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Al AYAE. WITHDRAWN: Broadcasters, receivers, functional groups of metabolites and the link to heart failure using polygenic factors. RESEARCH SQUARE 2024:rs.3.rs-3272974. [PMID: 37674714 PMCID: PMC10479558 DOI: 10.21203/rs.3.rs-3272974/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/08/2023]
Abstract
The full text of this preprint has been withdrawn, as it was submitted in error. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
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Yazdani A, Mendez-Giraldez R, Yazdani A, Schaid D, Won Kong S, Hadi M, Samiei A, Wittenbecher C, Lasky-Su J, Clish C, Marotta F, Kosorok M, Mora S, Muehlschlegel J, Chasman D, Larson M, Elsea S. Broadcasters, receivers, functional groups of metabolites and the link to heart failure progression using polygenic factors. RESEARCH SQUARE 2023:rs.3.rs-3246406. [PMID: 37645766 PMCID: PMC10462252 DOI: 10.21203/rs.3.rs-3246406/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline. 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. We identified metabolites associated with higher or lower risk of HF incidence, the associations that were not confounded by the other metabolites, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. We revealed the underlying relationships of the findings. For example, asparagine directly influenced glycine, and both 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 come directly from the diet. Metabolites may play a critical role in linking genetic background and lifestyle factors to HF progression. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates a mechanistic understanding of HF progression.
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Affiliation(s)
| | | | - Akram Yazdani
- Division of Clinical and Translational Sciences, Department of Internal Medicine, at The University of Texas Health Science Center at Houston, McGovern Medical School
| | - Daniel Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55902
| | | | - Mohamad Hadi
- School of Mathematics, University of science and technology of Iran, Tehran
| | - Ahmad Samiei
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA
| | | | | | | | | | | | - Samia Mora
- Brigham and Women's Hospital and Harvard Medical School
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Fan Z, Kernan KF, Sriram A, Benos PV, Canna SW, Carcillo JA, Kim S, Park HJ. Deep neural networks with knockoff features identify nonlinear causal relations and estimate effect sizes in complex biological systems. Gigascience 2022; 12:giad044. [PMID: 37395630 PMCID: PMC10316696 DOI: 10.1093/gigascience/giad044] [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: 09/15/2022] [Revised: 01/31/2023] [Accepted: 05/29/2023] [Indexed: 07/04/2023] Open
Abstract
BACKGROUND Learning the causal structure helps identify risk factors, disease mechanisms, and candidate therapeutics for complex diseases. However, although complex biological systems are characterized by nonlinear associations, existing bioinformatic methods of causal inference cannot identify the nonlinear relationships and estimate their effect size. RESULTS To overcome these limitations, we developed the first computational method that explicitly learns nonlinear causal relations and estimates the effect size using a deep neural network approach coupled with the knockoff framework, named causal directed acyclic graphs using deep learning variable selection (DAG-deepVASE). Using simulation data of diverse scenarios and identifying known and novel causal relations in molecular and clinical data of various diseases, we demonstrated that DAG-deepVASE consistently outperforms existing methods in identifying true and known causal relations. In the analyses, we also illustrate how identifying nonlinear causal relations and estimating their effect size help understand the complex disease pathobiology, which is not possible using other methods. CONCLUSIONS With these advantages, the application of DAG-deepVASE can help identify driver genes and therapeutic agents in biomedical studies and clinical trials.
