Citations

Objective The newly proposed National Institute on Aging–Alzheimer's Association (NIA‐AA) criteria for mild cognitive impairment (MCI) due to Alzheimer disease (AD) suggest a combination of clinical features and biomarker measures, but their performance in the community is not known. Methods The Mayo Clinic Study of Aging (MCSA) is a population‐based longitudinal study of nondemented subjects in Olmsted County, Minnesota. A sample of 154 MCI subjects from the MCSA was compared to a sample of 58 amnestic MCI subjects from the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI‐1) to assess the applicability of the criteria in both settings and to assess their outcomes. Results Fourteen percent of MCSA and 16% of ADNI‐1 of subjects were biomarker negative. In addition, 14% of MCSA and 12% of ADNI‐1 subjects had evidence for amyloid deposition only, whereas 43% of MCSA and 55% of ADNI‐1 subjects had evidence for amyloid deposition plus neurodegeneration (magnetic resonance imaging atrophy, fluorodeoxyglucose positron emission tomography hypometabolism, or both). However, a considerable number of subjects had biomarkers inconsistent with the proposed AD model; for example, 29% of MCSA subjects and 17% of ADNI‐1 subjects had evidence for neurodegeneration without amyloid deposition. These subjects may not be on an AD pathway. Neurodegeneration appears to be a key factor in predicting progression relative to amyloid deposition alone. Interpretation The NIA‐AA criteria apply to most MCI subjects in both the community and clinical trials settings; however, a sizeable proportion of subjects had conflicting biomarkers, which may be very important and need to be explored. Ann Neurol 2013;74:199–208

Objective Knowledge of potentially modifiable risk factors for neuropsychiatric symptoms (NPS) in Alzheimer's disease (AD) is important. This study longitudinally explores modifiable vascular risk factors for NPS in AD. Methods Participants enrolled in the Cache County Study on Memory in Aging with no dementia at baseline were subsequently assessed over three additional waves, and those with incident (new onset) dementia were invited to join the Dementia Progression Study for longitudinal follow‐up. A total of 327 participants with incident AD were identified and assessed for the following vascular factors: atrial fibrillation, hypertension, diabetes mellitus, angina, coronary artery bypass surgery, myocardial infarction, cerebrovascular accident, and use of antihypertensive or diabetes medicines. A vascular index (VI) was also calculated. NPS were assessed over time using the Neuropsychiatric Inventory (NPI). Affective and Psychotic symptom clusters were assessed separately. The association between vascular factors and change in NPI total score was analyzed using linear mixed model and in symptom clusters using a random effects model. Results No individual vascular risk factors or the VI significantly predicted change in any individual NPS. The use of antihypertensive medications more than four times per week was associated with higher total NPI and Affective cluster scores. Conclusions Use of antihypertensive medication was associated with higher total NPI and Affective cluster scores. The results of this study do not otherwise support vascular risk factors as modifiers of longitudinal change in NPS in AD. Copyright © 2013 John Wiley & Sons, Ltd.

AbstractBackgroundType 2 diabetes may increase the risk of amnestic mild cognitive impairment (aMCI) through Alzheimer's disease (AD)‐related and vascular pathology and may also increase the risk of nonamnestic MCI (naMCI) through vascular disease mechanisms. We examined the association of type 2 diabetes with mild cognitive impairment (MCI) and MCI subtype (aMCI and naMCI) overall and by sex.MethodsParticipants were Olmsted County, Minnesota residents (70 years and older) enrolled in a prospective, population‐based study. At baseline and every 15 months thereafter, participants were evaluated using the Clinical Dementia Rating scale, a neurological evaluation, and neuropsychological testing for a diagnosis of normal cognition, MCI, and dementia by a consensus panel. Type 2 diabetes was ascertained from the medical records of participants at baseline.ResultsOver a median 4.0 years of follow‐up, 348 of 1450 subjects developed MCI. Type 2 diabetes was associated (hazard ratio [95% confidence interval]) with MCI (1.39 [1.08–1.79]), aMCI (1.58 [1.17–2.15]; multiple domain: 1.58 [1.01–2.47]; single domain: 1.49 [1.09–2.05]), and the hazard ratio for naMCI was elevated (1.37 [0.84–2.24]). Diabetes was strongly associated with multiple‐domain aMCI in men (2.42 [1.31–4.48]) and an elevated risk of multiple domain naMCI in men (2.11 [0.70–6.33]), and with single domain naMCI in women (2.32 [1.04–5.20]).ConclusionsDiabetes was associated with an increased risk of MCI in elderly persons. The association of diabetes with MCI may vary with subtype, number of domains, and sex. Prevention and control of diabetes may reduce the risk of MCI and Alzheimer's disease.

Abstract Background Diffusion tensor imaging (DTI) is a promising method for identifying significant cross‐sectional differences of white‐matter tracts in normal controls (NC) and those with mild cognitive impairment (MCI) or Alzheimer's disease (AD). There have not been many studies establishing its longitudinal utility. Methods Seventy‐five participants (25 NC, 25 amnestic MCI, and 25 AD) had 3‐Tesla MRI scans and clinical evaluations at baseline and 3, 6, and 12 months. Fractional anisotropy (FA) and mean diffusivity (MD) were analyzed at each time‐point and longitudinally in eight a priori –selected areas taken from four regions of interest (ROIs). Results Cross‐sectionally, MD values were higher, and FA values lower in the fornix and splenium of the AD group compared with either MCI or NC ( P < .01). Within‐group change was more evident in MD than in FA over 12 months: MD increased in the inferior, anterior cingulum, and fornix in both the MCI and AD groups ( P < .01). Conclusions There were stable, cross‐sectional, region‐specific differences between the NC and AD groups in both FA and MD at each time‐point over 12 months. Longitudinally, MD was a better indicator of change than FA. Significant increases of fornix MD in the MCI group suggest this is an early indicator of progression.

ObjectiveThe new criteria for preclinical Alzheimer disease (AD) proposed 3 stages: abnormal levels of β‐amyloid (stage 1), stage 1 plus evidence of brain injury (stage 2), and stage 2 plus subtle cognitive changes (stage 3). However, a large group of subjects with normal β‐amyloid biomarkers have evidence of brain injury; we labeled them as the “suspected non‐Alzheimer pathophysiology” (sNAP) group. The characteristics of the sNAP group are poorly understood.MethodsUsing the preclinical AD classification, 430 cognitively normal subjects from the Mayo Clinic Study of Aging who underwent brain magnetic resonance (MR), 18fluorodeoxyglucose (FDG), and Pittsburgh compound B positron emission tomography (PET) were evaluated for FDG PET regional volumetrics, MR regional brain volumetrics, white matter hyperintensity volume, and number of infarcts. We examined cross‐sectional associations across AD preclinical stages, those with all biomarkers normal, and the sNAP group.ResultsThe sNAP group had a lower proportion (14%) with apolipoprotein E ε4 genotype than the preclinical AD stages 2 + 3. The sNAP group did not show any group differences compared to stages 2 + 3 of the preclinical AD group on measures of FDG PET regional hypometabolism, MR regional brain volume loss, cerebrovascular imaging lesions, vascular risk factors, imaging changes associated with α‐synucleinopathy, or physical findings of parkinsonism.InterpretationCognitively normal persons with brain injury biomarker abnormalities, with or without abnormal levels of β‐amyloid, were indistinguishable on a variety of imaging markers, clinical features, and risk factors. The initial appearance of brain injury biomarkers that occurs in cognitively normal persons with preclinical AD may not depend on β‐amyloidosis. ANN NEUROL 2013;73:472–480