1
|
Li P, Zhu X, Huang C, Tian S, Li Y, Qiao Y, Liu M, Su J, Tian D. Effects of obesity on aging brain and cognitive decline: A cohort study from the UK Biobank. IBRO Neurosci Rep 2025; 18:148-157. [PMID: 39896714 PMCID: PMC11786748 DOI: 10.1016/j.ibneur.2025.01.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2024] [Revised: 12/19/2024] [Accepted: 01/04/2025] [Indexed: 02/04/2025] Open
Abstract
Objective To investigate the impact of obesity on brain structure and cognition using large neuroimaging and genetic data. Methods Associations between body mass index (BMI), gray matter volume (GMV), whiter matter hyper-intensities (WMH), and fluid intelligence score (FIS) were estimated in 30283 participants from the UK Biobank. Longitudinal data analysis was conducted. Genome-wide association studies were applied to explore the genetic loci associations among BMI, GMV, WMH, and FIS. Mendelian Randomization analyses were applied to further estimate the effects of obesity on changes in the brain and cognition. Results The observational analysis revealed that BMI was negatively associated with GMV (r = -0.15, p < 1 × 10-24) and positively associated with WMH (r = 0.08, p < 1 × 10-16). The change in BMI was negatively associated with the change in GMV (r = -0.04, p < 5 × 10-5). Genetic overlap was observed among BMI, GMV, and FIS at SBK1 (rs2726032), SGF29 (rs17707300), TUFM (rs3088215), AKAP6 (rs1051695), IL27 (rs4788084), and SPI1 (rs3740689 and rs935914). The MR analysis provided evidence that higher BMI was associated with lower GMV (β=-1119.12, p = 5.77 ×10-6), higher WMH (β=42.76, p = 6.37 ×10-4), and lower FIS (β=-0.081, p = 1.92 ×10-23). Conclusions The phenotypic and genetic association between obesity and aging brain and cognitive decline suggested that weight control could be a promising strategy for slowing the aging brain.
Collapse
Affiliation(s)
- Panlong Li
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Xirui Zhu
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Chun Huang
- School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Shan Tian
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuna Li
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yuan Qiao
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Min Liu
- Department of Hypertension, Henan Provincial People’s Hospital & Zhengzhou University People’s Hospital, Zhengzhou, China
| | - Jingjing Su
- Department of Neurology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Dandan Tian
- Department of Hypertension, Henan Provincial People’s Hospital & Zhengzhou University People’s Hospital, Zhengzhou, China
| |
Collapse
|
2
|
Dong Y, Zhang P, Zhong J, Wang J, Xu Y, Huang H, Liu X, Sun W. Modifiable lifestyle factors influencing neurological and psychiatric disorders mediated by structural brain reserve: An observational and Mendelian randomization study. J Affect Disord 2025; 372:440-450. [PMID: 39672473 DOI: 10.1016/j.jad.2024.12.038] [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: 06/18/2024] [Revised: 09/27/2024] [Accepted: 12/08/2024] [Indexed: 12/15/2024]
Abstract
BACKGROUND Modifiable lifestyle factors are implicated as risk factors for neurological and psychiatric disorders, but whether these associations are causal remains uncertain. We aimed to evaluate associations and ascertain causal relationships between modifiable lifestyle factors, neurological and psychiatric disorder risk, and brain structural magnetic resonance imaging (MRI) markers. METHODS We analyzed data from over 50,000 UK Biobank participants with self-reported lifestyle factors, including alcohol consumption, smoking, physical activity, diet, sleep, electronic device use, and sexual factors. Primary outcomes were stroke, all-cause dementia, Parkinson's disease (PD), Major depression disorder (MDD), Anxiety Disorders (ANX), and Bipolar Disorder (BIP), alongside MRI markers. Summary statistics were obtained from genome-wide association studies and Mendelian randomization (MR) analyses investigated bidirectional associations between lifestyle factors, neurological/psychiatric disorders, and MRI markers, with mediation assessed using multivariable Mendelian randomization (MVMR). RESULTS Cross-sectional analyses identified lifestyle factors were associated with neurological and psychiatric disorders and brain morphology. MR confirmed causal relationships, including lifetime smoking index on Stroke, PD, MDD, ANX and BIP; play computer games on BIP; leisure screen time on Stroke and MDD; automobile speeding propensity on MDD; sexual factors on MDD and BIP; sleep characteristics on BIP and MDD. Brain structure mediated several lifestyle-disorder associations, such as daytime dozing and dementia, lifetime smoking and PD and age first had sexual intercourse and PD. CONCLUSION Our results provide support for a causal effect of multiple lifestyle measures on the risk of neurological and psychiatric disorders, with brain structural morphology serving as a potential biological mediator in their associations.
Collapse
Affiliation(s)
- Yiran Dong
- Department of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China
| | - Pan Zhang
- Department of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China
| | - Jinghui Zhong
- Department of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China
| | - Jinjing Wang
- Department of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China
| | - Yingjie Xu
- Department of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China
| | - Hongmei Huang
- Department of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China
| | - Xinfeng Liu
- Department of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China.
| | - Wen Sun
- Department of Neurology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230001, China.
| |
Collapse
|
3
|
Wang J, Xu W, Dove A, Salami A, Yang W, Ma X, Bennett DA, Xu W. Influence of lung function on macro- and micro-structural brain changes in mid- and late-life. Int J Surg 2025; 111:2467-2477. [PMID: 39869397 DOI: 10.1097/js9.0000000000002228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2024] [Accepted: 11/29/2024] [Indexed: 01/28/2025]
Abstract
INTRODUCTION Lung function has been associated with cognitive decline and dementia, but the extent to which lung function impacts brain structural changes remains unclear. We aimed to investigate the association of lung function with structural macro- and micro-brain changes across mid- and late-life. METHODS The study included a total of 37 164 neurologic disorder-free participants aged 40-70 years from the UK Biobank, who underwent brain MRI scans 9 years after baseline. After 2.5 years, a subsample (n = 3895) underwent a second MRI scan. Lung function was assessed using a composite score based on forced expiratory volume in 1 second, forced vital capacity, and peak expiratory flow, and divided into tertiles (i.e., low, moderate, and high). Structural brain volumes (including total brain, gray matter, white matter, hippocampus, and white matter hyperintensities) and diffusion markers (fractional anisotropy [FA] and mean diffusivity [MD]) were assessed. Data were analyzed using linear regression and mixed-effects models. RESULTS Compared to high lung function, low lung function was associated with smaller total brain, gray matter, white matter, and hippocampal volume, as well as lower white matter integrity. Over the 2.5-year follow-up, low lung function was associated with reduced white matter and hippocampal volume, reduced FA, and increased white matter hyperintensity volume and MD. After stratification by age, the associations remained significant among adults aged 40-60 years and 60+ years. CONCLUSION Low lung function is associated with macro- and micro-structural brain changes involving both neurodegenerative and vascular pathologies. This association is significant in both mid- and late-life.
Collapse
Affiliation(s)
- Jiao Wang
- Department of Epidemiology, College of Preventive Medicine,Third Military Medical University, Chongqing, China
| | - Weige Xu
- Department of Radiology, Tianjin Gongan Hospital, Tianjin, China
| | - Abigail Dove
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Alireza Salami
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Wenzhe Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Xiangyu Ma
- Department of Epidemiology, College of Preventive Medicine,Third Military Medical University, Chongqing, China
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, Illinois
| | - Weili Xu
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
4
|
Liu D, Li N, Zhu Y, Chen Q, Feng J. Asymmetric U-shaped relationship between blood glucose and white matter lesions: results of a cross-sectional study. BMC Neurol 2025; 25:65. [PMID: 39953442 PMCID: PMC11827292 DOI: 10.1186/s12883-025-04077-9] [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: 12/18/2023] [Accepted: 02/07/2025] [Indexed: 02/17/2025] Open
Abstract
BACKGROUND Elderly individuals are susceptible to the accrual of White Matter Lesions (WMLs), a subcategory of cerebral small-vessel disease. WMLs are strongly linked to an increased risk of strokes, intracerebral hemorrhages, and dementia. While the relationship between blood glucose levels and the development of WMLs has been investigated in previous studies, the findings remain inconsistent. Some evidence suggests that glucose dysregulation, including both hypo- and hyperglycemia, may contribute to WML formation through mechanisms such as endothelial dysfunction and chronic inflammation. However, other studies report no significant correlation. This inconsistency underscores the need for further investigation. METHODS In this investigation, the primary data were derived from a predictive mathematical model designed to estimate WMLs based on parameters obtained from routine medical examinations, with head MRI scans serving as the reference standard for WML diagnosis and quantification. We leveraged multivariable logistic regression analysis to scrutinize the relationship between blood glucose concentrations and WMLs. Additionally, we employed a restricted cubic spline regression model to investigate a potential non-linear relationship between these variables. RESULTS There were 1904 participants who underwent medical check-ups which included a head MRI. Generally, the relationship between blood glucose levels and white matter lesions followed an asymmetric U-shaped curve (P for non-linearity = 0.004). A consistent finding was that compared to the individuals in the 2nd and 3rd quartiles (95 to 107 mg/dl), the 1st quartile (OR, 1.71; 95% CI: 1.26-2.30) and 4th quartile (OR, 1.57; 95%CI: 1.12-2.20) had white matter lesions were significantly higher. CONCLUSION An asymmetric U-shaped relationship exists between blood glucose and WMLs, with the lowest risk occurring at 95-107 mg/dl. Management of blood glucose can help prevent the occurrence and development of WMLs. However, the study's cross-sectional design limits causal inference, and the reliance on pre-existing data constrained the availability of variables.
Collapse
Affiliation(s)
- Dayuan Liu
- Department of Neurosurgery, The Second Affiliated Hospital of Hainan Medical University, 368 Yehai Avenue, Longhua District, Haikou City, Hainan Province, 570311, China
| | - Ning Li
- Department of Neurosurgery, The Second Affiliated Hospital of Hainan Medical University, 368 Yehai Avenue, Longhua District, Haikou City, Hainan Province, 570311, China
| | - Yubo Zhu
- Department of Neurosurgery, The Second Affiliated Hospital of Hainan Medical University, 368 Yehai Avenue, Longhua District, Haikou City, Hainan Province, 570311, China
| | - Qianhua Chen
- Hainan Medical University, No.3 Xueyuan Road, Longhua District, Haikou City, Hainan Province, 571199, China
| | - Jigao Feng
- Department of Neurosurgery, The Second Affiliated Hospital of Hainan Medical University, 368 Yehai Avenue, Longhua District, Haikou City, Hainan Province, 570311, China.
| |
Collapse
|
5
|
Zawada SJ, Ganjizadeh A, Demaerschalk BM, Erickson BJ. Behavioral Monitoring in Transient Ischemic Attack and Stroke Patients: Exploratory Micro- and Macrostructural Imaging Insights for Identifying Post-Stroke Depression with Accelerometers in UK Biobank. SENSORS (BASEL, SWITZERLAND) 2025; 25:963. [PMID: 39943601 PMCID: PMC11820421 DOI: 10.3390/s25030963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2024] [Revised: 01/23/2025] [Accepted: 01/30/2025] [Indexed: 02/16/2025]
Abstract
To examine the association between post-stroke depression (PSD) and macrostructural and microstructural brain measures, and to explore whether changes in accelerometer-measured physical activity (PA) are associated with PSD, we conducted an exploratory study in UK Biobank with dementia-free participants diagnosed with at least one prior stroke. Eligible participants (n = 1186) completed an MRI scan. Depression was classified based on positive depression screening scores (PHQ-2 ≥ 3). Multivariate linear regression models assessed the relationships between depression and structural and diffusion measures generated from brain MRI scans. Logistic regression models were used to examine the relationship between accelerometer-measured daily PA and future depression (n = 367). Depression was positively associated with total white matter hyperintensities (WMHs) volume (standardized β [95% CI]-0.1339 [0.012, 0.256]; FDR-adjusted p-value-0.039), periventricular WMHs volume (standardized β [95% CI]-0.1351 [0.020, 0.250]; FDR-adjusted p-value-0.027), and reduced MD for commissural fibers (standardized β [95% CI]--0.139 [-0.255, -0.024]; adjusted p-value-0.045). The odds of depression decreased by 0.3% for each daily minute spent in objectively measured light PA, while each minute spent in sleep from midnight to 6:00 AM was associated with a 0.9% decrease in the odds of depression. This early-stage analysis using a population cohort offers a scientific rationale for researchers using multimodal data sources to investigate the heterogenous nature of PSD and, potentially, identify stroke patients at risk of poor outcomes.
Collapse
Affiliation(s)
| | - Ali Ganjizadeh
- Mayo Clinic Artificial Intelligence Laboratory, Rochester, MN 55905, USA; (A.G.); (B.J.E.)
| | - Bart M. Demaerschalk
- Mayo Clinic Department of Neurology, Division of Cerebrovascular Diseases, Phoenix, AZ 85054, USA;
| | - Bradley J. Erickson
- Mayo Clinic Artificial Intelligence Laboratory, Rochester, MN 55905, USA; (A.G.); (B.J.E.)
| |
Collapse
|
6
|
Möller HE. Editorial for "Associations of Central Arterial Stiffness With Brain White Matter Integrity and Gray Matter Volume in MRI Across the Adult Lifespan". J Magn Reson Imaging 2025. [PMID: 39902718 DOI: 10.1002/jmri.29733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2025] [Accepted: 01/23/2025] [Indexed: 02/06/2025] Open
Affiliation(s)
- Harald E Möller
- NMR Methods & Development Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| |
Collapse
|
7
|
Qiu Y, Cheng L, Xiong Y, Liu Z, Shen C, Wang L, Lu Y, Wei S, Zhang L, Yang SB, Zhang X. Advances in the Study of Necroptosis in Vascular Dementia: Focus on Blood-Brain Barrier and Neuroinflammation. CNS Neurosci Ther 2025; 31:e70224. [PMID: 39915907 PMCID: PMC11802338 DOI: 10.1111/cns.70224] [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: 08/06/2024] [Revised: 12/18/2024] [Accepted: 01/09/2025] [Indexed: 02/09/2025] Open
Abstract
BACKGROUND Vascular dementia (VaD) includes a group of brain disorders that are characterized by cerebrovascular pathology.Neuroinflammation, disruption of the blood-brain barrier (BBB) permeability, white matter lesions, and neuronal loss are all significant pathological manifestations of VaD and play a key role in disease progression. Necroptosis, also known asprogrammed necrosis, is a mode of programmed cell death distinct from apoptosis and is closely associated with ischemic injury and neurodegenerative diseases. Recent studies have shown that necroptosis in VaD exacerbates BBB destruction, activates neuroinflammation, promotes neuronal loss, and severely affects VaD prognosis. RESULTS AND CONCLUSIONS In this review, we outline the significant roles of necroptosis and its molecular mechanisms in the pathological process of VaD, with a particular focus on the role of necroptosis in modulating neuroinflammation and exacerbating the disruption of BBB permeability in VaD, and elaborate on the molecular regulatory mechanisms and the centrally involved cells of necroptosis mediated by tumor necrosis factor-α in neuroinflammation in VaD. We also analyze the possibility and specific strategy that targeting necroptosis would help inhibit neuroinflammation and BBB destruction in VaD. With a focus on necroptosis, this study delved into its impact on the pathological changes and prognosis of VaD to provide new treatment ideas.
Collapse
Affiliation(s)
- Yuemin Qiu
- Department of PathologyAffiliated Hospital of Jiujiang UniversityJiujiangJiangxiChina
- Department of PathologyJiujiang Clinical Precision Medicine Research CenterJiujiangJiangxiChina
| | - Lin Cheng
- Department of PathologyJiujiang Clinical Precision Medicine Research CenterJiujiangJiangxiChina
- Department of NeurologyAffiliated Hospital of Jiujiang UniversityJiujiangJiangxiChina
| | - Yinyi Xiong
- Department of PathologyJiujiang Clinical Precision Medicine Research CenterJiujiangJiangxiChina
- Department of RehabilitationAffiliated Hospital of Jiujiang UniversityJiujiangJiangxiChina
| | - Ziying Liu
- Department of PathologyAffiliated Hospital of Jiujiang UniversityJiujiangJiangxiChina
- Department of PathologyJiujiang Clinical Precision Medicine Research CenterJiujiangJiangxiChina
| | - Chunxiao Shen
- Department of PathologyAffiliated Hospital of Jiujiang UniversityJiujiangJiangxiChina
- Department of PathologyJiujiang Clinical Precision Medicine Research CenterJiujiangJiangxiChina
| | - Liangliang Wang
- Department of PathologyAffiliated Hospital of Jiujiang UniversityJiujiangJiangxiChina
- Department of PathologyJiujiang Clinical Precision Medicine Research CenterJiujiangJiangxiChina
| | - Yujia Lu
- Department of PathologyAffiliated Hospital of Jiujiang UniversityJiujiangJiangxiChina
- Department of PathologyJiujiang Clinical Precision Medicine Research CenterJiujiangJiangxiChina
| | - Shufei Wei
- Department of PathologyAffiliated Hospital of Jiujiang UniversityJiujiangJiangxiChina
- Department of PathologyJiujiang Clinical Precision Medicine Research CenterJiujiangJiangxiChina
| | - Lushun Zhang
- Department of PathologyAffiliated Hospital of Jiujiang UniversityJiujiangJiangxiChina
- Department of PathologyJiujiang Clinical Precision Medicine Research CenterJiujiangJiangxiChina
| | - Seung Bum Yang
- Department of Medical Non‐Commissioned OfficerWonkwang Health Science UniversityIksanRepublic of Korea
| | - Xiaorong Zhang
- Department of PathologyAffiliated Hospital of Jiujiang UniversityJiujiangJiangxiChina
- Department of PathologyJiujiang Clinical Precision Medicine Research CenterJiujiangJiangxiChina
| |
Collapse
|
8
|
Vadinova V, Brownsett SLE, Garden KL, Roxbury T, O’Brien K, Copland DA, McMahon KL, Sihvonen AJ. Early subacute frontal callosal microstructure and language outcomes after stroke. Brain Commun 2025; 7:fcae370. [PMID: 39845737 PMCID: PMC11753390 DOI: 10.1093/braincomms/fcae370] [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: 12/30/2023] [Revised: 05/31/2024] [Accepted: 12/09/2024] [Indexed: 01/24/2025] Open
Abstract
The integrity of the frontal segment of the corpus callosum, forceps minor, is particularly susceptible to age-related degradation and has been associated with cognitive outcomes in both healthy and pathological ageing. The predictive relevance of forceps minor integrity in relation to cognitive outcomes following a stroke remains unexplored. Our goal was to evaluate whether the heterogeneity of forceps minor integrity, assessed early after stroke onset (2-6 weeks), contributes to explaining variance in longitudinal outcomes in post-stroke aphasia. Both word- and sentence-level tasks were employed to assess language comprehension and language production skills in individuals with first-ever left-hemisphere stroke during the early subacute and chronic phases of recovery (n = 25). Structural and diffusion neuroimaging data from the early subacute phase were used to quantify stroke lesion load and bilateral forceps minor radial diffusivity. Multiple linear regression models examined whether early subacute radial diffusivity within the forceps minor, along with other factors (stroke lesion load, age, sex and education), explained variance in early subacute performance and longitudinal recovery (i.e. change in behavioural performance). Increased early subacute radial diffusivity in the forceps minor was associated with poor early subacute comprehension (t = -2.36, P = 0.02) but not production (P = 0.35) when controlling for stroke lesion load, age, sex and education. When considering longitudinal recovery, early subacute radial diffusivity in the forceps minor was not linked to changes in performance in either comprehension (P = 0.11) or production (P = 0.36) under the same control variables. The examination of various language components and processes led to novel insights: (i) language comprehension may be more susceptible to white matter brain health than language production and (ii) the influence of white matter brain health is reflected in early comprehension performance rather than longitudinal changes in comprehension. These results suggest that evaluating baseline callosal integrity is a valuable approach for assessing the risk of impaired language comprehension post-stroke, while also underscoring the importance of nuanced analyses of behavioural outcomes to enhance our understanding of the clinical applicability of baseline brain health measures.
