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Yazdani A, Mendez-Giraldez R, Yazdani A, Wang RS, Schaid DJ, Kong SW, Hadi MR, Samiei A, Samiei E, Wittenbecher C, Lasky-Su J, Clish CB, Muehlschlegel JD, Marotta F, Loscalzo J, Mora S, Chasman DI, Larson MG, Elsea SH. Broadcasters, receivers, functional groups of metabolites, and the link to heart failure by revealing metabolomic network connectivity. Metabolomics 2024; 20:71. [PMID: 38972029 DOI: 10.1007/s11306-024-02141-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 06/10/2024] [Indexed: 07/08/2024]
Abstract
BACKGROUND AND OBJECTIVE Blood-based small molecule metabolites offer easy accessibility and hold significant potential for insights into health processes, the impact of lifestyle, and genetic variation on disease, enabling precise risk prevention. In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline. METHODS We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites. RESULTS We identified metabolites associated with higher and lower risk of HF incidence, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. These associations were not confounded by the other metabolites due to uncovering the connectivity among metabolites and adjusting each association for the confounding metabolites. Examples of our findings include the direct influence of asparagine on glycine, both of which were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids, which are not synthesized in the human body and are obtained directly from the diet. CONCLUSION Metabolites may play a critical role in linking genetic background and lifestyle factors to HF incidence. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates studying complex conditions like HF.
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Affiliation(s)
- Azam Yazdani
- Division of Preventive Medicine, Department of Medicine, Brigham Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Harvard Data Science Initiative, The Broad Institute, Harvard Medical School, Boston, USA.
| | | | - Akram Yazdani
- Division of Clinical and Translational Sciences, Department of Internal Medicine, The University of Texas Health Science Center at Houston, McGovern Medical School, Houston, USA
| | - Rui-Sheng Wang
- Department of Medicine, Brigham Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55902, USA
| | - Sek Won Kong
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
| | - M Reza Hadi
- School of Mathematics, University of Science and Technology of Iran, Tehran, Iran
| | - Ahmad Samiei
- Division of Pulmonary Medicine, Boston Children's Hospital, Boston, USA
| | | | - Clemens Wittenbecher
- Division of Food and Nutrition Science, Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden
| | - Jessica Lasky-Su
- Department of Medicine, Brigham Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Jochen D Muehlschlegel
- Department of Anesthesia, Brigham Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Francesco Marotta
- ReGenera R&D International for Aging Intervention and Vitality & Longevity Medical Science Commission, Femtec, Milano, Italy
| | - Joseph Loscalzo
- The Division of Cardiovascular Medicine, Department of Medicine, Brigham Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Samia Mora
- Department of Medicine, Brigham Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel I Chasman
- Department of Medicine, Brigham Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Martin G Larson
- Department of Biostatistics, Boston University, Boston, MA, 02118, USA
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Gani MO, Kethireddy S, Adib R, Hasan U, Griffin P, Adibuzzaman M. Structural causal model with expert augmented knowledge to estimate the effect of oxygen therapy on mortality in the ICU. Artif Intell Med 2023; 137:102493. [PMID: 36868692 PMCID: PMC9992896 DOI: 10.1016/j.artmed.2023.102493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 01/17/2023] [Accepted: 01/17/2023] [Indexed: 02/01/2023]
Abstract
Recent advances in causal inference techniques, more specifically, in the theory of structural causal models, provide the framework for identifying causal effects from observational data in cases where the causal graph is identifiable, i.e., the data generation mechanism can be recovered from the joint distribution. However, no such studies have been performed to demonstrate this concept with a clinical example. We present a complete framework to estimate the causal effects from observational data by augmenting expert knowledge in the model development phase and with a practical clinical application. Our clinical application entails a timely and essential research question, the effect of oxygen therapy intervention in the intensive care unit (ICU). The result of this project is helpful in a variety of disease conditions, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) patients in the ICU. We used data from the MIMIC-III database, a widely used health care database in the machine learning community with 58,976 admissions from an ICU in Boston, MA, to estimate the oxygen therapy effect on morality. We also identified the model's covariate-specific effect on oxygen therapy for more personalized intervention.
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Affiliation(s)
- Md Osman Gani
- Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA.
| | | | - Riddhiman Adib
- Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR, USA.
| | - Uzma Hasan
- Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA.
| | - Paul Griffin
- Department of Industrial and Manufacturing Engineering, Penn State University, University Park, PA, USA.
| | - Mohammad Adibuzzaman
- Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR, USA.
