OBJECTIVE: To evaluate relationships between magnetic resonance imaging (MRI)-based measures of white matter hyperintensities (WMHs), measured at baseline and longitudinally, and 1-year cognitive decline using a large convenience sample in a clinical trial design with a relatively mild profile of cardiovascular risk factors. DESIGN: Convenience sample in a clinical trial design. SUBJECTS: A total of 804 participants in the Alzheimer Disease Neuroimaging Initiative who received MRI scans, cognitive testing, and clinical evaluations at baseline, 6-month follow-up, and 12-month follow-up visits. For each scan, WMHs were detected automatically on coregistered sets of T1, proton density, and T2 MRI images using a validated method. Mixed-effects regression models evaluated relationships between risk factors for WMHs, WMH volume, and change in outcome measures including Mini-Mental State Examination (MMSE), Alzheimer Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), and Clinical Dementia Rating Scale sum of boxes scores. Covariates in these models included race, sex, years of education, age, apolipoprotein E genotype, baseline clinical diagnosis (cognitively normal, mild cognitive impairment, or Alzheimer disease), cardiovascular risk score, and MRI-based hippocampal and brain volumes. RESULTS: Higher baseline WMH volume was associated with greater subsequent 1-year increase in ADAS-Cog and decrease in MMSE scores. Greater WMH volume at follow-up was associated with greater ADAS-Cog and lower MMSE scores at follow-up. Higher baseline age and cardiovascular risk score and more impaired baseline clinical diagnosis were associated with higher baseline WMH volume. CONCLUSIONS: White matter hyperintensity volume predicts 1-year cognitive decline in a relatively healthy convenience sample that was similar to clinical trial samples, and therefore should be considered as a covariate of interest at baseline and longitudinally in future AD treatment trials.
OBJECTIVE: To evaluate relationships between magnetic resonance imaging (MRI)-based measures of white matter hyperintensities (WMHs), measured at baseline and longitudinally, and 1-year cognitive decline using a large convenience sample in a clinical trial design with a relatively mild profile of cardiovascular risk factors. DESIGN: Convenience sample in a clinical trial design. SUBJECTS: A total of 804 participants in the Alzheimer Disease Neuroimaging Initiative who received MRI scans, cognitive testing, and clinical evaluations at baseline, 6-month follow-up, and 12-month follow-up visits. For each scan, WMHs were detected automatically on coregistered sets of T1, proton density, and T2 MRI images using a validated method. Mixed-effects regression models evaluated relationships between risk factors for WMHs, WMH volume, and change in outcome measures including Mini-Mental State Examination (MMSE), Alzheimer Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), and Clinical Dementia Rating Scale sum of boxes scores. Covariates in these models included race, sex, years of education, age, apolipoprotein E genotype, baseline clinical diagnosis (cognitively normal, mild cognitive impairment, or Alzheimer disease), cardiovascular risk score, and MRI-based hippocampal and brain volumes. RESULTS: Higher baseline WMH volume was associated with greater subsequent 1-year increase in ADAS-Cog and decrease in MMSE scores. Greater WMH volume at follow-up was associated with greater ADAS-Cog and lower MMSE scores at follow-up. Higher baseline age and cardiovascular risk score and more impaired baseline clinical diagnosis were associated with higher baseline WMH volume. CONCLUSIONS: White matter hyperintensity volume predicts 1-year cognitive decline in a relatively healthy convenience sample that was similar to clinical trial samples, and therefore should be considered as a covariate of interest at baseline and longitudinally in future AD treatment trials.
Authors: P Kochunov; J L Lancaster; P Thompson; R Woods; J Mazziotta; J Hardies; P Fox Journal: J Comput Assist Tomogr Date: 2001 Sep-Oct Impact factor: 1.826
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Authors: C DeCarli; D G Murphy; M Tranh; C L Grady; J V Haxby; J A Gillette; J A Salerno; A Gonzales-Aviles; B Horwitz; S I Rapoport Journal: Neurology Date: 1995-11 Impact factor: 9.910
Authors: Warren D Taylor; James R MacFall; James M Provenzale; Martha E Payne; Douglas R McQuoid; David C Steffens; K Ranga Rama Krishnan Journal: AJR Am J Roentgenol Date: 2003-08 Impact factor: 3.959
Authors: R C Petersen; P S Aisen; L A Beckett; M C Donohue; A C Gamst; D J Harvey; C R Jack; W J Jagust; L M Shaw; A W Toga; J Q Trojanowski; M W Weiner Journal: Neurology Date: 2009-12-30 Impact factor: 9.910
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