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Zhao Y. Mediation Analysis with Multiple Exposures and Multiple Mediators. Stat Med 2024. [PMID: 39250913 DOI: 10.1002/sim.10215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 04/25/2024] [Accepted: 08/23/2024] [Indexed: 09/11/2024]
Abstract
A mediation analysis approach is proposed for multiple exposures, multiple mediators, and a continuous scalar outcome under the linear structural equation modeling framework. It assumes that there exist orthogonal components that demonstrate parallel mediation mechanisms on the outcome, and thus is named principal component mediation analysis (PCMA). Likelihood-based estimators are introduced for simultaneous estimation of the component projections and effect parameters. The asymptotic distribution of the estimators is derived for low-dimensional data. A bootstrap procedure is introduced for inference. Simulation studies illustrate the superior performance of the proposed approach. Applied to a proteomics-imaging dataset from the Alzheimer's disease neuroimaging initiative (ADNI), the proposed framework identifies protein deposition - brain atrophy - memory deficit mechanisms consistent with existing knowledge and suggests potential AD pathology by integrating data collected from different modalities.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana
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2
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Li Y, Lee SH, Yu C, Hsu LM, Wang TWW, Do K, Kim HJ, Shih YYI, Grill WM. Optogenetic fMRI reveals therapeutic circuits of subthalamic nucleus deep brain stimulation. Brain Stimul 2024; 17:947-957. [PMID: 39096961 PMCID: PMC11364984 DOI: 10.1016/j.brs.2024.07.022] [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: 04/12/2024] [Revised: 07/11/2024] [Accepted: 07/31/2024] [Indexed: 08/05/2024] Open
Abstract
While deep brain stimulation (DBS) is widely employed for managing motor symptoms in Parkinson's disease (PD), its exact circuit mechanisms remain controversial. To identify the neural targets affected by therapeutic DBS in PD, we analyzed DBS-evoked whole brain activity in female hemi-parkinsonian rats using functional magnetic resonance imaging (fMRI). We delivered subthalamic nucleus (STN) DBS at various stimulation pulse repetition rates using optogenetics, allowing unbiased examination of cell-type specific STN feedforward neural activity. Unilateral optogenetic STN DBS elicited pulse repetition rate-dependent alterations of blood-oxygenation-level-dependent (BOLD) signals in SNr (substantia nigra pars reticulata), GP (globus pallidus), and CPu (caudate putamen). Notably, this modulation effectively ameliorated pathological circling behavior in animals expressing the kinetically faster Chronos opsin, but not in animals expressing ChR2. Furthermore, mediation analysis revealed that the pulse repetition rate-dependent behavioral rescue was significantly mediated by optogenetic DBS induced activity changes in GP and CPu, but not in SNr. This suggests that the activation of GP and CPu are critically involved in the therapeutic mechanisms of STN DBS.
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Affiliation(s)
- Yuhui Li
- Department of Biomedical Engineering, USA
| | - Sung-Ho Lee
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA; Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Chunxiu Yu
- Department of Biomedical Engineering, USA
| | - Li-Ming Hsu
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA; Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Tzu-Wen W Wang
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Khoa Do
- Department of Biomedical Engineering, USA
| | - Hyeon-Joong Kim
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA; Department of Neurology, University of North Carolina, Chapel Hill, NC, USA
| | - Yen-Yu Ian Shih
- Center for Animal MRI, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA; Department of Neurology, University of North Carolina, Chapel Hill, NC, USA.
| | - Warren M Grill
- Department of Biomedical Engineering, USA; Department of Electrical and Computer Engineering, USA; Department of Neurobiology, Duke University, Durham, NC, USA; Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA.
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3
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Roy S, Daniels MJ, Roy J. A Bayesian nonparametric approach for multiple mediators with applications in mental health studies. Biostatistics 2024; 25:919-932. [PMID: 38332624 PMCID: PMC11247183 DOI: 10.1093/biostatistics/kxad038] [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: 05/15/2023] [Revised: 12/14/2023] [Accepted: 12/15/2023] [Indexed: 02/10/2024] Open
Abstract
Mediation analysis with contemporaneously observed multiple mediators is a significant area of causal inference. Recent approaches for multiple mediators are often based on parametric models and thus may suffer from model misspecification. Also, much of the existing literature either only allow estimation of the joint mediation effect or estimate the joint mediation effect just as the sum of individual mediator effects, ignoring the interaction among the mediators. In this article, we propose a novel Bayesian nonparametric method that overcomes the two aforementioned drawbacks. We model the joint distribution of the observed data (outcome, mediators, treatment, and confounders) flexibly using an enriched Dirichlet process mixture with three levels. We use standardization (g-computation) to compute all possible mediation effects, including pairwise and all other possible interaction among the mediators. We thoroughly explore our method via simulations and apply our method to a mental health data from Wisconsin Longitudinal Study, where we estimate how the effect of births from unintended pregnancies on later life mental depression (CES-D) among the mothers is mediated through lack of self-acceptance and autonomy, employment instability, lack of social participation, and increased family stress. Our method identified significant individual mediators, along with some significant pairwise effects.
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Affiliation(s)
- Samrat Roy
- Operations and Decision Sciences, Indian Institute of Management Ahmedabad, Gujarat, India
| | | | - Jason Roy
- Department of Biostatistics and Epidemiology, Rutgers University, New Brunswick, USA
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4
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Glover NA, Chaudhari AM. Neuromuscular and trunk control mediate factors associated with injury in fatigued runners. J Biomech 2024; 170:112176. [PMID: 38820995 DOI: 10.1016/j.jbiomech.2024.112176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/20/2024] [Accepted: 05/26/2024] [Indexed: 06/02/2024]
Abstract
This study aimed to determine how fatigue affects factors associated with injury, neuromuscular activity, and control in recreational runners. Previously identified injury risk factors were defined as peak vertical instantaneous loading rates (pVILR) for tibial stress fracture (TSF) and peak hip adduction (pHADD) for patellofemoral pain syndrome and iliotibial band syndrome. Kinematics, kinetics, and electromyography data were collected from 11 recreational runners throughout a fatiguing run. Three trials were collected in the first and final minutes of the run. Coactivation was quantified about the knee and ankle for the entire stance phase and anticipatory, weight acceptance (WA), and propulsion sub-phases of stance. Trunk control was quantified by the peak mediolateral lean, peak forward lean, and flexion range of motion (ROM). There were significant increases in pHADD and pVILR when fatigued. Significant decreases in coactivation around the knee were found over the entire stance phase, in the anticipatory phase, and WA phase. Coactivation decreased about the ankle during WA. Lateral trunk lean significantly increased when fatigued, but no significant changes were found in flexion ROM or lean. Mediation analyses showed changes in ankle coactivation during WA, and lateral trunk lean are significant influences on pVILR, a measure associated with TSF. Fatigue-induced adaptations of decreasing ankle coactivation during WA and increased lateral trunk lean may increase the likelihood of TSF. In this study, a fatiguing run influenced changes in control in recreational runners. Further investigation of causal fatigue-induced injuries is necessary to better understand the effects of coactivation and trunk control.
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Affiliation(s)
- Nelson A Glover
- Department of Bioengineering, George Mason University, Fairfax, VA, United States.
| | - Ajit Mw Chaudhari
- School of Health and Rehabilitation Sciences, The Ohio State University, Columbus, OH, United States
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5
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Li Y, Lee SH, Yu C, Hsu LM, Wang TWW, Do K, Kim HJ, Shih YYI, Grill WM. Optogenetic fMRI reveals therapeutic circuits of subthalamic nucleus deep brain stimulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.22.581627. [PMID: 38464010 PMCID: PMC10925223 DOI: 10.1101/2024.02.22.581627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
While deep brain stimulation (DBS) is widely employed for managing motor symptoms in Parkinson's disease (PD), its exact circuit mechanisms remain controversial. To identify the neural targets affected by therapeutic DBS in PD, we analyzed DBS-evoked whole brain activity in female hemi-parkinsonian rats using function magnetic resonance imaging (fMRI). We delivered subthalamic nucleus (STN) DBS at various stimulation pulse repetition rates using optogenetics, allowing unbiased examinations of cell-type specific STN feed-forward neural activity. Unilateral STN optogenetic stimulation elicited pulse repetition rate-dependent alterations of blood-oxygenation-level-dependent (BOLD) signals in SNr (substantia nigra pars reticulata), GP (globus pallidus), and CPu (caudate putamen). Notably, these manipulations effectively ameliorated pathological circling behavior in animals expressing the kinetically faster Chronos opsin, but not in animals expressing ChR2. Furthermore, mediation analysis revealed that the pulse repetition rate-dependent behavioral rescue was significantly mediated by optogenetically induced activity changes in GP and CPu, but not in SNr. This suggests that the activation of GP and CPu are critically involved in the therapeutic mechanisms of STN DBS.
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6
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Jiang S, Colditz GA. Causal mediation analysis using high-dimensional image mediator bounded in irregular domain with an application to breast cancer. Biometrics 2023; 79:3728-3738. [PMID: 36853975 PMCID: PMC10460830 DOI: 10.1111/biom.13847] [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: 08/22/2022] [Accepted: 02/16/2023] [Indexed: 03/02/2023]
Abstract
Mammography is the primary breast cancer screening strategy. Recent methods have been developed using the mammogram image to improve breast cancer risk prediction. However, it is unclear on the extent to which the effect of risk factors on breast cancer risk is mediated through tissue features summarized in mammogram images and the extent to which it is through other pathways. While mediation analysis has been conducted using mammographic density (a summary measure within the image), the mammogram image is not necessarily well described by a single summary measure and, in addition, such a measure provides no spatial information about the relationship between the exposure risk factor and the risk of breast cancer. Thus, to better understand the role of the mammogram images that provide spatial information about the state of the breast tissue that is causally predictive of the future occurrence of breast cancer, we propose a novel method of causal mediation analysis using mammogram image mediator while accommodating the irregular shape of the breast. We apply the proposed method to data from the Joanne Knight Breast Health Cohort and leverage new insights on the decomposition of the total association between risk factor and breast cancer risk that was mediated by the texture of the underlying breast tissue summarized in the mammogram image.
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Affiliation(s)
- Shu Jiang
- Division of Public Health Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Graham A Colditz
- Division of Public Health Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
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7
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Clark-Boucher D, Zhou X, Du J, Liu Y, Needham BL, Smith JA, Mukherjee B. Methods for mediation analysis with high-dimensional DNA methylation data: Possible choices and comparisons. PLoS Genet 2023; 19:e1011022. [PMID: 37934796 PMCID: PMC10655967 DOI: 10.1371/journal.pgen.1011022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 11/17/2023] [Accepted: 10/18/2023] [Indexed: 11/09/2023] Open
Abstract
Epigenetic researchers often evaluate DNA methylation as a potential mediator of the effect of social/environmental exposures on a health outcome. Modern statistical methods for jointly evaluating many mediators have not been widely adopted. We compare seven methods for high-dimensional mediation analysis with continuous outcomes through both diverse simulations and analysis of DNAm data from a large multi-ethnic cohort in the United States, while providing an R package for their seamless implementation and adoption. Among the considered choices, the best-performing methods for detecting active mediators in simulations are the Bayesian sparse linear mixed model (BSLMM) and high-dimensional mediation analysis (HDMA); while the preferred methods for estimating the global mediation effect are high-dimensional linear mediation analysis (HILMA) and principal component mediation analysis (PCMA). We provide guidelines for epigenetic researchers on choosing the best method in practice and offer suggestions for future methodological development.
