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Orellana SC, Bethlehem RAI, Simpson-Kent IL, van Harmelen AL, Vértes PE, Bullmore ET. Childhood maltreatment influences adult brain structure through its effects on immune, metabolic, and psychosocial factors. Proc Natl Acad Sci U S A 2024; 121:e2304704121. [PMID: 38593073 PMCID: PMC11032474 DOI: 10.1073/pnas.2304704121] [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: 04/13/2023] [Accepted: 02/16/2024] [Indexed: 04/11/2024] Open
Abstract
Childhood maltreatment (CM) leads to a lifelong susceptibility to mental ill-health which might be reflected by its effects on adult brain structure, perhaps indirectly mediated by its effects on adult metabolic, immune, and psychosocial systems. Indexing these systemic factors via body mass index (BMI), C-reactive protein (CRP), and rates of adult trauma (AT), respectively, we tested three hypotheses: (H1) CM has direct or indirect effects on adult trauma, BMI, and CRP; (H2) adult trauma, BMI, and CRP are all independently related to adult brain structure; and (H3) childhood maltreatment has indirect effects on adult brain structure mediated in parallel by BMI, CRP, and AT. Using path analysis and data from N = 116,887 participants in UK Biobank, we find that CM is related to greater BMI and AT levels, and that these two variables mediate CM's effects on CRP [H1]. Regression analyses on the UKB MRI subsample (N = 21,738) revealed that greater CRP and BMI were both independently related to a spatially convergent pattern of cortical effects (Spearman's ρ = 0.87) characterized by fronto-occipital increases and temporo-parietal reductions in thickness. Subcortically, BMI was associated with greater volume, AT with lower volume and CPR with effects in both directions [H2]. Finally, path models indicated that CM has indirect effects in a subset of brain regions mediated through its direct effects on BMI and AT and indirect effects on CRP [H3]. Results provide evidence that childhood maltreatment can influence brain structure decades after exposure by increasing individual risk toward adult trauma, obesity, and inflammation.
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Affiliation(s)
- Sofia C. Orellana
- Department of Psychiatry, University of Cambridge, CambridgeCB2 0SZ, United Kingdom
| | - Richard A. I. Bethlehem
- Department of Psychiatry, University of Cambridge, CambridgeCB2 0SZ, United Kingdom
- Department of Psychology, University of Cambridge, CambridgeCB2 3EB, United Kingdom
| | - Ivan L. Simpson-Kent
- Institute of Psychology, Leiden University, Leiden2333AK, The Netherlands
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, CambridgeCB2 7EF, United Kingdom
- Department of Psychology, University of Pennsylvania, Philadelphia, PA19104-6241
| | - Anne-Laura van Harmelen
- Department of Psychiatry, University of Cambridge, CambridgeCB2 0SZ, United Kingdom
- Institute of Education and Child Studies, Leiden University, Leiden2333AK, The Netherlands
| | - Petra E. Vértes
- Department of Psychiatry, University of Cambridge, CambridgeCB2 0SZ, United Kingdom
| | - Edward T. Bullmore
- Department of Psychiatry, University of Cambridge, CambridgeCB2 0SZ, United Kingdom
- Cambridgeshire & Peterborough NHS Foundation Trust, CambridgeCB21 5EF, United Kingdom
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Larsen BA, Klinedinst BS, Wolf T, McLimans KE, Wang Q, Pollpeter A, Li T, Mohammadiarvejeh P, Fili M, Grundy JG, Willette AA. Adiposity and insulin resistance moderate the links between neuroelectrophysiology and working and episodic memory functions in young adult males but not females. Physiol Behav 2023; 271:114321. [PMID: 37567373 PMCID: PMC10592072 DOI: 10.1016/j.physbeh.2023.114321] [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: 05/19/2023] [Revised: 07/26/2023] [Accepted: 08/08/2023] [Indexed: 08/13/2023]
Abstract
