Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. We thank Drs. D. Stephen Snyder and Marilyn Miller from NIA who are ex-officio ADGC members. EADI. This work has been developed and supported by the LABEX (laboratory of excellence program investment for the future) DISTALZ grant (Development of Innovative Strategies for a Transdisciplinary approach to ALZheimer's disease) including funding from MEL (Metropole européenne de Lille), ERDF (European Regional Development Fund) and Conseil Régional Rotterdam, Netherlands Organization for the Health Research and Development (ZonMw), the Research Institute for Diseases in the Elderly (RIDE), the Ministry of Education, Culture and Science, the Ministry for Health, Welfare and Sports, the European Commission (DG XII), and the Municipality of Rotterdam. The authors are grateful to the study participants, the staff from the Rotterdam Study and the participating general practitioners and pharmacists. The generation and management of GWAS genotype data for the Rotterdam Study (RS-I, RS-II, RS-III) was executed by the Human Genotyping Facility of the Genetic Laboratory of the

The Alzheimer Disease Genetics Consortium (ADGC) performed a genome-wide association study (GWAS) of late-onset Alzheimer disease (LOAD) using a 3 stage design consisting of a discovery stage (Stage 1) and two replication stages (Stages 2 and 3). Both joint and meta-analysis analysis approaches were used. We obtained genome-wide significant results at MS4A4A [rs4938933; Stages 1+2, meta-analysis (PM) = 1.7 × 10−9, joint analysis (PJ) = 1.7 × 10−9; Stages 1–3, PM = 8.2 × 10−12], CD2AP (rs9349407; Stages 1–3, PM = 8.6 × 10−9), EPHA1 (rs11767557; Stages 1–3 PM = 6.0 × 10−10), and CD33 (rs3865444; Stages 1–3, PM = 1.6 × 10−9). We confirmed that CR1 (rs6701713; PM = 4.6×10−10, PJ = 5.2×10−11), CLU (rs1532278; PM = 8.3 × 10−8, PJ = 1.9×10−8), BIN1 (rs7561528; PM = 4.0×10−14; PJ = 5.2×10−14), and PICALM (rs561655; PM = 7.0 × 10−11, PJ = 1.0×10−10) but not EXOC3L2 are LOAD risk loci1–3.

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We recommend a new term, “primary age-related tauopathy” (PART), to describe a pathology that is commonly observed in the brains of aged individuals. Many autopsy studies have reported brains with neurofibrillary tangles (NFT) that are indistinguishable from those of Alzheimer's disease (AD), in the absence of amyloid (Aβ) plaques. For these “NFT+/Aβ−” brains, for which formal criteria for AD neuropathologic changes are not met, the NFT are mostly restricted to structures in the medial temporal lobe, basal forebrain, brainstem, and olfactory areas (bulb and cortex). Symptoms in persons with PART usually range from normal to amnestic cognitive changes, with only a minority exhibiting profound impairment. Because cognitive impairment is often mild, existing clinicopathologic designations, such as “tangle-only dementia” and “tangle-predominant senile dementia”, are imprecise and not appropriate for most subjects. PART is almost universally detectable at autopsy among elderly individuals, yet this pathological process cannot be specifically identified pre-mortem at the present time. Improved biomarkers and tau imaging may enable diagnosis of PART in clinical settings in the future. Indeed, recent studies have identified a common biomarker profile consisting of temporal lobe atrophy and tauopathy without evidence of Aβ accumulation. For both researchers and clinicians, a revised nomenclature will raise awareness of this extremely common pathologic change while providing a conceptual foundation for future studies. Prior reports that have elucidated features of the pathologic entity we refer to as PART are discussed, and working neuropathological diagnostic criteria are proposed.

