IntroductionLate-onset Alzheimer's disease (LOAD, onset age > 60 years) is the most prevalent dementia in the elderly 1 , and risk is partially driven by genetics 2 . Many of the loci responsible for this genetic risk were identified by genome-wide association studies (GWAS) [3][4][5][6][7][8] . To identify additional LOAD risk loci, the we performed the largest GWAS to date (89,769 individuals), analyzing both common and rare variants. We confirm 20 previous LOAD risk loci and identify four new genome-wide loci (IQCK, ACE, ADAM10, and ADAMTS1). Pathway analysis of these data implicates the immune system and lipid metabolism, and for the first time tau binding proteins and APP metabolism. These findings show that genetic variants affecting APP and Aβ processing are not only associated with early-onset autosomal dominant AD but also with LOAD. Analysis of AD risk genes and pathways show enrichment for rare variants (P = 1.32 x 10 -7 ) indicating that additional rare variants remain to be identified. Main TextOur previous work identified 19 genome-wide significant common variant signals in addition to APOE 9 , that influence risk for LOAD. These signals, combined with 'subthreshold' common variant associations, account for ~31% of the genetic variance of LOAD 2 , leaving the majority of genetic risk uncharacterized 10 . To search for additional signals, we conducted a GWAS metaanalysis of non-Hispanic Whites (NHW) using a larger sample (17 new, 46 total datasets) from our group, the International Genomics of Alzheimer's Project (IGAP) (composed of four AD consortia: ADGC, CHARGE, EADI, and GERAD). This sample increases our previous discovery sample (Stage 1) by 29% for cases and 13% for controls (N=21,982 cases; 41,944 controls) ( Supplementary Table 1 and 2, and Supplementary Note). To sample both common and rare variants (minor allele frequency MAF ≥ 0.01, and MAF < 0.01, respectively), we imputed the discovery datasets using a 1000 Genomes reference panel consisting of . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under a 11 36,648,992 single-nucleotide variants, 1,380,736 insertions/deletions, and 13,805 structural variants. After quality control, 9,456,058 common variants and 2,024,574 rare variants were selected for analysis (a 63% increase from our previous common variant analysis in 2013).Genotype dosages were analyzed within each dataset, and then combined with meta-analysis ( Supplementary Figures 1 and 2 and Supplementary Table 3). The Stage 1 discovery metaanalysis was first followed by Stage 2 using the I-select chip we previously developed in Lambert et al (including 11,632 variants, N=18,845) and finally stage 3A (N=6,998). The final sample was 33,692 clinical AD cases and 56,077 controls.Meta-analysis of Stages 1 and 2 produced 21 associations with P ≤ 5x10 -8 (Table 1 and Figure 1). Of these, 18 were previously reported as genome-wide significant and three of them are signals not initially described in Lambert et al: the rare R47H TREM2 coding va...
Objectives-i) to assess the diagnostic specificity of MRI-defined hippocampal atrophy for Alzheimer's disease (AD) among individuals with a variety of pathologically confirmed conditions associated with dementia as well as changes attributable to typical aging, and, ii) to measure correlations among pre-mortem MRI measurements of hippocampal atrophy, mental status exam performance, and the pathologic stage of AD. Methods-An un-selected series of 67 individuals participating in the Mayo Alzheimer's DiseaseResearch Center/Alzheimer's Disease Patient Registry were identified who had undergone a standardized antemortem MRI study and also post-mortem examination. Hippocampal volumes were measured from antemortem MRI. Each post-mortem specimen was assigned a pathologic diagnosis, and in addition, the severity of AD pathology was staged using the method of Braak and Braak.Results-Individuals with an isolated pathologic diagnosis of AD, hippocampal sclerosis, frontotemporal degeneration, and neurofibrillary tangle-only degeneration usually had substantial hippocampal atrophy while those with changes of typical aging did not. Among all 67 subjects, correlations (all p<0.001) were observed between hippocampal volume and Braak stage (r = −0.39), between hippocampal volume and MMSE score (r = 0.60), and between MMSE score and Braak stage (r = −0.41).Conclusions-Hippocampal atrophy, while not specific for AD, was a fairly sensitive marker of the pathologic AD stage [particularly among subjects with isolated AD pathology (r = −0.63, p = 0.001)] and consequent cognitive status.
