Mendelian randomization of chronic hepatitis B and cardiovascular disease.

Background: Evidence from observational studies suggests that chronic hepatitis B (CHB) is associated with cardiovascular disease (CVD). However, results have been inconsistent and causality remains to be established. We utilized two-sample Mendelian randomization (MR) to investigate potential causal associations between CHB and CVD, including atherosclerosis, coronary heart disease, hypertension, and ischemic stroke. Methods: The analysis was conducted through genome-wide association studies (GWAS), considering chronic hepatitis B as the exposure and cardiovascular disease as the endpoint. The primary method for evaluating causality in this analysis was the inverse-variance weighted (IVW) technique. Additionally, we employed the weighted median, MR-Egger regression, weighted mode, and simple mode methods for supplementary analyses. Finally, heterogeneity tests, sensitivity analyses, and multiple effects analyses were conducted. Results: In a random-effects IVW analysis, we found that genetic susceptibility to chronic hepatitis B was associated with an increased risk of atherosclerosis [OR = 1.048, 95% CI (1.022-1.075), P = 3.08E-04], as well as an increased risk of coronary heart disease [OR = 1.039, 95% CI (1.006-1.072), P = 0.020]. However, it was found to be inversely correlated with ischemic stroke risk [OR = 0.972, 95% CI (0.957-0.988), P = 4.13E-04]. There was no evidence that chronic hepatitis B was associated with hypertension [OR = 1.021, 95% CI (0.994-1.049), P = 0.121]. Conclusion: Our research indicates that chronic hepatitis B has a correlation with an elevated risk of developing atherosclerosis and coronary heart disease, while it is associated with a decreased risk of experiencing an ischemic stroke.

The impact of economy policy uncertainty and oil price shocks on G20 banks' stock performance: Wavelet coherence and non-parametric causality in quantiles approach.

This study employs nonparametric causality-in-quantiles and wavelet coherence techniques to examine the impact of economic policy uncertainty and oil price variations on bank stocks in twelve prominent global economies. The results reveal that the effects of both economic policy uncertainty and oil prices on bank stock values vary significantly across countries and over time. Notably, during stress periods, we observe an inverse relationship between economic policy uncertainty and bank stocks in multiple countries, namely, Brazil, Canada, France, India, Russia, and the USA, with Japan exhibiting a particularly strong and long-term adverse correlation. Similarly, the influence of oil prices is primarily observed during crisis periods, but it demonstrates a substantial co-movement with bank stocks across the sample countries except Brazil. Our empirical analysis holds valuable implications for policymakers, bankers, investors, and portfolio managers.

Causal relationship between inflammatory cytokines and autoimmune thyroid disease: a bidirectional two-sample Mendelian randomization analysis.

Background: Autoimmune thyroid disease (AITD) ranks among the most prevalent thyroid diseases, with inflammatory cytokines playing a decisive role in its pathophysiological process. However, the causal relationship between the inflammatory cytokines and AITD remains elusive. Methods: A two-sample Mendelian randomization (MR) analysis was performed to elucidate the causal connection between AITD and 41 inflammatory cytokines. Genetic variations associated with inflammatory cytokines were sourced from the FinnGen biobank, whereas a comprehensive meta-analysis of genome-wide association studies (GWASs) yielded data on Graves' disease (GD) and Hashimoto thyroiditis. Regarding the MR analysis, the inverse variance-weighted, MR-Egger, and weighted median methods were utilized. Additionally, sensitivity analysis was conducted using MR-Egger regression, MR-pleiotropy residual sum, and outliers. Results: Seven causal associations were identified between inflammatory cytokines and AITD. High levels of tumor necrosis factor-ß and low levels of stem cell growth factor-ß were indicative of a higher risk of GD. In contrast, high levels of interleukin-12p70 (IL-12p70), IL-13, and interferon-γ and low levels of monocyte chemotactic protein-1 (MCP-1) and TNF-α suggested a higher risk of HD. Moreover, 14 causal associations were detected between AITD and inflammatory cytokines. GD increases the levels of macrophage inflammatory protein-1ß, MCP-1, monokine induced by interferon-γ (MIG), interferon γ-induced protein 10 (IP-10), stromal cell-derived factor-1α, platelet-derived growth factor BB, ß-nerve growth factor, IL-2ra, IL-4, and IL-17 in blood, whereas HD increases the levels of MIG, IL-2ra, IP-10, and IL-16 levels. Conclusion: Our bidirectional MR analysis revealed a causal relationship between inflammatory cytokines and AITD. These findings offer valuable insights into the pathophysiological mechanisms underlying AITD.

