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Article ; Online: Genetic-based patient stratification in Alzheimer's disease.

Hernández-Lorenzo, Laura / García-Gutiérrez, Fernando / Solbas-Casajús, Ana / Corrochano, Silvia / Matías-Guiu, Jordi A / Ayala, Jose L

Scientific reports

2024 Volume 14, Issue 1, Page(s) 9970

Abstract: 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: ... ...

Abstract	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.
MeSH term(s)	Alzheimer Disease/genetics ; Alzheimer Disease/diagnostic imaging ; Humans ; tau Proteins/genetics ; Apolipoproteins E/genetics ; Positron-Emission Tomography ; Male ; Female ; Aged ; Genetic Predisposition to Disease ; Amyloid beta-Protein Precursor/genetics ; Protein Interaction Maps/genetics ; Biomarkers ; Brain/diagnostic imaging ; Brain/pathology ; Brain/metabolism
Chemical Substances	tau Proteins ; Apolipoproteins E ; MAPT protein, human ; Amyloid beta-Protein Precursor ; Biomarkers ; ApoE protein, human ; APP protein, human
Language	English
Publishing date	2024-04-30
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-60707-1
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Article ; Online: FACEmemory®, an Innovative Online Platform for Episodic Memory Pre-Screening: Findings from the First 3,000 Participants.

Alegret, Montserrat / García-Gutiérrez, Fernando / Muñoz, Nathalia / Espinosa, Ana / Ortega, Gemma / Lleonart, Núria / Rodríguez, Isabel / Rosende-Roca, Maitee / Pytel, Vanesa / Cantero-Fortiz, Yahveth / Rentz, Dorene M / Marquié, Marta / Valero, Sergi / Ruiz, Agustín / Butler, Christopher / Boada, Mercè

Journal of Alzheimer's disease : JAD

2024 Volume 97, Issue 3, Page(s) 1173–1187

Abstract: Background: The FACEmemory® online platform comprises a complex memory test and sociodemographic, medical, and family questions. This is the first study of a completely self-administered memory test with voice recognition, pre-tested in a memory clinic, ...

Abstract	Background: The FACEmemory® online platform comprises a complex memory test and sociodemographic, medical, and family questions. This is the first study of a completely self-administered memory test with voice recognition, pre-tested in a memory clinic, sensitive to Alzheimer's disease, using information and communication technologies, and offered freely worldwide. Objective: To investigate the demographic and clinical variables associated with the total FACEmemory score, and to identify distinct patterns of memory performance on FACEmemory. Methods: Data from the first 3,000 subjects who completed the FACEmemory test were analyzed. Descriptive analyses were applied to demographic, FACEmemory, and medical and family variables; t-test and chi-square analyses were used to compare participants with preserved versus impaired performance on FACEmemory (cut-off = 32); multiple linear regression was used to identify variables that modulate FACEmemory performance; and machine learning techniques were applied to identify different memory patterns. Results: Participants had a mean age of 50.57 years and 13.65 years of schooling; 64.07% were women, and 82.10% reported memory complaints with worries. The group with impaired FACEmemory performance (20.40%) was older, had less schooling, and had a higher prevalence of hypertension, diabetes, dyslipidemia, and family history of neurodegenerative disease than the group with preserved performance. Age, schooling, sex, country, and completion of the medical and family history questionnaire were associated with the FACEmemory score. Finally, machine learning techniques identified four patterns of FACEmemory performance: normal, dysexecutive, storage, and completely impaired. Conclusions: FACEmemory is a promising tool for assessing memory in people with subjective memory complaints and for raising awareness about cognitive decline in the community.
MeSH term(s)	Humans ; Female ; Male ; Memory, Episodic ; Neurodegenerative Diseases ; Cognition ; Cognitive Dysfunction/psychology ; Alzheimer Disease/diagnosis ; Alzheimer Disease/epidemiology ; Alzheimer Disease/psychology ; Neuropsychological Tests
Language	English
Publishing date	2024-01-12
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1440127-7
ISSN	1875-8908 ; 1387-2877
ISSN (online)	1875-8908
ISSN	1387-2877
DOI	10.3233/JAD-230983
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Article ; Online: Biomarkers of Alzheimer's disease and neurodegeneration in dried blood spots-A new collection method for remote settings.

