Objective: To identify factors influencing age at symptom onset and disease course in autosomal dominant Alzheimer disease (ADAD), and develop evidence-based criteria for predicting symptom onset in ADAD.Methods: We have collected individual-level data on ages at symptom onset and death from 387 ADAD pedigrees, compiled from 137 peer-reviewed publications, the Dominantly Inherited Alzheimer Network (DIAN) database, and 2 large kindreds of Colombian (PSEN1 E280A) and Volga German (PSEN2 N141I) ancestry. Our combined dataset includes 3,275 individuals, of whom 1,307 were affected by ADAD with known age at symptom onset. We assessed the relative contributions of several factors in influencing age at onset, including parental age at onset, age at onset by mutation type and family, and APOE genotype and sex. We additionally performed survival analysis using data on symptom onset collected from 183 ADAD mutation carriers followed longitudinally in the DIAN Study.Results: We report summary statistics on age at onset and disease course for 174 ADAD mutations, and discover strong and highly significant (p , 10 216 , r 2 . 0.38) correlations between individual age at symptom onset and predicted values based on parental age at onset and mean ages at onset by mutation type and family, which persist after controlling for APOE genotype and sex.Conclusions: Significant proportions of the observed variance in age at symptom onset in ADAD can be explained by family history and mutation type, providing empirical support for use of these data to estimate onset in clinical research. Researchers have identified more than 230 different autosomal dominant Alzheimer disease (ADAD) mutations located in the genes for amyloid precursor protein (APP), presenilin 1 (PSEN1), and presenilin 2 (PSEN2), including the canonical case discovered by Alois Alzheimer.1 There are significant differences between mutation types in age at symptom onset, and many result in onset as early as the third or fourth decade of life.2,3 Some families carrying an identical ADAD mutation can have significantly different ages at onset, suggesting the presence of other genetic or environmental modifiers of the disease process.4,5 APOE genotype was found to slightly modify age at onset in 2 ADAD kindreds, 5,6 although it is not yet clear whether this is the case for ADAD in general. The factors influencing symptom onset and progression in From the

Summary Background We previously detected functional brain imaging abnormalities in young adults at genetic risk for late-onset Alzheimer’s disease (AD). Here, we sought to characterize structural and functional magnetic resonance imaging (MRI), cerebrospinal fluid (CSF), and plasma biomarker abnormalities in young adults at risk for autosomal dominant early-onset AD. Biomarker measurements were characterized and compared in presenilin 1 (PSEN1) E280A mutation carriers and non-carriers from the world’s largest known autosomal dominant early-onset AD kindred, more than two decades before the carriers’ estimated median age of 44 at the onset of mild cognitive impairment (MCI) and before their estimated age of 28 at the onset of amyloid-β (Aβ) plaque deposition. Methods Biomarker data for this cross-sectional study were acquired in Antioquia, Colombia between July and August, 2010. Forty-four participants from the Colombian Alzheimer’s Prevention Initiative (API) Registry had structural MRIs, functional MRIs during associative memory encoding/novel viewing and control tasks, and cognitive assessments. They included 20 mutation carriers and 24 non-carriers, who were cognitively normal, 18-26 years old and matched for their gender, age, and educational level. Twenty of the participants, including 10 mutation carriers and 10 non-carriers, had lumbar punctures and venipunctures. Primary outcome measures included task-dependent hippocampal/parahippocampal activations and precuneus/posterior cingulate deactivations, regional gray matter reductions, CSF Aβ1-42, total tau and phospho-tau181 levels, and plasma Aβ1-42 levels and Aβ1-42/Aβ1-40 ratios. Structural and functional MRI data were compared using automated brain mapping algorithms and AD-related search regions. Cognitive and fluid biomarkers were compared using Mann-Whitney tests. Findings The mutation carrier and non-carrier groups did not differ significantly in their dementia ratings, neuropsychological test scores, or proportion of apolipoprotein E (APOE) ε4 carriers. Compared to the non-carriers, carriers had higher CSF Aβ1-42 levels (p=0·008), plasma Aβ1-42 levels (p=0·01), and plasma Aβ1-42/Aβ1-40 ratios (p=0·001), consistent with Aβ1-42 overproduction. They also had greater hippocampal/parahippocampal activations (as low as p=0·008, after correction for multiple comparisons), less precuneus/posterior cingulate deactivations (as low as p=0·001, after correction), less gray matter in several regions (p-values <0·005, uncorrected, and corrected p=0·008 in the parietal search region), similar to findings in the later preclinical and clinical stages of autosomal dominant and late-onset AD. Interpretation Young adults at genetic risk for autosomal dominant AD have functional and structural MRI abnormalities, along with CSF and plasma biomarker findings consistent with Aβ1-42 over-production. While the extent to which the underlying brain changes are progressive or developmental remain to be determined, this study demonstrates the earliest known biomarker cha...

