Robust multivariable Mendelian randomization based on constrained maximum likelihood.

Mendelian randomization (MR) is a powerful tool for causal inference with observational genome-wide association study (GWAS) summary data. Compared to the more commonly used univariable MR (UVMR), multivariable MR (MVMR) not only is more robust to the notorious problem of genetic (horizontal) pleiotropy but also estimates the direct effect of each exposure on the outcome after accounting for possible mediating effects of other exposures. Despite promising applications, there is a lack of studies on MVMR's theoretical properties and robustness in applications. In this work, we propose an efficient and robust MVMR method based on constrained maximum likelihood (cML), called MVMR-cML, with strong theoretical support. Extensive simulations demonstrate that MVMR-cML performs better than other existing MVMR methods while possessing the above two advantages over its univariable counterpart. An application to several large-scale GWAS summary datasets to infer causal relationships between eight cardiometabolic risk factors and coronary artery disease (CAD) highlights the usefulness and some advantages of the proposed method. For example, after accounting for possible pleiotropic and mediating effects, triglyceride (TG), low-density lipoprotein cholesterol (LDL), and systolic blood pressure (SBP) had direct effects on CAD; in contrast, the effects of high-density lipoprotein cholesterol (HDL), diastolic blood pressure (DBP), and body height diminished after accounting for other risk factors.

Precursor-mediated in situ growth of hierarchical N-doped graphene nanofibers confining nickel single atoms for CO2 electroreduction.

Despite the various strategies for achieving metal-nitrogen-carbon (M-N-C) single-atom catalysts (SACs) with different microenvironments for electrochemical carbon dioxide reduction reaction (CO2RR), the synthesis-structure-performance correlation remains elusive due to the lack of well-controlled synthetic approaches. Here, we employed Ni nanoparticles as starting materials for the direct synthesis of nickel (Ni) SACs in one spot through harvesting the interaction between metallic Ni and N atoms in the precursor during the chemical vapor deposition growth of hierarchical N-doped graphene fibers. By combining with first-principle calculations, we found that the Ni-N configuration is closely correlated to the N contents in the precursor, in which the acetonitrile with a high N/C ratio favors the formation of Ni-N3, while the pyridine with a low N/C ratio is more likely to promote the evolution of Ni-N2. Moreover, we revealed that the presence of N favors the formation of H-terminated edge of sp2 carbon and consequently leads to the formation of graphene fibers consisting of vertically stacked graphene flakes, instead of the traditional growth of carbon nanotubes on Ni nanoparticles. With a high capability in balancing the *COOH formation and *CO desorption, the as-prepared hierarchical N-doped graphene nanofibers with Ni-N3 sites exhibit a superior CO2RR performance compared to that with Ni-N2 and Ni-N4 ones.

Practical causal mediation analysis: extending nonparametric estimators to accommodate multiple mediators and multiple intermediate confounders.

Mediation analysis is appealing for its ability to improve understanding of the mechanistic drivers of causal effects, but real-world data complexities challenge its successful implementation, including (i) the existence of post-exposure variables that also affect mediators and outcomes (thus, confounding the mediator-outcome relationship), that may also be (ii) multivariate, and (iii) the existence of multivariate mediators. All three challenges are present in the mediation analysis we consider here, where our goal is to estimate the indirect effects of receiving a Section 8 housing voucher as a young child on the risk of developing a psychiatric mood disorder in adolescence that operate through mediators related to neighborhood poverty, the school environment, and instability of the neighborhood and school environments, considered together and separately. Interventional direct and indirect effects (IDE/IIE) accommodate post-exposure variables that confound the mediator-outcome relationship, but currently, no readily implementable nonparametric estimator for IDE/IIE exists that allows for both multivariate mediators and multivariate post-exposure intermediate confounders. The absence of such an IDE/IIE estimator that can easily accommodate both multivariate mediators and post-exposure confounders represents a significant limitation for real-world analyses, because when considering each mediator subgroup separately, the remaining mediator subgroups (or a subset of them) become post-exposure intermediate confounders. We address this gap by extending a recently developed nonparametric estimator for the IDE/IIE to allow for easy incorporation of multivariate mediators and multivariate post-exposure confounders simultaneously. We apply the proposed estimation approach to our analysis, including walking through a strategy to account for other, possibly co-occurring intermediate variables when considering each mediator subgroup separately.

