The effects of Mediterranean diet on cardiovascular risk factors, glycemic control and weight loss in patients with type 2 diabetes: a meta-analysis.

To explore the impact of the Mediterranean diet on cardiovascular risk factors, glycemic control and weight loss in patients with type 2 diabetes(T2D) by a meta-analysis of randomized controlled trials (RCTs). We systematically searched PubMed, Cochrance Library, EMBASE and four Chinese databases to identify RCTs that compared the Mediterranean diet with control diets in patients with T2D up to December 2021. The Risk of Bias of the included studies was assessed using the version 2 of the Cochrane risk-of-bias tools for randomized trials (ROB 2). Seven RCTs with 1371 patients met the eligibility criteria and entered into the meta-analysis. Compared to control diets, the beneficial effects of Mediterranean diet were not statistically significant in high-density lipoprotein (MD = 2.33; 95% CI: -0.27 to 4.92), low-density lipoprotein (MD = -2.34; 95% CI -5.67 to 0.99) and total cholesterol (MD = 2.60; 95% CI: -0.95 to 6.15). But Mediterranean diet led to reduce the level of diastolic blood pressure (MD = -1.20; 95% CI: -2.21 to -0.19) and systolic blood pressure (MD = -4.17; 95% CI: -7.12 to -1.22). Meanwhile, Mediterranean diet showed beneficial effects in glycemic control (HbA1[%]: MD = -0.39, 95% CI: -0.58 to -0.20; fasting plasma glucose: MD = -15.12, 95% CI: -24.69 to -5.55) and weight loss (BMI: MD = -0.71, 95% CI: -1.30 to -0.78; WC: MD = -1.69; 95% CI: -3.35 to -0.02) compared to the control diets. The meta-analysis presented evidence supporting the beneficial effects of the Mediterranean diet on blood pressure, glycemic control, and weight loss. However, the impact of the Mediterranean diet on the lipid profile was not found to be significant, warranting further verification. This Meta-analysis was registered on the INPLASY website (Registration number: INPLASY 202160096).

MHCII-peptide presentation: an assessment of the state-of-the-art prediction methods.

Major histocompatibility complex Class II (MHCII) proteins initiate and regulate immune responses by presentation of antigenic peptides to CD4+ T-cells and self-restriction. The interactions between MHCII and peptides determine the specificity of the immune response and are crucial in immunotherapy and cancer vaccine design. With the ever-increasing amount of MHCII-peptide binding data available, many computational approaches have been developed for MHCII-peptide interaction prediction over the last decade. There is thus an urgent need to provide an up-to-date overview and assessment of these newly developed computational methods. To benchmark the prediction performance of these methods, we constructed an independent dataset containing binding and non-binding peptides to 20 human MHCII protein allotypes from the Immune Epitope Database, covering DP, DR and DQ alleles. After collecting 11 known predictors up to January 2022, we evaluated those available through a webserver or standalone packages on this independent dataset. The benchmarking results show that MixMHC2pred and NetMHCIIpan-4.1 achieve the best performance among all predictors. In general, newly developed methods perform better than older ones due to the rapid expansion of data on which they are trained and the development of deep learning algorithms. Our manuscript not only draws a full picture of the state-of-art of MHCII-peptide binding prediction, but also guides researchers in the choice among the different predictors. More importantly, it will inspire biomedical researchers in both academia and industry for the future developments in this field.

MRSL: a causal network pruning algorithm based on GWAS summary data.

Causal discovery is a powerful tool to disclose underlying structures by analyzing purely observational data. Genetic variants can provide useful complementary information for structure learning. Recently, Mendelian randomization (MR) studies have provided abundant marginal causal relationships of traits. Here, we propose a causal network pruning algorithm MRSL (MR-based structure learning algorithm) based on these marginal causal relationships. MRSL combines the graph theory with multivariable MR to learn the conditional causal structure using only genome-wide association analyses (GWAS) summary statistics. Specifically, MRSL utilizes topological sorting to improve the precision of structure learning. It proposes MR-separation instead of d-separation and three candidates of sufficient separating set for MR-separation. The results of simulations revealed that MRSL had up to 2-fold higher F1 score and 100 times faster computing time than other eight competitive methods. Furthermore, we applied MRSL to 26 biomarkers and 44 International Classification of Diseases 10 (ICD10)-defined diseases using GWAS summary data from UK Biobank. The results cover most of the expected causal links that have biological interpretations and several new links supported by clinical case reports or previous observational literatures.