Collapse
Affiliation(s)
- Veronika Vadinova
- Queensland Aphasia Research Centre, University of Queensland, Brisbane 4029, Australia
- School of Health and Rehabilitation Sciences, University of Queensland, Brisbane 4072, Australia
- NHMRC Centre for Research Excellence in Aphasia Recovery & Rehabilitation, La Trobe University, Melbourne 3086, Australia
| | - Sonia L E Brownsett
- Queensland Aphasia Research Centre, University of Queensland, Brisbane 4029, Australia
- School of Health and Rehabilitation Sciences, University of Queensland, Brisbane 4072, Australia
- NHMRC Centre for Research Excellence in Aphasia Recovery & Rehabilitation, La Trobe University, Melbourne 3086, Australia
| | - Kimberley L Garden
- Queensland Aphasia Research Centre, University of Queensland, Brisbane 4029, Australia
- School of Health and Rehabilitation Sciences, University of Queensland, Brisbane 4072, Australia
- NHMRC Centre for Research Excellence in Aphasia Recovery & Rehabilitation, La Trobe University, Melbourne 3086, Australia
| | - Tracy Roxbury
- Queensland Aphasia Research Centre, University of Queensland, Brisbane 4029, Australia
| | - Katherine O’Brien
- Queensland Aphasia Research Centre, University of Queensland, Brisbane 4029, Australia
| | - David A Copland
- Queensland Aphasia Research Centre, University of Queensland, Brisbane 4029, Australia
- School of Health and Rehabilitation Sciences, University of Queensland, Brisbane 4072, Australia
- NHMRC Centre for Research Excellence in Aphasia Recovery & Rehabilitation, La Trobe University, Melbourne 3086, Australia
| | - Katie L McMahon
- School of Clinical Sciences, Centre for Biomedical Technologies, Queensland University of Technology, Brisbane 4001, Australia
| | - Aleksi J Sihvonen
- Queensland Aphasia Research Centre, University of Queensland, Brisbane 4029, Australia
- School of Health and Rehabilitation Sciences, University of Queensland, Brisbane 4072, Australia
- NHMRC Centre for Research Excellence in Aphasia Recovery & Rehabilitation, La Trobe University, Melbourne 3086, Australia
- Cognitive Brain Research Unit (CBRU), University of Helsinki, Helsinki 00290, Finland
- Centre of Excellence in Music, Mind, Body and Brain, University of Helsinki, Helsinki FI-40014, Finland
| |
Collapse
|
9
|
Sultan AA, Karthikeyan S, Grigorian A, Kennedy KG, Mio M, MacIntosh BJ, Goldstein BI. Cerebral blood flow in relation to peripheral endothelial function in youth bipolar disorder. Prog Neuropsychopharmacol Biol Psychiatry 2024; 135:111087. [PMID: 39004332 DOI: 10.1016/j.pnpbp.2024.111087] [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: 02/29/2024] [Revised: 07/03/2024] [Accepted: 07/10/2024] [Indexed: 07/16/2024]
Abstract
INTRODUCTION Anomalous cerebral blood flow (CBF) is evident in bipolar disorder (BD), however the extent to which CBF reflects peripheral vascular function in BD is unknown. This study investigated endothelial function, an index of early atherosclerosis and cardiovascular disease risk, in relation to CBF among youth with BD. METHODS Participants included 113 youth, 13-20 years old (66 BD; 47 healthy controls [HC]). CBF was measured using arterial spin labeling with 3T MRI. Region of interest analyses (ROI; global grey matter, middle frontal gyrus, anterior cingulate cortex, temporal cortex, caudate) were undertaken alongside voxel-wise analyses. Reactive hyperemia index (RHI), a measure of endothelial function, was assessed non-invasively via pulse amplitude tonometry. General linear models were used to examine RHI and RHI-by-diagnosis associations with CBF, controlling for age, sex, and body mass index. Bonferroni correction for multiple comparisons was used for ROI analyses, such that the significance level was divided by the number of ROIs (α = 0.05/5 = 0.01). Cluster-extent thresholding was used to correct for multiple comparisons for voxel-wise analyses. RESULTS ROI findings were not significant after correction. Voxel-wise analyses found that higher RHI was associated with lower left thalamus CBF in the whole group (p < 0.001). Additionally, significant RHI-by-diagnosis associations with CBF were found in three clusters: left intracalcarine cortex (p < 0.001), left thalamus (p < 0.001), and right frontal pole (p = 0.006). Post-hoc analyses showed that in each cluster, higher RHI was associated with lower CBF in BD, but higher CBF in HC. CONCLUSION We found that RHI was differentially associated with CBF in youth with BD versus HC. The unanticipated association of higher RHI with lower CBF in BD could potentially reflect a compensatory mechanism. Future research, including prospective studies and experimental designs are warranted to build on the current findings.
Collapse
Affiliation(s)
- Alysha A Sultan
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Sudhir Karthikeyan
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Anahit Grigorian
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Kody G Kennedy
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Megan Mio
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Bradley J MacIntosh
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Physical Sciences, Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada; Department of Physics and Computational Radiology, Oslo University Hospital, Oslo, Norway
| | - Benjamin I Goldstein
- Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, ON, Canada; Department of Pharmacology, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
| |
Collapse
|
10
|
Moodie JE, Buchanan C, Furtjes A, Conole E, Stolicyn A, Corley J, Ferguson K, Hernandez MV, Maniega SM, Russ TC, Luciano M, Whalley H, Bastin ME, Wardlaw J, Deary I, Cox S. Brain maps of general cognitive function and spatial correlations with neurobiological cortical profiles. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.12.17.628670. [PMID: 39764021 PMCID: PMC11702631 DOI: 10.1101/2024.12.17.628670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/30/2025]
Abstract
In this paper, we attempt to answer two questions: 1) which regions of the human brain, in terms of morphometry, are most strongly related to individual differences in domain-general cognitive functioning (g)? and 2) what are the underlying neurobiological properties of those regions? We meta-analyse vertex-wise g-cortical morphometry (volume, surface area, thickness, curvature and sulcal depth) associations using data from 3 cohorts: the UK Biobank (UKB), Generation Scotland (GenScot), and the Lothian Birth Cohort 1936 (LBC1936), with the meta-analytic N = 38,379 (age range = 44 to 84 years old). These g-morphometry associations vary in magnitude and direction across the cortex (|β| range = -0.12 to 0.17 across morphometry measures) and show good cross-cohort agreement (mean spatial correlation r = 0.57, SD = 0.18). Then, to address (2), we bring together existing - and derive new - cortical maps of 33 neurobiological characteristics from multiple modalities (including neurotransmitter receptor densities, gene expression, functional connectivity, metabolism, and cytoarchitectural similarity). We discover that these 33 profiles spatially covary along four major dimensions of cortical organisation (accounting for 65.9% of the variance) and denote aspects of neurobiological scaffolding that underpin the spatial patterning of MRI-cognitive associations we observe (significant |r| range = 0.21 to 0.56). Alongside the cortical maps from these analyses, which we make openly accessible, we provide a compendium of cortex-wide and within-region spatial correlations among general and specific facets of brain cortical organisation and higher order cognitive functioning, which we hope will serve as a framework for analysing other aspects of behaviour-brain MRI associations.
Collapse
Affiliation(s)
- Joanna E. Moodie
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Colin Buchanan
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| | - Anna Furtjes
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
| | - Eleanor Conole
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
| | - Aleks Stolicyn
- Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - Janie Corley
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
| | - Karen Ferguson
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - Maria Valdes Hernandez
- Centre for Clinical Brain Sciences, University of Edinburgh, UK
- Row Fogo Centre for Research into Small Vessel Diseases
| | - Susana Munoz Maniega
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - Tom C. Russ
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, UK
- Alzheimer Scotland Dementia Research Centre, University of Edinburgh, UK
- Dementia Network, NHS Research Scotland
| | | | - Heather Whalley
- Centre for Clinical Brain Sciences, University of Edinburgh, UK
| | - Mark E. Bastin
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
| | - Joanna Wardlaw
- Centre for Clinical Brain Sciences, University of Edinburgh, UK
- UK Dementia Research Institute
- Row Fogo Centre for Research into Small Vessel Diseases
| | - Ian Deary
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
| | - Simon Cox
- Lothian Birth Cohorts, Department of Psychology, The University of Edinburgh, UK
- Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
| |
Collapse
|
11
|
Zhang X, Huang Y, Liu S, Ma S, Li M, Zhu Z, Wang W, Zhang X, Liu J, Tang S, Hu Y, Ge Z, Yu H, He M, Shang X. Machine learning based metabolomic and genetic profiles for predicting multiple brain phenotypes. J Transl Med 2024; 22:1098. [PMID: 39627804 PMCID: PMC11613467 DOI: 10.1186/s12967-024-05868-3] [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: 07/08/2024] [Accepted: 11/09/2024] [Indexed: 12/08/2024] Open
Abstract
BACKGROUND It is unclear regarding the association between metabolomic state/genetic risk score(GRS) and brain volumes and how much of variance of brain volumes is attributable to metabolomic state or GRS. METHODS Our analysis included 8635 participants (52.5% females) aged 40-70 years at baseline from the UK Biobank. Metabolomic profiles were assessed using nuclear magnetic resonance at baseline (between 2006 and 2010). Brain volumes were measured using magnetic resonance imaging between 2014 and 2019. Machine learning was used to generate metabolomic state and GRS for each of 21 brain phenotypes. RESULTS Individuals in the top 20% of metabolomic state had 2.4-35.7% larger volumes of 21 individual brain phenotypes compared to those in the bottom 20% while the corresponding number for GRS ranged from 1.5 to 32.8%. The proportion of variance of brain volumes (R [2]) explained by the corresponding metabolomic state ranged from 2.2 to 19.4%, and the corresponding number for GRS ranged from 0.8 to 8.7%. Metabolomic state provided no or minimal additional prediction values of brain volumes to age and sex while GRS provided moderate additional prediction values (ranging from 0.8 to 8.8%). No significant interplay between metabolomic state and GRS was observed, but the association between metabolomic state and some regional brain volumes was stronger in men or younger individuals. Individual metabolomic profiles including lipids and fatty acids were strong predictors of brain volumes. CONCLUSIONS In conclusion, metabolomic state is strongly associated with multiple brain volumes but provides minimal additional prediction value of brain volumes to age + sex. Although GRS is a weaker contributor to brain volumes than metabolomic state, it provides moderate additional prediction value of brain volumes to age + sex. Our findings suggest metabolomic state and GRS are important predictors for multiple brain phenotypes.
Collapse
Affiliation(s)
- Xueli Zhang
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China
| | - Yu Huang
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Shunming Liu
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Shuo Ma
- Medical Big Data Center, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences, Southern Medical University), Guangzhou, China
| | - Min Li
- Medical Research Institute, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Zhuoting Zhu
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
- Centre for Eye Research Australia, Melbourne, VIC, 3002, Australia
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China
| | - Xiayin Zhang
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Jiahao Liu
- Centre for Eye Research Australia, Melbourne, VIC, 3002, Australia
| | - Shulin Tang
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Yijun Hu
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China
| | - Zongyuan Ge
- Monash e-Research Center, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Center, Monash University, Melbourne, VIC, 3800, Australia
| | - Honghua Yu
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
| | - Mingguang He
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
- Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong, Science Park, Hong Kong, China.
| | - Xianwen Shang
- Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
- Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China.
- Centre for Eye Research Australia, Melbourne, VIC, 3002, Australia.
- Department of Medicine (Royal Melbourne Hospital), University of Melbourne, Melbourne, VIC, 3050, Australia.
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
- Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong, China.
| |
Collapse
|
12
|
Feng L, Ye Z, Pan Y, McCoy RG, Mitchell BD, Kochunov P, Thompson PM, Chen J, Liang M, Nguyen TT, Shenassa E, Li Y, Canida T, Ke H, Lee H, Liu S, Hong LE, Chen C, Lei DKY, Chen S, Ma T. Adherence to Life's Essential 8 is associated with delayed white matter aging. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.12.02.24318274. [PMID: 39677426 PMCID: PMC11643169 DOI: 10.1101/2024.12.02.24318274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2024]
Abstract
Importance The American Heart Association introduced Life's Essential 8 (LE8) as a checklist of healthy lifestyle factors to help older individuals maintain and improve cardiovascular health and live longer. How LE8 can foster healthy brain aging and interact with genetic risk factors to render the aging brain less vulnerable to dementia is not well understood. Objective To investigate the impact of LE8 on the white matter brain aging and the moderating effects of the APOE4 allele. Design Setting and Participants This cross-sectional study uses genetic, imaging, and other health-related data collected in the UK Biobank cohort. Participants included non-pregnant whites with LE8 variables, diffusion tensor imaging data, and genetic data on APOE4 available, and excluded the extreme white matter hyperintensities. The baseline assessment was performed from 2006 to 2010. The diffusion tensor imaging data were collected since 2014. Exposures LE8 variables, encompassing diet, physical activity, smoking, sleep, body mass index, lipids, hemoglobin, and blood pressure. Main Outcomes and Measures The white matter brain age was predicted from regional fractional anisotropy measures derived from diffusion tensor imaging data using the random forest regression method. The outcome white matter brain age gap was calculated by subtracting individuals' chronological age from their predicted brain age. Results The analysis included 9,430 women and 9,387 men (mean age 55.45 [SD: 7.46] years). Higher LE8 scores were associated with lower white matter brain age gap, indicating delayed brain aging. The findings are consistent for each of the individual LE8 variables. The effect was stronger among non- APOE4 carriers (124 days younger per 10-point increase, 95% CI, 102 to 146 days; P<0.001) than APOE4 carriers (84 days younger per 10-point increase, 95% CI, 47 to 120 days; P<0.001). Notably, early middle-aged women with APOE4 showed significant interactions between LE8 scores and brain aging (P interaction = 0.048), not observed in men. Conclusions and Relevance Adherence to LE8 is associated with delayed brain aging, moderated by genetic factors such as APOE4 . These findings highlight the potential of behavioral and lifestyle interventions in reducing dementia risk, emphasizing tailored prevention plans for those with different genetic predispositions to dementia and sex.
Collapse
|
13
|
Teo TWJ, Saffari SE, Chan LL, Welton T. Comparison of MRI head motion indicators in 40,969 subjects informs neuroimaging study design. Sci Rep 2024; 14:29430. [PMID: 39604510 PMCID: PMC11603305 DOI: 10.1038/s41598-024-79827-9] [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: 07/09/2024] [Accepted: 11/12/2024] [Indexed: 11/29/2024] Open
Abstract
Head motion during MRI compromises image quality for clinical assessments and research. Active motion reduction strategies are effective but rarely applied due to uncertainty in their value for a given study. The ability to anticipate motion based on group characteristics would aid effective neuroimaging study design. This study compared putative motion indicators for their association to fMRI head motion in a large UK Biobank cohort (n = 40,969, aged 54.9 ± 7.5 years, 53% male). Body Mass Index (BMI; βadj = .050, p < .001) and ethnicity (βadj = 0.068, p < 0.001) were the strongest indicators of head motion. A ten-point increase in BMI, which is the difference between "healthy" and "obese", corresponded to a 51% increase in motion. Findings were similar in a subgroup with no lifetime diagnoses (n = 6858). Motion was not significantly increased in individuals with psychiatric disorders, musculoskeletal disorders, or diabetes. The hypertension subgroup exhibited significantly increased motion (p = 0.048). Cognitive task performance (t = 110.83, p < 0.001) and prior scan experience (t = 7.16, p < 0.001) were associated with increased head motion. Our results inform decision making for implementation of motion reduction strategies in MRI. BMI outweighs other motion indicators, while blood pressure, age, smoking and caffeine consumption are relatively less influential. Disease diagnosis alone is not a good indicator of MRI head motion.
