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Wang J, Ackley S, Woodworth DC, Sajjadi SA, Decarli CS, Fletcher EF, Glymour MM, Jiang L, Kawas C, Corrada MM. Associations of Amyloid Burden, White Matter Hyperintensities, and Hippocampal Volume With Cognitive Trajectories in the 90+ Study. Neurology 2024; 103:e209665. [PMID: 39008782 PMCID: PMC11249511 DOI: 10.1212/wnl.0000000000209665] [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: 02/16/2024] [Accepted: 05/10/2024] [Indexed: 07/17/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Amyloid pathology, vascular disease pathology, and pathologies affecting the medial temporal lobe are associated with cognitive trajectories in older adults. However, only limited evidence exists on how these pathologies influence cognition in the oldest old. We evaluated whether amyloid burden, white matter hyperintensity (WMH) volume, and hippocampal volume (HV) are associated with cognitive level and decline in the oldest old. METHODS This was a longitudinal, observational community-based cohort study. We included participants with 18F-florbetapir PET and MRI data from the 90+ Study. Amyloid load was measured using the standardized uptake value ratio in the precuneus/posterior cingulate with eroded white matter mask as reference. WMH volume was log-transformed. All imaging measures were standardized using sample means and SDs. HV and log-WMH volume were normalized by total intracranial volume using the residual approach. Global cognitive performance was measured by the Mini-Mental State Examination (MMSE) and modified MMSE (3MS) tests, repeated every 6 months. We used linear mixed-effects models with random intercepts; random slopes; and interaction between time, time squared, and imaging variables to estimate the associations of imaging variables with cognitive level and cognitive decline. Models were adjusted for demographics, APOE genotype, and health behaviors. RESULTS The sample included 192 participants. The mean age was 92.9 years, 125 (65.1%) were female, 71 (37.0%) achieved a degree beyond college, and the median follow-up time was 3.0 years. A higher amyloid load was associated with a lower cognitive level (βMMSE = -0.82, 95% CI -1.17 to -0.46; β3MS = -2.77, 95% CI -3.69 to -1.84). A 1-SD decrease in HV was associated with a 0.70-point decrease in the MMSE score (95% CI -1.14 to -0.27) and a 2.27-point decrease in the 3MS score (95% CI -3.40 to -1.14). Clear nonlinear cognitive trajectories were detected. A higher amyloid burden and smaller HV were associated with faster cognitive decline. WMH volume was not significantly associated with cognitive level or decline. DISCUSSION Amyloid burden and hippocampal atrophy are associated with both cognitive level and cognitive decline in the oldest old. Our findings shed light on how different pathologies contributed to driving cognitive function in the oldest old.
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Affiliation(s)
- Jingxuan Wang
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Sarah Ackley
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Davis C Woodworth
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Seyed Ahmad Sajjadi
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Charles S Decarli
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Evan F Fletcher
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - M Maria Glymour
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Luohua Jiang
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Claudia Kawas
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
| | - Maria M Corrada
- From the Department of Epidemiology and Biostatistics (J.W.), University of California, San Francisco; Department of Epidemiology (J.W., S.A., M.M.G.), Boston University, MA; Department of Neurology (D.C.W., S.A.S., C.K., M.M.C.), University of California, Irvine; Imaging of Dementia and Aging Laboratory (C.S.D., E.F.F.), Department of Neurology, University of California, Davis; and Department of Epidemiology and Biostatistics (L.J., M.M.C.), and Department of Neurobiology and Behavior (C.K.), University of California, Irvine