Causal dynamics of sleep, circadian rhythm, and mood symptoms in patients with major depression and bipolar disorder: insights from longitudinal wearable device data.

BACKGROUND: Sleep and circadian rhythm disruptions are common in patients with mood disorders. The intricate relationship between these disruptions and mood has been investigated, but their causal dynamics remain unknown. METHODS: We analysed data from 139 patients (76 female, mean age = 23.5 ± 3.64 years) with mood disorders who participated in a prospective observational study in South Korea. The patients wore wearable devices to monitor sleep and engaged in smartphone-delivered ecological momentary assessment of mood symptoms. Using a mathematical model, we estimated their daily circadian phase based on sleep data. Subsequently, we obtained daily time series for sleep/circadian phase estimates and mood symptoms spanning >40,000 days. We analysed the causal relationship between the time series using transfer entropy, a non-linear causal inference method. FINDINGS: The transfer entropy analysis suggested causality from circadian phase disturbance to mood symptoms in both patients with MDD (n = 45) and BD type I (n = 35), as 66.7% and 85.7% of the patients with a large dataset (>600 days) showed causality, but not in patients with BD type II (n = 59). Surprisingly, no causal relationship was suggested between sleep phase disturbances and mood symptoms. INTERPRETATION: Our findings suggest that in patients with mood disorders, circadian phase disturbances directly precede mood symptoms. This underscores the potential of targeting circadian rhythms in digital medicine, such as sleep or light exposure interventions, to restore circadian phase and thereby manage mood disorders effectively. FUNDING: Institute for Basic Science, the Human Frontiers Science Program Organization, the National Research Foundation of Korea, and the Ministry of Health & Welfare of South Korea.

Causal Artificial Intelligence Models of Food Quality Data.

Research background: The aim of this study is to emphasize the importance of artificial intelligence (AI) and causality modelling of food quality and analysis with 'big data'. AI with structural causal modelling (SCM), based on Bayesian networks and deep learning, enables the integration of theoretical field knowledge in food technology with process production, physicochemical analytics and consumer organoleptic assessments. Food products have complex nature and data are highly dimensional, with intricate interrelations (correlations) that are difficult to relate to consumer sensory perception of food quality. Standard regression modelling techniques such as multiple ordinary least squares (OLS) and partial least squares (PLS) are effectively applied for the prediction by linear interpolations of observed data under cross-sectional stationary conditions. Upgrading linear regression models by machine learning (ML) accounts for nonlinear relations and reveals functional patterns, but is prone to confounding and failed predictions under unobserved nonstationary conditions. Confounding of data variables is the main obstacle to applications of the regression models in food innovations under previously untrained conditions. Hence, this manuscript focuses on applying causal graphical models with Bayesian networks to infer causal relationships and intervention effects between process variables and consumer sensory assessment of food quality. Experimental approach: This study is based on the data available in the literature on the process of wheat bread baking quality, consumer sensory quality assessments of fermented milk products, and professional wine tasting data. The data for wheat baking quality were regularized by the least absolute shrinkage and selection operator (LASSO elastic net). Bayesian statistics was applied for the evaluation of the model joint probability function for inferring the network structure and parameters. The obtained SCMs are presented as directed acyclic graphs (DAG). D-separation criteria were applied to block confounding effects in estimating direct and total causal effects of process variables and consumer perception on food quality. Probability distributions of causal effects of the intervention of individual process variables on quality are presented as partial dependency plots determined by Bayesian neural networks. In the case of wine quality causality, the total causal effects determined by SCMs are positively validated by the double machine learning (DML) algorithm. Results and conclusions: The data set of 45 continuous variables corresponding to different chemical, physical and biochemical variables of wheat properties from seven Croatian cultivars during two years of controlled cultivation were analysed. LASSO regularization of the data set yielded the ten key predictors, accounting for 98 % variance of the baking quality data. Based on the key variables, the quality predictive random forest model with 75 % cross-validation accuracy was derived. Causal analysis between the quality and key predictors was based on the Bayesian model shown as a DAG graph. Protein content shows the most important direct causal effect with the corresponding path coefficient of 0.71, and THMM (total high-molecular-mass glutenin subunits) content was an indirect cause with a path coefficient of 0.42, and protein total average causal effect (ACE) was 0.65. The large data set of the quality of fermented milk products included binary consumer sensory data (taste, odour, turbidity), continuous physical variables (temperature, fat, pH, colour) and three grade classes of products by consumer quality assessment. A random forest model was derived for the prediction of the quality classification with an out-of-bag (OOB) error of 0.28 %. The Bayesian network model predicts that the direct causes of the taste classification are temperature, colour and fat content, while the direct causes of the quality classification are temperature, turbidity, odour and fat content. The key quality grade ACE of temperature -0.04 grade/°C and 0.3 quality grade/fat content were estimated. The temperature ACE dependency shows a nonlinear type as negative saturation with the 'breaking' point at 60 °C, while for fat ACE had a positive linear trend. Causal quality analysis of red and white wine was based on the large data set of eleven continuous variables of physical and chemical properties and quality assessments classified in ten classes, from 1 to 10. Each classification was obtained in triplicate by a panel of professional wine tasters. A non-structural double machine learning (DML) algorithm was applied for total ACE quality assessment. The alcohol content of red and white wine had the key positive ACE relative factor of 0.35 quality/alcohol, while volatile acidity had the key negative ACE of -0.2 quality/acidity. The obtained ACE predictions by the unstructured DML algorithm are in close agreement with the ACE obtained by the structural SCM. Novelty and scientific contribution: Novel methodologies and results for the application of causal artificial intelligence models in the analysis of consumer assessment of the quality of food products are presented. The application of Bayesian network structural causal models (SCM) enables the d-separation of pronounced effects of confounding between parameters in noncausal regression models. Based on the SCM, inference of ACE provides substantiated and validated research hypotheses for new products and support for decisions of potential interventions for improvement in product design, new process introduction, process control, management and marketing.

