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Investigating neuroimaging differences by cognitive and metabolic status in a community‐dwelling cohort
Author(s) -
Lockhart Samuel N.,
OkonmahObazee Stephanie,
Bateman James R.,
Williams Benjamin J.,
Cleveland Maryjo,
Yang Mia,
Haq Ihtsham,
Rogers Samantha D.,
Sachs Bonnie C.,
Espeland Mark A.,
Hughes Timothy M.,
Fischer Eric,
Sai Kiran Solingapuram,
Williamson Jeff D.,
Jung Youngkyoo,
Whitlow Christopher T.,
Craft Suzanne
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.045658
Subject(s) - hyperintensity , cohort , montreal cognitive assessment , neuroimaging , medicine , fractional anisotropy , white matter , cognition , cognitive decline , brain size , effects of sleep deprivation on cognitive performance , psychology , magnetic resonance imaging , dementia , psychiatry , radiology , disease
Background Little is know about how metabolic function relates to late‐life physical and cognitive health. In a community‐dwelling cohort of older adults with normal cognition (CN) and mild cognitive impairment (MCI), we investigated whether cognitive and metabolic status predicted neuroimaging biomarkers, and cognitive and physical function. Method Participants enrolled in the Wake Forest Alzheimer’s Disease Research Center (ADRC) Clinical Core cohort received neuropsychological assessment, MR imaging (T1, FLAIR, DTI, NODDI, pcASL), and PiB PET imaging on a subset. Participants were adjudicated as CN or MCI using NIA‐AA criteria. T1 MRI were processed (FreeSurfer v5.3) to calculate “AD‐signature” meta‐ROI cortical thickness. A set of AD‐sensitive cortical ROIs was used to extract mean PET SUVR signal. White matter hyperintensity (WMH) volume was calculated on FLAIR with LST in SPM12. DTI were processed using FSL, and NODDI using AMICO, to generate mean white matter (WM) Fractional Anisotropy (FA) and Free Water (FW), to assess WM microstructure. PcASL were processed to yield mean GM and WM cerebral blood flow (CBF). Cognitive measures included Montreal Cognitive Assessment (MoCA) and Free and Cued Selective Reminding Test (total free recall; FCSRT). Mobility function was assessed using the expanded Short Physical Performance Battery (eSPPB). Oral Glucose Tolerance Testing (OGTT) assessed blood glucose 120 minutes post‐challenge; OGTT was thresholded (>=140 mg/dl) to classify participants with impaired glucose tolerance (IGT) or normoglycemic (NG). We assessed group differences using t‐tests and multivariable linear regression (covariates: age, sex, race). Result Among 391 participants with both MRI and cognitive testing, MCI participants were older, less educated and had fewer females relative to CN (Table 1). IGT participants were older and less educated than NG participants. MCI participants had lower cortical thickness, higher WMH volume and FW, and higher PiB than CN (Table 2). MCI participants had lower MOCA and FCSRT (as expected) and eSPPB scores than CN, which remained significant in fully adjusted models. GM CBF differences did not survive adjustment. IGT was only associated with lower eSPPB score (p=.00004; adjusted p=.0051). Conclusion Cognitive impairment is associated with reduced brain health and physical function, while IGT relates more specifically with reduced physical function.