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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.
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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
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Liu SW, Ma XT, Yu S, Weng XF, Li M, Zhu J, Liu CF, Hu H. Bridging Reduced Grip Strength and Altered Executive Function: Specific Brain White Matter Structural Changes in Patients with Alzheimer's Disease. Clin Interv Aging 2024; 19:93-107. [PMID: 38250174 PMCID: PMC10799618 DOI: 10.2147/cia.s438782] [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: 10/02/2023] [Accepted: 01/09/2024] [Indexed: 01/23/2024] Open
Abstract
Objective To investigate the correlation between specific fiber tracts and grip strength and cognitive function in patients with Alzheimer's disease (AD) by fixel-based analysis (FBA). Methods AD patients were divided into AD with low grip strength (AD-LGS, n=29) and AD without low grip strength (AD-nLGS, n=25), along with 31 normal controls (NC). General data, neuropsychological tests, grip strength and cranial magnetic resonance imaging (MRI) scans were collected. FBA evaluated white matter (WM) fiber metrics, including fiber density (FD), fiber cross-sectional (FC), and fiber density and cross-sectional area (FDC). The mean fiber indicators of the fiber tracts of interest (TOI) were extracted in cerebral region of significant statistical differences in FBA to further compare the differences between groups and analyze the correlation between fiber properties and neuropsychological test scores. Results Compared to AD-nLGS group, AD-LGS group showed significant reductions in FDC in several cerebral regions. In AD patients, FDC values of bilateral uncinate fasciculus and left superior longitudinal fasciculus were positively correlated with Clock Drawing Test scores, while FDC of splenium of corpus callosum, bilateral anterior cingulate tracts, forceps major, and bilateral inferior longitudinal fasciculus were positively correlated with the Executive Factor Score of Memory and Executive Screening scale scores. Conclusion Reduced grip strength in AD patients is associated with extensive impairment of WM structural integrity. Changes in FDC of specific WM fiber tracts related to executive function play a significant mediating role in the reduction of grip strength in AD patients.
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Affiliation(s)
- Shan-Wen Liu
- Department of Neurology, the Second Affiliated Hospital of Soochow University, Suzhou, 215004, People’s Republic of China
| | - Xiao-Ting Ma
- Department of Neurology, the Second Affiliated Hospital of Soochow University, Suzhou, 215004, People’s Republic of China
| | - Shuai Yu
- Department of Neurology, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, 215000, People’s Republic of China
| | - Xiao-Fen Weng
- Department of Geriatric Medicine, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Suzhou, 215000, People’s Republic of China
| | - Meng Li
- Department of Imaging, the Second Affiliated Hospital of Soochow University, Suzhou, 215004, People’s Republic of China
| | - Jiangtao Zhu
- Department of Imaging, the Second Affiliated Hospital of Soochow University, Suzhou, 215004, People’s Republic of China
| | - Chun-Feng Liu
- Department of Neurology, the Second Affiliated Hospital of Soochow University, Suzhou, 215004, People’s Republic of China
| | - Hua Hu
- Department of Neurology, the Second Affiliated Hospital of Soochow University, Suzhou, 215004, People’s Republic of China
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Brain J, Kafadar AH, Errington L, Kirkley R, Tang EY, Akyea RK, Bains M, Brayne C, Figueredo G, Greene L, Louise J, Morgan C, Pakpahan E, Reeves D, Robinson L, Salter A, Siervo M, Tully PJ, Turnbull D, Qureshi N, Stephan BC. What's New in Dementia Risk Prediction Modelling? An Updated Systematic Review. Dement Geriatr Cogn Dis Extra 2024; 14:49-74. [PMID: 39015518 PMCID: PMC11250535 DOI: 10.1159/000539744] [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: 02/25/2024] [Accepted: 06/07/2024] [Indexed: 07/18/2024] Open
Abstract
Introduction Identifying individuals at high risk of dementia is critical to optimized clinical care, formulating effective preventative strategies, and determining eligibility for clinical trials. Since our previous systematic reviews in 2010 and 2015, there has been a surge in dementia risk prediction modelling. The aim of this study was to update our previous reviews to explore, and critically review, new developments in dementia risk modelling. Methods MEDLINE, Embase, Scopus, and Web of Science were searched from March 2014 to June 2022. Studies were included if they were population- or community-based cohorts (including electronic health record data), had developed a model for predicting late-life incident dementia, and included model performance indices such as discrimination, calibration, or external validation. Results In total, 9,209 articles were identified from the electronic search, of which 74 met the inclusion criteria. We found a substantial increase in the number of new models published from 2014 (>50 new models), including an increase in the number of models developed using machine learning. Over 450 unique predictor (component) variables have been tested. Nineteen studies (26%) undertook external validation of newly developed or existing models, with mixed results. For the first time, models have also been developed in low- and middle-income countries (LMICs) and others validated in racial and ethnic minority groups. Conclusion The literature on dementia risk prediction modelling is rapidly evolving with new analytical developments and testing in LMICs. However, it is still challenging to make recommendations about which one model is the most suitable for routine use in a clinical setting. There is an urgent need to develop a suitable, robust, validated risk prediction model in the general population that can be widely implemented in clinical practice to improve dementia prevention.
