Scientific publications
This section contains all scientific publications produced by the project.
See all scientific publications
DYNAMITE: Integrating Archetypal Analysis and Process Mining for Interpretable Disease Progression Modelling
#Open access #Journal
Isotta Trescato, Erica Tavazzi, Martina Vettoretti, Roberto Gatta, Rosario Vasta, Adriano Chio, Barbara Di Camillo
DYNAMITE, an acronym for DYNamic Archetypal analysis for MIning disease TrajEctories, is a new methodology developed specifically to model disease progression by exploiting information available in longitudinal clinical datasets. First, archetypal analysis is applied to data organised in matrix form, with the aim of finding extreme and representative disease states (archetypes) linked to the original data through convex coefficients. Then, each original observation is associated with a single archetype based on their similarity; finally, an event log is created encoding the progression of disease states for each patient in terms of archetype states. In the last stage of the procedure, archetypal analysis is coupled with process mining, which allows the event log archetypes to be visualised graphically as sequences of disease states, allowing the clinical trajectories of patients to be extracted and examined.
iDPP@CLEF 2024 - Participants' repositories for the Intelligent Disease Prediction Progression Challenge
#Workshop
Birolo, Giovanni, Bosoni, Pietro, Faggioli, Guglielmo, Aidos, Helena, Bergamaschi, Roberto, Cavalla, Paola, Chiò, Adriano, Dagliati, arianna, de Carvalho, Mamede, Di Nunzio, Giorgio Maria, Fariselli, Pietro, Garcìa Dominguez, Jose Manuel, Gromicho, Marta, Guazzo, Alessandro, Longato, Enrico, Madeira, Sara C., Manera, Umberto, Marchesin, Stefano, Menotti, Laura, Silvello, Gianmaria, Tavazzi, Eleonora, Tavazzi, Erica, Trescato, Isotta, Vettoretti, Martina, Di Camillo, Barbara, Ferro, Nicola
iDPP@CLEF 2024 (Intelligent Disease Progression Prediction at CLEF) is a challenge organized by the BRAINTEASER Horizon 2020 project and co-located with CLEF 2024 (Conference and Labs of the Evaluation Forum).
Deep Temporal Consensus Clustering for Patient Stratification in Amyotrophic Lateral Sclerosis
#Workshop
Miguel Pego Roque, Andreia S Martins, Marta Gromicho, Mamede de Carvalho, Sara C Madeira, Pedro Tomás, Helena Aidos
Amyotrophic Lateral Sclerosis (ALS) is a fast-acting neurodegenerative disease, characterized by loss of muscle movement and heterogeneity in disease evolution. This poses a challenge in predicting the best time for therapy administration. Here, we propose Deep Temporal Consensus Clustering (DTCC), a stratification method to uncover patient groups with similar disease progression. Using only the initial 6-month follow-up period, DTCC uncovered five clusters that were evaluated in terms of disease evolution and time-to-event. For three critical events (non-invasive ventilation, gastrostomy and death) the attained groups show distinct 10- year progressions, validating the approach.
Parallel intersection counting on shared-memory multiprocessors and GPUs
#Open access #Journal
Moreno Marzolla, Giovanni Birolo, Gabriele D’Angelo, Piero Fariselli
Computing intersections among sets of one-dimensional intervals is an ubiquitous problem in computational geometry with important applications in bioinformatics, where the size of typical inputs is large and it is therefore important to use efficient algorithms. In this paper we propose a parallel algorithm for the 1D intersection-counting problem, that is, the problem of counting the number of intersections between each interval in a given set A and every interval in a set B. Our algorithm is suitable for shared-memory architectures (e.g., multicore CPUs) and GPUs. The algorithm is work-efficient because it performs the same amount of work as the best serial algorithm for this kind of problem. Our algorithm has been implemented in C++ using the Thrust parallel algorithms library, enabling the generation of optimized programs for multicore CPUs and GPUs from the same source code. The performance of our algorithm is evaluated on synthetic and real datasets, showing good scalability on different generations of hardware.
Predicting clinical events characterizing the progression of amyotrophic lateral sclerosis via machine learning approaches using routine visits data: a feasibility study
#Open access #Peer-reviewed #Journal
Alessandro Guazzo, Michele Atzeni, Elena Idi, Isotta Trescato, Erica Tavazzi, Enrico Longato, Umberto Manera, Adriano Chiò, Marta Gromicho, Ines Alves, Mamede de Carvalho, Martina Vettoretti, Barbara Di Camillo
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that results in death within a short time span (3-5 years). One of the major challenges in treating ALS is its highly heterogeneous disease progression and the lack of effective prognostic tools to forecast it. The main aim of this study was, then, to test the feasibility of predicting relevant clinical outcomes that characterize the progression of ALS with a two-year prediction horizon via artificial intelligence techniques using routine visits data.
