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Feng L, Milleson HS, Ye Z, Canida T, Ke H, Liang M, Gao S, Chen S, Hong LE, Kochunov P, Lei DKY, Ma T. Nongenetic and Genetic Factors Associated with White Matter Brain Aging: Exposome-Wide and Genome-Wide Association Study. Genes (Basel) 2024; 15:1285. [PMID: 39457408 PMCID: PMC11507416 DOI: 10.3390/genes15101285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2024] [Revised: 09/18/2024] [Accepted: 09/19/2024] [Indexed: 10/28/2024] Open
Abstract
BACKGROUND/OBJECTIVES Human brain aging is a complex process that affects various aspects of brain function and structure, increasing susceptibility to neurological and psychiatric disorders. A number of nongenetic (e.g., environmental and lifestyle) and genetic risk factors are found to contribute to the varying rates at which the brain ages among individuals. METHODS In this paper, we conducted both an exposome-wide association study (XWAS) and a genome-wide association study (GWAS) on white matter brain aging in the UK Biobank, revealing the multifactorial nature of brain aging. We applied a machine learning algorithm and leveraged fractional anisotropy tract measurements from diffusion tensor imaging data to predict the white matter brain age gap (BAG) and treated it as the marker of brain aging. For XWAS, we included 107 variables encompassing five major categories of modifiable exposures that potentially impact brain aging and performed both univariate and multivariate analysis to select the final set of nongenetic risk factors. RESULTS We found current tobacco smoking, dietary habits including oily fish, beef, lamb, cereal, and coffee intake, length of mobile phone use, use of UV protection, and frequency of solarium/sunlamp use were associated with the BAG. In genetic analysis, we identified several SNPs on chromosome 3 mapped to genes IP6K1, GMNC, OSTN, and SLC25A20 significantly associated with the BAG, showing the high heritability and polygenic architecture of human brain aging. CONCLUSIONS The critical nongenetic and genetic risk factors identified in our study provide insights into the causal relationship between white matter brain aging and neurodegenerative diseases.
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Affiliation(s)
- Li Feng
- Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, MD 20740, USA; (L.F.); (D.K.Y.L.)
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20740, USA; (H.S.M.); (T.C.); (H.K.); (M.L.)
| | - Halley S. Milleson
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20740, USA; (H.S.M.); (T.C.); (H.K.); (M.L.)
- Department of Mathematics, The College of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, MD 20740, USA
| | - Zhenyao Ye
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD 21228, USA; (Z.Y.); (S.G.); (S.C.)
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD 21201, USA
| | - Travis Canida
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20740, USA; (H.S.M.); (T.C.); (H.K.); (M.L.)
- Department of Mathematics, The College of Computer, Mathematical, and Natural Sciences, University of Maryland, College Park, MD 20740, USA
| | - Hongjie Ke
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20740, USA; (H.S.M.); (T.C.); (H.K.); (M.L.)
| | - Menglu Liang
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20740, USA; (H.S.M.); (T.C.); (H.K.); (M.L.)
| | - Si Gao
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD 21228, USA; (Z.Y.); (S.G.); (S.C.)
- Louis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (L.E.H.); (P.K.)
| | - Shuo Chen
- Maryland Psychiatric Research Center, Department of Psychiatry, School of Medicine, University of Maryland, Baltimore, MD 21228, USA; (Z.Y.); (S.G.); (S.C.)
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, School of Medicine, University of Maryland, Baltimore, MD 21201, USA
| | - L. Elliot Hong
- Louis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (L.E.H.); (P.K.)
| | - Peter Kochunov
- Louis A. Faillace Department of Psychiatry & Behavioral Sciences, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (L.E.H.); (P.K.)
| | - David K. Y. Lei
- Department of Nutrition and Food Science, College of Agriculture & Natural Resources, University of Maryland, College Park, MD 20740, USA; (L.F.); (D.K.Y.L.)
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20740, USA; (H.S.M.); (T.C.); (H.K.); (M.L.)
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Neuffer J, Wagner M, Moreno E, Le Grand Q, Mishra A, Trégouët D, Leffondre K, Proust‐Lima C, Foubert‐Samier A, Berr C, Tzourio C, Helmer C, Debette S, Samieri C. Association of LIfestyle for BRAin health risk score (LIBRA) and genetic susceptibility with incident dementia and cognitive decline. Alzheimers Dement 2024; 20:4250-4259. [PMID: 38775256 PMCID: PMC11180843 DOI: 10.1002/alz.13801] [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: 10/12/2023] [Revised: 02/16/2024] [Accepted: 02/26/2024] [Indexed: 06/18/2024]
Abstract
INTRODUCTION Evaluating whether genetic susceptibility modifies the impact of lifestyle-related factors on dementia is critical for prevention. METHODS We studied 5170 participants from a French cohort of older persons free of dementia at baseline and followed for up to 17 years. The LIfestyle for BRAin health risk score (LIBRA) including 12 modifiable factors was constructed at baseline (higher score indicating greater risk) and was related to both subsequent cognitive decline and dementia incidence, according to genetic susceptibility to dementia (reflected by the apolipoprotein E [APOE] ε4 allele and a genetic risk score [GRS]). RESULTS The LIBRA was associated with higher dementia incidence, with no significant effect modification by genetics (hazard ratio for one point score = 1.09 [95% confidence interval, 1.05; 1.13]) in APOE ε4 non-carriers and = 1.15 [1.08; 1.22] in carriers; P = 0.15 for interaction). Similar findings were obtained with the GRS and with cognitive decline. DISCUSSION Lifestyle-based prevention may be effective whatever the genetic susceptibility to dementia.