Risk of Major Types of Dementias Following Hospital-Diagnosed Infections and Autoimmune Diseases.

Background: Population-based studies have shown an increased risk of dementia after infections, but weaker links were reported for autoimmune diseases. Evidence is scarce for whether the links may be modified by the dementia or exposure subtype. Objective: We aimed to investigate the association between infections and/or autoimmune diseases and rates of major types of dementias in the short- and long terms. Methods: Nationwide nested case-control study of dementia cases (65+ years) diagnosed in Denmark 2016-2020 and dementia-free controls. Exposures were hospital-diagnosed infections and autoimmune diseases in the preceding 35 years. Two groups of dementia cases were those diagnosed in memory clinics (MC) and those diagnosed outside memory clinics (non-memory clinic cases, NMC). Results: In total, 26,738 individuals were MC and 12,534 were NMC cases. Following any infection, the incidence rate ratio (IRR) for MC cases was 1.23 (95% CI 1.20-1.27) and 1.70 for NMC cases (1.62-1.76). Long-term increased rates were seen for vascular dementia and NMC cases. IRRs for autoimmune diseases were overall statistically insignificant. Conclusions: Cases with vascular dementia and not Alzheimer's disease, and a subgroup of cases identified with poorer health have increased long-term risk following infections. Autoimmune diseases were not associated with any type of dementia. Notably increased risks (attributed to the short term) and for NMC cases may indicate that immunosenescence rather than de novo infection explains the links. Future focus on such groups and on the role of vascular pathology will explain the infection-dementia links, especially in the long term.

Causal interactions in brain networks predict pain levels in trigeminal neuralgia.

Trigeminal neuralgia (TN) is a highly debilitating facial pain condition. Magnetic resonance imaging (MRI) is the main method for generating insights into the central mechanisms of TN pain in humans. Studies have found both structural and functional abnormalities in various brain structures in TN patients as compared with healthy controls. Whereas studies have also examined aberrations in brain networks in TN, no studies have to date investigated causal interactions in these brain networks and related these causal interactions to the levels of TN pain. We recorded fMRI data from 39 TN patients who either rested comfortably in the scanner during the resting state session or tracked their pain levels during the pain tracking session. Applying Granger causality to analyze the data and requiring consistent findings across the two scanning sessions, we found 5 causal interactions, including: (1) Thalamus → dACC, (2) Caudate → Inferior temporal gyrus, (3) Precentral gyrus → Inferior temporal gyrus, (4) Supramarginal gyrus → Inferior temporal gyrus, and (5) Bankssts → Inferior temporal gyrus, that were consistently associated with the levels of pain experienced by the patients. Utilizing these 5 causal interactions as predictor variables and the pain score as the predicted variable in a linear multiple regression model, we found that in both pain tracking and resting state sessions, the model was able to explain ∼36 % of the variance in pain levels, and importantly, the model trained on the 5 causal interaction values from one session was able to predict pain levels using the 5 causal interaction values from the other session, thereby cross-validating the models. These results, obtained by applying novel analytical methods to neuroimaging data, provide important insights into the pathophysiology of TN and could inform future studies aimed at developing innovative therapies for treating TN.