Genetic control of RNA editing in neurodegenerative disease.

A-to-I RNA editing diversifies human transcriptome to confer its functional effects on the downstream genes or regulations, potentially involving in neurodegenerative pathogenesis. Its variabilities are attributed to multiple regulators, including the key factor of genetic variants. To comprehensively investigate the potentials of neurodegenerative disease-susceptibility variants from the view of A-to-I RNA editing, we analyzed matched genetic and transcriptomic data of 1596 samples across nine brain tissues and whole blood from two large consortiums, Accelerating Medicines Partnership-Alzheimer's Disease and Parkinson's Progression Markers Initiative. The large-scale and genome-wide identification of 95 198 RNA editing quantitative trait loci revealed the preferred genetic effects on adjacent editing events. Furthermore, to explore the underlying mechanisms of the genetic controls of A-to-I RNA editing, several top RNA-binding proteins were pointed out, such as EIF4A3, U2AF2, NOP58, FBL, NOP56 and DHX9, since their regulations on multiple RNA-editing events were probably interfered by these genetic variants. Moreover, these variants may also contribute to the variability of other molecular phenotypes associated with RNA editing, including the functions of 3 proteins, expressions of 277 genes and splicing of 449 events. All the analyses results shown in NeuroEdQTL (https://relab.xidian.edu.cn/NeuroEdQTL/) constituted a unique resource for the understanding of neurodegenerative pathogenesis from genotypes to phenotypes related to A-to-I RNA editing.

Quantification of mediation effects of white matter functional characteristics on cognitive decline in aging.

Cognitive decline with aging involves multifactorial processes, including changes in brain structure and function. This study focuses on the role of white matter functional characteristics, as reflected in blood oxygenation level-dependent signals, in age-related cognitive deterioration. Building on previous research confirming the reproducibility and age-dependence of blood oxygenation level-dependent signals acquired via functional magnetic resonance imaging, we here employ mediation analysis to test if aging affects cognition through white matter blood oxygenation level-dependent signal changes, impacting various cognitive domains and specific white matter regions. We used independent component analysis of resting-state blood oxygenation level-dependent signals to segment white matter into coherent hubs, offering a data-driven view of white matter's functional architecture. Through correlation analysis, we constructed a graph network and derived metrics to quantitatively assess regional functional properties based on resting-state blood oxygenation level-dependent fluctuations. Our analysis identified significant mediators in the age-cognition relationship, indicating that aging differentially influences cognitive functions by altering the functional characteristics of distinct white matter regions. These findings enhance our understanding of the neurobiological basis of cognitive aging, highlighting the critical role of white matter in maintaining cognitive integrity and proposing new approaches to assess interventions targeting cognitive decline in older populations.

Cortical microstructural brain network mediates the association between personality trait of agreeableness and life satisfaction.

Personality traits are commonly regarded as relatively stable, whereas life satisfaction can fluctuate with time and circumstances, shaped by external influences and personal encounters. The correlation between personality traits and life satisfaction is well-established, yet the underlying neural mechanisms of the myelin-based microstructural brain network connecting them remain unclear. Here, we constructed individual-level whole-brain myelin microstructural networks from the MRI data of 1,043 healthy adults and performed correlation analysis to detect significant personality trait-related and life satisfaction-related subnetworks. A mediation analysis was used to verify whether the shared structural basis of personality traits and life satisfaction significantly mediated their association. The results showed that agreeableness positively correlated with life satisfaction. We identified a shared structural basis of the personality trait of agreeableness and life satisfaction. The regions comprising this overlapping network include the superior parietal lobule, inferior parietal lobule, and temporoparietal junction. Moreover, the shared microstructural connections mediate the association between the personality trait of agreeableness and life satisfaction. This large-scale neuroimaging investigation substantiates a mediation framework for understanding the microstructural connections between personality and life satisfaction, offering potential targets for assessment and interventions to promote human well-being.