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Affiliation(s)
- Jacob Brain
- Institute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK
- Freemasons Foundation Centre for Men’s Health, Discipline of Medicine, School of Psychology, The University of Adelaide, Adelaide, SA, Australia
| | - Aysegul Humeyra Kafadar
- Institute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK
| | - Linda Errington
- Walton Library, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - Rachael Kirkley
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Eugene Y.H. Tang
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Ralph K. Akyea
- PRISM Group, Centre for Academic Primary Care, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Manpreet Bains
- Nottingham Centre for Public Health and Epidemiology, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Carol Brayne
- Cambridge Public Health, University of Cambridge, Cambridge, UK
| | | | - Leanne Greene
- Exeter Clinical Trials Unit, Department of Health and Community Sciences, University of Exeter Medical School, Exeter, UK
| | - Jennie Louise
- Women’s and Children’s Hospital Research Centre and South Australian Health and Medical Research Institute, Adelaide, SA, Australia
| | - Catharine Morgan
- Division of Population Health, Health Services Research and Primary Care, University of Manchester, Manchester, UK
| | - Eduwin Pakpahan
- Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne, UK
| | - David Reeves
- School for Health Sciences, University of Manchester, Manchester, UK
| | - Louise Robinson
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK
| | - Amy Salter
- School of Public Health, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia
| | - Mario Siervo
- School of Population Health, Curtin University, Perth, WA, Australia
- Dementia Centre of Excellence, Curtin enAble Institute, Faculty of Health Sciences, Curtin University, Perth, WA, Australia
| | - Phillip J. Tully
- Freemasons Foundation Centre for Men’s Health, Discipline of Medicine, School of Psychology, The University of Adelaide, Adelaide, SA, Australia
- Faculty of Medicine and Health, School of Psychology, University of New England, Armidale, NSW, Australia
| | - Deborah Turnbull
- Freemasons Foundation Centre for Men’s Health, Discipline of Medicine, School of Psychology, The University of Adelaide, Adelaide, SA, Australia
| | - Nadeem Qureshi
- PRISM Group, Centre for Academic Primary Care, Lifespan and Population Health, School of Medicine, University of Nottingham, Nottingham, UK
| | - Blossom C.M. Stephan
- Institute of Mental Health, School of Medicine, University of Nottingham, Innovation Park, Jubilee Campus, Nottingham, UK
- Dementia Centre of Excellence, Curtin enAble Institute, Faculty of Health Sciences, Curtin University, Perth, WA, Australia
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Kim REY, Lee M, Kang DW, Wang SM, Kim D, Lim HK. Increased Likelihood of Dementia with Coexisting Atrophy of Multiple Regions of Interest. J Alzheimers Dis 2024; 97:259-271. [PMID: 38143346 DOI: 10.3233/jad-230602] [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: 12/26/2023]
Abstract
BACKGROUND Brain volume is associated with cognitive decline in later life, and cortical brain atrophy exceeding the normal range is related to inferior cognitive and behavioral outcomes in later life. OBJECTIVE To investigate the likelihood of cognitive decline, mild cognitive impairment (MCI), or dementia, when regional atrophy is present in participants' magnetic resonance imaging (MRI). METHODS Multi-center MRI data of 2,545 adults were utilized to measure regional volumes using NEUROPHET AQUA. Four lobes (frontal, parietal, temporal, and occipital), four Alzheimer's disease-related regions (entorhinal, fusiform, inferior temporal, and middle temporal area), and the hippocampus in the left and right hemispheres were measured and analyzed. The presence of regional atrophy from brain MRI was defined as ≤1.5 standard deviation (SD) compared to the age- and sex-matched cognitively normal population. The risk ratio for cognitive decline was investigated for participants with regional atrophy in contrast to those without regional atrophy. RESULTS The risk ratio for cognitive decline was significantly higher when hippocampal atrophy was present (MCI, 1.84, p < 0.001; dementia, 4.17, p < 0.001). Additionally, participants with joint atrophy in multiple regions showed a higher risk ratio for dementia, e.g., 9.6 risk ratio (95% confidence interval, 8.0-11.5), with atrophy identified in the frontal, temporal, and hippocampal gray matter, than those without atrophy. CONCLUSIONS Our study showed that individuals with multiple regional atrophy (either lobar or AD-specific regions) have a higher likelihood of developing dementia compared to the age- and sex-matched population without atrophy. Thus, further consideration is needed when assessing MRI findings.