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Gromicho M, Leão T, Oliveira Santos M, Pinto S, Carvalho AM, Madeira SC, de Carvalho M. Dynamic Bayesian Networks for stratification of disease progression in Amyotrophic Lateral Sclerosis. Eur J Neurol 2022; 29:2201-2210. [DOI: 10.1111/ene.15357] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/31/2022] [Indexed: 11/27/2022]
Affiliation(s)
- Marta Gromicho
- Instituto de Medicina Molecular Faculdade de Medicina Universidade de Lisboa Lisbon Portugal
| | - Tiago Leão
- Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal
| | - Miguel Oliveira Santos
- Instituto de Medicina Molecular Faculdade de Medicina Universidade de Lisboa Lisbon Portugal
- Department of Neurosciences and Mental Health Centro Hospitalar Universitário de Lisboa‐Norte Lisbon Portugal
| | - Susana Pinto
- Instituto de Medicina Molecular Faculdade de Medicina Universidade de Lisboa Lisbon Portugal
| | - Alexandra M. Carvalho
- Instituto de Telecomunicações and Lisbon ELLIS Unit (LUMLIS) Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal
| | - Sara C. Madeira
- LASIGE Faculdade de Ciências Universidade de Lisboa Lisbon Portugal
| | - Mamede de Carvalho
- Instituto de Medicina Molecular Faculdade de Medicina Universidade de Lisboa Lisbon Portugal
- Department of Neurosciences and Mental Health Centro Hospitalar Universitário de Lisboa‐Norte Lisbon Portugal
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Ahangaran M, Chiò A, D'Ovidio F, Manera U, Vasta R, Canosa A, Moglia C, Calvo A, Minaei-Bidgoli B, Jahed-Motlagh MR. Causal associations of genetic factors with clinical progression in amyotrophic lateral sclerosis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 216:106681. [PMID: 35151113 DOI: 10.1016/j.cmpb.2022.106681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 01/08/2022] [Accepted: 02/02/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVE Recent advances in the genetic causes of ALS reveals that about 10% of ALS patients have a genetic origin and that more than 30 genes are likely to contribute to this disease. However, four genes are more frequently associated with ALS: C9ORF72, TARDBP, SOD1, and FUS. The relationship between genetic factors and ALS progression rate is not clear. In this study, we carried out a causal analysis of ALS disease with a genetics perspective in order to assess the contribution of the four mentioned genes to the progression rate of ALS. METHODS In this work, we applied a novel causal learning model to the CRESLA dataset which is a longitudinal clinical dataset of ALS patients including genetic information of such patients. This study aims to discover the relationship between four mentioned genes and ALS progression rate from a causation perspective using machine learning and probabilistic methods. RESULTS The results indicate a meaningful association between genetic factors and ALS progression rate with causality viewpoint. Our findings revealed that causal relationships between ALSFRS-R items associated with bulbar regions have the strongest association with genetic factors, especially C9ORF72; and other three genes have the greatest contribution to the respiratory ALSFRS-R items with a causation point of view. CONCLUSIONS The findings revealed that genetic factors have a significant causal effect on the rate of ALS progression. Since C9ORF72 patients have higher proportion compared to those carrying other three gene mutations in the CRESLA cohort, we need a large multi-centric study to better analyze SOD1, TARDBP and FUS contribution to the ALS clinical progression. We conclude that causal associations between ALSFRS-R clinical factors is a suitable predictor for designing a prognostic model of ALS.
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Affiliation(s)
- Meysam Ahangaran
- Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran; Department of Computer Engineering, Mazandaran University of Science and Technology, Babol, Iran.
| | - Adriano Chiò
- Department of Computer Engineering, Mazandaran University of Science and Technology, Babol, Iran; 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Turin, Italy; Neurology 1, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza of Torino, Turin, Italy; National Research Council, Institute of Cognitive Sciences and Technologies, Rome, Italy.
| | - Fabrizio D'Ovidio
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Turin, Italy
| | - Umberto Manera
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Turin, Italy
| | - Rosario Vasta
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Turin, Italy
| | - Antonio Canosa
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Turin, Italy; Neurology 1, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza of Torino, Turin, Italy
| | - Cristina Moglia
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Turin, Italy; Neurology 1, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza of Torino, Turin, Italy
| | - Andrea Calvo
- 'Rita Levi Montalcini' Department of Neuroscience, University of Turin, Turin, Italy; Neurology 1, Azienda Ospedaliero-Universitaria Città della Salute e della Scienza of Torino, Turin, Italy
| | - Behrouz Minaei-Bidgoli
- Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
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Yazdani A, Yazdani A, Mendez-Giraldez R, Samiei A, Kosorok MR, Schaid DJ. From classical mendelian randomization to causal networks for systematic integration of multi-omics. Front Genet 2022; 13:990486. [PMID: 36186433 PMCID: PMC9520987 DOI: 10.3389/fgene.2022.990486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 08/17/2022] [Indexed: 11/17/2022] Open
Abstract
The number of studies with information at multiple biological levels of granularity, such as genomics, proteomics, and metabolomics, is increasing each year, and a biomedical questaion is how to systematically integrate these data to discover new biological mechanisms that have the potential to elucidate the processes of health and disease. Causal frameworks, such as Mendelian randomization (MR), provide a foundation to begin integrating data for new biological discoveries. Despite the growing number of MR applications in a wide variety of biomedical studies, there are few approaches for the systematic analysis of omic data. The large number and diverse types of molecular components involved in complex diseases interact through complex networks, and classical MR approaches targeting individual components do not consider the underlying relationships. In contrast, causal network models established in the principles of MR offer significant improvements to the classical MR framework for understanding omic data. Integration of these mostly distinct branches of statistics is a recent development, and we here review the current progress. To set the stage for causal network models, we review some recent progress in the classical MR framework. We then explain how to transition from the classical MR framework to causal networks. We discuss the identification of causal networks and evaluate the underlying assumptions. We also introduce some tests for sensitivity analysis and stability assessment of causal networks. We then review practical details to perform real data analysis and identify causal networks and highlight some of the utility of causal networks. The utilities with validated novel findings reveal the full potential of causal networks as a systems approach that will become necessary to integrate large-scale omic data.
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Affiliation(s)
- Azam Yazdani
- Center of Perioperative Genetics and Genomics, Brigham Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Akram Yazdani
- Health Science Center at Houston, McGovern Medical School, Division of Clinical and Translational Sciences, University of Texas, Houston, TX, United States
| | - Raul Mendez-Giraldez
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, Durham, NC, United States
| | - Ahmad Samiei
- Division of Pulmonary Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Michael R Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, United States
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AIM in Amyotrophic Lateral Sclerosis. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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