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Affiliation(s)
- Jeanne Neuffer
- Bordeaux Population Health Research Center, INSERMUniversity of BordeauxBordeauxFrance
| | - Maude Wagner
- Bordeaux Population Health Research Center, INSERMUniversity of BordeauxBordeauxFrance
- Rush Alzheimer's Disease Center, Rush University Medical CenterChicagoIllinoisUSA
| | - Elisa Moreno
- Bordeaux Population Health Research Center, INSERMUniversity of BordeauxBordeauxFrance
| | - Quentin Le Grand
- Bordeaux Population Health Research Center, INSERMUniversity of BordeauxBordeauxFrance
| | - Aniket Mishra
- Bordeaux Population Health Research Center, INSERMUniversity of BordeauxBordeauxFrance
| | | | - Karen Leffondre
- Bordeaux Population Health Research Center, INSERMUniversity of BordeauxBordeauxFrance
- Clinical and Epidemiological Research UnitINSERM CIC1401BordeauxFrance
| | - Cécile Proust‐Lima
- Bordeaux Population Health Research Center, INSERMUniversity of BordeauxBordeauxFrance
| | | | - Claudine Berr
- University of Montpellier, INSERM, Institute for Neurosciences of Montpellier (INM)MontpellierFrance
| | - Christophe Tzourio
- Bordeaux Population Health Research Center, INSERMUniversity of BordeauxBordeauxFrance
| | - Catherine Helmer
- Bordeaux Population Health Research Center, INSERMUniversity of BordeauxBordeauxFrance
- Clinical and Epidemiological Research UnitINSERM CIC1401BordeauxFrance
| | - Stéphanie Debette
- Bordeaux Population Health Research Center, INSERMUniversity of BordeauxBordeauxFrance
| | - Cécilia Samieri
- Bordeaux Population Health Research Center, INSERMUniversity of BordeauxBordeauxFrance
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Sun K, Jin S, Yang Z, Li X, Li C, Zhang J, Yang G, Yang C, Abdelrahman Z, Liu Z. Transition to healthier lifestyle associated with reduced risk of incident dementia and decreased hippocampal atrophy. J Affect Disord 2024; 349:552-558. [PMID: 38195008 DOI: 10.1016/j.jad.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/23/2023] [Accepted: 01/03/2024] [Indexed: 01/11/2024]
Abstract
BACKGROUND Research has estimated the associations of lifestyle at one-time point with the risk of dementia and hippocampal volume, but the impact of lifestyle transition on dementia and hippocampal volume remains unclear. This study aims to examine the associations of lifestyle transition with the risk of dementia and hippocampal volume. METHODS Based on data from the UK Biobank, a weighted lifestyle score was constructed by incorporating six lifestyle factors. Within each baseline lifestyle group (i.e., healthy, intermediate, and unhealthy), lifestyle transition was classified into decline, maintenance, and improvement. Cox proportional hazard regression was used to estimate the association of lifestyle transition and incident dementia (N = 16,305). A multiple linear regression model was used to estimate the association between lifestyle transition and hippocampal volume (N = 5849). RESULTS During a median follow-up period of 8.6 years, 120 (0.7 %) dementia events were documented. Among participants with healthy baseline lifestyles, the improvement group had a lower risk of incident dementia (HR: 0.18, 95 % CI: 0.04-0.81) and a larger hippocampal volume (β = 111.69, P = 0.026) than the decline group. Similar results were observed among participants with intermediate baseline lifestyles regarding dementia risk but not hippocampal volume. No benefits were observed in the improvement group among those with unhealthy baseline lifestyles. LIMITATIONS A lower incidence of dementia than other cohort study and this may have resulted in an underestimation of the risk of dementia. CONCLUSIONS Earlier transitions to healthier lifestyle were associated with reduced risk of incident dementia and decreased hippocampal atrophy.