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Affiliation(s)
- Dylan Clark-Boucher
- Department of Biostatistics, Harvard T.H. Chan School of Public Health; Boston, Massachusetts, United States of America
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan; Ann Arbor, Michigan, United States of America
| | - Jiacong Du
- Department of Biostatistics, University of Michigan; Ann Arbor, Michigan, United States of America
| | - Yongmei Liu
- Department of Medicine, Divisions of Cardiology and Neurology, Duke University Medical Center; Durham, North Carolina, United States of America
| | - Belinda L. Needham
- Department of Epidemiology, University of Michigan; Ann Arbor, Michigan, United States of America
| | - Jennifer A. Smith
- Department of Epidemiology, University of Michigan; Ann Arbor, Michigan, United States of America
- Survey Research Center, Institute for Social Research, University of Michigan; Ann Arbor, Michigan, United States of America
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan; Ann Arbor, Michigan, United States of America
- Department of Epidemiology, University of Michigan; Ann Arbor, Michigan, United States of America
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8
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Wang JX, Li Y, Reddick WE, Conklin HM, Glass JO, Onar-Thomas A, Gajjar A, Cheng C, Lu ZH. A high-dimensional mediation model for a neuroimaging mediator: Integrating clinical, neuroimaging, and neurocognitive data to mitigate late effects in pediatric cancer. Biometrics 2023; 79:2430-2443. [PMID: 35962595 DOI: 10.1111/biom.13729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Accepted: 07/06/2022] [Indexed: 11/30/2022]
Abstract
Pediatric cancer treatment, especially for brain tumors, can have profound and complicated late effects. With the survival rates increasing because of improved detection and treatment, a more comprehensive understanding of the impact of current treatments on neurocognitive function and brain structure is critically needed. A frontline medulloblastoma clinical trial (SJMB03) has collected data, including treatment, clinical, neuroimaging, and cognitive variables. Advanced methods for modeling and integrating these data are critically needed to understand the mediation pathway from the treatment through brain structure to neurocognitive outcomes. We propose an integrative Bayesian mediation analysis approach to model jointly a treatment exposure, a high-dimensional structural neuroimaging mediator, and a neurocognitive outcome and to uncover the mediation pathway. The high-dimensional imaging-related coefficients are modeled via a binary Ising-Gaussian Markov random field prior (BI-GMRF), addressing the sparsity, spatial dependency, and smoothness and increasing the power to detect brain regions with mediation effects. Numerical simulations demonstrate the estimation accuracy, power, and robustness. For the SJMB03 study, the BI-GMRF method has identified white matter microstructure that is damaged by cancer-directed treatment and impacts late neurocognitive outcomes. The results provide guidance on improving treatment planning to minimize long-term cognitive sequela for pediatric brain tumor patients.
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Affiliation(s)
- Jade Xiaoqing Wang
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Yimei Li
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Wilburn E Reddick
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Heather M Conklin
- Department of Psychology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - John O Glass
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Arzu Onar-Thomas
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Amar Gajjar
- Department of Pediatric Medicine, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Cheng Cheng
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Zhao-Hua Lu
- Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
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9
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Zhao Z, Chen C, Mani Adhikari B, Hong LE, Kochunov P, Chen S. Mediation Analysis for High-Dimensional Mediators and Outcomes with an Application to Multimodal Imaging Data. Comput Stat Data Anal 2023; 185:107765. [PMID: 37251499 PMCID: PMC10210585 DOI: 10.1016/j.csda.2023.107765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Multimodal neuroimaging data have attracted increasing attention for brain research. An integrated analysis of multimodal neuroimaging data and behavioral or clinical measurements provides a promising approach for comprehensively and systematically investigating the underlying neural mechanisms of different phenotypes. However, such an integrated data analysis is intrinsically challenging due to the complex interactive relationships between the multimodal multivariate imaging variables. To address this challenge, a novel multivariate-mediator and multivariate-outcome mediation model (MMO) is proposed to simultaneously extract the latent systematic mediation patterns and estimate the mediation effects based on a dense bi-cluster graph approach. A computationally efficient algorithm is developed for dense bicluster structure estimation and inference to identify the mediation patterns with multiple testing correction. The performance of the proposed method is evaluated by an extensive simulation analysis with comparison to the existing methods. The results show that MMO performs better in terms of both the false discovery rate and sensitivity compared to existing models. The MMO is applied to a multimodal imaging dataset from the Human Connectome Project to investigate the effect of systolic blood pressure on whole-brain imaging measures for the regional homogeneity of the blood oxygenation level-dependent signal through the cerebral blood flow.
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Affiliation(s)
- Zhiwei Zhao
- Department of Mathematics, University of Maryland, 4176 Campus Drive, CollegePark, 20742, MD, USA
| | - Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and PublicHealth, University of Maryland School of Medicine, 655 W. Baltimore, Street, Baltimore, 21201, MD, USA
| | - Bhim Mani Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University ofMaryland School of Medicine, 655 W. Baltimore Street, Baltimore, 21201, MD, USA
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University ofMaryland School of Medicine, 655 W. Baltimore Street, Baltimore, 21201, MD, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University ofMaryland School of Medicine, 655 W. Baltimore Street, Baltimore, 21201, MD, USA
| | - Shuo Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and PublicHealth, University of Maryland School of Medicine, 655 W. Baltimore, Street, Baltimore, 21201, MD, USA
- Maryland Psychiatric Research Center, Department of Psychiatry, University ofMaryland School of Medicine, 655 W. Baltimore Street, Baltimore, 21201, MD, USA
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Bogaerts K, Van Den Houte M, Jongen D, Ly HG, Coppens E, Schruers K, Van Diest I, Jan T, Van Wambeke P, Petre B, Kragel PA, Lindquist MA, Wager TD, Van Oudenhove L, Van den Bergh O. Brain mediators of negative affect-induced physical symptom reporting in patients with functional somatic syndromes. Transl Psychiatry 2023; 13:285. [PMID: 37604880 PMCID: PMC10442365 DOI: 10.1038/s41398-023-02567-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 07/19/2023] [Accepted: 07/20/2023] [Indexed: 08/23/2023] Open
Abstract
Functional somatic syndromes (FSS) include fibromyalgia, irritable bowel syndrome (IBS), and others. In FSS patients, merely viewing negative affective pictures can elicit increased physical symptoms. Our aim was to investigate the neural mechanisms underlying such negative affect-induced physical symptoms in FSS patients. Thirty patients with fibromyalgia and/or IBS and 30 healthy controls (all women) watched neutral, positive and negative affective picture blocks during functional MRI scanning and rated negative affect and physical symptoms after every block. We compared brain-wide activation during negative versus neutral picture viewing in FSS patients versus controls using robust general linear model analysis. Further, we compared neurologic pain signature (NPS), stimulus intensity-independent pain signature (SIIPS) and picture-induced negative emotion signature (PINES) responses to the negative versus neutral affect contrast and investigated whether they mediated between-group differences in affective picture-induced physical symptom reporting. More physical symptoms were reported after viewing negative compared to neutral pictures, and this effect was larger in patients than controls (p = 0.025). Accordingly, patients showed stronger activation in somatosensory regions during negative versus neutral picture viewing. NPS, but not SIIPS nor PINES, responses were higher in patients than controls during negative versus neutral pictures (p = 0.026). These differential NPS responses partially mediated between-group differences in physical symptoms. In conclusion, picture-induced negative affect elicits physical symptoms in FSS patients as a result of activation of somatosensory and nociceptive brain patterns, supporting the idea that affect-driven alterations in processing of somatic signals is a critical mechanism underlying FSS.
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Affiliation(s)
- Katleen Bogaerts
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium.
- Health Psychology, Psychology and Educational Sciences, University of Leuven, Leuven, Belgium.
| | - Maaike Van Den Houte
- REVAL - Rehabilitation Research Center, Faculty of Rehabilitation Sciences, Hasselt University, Diepenbeek, Belgium
- Laboratory for Brain-Gut Axis Studies (LaBGAS), Translational Research Center for Gastrointestinal Disorders (TARGID), University of Leuven, Leuven, Belgium
- Leuven Brain Institute, University of Leuven, Leuven, Belgium
| | - Daniëlle Jongen
- Laboratory for Brain-Gut Axis Studies (LaBGAS), Translational Research Center for Gastrointestinal Disorders (TARGID), University of Leuven, Leuven, Belgium
- Leuven Brain Institute, University of Leuven, Leuven, Belgium
| | - Huynh Giao Ly
- Laboratory for Brain-Gut Axis Studies (LaBGAS), Translational Research Center for Gastrointestinal Disorders (TARGID), University of Leuven, Leuven, Belgium
| | - Eline Coppens
- University Psychiatric Center KU Leuven, University Hospitals Leuven, Leuven, Belgium
| | - Koen Schruers
- MHeNS School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Ilse Van Diest
- Health Psychology, Psychology and Educational Sciences, University of Leuven, Leuven, Belgium
| | - Tack Jan
- GI motility and sensitivity research group, Translational Research Center for Gastrointestinal Disorders (TARGID), University of Leuven, Leuven, Belgium
| | - Peter Van Wambeke
- Department of Physical and Rehabilitation Medicine, University Hospitals Leuven, Leuven, Belgium
| | - Bogdan Petre
- Cognitive & Affective Neuroscience Lab (CANLab), Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Philip A Kragel
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Martin A Lindquist
- Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Tor D Wager
- Cognitive & Affective Neuroscience Lab (CANLab), Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Lukas Van Oudenhove
- Laboratory for Brain-Gut Axis Studies (LaBGAS), Translational Research Center for Gastrointestinal Disorders (TARGID), University of Leuven, Leuven, Belgium
- Leuven Brain Institute, University of Leuven, Leuven, Belgium
- Cognitive & Affective Neuroscience Lab (CANLab), Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Omer Van den Bergh
- Health Psychology, Psychology and Educational Sciences, University of Leuven, Leuven, Belgium
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11
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Müller S, Sicorello M, Moser D, Frach L, Limberg A, Gumpp AM, Ramo-Fernandez L, Köhler-Dauner F, Fegert JM, Waller C, Kumsta R, Kolassa IT. The DNA methylation landscape of the human oxytocin receptor gene (OXTR): data-driven clusters and their relation to gene expression and childhood adversity. Transl Psychiatry 2023; 13:265. [PMID: 37479681 PMCID: PMC10362059 DOI: 10.1038/s41398-023-02548-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 06/23/2023] [Accepted: 06/27/2023] [Indexed: 07/23/2023] Open
Abstract
The oxytocin receptor gene (OXTR) is of interest when investigating the effects of early adversity on DNA methylation. However, there is heterogeneity regarding the selection of the most promising CpG sites to target for analyses. The goal of this study was to determine functionally relevant clusters of CpG sites within the OXTR CpG island in 113 mother-infant dyads, with 58 of the mothers reporting childhood maltreatment (CM). OXTR DNA methylation was analyzed in peripheral/umbilical blood mononuclear cells. Different complexity reduction approaches were used to reduce the 188 CpG sites into clusters of co-methylated sites. Furthermore, associations between OXTR DNA methylation (cluster- and site-specific level) and OXTR gene expression and CM were investigated in mothers. Results showed that, first, CpG sections differed strongly regarding their statistical utility for research of individual differences in DNA methylation. Second, cluster analyses and Partial Least Squares (PLS) suggested two clusters consisting of intron1/exon2 and the protein-coding region of exon3, respectively, as most strongly associated with outcome measures. Third, cross-validated PLS regression explained 7% of variance in CM, with low cross-validated variance explained for the prediction of gene expression. Fourth, substantial mother-child correspondence was observed in correlation patterns within the identified clusters, but only modest correspondence outside these clusters. This study makes an important contribution to the mapping of the DNA methylation landscape of the OXTR CpG island by highlighting clusters of CpG sites that show desirable statistical properties and predictive value. We provide a Companion Web Application to facilitate the choice of CpG sites.
