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BackgroundWhite matter hyperintensities (WMH), a biomarker of small vessel disease, are often found in Alzheimer’s disease (AD) and their advanced detection and quantification can be beneficial for research and clinical applications. To investigate WMH in large-scale multicenter studies on cognitive impairment and AD, appropriate automated WMH segmentation algorithms are required. This study aimed to compare the performance of segmentation tools and provide information on their application in multicenter research.MethodsWe used a pseudo-randomly selected dataset (n = 50) from the DZNE-multicenter observational Longitudinal Cognitive Impairment and Dementia Study (DELCODE) that included 3D fluid-attenuated inversion recovery (FLAIR) images from participants across the cognitive continuum. Performances of top-rated algorithms for automated WMH segmentation [Brain Intensity Abnormality Classification Algorithm (BIANCA), lesion segmentation toolbox (LST), lesion growth algorithm (LGA), LST lesion prediction algorithm (LPA), pgs, and sysu_media] were compared to manual reference segmentation (RS).ResultsAcross tools, segmentation performance was moderate for global WMH volume and number of detected lesions. After retraining on a DELCODE subset, the deep learning algorithm sysu_media showed the highest performances with an average Dice’s coefficient of 0.702 (±0.109 SD) for volume and a mean F1-score of 0.642 (±0.109 SD) for the number of lesions. The intra-class correlation was excellent for all algorithms (>0.9) but BIANCA (0.835). Performance improved with high WMH burden and varied across brain regions.ConclusionTo conclude, the deep learning algorithm, when retrained, performed well in the multicenter context. Nevertheless, the performance was close to traditional methods. We provide methodological recommendations for future studies using automated WMH segmentation to quantify and assess WMH along the continuum of cognitive impairment and AD dementia.

Memory clinic patients are a heterogeneous population representing various aetiologies of pathological ageing. It is not known whether divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± standard deviation, age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (n = 342), mild cognitive impairment (n = 118) or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid Alzheimer's disease biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5) as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test whether baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and mild cognitive impairment conversion rates of cognitively unimpaired participants and those with subjective cognitive decline. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy initially affected the medial temporal lobes, followed by further temporal regions and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological Alzheimer's disease biomarker levels, APOE ε4 carriership and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe, with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive Alzheimer's disease biomarkers and was associated with more generalized cognitive impairment. Limbic-predominant atrophy, in all participants and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of mild cognitive impairment conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, at both the subject and the group level, was excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for Alzheimer's disease in applied settings. The implementation of atrophy subtype- and stage-specific end points might increase the statistical power of pharmacological trials targeting early Alzheimer's disease. [ABSTRACT FROM AUTHOR]

Background and purpose: The Scale for Assessment and Rating of Ataxia (SARA) is a widely used clinical scale. The objective was to study the age dependence of SARA in healthy adults and to define age‐specific cut‐off values to differentiate healthy from ataxic individuals. Methods: Data from 390 healthy individuals and 119 spinocerebellar ataxia patients were analyzed. SARA scores were mapped on functional SARA (fSARA). Age‐adjusted cut‐off values were determined by receiver operating characteristic curve analysis. Results: The cut‐off value was 3 for SARA and 1.5 for fSARA. Older patients had higher SARA cut‐off values (4.5 for 60–69 years and 6.5 for 70–79 years). Age‐adjusted cut‐off values for fSARA are 1 for 18–29, 30–39 and 50–59 years, 2 for 40–49 and 60–69 years and 3 for 70–79 years. Sensitivity and specificity were higher for SARA than for fSARA. Conclusion: In this study, age‐dependent cut‐off values were defined for SARA and fSARA. The results may be relevant for the design of future preventive trials in spinocerebellar ataxias that use conversion to ataxia as an outcome. [ABSTRACT FROM AUTHOR]

The gene for the hereditary form of isolated congenital alopecia has been mapped to a chromosome on 8p21-22, ALUNC, alopecia universalis congenitalis. Congenital absence of hair, or alopecia, may occur with or without associated defects. A majority of families with isolated congenital alopecia follow an autosomal-recessive inheritance mode. Genetic linkage analysis has been carried out on an inbred Pakistani family in which affected people have no hair at all using more than 175 microsatellite polymorphic markers. The condition was linked by haplotype analysis of recombination events to a 15-cM region between D8S261 and D8S1771.

Studies the gene responsible for autosomal recessive universal congenital alopecia. Cloning and characterization of the human homologue of the mouse hairless gene; Location of the gene in the critical region on chromosome 8p21-22.

