was associated with decreased risk of transition from FCMD to death (HR: 0.93; 95% CI: 0.90, 0.95) and from CMM to death (HR: 0.89; 95% CI: 0.88, 0.95), while per 1-SD increment in the biological scale score was associated with increased risk of transition from FCMD to death (HR: 1.10; 95% CI: 1.07, 1.14), and from CMM to death (HR: 1.22; 95% CI: 1.15, 1.29). CONCLUSION:The LE8 defined CVH influences the progression from a healthy state to FCMD, CMM, and death, highlighting the importance of improving CVH as a comprehensive approach for preventing CMM.

Currently, there is an absence of large-scale research focusing on the metabolome profiles of individuals prior to the development of sepsis. This study aimed to evaluate the associations of circulating Nuclear Magnetic Resonance (NMR) metabolic biomarkers with the risk of incident sepsis and the predictive ability of these metabolites for sepsis. The analysis utilized plasma metabolomic data measuring through NMR from the UK Biobank, which involved baseline plasma samples of 106,533 participants. The multivariable-adjusted Cox proportional hazard models were used to assess the associations of each circulating NMR metabolite biomarker with risk of incident sepsis. The full cohort was randomly assigned to a training set (n = 53,267) and a test set (n = 53,266) to develop and validate the sepsis risk prediction model. In training set, the least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression analyses were used to develop the prediction model. In test set, the predictive ability of conventional risk factors-based and combined metabolic biomarkers prediction model was assessed by Harrell’s C-index. The incremental predictive power of the metabolic biomarkers was evaluated with continuous net reclassification improvement (NRI) and integrated discrimination improvement (IDI). A total of 90 circulating metabolic biomarkers were significantly associated with risk of incident sepsis (all FDR adjusted P value < 0.05). Of these, triglycerides related lipid sub-classes, glycolysis, ketone bodies, and inflammation related metabolite biomarkers, creatinine, and phenylalanine were positively associated with risk of incident sepsis, while most of other lipid sub-classes, albumin, histidine, fatty acid and cholines related metabolic biomarkers were negatively associated with risk of sepsis. The Harrell’s C-index of the conventional prediction model was 0.733 (95

The prospective relationship between proteomics and inflammatory bowel disease (IBD) remains largely underexplored, presenting potential of therapeutic interventions. Using data from 48,800 IBD-free participants in the UK Biobank Pharma Proteomics Project (UKB-PPP), we assessed associations between 2923 plasma proteins and incident IBD risk using Cox analysis. Mendelian randomization (MR) meta-analysis, integrating cis-protein quantitative trait loci data from the UKB-PPP with IBD genome-wide association study data from the International Inflammatory Bowel Disease Genetics Consortium and FinnGen studies, identified causal proteins. Colocalization analysis strengthened evidence of shared common causal variants. Cohort study revealed associations of 673, 295, and 125 proteins with the risk of IBD, Crohn's disease (CD), and ulcerative colitis (UC), respectively. MR and colocalization analyses prioritized IL12B, CD6, MXRA8, CXCL9, IFNG, CCN3, RSPO3, and IL18 as having causal and high colocalization evidence with IBD or its subtypes. Our findings advance understanding of IBD's molecular etiology and highlight potential therapeutic targets.

Objectives:Limited epidemiological study has examined the association between rotating shift work and risk of irritable bowel syndrome (IBS). This study aimed to investigate the association between shift work and risk of IBS and explore the potential mediating factors for the association. Methods:A total of 268,290 participants from the UK Biobank were included. Cox proportional hazards model was used to examine the associations between shift work and the incidence of IBS. The mediation analyses were performed to investigate the mediating effects. Results:Participants engaged in always/usually shift work showed a significantly increased risk of developing IBS (HR: 1.12, 95% CI: 1.03-1.23). Joint analysis indicated that, participants with both always/usually shift work and inadequate sleep duration had a 54% increased risk of IBS (HR: 1.54, 95% CI: 1.35-1.82) compared to those with adequate sleep duration and never/rarely shift work; while participants with both always/usually shift work and insomnia-always had a 65% increased risk of IBS (HR: 1.65, 95% CI: 1.43-1.90) compared to those with never/rarely shift work and never/sometimes insomnia. Mediation analysis revealed that sleep quality and anxiety/depression partially mediated the relationship between shift work and IBS incidence, contributing 16.1% (6.8-25.4%) and 3.6% (0.4-6.8%) of the mediation effect, respectively. Conclusion:This study found that participants with always/usually shiftwork status had significantly increased risk of IBS, and this association may partially be mediated by anxiety/depression and sleep quality. Moreover, inadequate sleep duration and usually insomnia may intensify the effect of rotating shift work on the risk of incident IBS.