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Woodworth DC, Nguyen KM, Sordo L, Scambray KA, Head E, Kawas CH, Corrada MM, Nelson PT, Sajjadi SA. Comprehensive assessment of TDP-43 neuropathology data in the National Alzheimer's Coordinating Center database. Acta Neuropathol 2024; 147:103. [PMID: 38896163 PMCID: PMC11186885 DOI: 10.1007/s00401-024-02728-8] [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: 10/23/2023] [Revised: 03/02/2024] [Accepted: 04/02/2024] [Indexed: 06/21/2024]
Abstract
TDP-43 proteinopathy is a salient neuropathologic feature in a subset of frontotemporal lobar degeneration (FTLD-TDP), in amyotrophic lateral sclerosis (ALS-TDP), and in limbic-predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC), and is associated with hippocampal sclerosis of aging (HS-A). We examined TDP-43-related pathology data in the National Alzheimer's Coordinating Center (NACC) in two parts: (I) availability of assessments, and (II) associations with clinical diagnoses and other neuropathologies in those with all TDP-43 measures available. Part I: Of 4326 participants with neuropathology data collected using forms that included TDP-43 assessments, data availability was highest for HS-A (97%) and ALS (94%), followed by FTLD-TDP (83%). Regional TDP-43 pathologic assessment was available for 77% of participants, with hippocampus the most common region. Availability for the TDP-43-related measures increased over time, and was higher in centers with high proportions of participants with clinical FTLD. Part II: In 2142 participants with all TDP-43-related assessments available, 27% of participants had LATE-NC, whereas ALS-TDP or FTLD-TDP (ALS/FTLD-TDP) was present in 9% of participants, and 2% of participants had TDP-43 related to other pathologies ("Other TDP-43"). HS-A was present in 14% of participants, of whom 55% had LATE-NC, 20% ASL/FTLD-TDP, 3% Other TDP-43, and 23% no TDP-43. LATE-NC, ALS/FTLD-TDP, and Other TDP-43, were each associated with higher odds of dementia, HS-A, and hippocampal atrophy, compared to those without TDP-43 pathology. LATE-NC was associated with higher odds for Alzheimer's disease (AD) clinical diagnosis, AD neuropathologic change (ADNC), Lewy bodies, arteriolosclerosis, and cortical atrophy. ALS/FTLD-TDP was associated with higher odds of clinical diagnoses of primary progressive aphasia and behavioral-variant frontotemporal dementia, and cortical/frontotemporal lobar atrophy. When using NACC data for TDP-43-related analyses, researchers should carefully consider the incomplete availability of the different regional TDP-43 assessments, the high frequency of participants with ALS/FTLD-TDP, and the presence of other forms of TDP-43 pathology.
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Affiliation(s)
- Davis C Woodworth
- Department of Neurology, University of California, Irvine, Office 364, Med Surge II Building, Irvine, CA, 92697, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - Katelynn M Nguyen
- Department of Neurology, University of California, Irvine, Office 364, Med Surge II Building, Irvine, CA, 92697, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - Lorena Sordo
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
- Department of Pathology and Laboratory Medicine, University of California, Irvine, CA, USA
| | - Kiana A Scambray
- Department of Neurology, University of California, Irvine, Office 364, Med Surge II Building, Irvine, CA, 92697, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - Elizabeth Head
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
- Department of Pathology and Laboratory Medicine, University of California, Irvine, CA, USA
| | - Claudia H Kawas
- Department of Neurology, University of California, Irvine, Office 364, Med Surge II Building, Irvine, CA, 92697, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
- Department of Neurobiology and Behavior, University of California, Irvine, CA, USA
| | - María M Corrada
- Department of Neurology, University of California, Irvine, Office 364, Med Surge II Building, Irvine, CA, 92697, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
- Department of Epidemiology and Biostatistics, University of California, Irvine, CA, USA
| | - Peter T Nelson
- Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, KY, USA
| | - S Ahmad Sajjadi
- Department of Neurology, University of California, Irvine, Office 364, Med Surge II Building, Irvine, CA, 92697, USA.
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA.
- Department of Pathology and Laboratory Medicine, University of California, Irvine, CA, USA.