The Legacy of Redlining: Increasing Childhood Asthma Disparities Through Neighborhood Poverty.

RATIONALE: Identifying the root causes of racial disparities in childhood asthma is critical for health equity. OBJECTIVES: To determine if the 1930's racist policy of redlining led to present-day disparities in childhood asthma by increasing community-level poverty and decreasing neighborhood socioeconomic position (SEP). METHODS: We categorized census tracts at birth of participants from the Children's Respiratory and Environmental Workgroup birth cohort consortium into A, B, C, or D categories as defined by the Home Owners Loan Corporation (HOLC), with D being the highest perceived risk. Surrogates of present-day neighborhood-level SEP were determined for each tract including the percentage of low-income households, the CDC's social vulnerability index (SVI), and other tract-level variables. We performed causal mediation analysis, which, under the assumption of no unmeasured confounding, estimates the direct and mediated pathways by which redlining may cause asthma disparities through census tract-level mediators adjusting for individual-level covariates. MEASUREMENTS AND MAIN RESULTS: Of 4,849 children, the cumulative incidence of asthma through age 11 was 26.6% and 13.2% resided in census tracts with a HOLC grade of D. In mediation analyses, residing in grade D tracts (aOR = 1.03 [95%CI 1.01,1.05]) was significantly associated with childhood asthma, with 79% of this increased risk mediated by percentage of low-income households; results were similar for SVI and other tract-level variables. CONCLUSIONS: The historical structural racist policy of redlining led to present-day asthma disparities in part through decreased neighborhood SEP. Policies aimed at reversing the effects of structural racism should be considered to create more just, equitable, and healthy communities.

Biological Age, Chronological Age and Survival in Pulmonary Fibrosis: A Causal Mediation Analysis.

RATIONALE: Accelerated biological aging has been implicated in the development of interstitial lung disease (ILD) and other diseases of aging but remains poorly understood. OBJECTIVES: To identify plasma proteins that mediate the relationship between chronological age and survival association in patients with ILD. METHODS: Causal mediation analysis was performed to identify plasma proteins that mediated the chronological age-survival relationship in an idiopathic pulmonary fibrosis (IPF) discovery cohort. Proteins mediating this relationship after adjustment for false discovery were advanced for testing in an independent ILD validation cohort and explored in a chronic obstructive pulmonary disease (COPD) cohort. A proteomic-based measure of biological age was constructed and survival analysis performed assessing the impact of biological age and peripheral blood telomere length on the chronological age-survival relationship. RESULTS: Twenty-two proteins mediated the chronological age-survival relationship after adjustment for false discovery in the IPF discovery cohort (n=874), with nineteen remaining significant mediators of this relationship in the ILD validation cohort (n=983) and one mediating this relationship in the COPD cohort. Latent transforming growth factor beta binding protein 2 and ectodysplasin A2 receptor showed the strongest mediation across cohorts. A proteomic measure of biological age completely attenuated the chronological age-survival association and better discriminated survival than chronological age. Results were robust to adjustment for peripheral blood telomere length, which did not mediate the chronological age-survival relationship. CONCLUSIONS: Molecular measures of aging completely mediate the relationship between chronological age and survival, suggesting that chronological age has no direct effect on ILD survival.

Lower socioeconomic status may help explain racial disparities in asthma and atopic dermatitis prevalence: A mediation analysis.

