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Ng JCM, Schooling CM. Sex-specific Mendelian randomization phenome-wide association study of basal metabolic rate. Sci Rep 2025; 15:14368. [PMID: 40274879 PMCID: PMC12022104 DOI: 10.1038/s41598-025-98017-9] [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: 02/20/2024] [Accepted: 04/08/2025] [Indexed: 04/26/2025] Open
Abstract
Observationally, higher basal metabolic rate (BMR) is associated with metabolism-related disorders, cancer, aging, and mortality. In this Mendelian randomization (MR) phenome-wide association study, using two-sample MR methods, we systematically and comprehensively investigated the health effects of genetically predicted BMR across the phenome sex-specifically. We obtained sex-specific genetic variants strongly (p < 5 × 10- 8) and independently (r2 < 0.001) predicting BMR from the UK Biobank and applied them to over 1,000 phenotypes within the same study. We combined genetic variant-specific Wald estimates using inverse-variance weighting, supplemented by sensitivity analysis. We used a false-discovery rate correction to allow for multiple comparisons as well as multivariable MR adjusted for body mass index and testosterone to investigate the independent effects of BMR on phenotypes with significant univariable associations. We obtained 217/219 genetic variants predicting BMR and applied them to 1,150/1,242 phenotypes in men/women, respectively. BMR was associated with 190/270 phenotypes in univariable analysis and 122/123 phenotypes in multivariable analysis in men/women. Examples of robust associations in multivariable analysis included those with neoplasms, diseases of the circulatory system, and growth and reproductive investment. In conclusion, BMR might affect a wide range of health-related outcomes. The underlying mechanisms and interactions between phenotypes warrant further study, as BMR is modifiable.
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Affiliation(s)
- Jack C M Ng
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong Special Administrative Region, China
| | - C Mary Schooling
- School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong Special Administrative Region, China.
- Graduate School of Public Health and Health Policy, The City University of New York, 55 West 125th St, New York, NY, 10027, USA.
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2
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Zhao T, Li H, Zhang M, Xu Y, Zhang M, Chen L. Systematic evaluation of multifactorial causal associations for Alzheimer's disease and an interactive platform MRAD developed based on Mendelian randomization analysis. eLife 2024; 13:RP96224. [PMID: 39392298 PMCID: PMC11469671 DOI: 10.7554/elife.96224] [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] [Indexed: 10/12/2024] Open
Abstract
Alzheimer's disease (AD) is a complex degenerative disease of the central nervous system, and elucidating its pathogenesis remains challenging. In this study, we used the inverse-variance weighted (IVW) model as the major analysis method to perform hypothesis-free Mendelian randomization (MR) analysis on the data from MRC IEU OpenGWAS (18,097 exposure traits and 16 AD outcome traits), and conducted sensitivity analysis with six models, to assess the robustness of the IVW results, to identify various classes of risk or protective factors for AD, early-onset AD, and late-onset AD. We generated 400,274 data entries in total, among which the major analysis method of the IVW model consists of 73,129 records with 4840 exposure traits, which fall into 10 categories: Disease, Medical laboratory science, Imaging, Anthropometric, Treatment, Molecular trait, Gut microbiota, Past history, Family history, and Lifestyle trait. More importantly, a freely accessed online platform called MRAD (https://gwasmrad.com/mrad/) has been developed using the Shiny package with MR analysis results. Additionally, novel potential AD therapeutic targets (CD33, TBCA, VPS29, GNAI3, PSME1) are identified, among which CD33 was positively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. TBCA and VPS29 were negatively associated with the main outcome traits of AD, as well as with both EOAD and LOAD. GNAI3 and PSME1 were negatively associated with the main outcome traits of AD, as well as with LOAD, but had no significant causal association with EOAD. The findings of our research advance our understanding of the etiology of AD.