INTRODUCTION Obesity and insulin resistance negatively influence neural activity and cognitive function, but electrophysiological mechanisms underlying these interrelationships remain unclear. This study investigated whether adiposity and insulin resistance moderated neural activity and underlying cognitive functions in young adults. METHODS Real-time electroencephalography (EEG) was recorded in 38 lean (n = 12) and obese (n = 26) young adults with (n = 15) and without (n = 23) insulin resistance (18-38 years, 55.3% female) as participants completed three neurocognitive tasks in working memory (Operation Span), inhibitory control (Stroop), and episodic memory (Visual Association Test). Body fat percentage was quantified by a dual-energy X-ray absorptiometry scan (DEXA/DXA). Fasting serum insulin and glucose were quantified to calculate Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) values, for which a higher value indicates more insulin resistance. Hierarchical moderated regression analysis tested these interrelationships. RESULTS In males, greater frontal negative slow wave (fNSW) and positive slow wave (PSW) amplitudes were linked to higher working memory accuracy in participants with low, but not high, body fat percentage and HOMA-IR levels. In contrast, body fat percentage and HOMA-IR did not moderate these associations in females. Furthermore, body fat percentage and HOMA-IR values moderated the relationship between greater fNSW amplitudes and better episodic memory accuracy in males, but not females. Finally, body fat percentage and insulin resistance did not moderate the link between neural activity and inhibitory control for either sex. CONCLUSION Young adult males, but not females, with higher body adiposity and insulin resistance showed reduced neural activity and worse underlying working and episodic memory functions.
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Affiliation(s)
- Brittany A Larsen
- Department of Behavioral Science, MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, United States of America
| | - Brandon S Klinedinst
- Department of Medicine, University of Washington, RR-512, Health Sciences Building, Box 356420, 1959 NE Pacific St., Seattle, Washington, 98195, United States of America
| | - Tovah Wolf
- Lifecare Alliance, 1699 W Mound St., Columbus, Ohio, 43223, United States of America
| | - Kelsey E McLimans
- Nutrition and Dietetics Department, Viterbo University, 900 Viterbo Dr., La Crosse, Wisconsin, 54601, United States of America
| | - Qian Wang
- Department of Food Science and Human Nutrition, College of Human Sciences, Iowa State University, 2312 Food Sciences Building, 536 Farm House Ln., Ames, Iowa, 50011, United States of America
| | - Amy Pollpeter
- Bioinformatics and Computational Biology Graduate Program, Department of Biomedical Sciences, College of Veterinary Medicine, Iowa State University, 1800 Christensen Dr., Ames, Iowa, 50011, United States of America
| | - Tianqi Li
- Genetics and Genomics Graduate Program, Department of Food Science and Human Nutrition, College of Human Sciences, Iowa State University, 2312 Food Sciences Building, 536 Farm House Ln., Ames, Iowa, 50011, United States of America
| | - Parvin Mohammadiarvejeh
- Department of Industrial and Manufacturing Systems Engineering, College of Engineering, Iowa State University, 3004 Black Engineering, 2529 Union Dr., Ames, Iowa, 50011, United States of America
| | - Mohammad Fili
- Department of Industrial and Manufacturing Systems Engineering, College of Engineering, Iowa State University, 3004 Black Engineering, 2529 Union Dr., Ames, Iowa, 50011, United States of America
| | - John G Grundy
- Department of Psychology, College of Liberal Arts and Sciences, Iowa State University, 901 Stange Rd., Ames, Iowa, 50011, United States of America
| | - Auriel A Willette
- Department of Food Science and Human Nutrition, College of Human Sciences, Iowa State University, 2312 Food Sciences Building, 536 Farm House Ln., Ames, Iowa, 50011, United States of America; Department of Psychology, College of Liberal Arts and Sciences, Iowa State University, 901 Stange Rd., Ames, Iowa, 50011, United States of America; Department of Neurology, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, 200 Hawkins Dr., 2007 Roy Carver Pavilion, Iowa City, Iowa, 52242, United States of America.