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Affiliation(s)
- Zhenjiang Fan
- Department of Computer Science, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Kate F Kernan
- Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, Children's Hospital of Pittsburgh, Center for Critical Care Nephrology and Clinical Research Investigation and Systems Modeling of Acute Illness Center, University of Pittsburgh, Pittsburgh, PA 15260,USA
| | - Aditya Sriram
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Panayiotis V Benos
- Department of Epidemiology, University of Florida, Gainesville, FL 32610, USA
| | - Scott W Canna
- Pediatric Rheumatology, The Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Joseph A Carcillo
- Division of Pediatric Critical Care Medicine, Department of Critical Care Medicine, Children's Hospital of Pittsburgh, Center for Critical Care Nephrology and Clinical Research Investigation and Systems Modeling of Acute Illness Center, University of Pittsburgh, Pittsburgh, PA 15260,USA
| | - Soyeon Kim
- Division of Pediatric Pulmonary Medicine, Children's Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
- Department of Pediatrics, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Hyun Jung Park
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA 15213, USA
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Lee IH, Smith MR, Yazdani A, Sandhu S, Walker DI, Mandl KD, Jones DP, Kong SW. Comprehensive characterization of putative genetic influences on plasma metabolome in a pediatric cohort. Hum Genomics 2022; 16:67. [PMID: 36482414 PMCID: PMC9730628 DOI: 10.1186/s40246-022-00440-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels, while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype-metabotype associations. However, these associations have not been characterized in children. RESULTS We conducted the largest genome by metabolome-wide association study to date of children (N = 441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability (h2) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h2 (> 0.8) for 15.9% of features and low h2 (< 0.2) for most of features (62.0%). The features with high h2 were enriched for amino acid and nucleic acid metabolism, while carbohydrate and lipid concentrations showed low h2. For each feature, a metabolite quantitative trait loci (mQTL) analysis was performed to identify genetic variants that were potentially associated with plasma levels. Fifty-four associations among 29 features and 43 genetic variants were identified at a genome-wide significance threshold p < 3.5 × 10-12 (= 5 × 10-8/14,342 features). Previously reported associations such as UGT1A1 and bilirubin; PYROXD2 and methyl lysine; and ACADS and butyrylcarnitine were successfully replicated in our pediatric cohort. We found potential candidates for novel associations including CSMD1 and a monostearyl alcohol triglyceride (m/z 781.7483, retention time (RT) 89.3 s); CALN1 and Tridecanol (m/z 283.2741, RT 27.6). A gene-level enrichment analysis using MAGMA revealed highly interconnected modules for dADP biosynthesis, sterol synthesis, and long-chain fatty acid transport in the gene-feature network. CONCLUSION Comprehensive profiling of plasma metabolome across age groups combined with genome-wide genotyping revealed a wide range of genetic influence on diverse chemical species and metabolic pathways. The developmental trajectory of a biological system is shaped by gene-environment interaction especially in early life. Therefore, continuous efforts on generating metabolomics data in diverse human tissue types across age groups are required to understand gene-environment interaction toward healthy aging trajectories.
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Affiliation(s)
- In-Hee Lee
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA
| | - Matthew Ryan Smith
- grid.189967.80000 0001 0941 6502Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, GA 30602 USA ,grid.414026.50000 0004 0419 4084Atlanta Department of Veterans Affairs Medical Center, Decatur, GA 30033 USA
| | - Azam Yazdani
- grid.38142.3c000000041936754XCenter of Perioperative Genetics and Genomics, Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115 USA
| | - Sumiti Sandhu
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA
| | - Douglas I. Walker
- grid.59734.3c0000 0001 0670 2351Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029 USA
| | - Kenneth D. Mandl
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA ,grid.38142.3c000000041936754XDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA 02115 USA ,grid.38142.3c000000041936754XDepartment of Pediatrics, Harvard Medical School, Boston, MA 02115 USA
| | - Dean P. Jones
- grid.189967.80000 0001 0941 6502Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine, Emory University, Atlanta, GA 30602 USA
| | - Sek Won Kong
- grid.2515.30000 0004 0378 8438Computational Health Informatics Program, Boston Children’s Hospital, 401 Park Drive, Boston, MA 02215 USA ,grid.38142.3c000000041936754XDepartment of Pediatrics, Harvard Medical School, Boston, MA 02115 USA
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Kelly J, Berzuini C, Keavney B, Tomaszewski M, Guo H. A review of causal discovery methods for molecular network analysis. Mol Genet Genomic Med 2022; 10:e2055. [PMID: 36087049 PMCID: PMC9544222 DOI: 10.1002/mgg3.2055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/12/2022] [Accepted: 08/18/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND With the increasing availability and size of multi-omics datasets, investigating the casual relationships between molecular phenotypes has become an important aspect of exploring underlying biology andgenetics. There are an increasing number of methodlogies that have been developed and applied to moleular networks to investigate these causal interactions. METHODS We have introduced and reviewed the available methods for building large-scale causal molecular networks that have been developed and applied in the past decade. RESULTS In this review we have identified and summarized the existing methods for infering causality in large-scale causal molecular networks, and discussed important factors that will need to be considered in future research in this area. CONCLUSION Existing methods to infering causal molecular networks have their own strengths and limitations so there is no one best approach, and it is instead down to the discretion of the researcher. This review also to discusses some of the current limitations to biological interpretation of these networks, and important factors to consider for future studies on molecular networks.