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Zahr NM. Alcohol Use Disorder and Dementia: A Review. Alcohol Res 2024; 44:03. [PMID: 38812709 PMCID: PMC11135165 DOI: 10.35946/arcr.v44.1.03] [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: 05/31/2024] Open
Abstract
PURPOSE By 2040, 21.6% of Americans will be over age 65, and the population of those older than age 85 is estimated to reach 14.4 million. Although not causative, older age is a risk factor for dementia: every 5 years beyond age 65, the risk doubles; approximately one-third of those older than age 85 are diagnosed with dementia. As current alcohol consumption among older adults is significantly higher compared to previous generations, a pressing question is whether drinking alcohol increases the risk for Alzheimer's disease or other forms of dementia. SEARCH METHODS Databases explored included PubMed, Web of Science, and ScienceDirect. To accomplish this narrative review on the effects of alcohol consumption on dementia risk, the literature covered included clinical diagnoses, epidemiology, neuropsychology, postmortem pathology, neuroimaging and other biomarkers, and translational studies. Searches conducted between January 12 and August 1, 2023, included the following terms and combinations: "aging," "alcoholism," "alcohol use disorder (AUD)," "brain," "CNS," "dementia," "Wernicke," "Korsakoff," "Alzheimer," "vascular," "frontotemporal," "Lewy body," "clinical," "diagnosis," "epidemiology," "pathology," "autopsy," "postmortem," "histology," "cognitive," "motor," "neuropsychological," "magnetic resonance," "imaging," "PET," "ligand," "degeneration," "atrophy," "translational," "rodent," "rat," "mouse," "model," "amyloid," "neurofibrillary tangles," "α-synuclein," or "presenilin." When relevant, "species" (i.e., "humans" or "other animals") was selected as an additional filter. Review articles were avoided when possible. SEARCH RESULTS The two terms "alcoholism" and "aging" retrieved about 1,350 papers; adding phrases-for example, "postmortem" or "magnetic resonance"-limited the number to fewer than 100 papers. Using the traditional term, "alcoholism" with "dementia" resulted in 876 citations, but using the currently accepted term "alcohol use disorder (AUD)" with "dementia" produced only 87 papers. Similarly, whereas the terms "Alzheimer's" and "alcoholism" yielded 318 results, "Alzheimer's" and "alcohol use disorder (AUD)" returned only 40 citations. As pertinent postmortem pathology papers were published in the 1950s and recent animal models of Alzheimer's disease were created in the early 2000s, articles referenced span the years 1957 to 2024. In total, more than 5,000 articles were considered; about 400 are herein referenced. DISCUSSION AND CONCLUSIONS Chronic alcohol misuse accelerates brain aging and contributes to cognitive impairments, including those in the mnemonic domain. The consensus among studies from multiple disciplines, however, is that alcohol misuse can increase the risk for dementia, but not necessarily Alzheimer's disease. Key issues to consider include the reversibility of brain damage following abstinence from chronic alcohol misuse compared to the degenerative and progressive course of Alzheimer's disease, and the characteristic presence of protein inclusions in the brains of people with Alzheimer's disease, which are absent in the brains of those with AUD.
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Affiliation(s)
- Natalie M Zahr
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California. Center for Health Sciences, SRI International, Menlo Park, California
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Hanyu H, Koyama Y, Umekida K, Momose T, Watanabe S, Sato T. Factors and brain imaging features associated with cognition in oldest-old patients with Alzheimer-type dementia. J Neurol Sci 2024; 458:122929. [PMID: 38377704 DOI: 10.1016/j.jns.2024.122929] [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: 12/02/2023] [Revised: 01/20/2024] [Accepted: 02/09/2024] [Indexed: 02/22/2024]
Abstract
BACKGROUND The underlying pathophysiology of cognitive dysfunction in oldest-old patients with Alzheimer-type dementia (AD) has not been clarified to date. We aimed to determine the factors and brain imaging features associated with cognition in oldest-old patients with AD. METHODS We enrolled 456 consecutive outpatients with probable AD (145 men and 311 women, age range: 51-95 years). Demographic factors, such as education level, disease duration at initial visit, body mass index, comorbidities, frailty, and leisure activity, and brain imaging features, including severity of medial temporal lobe (MTL) atrophy, white matter lesions and infarcts, and frequency of posterior cerebral hypoperfusion were compared among pre-old (≤ 74 years), old (75 to 84 years), and oldest-old (≥ 85 years) subgroups. RESULTS The oldest-old subgroup showed significantly longer disease duration, lower education level, more severe frailty, less leisure activity, worse cognitive impairment, a tendency of slower progression of cognitive decline, greater MTL atrophy, more severe white matter hyperintensities and infarcts, and lower frequency of posterior hypoperfusion than the younger age subgroups. Regarding the brain imaging subtypes, there were significantly more patients with the limbic-predominant subtype and fewer patients with the hippocampal-sparing subtype in the oldest-old AD group than the pre-old AD group. CONCLUSIONS Oldest-old patients with AD show different factors and brain imaging features associated with cognition from pre-old and old patients. Our results are expected to provide useful information towards understanding the pathophysiology of oldest-old patients with AD, and for determining their clinical diagnosis and appropriate management methods.