The impact of health on labour market outcomes: A rapid systematic review.

The relationship between an individual's health and their labour market outcomes has long been a subject of health economics research. This review aims to provide an up-to-date, global review of the substantive findings in the existing literature. We pay particular attention to causal effects, acknowledging the methodological complexities that have long challenged the research and emphasizing the importance of overcoming them to present robust, policy-relevant evidence. The recent literature shows a notable advancement in addressing these methodological issues compared to previous work. The evidence reviewed suggests that individuals with better health overwhelmingly exhibit higher earnings and often enhanced labour supply. These findings extend beyond geographical boundaries, as evidence from diverse regions underscores the global significance of this association. The review covers evidence from a wide range of health indicators and conditions - including e.g. self-reported health, chronic diseases, disability, nutritional health, infections, mental health, addictions and others. Within and across the different health domains, the health-related factors exert varying degrees of influence on labour market outcomes, highlighting the multifaceted nature of the health-labour relationship and its potentially profound implications for individuals, communities, and economies.

Causal effect of gut microbiota on the risk of prostatitis: a two-sample Mendelian randomization study.

BACKGROUND: Recent studies demonstrated that chronic prostatitis (CP) is closely related to the gut microbiota (GM). Nevertheless, the causal relationship between GM and CP has not been fully elucidated. Therefore, the two-sample Mendelian randomization (MR) analysis was employed to investigate this association. METHODS: The summary data of gut microbiota derived from a genome-wide association study (GWAS) involving 18,340 individuals in the MiBioGen study served as the exposure, and the corresponding summary statistics for CP risk, representing the outcome, were obtained from the FinnGen databases (R9). The causal effects between GM and CP were estimated using the inverse-variance weighted (IVW) method supplemented with MR-Egger, weighted median, weighted mode, and simple mode methods. Additionally, the false discovery rate (FDR) correction was performed to adjust results. The detection and quantification of heterogeneity and pleiotropy were accomplished through the MR pleiotropy residual sum and outlier method, Cochran's Q statistics, and MR-Egger regression. RESULTS: The IVW estimates indicated that a total of 11 GM taxa were related to the risk of CP. Seven of them was correlated with an increased risk of CP, while the remained linked with a decreased risk of CP. However, only Methanobacteria (OR 0.86; 95% CI 0.74-0.99), Methanobacteriales (OR 0.86; 95% CI 0.74-0.99), NB1n (OR 1.16; 95% CI 1.16-1.34), Methanobacteriaceae (OR 0.86; 95% CI 0.74-0.99), Odoribactergenus Odoribacter (OR 1.43; 95% CI 1.05-1.94), and Sutterellagenus Sutterella (OR 1.33; 95% CI 1.01-1.76) still maintain significant association with CP after FDR correction. Consistent directional effects for all analyses were observed in the supplementary methods. Subsequently, sensitivity analyses indicated the absence of heterogeneity, directional pleiotropy, or outliers concerning the causal effect of specific gut microbiota on CP (p > 0.05). CONCLUSION: Our study demonstrated a gut microbiota-prostate axis, offering crucial data supporting the promising use of the GM as a candidate target for CP prevention, diagnosis, and treatment. There is a necessity for randomized controlled trials to validate the protective effect of the linked GM against the risk of CP, and to further investigate the underlying mechanisms involved.

Avoidance of causality outside experiments: Hypotheses from cognitive dissonance reduction.