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Williams T, John N, Calvi A, Bianchi A, De Angelis F, Doshi A, Wright S, Shatila M, Yiannakas MC, Chowdhury F, Stutters J, Ricciardi A, Prados F, MacManus D, Braisher M, Blackstone J, Ciccarelli O, Gandini Wheeler-Kingshott CAM, Barkhof F, Chataway J. Cardiovascular risk factors in secondary progressive multiple sclerosis: A cross-sectional analysis from the MS-STAT2 randomized controlled trial. Eur J Neurol 2023; 30:2769-2780. [PMID: 37318885 DOI: 10.1111/ene.15924] [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: 03/29/2023] [Revised: 05/31/2023] [Accepted: 06/05/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND AND PURPOSE There is increasing evidence that cardiovascular risk (CVR) contributes to disability progression in multiple sclerosis (MS). CVR is particularly prevalent in secondary progressive MS (SPMS) and can be quantified through validated composite CVR scores. The aim was to examine the cross-sectional relationships between excess modifiable CVR, whole and regional brain atrophy on magnetic resonance imaging, and disability in patients with SPMS. METHODS Participants had SPMS, and data were collected at enrolment into the MS-STAT2 trial. Composite CVR scores were calculated using the QRISK3 software. Prematurely achieved CVR due to modifiable risk factors was expressed as QRISK3 premature CVR, derived through reference to the normative QRISK3 dataset and expressed in years. Associations were determined with multiple linear regressions. RESULTS For the 218 participants, mean age was 54 years and median Expanded Disability Status Scale was 6.0. Each additional year of prematurely achieved CVR was associated with a 2.7 mL (beta coefficient; 95% confidence interval 0.8-4.7; p = 0.006) smaller normalized whole brain volume. The strongest relationship was seen for the cortical grey matter (beta coefficient 1.6 mL per year; 95% confidence interval 0.5-2.7; p = 0.003), and associations were also found with poorer verbal working memory performance. Body mass index demonstrated the strongest relationships with normalized brain volumes, whilst serum lipid ratios demonstrated strong relationships with verbal and visuospatial working memory performance. CONCLUSIONS Prematurely achieved CVR is associated with lower normalized brain volumes in SPMS. Future longitudinal analyses of this clinical trial dataset will be important to determine whether CVR predicts future disease worsening.
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Affiliation(s)
- Thomas Williams
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Nevin John
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Department of Medicine, School of Clinical Sciences, Monash University, Clayton, Victoria, Australia
| | - Alberto Calvi
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Alessia Bianchi
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Floriana De Angelis
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals, London, UK
| | - Anisha Doshi
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Sarah Wright
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Madiha Shatila
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Marios C Yiannakas
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Fatima Chowdhury
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Jon Stutters
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Antonio Ricciardi
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Ferran Prados
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Universitat Oberta de Catalunya, Barcelona, Spain
| | - David MacManus
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Marie Braisher
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - James Blackstone
- Comprehensive Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK
| | - Olga Ciccarelli
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals, London, UK
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Frederik Barkhof
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals, London, UK
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Department of Radiology & Nuclear Medicine, VU University Medical Centre, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jeremy Chataway
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
- National Institute for Health Research, Biomedical Research Centre, University College London Hospitals, London, UK
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Brain functional and structural magnetic resonance imaging of obesity and weight loss interventions. Mol Psychiatry 2023; 28:1466-1479. [PMID: 36918706 DOI: 10.1038/s41380-023-02025-y] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 02/26/2023] [Accepted: 02/28/2023] [Indexed: 03/16/2023]
Abstract
Obesity has tripled over the past 40 years to become a major public health issue, as it is linked with increased mortality and elevated risk for various physical and neuropsychiatric illnesses. Accumulating evidence from neuroimaging studies suggests that obesity negatively affects brain function and structure, especially within fronto-mesolimbic circuitry. Obese individuals show abnormal neural responses to food cues, taste and smell, resting-state activity and functional connectivity, and cognitive tasks including decision-making, inhibitory-control, learning/memory, and attention. In addition, obesity is associated with altered cortical morphometry, a lowered gray/white matter volume, and impaired white matter integrity. Various interventions and treatments including bariatric surgery, the most effective treatment for obesity in clinical practice, as well as dietary, exercise, pharmacological, and neuromodulation interventions such as transcranial direct current stimulation, transcranial magnetic stimulation and neurofeedback have been employed and achieved promising outcomes. These interventions and treatments appear to normalize hyper- and hypoactivations of brain regions involved with reward processing, food-intake control, and cognitive function, and also promote recovery of brain structural abnormalities. This paper provides a comprehensive literature review of the recent neuroimaging advances on the underlying neural mechanisms of both obesity and interventions, in the hope of guiding development of novel and effective treatments.
