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Supplementary Table S1: Spearman correlations (rho) between biomarkers; Supplementary Table S2: Pre-chemotherapy/bevacizumab concentrations of individual angiogenesis associated factors; Supplementary Table S3: comparisons between biomarker parameters by treatment Arm; Supplementary Table S4: comparisons between biomarker parameters by time since surgery; Supplementary Table S5: Validation of the identified biomarkers in the training dataset using bootstrap resampling technique; Supplementary Table S6: Tumour response by Ang1/Tie2 combination categories by treatment in the training set; Supplementary Table S7: Cox proportional hazard models confirming Ang1 and Tie2 as a joint biomarker in the validation dataset (N = 114)

Purpose: Randomized ovarian cancer trials, including ICON7, have reported improved progression-free survival (PFS) when bevacizumab was added to conventional cytotoxic therapy. The improvement was modest prompting the search for predictive biomarkers for bevacizumab.Experimental Design: Pretreatment training (n = 91) and validation (n = 114) blood samples were provided by ICON7 patients. Plasma concentrations of 15 angio-associated factors were determined using validated multiplex ELISAs. Our statistical approach adopted PFS as the primary outcome measure and involved (i) searching for biomarkers with prognostic relevance or which related to between-individual variation in bevacizumab effect; (ii) unbiased determination of cutoffs for putative biomarker values; (iii) investigation of biologically meaningfully predictive combinations of putative biomarkers; and (iv) replicating the analysis on candidate biomarkers in the validation dataset.Results: The combined values of circulating Ang1 (angiopoietin 1) and Tie2 (Tunica internal endothelial cell kinase 2) concentrations predicted improved PFS in bevacizumab-treated patients in the training set. Using median concentrations as cutoffs, high Ang1/low Tie2 values were associated with significantly improved PFS for bevacizumab-treated patients in both datasets (median, 23.0 months vs. 16.2; P = 0.003) for the interaction of Ang1–Tie2 treatment in Cox regression analysis. The prognostic indices derived from the training set also distinguished high and low probability for progression in the validation set (P = 0.008), generating similar values for HR (0.21 vs. 0.27) between treatment and control arms for patients with high Ang1 and low Tie2 values.Conclusions: The combined values of Ang1 and Tie2 are predictive biomarkers for improved PFS in bevacizumab-treated patients with ovarian cancer. These findings need to be validated in larger trials due to the limitation of sample size in this study. Clin Cancer Res; 20(17); 4549–58. ©2014 AACR.

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.

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 −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.

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.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.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).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.

Aims Data regarding long-term prognosis of MINOCA are very limited and conflicting. Methods and results The Italian Genetic Study on early-onset MI enrolled 2000 patients who had a first MI before they were 45. The median follow-up was 19.9 years, the equivalent of 39 535 person-years. The composite primary endpoint was cardiovascular (CV) death, non-fatal MI, and non-fatal stroke (MACE); the secondary endpoint was rehospitalization for coronary revascularization. MINOCA was experienced by 317 patients (15.9%). The risk of MACE was not significantly different between MINOCA patients and those with obstructive coronary artery disease (MICAD, 27.8% vs. 37.5%; adj. HR: 0.79, 95% CI: 0.57–1.09; P = 0.15, Figure 1). There was no between-group difference in the rate of non-fatal MI (17.3% vs. 25.4%; adj. HR: 0.76, 95% CI: 0.52–1.13; P = 0.18), non-fatal ischaemic stroke (9.5% vs. 3.7%; adj. HR: 1.79, 95% CI: 0.87–3.70; P = 0.12), or all-cause mortality (14.1% vs. 20.7%; adj. HR: 0.73, 95% CI: 0.43–1.25; P = 0.26), but the rates of CV death (6.2% vs. 8.4%; adj. HR: 0.26, 95% CI: 0.08–0.86; P = 0.03) and coronary revascularization (6.7% vs. 27.7%; HR: 0.27, 95% CI: 0.15–0.47; P Conclusions MINOCA is frequent in early-onset MI patients and is not benign with a long-term risk of MACE and overall mortality not significantly different from that of the MICAD patients. 189 Figure 1 Composite primary endpoint of CV death, non-fatal MI, and non-fatal stroke