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Affiliation(s)
- Haruo Hanyu
- Dementia Research Center, Tokyo General Hospital, Tokyo, Japan; Department of Geriatric Medicine, Tokyo Medical University, Tokyo, Japan.
| | - Yumi Koyama
- Department of Rehabilitation, Tokyo General Hospital, Tokyo, Japan
| | - Kazuki Umekida
- Department of Rehabilitation, Tokyo General Hospital, Tokyo, Japan
| | | | | | - Tomohiko Sato
- Department of Geriatric Medicine, Tokyo Medical University, Tokyo, Japan
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Li JX, Nguyen HL, Qian T, Woodworth DC, Sajjadi SA. Longitudinal hippocampal atrophy in hippocampal sclerosis of aging. AGING BRAIN 2023; 4:100092. [PMID: 37635712 PMCID: PMC10448324 DOI: 10.1016/j.nbas.2023.100092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 08/29/2023] Open
Abstract
Hippocampal sclerosis of aging (HS-A) is a common degenerative neuropathology in older individuals and is associated with dementia. HS-A is characterized by disproportionate hippocampal atrophy at autopsy but cannot be diagnosed during life. Therefore, little is known about the onset and progression of hippocampal atrophy in individuals with HS-A. To better understand the onset and progression of hippocampal atrophy in HS-A, we examined longitudinal hippocampal atrophy using serial MRI in participants with HS-A at autopsy (HS-A+, n = 8) compared to participants with limbic-predominant age-related TDP-43 encephalopathy neuropathological change (LATE-NC) without HS-A (n = 13), Alzheimer's disease neuropathologic change (ADNC) without HS-A or LATE-NC (n = 16), and those without these pathologies (n = 7). We found that participants with HS-A had lower hippocampal volumes compared to the other groups, and this atrophy preceded the onset of dementia. There was also some evidence that rates of hippocampal volume loss were slightly slower in those with HS-A. Together, these results suggest that the disproportionate hippocampal atrophy seen in HS-A may begin early prior to dementia.
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Affiliation(s)
- Janice X. Li
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - Hannah L. Nguyen
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - Tianchen Qian
- Department of Statistics, University of California, Irvine, Irvine, CA, USA
| | - Davis C. Woodworth
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
| | - S. Ahmad Sajjadi
- Department of Neurology, University of California, Irvine, CA, USA
- Institute for Memory Impairments and Neurological Disorders, University of California, Irvine, CA, USA
- Department of Pathology, University of California, Irvine, CA, USA
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Coon DW, Gómez-Morales A. Modifiable Risk Factors for Brain Health and Dementia and Opportunities for Intervention: A Brief Review. Clin Gerontol 2023; 46:143-154. [PMID: 35996225 DOI: 10.1080/07317115.2022.2114396] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
Maintaining brain health and promoting healthy lifestyle strategies to manage modifiable risk factors is vital to ensuring well-being for all - not only for the individuals with memory challenges but also their family caregivers and professional providers. In this brief review paper, we highlight modifiable risk and protective factors and opportunities for dementia risk reduction (e.g., limited alcohol use and reduced exposure to air pollution, secondhand smoke, and excessive noise); provide an overview of the World-Wide FINGERS Network and its goal to adapt the original Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) multidomain lifestyle approach in various settings to determine whether the protocol is effective across different populations in varied geographic, cultural, and economic settings and to optimize the model across a continuum of cognitive decline; and, comment on challenges and opportunities for researchers and clinicians including opportunities for risk reduction and intervention in primary care settings and the need to establish linkages across multiple levels of intervention to sustain behavior change in prevention, treatment, and care.