The distribution of neurofibrillary tangles (NFTs) and neuritic plaques (NPs) was mapped in 39 cortical areas of 11 brains of patients with Alzheimer's disease (AD). Whole hemisphere blocks were embedded in polyethylene glycol (Carbowax), sectioned coronally, and stained with thioflavin S and thionin. The densities of NFTs and NPs were assessed using a numerical rating scale for each area. Scores were grouped by type of cortex and by lobe for statistical analysis. Highly significant differences were obtained. For example, limbic periallocortex and allocortex had more NFTs than any other type of cortex. In descending order, the density of NFTs was as follows: periallocortex (area 28) greater than allocortex (subiculum/CA1 zones of hippocampal formation, area 51) greater than corticoid areas (accessory basal nucleus of amygdala, nucleus basalis of Meynert) greater than proisocortex (areas 11, 12, 24, 23, anterior insula, 38, 35) greater than nonprimary association cortex (32, 46, superior temporal sulcus, 40, 39, posterior parahippocampal cortex, 37, 36) greater than primary sensory association cortex (7, 18, 19, 22, 21, 20) greater than agranular cortex (44-5, 8, 6, 4) greater than primary sensory cortex (41-2, 3-1-2, 17). The laminar distribution of NFTs tended to be selective, involving primarily layers III and V of association areas and layers II and IV of limbic periallocortex. There were far more NFTs in both limbic and temporal lobes than in frontal, parietal, and occipital lobes. In general, NPs were more evenly distributed throughout the cortex, with the exceptions of limbic periallocortex and allocortex, which had notably fewer NPs than other cortical areas. Temporal and occipital lobes had the highest NP densities, limbic and frontal lobes had the lowest, and parietal lobe was intermediate. No significant left-right hemispheric differences for NFT or NP densities were found across the population, and there was no relationship between duration of illness and densities of NFTs or NPs. The regional and laminar distribution of NFTs (and, to a lesser degree, that of NPs) suggests a consistent pattern of vulnerability within the cerebral cortices that seems correlated to the hierarchies of cortico-cortical connections. The higher-order association cortices, especially those in the anterior and ventromedial sectors of temporal lobe, are the most vulnerable, while other cortices appear less vulnerable to a degree commensurate with their connectional "distance" (i.e., synapses removed) from the limbic areas.

Context: Social isolation in old age has been associated with risk of developing dementia, but the risk associated with perceived isolation, or loneliness, is not well understood.Objective: To test the hypothesis that loneliness is associated with increased risk of Alzheimer disease (AD).Design: Longitudinal clinicopathologic cohort study with up to 4 years of annual in-home follow-up.Participants: A total of 823 older persons free of dementia at enrollment were recruited from senior citizen facilities in and around Chicago, Ill. Loneliness was assessed with a 5-item scale at baseline (mean±SD, 2.3±0.6) and annually thereafter. At death, a uniform postmortem examination of the brain was conducted to quantify AD pathology in multiple brain regions and the presence of cerebral infarctions. Main Outcome Measures:Clinical diagnosis of AD and change in previously established composite measures of global cognition and specific cognitive functions.Results: During follow-up, 76 subjects developed clinical AD. Risk of AD was more than doubled in lonely persons (score 3.2, 90th percentile) compared with persons who were not lonely (score 1.4, 10th percentile), and controlling for indicators of social isolation did not affect the finding. Loneliness was associated with lower level of cognition at baseline and with more rapid cognitive decline during follow-up. There was no significant change in loneliness, and mean degree of loneliness during the study was robustly associated with cognitive decline and development of AD. In 90 participants who died and in whom autopsy of the brain was performed, loneliness was unrelated to summary measures of AD pathology or to cerebral infarction. Conclusion:Loneliness is associated with an increased risk of late-life dementia but not with its leading causes.

Multifactorial mechanisms underlying late-onset Alzheimer's disease (LOAD) are poorly characterized from an integrative perspective. Here spatiotemporal alterations in brain amyloid-β deposition, metabolism, vascular, functional activity at rest, structural properties, cognitive integrity and peripheral proteins levels are characterized in relation to LOAD progression. We analyse over 7,700 brain images and tens of plasma and cerebrospinal fluid biomarkers from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Through a multifactorial data-driven analysis, we obtain dynamic LOAD–abnormality indices for all biomarkers, and a tentative temporal ordering of disease progression. Imaging results suggest that intra-brain vascular dysregulation is an early pathological event during disease development. Cognitive decline is noticeable from initial LOAD stages, suggesting early memory deficit associated with the primary disease factors. High abnormality levels are also observed for specific proteins associated with the vascular system's integrity. Although still subjected to the sensitivity of the algorithms and biomarkers employed, our results might contribute to the development of preventive therapeutic interventions.

Considerable overlap has been identified in the risk factors, comorbidities and putative pathophysiological mechanisms of Alzheimer disease and related dementias (ADRDs) and type 2 diabetes mellitus (T2DM), two of the most pressing epidemics of our time. Much is known about the biology of each condition, but whether T2DM and ADRDs are parallel phenomena arising from coincidental roots in ageing or synergistic diseases linked by vicious pathophysiological cycles remains unclear. Insulin resistance is a core feature of T2DM and is emerging as a potentially important feature of ADRDs. Here, we review key observations and experimental data on insulin signalling in the brain, highlighting its actions in neurons and glia. In addition, we define the concept of ‘brain insulin resistance’ and review the growing, although still inconsistent, literature concerning cognitive impairment and neuropathological abnormalities in T2DM, obesity and insulin resistance. Lastly, we review evidence of intrinsic brain insulin resistance in ADRDs. By expanding our understanding of the overlapping mechanisms of these conditions, we hope to accelerate the rational development of preventive, disease-modifying and symptomatic treatments for cognitive dysfunction in T2DM and ADRDs alike.