Collapse
Affiliation(s)
- Thomas Wei Jun Teo
- National Neuroscience Institute, 11 Jalan Tan Tock Seng, Singapore, 308433, Singapore
| | - Seyed Ehsan Saffari
- National Neuroscience Institute, 11 Jalan Tan Tock Seng, Singapore, 308433, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Ling Ling Chan
- National Neuroscience Institute, 11 Jalan Tan Tock Seng, Singapore, 308433, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
| | - Thomas Welton
- National Neuroscience Institute, 11 Jalan Tan Tock Seng, Singapore, 308433, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
| |
Collapse
|
14
|
Yang S, Webb AJS. Reduced neurovascular coupling is associated with increased cardiovascular risk without established cerebrovascular disease: A cross-sectional analysis in UK biobank. J Cereb Blood Flow Metab 2024:271678X241302172. [PMID: 39576882 PMCID: PMC11585009 DOI: 10.1177/0271678x241302172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/13/2024] [Revised: 10/31/2024] [Accepted: 11/02/2024] [Indexed: 11/24/2024]
Abstract
Mid-life vascular risk factors predict late-life cerebrovascular diseases and poor global brain health. Although endothelial dysfunction is hypothesized to contribute to this process, evidence of impaired neurovascular function in early stages remains limited. In this cross-sectional study of 31,934 middle-aged individuals from UK Biobank without established cerebrovascular disease, the overall 10-year risk of cardiovascular events was associated with reduced neurovascular coupling (p < 2 × 10-16) during a visual task with functional MRI, including in participants with no clinically apparent brain injury on MRI. Diabetes, smoking, waist-hip ratio, and hypertension were each strongly associated with decreased neurovascular coupling with the strongest relationships for diabetes and smoking, whilst in older adults there was an inverted U-shaped relationship with DBP, peaking at 70-80 mmHg DBP. These findings indicate that mid-life vascular risk factors are associated with impaired cerebral endothelial-dependent neurovascular function in the absence of overt brain injury. Neurovascular dysfunction, measured by neurovascular coupling, may play a role in the development of late-life cerebrovascular disease, underscoring the need for further longitudinal studies to explore its potential as a mediator of long-term cerebrovascular risk.
Collapse
Affiliation(s)
- Sheng Yang
- Wolfson Centre for Prevention of Stroke and Dementia, Nuffield, Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Alastair John Stewart Webb
- Wolfson Centre for Prevention of Stroke and Dementia, Nuffield, Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Department of Brain Sciences, Hammersmith Hospital, Imperial College London, London, UK
| |
Collapse
|
15
|
Gassner GM, Damestani NL, Wheeler NS, Kufer JA, Yadav SM, Mellen SF, Maina KN, Salat DH, Juttukonda MR. Cerebral microvascular physiology associated with white matter lesion burden differs by level of vascular risk in typically aging older adults. J Cereb Blood Flow Metab 2024:271678X241300394. [PMID: 39568243 PMCID: PMC11580122 DOI: 10.1177/0271678x241300394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/21/2024] [Revised: 09/16/2024] [Accepted: 10/13/2024] [Indexed: 11/22/2024]
Abstract
White matter lesions (WMLs) are prevalent with aging, and higher WML burden has been observed in older adults with vascular diseases. While the physiology underlying the formation of WMLs is not known, various risk factors are associated with high WML burden. Here, we investigated the relationship between vascular risk factors and microvascular physiology (i.e., oxygen supply and oxygen extraction fraction [OEF]) and their association with WML burden. Forty-one typically aging adults (60-80 years) were classified into high or low vascular risk based on common modifiable vascular risk factors (hypertension, diabetes, hyperlipidemia, and overweight). These groups were subdivided into high or low WML burden. Differences in microvascular physiology (oxygen supply and OEF) were then compared between and within groups. Overall, OEF was significantly higher in the high vascular risk group compared to the low vascular risk group (p < 0.01). In the low vascular risk subgroup, OEF was uniquely lower in the individuals with high WML versus low WML burden (p = 0.02), despite no differences in oxygen supply between these subgroups (p = 0.87). The coupling of impaired OEF with the absence of compensatory physiology, such as elevated oxygen supply, may represent an important mechanism underlying WML burden in individuals with low vascular risk factors.
Collapse
Affiliation(s)
- Gabriele M Gassner
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Faculty of Medicine, Kiel University, Kiel, Germany
| | - Nikou L Damestani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Natalie S Wheeler
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jan A Kufer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Shrikanth M Yadav
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Sarah F Mellen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Katherine N Maina
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - David H Salat
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Neuroimaging for Veterans Center, VA Boston Healthcare System, Boston, MA, USA
| | - Meher R Juttukonda
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| |
Collapse
|
16
|
Shang X, Huang Y, Zhu S, Zhu Z, Zhang X, Wang W, Zhang X, Liu J, Liu J, Tang S, Ge Z, Hu Y, Yu H, Yang X, He M. Influence of intraocular and blood pressure on brain volumes: Observational and Mendelian randomization analyses. iScience 2024; 27:110817. [PMID: 39524355 PMCID: PMC11546435 DOI: 10.1016/j.isci.2024.110817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/22/2024] [Accepted: 08/22/2024] [Indexed: 11/16/2024] Open
Abstract
Intraocular pressure (IOP) is closely correlated with blood pressure (BP), and while BP has been linked to brain volumes, the effect of IOP on brain volumes remains unclear. This study analyzed participants from the UK Biobank with MRI-measured brain volumes. Observational analyses included 8,634 participants for IOP and 36,069 for BP, followed by Mendelian randomization (MR) analyses of 37,410 participants. Observational analyses revealed that each 10-mmHg increase in diastolic BP was linked to a 0.13 mL larger white matter hyperintensity (WMH) after adjusting for covariates. Associations between IOP and brain volumes were more pronounced in younger individuals or those without hypertension. MR analyses confirmed significant relationships between diastolic BP and WMH, and each 5-mmHg increase in IOP reduced gray matter volumes by 3.24 mL. The study suggests that targeting IOP and BP could help prevent brain volume reduction.
Collapse
Affiliation(s)
- Xianwen Shang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Centre for Eye Research Australia, Melbourne, VIC 3002, Australia
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
| | - Yu Huang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Susan Zhu
- Austin Hospital, University of Melbourne, Melbourne, VIC 3084, Australia
| | - Zhuoting Zhu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
- Centre for Eye Research Australia, Melbourne, VIC 3002, Australia
| | - Xueli Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
| | - Xiayin Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Guangdong Cardiovascular Institute, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, China
| | - Jing Liu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Jiahao Liu
- Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC 3010, Australia
| | - Shulin Tang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Zongyuan Ge
- Monash e-Research Center, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Center, Monash University, Melbourne, VIC 3800, Australia
| | - Yijun Hu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Honghua Yu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Xiaohong Yang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
| | - Mingguang He
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510080, China
- Centre for Eye Research Australia, Melbourne, VIC 3002, Australia
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China
- Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong, China
- Centre for Eye and Vision Research (CEVR), 17W Hong Kong Science Park, Hong Kong, China
| |
Collapse
|
17
|
Shang X, Wang W, Tian L, Shi D, Huang Y, Zhang X, Zhu Z, Zhang X, Liu J, Tang S, Hu Y, Ge Z, Yu H, He M. Association of greenspace and natural environment with brain volumes mediated by lifestyle and biomarkers among urban residents. Arch Gerontol Geriatr 2024; 126:105546. [PMID: 38941948 DOI: 10.1016/j.archger.2024.105546] [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: 04/24/2024] [Revised: 05/28/2024] [Accepted: 06/22/2024] [Indexed: 06/30/2024]
Abstract
OBJECTIVES To examine the associaiton between environmental measures and brain volumes and its potential mediators. STUDY DESIGN This was a prospective study. METHODS Our analysis included 34,454 participants (53.4% females) aged 40-73 years at baseline (between 2006 and 2010) from the UK Biobank. Brain volumes were measured using magnetic resonance imaging between 2014 and 2019. RESULTS Greater proximity to greenspace buffered at 1000 m at baseline was associated with larger volumes of total brain measured 8.8 years after baseline assessment (standardized β (95% CI) for each 10% increment in coverage: 0.013(0.005,0.020)), grey matter (0.013(0.006,0.020)), and white matter (0.011(0.004,0.017)) after adjustment for covariates and air pollution. The corresponding numbers for natural environment buffered at 1000 m were 0.010 (0.004,0.017), 0.009 (0.004,0.015), and 0.010 (0.004,0.016), respectively. Similar results were observed for greenspace and natural environment buffered at 300 m. The strongest mediator for the association between greenspace buffered at 1000 m and total brain volume was smoking (percentage (95% CI) of total variance explained: 7.9% (5.5-11.4%)) followed by mean sphered cell volume (3.3% (1.8-5.8%)), vitamin D (2.9% (1.6-5.1%)), and creatinine in blood (2.7% (1.6-4.7%)). Significant mediators combined explained 18.5% (13.2-25.3%) of the association with total brain volume and 32.9% (95% CI: 22.3-45.7%) of the association with grey matter volume. The percentage (95% CI) of the association between natural environment and total brain volume explained by significant mediators combined was 20.6% (14.7-28.1%)). CONCLUSIONS Higher coverage percentage of greenspace and environment may benefit brain health by promoting healthy lifestyle and improving biomarkers including vitamin D and red blood cell indices.
Collapse
Affiliation(s)
- Xianwen Shang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, PR China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, PR China; Centre for Eye Research Australia, University of Melbourne, Melbourne, VIC 3002, Australia; Department of Medicine (Royal Melbourne Hospital), University of Melbourne, Melbourne, VIC 3050, Australia; School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, PR China.
| | - Wei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, PR China
| | - Le Tian
- Comprehensive department, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, PR China
| | - Danli Shi
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, PR China; School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, PR China; Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong, PR China
| | - Yu Huang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, PR China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, PR China
| | - Xueli Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, PR China
| | - Zhuoting Zhu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, PR China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, PR China; Centre for Eye Research Australia, University of Melbourne, Melbourne, VIC 3002, Australia
| | - Xiayin Zhang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, PR China; Guangdong Cardiovascular Institute, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, PR China
| | - Jiahao Liu
- Centre for Eye Research Australia, University of Melbourne, Melbourne, VIC 3002, Australia
| | - Shulin Tang
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, PR China
| | - Yijun Hu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, PR China
| | - Zongyuan Ge
- Monash e-Research Center, Faculty of Engineering, Airdoc Research, Nvidia AI Technology Research Center, Monash University, Melbourne, VIC 3800, Australia
| | - Honghua Yu
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, PR China.
| | - Mingguang He
- Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou 510080, PR China; Centre for Eye Research Australia, University of Melbourne, Melbourne, VIC 3002, Australia; State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, PR China; School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong, PR China; Research Centre for SHARP Vision, The Hong Kong Polytechnic University, Kowloon, Hong Kong, PR China.
| |
Collapse
|
18
|
Beydoun MA, Beydoun HA, Fanelli-Kuczmarski MT, Hu YH, Shaked D, Weiss J, Waldstein SR, Launer LJ, Evans MK, Zonderman AB. Uncovering mediational pathways behind racial and socioeconomic disparities in brain volumes: insights from the UK Biobank study. GeroScience 2024:10.1007/s11357-024-01371-1. [PMID: 39388067 DOI: 10.1007/s11357-024-01371-1] [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: 07/25/2024] [Accepted: 09/29/2024] [Indexed: 10/15/2024] Open
Abstract
Mediation pathways explaining racial/ethnic and socioeconomic (SES) disparities in structural MRI markers of brain health remain underexplored. We examined racial/ethnic and SES disparities in sMRI markers and tested total, direct, and indirect effects through lifestyle, health-related, and cognition factors using a structural equations modeling approach among 36,184 UK Biobank participants aged 40-70 years at baseline assessment (47% men). Race (non-White vs. White) and lower SES-predicted poorer brain sMRI volumetric outcomes at follow-up, with racial/ethnic disparities in sMRI outcomes involving multiple pathways and SES playing a central role in those pathways. Mediational patterns differed across outcomes, with the SES-sMRI total effect being partially mediated for all outcomes. Over 20% of the total effect (TE) of race/ethnicity on WMH was explained by the indirect effect (IE), by a combination of different pathways going through SES, lifestyle, health-related, and cognition factors. This is in contrast to < 10% for total brain, gray matter (GM), white matter (WM), and frontal GM left/right. Another significant finding is that around 57% of the total effect for SES and the normalized white matter hyperintensity (WMH) was attributed to an indirect effect. This effect encompasses many pathways that involve lifestyle, health-related, and cognitive aspects. Aside from WMH, the percent of TE of SES mediated through various pathways ranged from ~ 5% for WM to > 15% up to 36% for most of the remaining sMRI outcomes, which are composed mainly of GM phenotypes. Race and SES were important determinants of brain volumetric outcomes, with partial mediation of racial/ethnic disparities through SES, lifestyle, health-related, and cognition factors.
Collapse
Affiliation(s)
- May A Beydoun
- Laboratory of Epidemiology and Population Sciences, National Institute On Aging, NIA/NIH/IRP, 251 Bayview Blvd., Suite 100, Room #: 04B118, Baltimore, MD, 21224, USA.
| | - Hind A Beydoun
- Department of Research Programs, Fort Belvoir Community Hospital, Fort Belvoir, VA, 22060, USA
| | - Marie T Fanelli-Kuczmarski
- Laboratory of Epidemiology and Population Sciences, National Institute On Aging, NIA/NIH/IRP, 251 Bayview Blvd., Suite 100, Room #: 04B118, Baltimore, MD, 21224, USA
| | - Yi-Han Hu
- Laboratory of Epidemiology and Population Sciences, National Institute On Aging, NIA/NIH/IRP, 251 Bayview Blvd., Suite 100, Room #: 04B118, Baltimore, MD, 21224, USA
| | | | - Jordan Weiss
- Stanford Center On Longevity, Stanford University, Stanford, CA, 94305, USA
| | - Shari R Waldstein
- Department of Psychology, University of Maryland Baltimore County, Catonsville, MD, 21250, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute On Aging, NIA/NIH/IRP, 251 Bayview Blvd., Suite 100, Room #: 04B118, Baltimore, MD, 21224, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute On Aging, NIA/NIH/IRP, 251 Bayview Blvd., Suite 100, Room #: 04B118, Baltimore, MD, 21224, USA
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute On Aging, NIA/NIH/IRP, 251 Bayview Blvd., Suite 100, Room #: 04B118, Baltimore, MD, 21224, USA
| |
Collapse
|
19
|
Huang L, Fu Y, Zhang Y, Hu H, Ma L, Ge Y, Zhao Y, Zhang Y, Chen S, Feng J, Cheng W, Tan L, Yu J. Identifying modifiable factors associated with neuroimaging markers of brain health. CNS Neurosci Ther 2024; 30:e70057. [PMID: 39404063 PMCID: PMC11474882 DOI: 10.1111/cns.70057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Revised: 08/20/2024] [Accepted: 09/07/2024] [Indexed: 10/19/2024] Open
Abstract
AIMS Brain structural alterations begin long before the presentation of brain disorders; therefore, we aimed to systematically investigate a wide range of influencing factors on neuroimaging markers of brain health. METHODS Utilizing data from 30,651 participants from the UK Biobank, we explored associations between 218 modifiable factors and neuroimaging markers of brain health. We conducted an exposome-wide association study using the least absolute shrinkage and selection operator (LASSO) technique. Restricted cubic splines (RCS) were further employed to estimate potential nonlinear correlations. Weighted standardized scores for neuroimaging markers were computed based on the estimates for individual factors. Finally, stratum-specific analyses were performed to examine differences in factors affecting brain health at different ages. RESULTS The identified factors related to neuroimaging markers of brain health fell into six domains, including systematic diseases, lifestyle factors, personality traits, social support, anthropometric indicators, and biochemical markers. The explained variance percentage of neuroimaging markers by weighted standardized scores ranged from 0.5% to 7%. Notably, associations between systematic diseases and neuroimaging markers were stronger in older individuals than in younger ones. CONCLUSION This study identified a series of factors related to neuroimaging markers of brain health. Targeting the identified factors might help in formulating effective strategies for maintaining brain health.
Collapse
Affiliation(s)
- Liang‐Yu Huang
- Department of NeurologyQingdao Municipal Hospital, Qingdao UniversityQingdaoChina
| | - Yan Fu
- Department of NeurologyQingdao Municipal Hospital, Qingdao UniversityQingdaoChina
| | - Yi Zhang
- Department of Neurology and Institute of NeurologyHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - He‐Ying Hu
- Department of NeurologyQingdao Municipal Hospital, Qingdao UniversityQingdaoChina
| | - Ling‐Zhi Ma
- Department of NeurologyQingdao Municipal Hospital, Qingdao UniversityQingdaoChina
| | - Yi‐Jun Ge
- Department of Neurology and Institute of NeurologyHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Yong‐Li Zhao
- Department of NeurologyQingdao Municipal Hospital, Qingdao UniversityQingdaoChina
| | - Ya‐Ru Zhang
- Department of Neurology and Institute of NeurologyHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Shi‐Dong Chen
- Department of Neurology and Institute of NeurologyHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| | - Jian‐Feng Feng
- Institute of Science and Technology for Brain‐Inspired Intelligence, Fudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence (Fudan University), Ministry of EducationShanghaiChina
- Fudan ISTBI—ZJNU Algorithm Centre for Brain‐Inspired IntelligenceZhejiang Normal UniversityJinhuaChina
- MOE Frontiers Center for Brain ScienceFudan UniversityShanghaiChina
| | - Wei Cheng
- Department of Neurology and Institute of NeurologyHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
- Institute of Science and Technology for Brain‐Inspired Intelligence, Fudan UniversityShanghaiChina
- Key Laboratory of Computational Neuroscience and Brain‐Inspired Intelligence (Fudan University), Ministry of EducationShanghaiChina
- Fudan ISTBI—ZJNU Algorithm Centre for Brain‐Inspired IntelligenceZhejiang Normal UniversityJinhuaChina
| | - Lan Tan
- Department of NeurologyQingdao Municipal Hospital, Qingdao UniversityQingdaoChina
| | - Jin‐Tai Yu
- Department of Neurology and Institute of NeurologyHuashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Shanghai Medical College, Fudan UniversityShanghaiChina
| |
Collapse
|
20
|
Chen HJ, Huang W, Dong X, Feng G, Liu Z, Wang Y, Peng J, Dai Z, Shu N. Effects of Vascular Risk Factors on the White Matter Network Architecture of the Brain. Neurosci Bull 2024; 40:1551-1556. [PMID: 39115758 PMCID: PMC11422303 DOI: 10.1007/s12264-024-01274-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 05/27/2024] [Indexed: 09/25/2024] Open
Affiliation(s)
- Hao-Jie Chen
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
| | - Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
- School of Systems Science, Beijing Normal University, Beijing, 100875, China
| | - Xinyi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
| | - Guozheng Feng
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
| | - Zhenzhao Liu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
| | - Yichen Wang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
| | - Junjie Peng
- School of Life Sciences, Tsinghua University, Beijing, 100084, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, 510275, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
- Beijing Aging Brain Rejuvenation Initiative Centre, Beijing Normal University, Beijing, 100875, China.