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Summary Background Fibrillar amyloid-β (Aβ) is thought to begin accumulating in the brain many years before the onset of clinical impairment in patients with Alzheimer’s disease. By assessing the accumulation of Aβ in people at risk of genetic forms of Alzheimer’s disease, we can identify how early preclinical changes start in individuals certain to develop dementia later in life. We sought to characterise the age-related accumulation of Aβ deposition in presenilin 1 (PSEN1) E280A mutation carriers across the spectrum of preclinical disease. Methods Between Aug 1 and Dec 6, 2011, members of the familial Alzheimer’s disease Colombian kindred aged 18–60 years were recruited from the Alzheimer’s Prevention Initiative’s registry at the University of Antioquia, Medellín, Colombia. Cross-sectional assessment using florbetapir PET was done in symptomatic mutation carriers with mild cognitive impairment or mild dementia, asymptomatic carriers, and asymptomatic non-carriers. These assessments were done at the Banner Alzheimer’s Institute in Phoenix, AZ, USA. A cortical grey matter mask consisting of six predefined regions. was used to measure mean cortical florbetapir PET binding. Cortical-to-pontine standard-uptake value ratios were used to characterise the cross-sectional accumulation of fibrillar Aβ deposition in carriers and non-carriers with regression analysis and to estimate the trajectories of fibrillar Aβ deposition. Findings We enrolled a cohort of 11 symptomatic individuals, 19 presymptomatic mutation carriers, and 20 asymptomatic non-carriers, ranging in age from 20 to 56 years. There was greater florbetapir binding in asymptomatic PSEN1 E280A mutation carriers than in age matched non-carriers. Fibrillar Aβ began to accumulate in PSEN 1E280A mutation carriers at a mean age of 28·2 years (95% CI 27·3–33·4), about 16 years and 21 years before the predicted median ages at mild cognitive impairment and dementia onset, respectively. 18F florbetapir binding rose steeply over the next 9·4 years and plateaued at a mean age of 37·6 years (95% CI 35·3–40·2), about 6 and 11 years before the expected respective median ages at mild cognitive impairment and dementia onset. Prominent florbetapir binding was seen in the anterior and posterior cingulate, precuneus, and parietotemporal and frontal grey matter, as well as in the basal ganglia. Binding in the basal ganglia was not seen earlier or more prominently than in other regions. Interpretation These findings contribute to the understanding of preclinical familial Alzheimer’s disease and help set the stage for assessment of amyloid-modifying treatments in the prevention of familial Alzheimer’s disease. Funding Avid Radiopharmaceuticals, Banner Alzheimer’s Foundation, Nomis Foundation, Anonymous Foundation, Forget Me Not Initiative, Colciencias, National Institute on Aging, and the State of Arizona.

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The literature on GWAS (genome-wide association studies) data suggests that very large sample sizes (for example, 50,000 cases and 50,000 controls) may be required to detect significant associations of genomic regions for complex disorders such as Alzheimer's disease (AD). Because of the challenges of obtaining such large cohorts, we describe here a novel sequential strategy that combines pooling of DNA and bootstrapping (pbGWAS) in order to significantly increase the statistical power and exponentially reduce expenses. We applied this method to a very homogeneous sample of patients belonging to a unique and clinically well-characterized multigenerational pedigree with one of the most severe forms of early onset AD, carrying the PSEN1 p.Glu280Ala mutation (often referred to as E280A mutation), which originated as a consequence of a founder effect. In this cohort, we identified novel loci genome-wide significantly associated as modifiers of the age of onset of AD (CD44, rs187116, P = 1.29 × 10–12; NPHP1, rs10173717, P = 1.74 × 10–12; CADPS2, rs3757536, P = 1.54 × 10–10; GREM2, rs12129547, P = 1.69 × 10–13, among others) as well as other loci known to be associated with AD. Regions identified by pbGWAS were confirmed by subsequent individual genotyping. The pbGWAS methodology and the genes it targeted could provide important insights in determining the genetic causes of AD and other complex conditions.

Objective-There is a need to identify a cognitive composite that is sensitive to tracking preclinical AD decline to be used as a primary endpoint in treatment trials.Method-We capitalized on longitudinal data, collected from 1995 to 2010, from cognitively unimpaired presenilin 1 (PSEN1) E280A mutation carriers from the world's largest known earlyonset autosomal dominant AD (ADAD) kindred to identify a composite cognitive test with the greatest statistical power to track preclinical AD decline and estimate the number of carriers age 30 and older needed to detect a treatment effect in the Alzheimer's Prevention Initiative's (API) preclinical AD treatment trial. The mean-to-standard-deviation ratios (MSDRs) of change over time were calculated in a search for the optimal combination of one to seven cognitive tests/sub-* To whom correspondence should be addressed: Napatkamon Ayutyanont, PhD, Banner Alzheimer's Institute, 901 E. Willetta Street, Phoenix, AZ 85006, telephone: 602.839.6825; Napatkamon.Ayutyanont@bannerhealth.com. ** These two authors are co-senior authors.Portions of this study were presented at the 2011 Alzheimer's Association International Conference, Paris, France and the 2011 Clinical Trials on Alzheimer's Disease (CTAD), San Diego, CA.They have no conflict of interest to report. Conclusions-We have identified a composite cognitive test score representing multiple cognitive domains that has improved power compared to the most sensitive single test item to track preclinical AD decline in ADAD mutation carriers and evaluate preclinical AD treatments. This API composite cognitive test score will be used as the primary endpoint in the first API trial in cognitively unimpaired ADAD carriers within 15 years of their estimated age at clinical onset. NIH Public AccessWe have independently confirmed our findings in a separate cohort of cognitively healthy older adults who progressed to the clinical stages of late-onset AD, described in a separate report, and continue to refine the composite in independent cohorts and compared with other analytical approaches.