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Affiliation(s)
- Jack Kelly
- Centre for Biostatistics, School of Health Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
| | - Carlo Berzuini
- Centre for Biostatistics, School of Health Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
| | - Bernard Keavney
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
- Division of Cardiology and Manchester Academic Health Science CentreManchester University NHS Foundation TrustManchesterUK
| | - Maciej Tomaszewski
- Division of Cardiovascular Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
- Manchester Heart Centre and Manchester Academic Health Science CentreManchester University NHS Foundation TrustManchesterUK
| | - Hui Guo
- Centre for Biostatistics, School of Health Sciences, Faculty of Medicine, Biology and HealthUniversity of ManchesterManchesterUK
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Debik J, Sangermani M, Wang F, Madssen TS, Giskeødegård GF. Multivariate analysis of NMR-based metabolomic data. NMR IN BIOMEDICINE 2022; 35:e4638. [PMID: 34738674 DOI: 10.1002/nbm.4638] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 09/08/2021] [Accepted: 09/29/2021] [Indexed: 06/13/2023]
Abstract
Nuclear magnetic resonance (NMR) spectroscopy allows for simultaneous detection of a wide range of metabolites and lipids. As metabolites act together in complex metabolic networks, they are often highly correlated, and optimal biological insight is achieved when using methods that take the correlation into account. For this reason, latent-variable-based methods, such as principal component analysis and partial least-squares discriminant analysis, are widely used in metabolomic studies. However, with increasing availability of larger population cohorts, and a shift from analysis of spectral data to using quantified metabolite levels, both more traditional statistical approaches and alternative machine learning methods have become more widely used. This review aims at providing an overview of the current state-of-the-art multivariate methods for the analysis of NMR-based metabolomic data as well as alternative methods, highlighting their strengths and limitations.
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Affiliation(s)
- Julia Debik
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
| | - Matteo Sangermani
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
| | - Feng Wang
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
- Clinic of Surgery, St. Olavs Hospital HF, Trondheim, Norway
| | - Torfinn S Madssen
- Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
| | - Guro F Giskeødegård
- Clinic of Surgery, St. Olavs Hospital HF, Trondheim, Norway
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology-NTNU, Trondheim, Norway
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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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Yazdani H, Cheng LL, Christiani DC, Yazdani A. Bounded Fuzzy Possibilistic Method Reveals Information about Lung Cancer through Analysis of Metabolomics. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:526-535. [PMID: 30222581 DOI: 10.1109/tcbb.2018.2869757] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Learning methods, such as conventional clustering and classification, have been applied in diagnosing diseases to categorize samples based on their features. Going beyond clustering samples, membership degrees represent to what degree each sample belongs to a cluster. Variation of membership degrees in each cluster provides information about the cluster as a whole and each sample individually which enables us to have insights toward precision medicine. Membership degrees are measured more accurately through removing restrictions from clustering samples. Bounded Fuzzy Possibilistic Method (BFPM) introduces a membership function that keeps the search space flexible to cluster samples with higher accuracy. The method evaluates samples for their movement from one cluster to another. This technique allows us to find critical samples in advance those with the potential ability to belong to other clusters in the near future. BFPM was applied on metabolomics of individuals in a lung cancer case-control study. Metabolomics as proximal molecular signals to the actual disease processes may serve as strong biomarkers of current disease process. The goal is to know whether serum metabolites of a healthy human can be differentiated from those with lung cancer. Using BFPM, some differences were observed, the pathology data were evaluated, and critical samples were recognized.