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.
| |
Collapse
|
21
|
Thornton V, Chang Y, Chaloemtoem A, Anokhin AP, Bijsterbosch J, Foraker R, Hancock DB, Johnson EO, White JD, Hartz SM, Bierut LJ. Alcohol, smoking, and brain structure: common or substance specific associations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.09.25.24313371. [PMID: 39399056 PMCID: PMC11469368 DOI: 10.1101/2024.09.25.24313371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/15/2024]
Abstract
Alcohol use and smoking are common substance-use behaviors with well-established negative health effects, including decreased brain health. We examined whether alcohol use and smoking were associated with the same neuroimaging-derived brain measures. We further explored whether the effects of alcohol use and smoking on the brain were additive or interactive. We leveraged a cohort of 36,309 participants with neuroimaging data from the UK Biobank. We used linear regression to determine the association between 354 neuroimaging-derived brain measures and alcohol use defined as drinks per week, pack years of smoking, and drinks per week × pack years smoking interaction. To assess whether the brain associations with alcohol are broadly similar or different from the associations with smoking, we calculated the correlation between z-scores of association for drinks per week and pack years smoking. Results indicated overall moderate positive correlation in the associations across measures representing brain structure, magnetic susceptibility, and white matter tract microstructure, indicating greater similarity than difference in the brain measures associated with alcohol use and smoking. The only evidence of an interaction between drinks per week and pack years smoking was seen in measures representing magnetic susceptibility in subcortical structures. The effects of alcohol use and smoking on brain health appeared to be additive rather than multiplicative for all other brain measures studied. 97% (224/230) of associations with alcohol and 100% (167/167) of the associations with smoking that surpassed a p value threshold are in a direction that can be interpreted to reflect reduced brain health. Our results underscore the similarity of the adverse associations between use of these substances and neuroimaging derived brain measures.
Collapse
Affiliation(s)
- Vera Thornton
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Yoonhoo Chang
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Ariya Chaloemtoem
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Andrey P. Anokhin
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Janine Bijsterbosch
- Department of Radiology, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Randi Foraker
- Department of Medicine, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Dana B. Hancock
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, North Carolina, USA
| | - Eric O. Johnson
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, North Carolina, USA
- Fellow Program, RTI International, Research Triangle Park, North Carolina, USA
| | - Julie D. White
- GenOmics and Translational Research Center, RTI International, Research Triangle Park, North Carolina, USA
| | - Sarah M. Hartz
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| | - Laura J. Bierut
- Department of Psychiatry, Washington University School of Medicine, St. Louis, Missouri, USA
| |
Collapse
|
22
|
Subramaniapillai S, Schindler LS, Redmond P, Bastin ME, Wardlaw JM, Valdés Hernández M, Maniega SM, Aribisala B, Westlye LT, Coath W, Groves J, Cash DM, Barnes J, James SN, Sudre CH, Barkhof F, Richards M, Corley J, Russ TC, Cox SR, Schott JM, Cole JH, de Lange AMG. Sex-Dependent Effects of Cardiometabolic Health and APOE4 on Brain Age: A Longitudinal Cohort Study. Neurology 2024; 103:e209744. [PMID: 39173100 PMCID: PMC11379441 DOI: 10.1212/wnl.0000000000209744] [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: 12/20/2023] [Accepted: 06/17/2024] [Indexed: 08/24/2024] Open
Abstract
BACKGROUND AND OBJECTIVES The aging population is growing faster than all other demographic strata. With older age comes a greater risk of health conditions such as obesity and high blood pressure (BP). These cardiometabolic risk factors (CMRs) exhibit prominent sex differences in midlife and aging, yet their influence on brain health in females vs males is largely unexplored. In this study, we investigated sex differences in relationships between BP, body mass index (BMI), and brain age over time and tested for interactions with APOE ε4 genotype (APOE4), a known genetic risk factor of Alzheimer disease. METHODS The sample included participants from 2 United Kingdom-based longitudinal birth cohorts, the Lothian Birth Cohort (1936) and Insight 46 (1946). Participants with MRI data from at least 1 time point were included to evaluate sex differences in associations between CMRs and brain age. The open-access software package brainageR 2.1 was used to estimate brain age for each participant. Linear mixed-effects models were used to assess the relationships between brain age, BMI, BP, and APOE4 status (i.e., carrier vs noncarrier) in males and females over time. RESULTS The combined sample comprised 1,120 participants (48% female) with a mean age (SD) of 73 (0.72) years in the Lothian Birth Cohort and 71 (0.68) years in Insight 46 at the time point 1 assessment. Approximately 30% of participants were APOE4 carriers. Higher systolic and diastolic BP was significantly associated with older brain age in females only (β = 0.43-0.56, p < 0.05). Among males, higher BMI was associated with older brain age across time points and APOE4 groups (β = 0.72-0.77, p < 0.05). In females, higher BMI was linked to older brain age among APOE4 noncarriers (β = 0.68-0.99, p < 0.05), whereas higher BMI was linked to younger brain age among carriers, particularly at the last time point (β = -1.75, p < 0.05). DISCUSSION This study indicates sex-dependent and time-dependent relationships between CMRs, APOE4 status, and brain age. Our findings highlight the necessity of sex-stratified analyses to elucidate the role of CMRs in individual aging trajectories, providing a basis for developing personalized preventive interventions.
Collapse
Affiliation(s)
- Sivaniya Subramaniapillai
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Louise S Schindler
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Paul Redmond
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Mark E Bastin
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Joanna M Wardlaw
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Maria Valdés Hernández
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Susana Muñoz Maniega
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Benjamin Aribisala
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Lars T Westlye
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - William Coath
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - James Groves
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - David M Cash
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Josephine Barnes
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Sarah-Naomi James
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Carole H Sudre
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Frederik Barkhof
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Marcus Richards
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Janie Corley
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Tom C Russ
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Simon R Cox
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Jonathan M Schott
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - James H Cole
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| | - Ann-Marie G de Lange
- From the Department of Clinical Neuroscience (S.S., L.S.S., A.-M.G.d.L.), Lausanne University Hospital and University of Lausanne, Switzerland; Department of Psychology (P.R., M.E.B., J.M.W., M.V.H., S.M.M., B.A., J.C., T.C.R., S.R.C.), University of Edinburgh, United Kingdom; Department of Psychology (L.T.W.), University of Oslo, Norway; Dementia Research Centre (W.C., J.G., D.M.C., J.B., S.-N.J., C.H.S., J.M.S.), Centre for Medical Image Computing (C.H.S., F.B., J.H.C.), and MRC Unit for Lifelong Health and Ageing (M.R., S.-N.J., C.H.S.), University College London, United Kingdom
| |
Collapse
|
23
|
Liu H, Jing J, Jiang J, Wen W, Zhu W, Li Z, Pan Y, Cai X, Liu C, Zhou Y, Meng X, Wang Y, Li H, Jiang Y, Zheng H, Wang S, Niu H, Kochan N, Brodaty H, Wei T, Sachdev PS, Fan Y, Liu T, Wang Y. Exploring the link between brain topological resilience and cognitive performance in the context of aging and vascular risk factors: A cross-ethnicity population-based study. Sci Bull (Beijing) 2024; 69:2735-2744. [PMID: 38664095 DOI: 10.1016/j.scib.2024.04.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2023] [Revised: 02/08/2024] [Accepted: 04/07/2024] [Indexed: 09/09/2024]
Abstract
Brain aging is typically associated with a significant decline in cognitive performance. Vascular risk factors (VRF) and subsequent atherosclerosis (AS) play a major role in this process. Brain resilience reflects the brain's ability to withstand external perturbations, but the relationship of brain resilience with cognition during the aging process remains unclear. Here, we investigated how brain topological resilience (BTR) is associated with cognitive performance in the face of aging and vascular risk factors. We used data from two cross-ethnicity community cohorts, PolyvasculaR Evaluation for Cognitive Impairment and Vascular Events (PRECISE, n = 2220) and Sydney Memory and Ageing Study (MAS, n = 246). We conducted an attack simulation on brain structural networks based on k-shell decomposition and node degree centrality. BTR was defined based on changes in the size of the largest subgroup of the network during the simulation process. Subsequently, we explored the negative correlations of BTR with age, VRF, and AS, and its positive correlation with cognitive performance. Furthermore, using structural equation modeling (SEM), we constructed path models to analyze the directional dependencies among these variables, demonstrating that aging, AS, and VRF affect cognition by disrupting BTR. Our results also indicated the specificity of this metric, independent of brain volume. Overall, these findings underscore the supportive role of BTR on cognition during aging and highlight its potential application as an imaging marker for objective assessment of brain cognitive performance.
Collapse
Affiliation(s)
- Hao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Jing Jing
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
| | - Jiyang Jiang
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Wei Wen
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Wanlin Zhu
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Zixiao Li
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Yuesong Pan
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Xueli Cai
- Department of Neurology, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Chang Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Yijun Zhou
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Xia Meng
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Yilong Wang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Hao Li
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China
| | - Yong Jiang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Huaguang Zheng
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China
| | - Suying Wang
- Cerebrovascular Research Lab, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Haijun Niu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Nicole Kochan
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Henry Brodaty
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Tiemin Wei
- Department of Cardiology, Lishui Hospital, Zhejiang University School of Medicine, Lishui 323000, China
| | - Perminder S Sachdev
- Neuropsychiatric Institute, Prince of Wales Hospital, Sydney NSW 2031, Australia; Centre for Healthy Brain Ageing (CHeBA), Discipline of Psychiatry and Mental Health, School of Clinical Medicine, UNSW Medicine, Sydney NSW 2052, Australia
| | - Yubo Fan
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China
| | - Tao Liu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191 , China.
| | - Yongjun Wang
- China National Clinical Research Center for Neurological Diseases, Beijing 100070, China; Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, China.
| |
Collapse
|
24
|
Li TR, Li BL, Xu XR, Zhong J, Wang TS, Liu FQ. Association of white matter hyperintensities with cognitive decline and neurodegeneration. Front Aging Neurosci 2024; 16:1412735. [PMID: 39328245 PMCID: PMC11425965 DOI: 10.3389/fnagi.2024.1412735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Accepted: 08/28/2024] [Indexed: 09/28/2024] Open
Abstract
Background The relationship between white matter hyperintensities (WMH) and the core features of Alzheimer's disease (AD) remains controversial. Further, due to the prevalence of co-pathologies, the precise role of WMH in cognition and neurodegeneration also remains uncertain. Methods Herein, we analyzed 1803 participants with available WMH volume data, extracted from the ADNI database, including 756 cognitively normal controls, 783 patients with mild cognitive impairment (MCI), and 264 patients with dementia. Participants were grouped according to cerebrospinal fluid (CSF) pathology (A/T profile) severity. Linear regression analysis was applied to evaluate the factors associated with WMH volume. Modeled by linear mixed-effects, the increase rates (Δ) of the WMH volume, cognition, and typical neurodegenerative markers were assessed. The predictive effectiveness of WMH volume was subsequently tested using Cox regression analysis, and the relationship between WMH/ΔWMH and other indicators such as cognition was explored through linear regression analyses. Furthermore, we explored the interrelationship among amyloid-β deposition, cognition, and WMH using mediation analysis. Results Higher WMH volume was associated with older age, lower CSF amyloid-β levels, hypertension, and smoking history (all p ≤ 0.001), as well as cognitive status (MCI, p < 0.001; dementia, p = 0.008), but not with CSF tau levels. These results were further verified in any clinical stage, except hypertension and smoking history in the dementia stage. Although WMH could not predict dementia conversion, its increased levels at baseline were associated with a worse cognitive performance and a more rapid memory decline. Longitudinal analyses showed that baseline dementia and positive amyloid-β status were associated with a greater accrual of WMH volume, and a higher ΔWMH was also correlated with a faster cognitive decline. In contrast, except entorhinal cortex thickness, the WMH volume was not found to be associated with any other neurodegenerative markers. To a lesser extent, WMH mediates the relationship between amyloid-β and cognition. Conclusion WMH are non-specific lesions that are associated with amyloid-β deposition, cognitive status, and a variety of vascular risk factors. Despite evidence indicating only a weak relationship with neurodegeneration, early intervention to reduce WMH lesions remains a high priority for preserving cognitive function in the elderly.
Collapse
Affiliation(s)
- Tao-Ran Li
- Department of Neurology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Bai-Le Li
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
- Beijing Pediatric Research Institute, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Xin-Ran Xu
- Department of Neurology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Jin Zhong
- Department of Neurology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| | - Tai-Shan Wang
- Department of Neurology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
- Department of Neurology, Yangzhou Friendship Hospital, Yangzhou, China
| | - Feng-Qi Liu
- Department of Neurology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
- Department of Hematology, The First Affiliated Hospital of Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China
| |
Collapse
|
25
|
Hassani S, Gorelick PB. What have observational studies taught us about brain health? An exploration of select cardiovascular risks and cognitive function. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2024; 7:100367. [PMID: 39309313 PMCID: PMC11414496 DOI: 10.1016/j.cccb.2024.100367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/27/2024] [Revised: 08/31/2024] [Accepted: 09/01/2024] [Indexed: 09/25/2024]
Abstract
Observational research studies serve as the cornerstone for gathering evidence on risk factors and contributors to cognitive decline and impairment. The evidence can then be combined with data from preclinical studies and randomized controlled trials to ultimately inform the development of effective interventions and the content of guidance statements. Observational cohort designs on modifiable risk factors and brain health can be particularly beneficial for studying questions that are unethical or impractical for a clinical trial setting, associations with dementia which may develop over decades, and underrepresented populations typically not included in clinical trials. This chapter will review the major observational, epidemiologic studies pertaining to the traditional vascular risk factors - hypertension, diabetes mellitus, hypercholesterolemia, smoking, and physical inactivity - and how they may impact brain health.
Collapse
Affiliation(s)
- Sara Hassani
- Duke University School of Medicine, Department of Neurology, USA
- Davee Department of Neurology, Division of Stroke and Neurocritical Care, Simpson Querrey Neurovascular Research Laboratory, Northwestern University Feinberg School of Medicine, 633 North St. Clair Street, 19th Floor, Chicago, IL 60611 USA
| | - Philip B. Gorelick
- Davee Department of Neurology, Division of Stroke and Neurocritical Care, Simpson Querrey Neurovascular Research Laboratory, Northwestern University Feinberg School of Medicine, 633 North St. Clair Street, 19th Floor, Chicago, IL 60611 USA
| |
Collapse
|
26
|
Wang Q, Yu R, Dong C, Zhou C, Xie Z, Sun H, Fu C, Zhu D. Association and prediction of Life's Essential 8 score, genetic susceptibility with MCI, dementia, and MRI indices: A prospective cohort study. J Affect Disord 2024; 360:394-402. [PMID: 38844164 DOI: 10.1016/j.jad.2024.06.008] [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: 03/19/2024] [Revised: 05/28/2024] [Accepted: 06/03/2024] [Indexed: 06/10/2024]
Abstract
BACKGROUND To examine the associations of Life's Essential 8 (LE8) and its predictive performance with mild cognitive impairment (MCI), dementia and brain MRI indices. METHODS We used cohort data from UK Biobank. LE8 was categorized into low (<50 score), moderate (50-79 score), and high (≥80 score) levels. Cox regression models considering death as a competing risk were used to estimate the hazard ratios (HRs) and 95%CI on the association between LE8 and MCI and dementia. Multivariable linear regression models were used to analyze LE8 every 10-score increase and brain MRI indices. Area under the curve (AUC) was used to measure the predictive performances of LE8. RESULTS We included 126,785 participants with a mean (SD) age of 56.0 (8.0) years and 53.5 % were female. The median follow-up was 13.0 years. Compared to individuals with a low LE8 score, those with a high LE8 score were associated with decreased risk of MCI (0.49, 95%CI: 0.40-0.62), all-cause dementia (0.60, 0.44-0.80), vascular dementia (VD, 0.44, 0.21-0.94), and non-Alzheimer non-vascular dementia (NAVD, 0.55, 0.35-0.84). High LE8 score was associated with increased total brain volume, hippocampus volume, grey matter volume, and grey matter in hippocampus volume (p all ≤0.001). LE8 combined age and sex had good performance for predicting all-cause dementia (AUC: 84.1 %), AD (85.4 %), VD (87.6 %), NAVD (81.4 %), and MCI (75.3 %). LIMITATIONS Our findings only reflect the characteristics of UKB participants. CONCLUSIONS High LE8 score was associated with reduced risk of MCI and dementia. It was also linked to brain MRI indices. LE8 score had good predicting performance for future risk of MCI and dementia.
