Genetic-based patient stratification in Alzheimer's disease.

Alzheimer's disease (AD) shows a high pathological and symptomatological heterogeneity. To study this heterogeneity, we have developed a patient stratification technique based on one of the most significant risk factors for the development of AD: genetics. We addressed this challenge by including network biology concepts, mapping genetic variants data into a brain-specific protein-protein interaction (PPI) network, and obtaining individualized PPI scores that we then used as input for a clustering technique. We then phenotyped each obtained cluster regarding genetics, sociodemographics, biomarkers, fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging, and neurocognitive assessments. We found three clusters defined mainly by genetic variants found in MAPT, APP, and APOE, considering known variants associated with AD and other neurodegenerative disease genetic architectures. Profiling of these clusters revealed minimal variation in AD symptoms and pathology, suggesting different biological mechanisms may activate the neurodegeneration and pathobiological patterns behind AD and result in similar clinical and pathological presentations, even a shared disease diagnosis. Lastly, our research highlighted MAPT, APP, and APOE as key genes where these genetic distinctions manifest, suggesting them as potential targets for personalized drug development strategies to address each AD subgroup individually.

A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers.

AIMS: The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values. METHODS: We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aß(1-42), Aß(1-42)/Aß(1-40) ratio, tTau, and pTau. RESULTS: The optimal solution yielded three clusters in both cohorts, significantly different in diagnosis, AT(N) classification, values distribution, and survival. We defined these three CSF groups as (i) non-defined or unrelated to AD, (ii) early stages and/or more delayed risk of conversion to dementia, and (iii) more severe cognitive impairment subjects with faster progression to dementia. CONCLUSION: We propose this data-driven three-group classification as a meaningful and straightforward approach to evaluating the risk of conversion to dementia, complementary to the AT(N) system classification.

Multi-Disease Validation of the RUDAS for Cognitive Screening in Alzheimer's Disease, Parkinson's Disease, and Multiple Sclerosis.

BACKGROUND: The Rowland Universal Dementia Assessment Scale (RUDAS) is a cognitive test with favorable diagnostic properties for detecting dementia and a low influence of education and cultural biases. OBJECTIVE: We aimed to validate the RUDAS in people with Alzheimer's disease (AD), Parkinson's disease (PD), and multiple sclerosis (MS). METHODS: We enrolled one hundred and fifty participants (60 with AD, 30 with PD, 60 with MS, and 120 healthy controls (HC)). All clinical groups completed a comprehensive neuropsychological battery, RUDAS, and standard cognitive tests of each disorder: MMSE, SCOPA-COG, and Symbol Digit Modalities Test. Intergroup comparisons between clinical groups and HC and ROC curves were estimated. Random Forest algorithms were trained and validated to detect cognitive impairment using RUDAS and rank the most relevant scores. RESULTS: The RUDAS scores were lower in patients with AD, and patients with PD and MS showed cognitive impairment compared to healthy controls. Effect sizes were generally large. The total score was the most discriminative, followed by the memory score. Correlations with standardized neuropsychological tests were moderate to high. Random Forest algorithms obtained accuracies over 80-90% using the RUDAS for diagnosing AD and cognitive impairment associated with PD and MS. CONCLUSION: Our results suggest the RUDAS is a valid test candidate for multi-disease cognitive screening tool in AD, PD, and MS.

On the limits of graph neural networks for the early diagnosis of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disease whose molecular mechanisms are activated several years before cognitive symptoms appear. Genotype-based prediction of the phenotype is thus a key challenge for the early diagnosis of AD. Machine learning techniques that have been proposed to address this challenge do not consider known biological interactions between the genes used as input features, thus neglecting important information about the disease mechanisms at play. To mitigate this, we first extracted AD subnetworks from several protein-protein interaction (PPI) databases and labeled these with genotype information (number of missense variants) to make them patient-specific. Next, we trained Graph Neural Networks (GNNs) on the patient-specific networks for phenotype prediction. We tested different PPI databases and compared the performance of the GNN models to baseline models using classical machine learning techniques, as well as randomized networks and input datasets. The overall results showed that GNNs could not outperform a baseline predictor only using the APOE gene, suggesting that missense variants are not sufficient to explain disease risk beyond the APOE status. Nevertheless, our results show that GNNs outperformed other machine learning techniques and that protein-protein interactions lead to superior results compared to randomized networks. These findings highlight that gene interactions are a valuable source of information in predicting disease status.

Validation of a brief cross-cultural cognitive screening test in Multiple Sclerosis.