The thoughts and feelings people have about pain (referred to as "Pain expectations") are known to alter the perception of pain. However we know very little about the cognitive processes that underpin pain expectations, or what drives the differing effect that pain expectations have between individuals. This paper details the testing of a model of pain perception which formalises the response to pain in terms of a Bayesian prior-to-posterior updating process. Using data acquired from a short and deception-free predictive cue task, it was found that this Bayesian model predicted ratings of pain better than other, simpler models. At the group level, the results confirmed two core predictions of Hierarchical Predictive Processing; that expectation alters perception and that increased uncertainty in the expectation reduces its impact on perception. The addition to the model of parameters relating to trait differences in pain expectation, improved its fit, suggesting that such traits (which have been relatively neglected in experimental work on pain perception) play a significant role in perception beyond those expectations triggered by the pain cue. When model parameters were allowed to vary by participant, the fit improved further. This final model produced a characterisation of the sensitivity of each individual to pain expectations. These findings are relevant for the understanding of the cognitive basis of pain expectations, and potentially useful for guiding patient stratification and clinical experimentation.

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.

From a cohort of 1352 consecutive patients admitted with coronavirus disease (Covid-19) to Papa Giovanni XXIII Hospital in Bergamo, Italy, between February and April 2020, we selected and studied 688 patients with arterial hypertension (254 deaths) to assess whether use of renin-angiotensin system inhibitors (RASIs) prior to hospital admission affects mortality from Covid-19. Prior use of RASIs was associated with a lower mortality in the over-68 group of patients, whereas no evidence of a similar effect (whether protective or adverse) was found in the younger group. There was positive relative excess due to a statistically significant (p =0.001) interaction between prior RASI exposure and an age greater than 68 years, corresponding to a positive relative excess risk. Next we used the subgroup of 411 hypertensive patients older than 68 yrs to separately assess the effects prior use of two RASI drug subclasses, angiotensin-converting enzyme inhibitors (ACEIs) and angiogiotensin receptor blockers (ARBs), by comparing these two exposures with no exposure to RASIs. We found both prior use of ACEIs and prior use of ARBs to be associated with a lower Covid-19 mortality, after adjusting for 32 medical history variables via propensity score matching. (ORACEI = 0.57, 95%CI 0.36 to 0.91, p =0.018) (ORARB = 0.49, 95%CI 0.29 to 0.82, p =0.006).

Background: Diagnoses from non-specific symptoms lead to reason over a huge space of hypotheses. Bayesian Networks (BN) can systematically address the task by integrating existing causal medical knowledge and clinical, possibly incomplete, case observations. Although prior specification of a causal BN structure can increase the generality of diagnostic predictions, particular care should be paid to avoid structural inconsistencies with the data at hand. Methods: The causal structure of a BN covering 129 cardiopulmonary disorders and 235 manifestations was elicited by experts. Markov chain Monte Carlo estimation of quantitative parameters was performed on the basis of 282 cases admitted to an Emergency Department and subsequently hospitalized. BN structural features underwent a refinement process to improve parameters sensitivity to data and diagnostic precision, until changes were consistent with published evidence. Discrimination/calibration measures of diagnostic performance (based on 284 test cases) were used to assess the predictive improvement after refinement. Results: Although one third of 1,417 parameter estimates remained anchored to prior settings, the BN updated by data returned adequately calibrated diagnostic predictions on the first six most observed disorders in the test sample, with an M-index of 0.91 (95% CI 0.87, 0.95) on 20 alternative diagnoses. Conclusions: Our large-sized BN effectively combines existing causal medical knowledge with sparse information contained in clinical records. It addresses a broad-spectrum differential diagnosis and informs physicians about possibly neglected cardiopulmonary conditions. The calibration analysis oriented the refinement of initial causal assumptions without loss of generality in diagnostic predictions.

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.

