Interactive molecular causal networks of hypertension using a fast machine learning algorithm MRdualPC.

BACKGROUND: Understanding the complex interactions between genes and their causal effects on diseases is crucial for developing targeted treatments and gaining insight into biological mechanisms. However, the analysis of molecular networks, especially in the context of high-dimensional data, presents significant challenges. METHODS: This study introduces MRdualPC, a computationally tractable algorithm based on the MRPC approach, to infer large-scale causal molecular networks. We apply MRdualPC to investigate the upstream causal transcriptomics influencing hypertension using a comprehensive dataset of kidney genome and transcriptome data. RESULTS: Our algorithm proves to be 100 times faster than MRPC on average in identifying transcriptomics drivers of hypertension. Through clustering, we identify 63 modules with causal driver genes, including 17 modules with extensive causal networks. Notably, we find that genes within one of the causal networks are associated with the electron transport chain and oxidative phosphorylation, previously linked to hypertension. Moreover, the identified causal ancestor genes show an over-representation of blood pressure-related genes. CONCLUSIONS: MRdualPC has the potential for broader applications beyond gene expression data, including multi-omics integration. While there are limitations, such as the need for clustering in large gene expression datasets, our study represents a significant advancement in building causal molecular networks, offering researchers a valuable tool for analyzing big data and investigating complex diseases.

Sudden cardiac death after early-onset myocardial infarction. A multicentre longitudinal cohort study with 20 years' follow-up.

BACKGROUND: Sudden cardiac death (SCD) is a serious consequence of a myocardial infarction (MI), but identifying patients at risk of developing SCD remains a major clinical challenge especially in the case of juvenile MI. The aim of this study was to identify predictors of SCD after early-onset MI using long-term follow-up data relating to a large nationwide patient cohort. METHODS: The Italian Genetic Study on Early-onset MI enrolled 2,000 patients experiencing a first MI before the age of 45 years, who were followed up for a median of 19.9 years. Fine-Gray proportional hazard models were used to assess the associations between their clinical, demographic and index event data and the occurrence of SCD. RESULTS: SCD occurred in 195 patients, who were more frequently males, hypertensive and/or diabetic; had a history of previous thromboembolic events with a greater atherosclerotic burden; and had a lower left ventricular ejection fraction (LVEF) after the index event. Multivariable analysis showed that the independent predictors of SCD were diabetes, hypertension, previous thromboembolic events, higher Syntax score, and a lower LVEF. There was no clear evidence of the clustering of SCD events during follow-up. SCD was the first post-MI clinical event in 101 patients; the remaining 94 experienced SCD after a non-fatal MI or hospitalisation for coronary revascularisation. CONCLUSIONS: SCD frequently occurs during the 20 years after early-onset MI. The nature of the identified predictors and the absence of clustering suggests that the pathophysiological basis of SCD may be related to progressive coronary atherosclerosis.

Bayesian Mendelian randomization with an interval causal null hypothesis: ternary decision rules and loss function calibration.

We enhance the Bayesian Mendelian Randomization (MR) framework of Berzuini et al. (Biostatistics 21(1):86-101, 2018) by allowing for interval null causal hypotheses, where values of the causal effect parameter that fall within a user-specified interval of "practical equivalence" (ROPE) (Kruschke, Adv Methods Pract Psychol Sci 1(2):270-80, 2018) are regarded as equivalent to "no effect". We motivate this move in the context of MR analysis. In this approach, the decision over the hypothesis test is taken on the basis of the Bayesian posterior odds for the causal effect parameter falling within the ROPE. We allow the causal effect parameter to have a mixture prior, with components corresponding to the null and the alternative hypothesis. Inference is performed via Markov chain Monte Carlo (MCMC) methods. We speed up the calculations by fitting to the data a simpler model than the intended, "true", one. We recover a set of samples from the "true" posterior distribution by weighted importance resampling of the MCMC-generated samples. From the final samples we obtain a simulation consistent estimate of the desired posterior odds, and ultimately of the Bayes factor for the interval-valued null hypothesis, [Formula: see text], vs [Formula: see text]. In those situations where the posterior odds is neither large nor small enough, we allow for an uncertain outcome of the test decision, thereby moving to a ternary decision logic. Finally, we present an approach to calibration of the proposed method via loss function. We illustrate the method with the aid of a study of the causal effect of obesity on risk of juvenile myocardial infarction based on a unique prospective dataset.