Greenness mitigate cause-specific mortality associated with air pollutants in ischemic and hemorrhagic stroke patients: An ecological health cohort study.

BACKGROUND: Air pollution is one of the most serious environmental risks to mortality of stroke. However, there exists a noteworthy knowledge gap concerning the different stroke subtypes, causes of death, the susceptibility of stroke patient, and the role of greenness in this context. METHODS: We analyzed data from an ecological health cohort, which included 334,261 patients aged ≥40 years with stroke (comprising 288,490 ischemic stroke and 45,771 hemorrhagic stroke) during the period 2013-2019. We used Cox proportional hazards models with time-varying exposure to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) to assess the associations of annual average fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) with both all-cause and cause-specific mortality. Additionally, we conducted analyses to examine the effect modification by greenness and identify potential susceptibility factors through subgroup analyses. RESULT: In multivariable-adjusted models, long-term exposure to PM2.5 and NO2 was associated with increased risk of all-cause mortality (HR: 1.038, 95% CI: 1.029-1.047 for PM2.5; HR: 1.055, 95% CI: 1.026-1.085 for NO2, per 10 µg/m3, for ischemic stroke patients; similar for hemorrhagic stroke patients). Gradually increasing effect sizes were shown for CVD mortality and stroke mortality. The HRs of mortality were slightly weaker with high versus low vegetation exposure. Cumulative exposures increased the HRs of pollutant-related mortality, and greater greenness decreased this risk. Two subtypes of stroke patients exhibited diverse patterns of benefit. CONCLUSION: Increasing residential greenness attenuates the increased risk of mortality with different patterns due to chronic air pollutants for ischemic and hemorrhagic stroke, offering valuable insights for precise tertiary stroke prevention strategies.

Epidemiological Features of Infectious Diseases in Children and Adolescents: A Population-Based Observational Study in Shandong Province, China, 2013-2017.

BACKGROUND: The arrival of the big-data era provides us with a chance to elaborate the spectrum and epidemiological characteristics of infectious diseases in children and adolescents aged 0-18 years in the pre-COVID-19 pandemic era. METHODS: We collected data on infectious diseases in 891,981 participants from the Cheeloo Lifespan Electronic Health Research Data-library. The incidence density of each infection was calculated and stratified by age and region. The annual percentage change (APC) in incidence was estimated by logarithmic linear regression. RESULTS: A total of 18,183 cases of 78 infections were diagnosed, with an overall incidence density of 626.33 per 100,000 person-years (PY). Of these, 6825 cases of 50 non-notifiable infectious diseases were identified. Children aged 1-3 years had the highest incidence of infections. The overall incidence revealed a significant increasing trend from 2013 to 2017 (APC = 36.9%, p < 0.05). Hand, foot, and mouth disease, pneumonia, and influenza were the three most common diseases. The incidence of pneumonia, rubella, scarlet fever, zoster, molluscum contagiosum, and syphilis increased significantly during the study period (all p < 0.05). Taian, Binzhou, and Weihai had the highest incidence of all other cities. The incidence of gastrointestinal infections increased markedly in the eastern coastal regions. CONCLUSIONS: More stress should be placed on a number of non-notifiable infectious diseases with a high burden and a significant increasing trend. Age-based and regional targeting efforts are needed to prevent and contain infectious diseases among children and adolescents.

Metabolites, Healthy Lifestyle, and Polygenic Risk Score Associated with Upper Gastrointestinal Cancer: Findings from the UK Biobank Study.