BackgroundThere is growing appreciation for sex-based differences in cardiovascular risk, but little is known about whether these extend to pre-menopausal females experiencing early-onset myocardial infarction (MI) who might benefit from the protective effects of estrogen exposure.MethodsThe Italian Genetic Study of Early-Onset Myocardial Infarction was a nationwide prospective cohort study involving 125 centres which, between 1998 and 2002, enrolled 2,000 consecutive patients who experienced a first MI before the age of 45 years and underwent coronary angiography at the time of the index event, and followed them up for a median of 19.9 years. The primary composite endpoint was cardiovascular death, non-fatal MI and non-fatal stroke.ResultsFollow-up was completed by 1,984 patients (98.2%) with a median age of 41 years (IQR 37-43): 1,778 men (88.9%) men and 222 women (11.1%). Smoking (46.5% vs 42.8%), dyslipidemia (62.9% vs 50.7%) and diabetes (7.8% vs 5.4%) were more frequent among the men (all pConclusionSignificant sex-based differences in baseline risk factors and outcomes were observed in patients with early-onset MI: women presented with a lower atherosclerotic disease burden and, despite having lower rates of secondary prevention measures, experienced better long-term outcomes.

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.

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. Electronic supplementary material The online version of this article (10.1186/s12859-018-2178-2) contains supplementary material, which is available to authorized users.

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 –4) and 18,426 MS-linked ( p 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.

Early-onset myocardial infarction (MI) can be a major cause of physical and psychological morbidity. The known chronic and degenerative nature of the coronary atherosclerosis underlying an acute event can lead to justified concerns regarding the future risk of recurrent ischemic events or even

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.

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.

We propose a fully probabilistic formulation of the notion of mechanistic interaction (interaction in some fundamental mechanistic sense) between the effects of putative (possibly continuous) causal factors A and B on a binary outcome variable Y indicating 'survival' vs 'failure'. We define mechanistic interaction in terms of departure from a generalized 'noisy OR' model, under which the multiplicative causal effect of A (resp., B) on the probability of failure cannot be enhanced by manipulating B (resp., A). We present conditions under which mechanistic interaction in the above sense can be assessed via simple tests on excess risk or superadditivity, in a possibly retrospective regime of observation. These conditions are defined in terms of generalized conditional independence relationships (generalised because they may involve non-stochastic 'regime indicators') that can often be checked on a graphical representation of the problem. Inference about mechanistic interaction between direct, or path-specific, causal effects can be accommodated in the proposed framework. The method is illustrated with the aid of a study in experimental psychology., 28 pages, 4 figures

ObjectivesBeing 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.DesignThe 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.Setting3 secondary and tertiary level centres in Greater Manchester, the UK.Participants392 hospitalised patients with a diagnosis of COVID-19.Results392 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.ConclusionsThis 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.

Summary 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.

ObjectiveThe 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.MethodsOne 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.ResultsThere 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).ConclusionsIndividualized 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.

The effect of estrogen on Alzheimer's Disease (AD) risk has received substantial research and media attention, especially in terms of hormone replacement therapy. But reproductive history is also an important modifier of estrogenic exposure, and deserves further investigation. Importantly, there is wide variation in reproductive patterns that modifies estrogen exposure during the reproductive span, which previous AD studies have not incorporated into their calculations. We measured degree of Alzheimer's-type dementia in a cohort of elderly British women, and collected detailed reproductive and medical history information, which we used to estimate number of months with estrogen exposure and number of months with menstrual cycles. Using Cox proportional-hazards models, we find that longer duration of estrogen exposure may have a protective effect against AD risk, such that for every additional month with estrogen, women experienced on average a 0.5% decrease in AD risk (N=89, p=0.02). More menstrual cycles may also have a protective effect against AD risk, although this result was of borderline statistical significance (p0.10). These results build upon previous methodologies by taking into account a variety of parameters including oral contraceptive use, breastfeeding, post-partum anovulation, abortions, and miscarriages. Additionally, Cox models revealed that longer reproductive span, age21 at first birth, and more months in lifetime spent pregnant had protective effects against AD risk.

