Weiner, Michael W.

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

AbstractBackgroundApolipoprotein E (APOE) ε4 allele's role as a modulator of the relationship between soluble plasma amyloid beta (Aβ) and fibrillar brain Aβ measured by Pittsburgh compound B positron emission tomography ([11C]PiB PET) has not been assessed.MethodsNinety‐six Alzheimer's Disease Neuroimaging Initiative participants with [11C]PiB scans and plasma Aβ1–40 and Aβ1–42 measurements at the time of PET scanning were included. Regional and voxelwise analyses of [11C]PiB data were used to determine the influence of APOE ε4 allele on association of plasma Aβ1–40, Aβ1–42, and Aβ1–40/Aβ1–42 with [11C]PiB uptake.ResultsIn APOE ε4− but not ε4+ participants, positive relationships between plasma Aβ1–40/Aβ1–42 and [11C]PiB uptake were observed. Modeling the interaction of APOE and plasma Aβ1–40/Aβ1–42 improved the explained variance in [11C]PiB binding compared with using APOE and plasma Aβ1–40/Aβ1–42 as separate terms.ConclusionsThe results suggest that plasma Aβ is a potential Alzheimer's disease biomarker and highlight the importance of genetic variation in interpretation of plasma Aβ levels.

Aberrant connectivity is implicated in many neurological and psychiatric disorders, including Alzheimer’s disease and schizophrenia. However, other than a few disease-associated candidate genes, we know little about the degree to which genetics play a role in the brain networks; we know even less about specific genes that influence brain connections. Twin and family-based studies can generate estimates of overall genetic influences on a trait, but genome-wide association scans (GWASs) can screen the genome for specific variants influencing the brain or risk for disease. To identify the heritability of various brain connections, we scanned healthy young adult twins with high-field, high-angular resolution diffusion MRI. We adapted GWASs to screen the brain’s connectivity pattern, allowing us to discover genetic variants that affect the human brain’s wiring. The association of connectivity with the SPON1 variant at rs2618516 on chromosome 11 (11p15.2) reached connectome-wide, genome-wide significance after stringent statistical corrections were enforced, and it was replicated in an independent subsample. rs2618516 was shown to affect brain structure in an elderly population with varying degrees of dementia. Older people who carried the connectivity variant had significantly milder clinical dementia scores and lower risk of Alzheimer’s disease. As a posthoc analysis, we conducted GWASs on several organizational and topological network measures derived from the matrices to discover variants in and around genes associated with autism ( MACROD2 ), development ( NEDD4 ), and mental retardation ( UBE2A ) significantly associated with connectivity. Connectome-wide, genome-wide screening offers substantial promise to discover genes affecting brain connectivity and risk for brain diseases.

Delta opioid receptors are implicated in a variety of psychiatric and neurological disorders. These receptors play a key role in the reinforcing properties of drugs of abuse, and polymorphisms in OPRD1 (the gene encoding delta opioid receptors) are associated with drug addiction. Delta opioid receptors are also involved in protecting neurons against hypoxic and ischemic stress. Here, we first examined a large sample of 738 elderly participants with neuroimaging and genetic data from the Alzheimer's Disease Neuroimaging Initiative. We hypothesized that common variants in OPRD1 would be associated with differences in brain structure, particularly in regions relevant to addictive and neurodegenerative disorders. One very common variant (rs678849) predicted differences in regional brain volumes. We replicated the association of this single‐nucleotide polymorphism with regional tissue volumes in a large sample of young participants in the Queensland Twin Imaging study. Although the same allele was associated with reduced volumes in both cohorts, the brain regions affected differed between the two samples. In healthy elderly, exploratory analyses suggested that the genotype associated with reduced brain volumes in both cohorts may also predict cerebrospinal fluid levels of neurodegenerative biomarkers, but this requires confirmation. If opiate receptor genetic variants are related to individual differences in brain structure, genotyping of these variants may be helpful when designing clinical trials targeting delta opioid receptors to treat neurological disorders. Hum Brain Mapp 35:1226–1236, 2014 . © 2013 Wiley Periodicals, Inc.