Clinical prediction models for aneurysmal subarachnoid hemorrhage: a systematic review update.

BACKGROUND: A systematic review of clinical prediction models for aneurysmal subarachnoid hemorrhage (aSAH) reported in 2011 noted that clinical prediction models for aSAH were developed using poor methods and were not externally validated. This study aimed to update the above review to guide the future development of predictive models in aSAH. METHODS: We systematically searched Embase and MEDLINE databases (January 2010 to February 2022) for articles that reported the development of a clinical prediction model to predict functional outcomes in aSAH. Our reviews are based on the items included in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) checklist, and on data abstracted from each study in accord with the Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) 2014 checklist. Bias and applicability were assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). RESULTS: We reviewed data on 30 466 patients contributing to 29 prediction models abstracted from 22 studies identified from an initial search of 7858 studies. Most models were developed using logistic regression (n=20) or machine learning (n=9) with prognostic variables selected through a range of methods. Age (n=13), World Federation of Neurological Surgeons (WFNS) grade (n=11), hypertension (n=6), aneurysm size (n=5), Fisher grade (n=12), Hunt and Hess score (n=5), and Glasgow Coma Scale (n=8) were the variables most frequently included in the reported models. External validation was performed in only four studies. All but one model had a high or unclear risk of bias due to poor performance or lack of validation. CONCLUSION: Externally validated models for the prediction of functional outcome in aSAH patients have now become available. However, most of them still have a high risk of bias.

A review of causal discovery methods for molecular network analysis.

BACKGROUND: With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology andgenetics. There are an increasing number of methodlogies that have been developed and applied to moleular networks to investigate these causal interactions. METHODS: We have introduced and reviewed the available methods for building large-scale causal molecular networks that have been developed and applied in the past decade. RESULTS: In this review we have identified and summarized the existing methods for infering causality in large-scale causal molecular networks, and discussed important factors that will need to be considered in future research in this area. CONCLUSION: Existing methods to infering causal molecular networks have their own strengths and limitations so there is no one best approach, and it is instead down to the discretion of the researcher. This review also to discusses some of the current limitations to biological interpretation of these networks, and important factors to consider for future studies on molecular networks.

Hunting for protective drugs at the break of a pandemic: Causal inference from hospital data.

At the break of a pandemic, the protective efficacy of therapeutic interventions needs rapid evaluation. An experimental approach to the problem will not always be appropriate. An alternative route are observational studies, whether based on regional health service data or hospital records. In this paper, we discuss the use of methods of causal inference for the analysis of such data, with special reference to causal questions that may arise in a pandemic. We apply the methods by using the aid of a directed acyclic graph (DAG) representation of the problem, to encode our causal assumptions and to logically connect the scientific questions. We illustrate the usefulness of DAGs in the context of a controversy over the effects of renin aldosterone system inhibitors (RASIs) in hypertensive individuals at risk of (or affected by) severe acute respiratory syndrome coronavirus 2 disease. We consider questions concerning the existence and the directions of those effects, their underlying mechanisms, and the possible dependence of the effects on context variables. This paper describes the cognitive steps that led to a DAG representation of the problem, based on background knowledge and evidence from past studies, and the use of the DAG to analyze our hospital data and assess the interpretive limits of the results. Our study contributed to subverting early opinions about RASIs, by suggesting that these drugs may indeed protect the older hypertensive Covid-19 patients from the consequences of the disease. Mechanistic interaction methods revealed that the benefit may be greater (in a sense to be made clear) in the older stratum of the population.

Sex-Related Differences in Long-Term Outcomes After Early-Onset Myocardial Infarction.