Colorectal cancer (CRC) is a frequently lethal disease, with stage II/III CRC accounting for ≈70%. Metabolic reprogramming plays a pivotal role in deciphering cancer heterogeneity and progression. Here, 9 datasets and 83 machine learning algorithm combinations are leveraged‌ to develop the Machine Learning-based Metabolic gene Prognostic Signature (MALMPS) model. The MALMPS model outperformed traditional clinical traits and molecular features in predicting prognosis for stage II/III CRC patients across training and validation datasets. COX7B, a key gene in MALMPS, is shown to promote CRC malignancy through multi-omics analysis and in vitro assays. CRC patients are stratified into high- and low-risk groups based on the median cutoff of MALMPS. Notably, the high-risk subgroup exhibited poor prognosis, activated inflammation, and enriched carbohydrate, glycosaminoglycan, and lipid metabolism, with therapeutic potential for IGF-1R and Wnt/β-catenin inhibitor. In contrast, the low-risk group displayed a TGF-β pathway inactivating mutation and enriched in nucleotides, cofactors, and amino acids metabolism. Metabolite profiling in the in-house SDCRC dataset validated the distinct metabolic alterations between the two groups. These findings indicate that MALMPS is a valuable instrument for predicting the recurrence risk of stage II/III colorectal cancer, particularly for identifying individuals at high risk.

The potential links between triglyceride-glucose (TyG) related indicators and breast cancer incidence after menopause have been less well studied, and the joint associations between genetic risk, TyG related indicators, and breast cancer are unknown. Simple surrogate indicators of insulin resistance including TyG, TyG-waist circumference (TyG-WC), TyG-waist to height ratio (TyG-WHtR), TyG-waist to hip ratio (TyG-WHR), TyG-body mass index (TyG-BMI). Genetic susceptibility in breast cancer was estimated by categorizing polygenic risk scores (PRS). For estimating the associations, we used Cox proportional hazards regression modeling. Correlation shapes were evaluated using restricted cubic splines (RCS). Mediation analyses for assessing the role of sex hormone-binding globulin (SHBG), C-reactive protein (CRP), testosterone, and glycosylated hemoglobin (HbA1c) in mediating the associations were conducted. The study included 83,873 UK biobank participants who were followed for a median of 13.8 years, with 3,561 new cases of postmenopausal breast cancer. Genetic risk and TyG related indicators were monotonically related to breast cancer, with additive but not multiplicative interactions between them. The highest quartiles of TyG, TyG-WC, TyG-WHtR, TyG-WHR, and TyG-BMI were significantly associated with increased breast cancer risk with hazard ratio (95

Stroke risk increases with chronological age, but the relationship with biological age (BA) acceleration is poorly understood. We aimed to examine the association between BA acceleration and incident stroke and its subtypes, explore the modifying effects on genetic susceptibility, and assess how BA acceleration mediates the effect of behavior score. We studied 253,932 UK Biobank participants and computed two BA measures (Klemera-Doubal Method [KDM], Phenotypic Age [PhenoAge]), with BA acceleration calculated by regressing BA on chronological age. The polygenic risk score (PRS) was derived from 87 genetic loci. The behaviors score was based on diet, physical activity, tobacco/nicotine, sleep, and BMI. During a median follow-up of 13.6 years, 5460 strokes, 4337 ischemic stroke (IS), 951 intracerebral hemorrhage (ICH), and 553 subarachnoid hemorrhage (SAH) cases were documented. Adjusting for confounding factors, each standard deviation increase in BA acceleration was associated with higher stroke risk: for KDM-BA acceleration, stroke (HR = 1.28, 95% CI = 1.25-1.32), IS (1.32, 1.28-1.36), ICH (1.15, 1.08-1.23), and SAH (1.16, 1.07-1.27); for PhenoAge acceleration, stroke (1.22, 1.19-1.25), IS (1.26, 1.22-1.29), ICH (1.08, 1.02-1.16), and SAH (1.08, 1.00-1.18). Compared to participants with the lowest PRS and BA acceleration, those with the highest PRS and BA acceleration had the highest stroke risk (KDM-BA acceleration: 2.19, 1.85-2.59; PhenoAge acceleration: 2.03, 1.69-2.42). Additionally, there was an additive interaction between KDM-BA acceleration and PRS. The mediation proportion of BA acceleration in associations of behaviors score with incident stroke and its subtypes ranged from 15.84% to 33.08%. BA acceleration may raise stroke risk, especially in those with high genetic risk. Maintaining healthy behaviors may help mitigate this risk.