The avoidance of causality in the design, analysis and interpretation of non-experimental studies has often been criticised as an untenable scientific stance, because theories are based on causal relations (and not associations) and a rich set of methodological tools for causal analysis has been developed in recent decades. Psychology researchers (n = 106 with complete data) participated in an online study presenting a causal statement about the results of a fictitious paper on the potential effect of drinking clear water for years on the risk of dementia. Two randomised groups of participants were then asked to reflect on the conflict between the goal of approaching a causal answer and the prevailing norm of avoiding doing so. One of the two groups was also instructed to think about possible benefits of addressing causality. Both groups then responded to a list of 19 items about attitudes to causal questions in science. A control group did this without reflecting on conflict or benefits. Free-text assessments were also collected during reflection, giving some indication of how and why causality is avoided. We condense the exploratory findings of this study into five new hypotheses about the how and why, filtered through what can be explained by cognitive dissonance reduction theory. These concern the cost of addressing causality, the variety of ways in which dissonance can be reduced, the need for profound intervention through teaching and social aspects. Predictions are derived from the hypotheses for confirmation trials in future studies and recommendations for teaching causality. Open data are provided for researchers' own analyses.

Factors affecting the quality of life of patients with medication-related osteonecrosis of the jaw during treatment: A quality-of-life survey and causal analysis.

This prospective cohort study aimed to investigate the impact of medication-related osteonecrosis of the jaw (MRONJ) on health-related quality of life (HRQOL) and oral health-related QOL (OHRQOL) and the association between the downstaging of MRONJ and OHRQOL. The HRQOL and OHRQOL of 44 patients with MRONJ were assessed using the SF-36v2 and the General Oral Health Assessment Index (GOHAI), respectively. Treatment was performed in accordance with the AAOMS position paper (2014). The SF-36v2 and GOHAI scores at the beginning of the survey were used to evaluate the impact of MRONJ on QOL. Potential confounders affecting the association between downstaging and QOL improvement were selected using directed acyclic graphs. Multiple regression analysis was performed to evaluate causal inferences. HRQOL scale scores declined below the national average. The three-component summary score (3CS), comprising the physical component summary (PCS), mental component summary (MCS), and role/social component summary (RCS), revealed that performance status and primary disease significantly affected the PCS and RCS (P = 0.005 and P < 0.001, respectively) and PCS and MCS (P = 0.024 and P = 0.003, respectively). The MRONJ stage did not influence the 3CS; however, OHRQOL declined in a stage-dependent manner (P = 0.005). Downstaging of MRONJ was independently associated with the improvement rate of the total GOHAI scores after adjusting for variables (P = 0.045). The HRQOL of patients with MRONJ declined; however, this may depend on the underlying disease status rather than the MRONJ stage. Improvement of the disease status can potentially predict an improvement in OHRQOL, regardless of the treatment modality.

Genetically identification of endometriosis and cancers risk in women through a two-sample Mendelian randomization study.

Endometriosis is a prevalent and chronic inflammatory gynecologic disorder affecting approximately 6-10% of women globally, and has been associated with an increased risk of cancer. Nevertheless, previous studies have been hindered by methodological limitations that compromise the validity and robustness of their findings. In this study we conducted a comprehensive two-sample Mendelian randomization analysis to explore the genetically driven causal relationship between endometriosis and the risk of cancer. We conducted the analysis via the inverse variance weighted method, MR Egger method, and weighted median method utilizing publicly available genome-wide association study summary statistics. Furthermore, we implemented additional sensitivity analyses to assess the robustness and validity of the causal associations identified. We found strong evidence of a significant causal effect of endometriosis on a higher risk of ovarian cancer via inverse-variance weighted method (OR = 1.19, 95% CI 1.11-1.29, p < 0.0001), MR-Egger regression, and weighted median methodologies. Remarkably, our findings revealed a significant association between endometriosis and an increased risk of clear cell ovarian cancer (OR = 2.04, 95% CI 1.66-2.51, p < 0.0001) and endometrioid ovarian cancer (OR = 1.45, 95% CI 1.27-1.65, p < 0.0001). No association between endometriosis and other types of cancer was observed. We uncovered a causal relationship between endometriosis and an elevated risk of ovarian cancer, particularly clear cell ovarian cancer and endometrioid ovarian cancer. No significant associations between endometriosis and other types of cancer could be identified.

PM2.5, component cause of severe metabolically abnormal obesity: An in silico, observational and analytical study.