Huber, Hanna / Blennow, Kaj / Zetterberg, Henrik / Boada, Mercé / Jeromin, Andreas / Weninger, Haley / Nuñez-Llaves, Raul / Aguilera, Núria / Ramis, Maribel / Simrén, Joel / Nilsson, Johanna / Lantero-Rodriguez, Juan / Orellana, Adelina / García-Gutiérrez, Fernando / Morató, Xavier / Ashton, Nicholas J / Montoliu-Gaya, Laia

Alzheimer's & dementia : the journal of the Alzheimer's Association

2024 Volume 20, Issue 4, Page(s) 2340–2352

Abstract: Background: We aimed to evaluate the precision of Alzheimer's disease (AD) and neurodegeneration biomarker measurements from venous dried plasma spots (DPS: Methods: In a discovery (n = 154) and a validation cohort (n = 115), glial fibrillary acidic ... ...

Abstract	Background: We aimed to evaluate the precision of Alzheimer's disease (AD) and neurodegeneration biomarker measurements from venous dried plasma spots (DPS Methods: In a discovery (n = 154) and a validation cohort (n = 115), glial fibrillary acidic protein (GFAP); neurofilament light (NfL); amyloid beta (Aβ) 40, Aβ42; and phosphorylated tau (p-tau181 and p-tau217) were measured in paired DPS Results: All DPS Discussion: Our data suggest that measuring blood biomarkers related to AD pathology and neurodegeneration from DPS Highlights: A wide array of biomarkers related to Alzheimer's disease (AD) and neurodegeneration were detectable in dried plasma spots (DPS
MeSH term(s)	Humans ; Alzheimer Disease/diagnosis ; Amyloid beta-Peptides ; Plasma ; Amyloidogenic Proteins ; Biomarkers ; tau Proteins
Chemical Substances	Amyloid beta-Peptides ; Amyloidogenic Proteins ; Biomarkers ; tau Proteins
Language	English
Publishing date	2024-01-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	2211627-8
ISSN	1552-5279 ; 1552-5260
ISSN (online)	1552-5279
ISSN	1552-5260
DOI	10.1002/alz.13697
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Article ; Online: Genome-wide association study and polygenic risk scores of retinal thickness across the cognitive continuum: data from the NORFACE cohort.

Alzheimer's research & therapy

2024 Volume 16, Issue 1, Page(s) 38

Abstract: Background: Several studies have reported a relationship between retinal thickness and dementia. Therefore, optical coherence tomography (OCT) has been proposed as an early diagnosis method for Alzheimer's disease (AD). In this study, we performed a ... ...

Abstract	Background: Several studies have reported a relationship between retinal thickness and dementia. Therefore, optical coherence tomography (OCT) has been proposed as an early diagnosis method for Alzheimer's disease (AD). In this study, we performed a genome-wide association study (GWAS) aimed at identifying genes associated with retinal nerve fiber layer (RNFL) and ganglion cell inner plexiform layer (GCIPL) thickness assessed by OCT and exploring the relationships between the spectrum of cognitive decline (including AD and non-AD cases) and retinal thickness. Methods: RNFL and GCIPL thickness at the macula were determined using two different OCT devices (Triton and Maestro). These determinations were tested for association with common single nucleotide polymorphism (SNPs) using adjusted linear regression models and combined using meta-analysis methods. Polygenic risk scores (PRSs) for retinal thickness and AD were generated. Results: Several genetic loci affecting retinal thickness were identified across the genome in accordance with previous reports. The genetic overlap between retinal thickness and dementia, however, was weak and limited to the GCIPL layer; only those observable with all-type dementia cases were considered. Conclusions: Our study does not support the existence of a genetic link between dementia and retinal thickness.
MeSH term(s)	Humans ; Genome-Wide Association Study ; Genetic Risk Score ; Nerve Fibers ; Tomography, Optical Coherence/methods ; Alzheimer Disease/diagnostic imaging ; Alzheimer Disease/genetics ; Alzheimer Disease/complications ; Cognition
Language	English
Publishing date	2024-02-16
Publishing country	England
Document type	Meta-Analysis ; Journal Article
ZDB-ID	2506521-X
ISSN	1758-9193 ; 1758-9193
ISSN (online)	1758-9193
ISSN	1758-9193
DOI	10.1186/s13195-024-01398-8
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Article ; Online: Unveiling the sound of the cognitive status: Machine Learning-based speech analysis in the Alzheimer's disease spectrum.

