Carlo Berzuini - Academia.edu (original) (raw)
Papers by Carlo Berzuini
British Journal of Haematology, Dec 1, 1988
The dependence of survival time on a set of prognostic factors was explored by means of Cox's... more The dependence of survival time on a set of prognostic factors was explored by means of Cox's regression model in 137 cases of myelofibrosis with myeloid metaplasia (MMM). The following parameters recorded at diagnosis proved to be important independent indicators of a poor prognosis: a higher value for age, a lower value for Hb concentration, a higher value for immature myeloid cells in peripheral blood (IMC), a lower value for total erythroid iron turnover (TEIT), and a bone marrow red cell aplasia (RCA). A prognostic classification tree was constructed whose terminal nodes (risk groups), described by simple logical conditions upon important indicators, were characterized by significantly different expected survival. The two extreme risk groups lend themselves to a simple, but complete description. The low‐risk group (19.7% of the sample) comprises cases who had the diagnosis of MMM before age 45 and a number of IMC constantly lower than 24%. The actuarial proportion of patients surviving at 15 years was 100%. The high‐risk group (29.9% of cases) comprises patients with age greater than 45 and Hb lower than 13 g/dl, associated with RCA, or with a relatively decreased erythropoiesis (TEIT lower than 2 times the normal) or with IMC greater than 24%. Seven out of the 11 who died within this group developed blastic crisis. Median survival time of the group was 69 months.
Lecture notes in medical informatics, 1991
This paper describes a knowledge-based approach to therapy planning based on an epistemological m... more This paper describes a knowledge-based approach to therapy planning based on an epistemological model of therapeutic reasoning. According to this model, therapy planning involves three basic inference types: abduction, deduction and induction. Representing knowledge needed for executing these tasks may require different formalisms: production rules, frames, probabilistic models and mathematical models. We show how a control blackboard architecture allows the exploitation of different knowledge sources adopting those different knowledge representation formalisms.
Purpose: Randomized ovarian cancer trials, including ICON7, have reported improved progression-fr... more 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 ¼ 91) and validation (n ¼ 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 ¼ 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 ¼ 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. Ó2014 AACR.
Supplementary Table S1: Spearman correlations (rho) between biomarkers; Supplementary Table S2: P... more 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)
IFAC Proceedings Volumes, Jun 1, 1982
Abstract A methodology for the study of erythroid disorders by means of a mathematical model of i... more Abstract A methodology for the study of erythroid disorders by means of a mathematical model of iron kinetics and classification techniques are presented. Iron flows along the pathway linking iron pools in the body can be quantitated by estimating model parameters from tracer data. This provides a quantitative assessment of erythropoiesis, since iron is used for hemoglobin synthesis. Multivariate analysis techniques in the space of estimated ferrokinetic parameters enabled us to obtain a new functional classification of erythroid disorders.
Multiple Sclerosis Journal, 2004
Giornale italiano di cardiologia, 1994
Aim of this study was to evaluate the factors affecting the duration of the recovery time (RT) af... more Aim of this study was to evaluate the factors affecting the duration of the recovery time (RT) after a positive exercise stress test and to define its relationship with the extent of coronary artery disease (CAD). We studied 109 consecutive patients with a positive exercise test and proven coronary disease. RT was neither related to the severity of CAD, nor to exercise duration, rate-pressure product at the end of the exercise and maximum ST segment depression. A significant linear relationship was found between RT and the time of ischemia during exercise (IT) (r = 0.66, p < .001). This relationship was analyzed separately in patients (pts) with advanced (Group I) and in pts with less severe CAD (Group II). The regression line of the data showed a similar slope but a higher y-axis intercept in Group I than in Group II (p < .05). The RT/IT ratio was in fact significantly higher in Group I than in Group II (3.0 +/- 1.3 vs 1.7 +/- 0.7, p < .0001). Discriminant analysis was per...
Scientific Reports, 2019
Acute myocardial infarction is primarily due to coronary atherosclerotic plaque rupture and subse... more 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...
BMC Bioinformatics, 2018
Background: Recent advances in data analysis methods based on principles of Mendelian Randomisati... more 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.