BackgroundPrevalence and risk factors for focal hemosiderin deposits are important considerations when planning amyloid‐modifying trials for treatment and prevention of Alzheimer's disease (AD).MethodsSubjects were cognitively normal (n = 171), early‐mild cognitive impairment (MCI) (n = 240), late‐MCI (n = 111), and AD (n = 40) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Microhemorrhages and superficial siderosis were assessed at baseline and on all available MRIs at 3, 6, and 12 months. β‐amyloid load was assessed with 18F‐florbetapir positron emission tomography.ResultsPrevalence of superficial siderosis was 1% and prevalence of microhemorrhages was 25% increasing with age (P < .001) and β‐amyloid load (P < .001). Topographic densities of microhemorrhages were highest in the occipital lobes and lowest in the deep/infratentorial regions. A greater number of microhemorrhages at baseline was associated with a greater annualized rate of additional microhemorrhages by last follow‐up (rank correlation = 0.49; P < .001).ConclusionsFocal hemosiderin deposits are relatively common in the ADNI cohort and are associated with β‐amyloid load.

AbstractThe Alzheimer's Disease Neuroimaging Initiative (ADNI) three‐dimensional T1‐weighted magnetic resonance imaging (MRI) acquisitions provide a rich data set for developing and testing analysis techniques for extracting structural endpoints. To promote greater rigor in analysis and meaningful comparison of different algorithms, the ADNI MRI Core has created standardized analysis sets of data comprising scans that met minimum quality control requirements. We encourage researchers to test and report their techniques against these data. Standard analysis sets of volumetric scans from ADNI‐1 have been created, comprising screening visits, 1‐year completers (subjects who all have screening, 6‐ and 12‐month scans), 2‐year annual completers (screening, 1‐year and 2‐year scans), 2‐year completers (screening, 6‐months, 1‐year, 18‐months [mild cognitive impaired (MCI) only], and 2‐year scans), and complete visits (screening, 6‐month, 1‐year, 18‐month [MCI only], 2‐year, and 3‐year [normal and MCI only] scans). As the ADNI‐GO/ADNI‐2 data become available, updated standard analysis sets will be posted regularly.

AbstractBackgroundThere is epidemiological evidence that cardiovascular risk factors (CVRF) also are risk factors for Alzheimer's disease, but there is limited information on this from neuropathological studies, and even less from in vivo studies. Therefore, we examined the relationship between CVRF and amyloid‐β (Aβ) brain burden measured by Pittsburgh Compound B‐positron emission tomography (PiB‐PET) studies in the Alzheimer's Disease Neuroimaging Initiative.MethodsNinety‐nine subjects from the Alzheimer's Disease Neuroimaging Initiative cohort who had a PiB‐PET study measure, apolipoprotein E genotyping data, and information available on CVRF (body mass index [BMI], systolic blood pressure, diastolic blood pressure [DBP], and cholesterol and fasting glucose test results) were included. Eighty‐one subjects also had plasma cortisol, C‐reactive protein, and superoxide dismutase 1 measurements. Stepwise regression models were used to assess the relation between the CVRF and the composite PiB‐PET score.ResultsThe first model included the following as baseline variables: age, clinical diagnosis, number of apolipoprotein ε4 alleles, BMI (P = .023), and DBP (P = .012). BMI showed an inverse relation with PiB‐PET score, and DBP had a positive relation with PiB‐PET score. In the second adjusted model, cortisol plasma levels were also associated with PiB‐PET score (P = .004). Systolic blood pressure, cholesterol, or impaired fasting glucose were not found to be associated with PiB‐PET values.ConclusionIn this cross‐sectional study, we found an association between Aβ brain burden measured in vivo and DBP and cortisol, indicating a possible link between these CVRF and Aβ burden measured by PiB‐PET. These findings highlight the utility of biomarkers to explore potential pathways linking diverse Alzheimer's disease risk factors.