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Affiliation(s)
- Svenja Müller
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
- Department of Clinical & Biological Psychology, Institute of Psychology and Education, Ulm University, 89081, Ulm, Germany
| | - Maurizio Sicorello
- Department of Psychosomatic Medicine and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, 68159, Mannheim, Germany
| | - Dirk Moser
- Department of Genetic Psychology, Faculty of Psychology, Ruhr Universität Bochum, 44801, Bochum, Germany
| | - Leonard Frach
- Department of Clinical, Educational and Health Psychology, Division of Psychology and Language Sciences, University College London, London, WC1H 0AP, United Kingdom
| | - Alicia Limberg
- Department of Genetic Psychology, Faculty of Psychology, Ruhr Universität Bochum, 44801, Bochum, Germany
| | - Anja M Gumpp
- Department of Clinical & Biological Psychology, Institute of Psychology and Education, Ulm University, 89081, Ulm, Germany
| | - Laura Ramo-Fernandez
- Department of Clinical & Biological Psychology, Institute of Psychology and Education, Ulm University, 89081, Ulm, Germany
| | - Franziska Köhler-Dauner
- Department of Child and Adolescent Psychiatry/Psychotherapy, University Hospital Ulm, 89075, Ulm, Germany
| | - Jörg M Fegert
- Department of Child and Adolescent Psychiatry/Psychotherapy, University Hospital Ulm, 89075, Ulm, Germany
| | - Christiane Waller
- Department of Psychosomatic Medicine and Psychotherapy, University Hospital Ulm, 89075, Ulm, Germany
- Department of Psychosomatics and Psychotherapeutic Medicine, Paracelsus Medical Private University of Nueremberg, 90419, Nueremberg, Germany
| | - Robert Kumsta
- Department of Genetic Psychology, Faculty of Psychology, Ruhr Universität Bochum, 44801, Bochum, Germany.
- Department of Behavioural and Cognitive Sciences, Laboratory for Stress and Gene-Environment Interplay, University of Luxemburg, Porte des Sciences L-4366, Esch-sur-Alzette, Luxemburg.
| | - Iris-Tatjana Kolassa
- Department of Clinical & Biological Psychology, Institute of Psychology and Education, Ulm University, 89081, Ulm, Germany.
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12
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Chen F, Mihaljevic M, Hou Z, Li Y, Lu H, Mori S, Sawa A, Faria AV. Relation between white matter integrity, perfusion, and processing speed in early-stage schizophrenia. J Psychiatr Res 2023; 163:166-171. [PMID: 37210835 DOI: 10.1016/j.jpsychires.2023.05.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 05/10/2023] [Accepted: 05/15/2023] [Indexed: 05/23/2023]
Abstract
OBJECTIVE Cerebral blood flow (CBF) plays a critical role in the maintenance of neuronal integrity, and CBF alterations have been linked to deleterious white matter changes. Several studies report CBF and white matter structural alterations individually. However, whether and how these pathological changes relate to each other remains elusive. By using our cohort of individuals with early-stage schizophrenia, we investigated the relationship between CBF and white matter structure. METHOD We studied 51 early-stage schizophrenia patients and age- and sex-matched healthy controls. We investigated the relationship among tissue structure (assessed with diffusion weighted imaging), perfusion (accessed by pseudo-continuous arterial labeling imaging), and neuropsychological indices (focusing on processing speed). We focused on the corpus callosum, due to its major role in associative functions and directness on revealing the architecture of a major white matter bundle. We performed mediation analysis to identify the possible mechanism underlay the relationship among cognition and white matter integrity and perfusion. RESULTS The CBF and the fractional anisotropy (FA) were inversely correlated in the corpus callosum of early-stage schizophrenia patients. While CBF negatively correlated with processing speed, FA correlated positively with this cognitive measure. These results were not observed in controls. Mediation analysis revealed that the effect of FA on processing speed was mediated via the CBF. CONCLUSIONS We provide evidence of a relationship between brain perfusion and white matter integrity in the corpus callosum in early-stage schizophrenia. These findings may shed the light on underlying metabolic support for structural changes with cognitive impact in schizophrenia.
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Affiliation(s)
- Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, 570311, China
| | - Marina Mihaljevic
- Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Zhipeng Hou
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Yang Li
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), Haikou, Hainan, 570311, China
| | - Hanzhang Lu
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Susumu Mori
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Akira Sawa
- Department of Neuroscience, The Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Psychiatry, School of Medicine, Johns Hopkins University, Baltimore, MD, USA; Department of Biomedical Engineering, The Whiting School of Engineering, Baltimore, MD, USA; Department of Mental Health, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Andreia V Faria
- Department of Radiology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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13
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Setti F, Handjaras G, Bottari D, Leo A, Diano M, Bruno V, Tinti C, Cecchetti L, Garbarini F, Pietrini P, Ricciardi E. A modality-independent proto-organization of human multisensory areas. Nat Hum Behav 2023; 7:397-410. [PMID: 36646839 PMCID: PMC10038796 DOI: 10.1038/s41562-022-01507-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 12/05/2022] [Indexed: 01/18/2023]
Abstract
The processing of multisensory information is based upon the capacity of brain regions, such as the superior temporal cortex, to combine information across modalities. However, it is still unclear whether the representation of coherent auditory and visual events requires any prior audiovisual experience to develop and function. Here we measured brain synchronization during the presentation of an audiovisual, audio-only or video-only version of the same narrative in distinct groups of sensory-deprived (congenitally blind and deaf) and typically developed individuals. Intersubject correlation analysis revealed that the superior temporal cortex was synchronized across auditory and visual conditions, even in sensory-deprived individuals who lack any audiovisual experience. This synchronization was primarily mediated by low-level perceptual features, and relied on a similar modality-independent topographical organization of slow temporal dynamics. The human superior temporal cortex is naturally endowed with a functional scaffolding to yield a common representation across multisensory events.
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Affiliation(s)
- Francesca Setti
- MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | | | - Davide Bottari
- MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | - Andrea Leo
- Department of Translational Research and Advanced Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy
| | - Matteo Diano
- Department of Psychology, University of Turin, Turin, Italy
| | - Valentina Bruno
- Manibus Lab, Department of Psychology, University of Turin, Turin, Italy
| | - Carla Tinti
- Department of Psychology, University of Turin, Turin, Italy
| | - Luca Cecchetti
- MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
| | | | - Pietro Pietrini
- MoMiLab, IMT School for Advanced Studies Lucca, Lucca, Italy
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14
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Du J, Zhou X, Clark-Boucher D, Hao W, Liu Y, Smith JA, Mukherjee B. Methods for large-scale single mediator hypothesis testing: Possible choices and comparisons. Genet Epidemiol 2023; 47:167-184. [PMID: 36465006 PMCID: PMC10329872 DOI: 10.1002/gepi.22510] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 09/30/2022] [Accepted: 11/11/2022] [Indexed: 12/12/2022]
Abstract
Mediation hypothesis testing for a large number of mediators is challenging due to the composite structure of the null hypothesis,H 0 : α β = 0 ${H}_{0}:\alpha \beta =0$ (α $\alpha $ : effect of the exposure on the mediator after adjusting for confounders;β $\beta $ : effect of the mediator on the outcome after adjusting for exposure and confounders). In this paper, we reviewed three classes of methods for large-scale one at a time mediation hypothesis testing. These methods are commonly used for continuous outcomes and continuous mediators assuming there is no exposure-mediator interaction so that the productα β $\alpha \beta $ has a causal interpretation as the indirect effect. The first class of methods ignores the impact of different structures under the composite null hypothesis, namely, (1)α = 0 , β ≠ 0 $\alpha =0,\beta \ne 0$ ; (2)α ≠ 0 , β = 0 $\alpha \ne 0,\beta =0$ ; and (3)α = β = 0 $\alpha =\beta =0$ . The second class of methods weights the reference distribution under each case of the null to form a mixture reference distribution. The third class constructs a composite test statistic using the three p values obtained under each case of the null so that the reference distribution of the composite statistic is approximatelyU ( 0 , 1 ) $U(0,1)$ . In addition to these existing methods, we developed the Sobel-comp method belonging to the second class, which uses a corrected mixture reference distribution for Sobel's test statistic. We performed extensive simulation studies to compare all six methods belonging to these three classes in terms of the false positive rates (FPRs) under the null hypothesis and the true positive rates under the alternative hypothesis. We found that the second class of methods which uses a mixture reference distribution could best maintain the FPRs at the nominal level under the null hypothesis and had the greatest true positive rates under the alternative hypothesis. We applied all methods to study the mediation mechanism of DNA methylation sites in the pathway from adult socioeconomic status to glycated hemoglobin level using data from the Multi-Ethnic Study of Atherosclerosis (MESA). We provide guidelines for choosing the optimal mediation hypothesis testing method in practice and develop an R package medScan available on the CRAN for implementing all the six methods.
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Affiliation(s)
- Jiacong Du
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Dylan Clark-Boucher
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Wei Hao
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
| | - Yongmei Liu
- Department of Medicine, Divisions of Cardiology and Neurology, Duke University Medical Center, Durham, North Carolina, USA
| | - Jennifer A Smith
- Department of Epidemiology, University of Michigan, Ann Arbor, Michigan, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA
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15
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Nath T, Caffo B, Wager T, Lindquist MA. A machine learning based approach towards high-dimensional mediation analysis. Neuroimage 2023; 268:119843. [PMID: 36586543 PMCID: PMC10332048 DOI: 10.1016/j.neuroimage.2022.119843] [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: 10/10/2022] [Revised: 12/02/2022] [Accepted: 12/27/2022] [Indexed: 12/30/2022] Open
Abstract
Mediation analysis is used to investigate the role of intermediate variables (mediators) that lie in the path between an exposure and an outcome variable. While significant research has focused on developing methods for assessing the influence of mediators on the exposure-outcome relationship, current approaches do not easily extend to settings where the mediator is high-dimensional. These situations are becoming increasingly common with the rapid increase of new applications measuring massive numbers of variables, including brain imaging, genomics, and metabolomics. In this work, we introduce a novel machine learning based method for identifying high dimensional mediators. The proposed algorithm iterates between using a machine learning model to map the high-dimensional mediators onto a lower-dimensional space, and using the predicted values as input in a standard three-variable mediation model. Hence, the machine learning model is trained to maximize the likelihood of the mediation model. Importantly, the proposed algorithm is agnostic to the machine learning model that is used, providing significant flexibility in the types of situations where it can be used. We illustrate the proposed methodology using data from two functional Magnetic Resonance Imaging (fMRI) studies. First, using data from a task-based fMRI study of thermal pain, we combine the proposed algorithm with a deep learning model to detect distributed, network-level brain patterns mediating the relationship between stimulus intensity (temperature) and reported pain at the single trial level. Second, using resting-state fMRI data from the Human Connectome Project, we combine the proposed algorithm with a connectome-based predictive modeling approach to determine brain functional connectivity measures that mediate the relationship between fluid intelligence and working memory accuracy. In both cases, our multivariate mediation model links exposure variables (thermal pain or fluid intelligence), high dimensional brain measures (single-trial brain activation maps or resting-state brain connectivity) and behavioral outcomes (pain report or working memory accuracy) into a single unified model. Using the proposed approach, we are able to identify brain-based measures that simultaneously encode the exposure variable and correlate with the behavioral outcome.
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Affiliation(s)
- Tanmay Nath
- The Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
| | - Brian Caffo
- The Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
| | - Tor Wager
- The Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Martin A Lindquist
- The Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
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16
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Clark-Boucher D, Zhou X, Du J, Liu Y, Needham BL, Smith JA, Mukherjee B. Methods for Mediation Analysis with High-Dimensional DNA Methylation Data: Possible Choices and Comparison. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.10.23285764. [PMID: 36824903 PMCID: PMC9949196 DOI: 10.1101/2023.02.10.23285764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
Epigenetic researchers often evaluate DNA methylation as a mediator between social/environmental exposures and disease, but modern statistical methods for jointly evaluating many mediators have not been widely adopted. We compare seven methods for high-dimensional mediation analysis with continuous outcomes through both diverse simulations and analysis of DNAm data from a large national cohort in the United States, while providing an R package for their implementation. Among the considered choices, the best-performing methods for detecting active mediators in simulations are the Bayesian sparse linear mixed model by Song et al. (2020) and high-dimensional mediation analysis by Gao et al. (2019); while the superior methods for estimating the global mediation effect are high-dimensional linear mediation analysis by Zhou et al. (2021) and principal component mediation analysis by Huang and Pan (2016). We provide guidelines for epigenetic researchers on choosing the best method in practice and offer suggestions for future methodological development.
