Clinical phenotype and genetic associations in autosomal dominant familial Alzheimer's disease: a case series

Background - The causes of phenotypic heterogeneity in familial Alzheimer’s disease with autosomal dominant inheritance are not well understood. We aimed to characterise clinical phenotypes and genetic associations with APP and PSEN1 mutations in symptomatic autosomal dominant familial Alzheimer’s disease (ADAD). Methods - We retrospectively analysed genotypic and phenotypic data (age at symptom onset, initial cognitive or behavioural symptoms, and presence of myoclonus, seizures, pyramidal signs, extrapyramidal signs, and cerebellar signs) from all individuals with ADAD due to APP or PSEN1 mutations seen at the Dementia Research Centre in London, UK. We examined the frequency of presenting symptoms and additional neurological features, investigated associations with age at symptom onset, APOE genotype, and mutation position, and explored phenotypic differences between APP and PSEN1 mutation carriers. The proportion of individuals presenting with various symptoms was analysed with descriptive statistics, stratified by mutation type. Findings - Between July 1, 1987, and Oct 31, 2015, age at onset was recorded for 213 patients (168 with PSEN1 mutations and 45 with APP mutations), with detailed history and neurological examination findings available for 121 (85 with PSEN1 mutations and 36 with APP mutations). We identified 38 different PSEN1 mutations (four novel) and six APP mutations (one novel). Age at onset differed by mutation, with a younger onset for individuals with PSEN1 mutations than for those with APP mutations (mean age 43·6 years [SD 7·2] vs 50·4 years [SD 5·2], respectively, p<0·0001); within the PSEN1 group, 72% of age at onset variance was explained by the specific mutation. A cluster of five mutations with particularly early onset (mean age at onset <40 years) involving PSEN1’s first hydrophilic loop suggests critical functional importance of this region. 71 (84%) individuals with PSEN1 mutations and 35 (97%) with APP mutations presented with amnestic symptoms, making atypical cognitive presentations significantly more common in PSEN1 mutation carriers (n=14; p=0·037). Myoclonus and seizures were the most common additional neurological features; individuals with myoclonus (40 [47%] with PSEN1 mutations and 12 [33%] with APP mutations) were significantly more likely to develop seizures (p=0·001 for PSEN1; p=0·036 for APP), which affected around a quarter of the patients in each group (20 [24%] and nine [25%], respectively). A number of patients with PSEN1 mutations had pyramidal (21 [25%]), extrapyramidal (12 [14%]), or cerebellar (three [4%]) signs. Interpretation - ADAD phenotypes are heterogeneous, with both age at onset and clinical features being influenced by mutation position as well as causative gene. This highlights the importance of considering genetic testing in young patients with dementia and additional neurological features in order to appropriately diagnose and treat their symptoms, and of examining different mutation types separately in future research. Funding - Medical Research Council and National Institute for Health Research

A new approach for developing comprehensive agricultural drought index using satellite-derived biophysical parameters and factor analysis method

The accurate assessment of drought and its monitoring is highly depending on the selection of appropriate indices. Despite the availability of countless drought indices, due to variability in environmental properties, a single universally drought index has not been presented yet. In this study, a new approach for developing comprehensive agricultural drought index from satellite-derived biophysical parameters is presented. Therefore, the potential of satellite-derived biophysical parameters for improved understanding of the water status of pistachio (Pistachio vera L.) crop grown in a semiarid area is evaluated. Exploratory factor analysis with principal component extraction method is performed to select the most in?uential parameters from seven biophysical parameters including surface temperature (Ts), surface albedo (a), leaf area index (LAI), soil heat ?ux (Go), soil-adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), and net radiation (Rn). Ts and Gowere found as the most effective parameters by this method. However, Ts, LAI, a, and SAVI that accounts for 99.6 % of the total variance of seven inputs were selected to model a new biophysical water stress index (BPWSI). The values of BPWSI were stretched independently and compared with the range of actual evapotranspiration estimated through well-known METRIC (mapping evapotranspiration at high resolution with internal calibration) energy balance model. The results showed that BPWSI can be ef?ciently used for the prediction of the pistachio water status (RMSE of 0.52, 0.31, and 0.48 mm/day on three image dates of April 28, July 17, and August 2, 2010). The study con?rmed that crop water status is accounted by several satellite-based biophysical parameters rather than single parameter