Importance: There is growing awareness of sex-related differences in cardiovascular risk profiles, but less is known about whether these extend to pre-menopausal females experiencing an early-onset myocardial infarction (MI), who may benefit from the protective effects of estrogen exposure. Methods: A nationwide study involving 125 Italian Coronary Care Units recruited 2,000 patients between 1998 and 2002 hospitalized for a type I myocardial infarction before the age of 45 years (male, n = 1,778 (88.9%). Patients were followed up for a median of 19.9 years (IQR 18.1-22.6). The primary composite endpoint was the occurrence of cardiovascular death, non-fatal myocardial re-infarction or non-fatal stroke, and the secondary endpoint of hospitalization for revascularisation by means of a percutaneous coronary intervention (PCI) or coronary artery bypass surgery (CABG). Results: ST-elevation MI was the most frequent presentation among both men and women (85.1 vs. 87.4%, p = ns), but the men had a greater baseline coronary atherosclerotic burden (median Duke Coronary Artery Disease Index: 48 vs. 23; median Syntax score 9 vs. 7; both p < 0.001). The primary composite endpoint occurred less frequently among women (25.7% vs. 37.0%; adjusted hazard ratio: 0.69, 95% CI 0.52-0.91; p = 0.01) despite being less likely to receive treatment with most secondary prevention medications during follow up. Conclusions: There are significant sex-related differences in baseline risk factors and outcomes among patients with early-onset MI: women present with a lower atherosclerotic disease burden and, although they are less frequently prescribed secondary prevention measures, experience better long-term outcomes. Trial Registration: 4272/98 Ospedale Niguarda, Ca' Granda 03/09/1998.

Bayesian mendelian randomization with study heterogeneity and data partitioning for large studies.

BACKGROUND: Mendelian randomization (MR) is a useful approach to causal inference from observational studies when randomised controlled trials are not feasible. However, study heterogeneity of two association studies required in MR is often overlooked. When dealing with large studies, recently developed Bayesian MR can be computationally challenging, and sometimes even prohibitive. METHODS: We addressed study heterogeneity by proposing a random effect Bayesian MR model with multiple exposures and outcomes. For large studies, we adopted a subset posterior aggregation method to overcome the problem of computational expensiveness of Markov chain Monte Carlo. In particular, we divided data into subsets and combined estimated causal effects obtained from the subsets. The performance of our method was evaluated by a number of simulations, in which exposure data was partly missing. RESULTS: Random effect Bayesian MR outperformed conventional inverse-variance weighted estimation, whether the true causal effects were zero or non-zero. Data partitioning of large studies had little impact on variations of the estimated causal effects, whereas it notably affected unbiasedness of the estimates with weak instruments and high missing rate of data. For the cases being simulated in our study, the results have indicated that the "divide (data) and combine (estimated subset causal effects)" can help improve computational efficiency, for an acceptable cost in terms of bias in the causal effect estimates, as long as the size of the subsets is reasonably large. CONCLUSIONS: We further elaborated our Bayesian MR method to explicitly account for study heterogeneity. We also adopted a subset posterior aggregation method to ease computational burden, which is important especially when dealing with large studies. Despite the simplicity of the model we have used in the simulations, we hope the present work would effectively point to MR studies that allow modelling flexibility, especially in relation to the integration of heterogeneous studies and computational practicality.

Plasma Protein Levels Analysis in Multiple Sclerosis Sardinian Families Identified C9 and CYP24A1 as Candidate Biomarkers.

Here we investigate protein levels in 69 multiple sclerosis (MS) cases and 143 healthy controls (HC) from twenty Sardinian families to search for promising biomarkers in plasma. Using antibody suspension bead array technology, the plasma levels of 56 MS-related proteins were obtained. Differences between MS cases and HC were estimated using Linear Mixed Models or Linear Quantile Mixed Models. The proportion of proteins level variability, explained by a set of 119 MS-risk SNPs as to the literature, was also quantified. Higher plasma C9 and CYP24A1 levels were found in MS cases compared to HC (p < 0.05 after Holm multiple testing correction), with protein level differences estimated as, respectively, 0.53 (95% CI: 0.25, 0.81) and 0.42 (95% CI: 0.19, 0.65) times plasma level standard deviation measured in HC. Furthermore, C9 resulted in both statistically significantly higher relapsing-remitting MS (RRMS) and secondary-progressive MS (SPMS) compared to HC, with SPMS showing the highest differences. Instead, CYP24A1 was statistically significantly higher only in RRMS as compared to HC. Respectively, 26% (95% CI: 10%, 44%) and 16% (95% CI: 9%, 39%) of CYP24A1 and C9 plasma level variability was explained by known MS-risk SNPs. Our results highlight C9 and CYP24A1 as potential biomarkers in plasma for MS and allow us to gain insight into molecular disease mechanisms.