Previous metabolomics studies have highlighted the predictive value of metabolites on upper gastrointestinal (UGI) cancer, while most of them ignored the potential effects of lifestyle and genetic risk on plasma metabolites. This study aimed to evaluate the role of lifestyle and genetic risk in the metabolic mechanism of UGI cancer. Differential metabolites of UGI cancer were identified using partial least-squares discriminant analysis and the Wilcoxon test. Then, we calculated the healthy lifestyle index (HLI) score and polygenic risk score (PRS) and divided them into three groups, respectively. A total of 15 metabolites were identified as UGI-cancer-related differential metabolites. The metabolite model (AUC = 0.699) exhibited superior discrimination ability compared to those of the HLI model (AUC = 0.615) and the PRS model (AUC = 0.593). Moreover, subgroup analysis revealed that the metabolite model showed higher discrimination ability for individuals with unhealthy lifestyles compared to that with healthy individuals (AUC = 0.783 vs 0.684). Furthermore, in the genetic risk subgroup analysis, individuals with a genetic predisposition to UGI cancer exhibited the best discriminative performance in the metabolite model (AUC = 0.770). These findings demonstrated the clinical significance of metabolic biomarkers in UGI cancer discrimination, especially in individuals with unhealthy lifestyles and a high genetic risk.

Ideal cardiovascular health and risk of death in a large Swedish cohort.

BACKGROUND: Ideal cardiovascular health (CVH) can be assessed by 7 metrics: smoking, body mass index, physical activity, diet, hypertension, dyslipidemia and diabetes, proposed by the American Heart Association. We examined the association of ideal CVH metrics with risk of all-cause, CVD and non-CVD death in a large cohort. METHODS: A total of 29,557 participants in the Swedish National March Cohort were included in this study. We ascertained 3,799 deaths during a median follow-up of 19 years. Cox regression models were used to estimate hazard ratios with 95% confidence intervals (95% CIs) of the association between CVH metrics with risk of death. Laplace regression was used to estimate 25th, 50th and 75th percentiles of age at death. RESULTS: Compared with those having 6-7 ideal CVH metrics, participants with 0-2 ideal metrics had 107% (95% CI = 46-192%) excess risk of all-cause, 224% (95% CI = 72-509%) excess risk of CVD and 108% (31-231%) excess risk of non-CVD death. The median age at death among those with 6-7 vs. 0-2 ideal metrics was extended by 4.2 years for all-causes, 5.8 years for CVD and 2.9 years for non-CVD, respectively. The observed associations were stronger among females than males. CONCLUSIONS: The strong inverse association between number of ideal CVH metrics and risk of death supports the application of the proposed seven metrics for individual risk assessment and general health promotion.

Comorbidity increases the risk of pulmonary tuberculosis: a nested case-control study using multi-source big data.

BACKGROUND: Some medical conditions may increase the risk of developing pulmonary tuberculosis (PTB); however, no systematic study on PTB-associated comorbidities and comorbidity clusters has been undertaken. METHODS: A nested case-control study was conducted from 2013 to 2017 using multi-source big data. We defined cases as patients with incident PTB, and we matched each case with four event-free controls using propensity score matching (PSM). Comorbidities diagnosed prior to PTB were defined with the International Classification of Diseases-10 (ICD-10). The longitudinal relationships between multimorbidity burden and PTB were analyzed using a generalized estimating equation. The associations between PTB and 30 comorbidities were examined using conditional logistic regression, and the comorbidity clusters were identified using network analysis. RESULTS: A total of 4265 cases and 17,060 controls were enrolled during the study period. A total of 849 (19.91%) cases and 1141 (6.69%) controls were multimorbid before the index date. Having 1, 2, and ≥ 3 comorbidities was associated with an increased risk of PTB (aOR 2.85-5.16). Fourteen out of thirty comorbidities were significantly associated with PTB (aOR 1.28-7.27), and the associations differed by sex and age. Network analysis identified three major clusters, mainly in the respiratory, circulatory, and endocrine/metabolic systems, in PTB cases. CONCLUSIONS: Certain comorbidities involving multiple systems may significantly increase the risk of PTB. Enhanced awareness and surveillance of comorbidity are warranted to ensure early prevention and timely control of PTB.

Conditional transcriptome-wide association study for fine-mapping candidate causal genes.

Transcriptome-wide association studies (TWASs) aim to integrate genome-wide association studies with expression-mapping studies to identify genes with genetically predicted expression (GReX) associated with a complex trait. In the present report, we develop a method, GIFT (gene-based integrative fine-mapping through conditional TWAS), that performs conditional TWAS analysis by explicitly controlling for GReX of all other genes residing in a local region to fine-map putatively causal genes. GIFT is frequentist in nature, explicitly models both expression correlation and cis-single nucleotide polymorphism linkage disequilibrium across multiple genes and uses a likelihood framework to account for expression prediction uncertainty. As a result, GIFT produces calibrated P values and is effective for fine-mapping. We apply GIFT to analyze six traits in the UK Biobank, where GIFT narrows down the set size of putatively causal genes by 32.16-91.32% compared with existing TWAS fine-mapping approaches. The genes identified by GIFT highlight the importance of vessel regulation in determining blood pressures and lipid metabolism for regulating lipid levels.