The effect of early and midlife factors on later-life cognitive function has attracted scientific and public interest in recent years, especially with respect to hormonal risk factors for dementia. There is substantial evidence for reproductive history affecting Alzheimer's disease (AD) etiology. Here, we demonstrate how breastfeeding history affects women's risk of AD. Reproductive history data was collected, and AD diagnostic interviews were performed, for a cohort of elderly British women. Using Cox proportional-hazard models, we find that longer breastfeeding duration corresponded to reduced risk of AD (p < 0.01, n = 81). Women who breastfed had lower AD risk than women who did not breastfeed (p = 0.017, n = 81). Breastfeeding practices are an important modifier of cumulative endogenous hormone exposure for mothers. Ovarian hormone deprivation and/or insulin sensitivity benefits of breastfeeding may be responsible for the observed reduction in AD risk. Future studies concerning hormone effects on AD risk should consider how reproductive history leads to variation in endogenous hormone exposure and how this may influence the relationship between hormones and AD.

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.

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

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.

Of interest here are sequential data-gathering and decision-making processes, started in the preceding chapter in this volume. The archetypical context is that of a sequence of medical decisions, taken at different time points during the follow-up of the patient, each decision involving choice of a treatment in the light of any interim responses or adverse reactions to earlier treatments. The problem consists of predicting the consequences that a (possibly new and possibly hypothetical) treatment plan will have on a future patient, on the basis of what we have learnt from the performances of past medical decision makers on past patients. While we make constant reference to a medical application context, the scope of the method is of much broader relevance. Our approach to this problem owes immensely to the seminal work of James Robins. In a rich series of papers, Robins (1986, 1987, 1988, 1989, 1992, 1997, 2000, 2004), Robins and Wasserman (1997) and Robins et al. (1999) introduce the idea of different treatment strategies applied in different hypothetical studies (these studies being analogous to our notion of ?regimes'). These papers examine conditions on the relationships between these studies, under which those treatment strategies can be compared. Among these, the sequential ran-domization condition is closely related to the ?stability' assumption used in this chapter. Furthermore, Robins (1986) introduced the G-computation algorithm (a special version of dynamic programming) to evaluate a sequential treatment strategy, which works where stan-dard regression ? even under the sequential randomization assumption ? fails. Most of the concepts expounded in this chapter have a counterpart in Robins' work, although, in the interest of readability, we shall frequently abstain from making the relationships with his work explicit. We shall assume that we have an idea of which variables informed past decisions, without assuming that these variables have all been observed in the past. Nor shall we assume that the rules that governed past decisions are discernible or similar to those that will guide future actions. In contrast with the previous chapter, we do use (fully stochastic) causal diagrams (more precisely, influence diagrams). In the context of sequential plan identification, graphical rep-resentations of causality were first advocated by Pearl and Robins (1995). In accord with these authors, we assume that the relevant causal knowledge is encoded in the form of a diagram with a completely specified topology, whose associated numerical conditional probabilities are given only for a subset of its variables, so-called observed variables. The remaining variables in the diagram are ?unobserved' or ?latent'. We shall, however, introduce a form of graphical representation of causality that differs from Pearl and Robins's diagrams in some respects. Part of the problem will be to characterize situations in which the estimation of the causal effects of the treatment plan of interest is not invalidated by unobserved confounding introduced by the latent variables. Throughout this chapter we use previous results by Dawid and Didelez (2010). Elja Arjas, both in his chapter (Chapter 7) in this volume and in the joint paper Arjas and Parner (2004), considers two probability models. One (called the ?obs' model) models the observational study that has generated the data. The other (called the ?ex' model) models the consequences of the future (perhaps hypothetical) application of the treatment plan of interest. In our approach, this distinction is embodied in a decision parameter, called the ?regime indicator', which indicates the particular regime, ?obs' or ?ex', in operation. Supplementing the set of domain variables with the mentioned regime indicator results in an ?augmented' set of variables. In our approach, conditions for the equivalence of Arjas's ?obs' and ?ex' distributions are phrased in terms of conditional independence conditions on this augmented set. Both chapters, ours and that of Arjas, avoid formulations based on ideas of potential outcomes or counterfactuals. We illustrate the methods and their motivations with the aid of two examples. Following Robins, we choose the first example in the area of the treatment of HIV infection. The second example will be in the treatment of abdominal aortic aneurysm. This chapter is about the identifiability of treatment plan effects, rather than about the methods of estimation and computation one is supposed to use if the problem turns out to be identifiable. The latter problems are discussed in greater detail in Chapter 17 in this volume, by Daniel, De Stavola and Cousens, who illustrate their method with the aid of the same HIV example we use here.