Long-term outcomes of early-onset myocardial infarction with non-obstructive coronary artery disease (MINOCA).

BACKGROUND: Acute myocardial infarction with non-obstructive coronary artery disease (MINOCA) is frequent in patients experiencing an early-onset MI, but data concerning its long-term prognosis are limited and conflicting. METHODS: The Italian Genetic Study on Early-onset MI enrolled 2000 patients experiencing a first MI before the age of 45 years, and had a median follow-up of 19.9 years. The composite primary endpoint was cardiovascular (CV) death, non-fatal MI, and non-fatal stroke (MACE); the secondary endpoint was rehospitalisation for coronary revascularisation. RESULTS: MINOCA occurred in 317 patients (15.9%) and, during the follow-up, there was no significant difference in MACE rates between them and the patients with obstructive coronary artery disease (MICAD: 27.8% vs 37.5%; adjusted hazard ratio [HR] 0.79, 95% confidence interval [CI] 0.57-1.09;p = 0.15). The CV death rate was lower in the MINOCA group (4.2% vs 8.4%, HR 0.26, 95%CI 0.08-0.86;p = 0.03), whereas the rates of non-fatal reinfarction (17.3% vs 25.4%; HR 0.76, 95%CI 0.52-1.13;p = 0.18), non-fatal ischemic stroke (9.5% vs 3.7%; HR 1.79, 95%CI 0.87-3.70;p = 0.12), and all-cause mortality (14.1% vs 20.7%, HR 0.73, 95%CI 0.43-1.25;p = 0.26) were not significantly different in the two groups. The rate of rehospitalisation for coronary revascularisation was lower among the MINOCA patients (6.7% vs 27.7%; HR 0.27, 95% CI 0.15-0.47;p < 0.001). CONCLUSIONS: MINOCA is frequent and not benign in patients with early-onset MI. Although there is a lower likelihood of CV death,the long-term risk of MACE and overall mortality is not significantly different from that of MICAD patients.

Antecedent use of renin-angiotensin system inhibitors is associated with reduced mortality in elderly hypertensive Covid-19 patients.

OBJECTIVES: The effect of renin-angiotensin system inhibitors (RASIs) on mortality in patients with coronavirus disease (Covid-19) is debated. From a cohort of 1352 consecutive patients admitted with Covid-19 to Papa Giovanni XXIII Hospital in Bergamo, Italy, between February and April 2020, we selected and studied hypertensive patients to assess whether antecedent (prior to hospitalization) use of RASIs might affect mortality from Covid-19 according to age. METHODS AND RESULTS: Arterial hypertension was present in 688 patients. Overall mortality (in-hospital or shortly after discharge) was 35% (N = 240). After adjusting for 26 medical history variables via propensity score matching, antecedent use of RASIs (N = 459, 67%) was associated with a lower mortality in older hypertensive patients (age above the median of 68 years in the whole series), whereas no evidence of a significant effect was found in the younger group of the same population (P interaction = 0.001). In an analysis of the subgroup of 432 hypertensive patients older than 68 years, we considered two RASI drug subclasses, angiotensin-converting enzyme inhibitors (ACEIs, N = 156) and angiotensin receptor blockers (ARBs, N = 140), and assessed their respective effects by taking no-antecedent-use of RASIs as reference. This analysis showed that both antecedent use of ACEIs and antecedent use of ARBs were associated with a lower Covid-19 mortality (odds ratioACEI = 0.57, 95% confidence interval 0.36--0.91, P = 0.018) (odds ratioARB = 0.49, 95% confidence interval 0.29--0.82, P = 0.006). CONCLUSION: In the population of over-68 hypertensive Covid-19 patients, antecedent use of ACEIs or ARBs was associated with a lower all-cause mortality, whether in-hospital or shortly after discharge, compared with no-antecedent-use of RASIs.