BACKGROUND: Racial disparities in atopic disease (atopic dermatitis [AD], asthma, and allergies) prevalence are well documented. Despite strong associations between race and socioeconomic deprivation in the United States, and socioeconomic status (SES) and atopic diseases, the extent to which SES explains these disparities is not fully understood. OBJECTIVE: We sought to identify racial disparities in childhood atopic disease prevalence and determine what proportion of those disparities is mediated by SES. METHODS: This study used the National Health Interview Survey (2011-2018) to investigate AD, asthma, and respiratory allergy prevalence in Black and White children and the extent to which measures of SES explain any identified disparities. RESULTS: By race, prevalences were as follows: AD, White 11.8% (95% CI: 11.4%, 12.2%) and Black 17.4% (95% CI: 16.6%, 18.3%); asthma prevalence, White 7.4% (95% CI: 7.0%, 7.7%) and Black 14.3% (95% CI: 13.5%, 15.0%); respiratory allergy, White 11.4% (95% CI: 11.0%, 11.9%) and Black 10.9% (95% CI: 10.3%, 11.6%). The percentage of the disparity between racial groups and disease prevalence explained by a multivariable measure of SES was 25% (95% CI: 15%, 36%) for Black versus White children with AD and 47% (95% CI: 40%, 54%) for Black versus White children with asthma. CONCLUSIONS: In a nationally representative US population, Black children had higher prevalence of AD and asthma than White children did and similar prevalence of respiratory allergy; a multivariable SES measure explained a proportion of the association between Black versus White race and AD and a much larger proportion for asthma.

Key risk factors of asthma-like symptoms are mediated through infection burden in early childhood.

BACKGROUND: Risk factors of asthma-like symptoms in childhood may act through an increased infection burden because infections often trigger these symptoms. OBJECTIVE: We sought to investigate whether the effect of established risk factors of asthma-like episodes in early childhood is mediated through burden and subtypes of common infections. METHODS: The study included 662 children from the Copenhagen Prospective Studies on Asthma in Childhood 2010 mother-child cohort, in which infections were registered prospectively in daily diaries from age 0 to 3 years. The association between established risk factors of asthma-like episodes and infection burden was analyzed by quasi-Poisson regressions, and mediation analyses were performed for significant risk factors. RESULTS: In the first 3 years of life, the children experienced a median of 16 (interquartile range, 12-23) infectious episodes. We found that the infection burden significantly (PACME < .05) mediated the association of maternal asthma (36.6% mediated), antibiotics during pregnancy (47.3%), siblings at birth (57.7%), an asthma exacerbation polygenic risk score (30.6%), and a bacterial airway immune score (80.2%) with number of asthma-like episodes, whereas the higher number of episodes from male sex, low birth weight, low gestational age, and maternal antibiotic use after birth was not mediated through an increased infection burden. Subtypes of infections driving the mediation were primarily colds, pneumonia, gastroenteritis, and fever, but not acute otitis media or acute tonsillitis. CONCLUSIONS: Several risk factors of asthma-like symptoms in early childhood act through an increased infection burden in the first 3 years of life. Prevention of infectious episodes may therefore be beneficial to reduce the burden of asthma-like symptoms in early childhood.

Joint transcriptomic and cytometric study of children with peanut allergy reveals molecular and cellular cross talk in reaction thresholds.

BACKGROUND: Reaction thresholds in peanut allergy are highly variable. Elucidating causal relationships between molecular and cellular processes associated with variable thresholds could point to therapeutic pathways for raising thresholds. OBJECTIVE: The aim of this study was to characterize molecular and cellular systemic processes associated with reaction threshold in peanut allergy and causal relationships between them. METHODS: A total of 105 children aged 4 to 14 years with suspected peanut allergy underwent double-blind, placebo-controlled food challenge to peanut. The cumulative peanut protein quantity eliciting allergic symptoms was considered the reaction threshold for each child. Peripheral blood samples collected at 0, 2, and 4 hours after challenge start were used for RNA sequencing, whole blood staining, and cytometry. Statistical and network analyses were performed to identify associations and causal mediation between the molecular and cellular profiles and peanut reaction threshold. RESULTS: Within the cohort (N = 105), 81 children (77%) experienced allergic reactions after ingesting varying quantities of peanut, ranging from 43 to 9043 mg of cumulative peanut protein. Peripheral blood expression of transcripts (eg, IGF1R [false discovery rate (FDR) = 5.4e-5] and PADI4 [FDR = 5.4e-5]) and neutrophil abundance (FDR = 9.5e-4) were associated with peanut threshold. Coexpression network analyses revealed that the threshold-associated transcripts were enriched in modules for FcγR-mediated phagocytosis (FDR = 3.2e-3) and Toll-like receptor (FDR = 1.4e-3) signaling. Bayesian network, key driver, and causal mediation analyses identified key drivers (AP5B1, KLHL21, VASP, TPD52L2, and IGF2R) within these modules that are involved in bidirectional causal mediation relationships with neutrophil abundance. CONCLUSION: Key driver transcripts in FcγR-mediated phagocytosis and Toll-like receptor signaling interact bidirectionally with neutrophils in peripheral blood and are associated with reaction threshold in peanut allergy.