BackgroundThere is growing evidence of bidirectional associations between rheumatoid arthritis and adverse pregnancy outcomes (APOs) in observational studies, but little is known about the causal direction of these associations. Therefore, we explored the potential causal relationships between rheumatoid arthritis and APOs using a bidirectional two-sample Mendelian randomization (MR) in European and Asian populations.MethodsWe conducted a bidirectional two-sample Mendelian randomization analysis using available summary statistics from released genome-wide association studies. Summary statistics for instrument-outcome associations were retrieved from two separate databases for rheumatoid arthritis and adverse pregnancy outcomes, respectively. The inverse-variance weighted method was used as the primary MR analysis, and cML-MA-BIC was used as the supplementary analysis. MR-Egger, MR pleiotropy residual sum and outlier (MR-PRESSO), and Cochran Q statistic method were implemented as sensitivity analyses approach to ensure the robustness of the results.ResultsOur study showed that a higher risk of rheumatoid arthritis in the European population was associated with gestational hypertension (OR: 1.04, 95%CI: 1.02-1.06), pre-eclampsia (OR: 1.06, 95%CI: 1.01-1.11), fetal growth restriction (OR: 1.08, 95%CI: 1.04-1.12), preterm delivery (OR:1.04, 95%CI: 1.01-1.07). Furthermore, we found no evidence that APOs had causal effects on rheumatoid arthritis in the reverse MR analysis. No association between rheumatoid arthritis and APOs was found in East Asian population. There was no heterogeneity or horizontal pleiotropy.ConclusionsThis MR analysis provides the positive causal association from rheumatoid arthritis to gestational hypertension, pre-eclampsia, fetal growth restriction and preterm delivery genetically. It highlights the importance of more intensive prenatal care and early intervention among pregnant women with rheumatoid arthritis to prevent potential adverse obstetric outcomes.

Exploring the association between exposure to polycyclic aromatic hydrocarbons (PAHs) and the risk of dyslipidemia and possible mediating effects is essential for conducting epidemiological health studies on related lipid disorders. Therefore, our study aimed to elucidate the potential association between PAH exposure and dyslipidemia risk and further identify the mediating effects based on blood cell-based inflammatory biomarkers. This cross-sectional study was conducted on 8380 individuals with complete survey data from the National Health and Nutrition Examination Survey (2001-2016). Multiple models (generalized linear regression model, restricted cubic spline model, Bayesian kernel machine regression, weighted quantiles sum regression) were used to assess the relationship between PAH co-exposure and the dyslipidemia risk and further identify potential mediating effects. Among the 8380 subjects, 2886 (34.44 %) had dyslipidemia. After adjusting for the confounding factors, the adjusted OR and 95% CI for dyslipidemia in the highest quartile of subjects were 1.30 (1.11, 1.51), 1. 22 (1.04, 1.43), 1.21 (1.03, 1.42), 1.29 (1.10, 1.52), 1.18 (1.01, 1.37), and 1.04 (0.89, 1.23) for 1-hydroxynaphthalene, 2-hydroxynaphthalene, 3-hydroxyfluorene, 2-hydroxyfluorene (2-FLU), 1-hydroxyphenanthrene, and 1-hydroxypyrene. The Bayesian kernel machine regression model also showed a positive correlation between PAH mixtures and dyslipidemia, and 2-FLU has the highest contribution. Mediation effect analyses showed that white blood cells and neutrophils were statistically significant in the association between PAHs and dyslipidemia. The present study suggests that individual and mixed PAH exposures may increase the risk of dyslipidemia in adults. Inflammatory biomarkers significantly mediated the relationship between PAH exposure and dyslipidemia. Environmental pollutants and their mechanisms should be more intensively monitored and studied.