A hierarchical statistical modeling approach to analyze proteomic isobaric tag for relative and a... more A hierarchical statistical modeling approach to analyze proteomic isobaric tag for relative and absolute quantitation data
We continue the discussion of sequential data-gathering and decision-making processes, started in... more We continue the discussion of 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
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996
Abstract-This paper considers biomedical problems in which a sample of subjects, for example clin... more Abstract-This paper considers biomedical problems in which a sample of subjects, for example clinical patients, is monitored through time for purposes of individual prediction. Emphasis is on situations in which the monitoring generates data both in the form of a time series and ...
arXiv: Statistics Theory, 2016
Our Bayesian approach to Mendelian Randomisation uses multiple instruments to assess the putative... more Our Bayesian approach to Mendelian Randomisation uses multiple instruments to assess the putative causal effect of an exposure on an outcome. The approach is robust to violations of the (untestable) Exclusion Restriction condition, and hence it does not require instruments to be independent of the outcome conditional on the exposure and on the confounders of the exposure-outcome relationship. The Bayesian approach offers a rigorous handling of the uncertainty (e.g. about the estimated instrument-exposure associations), freedom from asymptotic approximations of the null distribution and the possibility to elaborate the model in any direction of scientific relevance. We illustrate the last feature with the aid of a study of the metabolic mediators of the disease-inducing effects of obesity, where we elaborate the model to investigate whether the causal effect of interest interacts with a covariate. The proposed model contains a vector of unidentifiable parameters, beta\betabeta, whose jjjth...
arXiv: Applications, 2019
Background In a study performed on multiplex Multiple Sclerosis (MS) Sardinian families to identi... more Background In a study performed on multiplex Multiple Sclerosis (MS) Sardinian families to identify disease causing plasma proteins, application of Mendelian Randomization (MR) methods encounters difficulties due to relatedness of individuals, correlation between finely mapped genotype instrumental variables (IVs) and presence of missing exposures. Method We specialize the method of Berzuini et al (2018) to deal with these difficulties. The proposed method allows pedigree structure to enter the specification of the outcome distribution via kinship matrix, and treating missing exposures as additional parameters to be estimated from the data. It also acknowledges possible correlation between instruments by replacing the originally proposed independence prior for IV-specific pleiotropic effect with a g-prior. Based on correlated (r2< 0.2) IVs, we analysed the data of four candidate MS-causing proteins by using both the independence and the g-prior. Results 95% credible intervals for...
Schizophrenia Bulletin, 2021
Acute and transient psychotic disorders (ATPDs) include short-lived psychotic episodes with a hig... more 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 recur...
Nature Genetics, 2021
The kidney is an organ of key relevance to blood pressure (BP) regulation, hypertension and antih... more 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.
BMJ Open, 2020
ObjectivesBeing able to predict which patients with COVID-19 are going to deteriorate is importan... more 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 cov...
European Heart Journal Supplements, 2021
Data regarding long-term prognosis of MINOCA are very limited and conflicting. The Italian Geneti... more Data regarding long-term prognosis of MINOCA are very limited and conflicting. 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)...
Contains an Excel Table with the metabolite labels, in a cluster by cluster fashion. (XLSX 8 kb)
British Journal of Haematology, Dec 1, 1988
The dependence of survival time on a set of prognostic factors was explored by means of Cox's... more The dependence of survival time on a set of prognostic factors was explored by means of Cox's regression model in 137 cases of myelofibrosis with myeloid metaplasia (MMM). The following parameters recorded at diagnosis proved to be important independent indicators of a poor prognosis: a higher value for age, a lower value for Hb concentration, a higher value for immature myeloid cells in peripheral blood (IMC), a lower value for total erythroid iron turnover (TEIT), and a bone marrow red cell aplasia (RCA). A prognostic classification tree was constructed whose terminal nodes (risk groups), described by simple logical conditions upon important indicators, were characterized by significantly different expected survival. The two extreme risk groups lend themselves to a simple, but complete description. The low‐risk group (19.7% of the sample) comprises cases who had the diagnosis of MMM before age 45 and a number of IMC constantly lower than 24%. The actuarial proportion of patients surviving at 15 years was 100%. The high‐risk group (29.9% of cases) comprises patients with age greater than 45 and Hb lower than 13 g/dl, associated with RCA, or with a relatively decreased erythropoiesis (TEIT lower than 2 times the normal) or with IMC greater than 24%. Seven out of the 11 who died within this group developed blastic crisis. Median survival time of the group was 69 months.
Lecture notes in medical informatics, 1991
This paper describes a knowledge-based approach to therapy planning based on an epistemological m... more This paper describes a knowledge-based approach to therapy planning based on an epistemological model of therapeutic reasoning. According to this model, therapy planning involves three basic inference types: abduction, deduction and induction. Representing knowledge needed for executing these tasks may require different formalisms: production rules, frames, probabilistic models and mathematical models. We show how a control blackboard architecture allows the exploitation of different knowledge sources adopting those different knowledge representation formalisms.
Purpose: Randomized ovarian cancer trials, including ICON7, have reported improved progression-fr... more 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 ¼ 91) and validation (n ¼ 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 ¼ 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 ¼ 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. Ó2014 AACR.