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Melo Van Lent D, Gokingco H, Short MI, Yuan C, Jacques PF, Romero JR, DeCarli CS, Beiser AS, Seshadri S, Himali JJ, Jacob ME. Higher Dietary Inflammatory Index scores are associated with brain MRI markers of brain aging: Results from the Framingham Heart Study Offspring cohort. Alzheimers Dement 2023; 19:621-631. [PMID: 35522830 PMCID: PMC9637238 DOI: 10.1002/alz.12685] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 03/25/2022] [Accepted: 03/28/2022] [Indexed: 12/12/2022]
Abstract
INTRODUCTION We investigated cross-sectional associations between the Dietary Inflammatory Index (DII) and measures of brain volume and cerebral small vessel disease among participants of the Framingham Heart Study Offspring cohort. METHODS A total of 1897 participants (mean ± standard deviation, age 62±9) completed Food Frequency Questionnaires and brain magnetic resonance imaging (MRI). RESULTS Higher (pro-inflammatory) DII scores, averaged across a maximum of three time points, were associated with smaller total brain volume (beta ± standard error: -0.16 ± 0.03; P < .0001) after adjustment for demographic, clinical, and lifestyle covariates. In addition, higher DII scores were associated with smaller total gray matter volume (-0.08 ± 0.03; P = .003) and larger lateral ventricular volume (0.04 ± 0.02; P = .03). No associations were observed with other brain MRI measures. DISCUSSION Our findings showed associations between higher DII scores and global brain MRI measures. As we are one of the first groups to report on the associations between higher DII scores and brain volume, replication is needed to confirm our findings.
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Affiliation(s)
- Debora Melo Van Lent
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
| | - Hannah Gokingco
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
| | - Meghan I Short
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
| | - Changzheng Yuan
- School of Public Health, Zhejiang University Medical School, Hangzhou, China
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA
| | - Paul F Jacques
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Jean Mayer US Department of Agriculture Human Nutrition Research Center on Aging, Tufts University, Boston, Massachusetts, USA
| | - José R Romero
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
| | - Charles S DeCarli
- Department of Neurology, School of Medicine & Imaging of Dementia and Aging Laboratory, Center for Neuroscience, University of California Davis, Davis, California, USA
| | - Alexa S Beiser
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
| | - Sudha Seshadri
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
| | - Jayandra J Himali
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
- Department of Neurology, Boston University School of Medicine, Boston, Massachusetts, USA
- The Framingham Heart Study, Framingham, Massachusetts, USA
- Department of Biostatistics, Boston University School of Public Health, Boston, Massachusetts, USA
- Department of Population Health Sciences, UT Health San Antonio, San Antonio, Texas, USA
| | - Mini E Jacob
- Glenn Biggs Institute for Alzheimer's & Neurodegenerative Diseases, UT Health San Antonio, San Antonio, Texas, USA
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