Network Communications Flexibly Predict Visual Contents That Enhance Representations for Faster Visual Categorization.

Models of visual cognition generally assume that brain networks predict the contents of a stimulus to facilitate its subsequent categorization. However, understanding prediction and categorization at a network level has remained challenging, partly because we need to reverse engineer their information processing mechanisms from the dynamic neural signals. Here, we used connectivity measures that can isolate the communications of a specific content to reconstruct these network mechanisms in each individual participant (N = 11, both sexes). Each was cued to the spatial location (left vs right) and contents [low spatial frequency (LSF) vs high spatial frequency (HSF)] of a predicted Gabor stimulus that they then categorized. Using each participant's concurrently measured MEG, we reconstructed networks that predict and categorize LSF versus HSF contents for behavior. We found that predicted contents flexibly propagate top down from temporal to lateralized occipital cortex, depending on task demands, under supervisory control of prefrontal cortex. When they reach lateralized occipital cortex, predictions enhance the bottom-up LSF versus HSF representations of the stimulus, all the way from occipital-ventral-parietal to premotor cortex, in turn producing faster categorization behavior. Importantly, content communications are subsets (i.e., 55-75%) of the signal-to-signal communications typically measured between brain regions. Hence, our study isolates functional networks that process the information of cognitive functions.SIGNIFICANCE STATEMENT An enduring cognitive hypothesis states that our perception is partly influenced by the bottom-up sensory input but also by top-down expectations. However, cognitive explanations of the dynamic brain networks mechanisms that flexibly predict and categorize the visual input according to task-demands remain elusive. We addressed them in a predictive experimental design by isolating the network communications of cognitive contents from all other communications. Our methods revealed a Prediction Network that flexibly communicates contents from temporal to lateralized occipital cortex, with explicit frontal control, and an occipital-ventral-parietal-frontal Categorization Network that represents more sharply the predicted contents from the shown stimulus, leading to faster behavior. Our framework and results therefore shed a new light of cognitive information processing on dynamic brain activity.

Comparison of regmed and BayesNetty for exploring causal models with many variables.

Here we compare a recently proposed method and software package, regmed, with our own previously developed package, BayesNetty, designed to allow exploratory analysis of complex causal relationships between biological variables. We find that regmed generally has poorer recall but much better precision than BayesNetty. This is perhaps not too surprising as regmed is specifically designed for use with high-dimensional data. BayesNetty is found to be more sensitive to the resulting multiple testing problem encountered in these circumstances. However, as regmed is not designed to handle missing data, its performance is severely affected when missing data is present, whereas the performance of BayesNetty is only slightly affected. The performance of regmed can be rescued in this situation by first using BayesNetty to impute the missing data, and then applying regmed to the resulting "filled-in" data set.

Methods for large-scale single mediator hypothesis testing: Possible choices and comparisons.