Alzheimer's research & therapy

2024 Volume 16, Issue 1, Page(s) 26

Abstract: Background: Advancement in screening tools accessible to the general population for the early detection of Alzheimer's disease (AD) and prediction of its progression is essential for achieving timely therapeutic interventions and conducting ... ...

Abstract	Background: Advancement in screening tools accessible to the general population for the early detection of Alzheimer's disease (AD) and prediction of its progression is essential for achieving timely therapeutic interventions and conducting decentralized clinical trials. This study delves into the application of Machine Learning (ML) techniques by leveraging paralinguistic features extracted directly from a brief spontaneous speech (SS) protocol. We aimed to explore the capability of ML techniques to discriminate between different degrees of cognitive impairment based on SS. Furthermore, for the first time, this study investigates the relationship between paralinguistic features from SS and cognitive function within the AD spectrum. Methods: Physical-acoustic features were extracted from voice recordings of patients evaluated in a memory unit who underwent a SS protocol. We implemented several ML models evaluated via cross-validation to identify individuals without cognitive impairment (subjective cognitive decline, SCD), with mild cognitive impairment (MCI), and with dementia due to AD (ADD). In addition, we established models capable of predicting cognitive domain performance based on a comprehensive neuropsychological battery from Fundació Ace (NBACE) using SS-derived information. Results: The results of this study showed that, based on a paralinguistic analysis of sound, it is possible to identify individuals with ADD (F1 = 0.92) and MCI (F1 = 0.84). Furthermore, our models, based on physical acoustic information, exhibited correlations greater than 0.5 for predicting the cognitive domains of attention, memory, executive functions, language, and visuospatial ability. Conclusions: In this study, we show the potential of a brief and cost-effective SS protocol in distinguishing between different degrees of cognitive impairment and forecasting performance in cognitive domains commonly affected within the AD spectrum. Our results demonstrate a high correspondence with protocols traditionally used to assess cognitive function. Overall, it opens up novel prospects for developing screening tools and remote disease monitoring.
MeSH term(s)	Humans ; Alzheimer Disease/diagnosis ; Alzheimer Disease/psychology ; Speech ; Neuropsychological Tests ; Cognitive Dysfunction/diagnosis ; Cognitive Dysfunction/psychology ; Cognition ; Machine Learning ; Disease Progression
Language	English
Publishing date	2024-02-02
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2506521-X
ISSN	1758-9193 ; 1758-9193
ISSN (online)	1758-9193
ISSN	1758-9193
DOI	10.1186/s13195-024-01394-y
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Article ; Online: GA-MADRID: design and validation of a machine learning tool for the diagnosis of Alzheimer's disease and frontotemporal dementia using genetic algorithms.

García-Gutierrez, Fernando / Díaz-Álvarez, Josefa / Matias-Guiu, Jordi A / Pytel, Vanesa / Matías-Guiu, Jorge / Cabrera-Martín, María Nieves / Ayala, José L

Medical & biological engineering & computing

2022 Volume 60, Issue 9, Page(s) 2737–2756

Abstract: Artificial Intelligence aids early diagnosis and development of new treatments, which is key to slow down the progress of the diseases, which to date have no cure. The patients' evaluation is carried out through diagnostic techniques such as clinical ... ...

