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.

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.

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.

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.

Acid sensing ion channel 2: A new potential player in the pathophysiology of multiple sclerosis.

Acid-sensing ion channels (ASICs) are proton-gated channels involved in multiple biological functions such as: pain modulation, mechanosensation, neurotransmission, and neurodegeneration. Earlier, we described the genetic association, within the Nuoro population, between Multiple Sclerosis (MS) and rs28936, located in ASIC2 3'UTR. Here we investigated the potential involvement of ASIC2 in MS inflammatory process. We induced experimental autoimmune encephalomyelitis (EAE) in wild-type (WT), knockout Asic1-/- and Asic2-/- mice and observed a significant reduction of clinical score in Asic1-/- mice and a significant reduction in the clinical score in Asic2-/- mice in a limited time window (i.e., at days 20-23 after immunization). Immunohistochemistry confirmed the reduction in adaptive immune cell infiltrates in the spinal cord of EAE Asic1-/- mice. Analysis of mechanical allodynia, showed a significant higher pain threshold in Asic2-/- mice under physiological conditions, before immunization, as compared to WT mice and Asic1-/- . A significant reduction in pain threshold was observed in all three strains of mice after immunization. More importantly, analysis of human autoptic brain tissue in MS and control samples showed an increase of ASIC2 mRNA in MS samples. Subsequently, in vitro luciferase reporter gene assays, showed that ASIC2 expression is under possible miRNA regulation, in a rs28936 allele-specific manner. Taken together, these findings suggest a potential role of ASIC2 in the pathophysiology of MS.

Mendelian randomisation analysis of clustered causal effects of body mass on cardiometabolic biomarkers.

BACKGROUND: Recent advances in data analysis methods based on principles of Mendelian Randomisation, such as Egger regression and the weighted median estimator, add to the researcher's ability to infer cause-effect links from observational data. Now is the time to gauge the potential of these methods within specific areas of biomedical research. In this paper, we choose a study in metabolomics as an illustrative testbed. We apply Mendelian Randomisation methods in the analysis of data from the DILGOM (Dietary, Lifestyle and Genetic determinants of Obesity and Metabolic syndrome) study, in the context of an effort to identify molecular pathways of cardiovascular disease. In particular, our illustrative analysis addresses the question whether body mass, as measured by body mass index (BMI), exerts a causal effect on the concentrations of a collection of 137 cardiometabolic markers with different degrees of atherogenic power, such as the (highly atherogenic) lipoprotein metabolites with very low density (VLDLs) and the (protective) high density lipoprotein metabolites. RESULTS: We found strongest evidence of a positive BMI effect (that is, evidence that an increase in BMI causes an increase in the metabolite concentration) on those metabolites known to represent strong risk factors for coronary artery disease, such as the VLDLs, and evidence of a negative effect on protective biomarkers. CONCLUSIONS: The methods discussed represent a useful scientific tool, although they assume the validity of conditions that are (at best) only partially verifiable. This paper provides a rigorous account of such conditions. The results of our analysis provide a proof-of-concept illustration of the potential usefulness of Mendelian Randomisation in genomic biobank studies aiming to dissect the molecular causes of disease, and to identify candidate pharmacological targets.

Investigating multiple sclerosis genetic susceptibility on the founder population of east-central Sardinia via association and linkage analysis of immune-related loci.

BACKGROUND: A wealth of single-nucleotide polymorphisms (SNPs) responsible for multiple sclerosis (MS) susceptibility have been identified; however, they explain only a fraction of MS heritability. OBJECTIVES: We contributed to discovery of new MS susceptibility SNPs by studying a founder population with high MS prevalence. METHODS: We analyzed ImmunoChip data from 15 multiplex families and 94 unrelated controls from the Nuoro Province, Sardinia, Italy. We tested each SNP for both association and linkage with MS, the linkage being explored in terms of identity-by-descent (IBD) sharing excess and using gene dropping to compute a corresponding empirical p-value. By targeting regions that are both associated and in linkage with MS, we increase chances of identifying interesting genomic regions. RESULTS: We identified 486 MS-associated (p < 1 × 10-4) and 18,426 MS-linked (p < 0.05) SNPs. A total of 111 loci were both linked and associated with MS, 18 of them pointing to 14 non-major histocompatibility complex (MHC) genes, and 93 of them located in the MHC region. CONCLUSION: We discovered new suggestive signals and confirmed some previously identified ones. We believe this to represent a significant step toward an understanding of the genetic basis of MS.