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Affiliation(s)
- Regina E Y Kim
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
- Institute of Human Genomic Study, College of Medicine, Korea University, Seoul, Republic of Korea
- Department of Psychiatry, Iowa City, IA, University of Iowa, United States of America
| | - Minho Lee
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
| | - Dong Woo Kang
- Department of Psychiatry, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul
| | - Sheng-Min Wang
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Donghyeon Kim
- Research Institute, NEUROPHET Inc., Seoul, Republic of Korea
| | - Hyun Kook Lim
- Department of Psychiatry, Yeouido St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
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Clocchiatti-Tuozzo S, Rivier C, Renedo D, Lopez VMT, Geer J, Miner B, Yaggi H, de Havenon A, Payabvash S, Sheth KN, Gill TM, Falcone GJ. Suboptimal Sleep Duration is Associated with Poorer Neuroimaging Brain Health Profiles. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.20.23288891. [PMID: 37162933 PMCID: PMC10168497 DOI: 10.1101/2023.04.20.23288891] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Background Cardiovascular health optimization during middle age benefits brain health. The American Heart Association's Life's Simple 7 recently added sleep duration as a key determinant of cardiovascular health becoming the Life's Essential 8. We tested the hypothesis that suboptimal sleep duration is associated with poorer neuroimaging brain health profiles in asymptomatic middle-aged adults. Methods We conducted a prospective MRI neuroimaging study in middle-aged persons without stroke, dementia, or multiple sclerosis enrolled in the UK Biobank. Self-reported sleep duration was categorized as short (<7 hours), optimal (7-<9 hours), or long (≥9 hours). Evaluated neuroimaging markers of brain health included white matter hyperintensities (presence and volume) and diffusion tensor imaging metrics (fractional anisotropy and mean diffusivity) evaluated in 48 distinct neuroanatomical regions. We used multivariable logistic and linear regression models, as appropriate, to test for association between sleep duration and neuroimaging markers of brain health. Results We evaluated 39,502 middle-aged persons (mean age 55, 53% female). Of these, 28,712 (72.7%) had optimal, 8,422 (21.3%) short, and 2,368 (6%) long sleep. Compared to optimal sleep, short sleep was associated with higher risk (OR 1.11; 95% CI 1.05-1.17; P<0.001) and larger volume (beta=0.06, SE=0.01; P<0.001) of white matter hyperintensities, while long sleep was associated with higher volume (beta=0.04, SE=0.02; P=0.01) but not higher risk (P>0.05) of white matter hyperintensities. Short (beta=0.03, SE=0.01; P=0.004) and long sleep (beta=0.07, SE=0.02; P<0.001) were associated with worse fractional anisotropy, while only long sleep associated with worse mean diffusivity (beta=0.05, SE=0.02; P=0.005). Conclusions Among middle-aged adults without clinically observed neurological disease, suboptimal sleep duration is associated with poorer neuroimaging brain health profiles. Because the evaluated neuroimaging markers precede stroke and dementia by several years, our findings support early interventions aimed at correcting this modifiable risk factor.