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Affiliation(s)
- David W Coon
- Center for Innovation in Healthy and Resilient Aging, Arizona State University, Tempe, Arizona, USA
- Edson College of Nursing and Health Innovation, Arizona State University, Tempe, Arizona, USA
| | - Abigail Gómez-Morales
- Edson College of Nursing and Health Innovation, Arizona State University, Tempe, Arizona, USA
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Sebastian MJ, Khan SKA, Pappachan JM, Jeeyavudeen MS. Diabetes and cognitive function: An evidence-based current perspective. World J Diabetes 2023; 14:92-109. [PMID: 36926658 PMCID: PMC10011899 DOI: 10.4239/wjd.v14.i2.92] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 12/26/2022] [Accepted: 01/17/2023] [Indexed: 02/14/2023] Open
Abstract
Several epidemiological studies have clearly identified diabetes mellitus (DM) as a major risk factor for cognitive dysfunction, and it is going to be a major public health issue in the coming years because of the alarming rise in diabetes prevalence across the world. Brain and neural tissues predominantly depend on glucose as energy substrate and hence, any alterations in carbohydrate meta-bolism can directly impact on cerebral functional output including cognition, executive capacity, and memory. DM affects neuronal function and mental capacity in several ways, some of which include hypoperfusion of the brain tissues from cerebrovascular disease, diabetes-related alterations of glucose transporters causing abnormalities in neuronal glucose uptake and metabolism, local hyper- and hypometabolism of brain areas from insulin resistance, and recurrent hypoglycemic episodes inherent to pharmacotherapy of diabetes resulting in neuronal damage. Cognitive decline can further worsen diabetes care as DM is a disease largely self-managed by patients. Therefore, it is crucial to understand the pathobiology of cognitive dysfunction in relation to DM and its management for optimal long-term care plan for patients. A thorough appraisal of normal metabolic characteristics of the brain, how alterations in neural metabolism affects cognition, the diagnostic algorithm for patients with diabetes and dementia, and the management and prognosis of patients when they have this dangerous combination of illnesses is imperative in this context. This evidence-based narrative with the back-up of latest clinical trial reviews elaborates the current understanding on diabetes and cognitive function to empower physicians to manage their patients in day-to-day clinical practice.
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Affiliation(s)
| | - Shahanas KA Khan
- Department of Endocrinology and Metabolism, Lancashire Teaching Hospitals NHS Trust, Preston PR2 9HT, United Kingdom
| | - Joseph M Pappachan
- Department of Endocrinology and Metabolism, Lancashire Teaching Hospitals NHS Trust, Preston PR2 9HT, United Kingdom
- Faculty of Science, Manchester Metropolitan University, Manchester M15 6BH, United Kingdom
- Faculty of Biology, Medicine and Health, The University of Manchester, Manchester M13 9PL, United Kingdom
| | - Mohammad Sadiq Jeeyavudeen
- Department of Endocrinology and Metabolism, University Hospitals of Edinburgh, Edinburgh EH16 4SA, United Kingdom
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He F, Luo H, Yin L, Roosaar A, Axéll T, Zhao H, Ye W. Poor Oral Health as a Risk Factor for Dementia in a Swedish Population: A Cohort Study with 40 Years of Follow-Up. J Alzheimers Dis 2023; 92:171-181. [PMID: 36710668 DOI: 10.3233/jad-215177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
BACKGROUND Whether poor oral health is associated with dementia risk remains unclear. OBJECTIVE We conducted a cohort study of 14,439 participants who were followed up for up to 40 years in Uppsala County, central Sweden, aiming to explore the association between poor oral health, namely the number of tooth loss, dental plaque status, and oral mucosal lesions, and the risk of dementia. METHODS We used Cox proportional hazards regression model to derive cause-specific hazard ratios (HR) and corresponding 95% confidence intervals (CI), while adjusting for baseline potential confounders as well as a time-varying covariate, Charlson's Comorbidity Index score. RESULTS Dementia risk was substantially higher among those with a higher number of tooth loss; compared to the group with tooth loss 0-10, the HRs were 1.21 (95% CI: 1.02, 1.42), 1.17 (95% CI: 0.97, 1.40), and 1.30 (95% CI: 1.09, 1.54) respectively for groups with increasing number of tooth loss. There was some evidence of dose-risk association in this study, with a HR of 1.10 (1.04, 1.18) comparing adjacent groups (ptrend = 0.001). In a stratified analysis by attained age, tooth loss was more pronouncedly associated with the risk of dementia onset before age 80 (those with 21-32 versus 0-10 lost teeth, HR = 1.82, (95% CI: 1.32, 2.51); HR = 1.22 (95% CI: 1.10, 1.35) comparing adjacent groups, ptrend < 0.001). CONCLUSION In summary, there are some indications that poor oral health, as indicated by more tooth loss, is positively associated with an increased risk of dementia, especially for dementia onset before age 80.