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Affiliation(s)
- Dylan Clark-Boucher
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI
| | - Jiacong Du
- Department of Biostatistics, University of Michigan, Ann Arbor, MI
| | - Yongmei Liu
- Department of Medicine, Divisions of Cardiology and Neurology, Duke University Medical Center, Durham, NC
| | | | - Jennifer A Smith
- Department of Epidemiology, University of Michigan, Ann Arbor, MI
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, MI
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan, Ann Arbor, MI
- Department of Epidemiology, University of Michigan, Ann Arbor, MI
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17
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Murtha K, Larsen B, Pines A, Parkes L, Moore TM, Adebimpe A, Bertolero M, Alexander-Bloch A, Calkins ME, Davila DG, Lindquist MA, Mackey AP, Roalf DR, Scott JC, Wolf DH, Gur RC, Gur RE, Barzilay R, Satterthwaite TD. Associations between neighborhood socioeconomic status, parental education, and executive system activation in youth. Cereb Cortex 2023; 33:1058-1073. [PMID: 35348659 PMCID: PMC9930626 DOI: 10.1093/cercor/bhac120] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 02/16/2022] [Accepted: 02/17/2022] [Indexed: 11/13/2022] Open
Abstract
Socioeconomic status (SES) can impact cognitive performance, including working memory (WM). As executive systems that support WM undergo functional neurodevelopment during adolescence, environmental stressors at both individual and community levels may influence cognitive outcomes. Here, we sought to examine how SES at the neighborhood and family level impacts task-related activation of the executive system during adolescence and determine whether this effect mediates the relationship between SES and WM performance. To address these questions, we studied 1,150 youths (age 8-23) that completed a fractal n-back WM task during functional magnetic resonance imaging at 3T as part of the Philadelphia Neurodevelopmental Cohort. We found that both higher neighborhood SES and parental education were associated with greater activation of the executive system to WM load, including the bilateral dorsolateral prefrontal cortex, posterior parietal cortex, and precuneus. The association of neighborhood SES remained significant when controlling for task performance, or related factors like exposure to traumatic events. Furthermore, high-dimensional multivariate mediation analysis identified distinct patterns of brain activity within the executive system that significantly mediated the relationship between measures of SES and task performance. These findings underscore the importance of multilevel environmental factors in shaping executive system function and WM in youth.
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Affiliation(s)
- Kristin Murtha
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Adam Pines
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Linden Parkes
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering & Applied Science, University of Philadelphia, Philadelphia, PA 19104, USA
| | - Tyler M Moore
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Azeez Adebimpe
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Maxwell Bertolero
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Aaron Alexander-Bloch
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Monica E Calkins
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Diego G Davila
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Martin A Lindquist
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Allyson P Mackey
- Department of Psychology, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - David R Roalf
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - James C Scott
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Daniel H Wolf
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ruben C Gur
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Ran Barzilay
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Neurodevelopment and Psychosis Section, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children's Hospital of Philadelphia and Perleman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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18
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Roberts EK, Boss J, Mukherjee B, Salerno S, Zota A, Needham BL. Persistent organic pollutant exposure contributes to Black/White differences in leukocyte telomere length in the National Health and Nutrition Examination Survey. Sci Rep 2022; 12:19960. [PMID: 36402910 PMCID: PMC9675834 DOI: 10.1038/s41598-022-24316-0] [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: 06/22/2022] [Accepted: 11/14/2022] [Indexed: 11/21/2022] Open
Abstract
Despite racial disparities in diseases of aging and premature mortality, non-Hispanic Black Americans tend to have longer leukocyte telomere length (LTL), a biomarker of cellular aging, than non-Hispanic White Americans. Previous findings suggest that exposure to certain persistent organic pollutants (POPs) is both racially-patterned and associated with longer LTL. We examine whether Black/White differences in LTL are explained by differences in exposure to 15 POPs by estimating the indirect effect (IE) of self-reported race on LTL that is mediated through nine polychlorinated biphenyls (PCBs), three furans, and three dioxins, as well as their mixtures. Our study population includes 1,251 adults from the 1999-2000 and 2001-2002 cycles of the cross-sectional National Health and Nutrition Examination Survey. We characterized single-pollutant mediation effects by constructing survey-weighted linear regression models. We also implemented various approaches to quantify a global mediation effect of all POPs, including unpenalized linear regression, ridge regression, and examination of three summary exposure scores. We found support for the hypothesis that exposure to PCBs partially mediates Black/White differences in LTL. In single-pollutant models, there were significant IEs of race on LTL through six individual PCBs (118, 138, 153, 170, 180, and 187). Ridge regression (0.013, CI 0.001, 0.023; 26.0% mediated) and models examining summative exposure scores with linear combinations derived from principal components analysis (0.019, CI 0.009, 0.029; 34.8% mediated) and Toxic Equivalency Quotient (TEQ) scores (0.016, CI 0.005, 0.026; 28.8% mediated) showed significant IEs when incorporating survey weights. Exposures to individual POPs and their mixtures, which may arise from residential and occupational segregation, may help explain why Black Americans have longer LTL than their White counterparts, providing an environmental explanation for counterintuitive race differences in cellular aging.
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Affiliation(s)
- Emily K Roberts
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109-2029, USA
| | - Jonathan Boss
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109-2029, USA
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109-2029, USA
- Department of Epidemiology and Center for Social Epidemiology and Population Health, University of Michigan, 1415 Washington Heights, 4659 SPH Tower, Ann Arbor, MI, 48109-2029, USA
| | - Stephen Salerno
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109-2029, USA
| | - Ami Zota
- Department of Environmental and Occupational Health, George Washington University Milken School of Public Health, Washington, USA
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, USA
| | - Belinda L Needham
- Department of Epidemiology and Center for Social Epidemiology and Population Health, University of Michigan, 1415 Washington Heights, 4659 SPH Tower, Ann Arbor, MI, 48109-2029, USA.
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19
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Cerebral blood flow and cardiovascular risk effects on resting brain regional homogeneity. Neuroimage 2022; 262:119555. [PMID: 35963506 PMCID: PMC10044499 DOI: 10.1016/j.neuroimage.2022.119555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 08/01/2022] [Accepted: 08/09/2022] [Indexed: 11/22/2022] Open
Abstract
Regional homogeneity (ReHo) is a measure of local functional brain connectivity that has been reported to be altered in a wide range of neuropsychiatric disorders. Computed from brain resting-state functional MRI time series, ReHo is also sensitive to fluctuations in cerebral blood flow (CBF) that in turn may be influenced by cerebrovascular health. We accessed cerebrovascular health with Framingham cardiovascular risk score (FCVRS). We hypothesize that ReHo signal may be influenced by regional CBF; and that these associations can be summarized as FCVRS→CBF→ReHo. We used three independent samples to test this hypothesis. A test-retest sample of N = 30 healthy volunteers was used for test-retest evaluation of CBF effects on ReHo. Amish Connectome Project (ACP) sample (N = 204, healthy individuals) was used to evaluate association between FCVRS and ReHo and testing if the association diminishes given CBF. The UKBB sample (N = 6,285, healthy participants) was used to replicate the effects of FCVRS on ReHo. We observed strong CBF→ReHo links (p<2.5 × 10-3) using a three-point longitudinal sample. In ACP sample, marginal and partial correlations analyses demonstrated that both CBF and FCVRS were significantly correlated with the whole-brain average (p<10-6) and regional ReHo values, with the strongest correlations observed in frontal, parietal, and temporal areas. Yet, the association between ReHo and FCVRS became insignificant once the effect of CBF was accounted for. In contrast, CBF→ReHo remained significantly linked after adjusting for FCVRS and demographic covariates (p<10-6). Analysis in N = 6,285 replicated the FCVRS→ReHo effect (p = 2.7 × 10-27). In summary, ReHo alterations in health and neuropsychiatric illnesses may be partially driven by region-specific variability in CBF, which is, in turn, influenced by cardiovascular factors.
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20
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High-dimensional causal mediation analysis based on partial linear structural equation models. Comput Stat Data Anal 2022. [DOI: 10.1016/j.csda.2022.107501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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21
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Zhao Y, Chen T, Cai J, Lichenstein S, Potenza MN, Yip SW. Bayesian network mediation analysis with application to the brain functional connectome. Stat Med 2022; 41:3991-4005. [PMID: 35795965 PMCID: PMC10131252 DOI: 10.1002/sim.9488] [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: 07/20/2021] [Revised: 04/12/2022] [Accepted: 05/18/2022] [Indexed: 11/10/2022]
Abstract
The brain functional connectome, the collection of interconnected neural circuits along functional networks, facilitates a cutting-edge understanding of brain functioning, and has a potential to play a mediating role within the effect pathway between an exposure and an outcome. While existing mediation analytic approaches are capable of providing insight into complex processes, they mainly focus on a univariate mediator or mediator vector, without considering network-variate mediators. To fill the methodological gap and accomplish this exciting and urgent application, in the article, we propose an integrative mediation analysis under a Bayesian paradigm with networks entailing the mediation effect. To parameterize the network measurements, we introduce individually specified stochastic block models with unknown block allocation, and naturally bridge effect elements through the latent network mediators induced by the connectivity weights across network modules. To enable the identification of truly active mediating components, we simultaneously impose a feature selection across network mediators. We show the superiority of our model in estimating different effect components and selecting active mediating network structures. As a practical illustration of this approach's application to network neuroscience, we characterize the relationship between a therapeutic intervention and opioid abstinence as mediated by brain functional sub-networks.
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Affiliation(s)
- Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Yale Center for Analytical Sciences, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Tianqi Chen
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Jiachen Cai
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Sarah Lichenstein
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Marc N Potenza
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut, USA
- Connecticut Mental Health Center, New Haven, Connecticut, USA
- Connecticut Council on Problem Gambling, Wethersfield, Connecticut, USA
- Wu Tsai Institute, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Sarah W Yip
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut, USA
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22
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Krimmel SR, Keaser ML, Speis D, Haythornthwaite JA, Seminowicz DA. Migraine disability, pain catastrophizing, and headache severity are associated with evoked pain and targeted by mind-body therapy. Pain 2022; 163:e1030-e1037. [PMID: 35297801 PMCID: PMC9288557 DOI: 10.1097/j.pain.0000000000002578] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 12/22/2021] [Indexed: 11/25/2022]
Abstract
ABSTRACT Meta-analysis suggests that migraine patients are no more sensitive to experimentally evoked pain than healthy control subjects. At the same time, studies have linked some migraine symptoms to quantitative sensory testing (QST) profiles. Unfortunately, previous studies associating migraine symptoms and QST have important methodological shortcomings, stemming from small sample sizes, and frequent use of univariate statistics for multivariate research questions. In the current study, we seek to address these limitations by using a large sample of episodic migraine patients (n = 103) and a multivariate analysis that associates pain ratings from many thermal intensities simultaneously with 12 clinical measures ranging from headache frequency to sleep abnormalities. We identified a single dimension of association between thermal QST and migraine symptoms that relates to pain ratings for all stimulus intensities and a subset of migraine symptoms relating to disability (Headache Impact Test 6 and Brief Pain Inventory interference), catastrophizing (Pain Catastrophizing Scale), and pain severity (average headache pain, Brief Pain Inventory severity, and Short-Form McGill Pain Questionnaire 2). Headache frequency, allodynia, affect, and sleep disturbances were unrelated to this dimension. Consistent with previous research, we did not observe any difference in QST ratings between migraine patients and healthy control subjects. Additionally, we found that the linear combination of symptoms related to QST was modified by the mind-body therapy enhanced mindfulness-based stress reduction (MBSR+). These results suggest that QST has a selective relationship with pain symptoms even in the absence of between-subjects differences between chronic pain patients and healthy control subjects.