Ovarian Cancer Follow-up: A Preliminary Comparison of 2 Approaches.

OBJECTIVE: The aim of the study was to perform a preliminary comparison of quality of life (QoL) and patient satisfaction in individualized nurse-led follow-up versus conventional medical follow-up in ovarian cancer. METHODS: One hundred twelve women who received a diagnosis of ovarian, fallopian tube, or peritoneal cancer, completed primary treatment by surgery alone or with chemotherapy, irrespective of outcome with regard to remission, and expected survival of more than 3 months. Fifty-seven participants were randomized to individualized follow-up and 55 patients to conventional follow-up. Well-being was measured at baseline and at 3, 6, 12, and 24 months after randomization for QoL (QLQ-C30 [European Organisation for Research and Treatment of Cancer Core Quality of Life Questionnaire], QLQ-Ov28), the Hospital Anxiety and Depression Scale (HADS), and a Patient Satisfaction Questionnaire (PSQ-III). The primary endpoints were the effects of follow-up on each of the scores (via hierarchical mixed-effects model) and on relapse-free time (via Cox model). The total cost of follow-up was compared between each group. RESULTS: There was evidence for a QoL and patient satisfaction benefit for individualized versus standard follow-up (QLQ-C30, P = 0.013; 95% confidence interval, -0.03 to -0.001; PSQ-III P = 0.002; 95% confidence interval, -0.003 to -0.015; QLQ-Ov28, P = 0.14). Hospital Anxiety and Depression Scale data provided no evidence in favor of either treatment (P = 0.42). Delivered to protocol individualized follow-up resulted in a delay in the presentation of symptomatic relapse (P = 0.04), although the effect on survival in this study is unknown. Cost was £700 lower on average for the individualized follow-up group, but the difference was not statistically significant at the 5% level (P = 0.07). CONCLUSIONS: Individualized follow-up was superior to conventional follow-up in 3 of the 4 QoL and patient satisfaction surveys in this preliminary study. Further prospective studies are needed in a larger population.Trial registration number is ISRCTN59149551.

Systematic analysis of circulating soluble angiogenesis-associated proteins in ICON7 identifies Tie2 as a biomarker of vascular progression on bevacizumab.

BACKGROUND: There is a critical need for predictive/resistance biomarkers for VEGF inhibitors to optimise their use. METHODS: Blood samples were collected during and following treatment and, where appropriate, upon progression from ovarian cancer patients in ICON7, a randomised phase III trial of carboplatin and paclitaxel with or without bevacizumab. Plasma concentrations of 15 circulating angio-biomarkers were measured using a validated multiplex ELISA, analysed through a novel network analysis and their relevance to the PFS then determined. RESULTS: Samples (n=650) were analysed from 92 patients. Bevacizumab induced correlative relationships between Ang1 and Tie2 plasma concentrations, which reduced after initiation of treatment and remained decreased until progressive disease occurred. A 50% increase from the nadir in the concentration of circulating Tie2 (or the product of circulating Ang1 and Tie2) predicted tumour progression. Combining Tie2 with GCIG-defined Ca125 data yielded a significant improvement in the prediction of progressive disease in patients receiving bevacizumab in comparison with Ca125 alone (74.1% vs 47.3%, P<1 × 10(-9)). CONCLUSIONS: Tie2 is a vascular progression marker for bevacizumab-treated ovarian cancer patients. Tie2 in combination with Ca125 provides superior information to clinicians on progressive disease in patients with VEGFi-treated ovarian cancers.

A hierarchical statistical modeling approach to analyze proteomic isobaric tag for relative and absolute quantitation data.

MOTIVATION: Isobaric tag for relative and absolute quantitation (iTRAQ) is a widely used method in quantitative proteomics. A robust data analysis strategy is required to determine protein quantification reliability, i.e. changes due to biological regulation rather than technical variation, so that proteins that are differentially expressed can be identified. METHODS: Samples were created by mixing 5, 10, 15 and 20 µg Escherichia coli cell lysate with 100 µg of cell lysate from mouse, corresponding to expected relative fold changes of one for mouse proteins and from 0.25 to 4 for E.coli proteins. Relative quantification was carried out using eight channel isobaric tagging with iTRAQ reagent, and proteins were identified using a TripleTOF 5600 mass spectrometer. Technical variation inherent in this iTRAQ dataset was systematically investigated. RESULTS: A hierarchical statistical model was developed to use quantitative information at peptide level and protein level simultaneously to estimate variation present in each individual peptide and protein. A novel data analysis strategy for iTRAQ, denoted in short as WHATraq, was subsequently proposed with its performance evaluated by the proportion of E.coli proteins that are successfully identified as differentially expressed. Compared with two benchmark data analysis strategies WHATraq was able to identify at least 62.8% more true positive proteins that are differentially expressed. Further validated using a biological iTRAQ dataset including multiple biological replicates from varied murine cell lines, WHATraq performed consistently and identified 375% more proteins as being differentially expressed among different cell lines than the other data analysis strategies.