Developing and Validating an Individualized Clinical Prediction Model to Forecast Psychotic Recurrence in Acute and Transient Psychotic Disorders: Electronic Health Record Cohort Study.

Acute and transient psychotic disorders (ATPDs) include short-lived psychotic episodes with a high probability of developing psychotic recurrences. Clinical care for ATPD is currently limited by the inability to predict outcomes. Real-world electronic health record (EHR)-based retrospective cohort study STROBE/RECORD compliant included all individuals accessing the South London and Maudsley NHS Trust between 2006 and 2017 and receiving a first diagnosis of ATPD (F23, ICD-10). After imputing missing data, stepwise and LASSO Cox regression methods employing a priori predictors (n = 23) were compared to develop and internally validate an individualized risk prediction model to forecast the risk of psychotic recurrences following TRIPOD guidelines. The primary outcome was prognostic accuracy (area under the curve [AUC]). 3018 ATPD individuals were included (average age = 33.75 years, 52.7% females). Over follow-up (average 1042 ± 1011 days, up to 8 years) there were 1160 psychotic recurrences (events). Stepwise (n = 12 predictors) and LASSO (n = 17 predictors) regression methods yielded comparable prognostic accuracy, with an events per variable ratio >100 for both models. Both models showed an internally validated adequate prognostic accuracy from 4 years follow-up (AUC 0.70 for both models) and good calibration. A refined model was adapted in view of the new ICD-11 criteria on 307 subjects with polymorphic ATPD, showing fair prognostic accuracy at 4 years (AUC: stepwise 0.68; LASSO 0.70). This study presents the first clinically based prediction model internally validated to adequately predict long-term psychotic recurrence in individuals with ATPD. The model can be automatable in EHRs, supporting further external validations and refinements to improve its prognostic accuracy.

Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney.

The kidney is an organ of key relevance to blood pressure (BP) regulation, hypertension and antihypertensive treatment. However, genetically mediated renal mechanisms underlying susceptibility to hypertension remain poorly understood. We integrated genotype, gene expression, alternative splicing and DNA methylation profiles of up to 430 human kidneys to characterize the effects of BP index variants from genome-wide association studies (GWASs) on renal transcriptome and epigenome. We uncovered kidney targets for 479 (58.3%) BP-GWAS variants and paired 49 BP-GWAS kidney genes with 210 licensed drugs. Our colocalization and Mendelian randomization analyses identified 179 unique kidney genes with evidence of putatively causal effects on BP. Through Mendelian randomization, we also uncovered effects of BP on renal outcomes commonly affecting patients with hypertension. Collectively, our studies identified genetic variants, kidney genes, molecular mechanisms and biological pathways of key relevance to the genetic regulation of BP and inherited susceptibility to hypertension.

Overlapping-sample Mendelian randomisation with multiple exposures: a Bayesian approach.