Introduction: Blood-based biomarkers are a cost-effective and minimally invasive method for diagnosing the early and preclinical stages of amyloid positivity (AP). Our study aims to investigate our novel immunoprecipitation-immunoassay (IP-IA) as a test for predicting cognitive decline., Methods: We measured levels of amyloid beta (Aβ)X-40 and AβX-42 in immunoprecipitated eluates from the DELCODE cohort. Receiver-operating characteristic (ROC) curves, regression analyses, and Cox proportional hazard regression models were constructed to predict AP by Aβ42/40 classification in cerebrospinal fluid (CSF) and conversion to mild cognitive impairment (MCI) or dementia., Results: We detected a significant correlation between AßX-42/X-40 in plasma and CSF (r = 0.473). Mixed-modeling analysis revealed a substantial prediction of AßX-42/X-40 with an area under the curve (AUC) of 0.81 for AP (sensitivity: 0.79, specificity: 0.74, positive predictive value [PPV]: 0.71, negative predictive value [NPV]: 0.81). In addition, lower AβX-42/X-40 ratios were associated with negative PACC5 slopes, suggesting cognitive decline., Discussion: Our results suggest that assessing the plasma AβX-42/X-40 ratio via our semiautomated IP-IA is a promising biomarker when examining patients with early or preclinical AD., Highlights: New plasma Aβ42/Aβ40 measurement using immunoprecipitation-immunoassay Plasma Aβ42/Aβ40 associated with longitudinal cognitive decline Promising biomarker to detect subjective cognitive decline at-risk for brain amyloid positivity., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Memory clinic patients are a heterogeneous population representing various aetiologies of pathological ageing. It is not known whether divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± standard deviation, age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (n = 342), mild cognitive impairment (n = 118) or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid Alzheimer's disease biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5) as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test whether baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and mild cognitive impairment conversion rates of cognitively unimpaired participants and those with subjective cognitive decline. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy initially affected the medial temporal lobes, followed by further temporal regions and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological Alzheimer's disease biomarker levels, APOE ε4 carriership and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe, with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive Alzheimer's disease biomarkers and was associated with more generalized cognitive impairment. Limbic-predominant atrophy, in all participants and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of mild cognitive impairment conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, at both the subject and the group level, was excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for Alzheimer's disease in applied settings. The implementation of atrophy subtype- and stage-specific end points might increase the statistical power of pharmacological trials targeting early Alzheimer's disease., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain.)

Measures of step variability and body sway during gait have shown to correlate with clinical ataxia severity in several cross-sectional studies. However, to serve as a valid progression biomarker, these gait measures have to prove their sensitivity to robustly capture longitudinal change, ideally within short time frames (eg, 1 year). We present the first multicenter longitudinal gait analysis study in spinocerebellar ataxias. We performed a combined cross-sectional (n = 28) and longitudinal (1-year interval, n = 17) analysis in Spinocerebellar Ataxia type 3 subjects (including seven preataxic mutation carriers). Longitudinal analysis showed significant change in gait measures between baseline and 1-year follow-up, with high effect sizes (stride length variability: P = 0.01, effect size r prb  = 0.66; lateral sway: P = 0.007, r prb  = 0.73). Sample size estimation for lateral sway indicates a required cohort size of n = 43 for detecting a 50% reduction of natural progression, compared with n = 240 for the clinical ataxia score Scale for the Assessment and Rating of Ataxia (SARA). These measures thus present promising motor biomarkers for upcoming interventional studies. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society., (© 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.)

Spinocerebellar ataxia type 3 (SCA3) is a late-onset neurodegenerative disorder caused by the expansion of a polyglutamine tract within the gene product, ataxin-3. We have previously shown that mutant ataxin-3 causes upregulation of inflammatory genes in transgenic SCA3 cell lines and human SCA3 pontine neurons. We report here a complex pattern of transcriptional changes by microarray gene expression profiling and Northern blot analysis in a SCA3 cell model. Twenty-three differentially expressed genes involved in inflammatory reactions, nuclear transcription, and cell surface-associated processes were identified. The identified corresponding proteins were analyzed by immunohistochemistry in human disease and control brain tissue to evaluate their implication in SCA3 pathogenesis. In addition to several inflammatory mediators upregulated in mutant ataxin-3 expressing cell lines and pontine neurons of SCA3 patients, we identified a profound repression of genes encoding cell surface-associated proteins in cells overexpressing normal ataxin-3. Correspondingly, these genes were upregulated in mutant ataxin-3 expressing cell lines and in pontine neurons of SCA3 patients. These findings identify for the first time target genes transcriptionally regulated by normal ataxin-3 and support the hypothesis that both loss of normal ataxin-3 and gain of function through protein-protein interacting properties of mutant ataxin-3 contribute to SCA3 pathogenesis.