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Yazdani A, Yazdani A, Elsea SH, Schaid DJ, Kosorok MR, Dangol G, Samiei A. Genome analysis and pleiotropy assessment using causal networks with loss of function mutation and metabolomics. BMC Genomics 2019; 20:395. [PMID: 31113383 PMCID: PMC6528192 DOI: 10.1186/s12864-019-5772-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2018] [Accepted: 05/03/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Many genome-wide association studies have detected genomic regions associated with traits, yet understanding the functional causes of association often remains elusive. Utilizing systems approaches and focusing on intermediate molecular phenotypes might facilitate biologic understanding. RESULTS The availability of exome sequencing of two populations of African-Americans and European-Americans from the Atherosclerosis Risk in Communities study allowed us to investigate the effects of annotated loss-of-function (LoF) mutations on 122 serum metabolites. To assess the findings, we built metabolomic causal networks for each population separately and utilized structural equation modeling. We then validated our findings with a set of independent samples. By use of methods based on concepts of Mendelian randomization of genetic variants, we showed that some of the affected metabolites are risk predictors in the causal pathway of disease. For example, LoF mutations in the gene KIAA1755 were identified to elevate the levels of eicosapentaenoate (p-value = 5E-14), an essential fatty acid clinically identified to increase essential hypertension. We showed that this gene is in the pathway to triglycerides, where both triglycerides and essential hypertension are risk factors of metabolomic disorder and heart attack. We also identified that the gene CLDN17, harboring loss-of-function mutations, had pleiotropic actions on metabolites from amino acid and lipid pathways. CONCLUSION Using systems biology approaches for the analysis of metabolomics and genetic data, we integrated several biological processes, which lead to findings that may functionally connect genetic variants with complex diseases.
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Affiliation(s)
| | - Akram Yazdani
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029 USA
| | - Sarah H. Elsea
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Daniel J. Schaid
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905 USA
| | - Michael R. Kosorok
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Gita Dangol
- Health Science Center, The University of Texas MD Anderson Cancer Center, Austin, TX 77030 USA
| | - Ahmad Samiei
- Hasso Plattner Institute, 14482 Potsdam, Germany
- Climax Data Pattern, Boston, MA USA
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Papandreou C, Bulló M, Ruiz-Canela M, Dennis C, Deik A, Wang D, Guasch-Ferré M, Yu E, Razquin C, Corella D, Estruch R, Ros E, Fitó M, Fiol M, Liang L, Hernández-Alonso P, Clish CB, Martínez-González MA, Hu FB, Salas-Salvadó J. Plasma metabolites predict both insulin resistance and incident type 2 diabetes: a metabolomics approach within the Prevención con Dieta Mediterránea (PREDIMED) study. Am J Clin Nutr 2019; 109:626-634. [PMID: 30796776 PMCID: PMC7307433 DOI: 10.1093/ajcn/nqy262] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2018] [Accepted: 08/29/2018] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Insulin resistance is a complex metabolic disorder and is often associated with type 2 diabetes (T2D). OBJECTIVES The aim of this study was to test whether baseline metabolites can additionally improve the prediction of insulin resistance beyond classical risk factors. Furthermore, we examined whether a multimetabolite model predicting insulin resistance in nondiabetics can also predict incident T2D. METHODS We used a case-cohort study nested within the Prevención con Dieta Mediterránea (PREDIMED) trial in subsets of 700, 500, and 256 participants without T2D at baseline and 1 and 3 y. Fasting plasma metabolites were semiquantitatively profiled with liquid chromatography-tandem mass spectrometry. We assessed associations between metabolite concentrations and the homeostasis model of insulin resistance (HOMA-IR) through the use of elastic net regression analysis. We subsequently examined associations between the baseline HOMA-IR-related multimetabolite model and T2D incidence through the use of weighted Cox proportional hazard models. RESULTS We identified a set of baseline metabolites associated with HOMA-IR. One-year changes in metabolites were also significantly associated with HOMA-IR. The area under the curve was significantly greater for the model containing the classical risk factors and metabolites together compared with classical risk factors alone at baseline [0.81 (95% CI: 0.79, 0.84) compared with 0.69 (95% CI: 0.66, 0.73)] and during a 1-y period [0.69 (95% CI: 0.66, 0.72) compared with 0.57 (95% CI: 0.53, 0.62)]. The variance in HOMA-IR explained by the combination of metabolites and classical risk factors was also higher in all time periods. The estimated HRs for incident T2D in the multimetabolite score (model 3) predicting high HOMA-IR (median value or higher) or HOMA-IR (continuous) at baseline were 2.00 (95% CI: 1.58, 2.55) and 2.24 (95% CI: 1.72, 2.90), respectively, after adjustment for T2D risk factors. CONCLUSIONS The multimetabolite model identified in our study notably improved the predictive ability for HOMA-IR beyond classical risk factors and significantly predicted the risk of T2D.