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Affiliation(s)
- Fei He
- Department of Epidemiology and Health Statistics, Fujian Medical University, Fuzhou, China.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Huizi Luo
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Li Yin
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Ann Roosaar
- Department of Dental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Tony Axéll
- Maxillofacial Unit, Halland Hospital Halmstad, Halmstad, Sweden
| | - Hongwei Zhao
- Department of Epidemiology and Biostatistics, Texas A & M University, College Station, TX, USA
| | - Weimin Ye
- Department of Epidemiology and Health Statistics, Fujian Medical University, Fuzhou, China.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
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Michalowska MM, Herholz K, Hinz R, Amadi C, McInnes L, Anton-Rodriguez JM, Karikari TK, Blennow K, Zetterberg H, Ashton NJ, Pendleton N, Carter SF. Evaluation of in vivo staging of amyloid deposition in cognitively unimpaired elderly aged 78-94. Mol Psychiatry 2022; 27:4335-4342. [PMID: 35858992 PMCID: PMC9718666 DOI: 10.1038/s41380-022-01685-6] [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] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/27/2022] [Accepted: 06/27/2022] [Indexed: 02/07/2023]
Abstract
Amyloid-beta (Aβ) deposition is common in cognitively unimpaired (CU) elderly >85 years. This study investigated amyloid distribution and evaluated three published in vivo amyloid-PET staging schemes from a cognitively unimpaired (CU) cohort aged 84.9 ± 4.3 years (n = 75). SUV-based principal component analysis (PCA) was applied to 18F-flutemetamol PET data to determine an unbiased regional covariance pattern of tracer uptake across grey matter regions. PET staging schemes were applied to the data and compared to the PCA output. Concentration of p-tau181 was measured in blood plasma. The PCA revealed three distinct components accounting for 91.2% of total SUV variance. PC1 driven by the large common variance of uptake in neocortical and striatal regions was significantly positively correlated with global SUVRs, APOE4 status and p-tau181 concentration. PC2 represented mainly non-specific uptake in typical amyloid-PET reference regions, and PC3 the occipital lobe. Application of the staging schemes demonstrated that the majority of the CU cohort (up to 93%) were classified as having pathological amount and distribution of Aβ. Good correspondence existed between binary (+/-) classification and later amyloid stages, however, substantial differences existed between schemes for low stages with 8-17% of individuals being unstageable, i.e., not following the sequential progression of Aβ deposition. In spite of the difference in staging outcomes there was broad spatial overlap between earlier stages and PC1, most prominently in default mode network regions. This study critically evaluated the utility of in vivo amyloid staging from a single PET scan in CU elderly and found that early amyloid stages could not be consistently classified. The majority of the cohort had pathological Aβ, thus, it remains an open topic what constitutes abnormal brain Aβ in the oldest-old and what is the best method to determine that.
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Affiliation(s)
- Malgorzata M Michalowska
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Department of Neuropsychology and Psychopharmacology, Maastricht University, Maastricht, The Netherlands
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - Karl Herholz
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Rainer Hinz
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
| | - Chinenye Amadi
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Lynn McInnes
- Department of Psychology, Faculty of Health and Life Sciences, Northumbria University, Newcastle upon Tyne, UK
| | - Jose M Anton-Rodriguez
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK
- Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
| | - Thomas K Karikari
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
| | - Nicholas J Ashton
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- King's College London, Institute of Psychiatry, Psychology and Neuroscience Maurice Wohl Institute Clinical Neuroscience Institute, London, UK
- NIHR Biomedical Research Centre for Mental Health and Biomedical Research Unit for Dementia at South London and Maudsley NHS Foundation, London, UK
- Centre for Age-Related Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Neil Pendleton
- Division of Neuroscience and Experimental Psychology, University of Manchester, Manchester, UK
| | - Stephen F Carter
- Wolfson Molecular Imaging Centre, University of Manchester, Manchester, UK.