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Affiliation(s)
- Kaili Sun
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Shuyi Jin
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Zhenqing Yang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Xueqin Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Chenxi Li
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Jingyun Zhang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Gan Yang
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China
| | - Chongming Yang
- Research Support Center, Brigham Young University, Provo, UT 84602, USA
| | - Zeinab Abdelrahman
- Centre for Public Health, Queen's University of Belfast, Belfast BT12 6BA, UK
| | - Zuyun Liu
- Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, and Department of Big Data in Health Science School of Public Health, The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou 310058, Zhejiang, China.
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Jiang J, Wang A, Shi H, Jiang S, Li W, Jiang T, Wang L, Zhang X, Sun M, Zhao M, Zou X, Xu J. Clinical and neuroimaging association between neuropsychiatric symptoms and nutritional status across the Alzheimer's disease continuum: a longitudinal cohort study. J Nutr Health Aging 2024; 28:100182. [PMID: 38336502 DOI: 10.1016/j.jnha.2024.100182] [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: 10/16/2023] [Revised: 01/24/2024] [Accepted: 01/26/2024] [Indexed: 02/12/2024]
Abstract
OBJECTIVES To investigate the association between neuropsychiatric symptoms (NPS) and nutritional status, and explore their shared regulatory brain regions on the Alzheimer's disease (AD) continuum. DESIGN A longitudinal, observational cohort study. SETTING Data were collected from the Chinese Imaging, Biomarkers, and Lifestyle study between June 1, 2021 and December 31, 2022. PARTICIPANTS Overall, 432 patients on the AD continuum, including amnestic mild cognitive impairment and AD dementia, were assessed at baseline, and only 165 patients completed the (10.37 ± 6.08) months' follow-up. MEASUREMENTS The Mini-Nutritional Assessment (MNA) and Neuropsychiatric Inventory (NPI) were used to evaluate nutritional status and NPS, respectively. The corrected cerebral blood flow (cCBF) measured by pseudo-continuous arterial spin labeling of the dietary nutrition-related brain regions was analyzed. The association between the NPS at baseline and subsequent change in nutritional status and the association between the changes in the severity of NPS and nutritional status were examined using generalized linear mixed models. RESULTS Increased cCBF in the left putamen was associated with malnutrition, general NPS, affective symptoms, and hyperactivity (P < 0.05). The presence of general NPS (β = -1.317, P = 0.003), affective symptoms (β = -1.887, P < 0.001), and appetite/eating disorders (β = -1.714, P < 0.001) at baseline were associated with a decline in the MNA scores during follow-up. The higher scores of general NPI (β = -0.048), affective symptoms (β = -0.181), and appetite/eating disorders (β = -0.416; all P < 0.001) were longitudinally associated with lower MNA scores after adjusting for confounding factors. CONCLUSIONS We found that baseline NPS were predictors of a decline in nutritional status on the AD continuum. The worse the severity of affective symptoms and appetite/eating disorders, the poorer the nutritional status. Furthermore, abnormal perfusion of the putamen may regulate the association between malnutrition and NPS, which suggests their potentially common neural regulatory basis.
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Affiliation(s)
- Jiwei Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China
| | - Anxin Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China
| | - Hanping Shi
- Department of Gastrointestinal Surgery, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China; Department of Clinical Nutrition, Beijing Shijitan Hospital, Capital Medical University, Beijing, 100038, China; Beijing International Science and Technology Cooperation Base for Cancer Metabolism and Nutrition, Beijing, 100038, China
| | - Shirui Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China
| | - Wenyi Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China
| | - Tianlin Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China
| | - Linlin Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China
| | - Xiaoli Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China
| | - Mengfan Sun
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China
| | - Min Zhao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China
| | - Xinying Zou
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China
| | - Jun Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, 100070, China; China National Clinical Research Center for Neurological Diseases, Beijing, 100070, China.
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Chen J, Li T, Zhao B, Chen H, Yuan C, Garden GA, Wu G, Zhu H. The interaction effects of age, APOE and common environmental risk factors on human brain structure. Cereb Cortex 2024; 34:bhad472. [PMID: 38112569 PMCID: PMC10793588 DOI: 10.1093/cercor/bhad472] [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: 05/04/2023] [Revised: 10/09/2023] [Accepted: 11/06/2023] [Indexed: 12/21/2023] Open
Abstract
Mounting evidence suggests considerable diversity in brain aging trajectories, primarily arising from the complex interplay between age, genetic, and environmental risk factors, leading to distinct patterns of micro- and macro-cerebral aging. The underlying mechanisms of such effects still remain unclear. We conducted a comprehensive association analysis between cerebral structural measures and prevalent risk factors, using data from 36,969 UK Biobank subjects aged 44-81. Participants were assessed for brain volume, white matter diffusivity, Apolipoprotein E (APOE) genotypes, polygenic risk scores, lifestyles, and socioeconomic status. We examined genetic and environmental effects and their interactions with age and sex, and identified 726 signals, with education, alcohol, and smoking affecting most brain regions. Our analysis revealed negative age-APOE-ε4 and positive age-APOE-ε2 interaction effects, respectively, especially in females on the volume of amygdala, positive age-sex-APOE-ε4 interaction on the cerebellar volume, positive age-excessive-alcohol interaction effect on the mean diffusivity of the splenium of the corpus callosum, positive age-healthy-diet interaction effect on the paracentral volume, and negative APOE-ε4-moderate-alcohol interaction effects on the axial diffusivity of the superior fronto-occipital fasciculus. These findings highlight the need of considering age, sex, genetic, and environmental joint effects in elucidating normal or abnormal brain aging.