Obesity is currently one of the most alarming pathological conditions due to the progressive increase in its prevalence. In the last decade, it has been associated with fine particulate matter suspended in the air (PM2.5). The purpose of this study was to explore the mechanistic interaction of PM2.5 with a high-fat diet (HFD) through the differential regulation of transcriptional signatures, aiming to identify the association of these particles with metabolically abnormal obesity. The research design was observational, using bioinformatic methods and an explanatory approach based on Rothman's causal model. We propose three new transcriptional signatures in murine adipose tissue. The sum of transcriptional differences between the group exposed to an HFD and PM2.5, compared to the control group, were 0.851, 0.265, and -0.047 (p > 0.05). The HFD group increased body mass by 20% with two positive biomarkers of metabolic impact. The group exposed to PM2.5 maintained a similar weight to the control group but exhibited three positive biomarkers. Enriched biological pathways (p < 0.05) included PPAR signaling, small molecule transport, adipogenesis genes, cytokine-cytokine receptor interaction, and HIF-1 signaling. Transcriptional regulation predictions revealed CpG islands and common transcription factors. We propose three new transcriptional signatures: FAT-PM2.5-CEJUS, FAT-PM2.5-UP, and FAT-PM2.5-DN, whose transcriptional regulation profile in adipocytes was statistically similar by dietary intake and HFD and exposure to PM2.5 in mice; suggesting a mechanistic interaction between both factors. However, HFD-exposed murines developed moderate metabolically abnormal obesity, and PM2.5-exposed murines developed severe abnormal metabolism without obesity. Therefore, in Rothman's terms, it is concluded that HFD is a sufficient cause of the development of obesity, and PM2.5 is a component cause of severe abnormal metabolism of obesity. These signatures would be integrated into a systemic biological process that would induce transcriptional regulation in trans, activating obesogenic biological pathways, restricting lipid mobilization pathways, decreasing adaptive thermogenesis and angiogenesis, and altering vascular tone thus inducing a severe metabolically abnormal obesity.

Editorial: Are government early years learning and development frameworks evidence-based? A scientist's perspective.

Not all young children attend nurseries, childminders or other group settings before they start school, but many do. It is common for countries to set out a framework to guide practice for early years providers (such as nurseries) to follow. The conundrum regarding these frameworks for young children is that proving evidence of a causal link between early environments and later outcomes is very challenging scientifically. So how do governments choose what learning and development practices and goals to make mandatory for childcare providers? And is it realistic to expect early years providers to meet the legal requirements that these frameworks impose? We do not know which learning and development practices impact positively on later outcomes, and we certainly do not know if there is a one-size-fits-all approach for an early years framework that is guaranteed to work.

The COVID-19 Vaccine Safety Research Center: a cornerstone for strengthening safety evidence for COVID-19 vaccination in the Republic of Korea.

The COVID-19 Vaccine Safety Research Committee (CoVaSC) was established in November 2021 to address the growing need for independent, in-depth scientific evidence on adverse events (AEs) following coronavirus disease 2019 (COVID-19) vaccination. This initiative was requested by the Korea Disease Control and Prevention Agency and led by the National Academy of Medicine of Korea. In September 2022, the COVID-19 Vaccine Safety Research Center was established, strengthening CoVaSC's initiatives. The center has conducted various studies on the safety of COVID-19 vaccines. During CoVaSC's second research year, from September 29, 2022 to July 19, 2023, the center was restructured into 4 departments: Epidemiological Research, Clinical Research, Communication & Education, and International Cooperation & Policy Research. Its main activities include (1) managing CoVaSC and the COVID-19 Vaccine Safety Research Center, (2) surveying domestic and international trends in AE causality investigation, (3) assessing AEs following COVID-19 vaccination, (4) fostering international collaboration and policy research, and (5) organizing regular fora and training sessions for the public and clinicians. Causality assessments have been conducted for 27 diseases, and independent research has been conducted after organizing ad hoc committees comprising both epidemiologists and clinical experts on each AE of interest. The research process included protocol development, data analysis, interpretation of results, and causality assessment. These research outcomes have been shared transparently with the public and healthcare experts through various fora. The COVID-19 Vaccine Safety Research Center plans to continue strengthening and expanding its research activities to provide reliable, high-quality safety information to the public.

Neurobiological Causal Models of Language Processing.