BACKGROUND: Several batteries have been developed for the cognitive assessment of people with multiple sclerosis (MS). However, all these tests have some limitations in general clinical practice and from a cross-cultural perspective. In this study, we aimed to validate a novel cognitive screening test, the Cross-Cultural Dementia screening test (CCD), in pwMS. METHODS: Seventy-five participants with relapsing-remitting MS and 75 healthy controls were enrolled and completed a comprehensive neuropsychological battery and the CCD. Intergroup comparisons, effect sizes, and correlations with previously validated tests were calculated for a majority and a pilot study of a minority sample. ROC curves were estimated, and random forest classification models were developed. RESULTS: There were statistically significant differences between cognitively impaired MS (MS-CI) group and healthy controls, and between MS-CI and non-cognitively impaired MS group in all subtests of CCD with medium to large effect sizes. Correlations with standardized neuropsychological tests were moderate to high, supporting concurrent validity. These results were replicated in the minority sample. The random forest models showed a very accurate classification using the CCD. This test showed good psychometric properties compared with SDMT. CONCLUSIONS: Our study validates the CCD for cognitive impairment screening in MS, showing advantages over other routinely used cognitive tests.

Identification of the main components of spontaneous speech in primary progressive aphasia and their neural underpinnings using multimodal MRI and FDG-PET imaging.

BACKGROUND: Primary progressive aphasia (PPA) is a clinical syndrome characterized by gradual loss of language skills. This study aimed to evaluate the diagnostic capacity of a connected speech task for the diagnosis of PPA and its variants, to determine the main components of spontaneous speech, and to examine their neural correlates. METHODS: A total of 118 participants (31 patients with nfvPPA, 11 with svPPA, 45 with lvPPA, and 31 healthy controls) were evaluated with the Cookie Theft picture description task and a comprehensive language assessment protocol. Patients also underwent 18F-fluorodeoxyglucose positron emission tomography and magnetic resonance imaging studies. Principal component analysis and machine learning were used to evaluate the main components of connected speech and the accuracy of connected speech parameters for diagnosing PPA. Voxel-based analyses were conducted to evaluate the correlation between spontaneous speech components and brain metabolism, brain volumes, and white matter microstructure. RESULTS: Discrimination between patients with PPA and controls was 91.67%, with 77.78% discrimination between PPA variants. Parameters related to speech rate and lexical variables were the most discriminative for classification. Three main components were identified: lexical features, fluency, and syntax. The lexical component was associated with ventrolateral frontal regions, while the fluency component was associated with the medial superior prefrontal cortex. Number of pauses was more related with the left parietotemporal region, while pauses duration with the bilateral frontal lobe. The lexical component was correlated with several tracts in the language network (left frontal aslant tract, left superior longitudinal fasciculus I, II, and III, left arcuate fasciculus, and left uncinate fasciculus), and fluency was linked to the frontal aslant tract. CONCLUSION: Spontaneous speech assessment is a useful, brief approach for the diagnosis of PPA and its variants. Neuroimaging correlates suggested a subspecialization within the left frontal lobe, with ventrolateral regions being more associated with lexical production and the medial superior prefrontal cortex with speech rate.

Diagnosis of Alzheimer's disease and behavioural variant frontotemporal dementia with machine learning-aided neuropsychological assessment using feature engineering and genetic algorithms.

BACKGROUND: Neuropsychological assessment is considered a valid tool in the diagnosis of neurodegenerative disorders. However, there is an important overlap in cognitive profiles between Alzheimer's disease (AD) and behavioural variant frontotemporal dementia (bvFTD), and the usefulness in diagnosis is uncertain. We aimed to develop machine learning-based models for the diagnosis using cognitive tests. METHODS: Three hundred and twenty-nine participants (170 AD, 72 bvFTD, 87 healthy control [HC]) were enrolled. Evolutionary algorithms, inspired by the process of natural selection, were applied for both mono-objective and multi-objective classification and feature selection. Classical algorithms (NativeBayes, Support Vector Machines, among others) were also used, and a meta-model strategy. RESULTS: Accuracies for the diagnosis of AD, bvFTD and the differential diagnosis between them were higher than 84%. Algorithms were able to significantly reduce the number of tests and scores needed. Free and Cued Selective Reminding Test, verbal fluency and Addenbrooke's Cognitive Examination were amongst the most meaningful tests. CONCLUSIONS: Our study found high levels of accuracy for diagnosis using exclusively neuropsychological tests, which supports the usefulness of cognitive assessment in diagnosis. Machine learning may have a role in improving the interpretation and test selection.

Spanish Version of the Mini-Linguistic State Examination for the Diagnosis of Primary Progressive Aphasia.

BACKGROUND: Primary progressive aphasia (PPA) is a neurodegenerative syndrome with three main clinical variants: non-fluent, semantic, and logopenic. Clinical diagnosis and accurate classification are challenging and often time-consuming. The Mini-Linguistic State Examination (MLSE) has been recently developed as a short language test to specifically assess language in neurodegenerative disorders. OBJECTIVE: Our aim was to adapt and validate the Spanish version of MLSE for PPA diagnosis. METHODS: Cross-sectional study involving 70 patients with PPA and 42 healthy controls evaluated with the MLSE. Patients were independently diagnosed and classified according to comprehensive cognitive evaluation and advanced neuroimaging. RESULTS: Internal consistency was 0.758. The influence of age and education was very low. The area under the curve for discriminating PPA patients and healthy controls was 0.99. Effect sizes were moderate-large for the discrimination between PPA and healthy controls. Motor speech, phonology, and semantic subscores discriminated between the three clinical variants. A random forest classification model obtained an F1-score of 81%for the three PPA variants. CONCLUSION: Our study provides a brief and useful language test for PPA diagnosis, with excellent properties for both clinical routine assessment and research purposes.