Predicting lung cancer survival prognosis based on the conditional survival bayesian network.

Lung cancer is a leading cause of cancer deaths and imposes an enormous economic burden on patients. It is important to develop an accurate risk assessment model to determine the appropriate treatment for patients after an initial lung cancer diagnosis. The Cox proportional hazards model is mainly employed in survival analysis. However, real-world medical data are usually incomplete, posing a great challenge to the application of this model. Commonly used imputation methods cannot achieve sufficient accuracy when data are missing, so we investigated novel methods for the development of clinical prediction models. In this article, we present a novel model for survival prediction in missing scenarios. We collected data from 5,240 patients diagnosed with lung cancer at the Weihai Municipal Hospital, China. Then, we applied a joint model that combined a BN and a Cox model to predict mortality risk in individual patients with lung cancer. The established prognostic model achieved good predictive performance in discrimination and calibration. We showed that combining the BN with the Cox proportional hazards model is highly beneficial and provides a more efficient tool for risk prediction.

Association between type 1 diabetes and systemic lupus erythematosus: a Mendelian randomization study.

OBJECTIVES: Observational studies have shown that there is a bidirectional relationship between type 1 diabetes (T1D) and systemic lupus erythematosus (SLE); the causality of this association remains elusive and may be affected by confusion and reverse causality. There is also a lack of large-scale randomized controlled trials to verify. Therefore, this Mendelian randomization (MR) study aimed to investigate the causal association between T1D and SLE. METHODS: We aggregated data using publicly available genome-wide association studies (GWAS), all from European populations. Select independent (R2 < 0.001) and closely related to exposure (P < 5 × 10-8) as instrumental variables (IVs). The inverse-variance weighted (IVW) method was used as the primary method. We also used MR-Egger, the weighted median method, MR-Robust, MR-Lasso, and other methods leveraged as supplements. RESULTS: T1D had a positive causal association with SLE (IVW, odds ratio [OR] = 1.358, 95% confidence interval [CI], 1.205 - 1.530; P < 0.001). The causal association was verified in an independent validation set (IVW, OR = 1.137, 95% CI, 1.033 - 1.251; P = 0.001). SLE had a positive causal association with T1D (IVW, OR = 1.108, 95% CI, 1.074 - 1.144; P < 0.001). The causal association was verified in an independent validation set (IVW, OR = 1.085, 95% CI, 1.046 - 1.127; P < 0.001). These results have also been verified by sensitivity analysis. CONCLUSION: The MR analysis results indicated a causal association between T1D and SLE. Therefore, further research is needed to clarify the potential biological mechanism between T1D and SLE. Key Points • Observational studies have shown that there is a bidirectional relationship between T1D and SLE. • We evaluated causal effects between T1D and SLE by Mendelian randomization analyses. • The MR analysis results indicated a causal association between T1D and SLE.

Development and Validation of a Bayesian Network-Based Model for Predicting Coronary Heart Disease Risk From Electronic Health Records.

BACKGROUND: Traditional risk evaluation models have been applied to guide public health and clinical practice in various studies. However, the application of existing methods to data sets with missing and censored data, as is often the case in electronic health records, requires additional considerations. We aimed to develop and validate a predictive model that exhibits high performance with data sets that contain missing and censored data. METHODS AND RESULTS: This is a retrospective cohort study of coronary heart disease at Weihai Municipal Hospital on unique patients aged 18 to 96 years between 2013 and 2021. A total of 169 692 participants formed our study population, of which 10 895 participants were diagnosed with coronary heart disease. Models were built for the risk of coronary heart disease based on demographic, laboratory, and medical history variables. All complete samples were assigned to the training set (n=110 325), whereas the remaining samples were assigned to the validation set (n=59 367). The area under the receiver operating characteristic curve value was 0.800 (95% CI, 0.794-0.805), and the C statistic was 0.796 (95% CI, 0.791-0.801) in the derivation cohort, and the corresponding values were 0.837 (95% CI, 0.821-0.853) and 0.838 (95% CI, 0.822-0.854) in the validation cohort. The calibration curve demonstrated its good calibration ability, and decision curve analysis showed its clinical usefulness. CONCLUSIONS: Our proposed risk prediction model has demonstrated significant effectiveness in handling the complexities of electronic health record data, which often involve extensive missing data and censoring. This approach may offer potential assistance in the use of electronic health records to enhance patient outcomes.