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Affiliation(s)
- Samuel R. Krimmel
- Department of Neural and Pain Sciences, School of
Dentistry, and Center to Advance Chronic Pain Research, University of Maryland,
Baltimore, MD, 21201, USA
- Program in Neuroscience, University of Maryland School of
Medicine, Baltimore, MD, 21201 USA
| | - Michael L. Keaser
- Department of Neural and Pain Sciences, School of
Dentistry, and Center to Advance Chronic Pain Research, University of Maryland,
Baltimore, MD, 21201, USA
| | - Darrah Speis
- Department of Neural and Pain Sciences, School of
Dentistry, and Center to Advance Chronic Pain Research, University of Maryland,
Baltimore, MD, 21201, USA
| | - Jennifer A. Haythornthwaite
- Department of Psychiatry and Behavioral Sciences, Johns
Hopkins University School of Medicine, Baltimore, MD, USA
| | - David A. Seminowicz
- Department of Neural and Pain Sciences, School of
Dentistry, and Center to Advance Chronic Pain Research, University of Maryland,
Baltimore, MD, 21201, USA
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23
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Djordjilović V, Hemerik J, Thoresen M. On optimal two-stage testing of multiple mediators. Biom J 2022; 64:1090-1108. [PMID: 35426161 PMCID: PMC9544827 DOI: 10.1002/bimj.202100190] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 10/18/2021] [Accepted: 11/28/2021] [Indexed: 11/27/2022]
Abstract
Mediation analysis in high-dimensional settings often involves identifying potential mediators among a large number of measured variables. For this purpose, a two-step familywise error rate procedure called ScreenMin has been recently proposed. In ScreenMin, variables are first screened and only those that pass the screening are tested. The proposed data-independent threshold for selection has been shown to guarantee asymptotic familywise error rate. In this work, we investigate the impact of the threshold on the finite-sample familywise error rate. We derive a power maximizing threshold and show that it is well approximated by an adaptive threshold of Wang et al. (2016, arXiv preprint arXiv:1610.03330). We illustrate the investigated procedures on a case-control study examining the effect of fish intake on the risk of colorectal adenoma. We also apply our procedure in the context of replicability analysis to identify single nucleotide polymorphisms (SNP) associated with crop yield in two distinct environments.
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Affiliation(s)
- Vera Djordjilović
- Department of EconomicsCa' Foscari University of VeniceDorsoduroVeniceItaly
| | - Jesse Hemerik
- BiometrisWageningen University & ResearchWageningenThe Netherlands
| | - Magne Thoresen
- Oslo Centre for Biostatistics and EpidemiologyDepartment of BiostatisticsUniversity of OsloBlindernOsloNorway
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24
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Perera C, Zhang H, Zheng Y, Hou L, Qu A, Zheng C, Xie K, Liu L. HIMA2: high-dimensional mediation analysis and its application in epigenome-wide DNA methylation data. BMC Bioinformatics 2022; 23:296. [PMID: 35879655 PMCID: PMC9310002 DOI: 10.1186/s12859-022-04748-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 05/23/2022] [Indexed: 11/28/2022] Open
Abstract
Mediation analysis plays a major role in identifying significant mediators in the pathway between environmental exposures and health outcomes. With advanced data collection technology for large-scale studies, there has been growing research interest in developing methodology for high-dimensional mediation analysis. In this paper we present HIMA2, an extension of the HIMA method (Zhang in Bioinformatics 32:3150-3154, 2016). First, the proposed HIMA2 reduces the dimension of mediators to a manageable level based on the sure independence screening (SIS) method (Fan in J R Stat Soc Ser B 70:849-911, 2008). Second, a de-biased Lasso procedure is implemented for estimating regression parameters. Third, we use a multiple-testing procedure to accurately control the false discovery rate (FDR) when testing high-dimensional mediation hypotheses. We demonstrate its practical performance using Monte Carlo simulation studies and apply our method to identify DNA methylation markers which mediate the pathway from smoking to reduced lung function in the Coronary Artery Risk Development in Young Adults (CARDIA) Study.
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Affiliation(s)
- Chamila Perera
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Haixiang Zhang
- Center for Applied Mathematics, Tianjin University, Tianjin, 300072, China
| | - Yinan Zheng
- Department of Preventive Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Annie Qu
- Department of Statistics, University of California, Irvine, CA, 92697, USA
| | - Cheng Zheng
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Ke Xie
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, 63110, USA
| | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, 63110, USA.
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25
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Ghosh D, Mastej E, Jain R, Choi YS. Causal Inference in Radiomics: Framework, Mechanisms, and Algorithms. Front Neurosci 2022; 16:884708. [PMID: 35812228 PMCID: PMC9261933 DOI: 10.3389/fnins.2022.884708] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Accepted: 05/20/2022] [Indexed: 12/30/2022] Open
Abstract
The widespread use of machine learning algorithms in radiomics has led to a proliferation of flexible prognostic models for clinical outcomes. However, a limitation of these techniques is their black-box nature, which prevents the ability for increased mechanistic phenomenological understanding. In this article, we develop an inferential framework for estimating causal effects with radiomics data. A new challenge is that the exposure of interest is latent so that new estimation procedures are needed. We leverage a multivariate version of partial least squares for causal effect estimation. The methodology is illustrated with applications to two radiomics datasets, one in osteosarcoma and one in glioblastoma.
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Affiliation(s)
- Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, United States
- *Correspondence: Debashis Ghosh
| | - Emily Mastej
- Computational Biosciences Program, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Rajan Jain
- Department of Radiology and Neurosurgery, New York University Langone Medical Center, New York, NY, United States
| | - Yoon Seong Choi
- Department of Radiology, Yonsei University College of Medicine, Seoul, South Korea
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26
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Zhao Y, Li L. Multimodal data integration via mediation analysis with high-dimensional exposures and mediators. Hum Brain Mapp 2022; 43:2519-2533. [PMID: 35129252 PMCID: PMC9057105 DOI: 10.1002/hbm.25800] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/06/2022] [Accepted: 01/23/2022] [Indexed: 12/28/2022] Open
Abstract
Motivated by an imaging proteomics study for Alzheimer's disease (AD), in this article, we propose a mediation analysis approach with high-dimensional exposures and high-dimensional mediators to integrate data collected from multiple platforms. The proposed method combines principal component analysis with penalized least squares estimation for a set of linear structural equation models. The former reduces the dimensionality and produces uncorrelated linear combinations of the exposure variables, whereas the latter achieves simultaneous path selection and effect estimation while allowing the mediators to be correlated. Applying the method to the AD data identifies numerous interesting protein peptides, brain regions, and protein-structure-memory paths, which are in accordance with and also supplement existing findings of AD research. Additional simulations further demonstrate the effective empirical performance of the method.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data ScienceIndiana University School of MedicineIndianapolisIndianaUSA
| | - Lexin Li
- Department of Biostatistics and EpidemiologyUniversity of California, BerkeleyBerkeleyCaliforniaUSA
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27
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Wu F, Cai J, Wen C, Tan H. Co-sparse Non-negative Matrix Factorization. Front Neurosci 2022; 15:804554. [PMID: 35095402 PMCID: PMC8790575 DOI: 10.3389/fnins.2021.804554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 11/29/2021] [Indexed: 02/05/2023] Open
Abstract
Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible interpretability with non-negativity property. Nowadays, especially in the neuroimaging field, it is common to have at least thousands of voxels while the sample size is only hundreds. The non-negative matrix factorization encounters both computational and theoretical challenge with such high-dimensional data, i.e., there is no guarantee for a sparse and part-based representation of data. To this end, we introduce a co-sparse non-negative matrix factorization method to high-dimensional data by simultaneously imposing sparsity in both two decomposed matrices. Instead of adding some sparsity induced penalty such as l 1 norm, the proposed method directly controls the number of non-zero elements, which can avoid the bias issues and thus yield more accurate results. We developed an alternative primal-dual active set algorithm to derive the co-sparse estimator in a computationally efficient way. The simulation studies showed that our method achieved better performance than the state-of-art methods in detecting the basis matrix and recovering signals, especially under the high-dimensional scenario. In empirical experiments with two neuroimaging data, the proposed method successfully detected difference between Alzheimer's patients and normal person in several brain regions, which suggests that our method may be a valuable toolbox for neuroimaging studies.
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Affiliation(s)
- Fan Wu
- International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, China
| | - Jiahui Cai
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
| | - Canhong Wen
- International Institute of Finance, School of Management, University of Science and Technology of China, Hefei, China
| | - Haizhu Tan
- Department of Preventive Medicine, Shantou University Medical College, Shantou, China
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28
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Mo C, Ye Z, Ke H, Lu T, Canida T, Liu S, Wu Q, Zhao Z, Ma Y, Elliot Hong L, Kochunov P, Ma T, Chen S. A new Mendelian Randomization method to estimate causal effects of multivariable brain imaging exposures. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2022; 27:73-84. [PMID: 34890138 PMCID: PMC8669774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The advent of simultaneously collected imaging-genetics data in large study cohorts provides an unprecedented opportunity to assess the causal effect of brain imaging traits on externally measured experimental results (e.g., cognitive tests) by treating genetic variants as instrumental variables. However, classic Mendelian Randomization methods are limited when handling high-throughput imaging traits as exposures to identify causal effects. We propose a new Mendelian Randomization framework to jointly select instrumental variables and imaging exposures, and then estimate the causal effect of multivariable imaging data on the outcome. We validate the proposed method with extensive data analyses and compare it with existing methods. We further apply our method to evaluate the causal effect of white matter microstructure integrity (WM) on cognitive function. The findings suggest that our method achieved better performance regarding sensitivity, bias, and false discovery rate compared to individually assessing the causal effect of a single exposure and jointly assessing the causal effect of multiple exposures without dimension reduction. Our application results indicated that WM measures across different tracts have a joint causal effect that significantly impacts the cognitive function among the participants from the UK Biobank.
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Affiliation(s)
- Chen Mo
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America
| | - Hongjie Ke
- Department of Mathematics, University of Maryland, College Park, Maryland 20740, United States of America
| | - Tong Lu
- Department of Mathematics, University of Maryland, College Park, Maryland 20740, United States of America
| | - Travis Canida
- Department of Mathematics, University of Maryland, College Park, Maryland 20740, United States of America
| | - Song Liu
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250353, China
| | - Qiong Wu
- Department of Mathematics, University of Maryland, College Park, Maryland 20740, United States of America
| | - Zhiwei Zhao
- Department of Mathematics, University of Maryland, College Park, Maryland 20740, United States of America
| | - Yizhou Ma
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, Maryland 20740, United States of America
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States of America
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29
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Abstract
DNA methylation alterations have been widely studied as mediators of environmentally induced disease risks. With new advances in technique, epigenome-wide DNA methylation data (EWAS) have become the new standard for epigenetic studies in human populations. However, to date most epigenetic studies of mediation effects only involve selected (gene-specific) candidate methylation markers. There is an urgent need for appropriate analytical methods for EWAS mediation analysis. In this chapter, we provide an overview of recent advances on high-dimensional mediation analysis, with application to two DNA methylation data.
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Affiliation(s)
- Haixiang Zhang
- Center for Applied Mathematics, Tianjin University, Tianjin, China
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA.