Collapse
Affiliation(s)
- Qi Wang
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ruihong Yu
- Pingyin Center for Disease Control and Prevention, No. 67 Dongguan Street, Pingyin, Jinan, China
| | - Caiyun Dong
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Chunmiao Zhou
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Ziwei Xie
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Huizi Sun
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Chunying Fu
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Dongshan Zhu
- Department of Epidemiology, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, China; Center for Clinical Epidemiology and Evidence-Based Medicine, Shandong University, Jinan, China.
| |
Collapse
|
27
|
Soldan A, Wang J, Pettigrew C, Davatzikos C, Erus G, Hohman TJ, Dumitrescu L, Bilgel M, Resnick SM, Rivera-Rivera LA, Langhough R, Johnson SC, Benzinger T, Morris JC, Laws SM, Fripp J, Masters CL, Albert MS. Alzheimer's disease genetic risk and changes in brain atrophy and white matter hyperintensities in cognitively unimpaired adults. Brain Commun 2024; 6:fcae276. [PMID: 39229494 PMCID: PMC11369827 DOI: 10.1093/braincomms/fcae276] [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: 12/05/2023] [Revised: 06/25/2024] [Accepted: 08/12/2024] [Indexed: 09/05/2024] Open
Abstract
Reduced brain volumes and more prominent white matter hyperintensities on MRI scans are commonly observed among older adults without cognitive impairment. However, it remains unclear whether rates of change in these measures among cognitively normal adults differ as a function of genetic risk for late-onset Alzheimer's disease, including APOE-ɛ4, APOE-ɛ2 and Alzheimer's disease polygenic risk scores (AD-PRS), and whether these relationships are influenced by other variables. This longitudinal study examined the trajectories of regional brain volumes and white matter hyperintensities in relationship to APOE genotypes (N = 1541) and AD-PRS (N = 1093) in a harmonized dataset of middle-aged and older individuals with normal cognition at baseline (mean baseline age = 66 years, SD = 9.6) and an average of 5.3 years of MRI follow-up (max = 24 years). Atrophy on volumetric MRI scans was quantified in three ways: (i) a composite score of regions vulnerable to Alzheimer's disease (SPARE-AD); (ii) hippocampal volume; and (iii) a composite score of regions indexing advanced non-Alzheimer's disease-related brain aging (SPARE-BA). Global white matter hyperintensity volumes were derived from fluid attenuated inversion recovery (FLAIR) MRI. Using linear mixed effects models, there was an APOE-ɛ4 gene-dose effect on atrophy in the SPARE-AD composite and hippocampus, with greatest atrophy among ɛ4/ɛ4 carriers, followed by ɛ4 heterozygouts, and lowest among ɛ3 homozygouts and ɛ2/ɛ2 and ɛ2/ɛ3 carriers, who did not differ from one another. The negative associations of APOE-ɛ4 with atrophy were reduced among those with higher education (P < 0.04) and younger baseline ages (P < 0.03). Higher AD-PRS were also associated with greater atrophy in SPARE-AD (P = 0.035) and the hippocampus (P = 0.014), independent of APOE-ɛ4 status. APOE-ɛ2 status (ɛ2/ɛ2 and ɛ2/ɛ3 combined) was not related to baseline levels or atrophy in SPARE-AD, SPARE-BA or the hippocampus, but was related to greater increases in white matter hyperintensities (P = 0.014). Additionally, there was an APOE-ɛ4 × AD-PRS interaction in relation to white matter hyperintensities (P = 0.038), with greater increases in white matter hyperintensities among APOE-ɛ4 carriers with higher AD-PRS. APOE and AD-PRS associations with MRI measures did not differ by sex. These results suggest that APOE-ɛ4 and AD-PRS independently and additively influence longitudinal declines in brain volumes sensitive to Alzheimer's disease and synergistically increase white matter hyperintensity accumulation among cognitively normal individuals. Conversely, APOE-ɛ2 primarily influences white matter hyperintensity accumulation, not brain atrophy. Results are consistent with the view that genetic factors for Alzheimer's disease influence atrophy in a regionally specific manner, likely reflecting preclinical neurodegeneration, and that Alzheimer's disease risk genes contribute to white matter hyperintensity formation.
Collapse
Affiliation(s)
- Anja Soldan
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Jiangxia Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Corinne Pettigrew
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| | - Christos Davatzikos
- Centre for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Guray Erus
- Centre for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Timothy J Hohman
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Logan Dumitrescu
- Department of Neurology, Vanderbilt University Medical Center, Nashville, TN 37212, USA
| | - Murat Bilgel
- Laboratory of Behavioral Neuroscience, National Institute on Aging Intramural Research Program, Baltimore, MD 21224, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging Intramural Research Program, Baltimore, MD 21224, USA
| | - Leonardo A Rivera-Rivera
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53726, USA
| | - Rebecca Langhough
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53726, USA
| | - Sterling C Johnson
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI 53726, USA
| | - Tammie Benzinger
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - John C Morris
- Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Simon M Laws
- Centre for Precision Health, Edith Cowan University, Joondalup, WA 6027, Australia
| | - Jurgen Fripp
- Australian E-Health Research Centre, CSIRO Health & Biosecurity, Herston, QLD 4029, Australia
| | - Colin L Masters
- The Florey Institute, University of Melbourne, Parkville, VIC 3052, Australia
| | - Marilyn S Albert
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
| |
Collapse
|
28
|
Mohammad S, Gentreau M, Dubol M, Rukh G, Mwinyi J, Schiöth HB. Association of polygenic scores for autism with volumetric MRI phenotypes in cerebellum and brainstem in adults. Mol Autism 2024; 15:34. [PMID: 39113134 PMCID: PMC11304666 DOI: 10.1186/s13229-024-00611-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 07/22/2024] [Indexed: 08/10/2024] Open
Abstract
Previous research on autism spectrum disorders (ASD) have showed important volumetric alterations in the cerebellum and brainstem. Most of these studies are however limited to case-control studies with small clinical samples and including mainly children or adolescents. Herein, we aimed to explore the association between the cumulative genetic load (polygenic risk score, PRS) for ASD and volumetric alterations in the cerebellum and brainstem, as well as global brain tissue volumes of the brain among adults at the population level. We utilized the latest genome-wide association study of ASD by the Psychiatric Genetics Consortium (18,381 cases, 27,969 controls) and constructed the ASD PRS in an independent cohort, the UK Biobank. Regression analyses controlled for multiple comparisons with the false-discovery rate (FDR) at 5% were performed to investigate the association between ASD PRS and forty-four brain magnetic resonance imaging (MRI) phenotypes among ~ 31,000 participants. Primary analyses included sixteen MRI phenotypes: total volumes of the brain, cerebrospinal fluid (CSF), grey matter (GM), white matter (WM), GM of whole cerebellum, brainstem, and ten regions of the cerebellum (I_IV, V, VI, VIIb, VIIIa, VIIIb, IX, X, CrusI and CrusII). Secondary analyses included twenty-eight MRI phenotypes: the sub-regional volumes of cerebellum including the GM of the vermis and both left and right lobules of each cerebellar region. ASD PRS were significantly associated with the volumes of seven brain areas, whereby higher PRS were associated to reduced volumes of the whole brain, WM, brainstem, and cerebellar regions I-IV, IX, and X, and an increased volume of the CSF. Three sub-regional volumes including the left cerebellar lobule I-IV, cerebellar vermes VIIIb, and X were significantly and negatively associated with ASD PRS. The study highlights a substantial connection between susceptibility to ASD, its underlying genetic etiology, and neuroanatomical alterations of the adult brain.
Collapse
Affiliation(s)
- Salahuddin Mohammad
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Mélissa Gentreau
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Manon Dubol
- Department of Women's and Children's Health, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Gull Rukh
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Jessica Mwinyi
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
| | - Helgi B Schiöth
- Functional Pharmacology and Neuroscience Unit, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
| |
Collapse
|
29
|
Contador I, Buch-Vicente B, del Ser T, Llamas-Velasco S, Villarejo-Galende A, Benito-León J, Bermejo-Pareja F. Charting Alzheimer's Disease and Dementia: Epidemiological Insights, Risk Factors and Prevention Pathways. J Clin Med 2024; 13:4100. [PMID: 39064140 PMCID: PMC11278014 DOI: 10.3390/jcm13144100] [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: 05/23/2024] [Revised: 07/08/2024] [Accepted: 07/09/2024] [Indexed: 07/28/2024] Open
Abstract
Alzheimer's disease (AD), the most common cause of dementia, is a complex and multifactorial condition without cure at present. The latest treatments, based on anti-amyloid monoclonal antibodies, have only a modest effect in reducing the progression of cognitive decline in AD, whereas the possibility of preventing AD has become a crucial area of research. In fact, recent studies have observed a decrease in dementia incidence in developed regions such as the US and Europe. However, these trends have not been mirrored in non-Western countries (Japan or China), and the contributing factors of this reduction remain unclear. The Lancet Commission has delineated a constrained classification of 12 risk factors across different life stages. Nevertheless, the scientific literature has pointed to over 200 factors-including sociodemographic, medical, psychological, and sociocultural conditions-related to the development of dementia/AD. This narrative review aims to synthesize the risk/protective factors of dementia/AD. Essentially, we found that risk/protective factors vary between individuals and populations, complicating the creation of a unified prevention strategy. Moreover, dementia/AD explanatory mechanisms involve a diverse array of genetic and environmental factors that interact from the early stages of life. In the future, studies across different population-based cohorts are essential to validate risk/protective factors of dementia. This evidence would help develop public health policies to decrease the incidence of dementia.
Collapse
Affiliation(s)
- Israel Contador
- Department of Basic Psychology, Psychobiology, and Methodology of Behavioral Sciences, Faculty of Psychology, University of Salamanca, 37005 Salamanca, Spain
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, 17117 Stockholm, Sweden
| | - Bárbara Buch-Vicente
- Department of Basic Psychology, Psychobiology, and Methodology of Behavioral Sciences, Faculty of Psychology, University of Salamanca, 37005 Salamanca, Spain
| | - Teodoro del Ser
- Alzheimer Centre Reina Sofia—CIEN Foundation, Institute of Health Carlos III, 28031 Madrid, Spain;
| | - Sara Llamas-Velasco
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain; (S.L.-V.); (A.V.-G.); (J.B.-L.)
- Department of Neurology, University Hospital 12 de Octubre, 28041 Madrid, Spain
| | - Alberto Villarejo-Galende
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain; (S.L.-V.); (A.V.-G.); (J.B.-L.)
- Department of Neurology, University Hospital 12 de Octubre, 28041 Madrid, Spain
| | - Julián Benito-León
- Instituto de Investigación Sanitaria Hospital 12 de Octubre (imas12), 28041 Madrid, Spain; (S.L.-V.); (A.V.-G.); (J.B.-L.)
- Department of Neurology, University Hospital 12 de Octubre, 28041 Madrid, Spain
- Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), 28029 Madrid, Spain
- Department of Medicine, Faculty of Medicine, Complutense University, 28040 Madrid, Spain
| | - Félix Bermejo-Pareja
- Centro de Investigación Biomédica en Red Sobre Enfermedades Neurodegenerativas (CIBERNED), 28029 Madrid, Spain
- Department of Medicine, Faculty of Medicine, Complutense University, 28040 Madrid, Spain
| |
Collapse
|
30
|
Im Y, Kang SH, Park G, Yoo H, Chun MY, Kim CH, Park CJ, Kim JP, Jang H, Kim HJ, Oh K, Koh SB, Lee JM, Na DL, Seo SW, Kim H. Ethnic differences in the effects of apolipoprotein E ɛ4 and vascular risk factors on accelerated brain aging. Brain Commun 2024; 6:fcae213. [PMID: 39007039 PMCID: PMC11242459 DOI: 10.1093/braincomms/fcae213] [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: 12/07/2023] [Revised: 04/30/2024] [Accepted: 07/09/2024] [Indexed: 07/16/2024] Open
Abstract
The frequency of the apolipoprotein E ɛ4 allele and vascular risk factors differs among ethnic groups. We aimed to assess the combined effects of apolipoprotein E ɛ4 and vascular risk factors on brain age in Korean and UK cognitively unimpaired populations. We also aimed to determine the differences in the combined effects between the two populations. We enrolled 2314 cognitively unimpaired individuals aged ≥45 years from Korea and 6942 cognitively unimpaired individuals from the UK, who were matched using propensity scores. Brain age was defined using the brain age index. The apolipoprotein E genotype (ɛ4 carriers, ɛ2 carriers and ɛ3/ɛ3 homozygotes) and vascular risk factors (age, hypertension and diabetes) were considered predictors. Apolipoprotein E ɛ4 carriers in the Korean (β = 0.511, P = 0.012) and UK (β = 0.302, P = 0.006) groups had higher brain age index values. The adverse effects of the apolipoprotein E genotype on brain age index values increased with age in the Korean group alone (ɛ2 carriers × age, β = 0.085, P = 0.009; ɛ4 carriers × age, β = 0.100, P < 0.001). The apolipoprotein E genotype, age and ethnicity showed a three-way interaction with the brain age index (ɛ2 carriers × age × ethnicity, β = 0.091, P = 0.022; ɛ4 carriers × age × ethnicity, β = 0.093, P = 0.003). The effects of apolipoprotein E on the brain age index values were more pronounced in individuals with hypertension in the Korean group alone (ɛ4 carriers × hypertension, β = 0.777, P = 0.038). The apolipoprotein E genotype, age and ethnicity showed a three-way interaction with the brain age index (ɛ4 carriers × hypertension × ethnicity, β=1.091, P = 0.014). We highlight the ethnic differences in the combined effects of the apolipoprotein E ɛ4 genotype and vascular risk factors on accelerated brain age. These findings emphasize the need for ethnicity-specific strategies to mitigate apolipoprotein E ɛ4-related brain aging in cognitively unimpaired individuals.
Collapse
Affiliation(s)
- Yanghee Im
- USC Steven Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea
| | - Sung Hoon Kang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul 08308, Korea
| | - Gilsoon Park
- USC Steven Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
| | - Heejin Yoo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
| | - Min Young Chun
- Department of Neurology, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin 16995, Korea
| | - Chi-Hun Kim
- Department of Neurology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang 14068, Korea
| | - Chae Jung Park
- Research Institute, National Cancer Center, Goyang 10408, Korea
| | - Jun Pyo Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
| | - Hyemin Jang
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
| | - Hee Jin Kim
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
| | - Kyungmi Oh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul 08308, Korea
| | - Seong-Beom Koh
- Department of Neurology, Korea University Guro Hospital, Korea University College of Medicine, Seoul 08308, Korea
| | - Jong-Min Lee
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Korea
| | - Duk L Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
| | - Sang Won Seo
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul 06355, Korea
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul 06355, Korea
- Alzheimer’s Disease Convergence Research Center, Samsung Medical Center, Seoul 06351, Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon 16419, Korea
| | - Hosung Kim
- USC Steven Neuroimaging and Informatics Institute, Keck School of Medicine of University of Southern California, Los Angeles, CA 90033, USA
| |
Collapse
|
31
|
Ward J, Cox SR, Quinn T, Lyall LM, Strawbridge RJ, Russell E, Pell JP, Stewart W, Cullen B, Whalley H, Lyall DM. Head motion in the UK Biobank imaging subsample: longitudinal stability, associations with psychological and physical health, and risk of incomplete data. Brain Commun 2024; 6:fcae220. [PMID: 39015764 PMCID: PMC11249925 DOI: 10.1093/braincomms/fcae220] [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: 09/06/2023] [Revised: 05/15/2024] [Accepted: 07/01/2024] [Indexed: 07/18/2024] Open
Abstract
Participant motion in brain magnetic resonance imaging is associated with processing problems including potentially non-useable/incomplete data. This has implications for representativeness in research. Few large studies have investigated predictors of increased motion in the first instance. We exploratively tested for association between multiple psychological and physical health traits with concurrent motion during T1 structural, diffusion, average resting-state and task functional magnetic resonance imaging in N = 52 951 UK Biobank imaging subsample participants. These traits included history of cardiometabolic, inflammatory, neurological and psychiatric conditions, as well as concurrent cognitive test scores and anthropometric traits. We tested for stability in motion in participants with longitudinal imaging data (n = 5305, average 2.64 years later). All functional and T1 structural motion variables were significantly intercorrelated (Pearson r range 0.3-0.8, all P < 0.001). Diffusion motion variables showed weaker correlations around r = 0.1. Most physical and psychological phenotypes showed significant association with at least one measure of increased motion including specifically in participants with complete useable data (highest β = 0.66 for diabetes versus resting-state functional magnetic resonance imaging motion). Poorer values in most health traits predicted lower odds of complete imaging data, with the largest association for history of traumatic brain injury (odds ratio = 0.720, 95% confidence interval = 0.562 to 0.923, P = 0.009). Worse psychological and physical health are consistent predictors of increased average functional and structural motion during brain imaging and associated with lower odds of complete data. Average motion levels were largely consistent across modalities and longitudinally in participants with repeat data. Together, these findings have implications for representativeness and bias in imaging studies of generally healthy population samples.
Collapse
Affiliation(s)
- Joey Ward
- School of Health and Wellbeing, University of Glasgow, G12 8TB, Glasgow, UK
| | - Simon R Cox
- School of Philosophy, Psychology and Language Sciences, University of Edinburgh, EH8 9JZ, Edinburgh, UK
| | - Terry Quinn
- School of Cardiovascular and Metabolic Sciences, University of Glasgow, G12 8TA, Glasgow, UK
| | - Laura M Lyall
- School of Health and Wellbeing, University of Glasgow, G12 8TB, Glasgow, UK
| | - Rona J Strawbridge
- School of Health and Wellbeing, University of Glasgow, G12 8TB, Glasgow, UK
- Cardiovascular Medicine Unit, Department of Medicine Solna, Karolinska Institute, 171 64, Stockholm, Sweden
- Health Data Research (HDR)-UK, NW1 2BE, London, UK
| | - Emma Russell
- School of Psychology and Neuroscience, University of Glasgow, G12 8QB, Glasgow, UK
| | - Jill P Pell
- School of Health and Wellbeing, University of Glasgow, G12 8TB, Glasgow, UK
| | - William Stewart
- School of Psychology and Neuroscience, University of Glasgow, G12 8QB, Glasgow, UK
- Department of Neuropathology, Queen Elizabeth University Hospital, G51 4TF, Glasgow, UK
| | - Breda Cullen
- School of Health and Wellbeing, University of Glasgow, G12 8TB, Glasgow, UK
| | - Heather Whalley
- Centre for Clinical Brain Sciences, University of Edinburgh, EH16 4SB, Edinburgh, UK
| | - Donald M Lyall
- School of Health and Wellbeing, University of Glasgow, G12 8TB, Glasgow, UK
| |
Collapse
|
32
|
Patel Y, Woo A, Shi S, Ayoub R, Shin J, Botta A, Ketela T, Sung HK, Lerch J, Nieman B, Paus T, Pausova Z. Obesity and the cerebral cortex: Underlying neurobiology in mice and humans. Brain Behav Immun 2024; 119:637-647. [PMID: 38663773 DOI: 10.1016/j.bbi.2024.04.033] [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: 12/20/2023] [Revised: 04/17/2024] [Accepted: 04/22/2024] [Indexed: 05/01/2024] Open
Abstract
Obesity is a major modifiable risk factor for Alzheimer's disease (AD), characterized by progressive atrophy of the cerebral cortex. The neurobiology of obesity contributions to AD is poorly understood. Here we show with in vivo MRI that diet-induced obesity decreases cortical volume in mice, and that higher body adiposity associates with lower cortical volume in humans. Single-nuclei transcriptomics of the mouse cortex reveals that dietary obesity promotes an array of neuron-adverse transcriptional dysregulations, which are mediated by an interplay of excitatory neurons and glial cells, and which involve microglial activation and lowered neuronal capacity for neuritogenesis and maintenance of membrane potential. The transcriptional dysregulations of microglia, more than of other cell types, are like those in AD, as assessed with single-nuclei cortical transcriptomics in a mouse model of AD and two sets of human donors with the disease. Serial two-photon tomography of microglia demonstrates microgliosis throughout the mouse cortex. The spatial pattern of adiposity-cortical volume associations in human cohorts interrogated together with in silico bulk and single-nucleus transcriptomic data from the human cortex implicated microglia (along with other glial cells and subtypes of excitatory neurons), and it correlated positively with the spatial profile of cortical atrophy in patients with mild cognitive impairment and AD. Thus, multi-cell neuron-adverse dysregulations likely contribute to the loss of cortical tissue in obesity. The dysregulations of microglia may be pivotal to the obesity-related risk of AD.