Mediation hypothesis testing for a large number of mediators is challenging due to the composite structure of the null hypothesis, H 0 : α ß = 0 ${H}_{0}:\alpha \beta =0$ ( α $\alpha $ : effect of the exposure on the mediator after adjusting for confounders; ß $\beta $ : effect of the mediator on the outcome after adjusting for exposure and confounders). In this paper, we reviewed three classes of methods for large-scale one at a time mediation hypothesis testing. These methods are commonly used for continuous outcomes and continuous mediators assuming there is no exposure-mediator interaction so that the product α ß $\alpha \beta $ has a causal interpretation as the indirect effect. The first class of methods ignores the impact of different structures under the composite null hypothesis, namely, (1) α = 0 , ß ≠ 0 $\alpha =0,\beta \ne 0$ ; (2) α ≠ 0 , ß = 0 $\alpha \ne 0,\beta =0$ ; and (3) α = ß = 0 $\alpha =\beta =0$ . The second class of methods weights the reference distribution under each case of the null to form a mixture reference distribution. The third class constructs a composite test statistic using the three p values obtained under each case of the null so that the reference distribution of the composite statistic is approximately U ( 0 , 1 ) $U(0,1)$ . In addition to these existing methods, we developed the Sobel-comp method belonging to the second class, which uses a corrected mixture reference distribution for Sobel's test statistic. We performed extensive simulation studies to compare all six methods belonging to these three classes in terms of the false positive rates (FPRs) under the null hypothesis and the true positive rates under the alternative hypothesis. We found that the second class of methods which uses a mixture reference distribution could best maintain the FPRs at the nominal level under the null hypothesis and had the greatest true positive rates under the alternative hypothesis. We applied all methods to study the mediation mechanism of DNA methylation sites in the pathway from adult socioeconomic status to glycated hemoglobin level using data from the Multi-Ethnic Study of Atherosclerosis (MESA). We provide guidelines for choosing the optimal mediation hypothesis testing method in practice and develop an R package medScan available on the CRAN for implementing all the six methods.

New proposal to address mediation analysis interrogations by using genetic variants as instrumental variables.

The application of causal mediation analysis (CMA) considering the mediation effect of a third variable is increasing in epidemiological studies; however, this requires fitting strong assumptions on confounding bias. To address this limitation, we propose an extension of CMA combining it with Mendelian randomization (MRinCMA). We applied the new approach to analyse the causal effect of obesity and diabetes on pancreatic cancer, considering each factor as potential mediator. To check the performance of MRinCMA under several conditions/scenarios, we used it in different simulated data sets and compared it with structural equation models. For continuous variables, MRinCMA and structural equation models performed similarly, suggesting that both approaches are valid to obtain unbiased estimates. When noncontinuous variables were considered, MRinCMA presented, overall, lower bias than structural equation models. By applying MRinCMA, we did not find any evidence of causality of obesity or diabetes on pancreatic cancer. With this new methodology, researchers would be able to address CMA hypotheses by appropriately accounting for the confounding bias assumption regardless of the conditions used in their studies in different settings.

Socioeconomic Disparities and Mediators for Recurrent Atherosclerotic Cardiovascular Disease Events After a First Myocardial Infarction.

BACKGROUND: Low socioeconomic status is associated with worse secondary prevention use and prognosis after myocardial infarction (MI). Actions for health equity improvements warrant identification of risk mediators. Therefore, we assessed mediators of the association between socioeconomic status and first recurrent atherosclerotic cardiovascular disease event (rASCVD) after MI. METHODS: In this cohort study on 1-year survivors of first-ever MI with Swedish universal health coverage ages 18 to 76 years, individual-level data from SWEDEHEART (Swedish Web System for Enhancement and Development of Evidence-Based Care in Heart Disease Evaluated According to Recommended Therapies) and linked national registries was collected from 2006 through 2020. Exposure was socioeconomic status by disposable income quintile (principal proxy), educational level, and marital status. The primary outcome was rASCVD and secondary outcomes were cardiovascular and all-cause mortality. We initially assessed the incremental attenuation of hazard ratios with 95% CIs in sequential multivariable models adding groups of potential mediators (ie, previous risk factors, acute presentation and infarct severity, initial therapies, and secondary prevention). Thereafter, the proportion of excess rASCVD associated with a low income mediated through nonparticipation in cardiac rehabilitation, suboptimal statin management, a cardiometabolic risk profile, persistent smoking, and blood pressure above target after MI were calculated using causal mediation analysis. RESULTS: Among 68 775 participants (73.8% men), 7064 rASCVD occurred during a mean 5.7-year follow-up. Income, adjusted for age, sex, and calendar year, was associated with rASCVD (hazard ratio, 1.63 [95% CI, 1.51-1.76] in the lowest versus highest income quintile). Risk attenuated most by adjustment for previous risk factors and by adding secondary prevention variables for a final model (hazard ratio, 1.38 [95% CI, 1.26-1.51]) in the lowest versus highest income quintile. The proportions of the excess 15-year rASCVD risk in the lowest income quintile mediated through nonparticipation in cardiac rehabilitation, cardiometabolic risk profile, persistent smoking, and poor blood pressure control were 3.3% (95% CI 2.1-4.8), 3.9% (95% CI, 2.9-5.5), 15.2% (95% 9.1-25.7), and 1.0% (95% CI 0.6-1.5), respectively. Risk mediation through optimal statin management was negligible. CONCLUSIONS: Nonparticipation in cardiac rehabilitation, a cardiometabolic risk profile, and persistent smoking mediate income-dependent prognosis after MI. In the absence of randomized trials, this causal inference approach may guide decisions to improve health equity.