Abstract	Artificial Intelligence aids early diagnosis and development of new treatments, which is key to slow down the progress of the diseases, which to date have no cure. The patients' evaluation is carried out through diagnostic techniques such as clinical assessments neuroimaging techniques, which provide high-dimensionality data. In this work, a computational tool is presented that deals with the data provided by the clinical diagnostic techniques. This is a Python-based framework implemented with a modular design and fully extendable. It integrates (i) data processing and management of missing values and outliers; (ii) implementation of an evolutionary feature engineering approach, developed as a Python package, called PyWinEA using Mono-objective and Multi-objetive Genetic Algorithms (NSGAII); (iii) a module for designing predictive models based on a wide range of machine learning algorithms; (iv) a multiclass decision stage based on evolutionary grammars and Bayesian networks. Developed under the eXplainable Artificial Intelligence and open science perspective, this framework provides promising advances and opens the door to the understanding of neurodegenerative diseases from a data-centric point of view. In this work, we have successfully evaluated the potential of the framework for early and automated diagnosis with neuroimages and neurocognitive assessments from patients with Alzheimer's disease (AD) and frontotemporal dementia (FTD).
MeSH term(s)	Algorithms ; Alzheimer Disease/diagnosis ; Artificial Intelligence ; Bayes Theorem ; Frontotemporal Dementia/diagnosis ; Frontotemporal Dementia/genetics ; Humans ; Machine Learning
Language	English
Publishing date	2022-07-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	282327-5
ISSN	1741-0444 ; 0025-696X ; 0140-0118
ISSN (online)	1741-0444
ISSN	0025-696X ; 0140-0118
DOI	10.1007/s11517-022-02630-z
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Article: Genetic Algorithms for Optimized Diagnosis of Alzheimer's Disease and Frontotemporal Dementia Using Fluorodeoxyglucose Positron Emission Tomography Imaging.

Díaz-Álvarez, Josefa / Matias-Guiu, Jordi A / Cabrera-Martín, María Nieves / Pytel, Vanesa / Segovia-Ríos, Ignacio / García-Gutiérrez, Fernando / Hernández-Lorenzo, Laura / Matias-Guiu, Jorge / Carreras, José Luis / Ayala, José L

Frontiers in aging neuroscience

2022 Volume 13, Page(s) 708932

Abstract: 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 ... ...

Abstract	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
Language	English
Publishing date	2022-02-03
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2558898-9
ISSN	1663-4365
ISSN	1663-4365
DOI	10.3389/fnagi.2021.708932
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Article: Harnessing acoustic speech parameters to decipher amyloid status in individuals with mild cognitive impairment.

Frontiers in neuroscience

2023 Volume 17, Page(s) 1221401

Abstract: Alzheimer's disease (AD) is a neurodegenerative condition characterized by a gradual decline in cognitive functions. Currently, there are no effective treatments for AD, underscoring the importance of identifying individuals in the preclinical stages of ... ...

Abstract	Alzheimer's disease (AD) is a neurodegenerative condition characterized by a gradual decline in cognitive functions. Currently, there are no effective treatments for AD, underscoring the importance of identifying individuals in the preclinical stages of mild cognitive impairment (MCI) to enable early interventions. Among the neuropathological events associated with the onset of the disease is the accumulation of amyloid protein in the brain, which correlates with decreased levels of A
Language	English
Publishing date	2023-09-07
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2411902-7
ISSN	1662-453X ; 1662-4548
ISSN (online)	1662-453X
ISSN	1662-4548
DOI	10.3389/fnins.2023.1221401
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Article ; Online: Diagnosis of Alzheimer's disease and behavioural variant frontotemporal dementia with machine learning-aided neuropsychological assessment using feature engineering and genetic algorithms.