Whole-Exome Sequencing and C9orf72 Analysis in Primary Progressive Aphasia.

Primary progressive aphasia (PPA) is mainly considered a sporadic disease and few studies have systematically analyzed its genetic basis. We here report the analyses of C9orf72 genotyping and whole-exome sequencing data in a consecutive and well-characterized cohort of 50 patients with PPA. We identified three pathogenic GRN variants, one of them unreported, and two cases with C9orf72 expansions. In addition, one likely pathogenic variant was found in the SQSTM1 gene. Overall, we found 12%of patients carrying pathogenic or likely pathogenic variants. These results support the genetic role in the pathophysiology of a proportion of patients with PPA.

Genetic Algorithms for Optimized Diagnosis of Alzheimer's Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.

Genetic algorithms have a proven capability to explore a large space of solutions, and deal with very large numbers of input features. We hypothesized that the application of these algorithms to 18F-Fluorodeoxyglucose Positron Emission Tomography (FDG-PET) may help in diagnosis of Alzheimer's disease (AD) and Frontotemporal Dementia (FTD) by selecting the most meaningful features and automating diagnosis. We aimed to develop algorithms for the three main issues in the diagnosis: discrimination between patients with AD or FTD and healthy controls (HC), differential diagnosis between behavioral FTD (bvFTD) and AD, and differential diagnosis between primary progressive aphasia (PPA) variants. Genetic algorithms, customized with K-Nearest Neighbor and BayesNet Naives as the fitness function, were developed and compared with Principal Component Analysis (PCA). K-fold cross validation within the same sample and external validation with ADNI-3 samples were performed. External validation was performed for the algorithms distinguishing AD and HC. Our study supports the use of FDG-PET imaging, which allowed a very high accuracy rate for the diagnosis of AD, FTD, and related disorders. Genetic algorithms identified the most meaningful features with the minimum set of features, which may be relevant for automated assessment of brain FDG-PET images. Overall, our study contributes to the development of an automated, and optimized diagnosis of neurodegenerative disorders using brain metabolism.

ACE2, TMPRSS2, and Furin variants and SARS-CoV-2 infection in Madrid, Spain.

It has been suggested that some individuals may present genetic susceptibility to SARS-CoV-2 infection, with particular research interest in variants of the ACE2 and TMPRSS2 genes, involved in viral penetration into cells, in different populations and geographic regions, although insufficient information is currently available. This study addresses the apparently reasonable hypothesis that variants of these genes may modulate viral infectivity, making some individuals more vulnerable than others. Through whole-exome sequencing, the frequency of exonic variants of the ACE2, TMPRSS2, and Furin genes was analyzed in relation to presence or absence of SARS-CoV-2 infection in a familial multiple sclerosis cohort including 120 individuals from Madrid. The ACE2 gene showed a low level of polymorphism, and none variant was significantly associated with SARS-CoV-2 infection. These variants have previously been detected in Italy. While TMPRSS2 is highly polymorphic, the variants found do not coincide with those described in other studies, with the exception of rs75603675, which may be associated with SARS-CoV-2 infection. The synonymous variants rs61735792 and rs61735794 showed a significant association with infection. Despite the limited number of patients with SARS-CoV-2 infection, some variants, especially in TMPRSS2, may be associated with COVID-19.

Cognitive Processes Underlying Verbal Fluency in Multiple Sclerosis.

Background: Verbal fluency (VF) has been associated with several cognitive functions, but the cognitive processes underlying verbal fluency deficits in Multiple Sclerosis (MS) are controversial. Further knowledge about VF could be useful in clinical practice, because these tasks are brief, applicable, and reliable in MS patients. In this study, we aimed to evaluate the cognitive processes related to VF and to develop machine-learning algorithms to predict those patients with cognitive deficits using only VF-derived scores. Methods: Two hundred participants with MS were enrolled and examined using a comprehensive neuropsychological battery, including semantic and phonemic fluencies. Automatic linear modeling was used to identify the neuropsychological test predictors of VF scores. Furthermore, machine-learning algorithms (support vector machines, random forest) were developed to predict those patients with cognitive deficits using only VF-derived scores. Results: Neuropsychological tests associated with attention-executive functioning, memory, and language were the main predictors of the different fluency scores. However, the importance of memory was greater in semantic fluency and clustering scores, and executive functioning in phonemic fluency and switching. Machine learning algorithms predicted general cognitive impairment and executive dysfunction, with F1-scores over 67-71%. Conclusions: VF was influenced by many other cognitive processes, mainly including attention-executive functioning, episodic memory, and language. Semantic fluency and clustering were more explained by memory function, while phonemic fluency and switching were more related to executive functioning. Our study supports that the multiple cognitive components underlying VF tasks in MS could serve for screening purposes and the detection of executive dysfunction.