Investigating the causal relationship of gut microbiota with GERD and BE: a bidirectional mendelian randomization.

BACKGROUND: Gut microbiota(GM) have been proven associated with lots of gastrointestinal diseases, but its causal relationship with Gastroesophageal reflux disease(GERD) and Barrett's esophagus(BE) hasn't been explored. We aimed to uncover the causal relation between GM and GERD/BE and potential mediators by utilizing Mendelian Randomization(MR) analysis. METHODS: Summary statistics of GM(comprising 301 bacteria taxa and 205 metabolism pathways) were extracted from MiBioGen Consortium(N = 18,340) and Dutch Microbiome Project(N = 7,738), GERD and BE from a multitrait meta-analysis(NGERD=602,604, NBE=56,429). Bidirectional two-sample MR analysis and linkage disequilibrium score regression(LDSC) were used to explore the genetic correlation between GM and GERD/BE. Mediation MR analysis was performed for the risk factors of GERD/BE, including Body mass index(BMI), weight, type 2 diabetes, major depressive disorder(MDD), smoking initiation, alcohol consumption, and dietary intake(including carbohydrate, sugar, fat, protein intake), to detect the potential mediators between GM and GERD/BE. RESULTS: 11 bacterial taxa and 13 metabolism pathways were found associated with GERD, and 18 taxa and 5 pathways exhibited causal relationship with BE. Mediation MR analysis suggested weight and BMI played a crucial role in these relationships. LDSC identified 1 taxon and 4 metabolism pathways related to GERD, and 1 taxon related to BE. Specie Faecalibacterium prausnitzii had a suggestive impact on both GERD(OR = 1.087, 95%CI = 1.01-1.17) and BE(OR = 1.388, 95%CI = 1.03-1.86) and LDSC had determined their correlation. Reverse MR indicated that BE impacted 10 taxa and 4 pathways. CONCLUSIONS: This study established a causal link between gut microbiota and GERD/BE, and identified the probable mediators. It offers new insights into the role of gut microbiota in the development and progression of GERD and BE in the host.

Elucidating genetic and molecular basis of altered higher-order brain structure-function coupling in major depressive disorder.