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30
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High-throughput mediation analysis of human proteome and metabolome identifies mediators of post-bariatric surgical diabetes control. Nat Commun 2021; 12:6951. [PMID: 34845204 PMCID: PMC8630169 DOI: 10.1038/s41467-021-27289-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 11/11/2021] [Indexed: 12/13/2022] Open
Abstract
To improve the power of mediation in high-throughput studies, here we introduce High-throughput mediation analysis (Hitman), which accounts for direction of mediation and applies empirical Bayesian linear modeling. We apply Hitman in a retrospective, exploratory analysis of the SLIMM-T2D clinical trial in which participants with type 2 diabetes were randomized to Roux-en-Y gastric bypass (RYGB) or nonsurgical diabetes/weight management, and fasting plasma proteome and metabolome were assayed up to 3 years. RYGB caused greater improvement in HbA1c, which was mediated by growth hormone receptor (GHR). GHR’s mediation is more significant than clinical mediators, including BMI. GHR decreases at 3 months postoperatively alongside increased insulin-like growth factor binding proteins IGFBP1/BP2; plasma GH increased at 1 year. Experimental validation indicates (1) hepatic GHR expression decreases in post-bariatric rats; (2) GHR knockdown in primary hepatocytes decreases gluconeogenic gene expression and glucose production. Thus, RYGB may induce resistance to diabetogenic effects of GH signaling. Trial Registration: Clinicaltrials.gov NCT01073020. Factors underlying the effects of gastric bypass surgery on glucose homeostasis are incompletely understood. Here the authors developed and applied high-throughput mediation analysis to identify proteome/metabolome mediators of improved glucose homeostasis after to gastric bypass surgery, and report that improved glycemia was mediated by the growth hormone receptor.
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31
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Zhang H, Zheng Y, Hou L, Zheng C, Liu L. Mediation analysis for survival data with high-dimensional mediators. Bioinformatics 2021; 37:3815-3821. [PMID: 34343267 PMCID: PMC8570823 DOI: 10.1093/bioinformatics/btab564] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 07/18/2021] [Accepted: 07/29/2021] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Mediation analysis has become a prevalent method to identify causal pathway(s) between an independent variable and a dependent variable through intermediate variable(s). However, little work has been done when the intermediate variables (mediators) are high-dimensional and the outcome is a survival endpoint. In this paper, we introduce a novel method to identify potential mediators in a causal framework of high-dimensional Cox regression. RESULTS We first reduce the data dimension through a mediation-based sure independence screening method. A de-biased Lasso inference procedure is used for Cox's regression parameters. We adopt a multiple-testing procedure to accurately control the false discovery rate when testing high-dimensional mediation hypotheses. Simulation studies are conducted to demonstrate the performance of our method. We apply this approach to explore the mediation mechanisms of 379 330 DNA methylation markers between smoking and overall survival among lung cancer patients in The Cancer Genome Atlas lung cancer cohort. Two methylation sites (cg08108679 and cg26478297) are identified as potential mediating epigenetic markers. AVAILABILITY AND IMPLEMENTATION Our proposed method is available with the R package HIMA at https://cran.r-project.org/web/packages/HIMA/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Haixiang Zhang
- Center for Applied Mathematics, Tianjin University, Tianjin 300072, China
| | - Yinan Zheng
- Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Cheng Zheng
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE 68198, USA
| | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO 63110, USA
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32
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Song Y, Zhou X, Kang J, Aung MT, Zhang M, Zhao W, Needham BL, Kardia SLR, Liu Y, Meeker JD, Smith JA, Mukherjee B. Bayesian Sparse Mediation Analysis with Targeted Penalization of Natural Indirect Effects. J R Stat Soc Ser C Appl Stat 2021; 70:1391-1412. [PMID: 34887595 PMCID: PMC8653861 DOI: 10.1111/rssc.12518] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Causal mediation analysis aims to characterize an exposure's effect on an outcome and quantify the indirect effect that acts through a given mediator or a group of mediators of interest. With the increasing availability of measurements on a large number of potential mediators, like the epigenome or the microbiome, new statistical methods are needed to simultaneously accommodate high-dimensional mediators while directly target penalization of the natural indirect effect (NIE) for active mediator identification. Here, we develop two novel prior models for identification of active mediators in high-dimensional mediation analysis through penalizing NIEs in a Bayesian paradigm. Both methods specify a joint prior distribution on the exposure-mediator effect and mediator-outcome effect with either (a) a four-component Gaussian mixture prior or (b) a product threshold Gaussian prior. By jointly modeling the two parameters that contribute to the NIE, the proposed methods enable penalization on their product in a targeted way. Resultant inference can take into account the four-component composite structure underlying the NIE. We show through simulations that the proposed methods improve both selection and estimation accuracy compared to other competing methods. We applied our methods for an in-depth analysis of two ongoing epidemiologic studies: the Multi-Ethnic Study of Atherosclerosis (MESA) and the LIFECODES birth cohort. The identified active mediators in both studies reveal important biological pathways for understanding disease mechanisms.
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Affiliation(s)
- Yanyi Song
- University of Michigan, Ann Arbor, MI, USA
| | - Xiang Zhou
- University of Michigan, Ann Arbor, MI, USA
| | - Jian Kang
- University of Michigan, Ann Arbor, MI, USA
| | - Max T Aung
- University of Michigan, Ann Arbor, MI, USA
| | - Min Zhang
- University of Michigan, Ann Arbor, MI, USA
| | - Wei Zhao
- University of Michigan, Ann Arbor, MI, USA
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33
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Zhang Q. High-Dimensional Mediation Analysis with Applications to Causal Gene Identification. STATISTICS IN BIOSCIENCES 2021. [DOI: 10.1007/s12561-021-09328-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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34
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Schaid DJ, Dikilitas O, Sinnwell JP, Kullo IJ. Penalized mediation models for multivariate data. Genet Epidemiol 2021; 46:32-50. [PMID: 34664742 DOI: 10.1002/gepi.22433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 08/04/2021] [Accepted: 10/04/2021] [Indexed: 11/11/2022]
Abstract
Statistical methods to integrate multiple layers of data, from exposures to intermediate traits to outcome variables, are needed to guide interpretation of complex data sets for which variables are likely contributing in a causal pathway from exposure to outcome. Statistical mediation analysis based on structural equation models provide a general modeling framework, yet they can be difficult to apply to high-dimensional data and they are not automated to select the best fitting model. To overcome these limitations, we developed novel algorithms and software to simultaneously evaluate multiple exposure variables, multiple intermediate traits, and multiple outcome variables. Our penalized mediation models are computationally efficient and simulations demonstrate that they produce reliable results for large data sets. Application of our methods to a study of vascular disease demonstrates their utility to identify novel direct effects of single-nucleotide polymorphisms (SNPs) on coronary heart disease and peripheral artery disease, while disentangling the effects of SNPs on the intermediate risk factors including lipids, cigarette smoking, systolic blood pressure, and type 2 diabetes.
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Affiliation(s)
- Daniel J Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Jason P Sinnwell
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA
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35
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Liao Y, Liu J, Coffman DL, Li R. Varying Coefficient Mediation Model and Application to Analysis of Behavioral Economics Data. JOURNAL OF BUSINESS & ECONOMIC STATISTICS : A PUBLICATION OF THE AMERICAN STATISTICAL ASSOCIATION 2021; 40:1759-1771. [PMID: 36330150 PMCID: PMC9624463 DOI: 10.1080/07350015.2021.1971089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
This article is concerned with causal mediation analysis with varying indirect and direct effects. We propose a varying coefficient mediation model, which can also be viewed as an extension of moderation analysis on a causal diagram. We develop a new estimation procedure for the direct and indirect effects based on B-splines. Under mild conditions, rates of convergence and asymptotic distributions of the resulting estimates are established. We further propose a F-type test for the direct effect. We conduct simulation study to examine the finite sample performance of the proposed methodology, and apply the new procedures for empirical analysis of behavioral economics data.
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Affiliation(s)
- Yujie Liao
- Department of Statistics, Pennsylvania State University, University Park, PA
| | - Jingyuan Liu
- MOE Key Laboratory of Econometrics, Department of Statistics and Data Science, School of Economics, Wang Yanan Institute for Studies in Economics, Xiamen University, Xiamen, China
- Fujian Key Lab of Statistics, Xiamen University, Xiamen, China
| | - Donna L. Coffman
- Department of Epidemiology and Biostatistics, Temple University, Philadelphia, PA
| | - Runze Li
- Department of Statistics, Pennsylvania State University, University Park, PA
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36
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Zhao Y, Luo X. Pathway Lasso: Pathway Estimation and Selection with High-Dimensional Mediators. STATISTICS AND ITS INTERFACE 2021; 15:39-50. [PMID: 35815003 PMCID: PMC9269990 DOI: 10.4310/21-sii673] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
In many scientific studies, it becomes increasingly important to delineate the pathways through a large number of mediators, such as genetic and brain mediators. Structural equation modeling (SEM) is a popular technique to estimate the pathway effects, commonly expressed as the product of coefficients. However, it becomes unstable and computationally challenging to fit such models with high-dimensional mediators. This paper proposes a sparse mediation model using a regularized SEM approach, where sparsity means that a small number of mediators have a nonzero mediation effect between a treatment and an outcome. To address the model selection challenge, we innovate by introducing a new penalty called Pathway Lasso. This penalty function is a convex relaxation of the non-convex product function for the mediation effects, and it enables a computationally tractable optimization criterion to estimate and select pathway effects simultaneously. We develop a fast ADMM-type algorithm to compute the model parameters, and we show that the iterative updates can be expressed in closed form. We also prove the asymptotic consistency of our Pathway Lasso estimator for the mediation effect. On both simulated data and an fMRI data set, the proposed approach yields higher pathway selection accuracy and lower estimation bias than competing methods.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, 410 West 10th Street, Indianapolis, IN USA
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, 1200 Pressler Street, Houston, TX USA
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Cadiou S, Basagaña X, Gonzalez JR, Lepeule J, Vrijheid M, Siroux V, Slama R. Performance of approaches relying on multidimensional intermediary data to decipher causal relationships between the exposome and health: A simulation study under various causal structures. ENVIRONMENT INTERNATIONAL 2021; 153:106509. [PMID: 33774494 DOI: 10.1016/j.envint.2021.106509] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 01/10/2021] [Accepted: 03/06/2021] [Indexed: 06/12/2023]
Abstract
Challenges in the assessment of the health effects of the exposome, defined as encompassing all environmental exposures from the prenatal period onwards, include a possibly high rate of false positive signals. It might be overcome using data dimension reduction techniques. Data from the biological layers lying between the exposome and its possible health consequences, such as the methylome, may help reducing exposome dimension. We aimed to quantify the performances of approaches relying on the incorporation of an intermediary biological layer to relate the exposome and health, and compare them with agnostic approaches ignoring the intermediary layer. We performed a Monte-Carlo simulation, in which we generated realistic exposome and intermediary layer data by sampling with replacement real data from the Helix exposome project. We generated a Gaussian outcome assuming linear relationships between the three data layers, in 2381 scenarios under five different causal structures, including mediation and reverse causality. We tested 3 agnostic methods considering only the exposome and the health outcome: ExWAS (for Exposome-Wide Association study), DSA, LASSO; and 3 methods relying on an intermediary layer: two implementations of our new oriented Meet-in-the-Middle (oMITM) design, using ExWAS and DSA, and a mediation analysis using ExWAS. Methods' performances were assessed through their sensitivity and FDP (False-Discovery Proportion). The oMITM-based methods generally had lower FDP than the other approaches, possibly at a cost in terms of sensitivity; FDP was in particular lower under a structure of reverse causality and in some mediation scenarios. The oMITM-DSA implementation showed better performances than oMITM-ExWAS, especially in terms of FDP. Among the agnostic approaches, DSA showed the highest performance. Integrating information from intermediary biological layers can help lowering FDP in studies of the exposome health effects; in particular, oMITM seems less sensitive to reverse causality than agnostic exposome-health association studies.
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Affiliation(s)
- Solène Cadiou
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | - Xavier Basagaña
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Juan R Gonzalez
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Johanna Lepeule
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | - Martine Vrijheid
- ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Spain
| | - Valérie Siroux
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France
| | - Rémy Slama
- Team of Environmental Epidemiology, IAB, Institute for Advanced Biosciences, Inserm, CNRS, CHU-Grenoble-Alpes, University Grenoble-Alpes, Grenoble, France.