The language faculty is physically realized in the neurobiological infrastructure of the human brain. Despite significant efforts, an integrated understanding of this system remains a formidable challenge. What is missing from most theoretical accounts is a specification of the neural mechanisms that implement language function. Computational models that have been put forward generally lack an explicit neurobiological foundation. We propose a neurobiologically informed causal modeling approach which offers a framework for how to bridge this gap. A neurobiological causal model is a mechanistic description of language processing that is grounded in, and constrained by, the characteristics of the neurobiological substrate. It intends to model the generators of language behavior at the level of implementational causality. We describe key features and neurobiological component parts from which causal models can be built and provide guidelines on how to implement them in model simulations. Then we outline how this approach can shed new light on the core computational machinery for language, the long-term storage of words in the mental lexicon and combinatorial processing in sentence comprehension. In contrast to cognitive theories of behavior, causal models are formulated in the "machine language" of neurobiology which is universal to human cognition. We argue that neurobiological causal modeling should be pursued in addition to existing approaches. Eventually, this approach will allow us to develop an explicit computational neurobiology of language.

The genetic relationship between systemic lupus erythematosus and risk of primary ovarian failure from a mendelian randomization study.

Previous studies investigating the relationship between systemic lupus erythematosus (SLE) and primary ovarian failure (POF) generated conflicting results. To data, no mendelian randomization study has been applied to examine this association. In this study, genetic instruments for exposure (SLE) were selected from a GWAS study with 5201 cases and 9066 noncases. Outcome data for POF and three reproductive traits (age at menarche, age at menopause, and age at first live birth) were obtained from other eligible GWASs. To estimate causal association, the inverse-variance weighted (IVW) method (the main analyse), MR Egger test, weighted median, simple mode, and weighted mode were applied. Moreover, sensitivity analyses were conducted to ensure the robustness of the results. Estimated by the IVW method, SLE was suggested to be causally related to the risk of POF (OR = 1.166, 95% CI 1.055-1.289, P = 0.003) and delayed age at first live birth (OR = 1.006, 95% CI 1.002-1.010, P = 0.007), with no evidence of a causal association between SLE and age at menopause or menarche. The estimates were robust according to sensitivity analysis. In conclusion, the two-sample MR study supported a causal association between SLE and POF from a genetic aspect.

Causal effect of gut microbiota and diabetic nephropathy: a Mendelian randomization study.

BACKGROUND: The interaction of dysbiosis of gut microbiota (GM) with diabetic nephropathy (DN) drew our attention and a better understanding of GM on DN might provide potential therapeutic approaches. However, the exact causal effect of GM on DN remains unknown. METHODS: We applied two-sample Mendelian Randomization (MR) analysis, including inverse variance weighted (IVW), MR-Egger methods, etc., to screen the significant bacterial taxa based on the GWAS data. Sensitivity analysis was conducted to assess the robustness of MR results. To identify the most critical factor on DN, Mendelian randomization-Bayesian model averaging (MR-BMA) method was utilized. Then, whether the reverse causality existed was verified by reverse MR analysis. Finally, transcriptome MR analysis was performed to investigate the possible mechanism of GM on DN. RESULTS: At locus-wide significance levels, the results of IVW suggested that order Bacteroidales (odds ratio (OR) = 1.412, 95% confidence interval (CI): 1.025-1.945, P = 0.035), genus Akkermansia (OR = 1.449, 95% CI: 1.120-1.875, P = 0.005), genus Coprococcus 1 (OR = 1.328, 95% CI: 1.066-1.793, P = 0.015), genus Marvinbryantia (OR = 1.353, 95% CI: 1.037-1.777, P = 0.030) and genus Parasutterella (OR = 1.276, 95% CI: 1.022-1.593, P = 0.032) were risk factors for DN. Reversely, genus Eubacterium ventriosum (OR = 0.756, 95% CI: 0.594-0.963, P = 0.023), genus Ruminococcus gauvreauii (OR = 0.663, 95% CI: 0.506-0.870, P = 0.003) and genus Erysipelotrichaceae (UCG003) (OR = 0.801, 95% CI: 0.644-0.997, P = 0.047) were negatively associated with the risk of DN. Among these taxa, genus Ruminococcus gauvreauii played a crucial role in DN. No significant heterogeneity or pleiotropy in the MR result was found. Mapped genes (FDR < 0.05) related to GM had causal effects on DN, while FCGR2B and VNN2 might be potential therapeutic targets. CONCLUSIONS: This work provided new evidence for the causal effect of GM on DN occurrence and potential biomarkers for DN. The significant bacterial taxa in our study provided new insights for the 'gut-kidney' axis, as well as unconventional prevention and treatment strategies for DN.