Previous studies have shown that major depressive disorder (MDD) patients exhibit structural and functional impairments, but few studies have investigated changes in higher-order coupling between structure and function. Here, we systematically investigated the effect of MDD on higher-order coupling between structural connectivity (SC) and functional connectivity (FC). Each brain region was mapped into embedding vector by the node2vec algorithm. We used support vector machine (SVM) with the brain region embedding vector to distinguish MDD patients from health controls (HCs) and identify the most discriminative brain regions. Our study revealed that MDD patients had decreased higher-order coupling in connections between the most discriminative brain regions and local connections in rich-club organization and increased higher-order coupling in connections between the ventral attentional network and limbic network compared with HCs. Interestingly, transcriptome-neuroimaging association analysis demonstrated the correlations between regional rSC-FC coupling variations between MDD patients and HCs and α/ß-hydrolase domain-containing 6 (ABHD6), ß 1,3-N-acetylglucosaminyltransferase-9(ß3GNT9), transmembrane protein 45B (TMEM45B), the correlation between regional dSC-FC coupling variations and retinoic acid early transcript 1E antisense RNA 1(RAET1E-AS1), and the correlations between regional iSC-FC coupling variations and ABHD6, ß3GNT9, katanin-like 2 protein (KATNAL2). In addition, correlation analysis with neurotransmitter receptor/transporter maps found that the rSC-FC and iSC-FC coupling variations were both correlated with neuroendocrine transporter (NET) expression, and the dSC-FC coupling variations were correlated with metabotropic glutamate receptor 5 (mGluR5). Further mediation analysis explored the relationship between genes, neurotransmitter receptor/transporter and MDD related higher-order coupling variations. These findings indicate that specific genetic and molecular factors underpin the observed disparities in higher-order SC-FC coupling between MDD patients and HCs. Our study confirmed that higher-order coupling between SC and FC plays an important role in diagnosing MDD. The identification of new biological evidence for MDD etiology holds promise for the development of innovative antidepressant therapies.

Do age at diagnosis, tumour thickness and tumour site explain sex differences in melanoma survival? A causal mediation analysis using cancer registry data.

Women diagnosed with melanoma have better survival than men, but little is known about potential intervention targets to reduce this survival gap by sex. We conducted a population-based study using Victorian Cancer Registry data including 5833 women and 6780 men aged 15 to 70 years when diagnosed with first primary melanoma between 2007 and 2015. Deaths to the end of 2020 were identified through linkage to the Victorian and national death registries. We estimated the effect of age at diagnosis, tumour thickness and tumour site on reducing the melanoma-specific survival gap by sex (ie, interventional indirect effects [IIEs]) on risk difference (RD) scale. Compared to women, there were 211 (95% CI: 145-278) additional deaths per 10 000 in men within 5 years following diagnosis. We estimated that 44% of this gap would be reduced by a hypothetical intervention shifting the distribution of melanoma thickness in men to be the same as that observed for women (IIEthickness RD 93 [95% CI: 75-118] per 10 000) and 20% by an intervention on tumour site (head and neck/trunk vs upper limb/lower limb; IIEsite RD 42 [95% CI: 15-72] per 10 000), while an intervention on age at diagnosis would have a negligible effect. Tumour thickness, tumour site and age at diagnosis mediated 65% of the effect of sex on 5-year melanoma survival in Victoria. Of these factors, tumour thickness had the most considerable mediating effect, suggesting that effective promotion of earlier detection of melanoma in men could potentially nearly halve the gap in melanoma-specific survival by sex.

Metabolomic landscape of overall and common cancers in the UK Biobank: A prospective cohort study.

Information about the NMR metabolomics landscape of overall, and common cancers is still limited. Based on a cohort of 83,290 participants from the UK Biobank, we used multivariate Cox regression to assess the associations between each of the 168 metabolites with the risks of overall cancer and 20 specific types of cancer. Then, we applied LASSO to identify important metabolites for overall cancer risk and obtained their associations using multivariate cox regression. We further conducted mediation analysis to evaluate the mediated role of metabolites in the effects of traditional factors on overall cancer risk. Finally, we included the 13 identified metabolites as predictors in prediction models, and compared the accuracies of our traditional models. We found that there were commonalities among the metabolic profiles of overall and specific types of cancer: the top 20 frequently identified metabolites for 20 specific types of cancer were all associated with overall cancer; most of the specific types of cancer had common identified metabolites. Meanwhile, the associations between the same metabolite with different types of cancer can vary based on the site of origin. We identified 13 metabolic biomarkers associated with overall cancer, and found that they mediated the effects of traditional factors. The accuracies of prediction models improved when we added 13 identified metabolites in models. This study is helpful to understand the metabolic mechanisms of overall and a wide range of cancers, and our results also indicate that NMR metabolites are potential biomarkers in cancer diagnosis and prevention.