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Affiliation(s)
- Christopher Papandreou
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Mònica Bulló
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Miguel Ruiz-Canela
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
| | - Courtney Dennis
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Amy Deik
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | | | - Marta Guasch-Ferré
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Departments of Nutrition, Boston, MA
| | - Edward Yu
- Departments of Nutrition, Boston, MA
| | - Cristina Razquin
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
| | - Dolores Corella
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine, University of Valencia, Valencia, Spain
| | - Ramon Estruch
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Departments of Internal Medicine, University of Barcelona, Barcelona, Spain
- Endocrinology and Nutrition, University of Barcelona, Barcelona, Spain
| | - Emilio Ros
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Lipid Clinic, Department of Endocrinology and Nutrition Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS), Hospital Clinic, University of Barcelona, Barcelona, Spain
| | - Montserrat Fitó
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Cardiovascular and Nutrition Research Group, Institut de Recerca Hospital del Mar, Barcelona, Spain
| | - Miquel Fiol
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- University Institute of Health Science Research (IUNICS), University of Balearic Islands and Hospital Son Espases, Palma de Mallorca, Spain
| | - Liming Liang
- Epidemiology and Statistics, Harvard TH Chan School of Public Health, Boston, MA
| | - Pablo Hernández-Alonso
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
| | - Clary B Clish
- Broad Institute of MIT and Harvard University, Cambridge, MA
| | - Miguel A Martínez-González
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
- Department of Preventive Medicine and Public Health, University of Navarra, Pamplona, Spain
- Departments of Nutrition, Boston, MA
| | - Frank B Hu
- Departments of Nutrition, Boston, MA
- Epidemiology and Statistics, Harvard TH Chan School of Public Health, Boston, MA
- Channing Division for Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA
| | - Jordi Salas-Salvadó
- Human Nutrition Unit, Faculty of Medicine and Health Sciences, Institut d'Investigació Sanitària Pere Virgili, Rovira i Virgili University, Reus, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III, Madrid, Spain
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11
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Causal discovery from sequential data in ALS disease based on entropy criteria. J Biomed Inform 2019; 89:41-55. [DOI: 10.1016/j.jbi.2018.10.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 10/14/2018] [Accepted: 10/15/2018] [Indexed: 11/21/2022]
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12
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Enhanced Molecular Appreciation of Psychiatric Disorders Through High-Dimensionality Data Acquisition and Analytics. Methods Mol Biol 2019; 2011:671-723. [PMID: 31273728 DOI: 10.1007/978-1-4939-9554-7_39] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
The initial diagnosis, molecular investigation, treatment, and posttreatment care of major psychiatric disorders (schizophrenia and bipolar depression) are all still significantly hindered by the current inability to define these disorders in an explicit molecular signaling manner. High-dimensionality data analytics, using large datastreams from transcriptomic, proteomic, or metabolomic investigations, will likely advance both the appreciation of the molecular nature of major psychiatric disorders and simultaneously enhance our ability to more efficiently diagnose and treat these debilitating conditions. High-dimensionality data analysis in psychiatric research has been heterogeneous in aims and methods and limited by insufficient sample sizes, poorly defined case definitions, methodological inhomogeneity, and confounding results. All of these issues combine to constrain the conclusions that can be extracted from them. Here, we discuss possibilities for overcoming methodological challenges through the implementation of transcriptomic, proteomic, or metabolomics signatures in psychiatric diagnosis and offer an outlook for future investigations. To fulfill the promise of intelligent high-dimensionality data-based differential diagnosis in mental disease diagnosis and treatment, future research will need large, well-defined cohorts in combination with state-of-the-art technologies.