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Affiliation(s)
- Santiago Clocchiatti-Tuozzo
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Cyprien Rivier
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Daniela Renedo
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | | | - Jacqueline Geer
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Brienne Miner
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Henry Yaggi
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Adam de Havenon
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Sam Payabvash
- Department of Radiology, Yale School of Medicine, New Haven, CT, USA
| | - Kevin N Sheth
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Thomas M Gill
- Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Guido J Falcone
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
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Development and internal validation of a prognostic model for 15-year risk of Alzheimer dementia in primary care patients. Neurol Sci 2022; 43:5899-5908. [DOI: 10.1007/s10072-022-06258-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 07/05/2022] [Indexed: 10/17/2022]
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7
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Guo W, Shi J. White matter hyperintensities volume and cognition: A meta-analysis. Front Aging Neurosci 2022; 14:949763. [PMID: 36118701 PMCID: PMC9476945 DOI: 10.3389/fnagi.2022.949763] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
Background Cerebral small vessel disease (CSVD) is prevalent in the elderly and leads to an increased risk of cognitive impairment and dementia. The volume of white matter hyperintensities (WMHs) increases with age, which affects cognition. Objective To explore the relationship between WMH volume and cognitive decline in patients with CSVD. Methods We performed a systematic search of PubMed, Embase, and the Web of Science databases from their respective creation dates to the 5 May 2022 to identify all the clinical studies on either mild cognitive impairment (MCI) or dementia in regards to WMH volume in CSVD. Results White matter hyperintensities was associated with the risk of both the MCI and dementia, with a 35% increased risk [relative risk (RR) = 1.35; (95% CI: 1.01–1.81)] of progression from cognitively unimpaired (CU) to MCI (six studies, n = 2,278) and a 49% increased risk [RR = 1.49; (95% CI: 1.21–1.84)] of progression to dementia (six studies, n = 6,330). In a subgroup analysis, a follow-up period of over 5 years increased the risk of MCI by 40% [RR = 1.40; (95% CI: 1.07–1.82)] and dementia by 48% [RR = 1.48; (95% CI: 1.15–1.92)]. Conclusion White matter hyperintensities was found to be substantially correlated with the risk of cognitive impairment. Furthermore, cognitive decline was found to be a chronic process, such that WMH predicted the rate of cognitive decline in CSVD beyond 5 years. The cognitive decline observed in patients with WMH may, therefore, be minimized by early intervention.
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Beydoun MA, Shaked D, Hossain S, Weiss J, Beydoun HA, Maldonado AI, Katzel LI, Davatzikos C, Gullapalli RP, Seliger SL, Erus G, Evans MK, Zonderman AB, Waldstein SR. Red cell distribution width, anemia and their associations with white matter integrity among middle-aged urban adults. Neurobiol Aging 2021; 105:229-240. [PMID: 34120091 PMCID: PMC8338752 DOI: 10.1016/j.neurobiolaging.2021.05.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 05/03/2021] [Accepted: 05/04/2021] [Indexed: 12/11/2022]
Abstract
Anemia (blood hemoglobin [Hb] <13 g/dL among males; <12 g/dL among females) and elevated red cell distribution width (RDW) are potential risk factors for reduced brain white matter integrity (WMI), reflected by lower fractional anisotropy or increased mean diffusivity. Cross-sectional data with exposure-outcome lag time was used, whereby hematological exposures (RDW and Hb) and covariates were compiled from the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study with available visit 1 (v1; 2004-2009) and/or v2 (2009-2013) data; while diffusion tensor magnetic resonance imaging (dMRI) outcome data were collected at HANDLS SCAN visit (vscan: 2011-2015, n = 214, mean follow-up from v1 ±SD: 5.6 ± 1.8 year). Multivariable-adjusted linear regression analyses were conducted, overall, stratifying by sex, and further restricting to the nonanemic for RDW exposures in part of the analyses. Among males, RDW(v1) was linked with lower global mean fractional anisotropy (standardized effect size b = -0.30, p= 0.003, q < 0.05; basic model), an association only slightly attenuated with further covariate adjustment. Anemia was not a risk factor for poor WMI, independently of RDW. Ultimately, pending further longitudinal evidence, initial RDW appears to be associated with poorer WMI among males.
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Affiliation(s)
- May A Beydoun
- Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA.
| | - Danielle Shaked
- Department of Psychology, VA Boston Healthcare System, Boston, MA, USA
| | - Sharmin Hossain
- Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Jordan Weiss
- Department of Demography, University of California, Berkeley, Berkeley, CA, USA
| | - Hind A Beydoun
- Department of Research Programs, Fort Belvoir Community Hospital, Fort Belvoir, VA, USA
| | - Ana I Maldonado
- Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA; Department of Psychology, University of Maryland Baltimore County, Catonsville, MD, USA
| | - Leslie I Katzel
- Geriatric Research Education and Clinical Center, Baltimore VA Medical Center, Baltimore, MD, USA; Division of Gerontology & Geriatric Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Christos Davatzikos
- Artificial Intelligence in Biomedical Imaging Lab, Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Rao P Gullapalli
- Department of Diagnostic Radiology, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Stephen L Seliger
- Division of Nephrology, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Guray Erus
- Artificial Intelligence in Biomedical Imaging Lab, Center for Biomedical Image Computing and Analytics (CBICA), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Michele K Evans
- Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Alan B Zonderman
- Laboratory of Epidemiology and Population Sciences, NIA/NIH/IRP, Baltimore, MD, USA
| | - Shari R Waldstein
- Department of Psychology, University of Maryland Baltimore County, Catonsville, MD, USA; Geriatric Research Education and Clinical Center, Baltimore VA Medical Center, Baltimore, MD, USA; Division of Gerontology & Geriatric Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
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