Collapse
Affiliation(s)
- Yash Patel
- The Hospital for Sick Children, Translational Medicine Program, Toronto, ON, Canada; Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
| | - Anita Woo
- The Hospital for Sick Children, Translational Medicine Program, Toronto, ON, Canada; Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - Sammy Shi
- The Hospital for Sick Children, Translational Medicine Program, Toronto, ON, Canada; Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - Ramy Ayoub
- The Hospital for Sick Children, Translational Medicine Program, Toronto, ON, Canada
| | - Jean Shin
- The Hospital for Sick Children, Translational Medicine Program, Toronto, ON, Canada; Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada
| | - Amy Botta
- The Hospital for Sick Children, Translational Medicine Program, Toronto, ON, Canada
| | - Troy Ketela
- Princess Margaret Genomics Centre, Toronto, ON, Canada
| | - Hoon-Ki Sung
- The Hospital for Sick Children, Translational Medicine Program, Toronto, ON, Canada
| | - Jason Lerch
- Nuffield Department of Clinical Neurosciences, Oxford University, Oxford, Great Britton
| | - Brian Nieman
- The Hospital for Sick Children, Translational Medicine Program, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Tomas Paus
- Department of Psychiatry and Addictology and Department of Neuroscience, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, QC, Canada
| | - Zdenka Pausova
- The Hospital for Sick Children, Translational Medicine Program, Toronto, ON, Canada; Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, ON, Canada; Department of Pediatrics and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, QC, Canada.
| |
Collapse
|
33
|
Raichlen DA, Ally M, Aslan DH, Sayre MK, Bharadwaj PK, Maltagliati S, Lai MHC, Wilcox RR, Habeck CG, Klimentidis YC, Alexander GE. Associations between accelerometer-derived sedentary behavior and physical activity with white matter hyperintensities in middle-aged to older adults. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e70001. [PMID: 39183745 PMCID: PMC11342350 DOI: 10.1002/dad2.70001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 07/30/2024] [Accepted: 08/02/2024] [Indexed: 08/27/2024]
Abstract
INTRODUCTION We examined the relationship between sedentary behavior (SB), moderate-to-vigorous physical activity (MVPA), and white matter hyperintensity (WMH) volumes, a common magnetic resonance imaging (MRI) marker associated with risk of neurodegenerative disease in middle-aged to older adults. METHODS We used data from the UK Biobank (n = 14,415; 45 to 81 years) that included accelerometer-derived measures of SB and MVPA, and WMH volumes from MRI. RESULTS Both MVPA and SB were associated with WMH volumes (βMVPA = -0.03 [-0.04, -0.01], p < 0.001; βSB = 0.02 [0.01, 0.03], p = 0.007). There was a significant interaction between SB and MVPA on WMH volumes (βSB×MVPA = -0.015 [-0.028, -0.001], p SB×MVPA = 0.03) where SB was positively associated with WMHs at low MVPA, and MVPA was negatively associated with WMHs at high SB. DISCUSSION While this study cannot establish causality, the results highlight the potential importance of considering both MVPA and SB in strategies aimed at reducing the accumulation of WMH volumes in middle-aged to older adults. Highlights SB is associated with greater WMH volumes and MVPA is associated with lower WMH volumes.Relationships between SB and WMH are strongest at low levels of MVPA.Associations between MVPA and WMH are strongest at high levels of SB.Considering both SB and MVPA may be effective strategies for reducing WMHs.
Collapse
Affiliation(s)
- David A. Raichlen
- Human and Evolutionary Biology SectionDepartment of Biological SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of AnthropologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Madeline Ally
- Department of PsychologyUniversity of ArizonaTucsonArizonaUSA
| | - Daniel H. Aslan
- Human and Evolutionary Biology SectionDepartment of Biological SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | | | | | - Silvio Maltagliati
- Human and Evolutionary Biology SectionDepartment of Biological SciencesUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Mark H. C. Lai
- Department of PsychologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Rand R. Wilcox
- Department of PsychologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Christian G. Habeck
- Cognitive Neuroscience DivisionDepartment of Neurology and Taub InstituteColumbia UniversityNew YorkNew YorkUSA
| | - Yann C. Klimentidis
- Department of Epidemiology and BiostatisticsMel and Enid Zuckerman College of Public HealthUniversity of ArizonaTucsonArizonaUSA
- BIO5 InstituteUniversity of ArizonaTucsonArizonaUSA
| | - Gene E. Alexander
- Department of PsychologyUniversity of ArizonaTucsonArizonaUSA
- BIO5 InstituteUniversity of ArizonaTucsonArizonaUSA
- Evelyn F. McKnight Brain InstituteUniversity of ArizonaTucsonArizonaUSA
- Department of PsychiatryUniversity of ArizonaTucsonArizonaUSA
- Neuroscience Graduate Interdisciplinary ProgramUniversity of ArizonaTucsonArizonaUSA
- Physiological Sciences Graduate Interdisciplinary ProgramUniversity of ArizonaTucsonArizonaUSA
- Arizona Alzheimer's ConsortiumPhoenixArizonaUSA
| |
Collapse
|
34
|
Wang Z, Li X, Wang J, Yang W, Dove A, Lu W, Qi X, Sindi S, Xu W. Association of past and current sleep duration with structural brain differences: A large population-based study from the UK Biobank. Sleep Med 2024; 119:179-186. [PMID: 38692219 DOI: 10.1016/j.sleep.2024.04.033] [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: 12/24/2023] [Revised: 04/19/2024] [Accepted: 04/24/2024] [Indexed: 05/03/2024]
Abstract
OBJECTIVE This study aimed to examine the association between past/current sleep duration and macro-/micro-structural brain outcomes and explore whether hypertension or social activity plays a role in such association. METHODS Within the UK Biobank, 40 436 dementia-free participants (age 40-70 years) underwent a baseline assessment followed by a brain magnetic resonance imaging (MRI) scan 9 years later. Past (baseline) and current (MRI scans) sleep duration (hours/day) were recorded and classified as short (≤5), intermediate (6-8), and long (≥9). Brain structural volumes and diffusion markers were assessed by MRI scans. RESULTS Compared with past intermediate sleep, past short sleep was related to smaller cortex volumes (standardized β [95 % CI]: -0.04 [-0.07, -0.02]) and lower regional fractional anisotropy (FA) (-0.08 [-0.13, -0.03]), while past long sleep was related to smaller regional subcortical volumes (standardized β: -0.04 to -0.07 for thalamus, accumbens, and hippocampus). Compared to current intermediate sleep, current short sleep was associated with smaller cortex volumes (-0.03 [-0.05, -0.01]), greater white matter hyperintensities (WMH) volumes (0.04 [0.01, 0.08]), and lower regional FA (-0.07 [-0.11, -0.02]). However, current long sleep was related to smaller total brain (-0.03 [-0.05, -0.02]), grey matter (-0.05 [-0.07, -0.03]), cortex (-0.05 [-0.07, -0.03]), regional subcortical volumes [standardized β: -0.05 to -0.09 for putamen, thalamus, hippocampus, and accumbens]), greater WMH volumes (0.06 [0.03, 0.09]), as well as lower regional FA (-0.05 [-0.09, -0.02]). The association between current long sleep duration and poor brain health was stronger among people with hypertension or low frequency of social activity (all Pinteraction <0.05). CONCLUSIONS Both past and current short/long sleep are associated with smaller brain volume and poorer white matter health in the brain, especially in individuals with hypertension and low frequency of social activity. Our findings highlight the need to maintain 6-8 h' sleep duration for healthy brain aging.
Collapse
Affiliation(s)
- Zhiyu Wang
- Department of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin, China; Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China; Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China
| | - Xuerui Li
- Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute, Tianjin, China
| | - Jiao Wang
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China; Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China; Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Wenzhe Yang
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China; Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China; Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Abigail Dove
- Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Wenli Lu
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China; Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China; Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Xiuying Qi
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China; Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China; Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China.
| | - Shireen Sindi
- Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden; Ageing Epidemiology Research Unit (AGE), School of Public Health, Faculty of Medicine, Imperial College London, UK
| | - Weili Xu
- Tianjin Key Laboratory of Environment, Nutrition and Public Health, Tianjin, China; Center for International Collaborative Research on Environment, Nutrition and Public Health, Tianjin, China; Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China; Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden.
| |
Collapse
|
35
|
Weiss J, Beydoun MA, Beydoun HA, Georgescu MF, Hu YH, Noren Hooten N, Banerjee S, Launer LJ, Evans MK, Zonderman AB. Pathways explaining racial/ethnic and socio-economic disparities in brain white matter integrity outcomes in the UK Biobank study. SSM Popul Health 2024; 26:101655. [PMID: 38562403 PMCID: PMC10982559 DOI: 10.1016/j.ssmph.2024.101655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Revised: 02/14/2024] [Accepted: 03/11/2024] [Indexed: 04/04/2024] Open
Abstract
Pathways explaining racial/ethnic and socio-economic status (SES) disparities in white matter integrity (WMI) reflecting brain health, remain underexplored, particularly in the UK population. We examined racial/ethnic and SES disparities in diffusion tensor brain magnetic resonance imaging (dMRI) markers, namely global and tract-specific mean fractional anisotropy (FA), and tested total, direct and indirect effects through lifestyle, health-related and cognition factors using a structural equations modeling approach among 36,184 UK Biobank participants aged 40-70 y at baseline assessment (47% men). Multiple linear regression models were conducted, testing independent associations of race/ethnicity, socio-economic and other downstream factors in relation to global mean FA, while stratifying by Alzheimer's Disease polygenic Risk Score (AD PRS) tertiles. Race (Non-White vs. White) and lower SES predicted poorer WMI (i.e. lower global mean FA) at follow-up, with racial/ethnic disparities in FAmean involving multiple pathways and SES playing a central role in those pathways. Mediational patterns differed across tract-specific FA outcomes, with SES-FAmean total effect being partially mediated (41% of total effect = indirect effect). Furthermore, the association of poor cognition with FAmean was markedly stronger in the two uppermost AD PRS tertiles compared to the lower tertile (T2 and T3: β±SE: -0.0009 ± 0.0001 vs. T1: β±SE: -0.0005 ± 0.0001, P < 0.001), independently of potentially confounding factors. Race and lower SES were generally important determinants of adverse WMI outcomes, with partial mediation of socio-economic disparities in global mean FA through lifestyle, health-related and cognition factors. The association of poor cognition with lower global mean FA was stronger at higher AD polygenic risk.
Collapse
Affiliation(s)
- Jordan Weiss
- Stanford Center on Longevity, Stanford University, Stanford, CA, USA
| | - May A. Beydoun
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| | - Hind A. Beydoun
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| | - Michael F. Georgescu
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| | - Yi-Han Hu
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| | - Nicole Noren Hooten
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| | - Sri Banerjee
- Public Health Doctoral Programs, Walden University, Minneapolis, MN, USA
| | - Lenore J. Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| | - Michele K. Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| | - Alan B. Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, USA
| |
Collapse
|
36
|
Jiang R, Noble S, Rosenblatt M, Dai W, Ye J, Liu S, Qi S, Calhoun VD, Sui J, Scheinost D. The brain structure, inflammatory, and genetic mechanisms mediate the association between physical frailty and depression. Nat Commun 2024; 15:4411. [PMID: 38782943 PMCID: PMC11116547 DOI: 10.1038/s41467-024-48827-8] [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: 02/07/2024] [Accepted: 05/13/2024] [Indexed: 05/25/2024] Open
Abstract
Cross-sectional studies have demonstrated strong associations between physical frailty and depression. However, the evidence from prospective studies is limited. Here, we analyze data of 352,277 participants from UK Biobank with 12.25-year follow-up. Compared with non-frail individuals, pre-frail and frail individuals have increased risk for incident depression independent of many putative confounds. Altogether, pre-frail and frail individuals account for 20.58% and 13.16% of depression cases by population attributable fraction analyses. Higher risks are observed in males and individuals younger than 65 years than their counterparts. Mendelian randomization analyses support a potential causal effect of frailty on depression. Associations are also observed between inflammatory markers, brain volumes, and incident depression. Moreover, these regional brain volumes and three inflammatory markers-C-reactive protein, neutrophils, and leukocytes-significantly mediate associations between frailty and depression. Given the scarcity of curative treatment for depression and the high disease burden, identifying potential modifiable risk factors of depression, such as frailty, is needed.
Collapse
Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA.
| | - Stephanie Noble
- Department of Psychology, Northeastern University, Boston, MA, USA
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Center for Cognitive and Brain Health, Northeastern University, Boston, MA, USA
| | - Matthew Rosenblatt
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
| | - Wei Dai
- Department of Biostatistics, Yale University, New Haven, CT, 06520, USA
| | - Jean Ye
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06520, USA
| | - Shu Liu
- Department of Psychiatry, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, 30303, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, 30303, USA
| | - Jing Sui
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, 06510, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06520, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, 06520, USA
- Department of Statistics & Data Science, Yale University, New Haven, CT, 06520, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, 06510, USA
| |
Collapse
|
37
|
Huang WQ, Lin Q, Tzeng CM. Leukoaraiosis: Epidemiology, Imaging, Risk Factors, and Management of Age-Related Cerebral White Matter Hyperintensities. J Stroke 2024; 26:131-163. [PMID: 38836265 PMCID: PMC11164597 DOI: 10.5853/jos.2023.02719] [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: 08/18/2023] [Accepted: 01/15/2024] [Indexed: 06/06/2024] Open
Abstract
Leukoaraiosis (LA) manifests as cerebral white matter hyperintensities on T2-weighted magnetic resonance imaging scans and corresponds to white matter lesions or abnormalities in brain tissue. Clinically, it is generally detected in the early 40s and is highly prevalent globally in individuals aged >60 years. From the imaging perspective, LA can present as several heterogeneous forms, including punctate and patchy lesions in deep or subcortical white matter; lesions with periventricular caps, a pencil-thin lining, and smooth halo; as well as irregular lesions, which are not always benign. Given its potential of having deleterious effects on normal brain function and the resulting increase in public health burden, considerable effort has been focused on investigating the associations between various risk factors and LA risk, and developing its associated clinical interventions. However, study results have been inconsistent, most likely due to potential differences in study designs, neuroimaging methods, and sample sizes as well as the inherent neuroimaging heterogeneity and multi-factorial nature of LA. In this article, we provided an overview of LA and summarized the current knowledge regarding its epidemiology, neuroimaging classification, pathological characteristics, risk factors, and potential intervention strategies.
Collapse
Affiliation(s)
- Wen-Qing Huang
- Department of Central Laboratory, Shanghai Children's Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Qing Lin
- Department of Neurology, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
- Xiamen Clinical Research Center for Neurological Diseases, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
- Fujian Provincial Clinical Research Center for Brain Diseases, The First Affiliated Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China
- The Third Clinical College, Fujian Medical University, Fuzhou, Fujian, China
| | - Chi-Meng Tzeng
- Translational Medicine Research Center, School of Pharmaceutical Sciences, Xiamen University, Xiamen, Fujian, China
| |
Collapse
|
38
|
Schrempft S, Trofimova O, Künzi M, Ramponi C, Lutti A, Kherif F, Latypova A, Vollenweider P, Marques-Vidal P, Preisig M, Kliegel M, Stringhini S, Draganski B. The Neurobiology of Life Course Socioeconomic Conditions and Associated Cognitive Performance in Middle to Late Adulthood. J Neurosci 2024; 44:e1231232024. [PMID: 38499361 PMCID: PMC11044112 DOI: 10.1523/jneurosci.1231-23.2024] [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: 07/03/2023] [Revised: 01/18/2024] [Accepted: 01/23/2024] [Indexed: 03/20/2024] Open
Abstract
Despite major advances, our understanding of the neurobiology of life course socioeconomic conditions is still scarce. This study aimed to provide insight into the pathways linking socioeconomic exposures-household income, last known occupational position, and life course socioeconomic trajectories-with brain microstructure and cognitive performance in middle to late adulthood. We assessed socioeconomic conditions alongside quantitative relaxometry and diffusion-weighted magnetic resonance imaging indicators of brain tissue microstructure and cognitive performance in a sample of community-dwelling men and women (N = 751, aged 50-91 years). We adjusted the applied regression analyses and structural equation models for the linear and nonlinear effects of age, sex, education, cardiovascular risk factors, and the presence of depression, anxiety, and substance use disorders. Individuals from lower-income households showed signs of advanced brain white matter (WM) aging with greater mean diffusivity (MD), lower neurite density, lower myelination, and lower iron content. The association between household income and MD was mediated by neurite density (B = 0.084, p = 0.003) and myelination (B = 0.019, p = 0.009); MD partially mediated the association between household income and cognitive performance (B = 0.017, p < 0.05). Household income moderated the relation between WM microstructure and cognitive performance, such that greater MD, lower myelination, or lower neurite density was only associated with poorer cognitive performance among individuals from lower-income households. Individuals from higher-income households showed preserved cognitive performance even with greater MD, lower myelination, or lower neurite density. These findings provide novel mechanistic insights into the associations between socioeconomic conditions, brain anatomy, and cognitive performance in middle to late adulthood.