- Department of Psychiatry, University of Cambridge, Cambridge, UK.
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He E, Liu M, Gong S, Fu X, Han Y, Deng F. White Matter Alterations in Depressive Disorder. Front Immunol 2022; 13:826812. [PMID: 35634314 PMCID: PMC9133348 DOI: 10.3389/fimmu.2022.826812] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 03/22/2022] [Indexed: 11/13/2022] Open
Abstract
Depressive disorder is the most prevalent affective disorder today. Depressive disorder has been linked to changes in the white matter. White matter changes in depressive disorder could be a result of impaired cerebral blood flow (CBF) and CBF self-regulation, impaired blood-brain barrier function, inflammatory factors, genes and environmental factors. Additionally, white matter changes in patients with depression are associated with clinical variables such as differential diagnosis, severity, treatment effect, and efficacy assessment. This review discusses the characteristics, possible mechanisms, clinical relevance, and potential treatment of white matter alterations caused by depressive disorders.
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Sakatani K, Oyama K, Hu L, Warisawa S. Estimation of Human Cerebral Atrophy Based on Systemic Metabolic Status Using Machine Learning. Front Neurol 2022; 13:869915. [PMID: 35585840 PMCID: PMC9109818 DOI: 10.3389/fneur.2022.869915] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Accepted: 04/05/2022] [Indexed: 12/22/2022] Open
Abstract
BackgroundBased on the assumption that systemic metabolic disorders affect cognitive function, we have developed a deep neural network (DNN) model that can estimate cognitive function based on basic blood test data that do not contain dementia-specific biomarkers. In this study, we used the same DNN model to assess whether basic blood data can be used to estimate cerebral atrophy.MethodsWe used data from 1,310 subjects (58.32 ± 12.91years old) enrolled in the Brain Doc Bank. The average Mini Mental State Examination score was 28.6 ± 1.9. The degree of cerebral atrophy was determined using the MRI-based index (GM-BHQ). First, we evaluated the correlations between the subjects' age, blood data, and GM-BHQ. Next, we developed DNN models to assess the GM-BHQ: one used subjects' age and blood data, while the other used only blood data for input items.ResultsThere was a negative correlation between age and GM-BHQ scores (r = -0.71). The subjects' age was positively correlated with blood urea nitrogen (BUN) (r = 0.40), alkaline phosphatase (ALP) (r = 0.22), glucose (GLU) (r = 0.22), and negative correlations with red blood cell counts (RBC) (r = −0.29) and platelet counts (PLT) (r = −0.26). GM-BHQ correlated with BUN (r = −0.30), GLU (r = −0.26), PLT (r = 0.26), and ALP (r = 0.22). The GM-BHQ estimated by the DNN model with subject age exhibited a positive correlation with the ground truth GM-BHQ (r = 0.70). Furthermore, even if the DNN model without subject age was used, the estimated GM-BHQ showed a significant positive correlation with ground truth GM-BHQ (r = 0.58). Age was the most important variable for estimating GM-BHQ.DiscussionAging had the greatest effect on cerebral atrophy. Aging also affects various organs, such as the kidney, and causes changes in systemic metabolic status, which may contribute to cerebral atrophy and cognitive impairment. The DNN model may serve as a new screening test for dementia using basic blood tests for health examinations. Finally, the blood data reflect systemic metabolic disorders in each subject—this method may thus contribute to personalized care.
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Affiliation(s)
- Kaoru Sakatani
- Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
- *Correspondence: Kaoru Sakatani
| | - Katsunori Oyama
- Department of Computer Science, College of Engineering, Nihon University, Koriyama, Japan
| | - Lizhen Hu
- Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
| | - Shin'ichi Warisawa
- Department of Human and Engineered Environmental Studies, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan
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