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Affiliation(s)
- Jie Chen
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill NC 27514, United States
| | - Tengfei Li
- Department of Radiology, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Bingxin Zhao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, 265 South 37th Street, 3rd & 4th Floors, Philadelphia, PA 19104-1686, United States
| | - Hui Chen
- School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, China
| | - Changzheng Yuan
- School of Public Health, Zhejiang University School of Medicine, 866 Yuhangtang Rd, Hangzhou 310058, China
- Department of Nutrition, Harvard T H Chan School of Public Health, 665 Huntington Avenue Boston, MA, 02115, United States
| | - Gwenn A Garden
- Department of Neurology, School of Medicine, University of North Carolina at Chapel Hill, 170 Manning Drive Chapel Hill, NC 27599-7025, United States
| | - Guorong Wu
- Department of Psychiatry, School of Medicine, University of North Carolina at Chapel Hill, 101 Manning Drive, Chapel Hill, NC 27514, United States
- Departments of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave #3260, Chapel Hill, NC 27599, United States
- Departments of Computer Science, University of North Carolina at Chapel Hill, 201 South Columbia Street, Chapel Hill, NC 27599, United States
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Dr, Chapel Hill, NC 27599, United States
- Carolina Institute for Developmental Disabilities, 101 Renee Lynne Ct, Carrboro, NC 27510, United States
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill NC 27514, United States
- Biomedical Research Imaging Center, School of Medicine, University of North Carolina at Chapel Hill, 125 Mason Farm Road, Chapel Hill, NC 27599, United States
- Departments of Statistics and Operations Research, University of North Carolina at Chapel Hill, 318 E Cameron Ave #3260, Chapel Hill, NC 27599, United States
- Departments of Computer Science, University of North Carolina at Chapel Hill, 201 South Columbia Street, Chapel Hill, NC 27599, United States
- Departments of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27514, United States
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Han Q, Xiao X, Wang S, Qin W, Yu C, Liang M. Characterization of the effects of outliers on ComBat harmonization for removing inter-site data heterogeneity in multisite neuroimaging studies. Front Neurosci 2023; 17:1146175. [PMID: 37304022 PMCID: PMC10249749 DOI: 10.3389/fnins.2023.1146175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 05/10/2023] [Indexed: 06/13/2023] Open
Abstract
Data harmonization is a key step widely used in multisite neuroimaging studies to remove inter-site heterogeneity of data distribution. However, data harmonization may even introduce additional inter-site differences in neuroimaging data if outliers are present in the data of one or more sites. It remains unclear how the presence of outliers could affect the effectiveness of data harmonization and consequently the results of analyses using harmonized data. To address this question, we generated a normal simulation dataset without outliers and a series of simulation datasets with outliers of varying properties (e.g., outlier location, outlier quantity, and outlier score) based on a real large-sample neuroimaging dataset. We first verified the effectiveness of the most commonly used ComBat harmonization method in the removal of inter-site heterogeneity using the normal simulation data, and then characterized the effects of outliers on the effectiveness of ComBat harmonization and on the results of association analyses between brain imaging-derived phenotypes and a simulated behavioral variable using the simulation datasets with outliers. We found that, although ComBat harmonization effectively removed the inter-site heterogeneity in multisite data and consequently improved the detection of the true brain-behavior relationships, the presence of outliers could damage severely the effectiveness of ComBat harmonization in the removal of data heterogeneity or even introduce extra heterogeneity in the data. Moreover, we found that the effects of outliers on the improvement of the detection of brain-behavior associations by ComBat harmonization were dependent on how such associations were assessed (i.e., by Pearson correlation or Spearman correlation), and on the outlier location, quantity, and outlier score. These findings help us better understand the influences of outliers on data harmonization and highlight the importance of detecting and removing outliers prior to data harmonization in multisite neuroimaging studies.
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Affiliation(s)
- Qichao Han
- School of Medical Technology, School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Xiaoxiao Xiao
- School of Medical Technology, School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Sijia Wang
- School of Medical Technology, School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Chunshui Yu
- School of Medical Technology, School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Meng Liang
- School of Medical Technology, School of Medical Imaging, Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, China
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