Association between variants of PRDM1 and NDP52 and Crohn's disease, based on exome sequencing and functional studies.

BACKGROUND & AIMS: Genome-wide association studies (GWAS) have identified 140 Crohn's disease (CD) susceptibility loci. For most loci, the variants that cause disease are not known and the genes affected by these variants have not been identified. We aimed to identify variants that cause CD through detailed sequencing, genetic association, expression, and functional studies. METHODS: We sequenced whole exomes of 42 unrelated subjects with CD and 5 healthy subjects (controls) and then filtered single nucleotide variants by incorporating association results from meta-analyses of CD GWAS and in silico mutation effect prediction algorithms. We then genotyped 9348 subjects with CD, 2868 subjects with ulcerative colitis, and 14,567 control subjects and associated variants analyzed in functional studies using materials from subjects and controls and in vitro model systems. RESULTS: We identified rare missense mutations in PR domain-containing 1 (PRDM1) and associated these with CD. These mutations increased proliferation of T cells and secretion of cytokines on activation and increased expression of the adhesion molecule L-selectin. A common CD risk allele, identified in GWAS, correlated with reduced expression of PRDM1 in ileal biopsy specimens and peripheral blood mononuclear cells (combined P = 1.6 × 10(-8)). We identified an association between CD and a common missense variant, Val248Ala, in nuclear domain 10 protein 52 (NDP52) (P = 4.83 × 10(-9)). We found that this variant impairs the regulatory functions of NDP52 to inhibit nuclear factor κB activation of genes that regulate inflammation and affect the stability of proteins in Toll-like receptor pathways. CONCLUSIONS: We have extended the results of GWAS and provide evidence that variants in PRDM1 and NDP52 determine susceptibility to CD. PRDM1 maps adjacent to a CD interval identified in GWAS and encodes a transcription factor expressed by T and B cells. NDP52 is an adaptor protein that functions in selective autophagy of intracellular bacteria and signaling molecules, supporting the role of autophagy in the pathogenesis of CD.

Deep determinism and the assessment of mechanistic interaction.

Given two variables that causally influence a binary response, we formalize the idea that their effects operate through a common mechanism, in which case we say that the two variables interact mechanistically. We introduce a mechanistic interaction relationship of "interference" that is asymmetric in the two causal factors. Conditions and assumptions under which such mechanistic interaction can be tested under a given regime of data collection, be it interventional or observational, are expressed in terms of conditional independence relationships between the problem variables, which can be manipulated with the aid of causal diagrams. The proposed method is able, under appropriate conditions, to test for interaction between direct effects, and to deal with the situation where one of the two factors is a dichotomized version of a continuous variable. The method is illustrated with the aid of a study on heart disease.

Direct genetic effects and their estimation from matched case-control data.

In genetic association studies, a single marker is often associated with multiple, correlated phenotypes (e.g., obesity and cardiovascular disease, or nicotine dependence and lung cancer). A pervasive question is then whether that marker exerts independent effects on all phenotypes. In this paper, we address this question by assessing whether there is a genetic effect on one phenotype that is not mediated through the other ones, so called direct genetic effect. Answering such question may represent an important step in the elucidation of the underlying biological mechanism. Under rather restrictive conditions, such direct genetic effects are known to be estimable by standard regression methods. Under more lenient conditions, in a prospective or unmatched case-control study, these effects can be estimated by using a previously proposed G-estimation method (Vansteelandt [2009] Epidemiology 20, 851-860). The present paper extends this method to matched case-control studies, and investigates the conditions under which this extension is valid. We illustrate the method on data from a matched case-control study, which we use to elucidate the pathway implications of a detected association between myocardial infarction and a genetic locus in the chromosomal region of the FTO gene.

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.

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.

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.

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.

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.

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.

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.