Opportunistic Screening With Low-Dose Computed Tomography and Lung Cancer Mortality in China.

Importance: Despite the recommendations of lung cancer screening guidelines and the evidence supporting the effectiveness of population-based lung screening, a common barrier to effective lung cancer screening is that the participation rates of low-dose computed tomography (LDCT) screening among individuals with the highest risk are not large. There are limited data from clinical practice regarding whether opportunistic LDCT screening is associated with reduced lung-cancer mortality. Objective: To evaluate whether opportunistic LDCT screening is associated with improved prognosis among adults with lung cancer in mainland China. Design, Setting, and Participants: This cohort study included patients diagnosed with lung cancer at Weihai Municipal Hospital Healthcare Group, Weihai City, China, from 2016 to 2021. Data were analyzed from January 2022 to February 2023. Exposures: Data collected included demographic indicators, tumor characteristics, comorbidities, blood indexes, and treatment information. Patients were classified into screened and nonscreened groups on the basis of whether or not their lung cancer diagnosis occurred through opportunistic screening. Main Outcomes and Measures: Follow-up outcome indicators included lung cancer-specific mortality and all-cause mortality. Propensity score matching (PSM) was adopted to account for potential imbalanced factors between groups. The associations between LDCT screening and outcomes were analyzed using Cox regression models based on the matched data. Propensity score regression adjustment and inverse probability treatment weighting were used for sensitivity analysis. Results: A total of 5234 patients (mean [SD] baseline age, 61.8 [9.8] years; 2518 [48.1%] female) with complete opportunistic screening information were included in the analytical sample, with 2251 patients (42.91%) receiving their lung cancer diagnosis through opportunistic screening. After 1:1 PSM, 2788 patients (1394 in each group) were finally included. The baseline characteristics of the matched patients were balanced between groups. Opportunistic screening with LDCT was associated with a 49% lower risk of lung cancer death (HR, 0.51; 95% CI, 0.42-0.62) and 46% lower risk of all-cause death (HR, 0.54; 95% CI, 0.45-0.64). Conclusions and Relevance: In this cohort study of patients with lung cancer, opportunistic lung cancer screening with LDCT was associated with lower lung cancer mortality and all-cause mortality. These findings suggest that opportunistic screening is an important supplement to population screening to improve prognosis of adults with lung cancer.

Protein-centric omics integration analysis identifies candidate plasma proteins for multiple autoimmune diseases.

It remains challenging to translate the findings from genome-wide association studies (GWAS) of autoimmune diseases (AIDs) into interventional targets, presumably due to the lack of knowledge on how the GWAS risk variants contribute to AIDs. In addition, current immunomodulatory drugs for AIDs are broad in action rather than disease-specific. We performed a comprehensive protein-centric omics integration analysis to identify AIDs-associated plasma proteins through integrating protein quantitative trait loci datasets of plasma protein (1348 proteins and 7213 individuals) and totally ten large-scale GWAS summary statistics of AIDs under a cutting-edge systematic analytic framework. Specifically, we initially screened out the protein-AID associations using proteome-wide association study (PWAS), followed by enrichment analysis to reveal the underlying biological processes and pathways. Then, we performed both Mendelian randomization (MR) and colocalization analyses to further identify protein-AID pairs with putatively causal relationships. We finally prioritized the potential drug targets for AIDs. A total of 174 protein-AID associations were identified by PWAS. AIDs-associated plasma proteins were significantly enriched in immune-related biological process and pathways, such as inflammatory response (P = 3.96 × 10-10). MR analysis further identified 97 protein-AID pairs with potential causal relationships, among which 21 pairs were highly supported by colocalization analysis (PP.H4 > 0.75), 10 of 21 were the newly discovered pairs and not reported in previous GWAS analyses. Further explorations showed that four proteins (TLR3, FCGR2A, IL23R, TCN1) have corresponding drugs, and 17 proteins have druggability. These findings will help us to further understand the biological mechanism of AIDs and highlight the potential of these proteins to develop as therapeutic targets for AIDs.

