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Altered cerebral blood flow in older adults with Alzheimer’s disease: a systematic review

  • Review Article
  • Published:
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Abstract

The prevalence of Alzheimer’s disease is projected to reach 13 million in the U.S. by 2050. Although major efforts have been made to avoid this outcome, so far there are no treatments that can stop or reverse the progressive cognitive decline that defines Alzheimer’s disease. The utilization of preventative treatment before significant cognitive decline has occurred may ultimately be the solution, necessitating a reliable biomarker of preclinical/prodromal disease stages to determine which older adults are most at risk. Quantitative cerebral blood flow is a promising potential early biomarker for Alzheimer’s disease, but the spatiotemporal patterns of altered cerebral blood flow in Alzheimer’s disease are not fully understood. The current systematic review compiles the findings of 81 original studies that compared resting gray matter cerebral blood flow in older adults with mild cognitive impairment or Alzheimer’s disease and that of cognitively normal older adults and/or assessed the relationship between cerebral blood flow and objective cognitive function. Individuals with Alzheimer’s disease had relatively decreased cerebral blood flow in all brain regions investigated, especially the temporoparietal and posterior cingulate, while individuals with mild cognitive impairment had consistent results of decreased cerebral blood flow in the posterior cingulate but more mixed results in other regions, especially the frontal lobe. Most papers reported a positive correlation between regional cerebral blood flow and cognitive function. This review highlights the need for more studies assessing cerebral blood flow changes both spatially and temporally over the course of Alzheimer’s disease, as well as the importance of including potential confounding factors in these analyses.

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Data Availability

We did not analyze or generate datasets because this work involved the compilation and synthesis of previously published results, all of which are cited and available to view.

Abbreviations

AD:

Alzheimer’s disease

ADAS:

Alzheimer’s Disease Assessment Scale

ASL:

Arterial spin labeled

APOE :

Apolipoprotein E

CAMCOG:

Cambridge Cognition Examination

CANTAB:

Cambridge Neuropsychological Test Automated Battery

CBF:

Cerebral blood flow

CN:

Cognitively normal

DCE:

Dynamic contrast enhanced

DSC:

Dynamic susceptibility contrast

GRADE:

Grading of Recommendations Assessment Development and Education

MCI:

Mild cognitive impairment

MeSH:

Medical Subject Headings

MMSE:

Mini-Mental State Examination

MRI:

Magnetic resonance imaging

PET:

Positron emission tomography

PICO:

Population intervention comparison and outcomes

SCD:

Subjective cognitive decline

SD:

Standard deviation

SPECT:

Single-photon emission computerized tomography

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Funding

Dr. Risacher receives support from the National Institute on Aging (K01 AG049050, R01 AG061788) and the New Vision Award. Dr. Wu receives support from the National Institute on Aging (R01 AG053993). Dr. Apostolova receives support from NIH, Alzheimer Association, AVID Pharmaceuticals, Life Molecular Imaging, and Roche Diagnostics. Dr. Saykin receives support from multiple NIH grants (P30 AG010133, P30 AG072976, R01 AG019771, R01 AG057739, U01 AG024904, R01 LM013463, R01 AG068193, T32 AG071444, and U01 AG068057 and U01 AG072177). He has also received support from Avid Radiopharmaceuticals, a subsidiary of Eli Lilly (in kind contribution of PET tracer precursor).

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  1. Department of Radiology and Imaging Sciences, Indiana University School of Medicine, 355 W 16th St. IU Neuroscience Center, GH 4101, 46202, Indianapolis, IN, USA

    Cecily G. Swinford, Shannon L. Risacher, Yu-Chien Wu, Liana G. Apostolova, Paula J. Bice & Andrew J. Saykin

  2. Indiana Alzheimer’s Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, USA

    Cecily G. Swinford, Shannon L. Risacher, Yu-Chien Wu, Liana G. Apostolova, Sujuan Gao, Paula J. Bice & Andrew J. Saykin

  3. Department of Neurology, Indiana University School of Medicine, Indianapolis, IN, USA

    Liana G. Apostolova & Andrew J. Saykin

  4. Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA

    Sujuan Gao

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  1. Cecily G. Swinford
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Contributions

Author contributions included conceptual and study design (CGS, SLR and AJS), literature search and selection of included publications (CGS, PJB), summarization, synthesis, and presentation of results (CGS, PJB), interpretations, conclusions, and overall messaging (all authors), and comments, revisions, and approval for final publication (all authors).

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Correspondence to Andrew J. Saykin.

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Dr. Apostolova receives support in the form of consulting fees from Biogen, Two Labs, IQVIA, NIH, Florida Department of Health, NIH Biobank, Eli Lilly, GE Healthcare, and Eisai; in the form of payment for lectures, etc. from AAN, MillerMed, AiSM, and Health and Hospitality; and in the form of travel and meeting support from Alzheimer’s Association. She also participates on Data Safety Monitoring or Advisory boards for IQVIA, NIA R01 AG061111, UAB Nathan Shock Center, and New Mexico Exploratory ADRC; in leadership roles for Medical Science Council Alzheimer Association Greater IN Chapter, Alzheimer Association Science Program Committee, and FDA PCNS Advisory Committee; stock or stock options Cassava Neurosciences and Golden Seeds, and receipt of materials, etc. from AVID Pharmaceuticals, Life Molecular Imaging, and Roche Diagnostics. Dr. Saykin serves or served on the following: Bayer Oncology (Scientific Advisory Board), Eisai (Scientific Advisory Board), Siemens Medical Solutions USA, Inc. (Dementia Advisory Board), and Springer-Nature Publishing (Editorial Office Support as Editor-in-Chief, Brain Imaging and Behavior). All other authors report no disclosures.

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Swinford, C.G., Risacher, S.L., Wu, YC. et al. Altered cerebral blood flow in older adults with Alzheimer’s disease: a systematic review. Brain Imaging and Behavior 17, 223–256 (2023). https://doi.org/10.1007/s11682-022-00750-6

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