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13
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Redefining environmental exposure for disease etiology. NPJ Syst Biol Appl 2018; 4:30. [PMID: 30181901 PMCID: PMC6119193 DOI: 10.1038/s41540-018-0065-0] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Revised: 05/14/2018] [Accepted: 05/23/2018] [Indexed: 12/16/2022] Open
Abstract
Etiological studies of human exposures to environmental factors typically rely on low-throughput methods that target only a few hundred chemicals or mixtures. In this Perspectives article, I outline how environmental exposure can be defined by the blood exposome—the totality of chemicals circulating in blood. The blood exposome consists of chemicals derived from both endogenous and exogenous sources. Endogenous chemicals are represented by the human proteome and metabolome, which establish homeostatic networks of functional molecules. Exogenous chemicals arise from diet, vitamins, drugs, pathogens, microbiota, pollution, and lifestyle factors, and can be measured in blood as subsets of the proteome, metabolome, metals, macromolecular adducts, and foreign DNA and RNA. To conduct ‘exposome-wide association studies’, blood samples should be obtained prospectively from subjects—preferably at critical stages of life—and then analyzed in incident disease cases and matched controls to find discriminating exposures. Results from recent metabolomic investigations of archived blood illustrate our ability to discover potentially causal exposures with current technologies.
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14
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Yazdani A, Yazdani A, Bowman TA, Marotta F, Cooke JP, Samiei A. Arachidonic acid as a target for treating hypertriglyceridemia reproduced by a causal network analysis and an intervention study. Metabolomics 2018; 14:78. [PMID: 30830364 DOI: 10.1007/s11306-018-1368-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2018] [Accepted: 05/05/2018] [Indexed: 02/07/2023]
Affiliation(s)
- Azam Yazdani
- University of Texas, Health Science Center, Houston, TX, 77030, USA.
- Climax Data Pattern, Houston, TX, USA.
| | - Akram Yazdani
- Climax Data Pattern, Houston, TX, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, 10029, USA
| | - Thomas A Bowman
- Jarrow Formulas, Inc., 1824 S. Robertson Blvd, Los Angeles, CA, 90035, USA
| | | | - John P Cooke
- Department of Cardiovascular Sciences, Houston Methodist, Houston, USA
| | - Ahmad Samiei
- Climax Data Pattern, Houston, TX, USA
- Hasso Plattner Institute, 14482, Potsdam, Germany
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15
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Kohler I, Hankemeier T, van der Graaf PH, Knibbe CA, van Hasselt JC. Integrating clinical metabolomics-based biomarker discovery and clinical pharmacology to enable precision medicine. Eur J Pharm Sci 2017; 109S:S15-S21. [DOI: 10.1016/j.ejps.2017.05.018] [Citation(s) in RCA: 68] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Accepted: 05/10/2017] [Indexed: 12/21/2022]
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16
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Identification of susceptible genes for complex chronic diseases based on disease risk functional SNPs and interaction networks. J Biomed Inform 2017; 74:137-144. [DOI: 10.1016/j.jbi.2017.09.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 09/15/2017] [Accepted: 09/16/2017] [Indexed: 01/05/2023]
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17
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Ainsworth HF, Shin SY, Cordell HJ. A comparison of methods for inferring causal relationships between genotype and phenotype using additional biological measurements. Genet Epidemiol 2017; 41:577-586. [PMID: 28691305 PMCID: PMC5655748 DOI: 10.1002/gepi.22061] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2017] [Revised: 05/19/2017] [Accepted: 05/19/2017] [Indexed: 11/23/2022]
Abstract
Genome wide association studies (GWAS) have been very successful over the last decade at identifying genetic variants associated with disease phenotypes. However, interpretation of the results obtained can be challenging. Incorporation of further relevant biological measurements (e.g. ‘omics’ data) measured in the same individuals for whom we have genotype and phenotype data may help us to learn more about the mechanism and pathways through which causal genetic variants affect disease. We review various methods for causal inference that can be used for assessing the relationships between genetic variables, other biological measures, and phenotypic outcome, and present a simulation study assessing the performance of the methods under different conditions. In general, the methods we considered did well at inferring the causal structure for data simulated under simple scenarios. However, the presence of an unknown and unmeasured common environmental effect could lead to spurious inferences, with the methods we considered displaying varying degrees of robustness to this confounder. The use of causal inference techniques to integrate omics and GWAS data has the potential to improve biological understanding of the pathways leading to disease. Our study demonstrates the suitability of various methods for performing causal inference under several biologically plausible scenarios.
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Affiliation(s)
- Holly F Ainsworth
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, United Kingdom
| | - So-Youn Shin
- MRC Integrative Epidemiology Unit (IEU), University of Bristol, Bristol, United Kingdom
| | - Heather J Cordell
- Institute of Genetic Medicine, Newcastle University, International Centre for Life, Central Parkway, Newcastle upon Tyne, United Kingdom
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