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38
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Zhang H, Chen J, Li Z, Liu L. Testing for mediation effect with application to human microbiome data. STATISTICS IN BIOSCIENCES 2021; 13:313-328. [PMID: 34093887 PMCID: PMC8177450 DOI: 10.1007/s12561-019-09253-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2019] [Revised: 05/22/2019] [Accepted: 07/19/2019] [Indexed: 12/27/2022]
Abstract
Mediation analysis has been commonly used to study the effect of an exposure on an outcome through a mediator. In this paper, we are interested in exploring the mediation mechanism of microbiome, whose special features make the analysis challenging. We consider the isometric logratio transformation of the relative abundance as the mediator variable. Then, we present a de-biased Lasso estimate for the mediator of interest and derive its standard error estimator, which can be used to develop a test procedure for the interested mediation effect. Extensive simulation studies are conducted to assess the performance of our method. We apply the proposed approach to test the mediation effect of human gut microbiome between the dietary fiber intake and body mass index.
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Affiliation(s)
- Haixiang Zhang
- Center for Applied Mathematics, Tianjin University, Tianjin, 300072, China
| | - Jun Chen
- Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN 55905, USA
| | - Zhigang Li
- Department of Biostatistics, University of Florida, Gainesville, FL 32610, USA
| | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO 63110, USA
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Yu Z, Cui Y, Wei T, Ma Y, Luo C. High-Dimensional Mediation Analysis With Confounders in Survival Models. Front Genet 2021; 12:688871. [PMID: 34262599 PMCID: PMC8273300 DOI: 10.3389/fgene.2021.688871] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/07/2021] [Indexed: 12/02/2022] Open
Abstract
Mediation analysis is a common statistical method for investigating the mechanism of environmental exposures on health outcomes. Previous studies have extended mediation models with a single mediator to high-dimensional mediators selection. It is often assumed that there are no confounders that influence the relations among the exposure, mediator, and outcome. This is not realistic for the observational studies. To accommodate the potential confounders, we propose a concise and efficient high-dimensional mediation analysis procedure using the propensity score for adjustment. Results from simulation studies demonstrate the proposed procedure has good performance in mediator selection and effect estimation compared with methods that ignore all confounders. Of note, as the sample size increases, the performance of variable selection and mediation effect estimation is as well as the results shown in the method which include all confounders as covariates in the mediation model. By applying this procedure to a TCGA lung cancer data set, we find that lung cancer patients who had serious smoking history have increased the risk of death via the methylation markers cg21926276 and cg20707991 with significant hazard ratios of 1.2093 (95% CI: 1.2019-1.2167) and 1.1388 (95% CI: 1.1339-1.1438), respectively.
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Affiliation(s)
- Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yidan Cui
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Ting Wei
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Yanran Ma
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Chengwen Luo
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
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40
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Zeng P, Shao Z, Zhou X. Statistical methods for mediation analysis in the era of high-throughput genomics: Current successes and future challenges. Comput Struct Biotechnol J 2021; 19:3209-3224. [PMID: 34141140 PMCID: PMC8187160 DOI: 10.1016/j.csbj.2021.05.042] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/21/2021] [Accepted: 05/21/2021] [Indexed: 12/12/2022] Open
Abstract
Mediation analysis investigates the intermediate mechanism through which an exposure exerts its influence on the outcome of interest. Mediation analysis is becoming increasingly popular in high-throughput genomics studies where a common goal is to identify molecular-level traits, such as gene expression or methylation, which actively mediate the genetic or environmental effects on the outcome. Mediation analysis in genomics studies is particularly challenging, however, thanks to the large number of potential mediators measured in these studies as well as the composite null nature of the mediation effect hypothesis. Indeed, while the standard univariate and multivariate mediation methods have been well-established for analyzing one or multiple mediators, they are not well-suited for genomics studies with a large number of mediators and often yield conservative p-values and limited power. Consequently, over the past few years many new high-dimensional mediation methods have been developed for analyzing the large number of potential mediators collected in high-throughput genomics studies. In this work, we present a thorough review of these important recent methodological advances in high-dimensional mediation analysis. Specifically, we describe in detail more than ten high-dimensional mediation methods, focusing on their motivations, basic modeling ideas, specific modeling assumptions, practical successes, methodological limitations, as well as future directions. We hope our review will serve as a useful guidance for statisticians and computational biologists who develop methods of high-dimensional mediation analysis as well as for analysts who apply mediation methods to high-throughput genomics studies.
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Affiliation(s)
- Ping Zeng
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
- Center for Medical Statistics and Data Analysis, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Zhonghe Shao
- Department of Epidemiology and Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu 221004, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor 48109, MI, USA
- Center for Statistical Genetics, University of Michigan, Ann Arbor 48109, MI, USA
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41
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Fang R, Yang H, Gao Y, Cao H, Goode EL, Cui Y. Gene-based mediation analysis in epigenetic studies. Brief Bioinform 2021; 22:bbaa113. [PMID: 32608480 PMCID: PMC8660163 DOI: 10.1093/bib/bbaa113] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 04/07/2020] [Accepted: 05/12/2020] [Indexed: 12/15/2022] Open
Abstract
Mediation analysis has been a useful tool for investigating the effect of mediators that lie in the path from the independent variable to the outcome. With the increasing dimensionality of mediators such as in (epi)genomics studies, high-dimensional mediation model is needed. In this work, we focus on epigenetic studies with the goal to identify important DNA methylations that act as mediators between an exposure disease outcome. Specifically, we focus on gene-based high-dimensional mediation analysis implemented with kernel principal component analysis to capture potential nonlinear mediation effect. We first review the current high-dimensional mediation models and then propose two gene-based analytical approaches: gene-based high-dimensional mediation analysis based on linearity assumption between mediators and outcome (gHMA-L) and gene-based high-dimensional mediation analysis based on nonlinearity assumption (gHMA-NL). Since the underlying true mediation relationship is unknown in practice, we further propose an omnibus test of gene-based high-dimensional mediation analysis (gHMA-O) by combing gHMA-L and gHMA-NL. Extensive simulation studies show that gHMA-L performs better under the model linear assumption and gHMA-NL does better under the model nonlinear assumption, while gHMA-O is a more powerful and robust method by combining the two. We apply the proposed methods to two datasets to investigate genes whose methylation levels act as important mediators in the relationship: (1) between alcohol consumption and epithelial ovarian cancer risk using data from the Mayo Clinic Ovarian Cancer Case-Control Study and (2) between childhood maltreatment and comorbid post-traumatic stress disorder and depression in adulthood using data from the Gray Trauma Project.
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42
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Aung MT, Song Y, Ferguson KK, Cantonwine DE, Zeng L, McElrath TF, Pennathur S, Meeker JD, Mukherjee B. Application of an analytical framework for multivariate mediation analysis of environmental data. Nat Commun 2020; 11:5624. [PMID: 33159049 PMCID: PMC7648785 DOI: 10.1038/s41467-020-19335-2] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 09/29/2020] [Indexed: 01/08/2023] Open
Abstract
Diverse toxicological mechanisms may mediate the impact of environmental toxicants (phthalates, phenols, polycyclic aromatic hydrocarbons, and metals) on pregnancy outcomes. In this study, we introduce an analytical framework for multivariate mediation analysis to identify mediation pathways (q = 61 mediators) in the relationship between environmental toxicants (p = 38 analytes) and gestational age at delivery. Our analytical framework includes: (1) conducting pairwise mediation for unique exposure-mediator combinations, (2) exposure dimension reduction by estimating environmental risk scores, and (3) multivariate mediator analysis using either Bayesian shrinkage mediation analysis, population value decomposition, or mediation pathway penalization. Dimension reduction demonstrates that a one-unit increase in phthalate risk score is associated with a total effect of 1.07 lower gestational age (in weeks) at delivery (95% confidence interval: 0.48-1.67) and eicosanoids from the cytochrome p450 pathway mediated 26% of this effect (95% confidence interval: 4-63%). Eicosanoid products derived from the cytochrome p450 pathway may be important mediators of phthalate toxicity.
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Affiliation(s)
- Max T Aung
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, US
| | - Yanyi Song
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, US
| | - Kelly K Ferguson
- Epidemiology Branch, National Institute of Environmental Health Sciences, Research Triangle Park, NC, Chapel Hill, US
| | - David E Cantonwine
- Division of Maternal and Fetal Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, US
| | - Lixia Zeng
- Department of Internal Medicine-Nephrology, University of Michigan, Ann Arbor, MI, US
| | - Thomas F McElrath
- Division of Maternal and Fetal Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, US
| | - Subramaniam Pennathur
- Department of Internal Medicine-Nephrology, University of Michigan, Ann Arbor, MI, US
- Michigan Regional Comprehensive Metabolomics Resource Core, University of Michigan, Ann Arbor, MI, US
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI, US
| | - John D Meeker
- Department of Environmental Health Sciences, University of Michigan School of Public Health, Ann Arbor, MI, US
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan School of Public Health, Ann Arbor, MI, US.
- Department of Epidemiology, University of Michigan School of Public Health, Ann Arbor, MI, US.
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43
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Identifying neural signatures mediating behavioral symptoms and psychosis onset: High-dimensional whole brain functional mediation analysis. Neuroimage 2020; 226:117508. [PMID: 33157263 PMCID: PMC7836235 DOI: 10.1016/j.neuroimage.2020.117508] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/19/2020] [Accepted: 10/22/2020] [Indexed: 11/26/2022] Open
Abstract
Along the pathway from behavioral symptoms to the development of psychotic disorders sits the multivariate mediating brain. The functional organization and structural topography of large-scale multivariate neural mediators among patients with brain disorders, however, are not well understood. Here, we design a high-dimensional brain-wide functional mediation framework to investigate brain regions that intermediate between baseline behavioral symptoms and future conversion to full psychosis among individuals at clinical high risk (CHR). Using resting-state functional magnetic resonance imaging (fMRI) data from 263 CHR subjects, we extract an α brain atlas and a β brain atlas: the former underlines brain areas associated with prodromal symptoms and the latter highlights brain areas associated with disease onset. In parallel, we identify and separate mediators that potentially positively and negatively mediate symptoms and psychosis, respectively, and quantify the effect of each neural mediator on disease development. Taken together, these results paint a brain-wide picture of neural markers that are potentially mediating behavioral symptoms and the development of psychotic disorders; additionally, they underscore a statistical framework that is useful to uncover large-scale intermediating variables in a regulatory biological system.
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44
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Song Y, Zhou X, Zhang M, Zhao W, Liu Y, Kardia SLR, Diez Roux AV, Needham BL, Smith JA, Mukherjee B. Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies. Biometrics 2020; 76:700-710. [PMID: 31733066 PMCID: PMC7228845 DOI: 10.1111/biom.13189] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 10/30/2019] [Accepted: 11/04/2019] [Indexed: 11/29/2022]
Abstract
Causal mediation analysis aims to examine the role of a mediator or a group of mediators that lie in the pathway between an exposure and an outcome. Recent biomedical studies often involve a large number of potential mediators based on high-throughput technologies. Most of the current analytic methods focus on settings with one or a moderate number of potential mediators. With the expanding growth of -omics data, joint analysis of molecular-level genomics data with epidemiological data through mediation analysis is becoming more common. However, such joint analysis requires methods that can simultaneously accommodate high-dimensional mediators and that are currently lacking. To address this problem, we develop a Bayesian inference method using continuous shrinkage priors to extend previous causal mediation analysis techniques to a high-dimensional setting. Simulations demonstrate that our method improves the power of global mediation analysis compared to simpler alternatives and has decent performance to identify true nonnull contributions to the mediation effects of the pathway. The Bayesian method also helps us to understand the structure of the composite null cases for inactive mediators in the pathway. We applied our method to Multi-Ethnic Study of Atherosclerosis and identified DNA methylation regions that may actively mediate the effect of socioeconomic status on cardiometabolic outcomes.