Collapse
Affiliation(s)
- Stephanie Schrempft
- Unit of Population Epidemiology, Division of Primary Care, Geneva University Hospitals, Geneva CH-1205, Switzerland
| | - Olga Trofimova
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne CH-1015, Switzerland
- Swiss Institute of Bioinformatics, Lausanne CH-1015, Switzerland
| | - Morgane Künzi
- Swiss National Centre of Competences in Research, "LIVES - Overcoming Vulnerability: Life Course Perspectives," University of Lausanne and University of Geneva, Lausanne CH-1015 and Carouge CH-1227, Switzerland
- Department of Psychology, University of Geneva, Geneva CH-1205, Switzerland
- Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Carouge CH-1227, Switzerland
| | - Cristina Ramponi
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Antoine Lutti
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Ferath Kherif
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Adeliya Latypova
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Peter Vollenweider
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Pedro Marques-Vidal
- Department of Medicine, Internal Medicine, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
| | - Martin Preisig
- Department of Psychiatry, Lausanne University Hospital and University of Lausanne, Lausanne CH-1008, Switzerland
| | - Matthias Kliegel
- Swiss National Centre of Competences in Research, "LIVES - Overcoming Vulnerability: Life Course Perspectives," University of Lausanne and University of Geneva, Lausanne CH-1015 and Carouge CH-1227, Switzerland
- Department of Psychology, University of Geneva, Geneva CH-1205, Switzerland
- Center for the Interdisciplinary Study of Gerontology and Vulnerability, University of Geneva, Carouge CH-1227, Switzerland
| | - Silvia Stringhini
- Unit of Population Epidemiology, Division of Primary Care, Geneva University Hospitals, Geneva CH-1205, Switzerland
- Department of Health and Community Medicine, Faculty of Medicine, University of Geneva, Geneva CH-1211, Switzerland
- University Centre for General Medicine and Public Health, University of Lausanne, Lausanne CH-1005, Switzerland
| | - Bogdan Draganski
- Laboratory for Research in Neuroimaging (LREN), Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne CH-1011, Switzerland
- Neurology Department, Max-Planck-Institute for Human Cognitive and Brain Sciences, D-04303 Leipzig, Germany
| |
Collapse
|
39
|
Yeshaw Y, Madakkatel I, Mulugeta A, Lumsden A, Hyppönen E. Uncovering Predictors of Low Hippocampal Volume: Evidence from a Large-Scale Machine-Learning-Based Study in the UK Biobank. Neuroepidemiology 2024; 58:369-382. [PMID: 38560977 PMCID: PMC11449190 DOI: 10.1159/000538565] [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: 01/25/2024] [Accepted: 03/20/2024] [Indexed: 04/04/2024] Open
Abstract
INTRODUCTION Hippocampal atrophy is an established biomarker for conversion from the normal ageing process to developing cognitive impairment and dementia. This study used a novel hypothesis-free machine-learning approach, to uncover potential risk factors of lower hippocampal volume using information from the world's largest brain imaging study. METHODS A combination of machine learning and conventional statistical methods were used to identify predictors of low hippocampal volume. We run gradient boosting decision tree modelling including 2,891 input features measured before magnetic resonance imaging assessments (median 9.2 years, range 4.2-13.8 years) using data from 42,152 dementia-free UK Biobank participants. Logistic regression analyses were run on 87 factors identified as important for prediction based on Shapley values. False discovery rate-adjusted p value <0.05 was used to declare statistical significance. RESULTS Older age, male sex, greater height, and whole-body fat-free mass were the main predictors of low hippocampal volume with the model also identifying associations with lung function and lifestyle factors including smoking, physical activity, and coffee intake (corrected p < 0.05 for all). Red blood cell count and several red blood cell indices such as haemoglobin concentration, mean corpuscular haemoglobin, mean corpuscular volume, mean reticulocyte volume, mean sphered cell volume, and red blood cell distribution width were among many biomarkers associated with low hippocampal volume. CONCLUSION Lifestyles, physical measures, and biomarkers may affect hippocampal volume, with many of the characteristics potentially reflecting oxygen supply to the brain. Further studies are required to establish causality and clinical relevance of these findings.
Collapse
Affiliation(s)
- Yigizie Yeshaw
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia,
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia,
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia,
- Department of Epidemiology and Biostatistics, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia,
| | - Iqbal Madakkatel
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Anwar Mulugeta
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
- Department of Pharmacology and Clinical Pharmacy, College of Health Sciences, Addis Ababa, Ethiopia
| | - Amanda Lumsden
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| |
Collapse
|
40
|
Dove A, Guo J, Wang J, Vetrano DL, Sakakibara S, Laukka EJ, Bennett DA, Xu W. Cardiometabolic disease, cognitive decline, and brain structure in middle and older age. ALZHEIMER'S & DEMENTIA (AMSTERDAM, NETHERLANDS) 2024; 16:e12566. [PMID: 38595913 PMCID: PMC11002777 DOI: 10.1002/dad2.12566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 02/06/2024] [Accepted: 02/07/2024] [Indexed: 04/11/2024]
Abstract
INTRODUCTION The presence of multiple cardiometabolic diseases (CMDs) has been linked to increased dementia risk, but the combined influence of CMDs on cognition and brain structure across the life course is unclear. METHODS In the UK Biobank, 46,562 dementia-free participants completed a cognitive test battery at baseline and a follow-up visit 9 years later, at which point 39,306 also underwent brain magnetic resonance imaging. CMDs (diabetes, heart disease, and stroke) were ascertained from medical records. Data were analyzed using age-stratified (middle age [< 60] versus older [≥ 60]) mixed-effects models and linear regression. RESULTS A higher number of CMDs was associated with significantly steeper global cognitive decline in older (β = -0.008; 95% confidence interval: -0.012, -0.005) but not middle age. Additionally, the presence of multiple CMDs was related to smaller total brain volume, gray matter volume, white matter volume, and hippocampal volume and larger white matter hyperintensity volume, even in middle age. DISCUSSION CMDs are associated with cognitive decline in older age and poorer brain structural health beginning already in middle age. Highlights We explored the association of CMDs with cognitive decline and brain MRI measures.CMDs accelerated cognitive decline in older (≥60y) but not middle (<60) age.CMDs were associated with poorer brain MRI parameters in both middle and older age.Results highlight the connection between CMDs and cognitive/brain aging.
Collapse
Affiliation(s)
- Abigail Dove
- Aging Research CenterDepartment of NeurobiologyCare Sciences and SocietyKarolinska InstitutetStockholmSweden
| | - Jie Guo
- Aging Research CenterDepartment of NeurobiologyCare Sciences and SocietyKarolinska InstitutetStockholmSweden
| | - Jiao Wang
- Department of Epidemiology and BiostatisticsSchool of Public HealthTianjin Medical UniversityTianjinChina
| | - Davide Liborio Vetrano
- Aging Research CenterDepartment of NeurobiologyCare Sciences and SocietyKarolinska InstitutetStockholmSweden
- Stockholm Gerontology Research CenterStockholmSweden
| | - Sakura Sakakibara
- Aging Research CenterDepartment of NeurobiologyCare Sciences and SocietyKarolinska InstitutetStockholmSweden
| | - Erika J. Laukka
- Aging Research CenterDepartment of NeurobiologyCare Sciences and SocietyKarolinska InstitutetStockholmSweden
- Stockholm Gerontology Research CenterStockholmSweden
| | - David A. Bennett
- Rush Alzheimer's Disease CenterRush University Medical CenterChicagoIllinoisUSA
| | - Weili Xu
- Aging Research CenterDepartment of NeurobiologyCare Sciences and SocietyKarolinska InstitutetStockholmSweden
- Department of Epidemiology and BiostatisticsSchool of Public HealthTianjin Medical UniversityTianjinChina
| |
Collapse
|
41
|
Petersen M, Hoffstaedter F, Nägele FL, Mayer C, Schell M, Rimmele DL, Zyriax BC, Zeller T, Kühn S, Gallinat J, Fiehler J, Twerenbold R, Omidvarnia A, Patil KR, Eickhoff SB, Thomalla G, Cheng B. A latent clinical-anatomical dimension relating metabolic syndrome to brain structure and cognition. eLife 2024; 12:RP93246. [PMID: 38512127 PMCID: PMC10957178 DOI: 10.7554/elife.93246] [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: 03/22/2024] Open
Abstract
The link between metabolic syndrome (MetS) and neurodegenerative as well as cerebrovascular conditions holds substantial implications for brain health in at-risk populations. This study elucidates the complex relationship between MetS and brain health by conducting a comprehensive examination of cardiometabolic risk factors, brain morphology, and cognitive function in 40,087 individuals. Multivariate, data-driven statistics identified a latent dimension linking more severe MetS to widespread brain morphological abnormalities, accounting for up to 71% of shared variance in the data. This dimension was replicable across sub-samples. In a mediation analysis, we could demonstrate that MetS-related brain morphological abnormalities mediated the link between MetS severity and cognitive performance in multiple domains. Employing imaging transcriptomics and connectomics, our results also suggest that MetS-related morphological abnormalities are linked to the regional cellular composition and macroscopic brain network organization. By leveraging extensive, multi-domain data combined with a dimensional stratification approach, our analysis provides profound insights into the association of MetS and brain health. These findings can inform effective therapeutic and risk mitigation strategies aimed at maintaining brain integrity.
Collapse
Affiliation(s)
- Marvin Petersen
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Felix Hoffstaedter
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center JülichJülichGermany
| | - Felix L Nägele
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Carola Mayer
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Maximilian Schell
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - D Leander Rimmele
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Birgit-Christiane Zyriax
- Midwifery Science-Health Services Research and Prevention, Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-EppendorfHamburgGermany
| | - Tanja Zeller
- Department of Cardiology, University Heart and Vascular CenterHamburgGermany
- German Center for Cardiovascular Research (DZHK), partner site Hamburg/Kiel/LuebeckHamburgGermany
- University Center of Cardiovascular Science, University Heart and Vascular CenterHamburgGermany
| | - Simone Kühn
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Jürgen Gallinat
- Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Jens Fiehler
- Department of Diagnostic and Interventional Neuroradiology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Raphael Twerenbold
- Department of Cardiology, University Heart and Vascular CenterHamburgGermany
- German Center for Cardiovascular Research (DZHK), partner site Hamburg/Kiel/LuebeckHamburgGermany
- University Center of Cardiovascular Science, University Heart and Vascular CenterHamburgGermany
- Epidemiological Study Center, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Amir Omidvarnia
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center JülichJülichGermany
| | - Kaustubh R Patil
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center JülichJülichGermany
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University DüsseldorfDüsseldorfGermany
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Center JülichJülichGermany
| | - Goetz Thomalla
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| | - Bastian Cheng
- Department of Neurology, University Medical Center Hamburg-EppendorfHamburgGermany
| |
Collapse
|
42
|
Beydoun MA, Beydoun HA, Hu YH, El-Hajj ZW, Georgescu MF, Noren Hooten N, Li Z, Weiss J, Lyall DM, Waldstein SR, Hedges DW, Gale SD, Launer LJ, Evans MK, Zonderman AB. Helicobacter pylori, persistent infection burden and structural brain imaging markers. Brain Commun 2024; 6:fcae088. [PMID: 38529358 PMCID: PMC10961948 DOI: 10.1093/braincomms/fcae088] [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: 03/28/2023] [Revised: 01/11/2024] [Accepted: 03/11/2024] [Indexed: 03/27/2024] Open
Abstract
Persistent infections, whether viral, bacterial or parasitic, including Helicobacter pylori infection, have been implicated in non-communicable diseases, including dementia and other neurodegenerative diseases. In this cross-sectional study, data on 635 cognitively normal participants from the UK Biobank study (2006-21, age range: 40-70 years) were used to examine whether H. pylori seropositivity (e.g. presence of antibodies), serointensities of five H. pylori antigens and a measure of total persistent infection burden were associated with selected brain volumetric structural MRI (total, white, grey matter, frontal grey matter (left/right), white matter hyperintensity as percent intracranial volume and bi-lateral sub-cortical volumes) and diffusion-weighted MRI measures (global and tract-specific bi-lateral fractional anisotropy and mean diffusivity), after an average 9-10 years of lag time. Persistent infection burden was calculated as a cumulative score of seropositivity for over 20 different pathogens. Multivariable-adjusted linear regression analyses were conducted, whereby selected potential confounders (all measures) and intracranial volume (sub-cortical volumes) were adjusted, with stratification by Alzheimer's disease polygenic risk score tertile when exposures were H. pylori antigen serointensities. Type I error was adjusted to 0.007. We report little evidence of an association between H. pylori seropositivity and persistent infection burden with various volumetric outcomes (P > 0.007, from multivariable regression models), unlike previously reported in past research. However, H. pylori antigen serointensities, particularly immunoglobulin G against the vacuolating cytotoxin A, GroEL and outer membrane protein antigens, were associated with poorer tract-specific white matter integrity (P < 0.007), with outer membrane protein serointensity linked to worse outcomes in cognition-related tracts such as the external capsule, the anterior limb of the internal capsule and the cingulum, specifically at low Alzheimer's disease polygenic risk. Vacuolating cytotoxin A serointensity was associated with greater white matter hyperintensity volume among individuals with mid-level Alzheimer's disease polygenic risk, while among individuals with the highest Alzheimer's disease polygenic risk, the urease serointensity was consistently associated with reduced bi-lateral caudate volumes and the vacuolating cytotoxin A serointensity was linked to reduced right putamen volume (P < 0.007). Outer membrane protein and urease were associated with larger sub-cortical volumes (e.g. left putamen and right nucleus accumbens) at middle Alzheimer's disease polygenic risk levels (P < 0.007). Our results shed light on the relationship between H. pylori seropositivity, H. pylori antigen levels and persistent infection burden with brain volumetric structural measures. These data are important given the links between infectious agents and neurodegenerative diseases, including Alzheimer's disease, and can be used for the development of drugs and preventive interventions that would reduce the burden of those diseases.
Collapse
Affiliation(s)
- May A Beydoun
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, USA
| | - Hind A Beydoun
- Department of Research Programs, Fort Belvoir Community Hospital, Fort Belvoir, VA 22060, USA
| | - Yi-Han Hu
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, USA
| | - Ziad W El-Hajj
- Department of Biology, McGill University, Montreal, QC H3A 1B1, Canada
| | - Michael F Georgescu
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, USA
| | - Nicole Noren Hooten
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, USA
| | - Zhiguang Li
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, USA
| | - Jordan Weiss
- Stanford Center on Longevity, Stanford University, Stanford, CA 94305, USA
| | - Donald M Lyall
- School of Health and Wellbeing, University of Glasgow, Glasgow G12 8QQ, Scotland, UK
| | - Shari R Waldstein
- Department of Psychology, University of Maryland, Catonsville, MD 21250, USA
- Division of Gerontology, Geriatrics, and Palliative Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Dawson W Hedges
- Department of Psychology, Brigham Young University, Provo, UT 84602, USA
| | - Shawn D Gale
- Department of Psychology, Brigham Young University, Provo, UT 84602, USA
| | - Lenore J Launer
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, USA
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, National Institute on Aging, NIA/NIH/IRP, Baltimore, MD 21224, USA
| |
Collapse
|
43
|
Valletta M, Vetrano DL, Calderón‐Larrañaga A, Kalpouzos G, Canevelli M, Marengoni A, Laukka EJ, Grande G. Association of mild and complex multimorbidity with structural brain changes in older adults: A population-based study. Alzheimers Dement 2024; 20:1958-1965. [PMID: 38170758 PMCID: PMC10984455 DOI: 10.1002/alz.13614] [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: 09/08/2023] [Revised: 11/01/2023] [Accepted: 11/27/2023] [Indexed: 01/05/2024]
Abstract
INTRODUCTION We quantified the association of mild (ie, involving one or two body systems) and complex (ie, involving ≥3 systems) multimorbidity with structural brain changes in older adults. METHODS We included 390 dementia-free participants aged 60+ from the Swedish National Study on Aging and Care in Kungsholmen who underwent brain magnetic resonance imaging at baseline and after 3 and/or 6 years. Using linear mixed models, we estimated the association between multimorbidity and changes in total brain tissue, ventricular, hippocampal, and white matter hyperintensities volumes. RESULTS Compared to non-multimorbid participants, those with complex multimorbidity showed the steepest reduction in total brain (β*time -0.03, 95% CI -0.05, -0.01) and hippocampal (β*time -0.05, 95% CI -0.08, -0.03) volumes, the greatest ventricular enlargement (β*time 0.03, 95% CI 0.01, 0.05), and the fastest white matter hyperintensities accumulation (β*time 0.04, 95% CI 0.01, 0.07). DISCUSSION Multimorbidity, particularly when involving multiple body systems, is associated with accelerated structural brain changes, involving both neurodegeneration and vascular pathology. HIGHLIGHTS Multimorbidity accelerates structural brain changes in cognitively intact older adults These brain changes encompass both neurodegeneration and cerebrovascular pathology The complexity of multimorbidity is associated with the rate of brain changes' progression.