BACKGROUND: Mendelian randomization (MR) has been widely applied to causal inference in medical research. It uses genetic variants as instrumental variables (IVs) to investigate putative causal relationship between an exposure and an outcome. Traditional MR methods have mainly focussed on a two-sample setting in which IV-exposure association study and IV-outcome association study are independent. However, it is not uncommon that participants from the two studies fully overlap (one-sample) or partly overlap (overlapping-sample). METHODS: We proposed a Bayesian method that is applicable to all the three sample settings. In essence, we converted a two- or overlapping- sample MR to a one-sample MR where data were partly unmeasured. Assume that all study individuals were drawn from the same population and unmeasured data were missing at random. Then the missing data were treated au pair with the model parameters as unknown quantities, and thus, were imputed iteratively conditioning on the observed data and estimated parameters using Markov chain Monte Carlo. We generalised our model to allow for pleiotropy and multiple exposures and assessed its performance by a number of simulations using four metrics: mean, standard deviation, coverage and power. We also compared our method with classic MR methods. RESULTS: In our proposed method, higher sample overlapping rate and instrument strength led to more precise estimated causal effects with higher power. Pleiotropy had a notably negative impact on the estimates. Nevertheless, the coverages were high and our model performed well in all the sample settings overall. In comparison with classic MR, our method provided estimates with higher precision. When the true causal effects were non-zero, power of their estimates was consistently higher from our method. The performance of our method was similar to classic MR in terms of coverage. CONCLUSIONS: Our model offers the flexibility of being applicable to any of the sample settings. It is an important addition to the MR literature which has restricted to one- or two- sample scenarios. Given the nature of Bayesian inference, it can be easily extended to more complex MR analysis in medical research.

Value of dynamic clinical and biomarker data for mortality risk prediction in COVID-19: a multicentre retrospective cohort study.

OBJECTIVES: Being able to predict which patients with COVID-19 are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and do not always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying 'state-of-the-art' statistical methods in the development of a prognostic model to predict death in hospitalised patients with COVID-19. DESIGN: The data were analysed from admissions with COVID-19 to three hospital sites. Exploratory data analysis included a graphical approach to partial correlations. Dynamic biomarkers were considered up to 5 days following admission rather than depending solely on baseline or single time-point data. Marked departures from linear effects of covariates were identified by employing smoothing splines within a generalised additive modelling framework. SETTING: 3 secondary and tertiary level centres in Greater Manchester, the UK. PARTICIPANTS: 392 hospitalised patients with a diagnosis of COVID-19. RESULTS: 392 patients with a COVID-19 diagnosis were identified. Area under the receiver operating characteristic curve increased from 0.73 using admission data alone to 0.75 when also considering results of baseline blood samples and to 0.83 when considering dynamic values of routinely collected markers. There was clear non-linearity in the association of age with patient outcome. CONCLUSIONS: This study shows that clinical prediction models to predict death in hospitalised patients with COVID-19 can be improved by taking into account both non-linear effects in covariates such as age and dynamic changes in values of biomarkers.

Integrative analysis of Mendelian randomization and Bayesian colocalization highlights four genes with putative BMI-mediated causal pathways to diabetes.

Genome-wide association studies have identified hundreds of single nucleotide polymorphisms (SNPs) that are associated with BMI and diabetes. However, lack of adequate data has for long time prevented investigations on the pathogenesis of diabetes where BMI was a mediator of the genetic causal effects on this disease. Of our particular interest is the underlying causal mechanisms of diabetes. We leveraged the summary statistics reported in two studies: UK Biobank (N = 336,473) and Genetic Investigation of ANthropometric Traits (GIANT, N = 339,224) to investigate BMI-mediated genetic causal pathways to diabetes. We first estimated the causal effect of BMI on diabetes by using four Mendelian randomization methods, where a total of 76 independent BMI-associated SNPs (R2 ≤ 0.001, P < 5 × 10-8) were used as instrumental variables. It was consistently shown that higher level of BMI (kg/m2) led to increased risk of diabetes. We then applied two Bayesian colocalization methods and identified shared causal SNPs of BMI and diabetes in genes TFAP2B, TCF7L2, FTO and ZC3H4. This study utilized integrative analysis of Mendelian randomization and colocalization to uncover causal relationships between genetic variants, BMI and diabetes. It highlighted putative causal pathways to diabetes mediated by BMI for four genes.

Investigating the Causal Effect of Brain Expression of CCL2, NFKB1, MAPK14, TNFRSF1A, CXCL10 Genes on Multiple Sclerosis: A Two-Sample Mendelian Randomization Approach.