Causal associations between female reproductive behaviors and psychiatric disorders: a lifecourse Mendelian randomization study.

BACKGROUND: The timings of reproductive life events have been examined to be associated with various psychiatric disorders. However, studies have not considered the causal pathways from reproductive behaviors to different psychiatric disorders. This study aimed to investigate the nature of the relationships between five reproductive behaviors and twelve psychiatric disorders. METHODS: Firstly, we calculated genetic correlations between reproductive factors and psychiatric disorders. Then two-sample Mendelian randomization (MR) was conducted to estimate the causal associations among five reproductive behaviors, and these reproductive behaviors on twelve psychiatric disorders, using genome-wide association study (GWAS) summary data from genetic consortia. Multivariable MR was then applied to evaluate the direct effect of reproductive behaviors on these psychiatric disorders whilst accounting for other reproductive factors at different life periods. RESULTS: Univariable MR analyses provide evidence that age at menarche, age at first sexual intercourse and age at first birth have effects on one (depression), seven (anxiety disorder, ADHD, bipolar disorder, bipolar disorder II, depression, PTSD and schizophrenia) and three psychiatric disorders (ADHD, depression and PTSD) (based on p<7.14×10-4), respectively. However, after performing multivariable MR, only age at first sexual intercourse has direct effects on five psychiatric disorders (Depression, Attention deficit or hyperactivity disorder, Bipolar disorder, Posttraumatic stress disorder and schizophrenia) when accounting for other reproductive behaviors with significant effects in univariable analyses. CONCLUSION: Our findings suggest that reproductive behaviors predominantly exert their detrimental effects on psychiatric disorders and age at first sexual intercourse has direct effects on psychiatric disorders.

Personalized prediction for multiple chronic diseases by developing the multi-task Cox learning model.

Personalized prediction of chronic diseases is crucial for reducing the disease burden. However, previous studies on chronic diseases have not adequately considered the relationship between chronic diseases. To explore the patient-wise risk of multiple chronic diseases, we developed a multitask learning Cox (MTL-Cox) model for personalized prediction of nine typical chronic diseases on the UK Biobank dataset. MTL-Cox employs a multitask learning framework to train semiparametric multivariable Cox models. To comprehensively estimate the performance of the MTL-Cox model, we measured it via five commonly used survival analysis metrics: concordance index, area under the curve (AUC), specificity, sensitivity, and Youden index. In addition, we verified the validity of the MTL-Cox model framework in the Weihai physical examination dataset, from Shandong province, China. The MTL-Cox model achieved a statistically significant (p<0.05) improvement in results compared with competing methods in the evaluation metrics of the concordance index, AUC, sensitivity, and Youden index using the paired-sample Wilcoxon signed-rank test. In particular, the MTL-Cox model improved prediction accuracy by up to 12% compared to other models. We also applied the MTL-Cox model to rank the absolute risk of nine chronic diseases in patients on the UK Biobank dataset. This was the first known study to use the multitask learning-based Cox model to predict the personalized risk of the nine chronic diseases. The study can contribute to early screening, personalized risk ranking, and diagnosing of chronic diseases.

Effect of neighbourhood greenness on the association between air pollution and risk of stroke first onset: A case-crossover study in shandong province, China.

BACKGROUND: Higher neighbourhood greenness is associated with beneficial health outcomes, and short-term exposure to air pollution is associated with an elevated risk of stroke onset. However, little is known about their interactions. METHODS: Daily data on stroke first onset were collected from 20 counties in Shangdong Province, China, from 2013 to 2019. The enhanced vegetation index (EVI) and concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and sulfur dioxide (SO2) were calculated for each individual at the village or community level based on their home address to measure their neighbourhood exposure to greenness and air pollution. EVI was categorised as low or high, and a time-stratified case-crossover design was used to estimate the percent excess risk (ER%) of stroke associated with short-term exposure to air pollution. We further stratified greenness on the basis of EVI values into quartiles and introduced interaction terms between air pollutant concentrations and the median EVI values of the quartiles to assess the effect of greenness on the associations between short-term exposure and stroke. RESULTS: Individuals living in the high-greenness areas had weaker associations between total stroke risk and exposure to NO2 (low greenness: ER% = 1.765% [95% CI 1.205%-2.328%]; high greenness: ER% = 0.368% [95% CI -0.252% to 0.991%]; P = 0.001), O3 (low greenness: 0.476% [95% CI 0.246%-0.706%]; high greenness: ER% = 0.085% [95% CI -0.156% to 0.327%]; P = 0.011), and SO2 (low greenness: 0.632% [95% CI 0.138%-1.129%]; high greenness: ER% = -0.177% [95% CI -0.782% to 0.431%]; P = 0.035). CONCLUSION: Residence in areas with higher greenness was related to weaker associations between air pollution and stroke risk, suggesting that effectively planning green spaces can improve public health.