Collapse
Affiliation(s)
- Martina Valletta
- Aging Research CenterDepartment of NeurobiologyCare Sciences and SocietyKarolinska Institutet and Stockholm UniversityStockholmSweden
| | - Davide Liborio Vetrano
- Aging Research CenterDepartment of NeurobiologyCare Sciences and SocietyKarolinska Institutet and Stockholm UniversityStockholmSweden
- Stockholm Gerontology Research CenterStockholmSweden
| | - Amaia Calderón‐Larrañaga
- Aging Research CenterDepartment of NeurobiologyCare Sciences and SocietyKarolinska Institutet and Stockholm UniversityStockholmSweden
- Stockholm Gerontology Research CenterStockholmSweden
| | - Grégoria Kalpouzos
- Aging Research CenterDepartment of NeurobiologyCare Sciences and SocietyKarolinska Institutet and Stockholm UniversityStockholmSweden
| | - Marco Canevelli
- Aging Research CenterDepartment of NeurobiologyCare Sciences and SocietyKarolinska Institutet and Stockholm UniversityStockholmSweden
- Department of Human NeuroscienceSapienza UniversityRomeItaly
| | - Alessandra Marengoni
- Aging Research CenterDepartment of NeurobiologyCare Sciences and SocietyKarolinska Institutet and Stockholm UniversityStockholmSweden
- Department of Clinical and Experimental SciencesUniversity of BresciaBresciaItaly
| | - Erika J Laukka
- Aging Research CenterDepartment of NeurobiologyCare Sciences and SocietyKarolinska Institutet and Stockholm UniversityStockholmSweden
- Stockholm Gerontology Research CenterStockholmSweden
| | - Giulia Grande
- Aging Research CenterDepartment of NeurobiologyCare Sciences and SocietyKarolinska Institutet and Stockholm UniversityStockholmSweden
- Stockholm Gerontology Research CenterStockholmSweden
| |
Collapse
|
44
|
Borchers F, Rumpel M, Laubrock J, Spies C, Kozma P, Slooter A, van Montfort SJT, Piper SK, Wiebach J, Winterer G, Pischon T, Feinkohl I. Cognitive reserve and the risk of postoperative neurocognitive disorders in older age. Front Aging Neurosci 2024; 15:1327388. [PMID: 38374990 PMCID: PMC10875020 DOI: 10.3389/fnagi.2023.1327388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 12/26/2023] [Indexed: 02/21/2024] Open
Abstract
Background Postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) are postoperative neurocognitive disorders (PNDs) that frequently occur in the aftermath of a surgical intervention. Cognitive reserve (CR) is a concept posited to explain why cognitive health varies between individuals. On this qualitative understanding of cognitive health, factors like IQ, education level, and occupational complexity can affect the impact of neuropathological processes on cognitive outcomes. Methods We investigated the association between CR and POD and CR and POCD on data from 713 patients aged≥65 years with elective surgery. Peak pre-morbid IQ was estimated from vocabulary. Occupational complexity was coded according to the Dictionary of Occupational Titles (DOT). Education level was classed according to the International Standard Classification of Education (ISCED). These three factors were used as proxies of CR. In a series of regression models, age, sex, depression, site of surgery, and several lifestyle and vascular factors were controlled for. Results Patients with a higher IQ had lower odds of developing POD. We found no significant association between the other two CR markers with POD. None of the CR markers was associated with POCD. Conclusion The significant association of a higher IQ with lower POD risk allows for the stratification of elderly surgical patients by risk. This knowledge can aid the prevention and/or early detection of POD. Further research should attempt to determine the lack of associations of CR markers with POCD in our study.
Collapse
Affiliation(s)
- Friedrich Borchers
- Department of Anesthesiology and Intensive Care Medicine (CCM, CVK), Humboldt-Universität zu Berlin, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Miriam Rumpel
- Department of Psychology, University of Potsdam, Potsdam, Germany
| | - Jochen Laubrock
- Department of Psychology, University of Potsdam, Potsdam, Germany
| | - Claudia Spies
- Department of Anesthesiology and Intensive Care Medicine (CCM, CVK), Humboldt-Universität zu Berlin, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Petra Kozma
- 2nd Department of Internal Medicine and Nephrological Center, University of Pécs Medical School, Pécs, Hungary
| | - Arjen Slooter
- Department of Intensive Care Medicine and Brain Center, University Medical Center Utrecht (UMC), Utrecht University, Utrecht, Netherlands
| | - Simone J. T. van Montfort
- Department of Intensive Care Medicine and Brain Center, University Medical Center Utrecht (UMC), Utrecht University, Utrecht, Netherlands
| | - Sophie K. Piper
- Institute of Medical Informatics, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Institute of Biometry and Clinical Epidemiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Janine Wiebach
- Institute of Medical Informatics, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Institute of Biometry and Clinical Epidemiology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Georg Winterer
- Department of Anesthesiology and Intensive Care Medicine (CCM, CVK), Humboldt-Universität zu Berlin, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Pharmaimage Biomarker Solutions Inc., Cambridge, MA, United States
- PI Health Solutions GmbH, Berlin, Germany
| | - Tobias Pischon
- Molecular Epidemiology Research Group, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Core Facility Biobank, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
- Biobank Technology Platform, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Insa Feinkohl
- Medical Biometry and Epidemiology Group, Witten/Herdecke University, Witten, Germany
| |
Collapse
|
45
|
Raghavan S, Przybelski SA, Lesnick TG, Fought AJ, Reid RI, Gebre RK, Windham BG, Algeciras‐Schimnich A, Machulda MM, Vassilaki M, Knopman DS, Jack CR, Petersen RC, Graff‐Radford J, Vemuri P. Vascular risk, gait, behavioral, and plasma indicators of VCID. Alzheimers Dement 2024; 20:1201-1213. [PMID: 37932910 PMCID: PMC10916988 DOI: 10.1002/alz.13540] [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: 07/11/2023] [Revised: 10/06/2023] [Accepted: 10/11/2023] [Indexed: 11/08/2023]
Abstract
INTRODUCTION Cost-effective screening tools for vascular contributions to cognitive impairment and dementia (VCID) has significant implications. We evaluated non-imaging indicators of VCID using magnetic resonance imaging (MRI)-measured white matter (WM) damage and hypothesized that these indicators differ based on age. METHODS In 745 participants from the Mayo Clinic Study of Aging (≥50 years of age) with serial WM assessments from diffusion MRI and fluid-attenuated inversion recovery (FLAIR)-MRI, we examined associations between baseline non-imaging indicators (demographics, vascular risk factors [VRFs], gait, behavioral, plasma glial fibrillary acidic protein [GFAP], and plasma neurofilament light chain [NfL]) and WM damage across three age tertiles. RESULTS VRFs and gait were associated with diffusion changes even in low age strata. All measures (VRFs, gait, behavioral, plasma GFAP, plasma NfL) were associated with white matter hyperintensities (WMHs) but mainly in intermediate and high age strata. DISCUSSION Non-imaging indicators of VCID were related to WM damage and may aid in screening participants and assessing outcomes for VCID. HIGHLIGHTS Non-imaging indicators of VCID can aid in prediction of MRI-measured WM damage but their importance differed by age. Vascular risk and gait measures were associated with early VCID changes measured using diffusion MRI. Plasma markers explained variability in WMH across age strata. Most non-imaging measures explained variability in WMH and vascular WM scores in intermediate and older age groups. The framework developed here can be used to evaluate new non-imaging VCID indicators proposed in the future.
Collapse
Affiliation(s)
| | | | - Timothy G. Lesnick
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | - Angela J. Fought
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | - Robert I. Reid
- Department of Information TechnologyMayo ClinicRochesterMinnesotaUSA
| | | | - B. Gwen Windham
- Department of MedicineUniversity of Mississippi Medical CenterJacksonUSA
| | | | | | - Maria Vassilaki
- Department of Quantitative Health SciencesMayo ClinicRochesterMinnesotaUSA
| | | | | | | | | | | |
Collapse
|
46
|
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.
Collapse
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
| |
Collapse
|
47
|
Estrella ML, Tarraf W, Kuwayama S, Gallo LC, Salazar CR, Stickel AM, Mattei J, Vásquez PM, Eldeirawi KM, Perreira KM, Penedo FJ, Isasi CR, Cai J, Zeng D, González HM, Daviglus ML, Lamar M. Associations of Allostatic Load with Level of and Change in Cognitive Function Among Middle-Aged and Older Hispanic/Latino Adults: The Study of Latinos-Investigation of Neurocognitive Aging (SOL-INCA). J Alzheimers Dis 2024; 99:1047-1064. [PMID: 38758999 PMCID: PMC11343490 DOI: 10.3233/jad-230796] [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: 05/19/2024]
Abstract
Background Higher allostatic load (AL), a multi-system measure of physiological dysregulation considered a proxy for chronic stress exposure, is associated with poorer global cognition (GC) in older non-Hispanic white adults. However, evidence of these associations in middle-aged and older US-based Hispanic/Latino adults is limited. Objective To examine associations of AL with level of cognition, performance in cognition 7 years later, and change in cognition over 7 years among middle-aged and older US-based Hispanic/Latino adults. Methods We used data (n = 5,799, 45-74 years at baseline) from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) and SOL-Investigation of Neurocognitive Aging (SOL-INCA). The AL score comprised 16 biomarkers representing cardiometabolic, glucose, cardiopulmonary, parasympathetic, and inflammatory systems (higher scores = greater dysregulation). Cognitive outcomes included GC and individual tests of verbal learning and memory, world fluency (WF), Digit Symbol Substitution (DSS), and Trail Making (Parts A & B). Survey-linear regressions assessed associations of AL with performance in cognition at baseline, 7 years later, and via 7-year cognitive change scores adjusting for sociodemographic characteristics, lifestyle factors, and depressive symptoms. Results Higher AL was associated with lower baseline performance in GC and WF; and lower 7-year follow-up performance in these same measures plus DSS and Trail Making Parts A & B. Higher AL was associated with more pronounced 7-year change (reduction) in GC and on WF and DSS tests. Conclusions Findings extend previous evidence in predominantly older non-Hispanic white cohorts to show that AL is related to level of and change in GC (as well as WF and DSS) among middle-aged and older US-based Hispanic/Latino adults.
Collapse
Affiliation(s)
- Mayra L. Estrella
- Rush Alzheimer’s Disease Center and the Department of Internal Medicine, Rush University Medical Center, Chicago, IL, USA
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Wassim Tarraf
- Institute of Gerontology and Department of Healthcare Sciences, Wayne State University, Detroit, MI, USA
| | - Sayaka Kuwayama
- Department of Neurosciences and Shiley-Marcos Alzheimer’s Disease Research Center, University of California, San Diego, San Diego, CA, USA
| | - Linda C. Gallo
- Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Christian R. Salazar
- University of California Irvine Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, Irvine, CA, USA
| | - Ariana M. Stickel
- Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Josiemer Mattei
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Priscilla M. Vásquez
- Department of Urban Public Health, Charles R. Drew University of Science and Medicine, Los Angeles, CA, USA
| | - Kamal M. Eldeirawi
- Department of Population Health Nursing Science, College of Nursing, University of Illinois Chicago, Chicago, IL, USA
| | - Krista M. Perreira
- Department of Social Medicine, University of North Carolina School of Medicine, Chapel Hill, NC, USA
| | - Frank J. Penedo
- Department of Psychology and Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL, USA
| | - Carmen R. Isasi
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, USA
| | - Jianwen Cai
- Department of Biostatistics, University of North Carolina at Chapel Hill Gillings School of Global Public Health, Chapel Hill, NC, USA
| | - Donglin Zeng
- Department of Biostatistics, University of North Carolina at Chapel Hill Gillings School of Global Public Health, Chapel Hill, NC, USA
| | - Hector M. González
- Department of Neurosciences and Shiley-Marcos Alzheimer’s Disease Research Center, University of California, San Diego, CA, USA
| | - Martha L. Daviglus
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
| | - Melissa Lamar
- Institute for Minority Health Research, University of Illinois at Chicago, Chicago, IL, USA
- Rush Alzheimer’s Disease Center and the Department of Psychiatry and Behavioral Sciences, Rush University Medical Center, Chicago, IL, USA
| |
Collapse
|
48
|
Pausova Z, Sliz E. Large-Scale Population-Based Studies of Blood Metabolome and Brain Health. Curr Top Behav Neurosci 2024; 68:177-219. [PMID: 38509405 DOI: 10.1007/7854_2024_463] [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: 03/22/2024]
Abstract
Metabolomics technologies enable the quantification of multiple metabolomic measures simultaneously, which provides novel insights into molecular aspects of human health and disease. In large-scale, population-based studies, blood is often the preferred biospecimen. Circulating metabolome may relate to brain health either by affecting or reflecting brain metabolism. Peripheral metabolites may act at or cross the blood-brain barrier and, subsequently, influence brain metabolism, or they may reflect brain metabolism if similar pathways are engaged. Peripheral metabolites may also include those penetrating the circulation from the brain, indicating, for example, brain damage. Most brain health-related metabolomics studies have been conducted in the context of neurodegenerative disorders and cognition, but some studies have also focused on neuroimaging markers of these disorders. Moreover, several metabolomics studies of neurodevelopmental disorders have been performed. Here, we provide a brief background on the types of blood metabolites commonly assessed, and we review the literature describing the relationships between human blood metabolome (n > 50 metabolites) and brain health reported in large-scale studies (n > 500 individuals).
Collapse
Affiliation(s)
- Zdenka Pausova
- Centre hospitalier universitaire Sainte-Justine and Department of Pediatrics, University of Montreal, Montreal, QC, Canada
| | - Eeva Sliz
- Research Unit of Population Health, Faculty of Medicine, University of Oulu, Oulu, Finland.
| |
Collapse
|
49
|
Wang W, Yang Y, Sang F, Chen Y, Li X, Chen K, Wang J, Zhang Z. Vascular Risk Factors and Brain Health in Aging: Insights from a Community-Based Cohort Study. J Alzheimers Dis 2024; 99:1361-1374. [PMID: 38788079 DOI: 10.3233/jad-240240] [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] [Indexed: 05/26/2024]
Abstract
Background The aging population and high rates of Alzheimer's disease (AD) create significant medical burdens, prompting a need for early prevention. Targeting modifiable risk factors like vascular risk factors (VRFs), closely linked to AD, may provide a promising strategy for intervention. Objective This study investigates how VRFs influence cognitive performance and brain structures in a community-based cohort. Methods In this cross-sectional study, 4,667 participants over 50 years old, drawn from the Beijing Ageing Brain Rejuvenation Initiative project, were meticulously examined. Cognitive function and VRFs (diabetes mellitus, hypertension, hyperlipidemia, obesity, and smoking), were comprehensively assessed through one-to-one interviews. Additionally, a subset of participants (n = 719) underwent MRI, encompassing T1-weighted and diffusion-weighted scans, to elucidate gray matter volume and white matter structural network organization. Results The findings unveil diabetes as a potent detriment to memory, manifesting in atrophy within the right supramarginal gyrus and diminished nodal efficiency and degree centrality in the right inferior parietal lobe. Hypertension solely impaired memory without significant structural changes. Intriguingly, individuals with comorbid diabetes and hypertension exhibited the most pronounced deficits in both brain structure and cognitive performance. Remarkably, hyperlipidemia emerged as a factor associated with enhanced cognition, and preservation of brain structure. Conclusions This study illuminates the intricate associations between VRFs and the varied patterns of cognitive and brain structural damage. Notably, the synergistic effect of diabetes and hypertension emerges as particularly deleterious. These findings underscore the imperative to tailor interventions for patients with distinct VRF comorbidities, especially when addressing cognitive decline and structural brain changes.
Collapse
Affiliation(s)
- Wenxiao Wang
- Faculty of Psychology, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Yiru Yang
- School of Nursing and Rehabilitation, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Feng Sang
- Faculty of Psychology, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI Centre), Beijing Normal University, Beijing, China
| | - Yaojing Chen
- Faculty of Psychology, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI Centre), Beijing Normal University, Beijing, China
| | - Xin Li
- Faculty of Psychology, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI Centre), Beijing Normal University, Beijing, China
| | - Kewei Chen
- Beijing Aging Brain Rejuvenation Initiative (BABRI Centre), Beijing Normal University, Beijing, China
- Banner Alzheimer's Institute, Phoenix, AZ, USA
| | - Jun Wang
- Faculty of Psychology, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI Centre), Beijing Normal University, Beijing, China
| | - Zhanjun Zhang
- Faculty of Psychology, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Beijing Aging Brain Rejuvenation Initiative (BABRI Centre), Beijing Normal University, Beijing, China
| |
Collapse
|
50
|
Blöchl M, Schaare HL, Kumral D, Gaebler M, Nestler S, Villringer A. Vascular risk factors, white matter microstructure, and depressive symptoms: a longitudinal analysis in the UK Biobank. Psychol Med 2024; 54:125-135. [PMID: 37016768 DOI: 10.1017/s0033291723000697] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2023]
Abstract
BACKGROUND Cumulative burden from vascular risk factors (VRFs) has been associated with an increased risk of depressive symptoms in mid- and later life. It has been hypothesised that this association arises because VRFs disconnect fronto-subcortical white matter tracts involved in mood regulation, which puts older adults at higher risk of developing depressive symptoms. However, evidence for the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms from longitudinal studies is scarce. METHODS This preregistered study analysed longitudinal data from 6,964 middle-aged and older adults from the UK Biobank who participated in consecutive assessments of VRFs, brain imaging, and depressive symptoms. Using mediation modelling, we directly tested to what extend white matter microstructure mediates the longitudinal association between VRF burden and depressive symptoms. RESULTS VRF burden showed a small association with depressive symptoms at follow-up. However, there was no evidence that fractional anisotropy (FA) of white matter tracts mediated this association. Additional analyses also yielded no mediating effects using alternative operationalisations of VRF burden, mean diffusivity (MD) of single tracts, or overall average of tract-based white matter microstructure (global FA, global MD, white matter hyperintensity volume). CONCLUSIONS Our results lend no support to the hypothesis that disconnection of white matter tracts underlies the association between VRF burden and depressive symptoms, while highlighting the relevance of using longitudinal data to directly test pathways linking vascular and mental health.
Collapse
Affiliation(s)
- Maria Blöchl
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- International Max Planck Research School: Neuroscience of Communication: Structure, Function, and Plasticity, Leipzig, Germany
- Department of Psychology, University of Münster, Münster, Germany
| | - H Lina Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour) Research Centre Jülich, Germany
| | - Deniz Kumral
- Institute of Psychology, Neuropsychology, University of Freiburg, Freiburg, Germany
- Clinical Psychology and Psychotherapy Unit, Institute of Psychology, University of Freiburg, Freiburg, Germany
| | - Michael Gaebler
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Faculty of Philosophy, Humboldt-Universität zu Berlin, Berlin School of Mind and Brain, MindBrainBody Institute
- Max Planck Dahlem Campus of Cognition, Berlin, Germany
| | - Steffen Nestler
- Department of Psychology, University of Münster, Münster, Germany
| | - Arno Villringer
- Department for Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University Clinic Leipzig, Leipzig, Germany
- Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, Berlin, Germany
| |
Collapse
|