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Affiliation(s)
- Tianyu Zhao
- Department of Pharmacology, College of Basic Medical Sciences, Jilin UniversityChangchunChina
| | - Hui Li
- Department of Neurology, Xuanwu Hospital, Capital Medical UniversityBeijingChina
- Neurology and Intracranial Hypertension & Cerebral Venous Disease Center National Health Commission of China, Xuanwu Hospital, Capital Medical UniversityBeijingChina
| | | | - Yang Xu
- Department of Pharmacology, College of Basic Medical Sciences, Jilin UniversityChangchunChina
| | - Ming Zhang
- Department of Pharmacology, College of Basic Medical Sciences, Jilin UniversityChangchunChina
| | - Li Chen
- Department of Pharmacology, College of Basic Medical Sciences, Jilin UniversityChangchunChina
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3
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Shi C, Ma D, Li M, Wang Z, Hao C, Liang Y, Feng Y, Hu Z, Hao X, Guo M, Li S, Zuo C, Sun Y, Tang M, Mao C, Zhang C, Xu Y, Sun S. Identifying potential causal effects of Parkinson's disease: A polygenic risk score-based phenome-wide association and mendelian randomization study in UK Biobank. NPJ Parkinsons Dis 2024; 10:166. [PMID: 39242620 PMCID: PMC11379879 DOI: 10.1038/s41531-024-00780-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Accepted: 08/20/2024] [Indexed: 09/09/2024] Open
Abstract
There is considerable uncertainty regarding the associations between various risk factors and Parkinson's Disease (PD). This study systematically screened and validated a wide range of potential PD risk factors from 502,364 participants in the UK Biobank. Baseline data for 1851 factors across 11 categories were analyzed through a phenome-wide association study (PheWAS). Polygenic risk scores (PRS) for PD were used to diagnose Parkinson's Disease and identify factors associated with PD diagnosis through PheWAS. Two-sample Mendelian randomization (MR) analysis was employed to assess causal relationships. PheWAS results revealed 267 risk factors significantly associated with PD-PRS among the 1851 factors, and of these, 27 factors showed causal evidence from MR analysis. Compelling evidence suggests that fluid intelligence score, age at first sexual intercourse, cereal intake, dried fruit intake, and average total household income before tax have emerged as newly identified risk factors for PD. Conversely, maternal smoking around birth, playing computer games, salt added to food, and time spent watching television have been identified as novel protective factors against PD. The integration of phenotypic and genomic data may help to identify risk factors and prevention targets for PD.
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Affiliation(s)
- Changhe Shi
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China.
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China.
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China.
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan, China.
| | - Dongrui Ma
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Mengjie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Zhiyun Wang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Chenwei Hao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Yuanyuan Liang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Yanmei Feng
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Zhengwei Hu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Xiaoyan Hao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Mengnan Guo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Shuangjie Li
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Chunyan Zuo
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Yuemeng Sun
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Mibo Tang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Chengyuan Mao
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Chan Zhang
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
| | - Yuming Xu
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan, China
| | - Shilei Sun
- Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Henan Key Laboratory of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, Henan, China
- Institute of Neuroscience, Zhengzhou University, Zhengzhou, Henan, China
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Zhang B, You J, Rolls ET, Wang X, Kang J, Li Y, Zhang R, Zhang W, Wang H, Xiang S, Shen C, Jiang Y, Xie C, Yu J, Cheng W, Feng J. Identifying behaviour-related and physiological risk factors for suicide attempts in the UK Biobank. Nat Hum Behav 2024; 8:1784-1797. [PMID: 38956227 DOI: 10.1038/s41562-024-01903-x] [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: 10/22/2023] [Accepted: 04/29/2024] [Indexed: 07/04/2024]
Abstract
Suicide is a global public health challenge, yet considerable uncertainty remains regarding the associations of both behaviour-related and physiological factors with suicide attempts (SA). Here we first estimated polygenic risk scores (PRS) for SA in 334,706 UK Biobank participants and conducted phenome-wide association analyses considering 2,291 factors. We identified 246 (63.07%) behaviour-related and 200 (10.41%, encompassing neuroimaging, blood and metabolic biomarkers, and proteins) physiological factors significantly associated with SA-PRS, with robust associations observed in lifestyle factors and mental health. Further case-control analyses involving 3,558 SA cases and 149,976 controls mirrored behaviour-related associations observed with SA-PRS. Moreover, Mendelian randomization analyses supported a potential causal effect of liability to 58 factors on SA, such as age at first intercourse, neuroticism, smoking, overall health rating and depression. Notably, machine-learning classification models based on behaviour-related factors exhibited high discriminative accuracy in distinguishing those with and without SA (area under the receiver operating characteristic curve 0.909 ± 0.006). This study provides comprehensive insights into diverse risk factors for SA, shedding light on potential avenues for targeted prevention and intervention strategies.
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Affiliation(s)
- Bei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Jia You
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Oxford Centre for Computational Neuroscience, Oxford, UK
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Xiang Wang
- Medical Psychological Centre, The Second Xiangya Hospital, Central South University, Changsha, China
- Medical Psychological Institute, Central South University, Changsha, China
- China National Clinical Research Centre on Mental Disorders (Xiangya), Changsha, China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Yuzhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Ruohan Zhang
- Department of Computer Science, University of Warwick, Coventry, UK
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Huifu Wang
- Department of Neurology, Qingdao Municipal Hospital, Qingdao University, Qingdao, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Chun Shen
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Yuchao Jiang
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Jintai Yu
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- MOE Frontiers Centre for Brain Science, Fudan University, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence and Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, UK.
- MOE Frontiers Centre for Brain Science, Fudan University, Shanghai, China.
- Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
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5
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Li Z, Ma Q, Deng Y, Rolls ET, Shen C, Li Y, Zhang W, Xiang S, Langley C, Sahakian BJ, Robbins TW, Yu JT, Feng J, Cheng W. Irritable Bowel Syndrome Is Associated With Brain Health by Neuroimaging, Behavioral, Biochemical, and Genetic Analyses. Biol Psychiatry 2024; 95:1122-1132. [PMID: 38199582 DOI: 10.1016/j.biopsych.2023.12.024] [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: 07/10/2023] [Revised: 11/14/2023] [Accepted: 12/13/2023] [Indexed: 01/12/2024]
Abstract
BACKGROUND Irritable bowel syndrome (IBS) interacts with psychopathology in a complex way; however, little is known about the underlying brain, biochemical, and genetic mechanisms. METHODS To clarify the phenotypic and genetic associations between IBS and brain health, we performed a comprehensive retrospective cohort study on a large population. Our study included 171,104 participants from the UK Biobank who underwent a thorough assessment of IBS, with the majority also providing neuroimaging, behavioral, biochemical, and genetic information. Multistage linked analyses were conducted, including phenome-wide association analysis, polygenic risk score calculation, and 2-sample Mendelian randomization analysis. RESULTS The phenome-wide association analysis showed that IBS was linked to brain health problems, including anxiety and depression, and poor cognitive performance. Significantly lower brain volumes associated with more severe IBS were found in key areas related to emotional regulation and higher-order cognition, including the medial orbitofrontal cortex/ventromedial prefrontal cortex, anterior insula, anterior and mid-cingulate cortices, dorsolateral prefrontal cortex, and hippocampus. Higher triglycerides, lower high-intensity lipoprotein, and lower platelets were also related (p < 1 × 10-10) to more severe IBS. Finally, Mendelian randomization analyses demonstrated potential causal relationships between IBS and brain health and indicated possible mediating effects of dyslipidemia and inflammation. CONCLUSIONS For the first time, this study provides a comprehensive understanding of the relationship between IBS and brain health phenotypes, integrating perspectives from neuroimaging, behavioral performance, biochemical factors, and genetics, which is of great significance for clinical applications to potentially address brain health impairments in patients with IBS.
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Affiliation(s)
- Zeyu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Qing Ma
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yueting Deng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Edmund T Rolls
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, United Kingdom; Oxford Centre for Computational Neuroscience, Oxford, United Kingdom.
| | - Chun Shen
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Yuzhu Li
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Wei Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Christelle Langley
- Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Barbara J Sahakian
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Trevor W Robbins
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Psychology, University of Cambridge, Cambridge, United Kingdom
| | - Jin-Tai Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Department of Computer Science, University of Warwick, Coventry, United Kingdom; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
| | - Wei Cheng
- Institute of Science and Technology for Brain-Inspired Intelligence, Department of Neurology, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China; Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, China.
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6
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Li C, Chen Z, He S, Chen Y, Liu J. Unveiling the influence of daily dietary patterns on brain cortical structure: insights from bidirectional Mendelian randomization. Food Funct 2023; 14:10418-10429. [PMID: 37960880 DOI: 10.1039/d3fo02879h] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Cognitive impairment is a significant concern in aging populations. This study utilized Mendelian randomization analysis to explore the impact of dietary habits and macro-nutrients on cortical structure. A bidirectional Mendelian randomization approach was employed, incorporating large-scale genetic data on dietary habits and brain cortical structure. The results did not reveal significant causal relationships between dietary factors and overall cortical structure and thickness. However, specific dietary factors showed associations with cortical structure in certain regions. For instance, fat intake affected six cortical regions, while milk, protein, fruits, and water were associated with changes in specific regions. Reverse analysis suggested that cortical thickness influenced the consumption of alcohol, carbohydrates, coffee, and fish. These findings contribute to understanding the potential mechanisms underlying the role of dietary factors in cognitive function changes and provide evidence supporting the existence of the gut-brain axis.
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Affiliation(s)
- Cong Li
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China.
| | - Zhe Chen
- Department of Thoracic Surgery, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China
| | - Shaqi He
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China.
| | - Yanjing Chen
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China.
| | - Jun Liu
- Department of Radiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, China.
- Clinical Research Center for Medical Imaging in Hunan Province, Changsha, Hunan Province, 410011, People's Republic of China
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7
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Zhang DF, Li M. Toward a Full Understanding of Causal and Modifiable Risk Factors for Alzheimer's Disease by Integrative Phenome-wide Association Studies. Biol Psychiatry 2023; 93:756-758. [PMID: 37045512 DOI: 10.1016/j.biopsych.2023.01.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 01/18/2023] [Indexed: 04/14/2023]
Affiliation(s)
- Deng-Feng Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province and the KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Yunnan, China, and the Kunming College of Life Science, University of Chinese Academy of Sciences, Yunnan, China.
| | - Ming Li
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences and Yunnan Province and the KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Yunnan, China, and the Kunming College of Life Science, University of Chinese Academy of Sciences, Yunnan, China.
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