Multiple Sclerosis (MS) exhibits considerable heterogeneity in phenotypic expression, course, prognosis and response to therapy. This suggests this disease involves multiple, as yet poorly understood, causal mechanisms. In this work we assessed the possible causal link between gene expression level of five selected genes related to the pro-inflammatory NF-κB signaling pathway (i.e., CCL2, NFKB1, MAPK14, TNFRSF1A, CXCL10) in ten different brain tissues (i.e., cerebellum, frontal cortex, hippocampus, medulla, occipital cortex, putamen, substantia nigra, thalamus, temporal cortex and intralobular white matter) and MS. We adopted a two-stage Mendelian Randomization (MR) approach for the estimation of the causal effects of interest, based on summary-level data from 20 multiplex Sardinian families and data provided by the United Kingdom Brain Expression Consortium (UKBEC). Through Radial-MR and Cochrane's Q statistics we identified and removed genetic variants which are most likely to be invalid instruments. To estimate the total causal effect, univariable MR was carried out separately for each gene and brain region. We used Inverse-Variance Weighted estimator (IVW) as main analysis and MR-Egger Regression estimator (MR-ER) and Weighted Median Estimator (WME) as sensitivity analysis. As these genes belong to the same pathway and thus they can be closely related, we also estimated their direct causal effects by applying IVW and MR-ER within a multivariable MR (MVMR) approach using set of genetic instruments specific and common (composite) to each multiple exposures represented by the expression of the candidate genes. Univariate MR analysis showed a significant positive total causal effect for CCL2 and NFKB1 respectively in medulla and cerebellum. MVMR showed a direct positive causal effect for NFKB1 and TNFRSF1A, and a direct negative causal effect for CCL2 in cerebellum; while in medulla we observed a direct positive causal effect for CCL2. Since in general we observed a different magnitude for the gene specific causal effect we hypothesize that in cerebellum and medulla the effect of each gene expression is direct but also mediated by the others. These results confirm the importance of the involvement of NF-κB signaling pathway in brain tissue for the development of the disease and improve our understanding in the pathogenesis of MS.

A Bayesian approach to Mendelian randomization with multiple pleiotropic variants.

We propose a Bayesian approach to Mendelian randomization (MR), where instruments are allowed to exert pleiotropic (i.e. not mediated by the exposure) effects on the outcome. By having these effects represented in the model by unknown parameters, and by imposing a shrinkage prior distribution that assumes an unspecified subset of the effects to be zero, we obtain a proper posterior distribution for the causal effect of interest. This posterior can be sampled via Markov chain Monte Carlo methods of inference to obtain point and interval estimates. The model priors require a minimal input from the user. We explore the performance of our method by means of a simulation experiment. Our results show that the method is reasonably robust to the presence of directional pleiotropy and moderate correlation between the instruments. One section of the article elaborates the model to deal with two exposures, and illustrates the possibility of using MR to estimate direct and indirect effects in this situation. A main objective of the article is to create a basis for developments in MR that exploit the potential offered by a Bayesian approach to the problem, in relation with the possibility of incorporating external information in the prior, handling multiple sources of uncertainty, and flexibly elaborating the basic model.

Sighting acute myocardial infarction through platelet gene expression.

Acute myocardial infarction is primarily due to coronary atherosclerotic plaque rupture and subsequent thrombus formation. Platelets play a key role in the genesis and progression of both atherosclerosis and thrombosis. Since platelets are anuclear cells that inherit their mRNA from megakaryocyte precursors and maintain it unchanged during their life span, gene expression profiling at the time of an acute myocardial infarction provides information concerning the platelet gene expression preceding the coronary event. In ST-segment elevation myocardial infarction (STEMI), a gene-by-gene analysis of the platelet gene expression identified five differentially expressed genes: FKBP5, S100P, SAMSN1, CLEC4E and S100A12. The logistic regression model used to combine the gene expression in a STEMI vs healthy donors score showed an AUC of 0.95. The same five differentially expressed genes were externally validated using platelet gene expression data from patients with coronary atherosclerosis but without thrombosis. Platelet gene expression profile highlights five genes able to identify STEMI patients and to discriminate them in the background of atherosclerosis. Consequently, early signals of an imminent acute myocardial infarction are likely to be found by platelet gene expression profiling before the infarction occurs.