Garcia-Gutierrez, Fernando / Delgado-Alvarez, Alfonso / Delgado-Alonso, Cristina / Díaz-Álvarez, Josefa / Pytel, Vanesa / Valles-Salgado, Maria / Gil, María Jose / Hernández-Lorenzo, Laura / Matías-Guiu, Jorge / Ayala, José L / Matias-Guiu, Jordi A

International journal of geriatric psychiatry

2021 Volume 37, Issue 2

Abstract: 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 ... ...

Abstract	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.
Language	English
Publishing date	2021-12-11
Publishing country	England
Document type	Journal Article
ZDB-ID	806736-3
ISSN	1099-1166 ; 0885-6230
ISSN (online)	1099-1166
ISSN	0885-6230
DOI	10.1002/gps.5667
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Article ; Online: Macular vessel density in the superficial plexus is not a proxy of cerebrovascular damage in non-demented individuals: data from the NORFACE cohort.

García-Sánchez, Ainhoa / Sotolongo-Grau, Oscar / Tartari, Juan Pablo / Sanabria, Ángela / Esteban-De Antonio, Ester / Pérez-Cordón, Alba / Alegret, Montserrat / Pytel, Vanesa / Martínez, Joan / Aguilera, Núria / de Rojas, Itziar / Cano, Amanda / García-González, Pablo / Puerta, Raquel / Olivé, Clàudia / Capdevila, Maria / García-Gutiérrez, Fernando / Vivas, Assumpta / Gómez-Chiari, Marta /

Giménez, Juan / Tejero, Miguel Ángel / Castilla-Martí, Miguel / Castilla-Martí, Luis / Tárraga, Lluís / Valero, Sergi / Ruiz, Agustín / Boada, Mercè / Marquié, Marta

Alzheimer's research & therapy

2024 Volume 16, Issue 1, Page(s) 42

Abstract: Introduction: Optical coherence tomography angiography (OCT-A) is a novel tool that allows the detection of retinal vascular changes. We investigated the association of macular vessel density (VD) in the superficial plexus assessed by OCT-A with ... ...

Abstract	Introduction: Optical coherence tomography angiography (OCT-A) is a novel tool that allows the detection of retinal vascular changes. We investigated the association of macular vessel density (VD) in the superficial plexus assessed by OCT-A with measures of cerebrovascular pathology and atrophy quantified by brain magnetic resonance imaging (MRI) in non-demented individuals. Methods: Clinical, demographical, OCT-A, and brain MRI data from non-demented research participants were included. We analyzed the association of regional macular VD with brain vascular burden using the Fazekas scale assessed in a logistic regression analysis, and the volume of white matter hyperintensities (WMH) assessed in a multiple linear regression analysis. We also explored the associations of macular VD with hippocampal volume, ventricle volume and Alzheimer disease cortical signature (ADCS) thickness assessed in multiple linear regression analyses. All analyses were adjusted for age, sex, syndromic diagnosis and cardiovascular variables. Results: The study cohort comprised 188 participants: 89 with subjective cognitive decline and 99 with mild cognitive impairment. No significant association of regional macular VD with the Fazekas categories (all, p > 0.111) and WMH volume (all, p > 0.051) were detected. VD in the nasal quadrant was associated to hippocampal volume (p = 0.007), but no other associations of macular VD with brain atrophy measures were detected (all, p > 0.05). Discussion: Retinal vascular measures were not a proxy of cerebrovascular damage in non-demented individuals, while VD in the nasal quadrant was associated with hippocampal atrophy independently of the amyloid status.
MeSH term(s)	Humans ; Fluorescein Angiography/methods ; Retinal Vessels/diagnostic imaging ; Retinal Vessels/pathology ; Atrophy/pathology ; Tomography, Optical Coherence/methods
Language	English
Publishing date	2024-02-20
Publishing country	England
Document type	Journal Article
ZDB-ID	2506521-X
ISSN	1758-9193 ; 1758-9193
ISSN (online)	1758-9193
ISSN	1758-9193
DOI	10.1186/s13195-024-01408-9
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