Supplementary Table S1: Spearman correlations (rho) between biomarkers; Supplementary Table S2: P... more 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)
IFAC Proceedings Volumes, Jun 1, 1982
Abstract A methodology for the study of erythroid disorders by means of a mathematical model of i... more Abstract A methodology for the study of erythroid disorders by means of a mathematical model of iron kinetics and classification techniques are presented. Iron flows along the pathway linking iron pools in the body can be quantitated by estimating model parameters from tracer data. This provides a quantitative assessment of erythropoiesis, since iron is used for hemoglobin synthesis. Multivariate analysis techniques in the space of estimated ferrokinetic parameters enabled us to obtain a new functional classification of erythroid disorders.
Multiple Sclerosis Journal, 2004
Giornale italiano di cardiologia, 1994
Aim of this study was to evaluate the factors affecting the duration of the recovery time (RT) af... more Aim of this study was to evaluate the factors affecting the duration of the recovery time (RT) after a positive exercise stress test and to define its relationship with the extent of coronary artery disease (CAD). We studied 109 consecutive patients with a positive exercise test and proven coronary disease. RT was neither related to the severity of CAD, nor to exercise duration, rate-pressure product at the end of the exercise and maximum ST segment depression. A significant linear relationship was found between RT and the time of ischemia during exercise (IT) (r = 0.66, p < .001). This relationship was analyzed separately in patients (pts) with advanced (Group I) and in pts with less severe CAD (Group II). The regression line of the data showed a similar slope but a higher y-axis intercept in Group I than in Group II (p < .05). The RT/IT ratio was in fact significantly higher in Group I than in Group II (3.0 +/- 1.3 vs 1.7 +/- 0.7, p < .0001). Discriminant analysis was per...
Scientific Reports, 2019
Acute myocardial infarction is primarily due to coronary atherosclerotic plaque rupture and subse... more 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...
BMC Bioinformatics, 2018
Background: Recent advances in data analysis methods based on principles of Mendelian Randomisati... more 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.
A hierarchical statistical modeling approach to analyze proteomic isobaric tag for relative and a... more A hierarchical statistical modeling approach to analyze proteomic isobaric tag for relative and absolute quantitation data
We continue the discussion of sequential data-gathering and decision-making processes, started in... more We continue the discussion of 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
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996
Abstract-This paper considers biomedical problems in which a sample of subjects, for example clin... more Abstract-This paper considers biomedical problems in which a sample of subjects, for example clinical patients, is monitored through time for purposes of individual prediction. Emphasis is on situations in which the monitoring generates data both in the form of a time series and ...
arXiv: Statistics Theory, 2016
Our Bayesian approach to Mendelian Randomisation uses multiple instruments to assess the putative... more Our Bayesian approach to Mendelian Randomisation uses multiple instruments to assess the putative causal effect of an exposure on an outcome. The approach is robust to violations of the (untestable) Exclusion Restriction condition, and hence it does not require instruments to be independent of the outcome conditional on the exposure and on the confounders of the exposure-outcome relationship. The Bayesian approach offers a rigorous handling of the uncertainty (e.g. about the estimated instrument-exposure associations), freedom from asymptotic approximations of the null distribution and the possibility to elaborate the model in any direction of scientific relevance. We illustrate the last feature with the aid of a study of the metabolic mediators of the disease-inducing effects of obesity, where we elaborate the model to investigate whether the causal effect of interest interacts with a covariate. The proposed model contains a vector of unidentifiable parameters, beta\betabeta, whose jjjth...
arXiv: Applications, 2019
Background In a study performed on multiplex Multiple Sclerosis (MS) Sardinian families to identi... more Background In a study performed on multiplex Multiple Sclerosis (MS) Sardinian families to identify disease causing plasma proteins, application of Mendelian Randomization (MR) methods encounters difficulties due to relatedness of individuals, correlation between finely mapped genotype instrumental variables (IVs) and presence of missing exposures. Method We specialize the method of Berzuini et al (2018) to deal with these difficulties. The proposed method allows pedigree structure to enter the specification of the outcome distribution via kinship matrix, and treating missing exposures as additional parameters to be estimated from the data. It also acknowledges possible correlation between instruments by replacing the originally proposed independence prior for IV-specific pleiotropic effect with a g-prior. Based on correlated (r2< 0.2) IVs, we analysed the data of four candidate MS-causing proteins by using both the independence and the g-prior. Results 95% credible intervals for...
Schizophrenia Bulletin, 2021
Acute and transient psychotic disorders (ATPDs) include short-lived psychotic episodes with a hig... more 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 recur...
Nature Genetics, 2021
The kidney is an organ of key relevance to blood pressure (BP) regulation, hypertension and antih... more 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.
BMJ Open, 2020
ObjectivesBeing able to predict which patients with COVID-19 are going to deteriorate is importan... more 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 cov...
European Heart Journal Supplements, 2021
Data regarding long-term prognosis of MINOCA are very limited and conflicting. The Italian Geneti... more Data regarding long-term prognosis of MINOCA are very limited and conflicting. 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)...
Contains an Excel Table with the metabolite labels, in a cluster by cluster fashion. (XLSX 8 kb)