The long-term mediation role of cytokines on the causal pathway from maternal gestational age to offspring eye diseases: Lifecourse-Network Mendelian randomization.

BACKGROUND: Gestational duration has a significant impact on eye diseases. A large number of evidences suggest that cytokines are associated with gestational duration and eye diseases. However, the causal relationships among cytokines, maternal gestational impairment and offspring eye diseases remain unclear. METHODS: We performed lifecourse-network Mendelian randomization (MR) to explore the causal relationships between maternal gestational duration (from the Early Growth Genetics (EGG) Consortium and the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH) study, N = 84,689), neonatal/adult cytokines (from the NHGRI-EBI Catalog, N = 764/4,618), and adult eye diseases (from FinnGen consotium, N = 309,154) using summary-level data from large genome-wide association studies. Multiplicative random effects inverse variance weighted (IVW) and multivariable-IVW methods were the main analysis methods, and the other 15 pleiotropy-robust methods, weak IV-robust methods, and outliers-robust methods were used as auxiliary methods. RESULTS: Maternal gestational age (early preterm birth, preterm birth, gestational duration, and post-term birth) had a causal relationship with 42 eye diseases. Four neonatal cytokines, Tumor Necrosis Factor-α(TNF-α), IL10, GROA, and CTACK, as well as four adult cytokines, CTACK, IL10, IL12p70 and IL6 are mediators in the causal relationships between early preterm birth and preterm birth in eight eye diseases. However, after adjusting for these mediators, a null direct causal effect of early preterm birth and preterm birth on eight eye diseases was found. In addition, there was no mediator in the causal relationship between gestational duration and post-term birth to eye diseases. CONCLUSION: The effects of maternal gestational duration on offspring eye diseases through cytokines are long-term and life-course effects.

Association between dietary habits and the risk of migraine: a Mendelian randomization study.

Objective: The important contribution of dietary triggers to migraine pathogenesis has been recognized. However, the potential causal roles of many dietary habits on the risk of migraine in the whole population are still under debate. The objective of this study was to determine the potential causal association between dietary habits and the risk of migraine (and its subtypes) development, as well as the possible mediator roles of migraine risk factors. Methods: Based on summary statistics from large-scale genome-wide association studies, we conducted two-sample Mendelian randomization (MR) and bidirectional MR to investigate the potential causal associations between 83 dietary habits and migraine and its subtypes, and network MR was performed to explore the possible mediator roles of 8 migraine risk factors. Results: After correcting for multiple testing, we found evidence for associations of genetically predicted coffee, cheese, oily fish, alcohol (red wine), raw vegetables, muesli, and wholemeal/wholegrain bread intake with decreased risk of migraine, those odds ratios ranged from 0.78 (95% CI: 0.63-0.95) for overall cheese intake to 0.61 (95% CI: 0.47-0.80) for drinks usually with meals among current drinkers (yes + it varies vs. no); while white bread, cornflakes/frosties, and poultry intake were positively associated with the risk of migraine. Additionally, genetic liability to white bread, wholemeal/wholegrain bread, muesli, alcohol (red wine), cheese, and oily fish intake were associated with a higher risk of insomnia and (or) major depression disorder (MDD), each of them may act as a mediator in the pathway from several dietary habits to migraine. Finally, we found evidence of a negative association between genetically predicted migraine and drinking types, and positive association between migraine and cups of tea per day. Significance: Our study provides evidence about association between dietary habits and the risk of migraine and demonstrates that some associations are partly mediated through one or both insomnia and MDD. These results provide new insights for further nutritional interventions for migraine prevention.