Background: Neurofibrillary tangles (NFTs), composed of hyperphosphorylated tau proteins, are one
Objective: To identify the patterns of diffusivity changes in patients with dementia with Lewy bodies (DLB) and Alzheimer disease (AD) and to determine whether diffusion tensor MRI (DTI) is complementary to structural MRI in depicting the tissue abnormalities characteristic of DLB and AD. Methods:We studied clinically diagnosed age-, gender-, and education-matched subjects with DLB (n ϭ 30), subjects with AD (n ϭ 30), and cognitively normal (CN) subjects (n ϭ 60) in a case-control study. DTI was performed at 3T with a fluid-attenuated inversion recovery-based DTI sequence that enabled cortical diffusion measurements. Mean diffusivity (MD) and gray matter (GM) density were measured from segmented cortical regions. Tract-based diffusivity was measured using color-coded fractional anisotropy (FA) maps. Results:Patients with DLB were characterized by elevated MD in the amygdala and decreased FA in the inferior longitudinal fasciculus (ILF). ILF diffusivity was associated with the presence of visual hallucinations (p ϭ 0.007), and amygdala diffusivity was associated with Unified Parkinson's Disease Rating Scale (r ϭ 0.50; p ϭ 0.005) in DLB. In contrast, patients with AD were characterized by elevated MD in the medial temporal, temporal, and parietal lobe association cortices and decreased FA in the fornix, cingulum, and ILF. Amygdala diffusivity was complementary to GM density in discriminating DLB from CN; hippocampal and parahippocampal diffusivity was complementary to GM density in discriminating AD from CN. Conclusion:Increased amygdalar diffusivity in the absence of tissue loss in dementia with Lewy bodies (DLB) may be related to microvacuolation, a common pathology associated with Lewy body disease in the amygdala. Diffusivity measurements were complementary to structural MRI, demonstrating that measures of diffusivity on diffusion tensor MRI are valuable tools for characterizing the tissue abnormalities characteristic of Alzheimer disease and DLB. Neurology ® 2010;74:1814 -1821 GLOSSARY AD ϭ Alzheimer disease; CN ϭ cognitively normal; DLB ϭ dementia with Lewy bodies; DTI ϭ diffusion tensor MRI; FA ϭ fractional anisotropy; FDR ϭ false discovery rate; FLAIR ϭ fluid-attenuated inversion recovery; GM ϭ gray matter; ILF ϭ inferior longitudinal fasciculus; LB ϭ Lewy body; MD ϭ mean diffusivity; RBD ϭ REM sleep behavior disorder; ROI ϭ region of interest; SLF ϭ superior longitudinal fasciculus; TE ϭ echo time; TI ϭ inversion time; TR ϭ repetition time; UPDRS ϭ Unified Parkinson's Disease Rating Scale; WM ϭ white matter.Dementia with Lewy bodies (DLB) is the second most common cause of neurodegenerative dementia after Alzheimer disease (AD).1 Although patients with Lewy body (LB) pathology typically have some AD pathology, 2 noninvasive imaging markers that can distinguish the contribution of these different pathologies to the dementia syndrome may be useful for the differential diagnosis and may provide insight into the pathologic mechanisms underlying these disorders.Diffusion tensor MRI (DTI) provides infor...
Specific cognitive domain functions are associated with distinct patterns of cortical and white matter diffusivity in elderly with no dementia. Posterior cingulum tract FA was associated with all 4 cognitive domain functions, in agreement with the hypothesis that the posterior cingulate cortex is the main connectivity hub for cognitive brain networks. Microstructural changes identified on DTI may be associated with neurodegenerative pathologies underlying cognitive changes in older adults without dementia.
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