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Affiliation(s)
- Yanyi Song
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A
| | - Min Zhang
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A
| | - Wei Zhao
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, U.S.A
| | - Yongmei Liu
- Department of Epidemiology and Prevention, Wake Forest School of Medicine, Winston-Salem, NC, U.S.A
| | | | - Ana V. Diez Roux
- Department of Epidemiology and Biostatistics, Drexel University, Philadelphia, PA, U.S.A
| | - Belinda L. Needham
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, U.S.A
| | - Jennifer A. Smith
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, U.S.A
| | - Bhramar Mukherjee
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, U.S.A
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45
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Derkach A, Moore SC, Boca SM, Sampson JN. Group testing in mediation analysis. Stat Med 2020; 39:2423-2436. [DOI: 10.1002/sim.8546] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 11/01/2019] [Accepted: 03/05/2020] [Indexed: 11/09/2022]
Affiliation(s)
- Andriy Derkach
- Biostatistics Branch, Division of Cancer Epidemiology and GeneticsNational Cancer Institute Rockville Maryland USA
| | - Steven C. Moore
- Metabolomics Epidemiology Branch, Division of Cancer Epidemiology and GeneticsNational Cancer Institute Rockville Maryland USA
| | - Simina M. Boca
- Innovation Center for Biomedical Informatics, Department of Oncology and Biostatistics, Bioinformatics and BiomathematicsGeorgetown University Medical Center Washington District of Columbia USA
| | - Joshua N. Sampson
- Biostatistics Branch, Division of Cancer Epidemiology and GeneticsNational Cancer Institute Rockville Maryland USA
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46
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Zhao Y, Li L, Caffo BS. Multimodal neuroimaging data integration and pathway analysis. Biometrics 2020; 77:879-889. [PMID: 32789850 DOI: 10.1111/biom.13351] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 07/15/2020] [Accepted: 06/05/2020] [Indexed: 12/01/2022]
Abstract
With advancements in technology, the collection of multiple types of measurements on a common set of subjects is becoming routine in science. Some notable examples include multimodal neuroimaging studies for the simultaneous investigation of brain structure and function and multi-omics studies for combining genetic and genomic information. Integrative analysis of multimodal data allows scientists to interrogate new mechanistic questions. However, the data collection and generation of integrative hypotheses is outpacing available methodology for joint analysis of multimodal measurements. In this article, we study high-dimensional multimodal data integration in the context of mediation analysis. We aim to understand the roles that different data modalities play as possible mediators in the pathway between an exposure variable and an outcome. We propose a mediation model framework with two data types serving as separate sets of mediators and develop a penalized optimization approach for parameter estimation. We study both the theoretical properties of the estimator through an asymptotic analysis and its finite-sample performance through simulations. We illustrate our method with a multimodal brain pathway analysis having both structural and functional connectivity as mediators in the association between sex and language processing.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Lexin Li
- Department of Biostatistics and Epidemiology, University of California, Berkeley, California
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland
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47
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Zhou RR, Wang L, Zhao SD. Estimation and inference for the indirect effect in high-dimensional linear mediation models. Biometrika 2020; 107:573-589. [PMID: 32831353 DOI: 10.1093/biomet/asaa016] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Indexed: 12/19/2022] Open
Abstract
Mediation analysis is difficult when the number of potential mediators is larger than the sample size. In this paper we propose new inference procedures for the indirect effect in the presence of high-dimensional mediators for linear mediation models. We develop methods for both incomplete mediation, where a direct effect may exist, and complete mediation, where the direct effect is known to be absent. We prove consistency and asymptotic normality of our indirect effect estimators. Under complete mediation, where the indirect effect is equivalent to the total effect, we further prove that our approach gives a more powerful test compared to directly testing for the total effect. We confirm our theoretical results in simulations, as well as in an integrative analysis of gene expression and genotype data from a pharmacogenomic study of drug response. We present a novel analysis of gene sets to understand the molecular mechanisms of drug response, and also identify a genome-wide significant noncoding genetic variant that cannot be detected using standard analysis methods.
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Affiliation(s)
- Ruixuan Rachel Zhou
- Department of Statistics, University of Illinois at Urbana-Champaign, 725 S. Wright Street, Champaign, Illinois 61820, U.S.A
| | - Liewei Wang
- Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, 200 First St. SW, Rochester, Minnesota 55905, U.S.A
| | - Sihai Dave Zhao
- Department of Statistics, University of Illinois at Urbana-Champaign, 725 S. Wright Street, Champaign, Illinois 61820, U.S.A
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48
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Blum MGB, Valeri L, François O, Cadiou S, Siroux V, Lepeule J, Slama R. Challenges Raised by Mediation Analysis in a High-Dimension Setting. ENVIRONMENTAL HEALTH PERSPECTIVES 2020; 128:55001. [PMID: 32379489 PMCID: PMC7263455 DOI: 10.1289/ehp6240] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 04/14/2020] [Accepted: 04/15/2020] [Indexed: 05/19/2023]
Abstract
BACKGROUND Mediation analysis is used in epidemiology to identify pathways through which exposures influence health. The advent of high-throughput (omics) technologies gives opportunities to perform mediation analysis with a high-dimension pool of covariates. OBJECTIVE We aimed to highlight some biostatistical issues of this expanding field of high-dimension mediation. DISCUSSION The mediation techniques used for a single mediator cannot be generalized in a straightforward manner to high-dimension mediation. Causal knowledge on the relation between covariates is required for mediation analysis, and it is expected to be more limited as dimension and system complexity increase. The methods developed in high dimension can be distinguished according to whether mediators are considered separately or as a whole. Methods considering each potential mediator separately do not allow efficient identification of the indirect effects when mutual influences exist among the mediators, which is expected for many biological (e.g., epigenetic) parameters. In this context, methods considering all potential mediators simultaneously, based, for example, on data reduction techniques, are more adapted to the causal inference framework. Their cost is a possible lack of ability to single out the causal mediators. Moreover, the ability of the mediators to predict the outcome can be overestimated, in particular because many machine-learning algorithms are optimized to increase predictive ability rather than their aptitude to make causal inference. Given the lack of overarching validated framework and the generally complex causal structure of high-dimension data, analysis of high-dimension mediation currently requires great caution and effort to incorporate a priori biological knowledge. https://doi.org/10.1289/EHP6240.
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Affiliation(s)
- Michaël G B Blum
- Laboratoire Techniques de l'Imagerie Médicale et de la Complexité (TIMC-IMAG; UMR 5525), French National Centre for Scientific Research (CNRS), University Grenoble Alpes, La Tronche, France
- OWKIN, Paris, France
| | - Linda Valeri
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York, New York, USA
| | - Olivier François
- Laboratoire Techniques de l'Imagerie Médicale et de la Complexité (TIMC-IMAG; UMR 5525), French National Centre for Scientific Research (CNRS), University Grenoble Alpes, La Tronche, France
| | - Solène Cadiou
- Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB) joint research center, Institut national de la santé et de la recherché médicale (Inserm), CNRS, University Grenoble-Alpes, Grenoble, France
| | - Valérie Siroux
- Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB) joint research center, Institut national de la santé et de la recherché médicale (Inserm), CNRS, University Grenoble-Alpes, Grenoble, France
| | - Johanna Lepeule
- Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB) joint research center, Institut national de la santé et de la recherché médicale (Inserm), CNRS, University Grenoble-Alpes, Grenoble, France
| | - Rémy Slama
- Team of Environmental Epidemiology applied to Reproduction and Respiratory Health, Institute for Advanced Biosciences (IAB) joint research center, Institut national de la santé et de la recherché médicale (Inserm), CNRS, University Grenoble-Alpes, Grenoble, France
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49
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Luo C, Fa B, Yan Y, Wang Y, Zhou Y, Zhang Y, Yu Z. High-dimensional mediation analysis in survival models. PLoS Comput Biol 2020; 16:e1007768. [PMID: 32302299 PMCID: PMC7190184 DOI: 10.1371/journal.pcbi.1007768] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 04/29/2020] [Accepted: 03/02/2020] [Indexed: 02/07/2023] Open
Abstract
Mediation analysis with high-dimensional DNA methylation markers is important in identifying epigenetic pathways between environmental exposures and health outcomes. There have been some methodology developments of mediation analysis with high-dimensional mediators. However, high-dimensional mediation analysis methods for time-to-event outcome data are still yet to be developed. To address these challenges, we propose a new high-dimensional mediation analysis procedure for survival models by incorporating sure independent screening and minimax concave penalty techniques for variable selection, with the Sobel and the joint method for significance test of indirect effect. The simulation studies show good performance in identifying correct biomarkers, false discovery rate control, and minimum estimation bias of the proposed procedure. We also apply this approach to study the causal pathway from smoking to overall survival among lung cancer patients potentially mediated by 365,307 DNA methylations in the TCGA lung cancer cohort. Mediation analysis using a Cox proportional hazards model estimates that patients who have serious smoking history increase the risk of lung cancer through methylation markers including cg21926276, cg27042065, and cg26387355 with significant hazard ratios of 1.2497(95%CI: 1.1121, 1.4045), 1.0920(95%CI: 1.0170, 1.1726), and 1.1489(95%CI: 1.0518, 1.2550), respectively. The three methylation sites locate in the three genes which have been showed to be associated with lung cancer event or overall survival. However, the three CpG sites (cg21926276, cg27042065 and cg26387355) have not been reported, which are newly identified as the potential novel epigenetic markers linking smoking and survival of lung cancer patients. Collectively, the proposed high-dimensional mediation analysis procedure has good performance in mediator selection and indirect effect estimation.
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Affiliation(s)
- Chengwen Luo
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Botao Fa
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Yuting Yan
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Yang Wang
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Yiwang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
| | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics, Shanghai Jiao Tong University, Shanghai, China
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Geuter S, Reynolds Losin EA, Roy M, Atlas LY, Schmidt L, Krishnan A, Koban L, Wager TD, Lindquist MA. Multiple Brain Networks Mediating Stimulus-Pain Relationships in Humans. Cereb Cortex 2020; 30:4204-4219. [PMID: 32219311 DOI: 10.1093/cercor/bhaa048] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The brain transforms nociceptive input into a complex pain experience comprised of sensory, affective, motivational, and cognitive components. However, it is still unclear how pain arises from nociceptive input and which brain networks coordinate to generate pain experiences. We introduce a new high-dimensional mediation analysis technique to estimate distributed, network-level patterns that formally mediate the relationship between stimulus intensity and pain. We applied the model to a large-scale analysis of functional magnetic resonance imaging data (N = 284), focusing on brain mediators of the relationship between noxious stimulus intensity and trial-to-trial variation in pain reports. We identify mediators in both traditional nociceptive pathways and in prefrontal, midbrain, striatal, and default-mode regions unrelated to nociception in standard analyses. The whole-brain mediators are specific for pain versus aversive sounds and are organized into five functional networks. Brain mediators predicted pain ratings better than previous brain measures, including the neurologic pain signature (Wager et al. 2013). Our results provide a broader view of the networks underlying pain experience, as well as novel brain targets for interventions.
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Affiliation(s)
- Stephan Geuter
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.,Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA.,Vorwerk International & Co. KmG, Zurich, Switzerland
| | | | - Mathieu Roy
- Department of Psychology, McGill University, Montreal, Quebec, Canada
| | - Lauren Y Atlas
- National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD, USA.,National Center on Drug Abuse, National Institutes of Health, Bethesda, MD, USA.,National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Liane Schmidt
- Control-Interoception-Attention Team, Institute du Cerveau et de la Moelle épinière, INSERM UMR 1127, CNRS UMR 7225, Sorbonne University, Paris, France
| | - Anjali Krishnan
- Department of Psychology, Brooklyn College of the City University of New York, Brooklyn, NY, USA
| | - Leonie Koban
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA.,Control-Interoception-Attention Team, Institute du Cerveau et de la Moelle épinière, INSERM UMR 1127, CNRS UMR 7225, Sorbonne University, Paris, France.,Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA.,Marketing Area, INSEAD, Fontainebleau, France
| | - Tor D Wager
- Institute of Cognitive Science, University of Colorado Boulder, Boulder, CO, USA.,Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA.,Presidential Cluster in Neuroscience and Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
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