TY - JOUR
T1 - Relationship Between Risk Factors and Brain Reserve in Late Middle Age
T2 - Implications for Cognitive Aging
AU - Neth, Bryan J.
AU - Graff-Radford, Jonathan
AU - Mielke, Michelle M.
AU - Przybelski, Scott A.
AU - Lesnick, Timothy G.
AU - Schwarz, Christopher G.
AU - Reid, Robert I.
AU - Senjem, Matthew L.
AU - Lowe, Val J.
AU - Machulda, Mary M.
AU - Petersen, Ronald C.
AU - Jr, Clifford R.Jack
AU - Knopman, David S.
AU - Vemuri, Prashanthi
N1 - Funding Information:
We thank all the study participants and staff in the Mayo Clinic Study of Aging, Mayo Alzheimer’s Disease Research Center, and Aging Dementia Imaging Research laboratory at the Mayo Clinic. Funding. This work was supported by NIH grants U01 AG006786 (PI: RP), R01 AG056366 (PI: PV), R01 NS097495 (PI: PV), P50 AG016574 (PI: RP), R37 AG011378 (PI: CJ), R01 AG041851 (PIs: CJ and DK), R01 AG034676 (Rochester Epidemiology Project), the Gerald and Henrietta Rauenhorst Foundation grant, the Millis Family, the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Foundation, Alzheimer’s Association (Zenith Fellows Award), Liston Award, Elsie and Marvin Dekelboum Family Foundation, Schuler Foundation, and Opus building NIH grant C06 RR018898.
Publisher Copyright:
© Copyright © 2020 Neth, Graff-Radford, Mielke, Przybelski, Lesnick, Schwarz, Reid, Senjem, Lowe, Machulda, Petersen, Jack, Knopman and Vemuri.
PY - 2020/1/9
Y1 - 2020/1/9
N2 - Background: Brain reserve can be defined as the individual variation in the brain structural characteristics that later in life are likely to modulate cognitive performance. Late midlife represents a point in aging where some structural brain imaging changes have become manifest but the effects of cognitive aging are minimal, and thus may represent an ideal opportunity to determine the relationship between risk factors and brain imaging biomarkers of reserve. Objective: We aimed to assess neuroimaging measures from multiple modalities to broaden our understanding of brain reserve, and the late midlife risk factors that may make the brain vulnerable to age related cognitive disorders. Methods: We examined multimodal [structural and diffusion Magnetic Resonance Imaging (MRI), FDG PET] neuroimaging measures in 50–65 year olds to examine the associations between risk factors (Intellectual/Physical Activity: education-occupation composite, physical, and cognitive-based activity engagement; General Health Factors: presence of cardiovascular and metabolic conditions (CMC), body mass index, hemoglobin A1c, smoking status (ever/never), CAGE Alcohol Questionnaire (>2, yes/no), Beck Depression Inventory score), brain reserve measures [Dynamic: genu corpus callosum fractional anisotropy (FA), posterior cingulate cortex FDG uptake, superior parietal cortex thickness, AD signature cortical thickness; Static: intracranial volume], and cognition (global, memory, attention, language, visuospatial) from a population-based sample. We quantified dynamic proxies of brain reserve (cortical thickness, glucose metabolism, microstructural integrity) and investigated various protective/risk factors. Results: Education-occupation was associated with cognition and total intracranial volume (static measure of brain reserve), but was not associated with any of the dynamic neuroimaging biomarkers. In contrast, many general health factors were associated with the dynamic neuroimaging proxies of brain reserve, while most were not associated with cognition in this late middle aged group. Conclusion: Brain reserve, as exemplified by the four dynamic neuroimaging features studied here, is itself at least partly influenced by general health status in midlife, but may be largely independent of education and occupation.
AB - Background: Brain reserve can be defined as the individual variation in the brain structural characteristics that later in life are likely to modulate cognitive performance. Late midlife represents a point in aging where some structural brain imaging changes have become manifest but the effects of cognitive aging are minimal, and thus may represent an ideal opportunity to determine the relationship between risk factors and brain imaging biomarkers of reserve. Objective: We aimed to assess neuroimaging measures from multiple modalities to broaden our understanding of brain reserve, and the late midlife risk factors that may make the brain vulnerable to age related cognitive disorders. Methods: We examined multimodal [structural and diffusion Magnetic Resonance Imaging (MRI), FDG PET] neuroimaging measures in 50–65 year olds to examine the associations between risk factors (Intellectual/Physical Activity: education-occupation composite, physical, and cognitive-based activity engagement; General Health Factors: presence of cardiovascular and metabolic conditions (CMC), body mass index, hemoglobin A1c, smoking status (ever/never), CAGE Alcohol Questionnaire (>2, yes/no), Beck Depression Inventory score), brain reserve measures [Dynamic: genu corpus callosum fractional anisotropy (FA), posterior cingulate cortex FDG uptake, superior parietal cortex thickness, AD signature cortical thickness; Static: intracranial volume], and cognition (global, memory, attention, language, visuospatial) from a population-based sample. We quantified dynamic proxies of brain reserve (cortical thickness, glucose metabolism, microstructural integrity) and investigated various protective/risk factors. Results: Education-occupation was associated with cognition and total intracranial volume (static measure of brain reserve), but was not associated with any of the dynamic neuroimaging biomarkers. In contrast, many general health factors were associated with the dynamic neuroimaging proxies of brain reserve, while most were not associated with cognition in this late middle aged group. Conclusion: Brain reserve, as exemplified by the four dynamic neuroimaging features studied here, is itself at least partly influenced by general health status in midlife, but may be largely independent of education and occupation.
KW - brain reserve
KW - cognitive aging
KW - dynamic
KW - multimodal imaging
KW - resilience
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UR - http://www.scopus.com/inward/citedby.url?scp=85078272135&partnerID=8YFLogxK
U2 - 10.3389/fnagi.2019.00355
DO - 10.3389/fnagi.2019.00355
M3 - Article
AN - SCOPUS:85078272135
SN - 1663-4365
VL - 11
JO - Frontiers in Aging Neuroscience
JF - Frontiers in Aging Neuroscience
M1 - 355
ER -