Cognitive reserve as a modulator of cognitive decline and of behavioral symptoms in patients with amyotrophic lateral sclerosis
#Open access #Peer-reviewed #Journal
Simão S, Oliveira Santos M, Gromicho M, Pavão Martins I, De Carvalho M
Amyotrophic lateral sclerosis (ALS) has heterogeneous manifestations ranging from motor neuron degeneration to cognitive and behavioral impairment. This study aims to clarify the interactions between cognition and behavioral symptoms with relevant disease predictors and with cognitive reserve (CR), quantified through education, physical activity, and occupation proxies.
C9orf72 gene repeat expansion phenotype profile of motor neurone disease in Portugal.
#Open access #Peer-reviewed #Journal
Silva CS, Gromicho M, Simão S, Pronto-Laborinho AC, Alves I, Pinto S, Santos MO, de Carvalho M
C9orf72 gene repeat expansion (C9RE) is the most frequent gene variant associated with amyotrophic lateral sclerosis (ALS). We aimed to study the phenotype of motor neurone disease (MND) patients with C9RE in a Portuguese cohort.
Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns
#Open access # Peer-reviewed #Journal
Daniela M. Amaral, Diogo F Soares, Marta Gromicho, Mamede de Carvalho, Sara C Madeira, Pedro Tomás, Helena Aidos
Understanding how different groups of patients experience disease progression is essential for improving care and guiding treatment decisions. This study introduces a new data-driven approach, ClusTric, which uses advanced clustering methods to uncover complex patterns in how diseases like amyotrophic lateral sclerosis (ALS) progress over time.
BRAINTEASER ALS and MS Datasets
#Workshop
Faggioli, Guglielmo, Marchesin, Stefano, Menotti, Laura, Aidos, Helena, Bergamaschi, Roberto, Birolo, Giovanni, Bosoni, Pietro, Cavalla, Paola, Chiò, Adriano, Dagliati, Arianna, de Carvalho, Mamede, Di Nunzio, Giorgio Maria, Fariselli, Piero, García Dominguez, Jose Manuel, Gromicho, Marta, Guazzo, Alessandro, Longato, Enrico, Madeira, Sara C., Manera, Umberto, Silvello, Gianmaria, Tavazzi, Eleonora, Tavazzi, Erica, Trescato, Isotta, Vettoretti, Martina, Di Camillo, Barbara, Ferro, Nicola
BRAINTEASER (Bringing Artificial Intelligence home for a better care of amyotrophic lateral sclerosis and multiple sclerosis) is a data science project that seeks to exploit the value of big data, including those related to health, lifestyle habits, and environment, to support patients with Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) and their clinicians. Taking advantage of cost-efficient sensors and apps, BRAINTEASER will integrate large, clinical datasets that host both patient-generated and environmental data.
The BrainTeaser Ontology for ALS and MS Clinical Data
#Workshop
Guglielmo Faggioli, Stefano Marchesin, Laura Menotti, Giorgio Maria Di Nunzio, Gianmaria Silvello, Nicola Ferro
This webpage describes the design and development of the BrainTeaser Ontology (BTO) whose purpose is to jointly model both Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) Clinical Data.
Exploring the Impact of Environmental Pollutants on Multiple Sclerosis Progression
#Conference
Elena Marinello, Erica Tavazzi, Enrico Longato, Pietro Bosoni, Arianna Dagliati, Mahin Vazifehdan, Riccardo Bellazzi, Isotta Trescato, Alessandro Guazzo, Martina Vettoretti, Eleonora Tavazzi, Lara Ahmad, Roberto Bergamaschi, Paola Cavalla, Umberto Manera, Adriano Chiò, Barbara Di Camillo
Multiple Sclerosis (MS) is a chronic autoimmune and inflammatory neurological disorder characterised by episodes of symptom exacerbation, known as relapses. In this study, we investigate the role of environmental factors in relapse occurrence among MS patients, using data from the H2020 BRAINTEASER project.
iDPP@CLEF 2024: The Intelligent Disease Progression Prediction Challenge
#Workshop
Aidos, H., Bergamaschi, S., Cavalla, P., Chiò, A., Dagliati, A., Di Camillo, B., de Carvalho, M., Ferro, N., Fariselli, P., García Dominguez, J. M., Madeira, S. C., and Tavazzi, E. (2024).
iDPP@CLEF 2024 focused on prospective patient data for ALS collected via a dedicated app developed by the BRAINTEASER project and sensor data in the context of clinical trials in Turin, Pavia, Lisbon, and Madrid. For MS, iDPP@CLEF 2024 will rely on retrospective patient data complemented with environmental and pollution data from clinical institutions in Pavia and Turin.
Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2024
#Workshop
Birolo, G., Bosoni, P., Faggioli, G., Aidos, H., Bergamaschi, R., Cavalla, P., Chiò, A., Dagliati, A., de Carvalho, M., Di Nunzio, G. M., Fariselli, P., Garcia Dominguez, J. M., Gromicho, M., Guazzo, A., Longato, E., Madeira, S., Manera, U., Marchesin, S., Menotti, L., Silvello, G., Tavazzi, E., Tavazzi, E., Trescato, I., Vettoretti, M., Di Camillo, B., and Ferro, N. (2024).
In this edition, we extended the MS dataset of iDPP@CLEF 2023 with environmental data. Furthermore, we introduced two new ALS tasks, focused on predicting the progression of the disease using data obtained from wearable devices, making it the first iDPP edition that uses prospective data collected directly from the patients involved in the BRAINTEASER project.
Overview of iDPP@CLEF 2024: The Intelligent Disease Progression Prediction Challenge
#Workshop
Birolo, G., Bosoni, P., Faggioli, G., Aidos, H., Bergamaschi, R., Cavalla, P., Chiò, A., Dagliati, A., de Carvalho, M., Di Nunzio, G. M., Fariselli, P., Garcia Dominguez, J. M., Gromicho, M., Guazzo, A., Longato, E., Madeira, S., Manera, U., Marchesin, S., Menotti, L., Silvello, G., Tavazzi, E., Tavazzi, E., Trescato, I., Vettoretti, M., Di Camillo, B., and Ferro, N. (2024).
iDPP@CLEF 2024 continues the work of the previous editions, iDPP@CLEF 2022 and 2023. The 2022 edition focused on predicting ALS progression and utilizing explainable AI. The 2023 edition expanded on this by including environmental data and introduced a new task for predicting MS progression. This edition extends the MS dataset with environmental data and introduces two new ALS tasks aimed at predicting disease progression using data from wearable devices. This marks the first iDPP edition to utilize prospective data directly collected from patients involved in the BRAINTEASER project.
Predicting the functional rating scale and self-assessment status of ALS patients with sensor data
#Workshop
Andreia S Martins, Daniela M Amaral, Eduardo N Castanho, Diogo F Soares, Ruben Branco, Sara C Madeira, Helena Aidos
iDPP @ CLEF 2024 aimed to develop novel methodologies for predicting ALS disease progression, enabling the community to combine efforts and improve current prognostic methods. This report discusses evaluation of the impact of sensor data on improving the prediction of ALSFRS-R scores.
Using Wearable and Environmental Data to Improve the Prediction of Amyotrophic Lateral Sclerosis and Multiple Sclerosis Progression: an Explorative Study
#Workshop
Elena Marinello, Alessandro Guazzo, Enrico Longato, Erica Tavazzi, Isotta Trescato, Martina Vettoretti, and Barbara Di Camillo.
Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) are chronic diseases with a severe impact on patients’ lives. Both diseases create significant psychological and economic burdens due to alternating acute phases requiring hospital and home care. One possible solution could be the employment of sensor data to develop predictive models that can assist clinicians in making treatment and therapeutic decisions. In the context of the iDPP@CLEF 2024 challenge, this work aimed to develop and compare different machine-learning approaches for predicting the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) scores in ALS patients, and relapses in MS patients, using wearable and environmental data, respectively.
Intermediate HTT CAG repeats worsen disease severity in amyotrophic lateral sclerosis
#Open access #Peer-reviewed #Journal
Maurizio Grassano, Antonio Canosa, Sandra D’Alfonso, Lucia Corrado, Giorgia Brodini, Emanuele Koumantakis, Paolo Cugnasco, Umberto Manera, Rosario Vasta, Francesca Palumbo, Letizia Mazzini, Salvatore Gallone, Cristina Moglia, Ramita Dewan, Ruth Chia, Jinhui Ding, Clifton Dalgard, Raphael J Gibbs, Sonja Scholz, Andrea Calvo, Bryan Traynor, Adriano Chio
Recent research has indicated a connection between amyotrophic lateral sclerosis (ALS) and Huntington’s disease (HD), an inherited neurological condition caused by a trinucleotide CAG repeat expansion within exon 1 of the Huntingtin gene (HTT; MIM:613004). The same pathogenic CAG repeat expansions (40 or more CAG repeats) observed in patients with HD have been identified in patients with frontotemporal dementia (FTD)/ALS. Similarly, non-pathogenic intermediate-length CAG repeats in the ATXN2 gene are a well-established factor associated with increased ALS risk and faster disease progression Given these observations, we investigated the impact of intermediate HTT alleles on survival in two cohorts of patients diagnosed with ALS.
Assessing disease progression in ALS: prognostic subgroups and outliers
#Open access #Peer-reviewed #Journal
Inês Alves, Marta Gromicho, Miguel Oliveira Santos, Susana Pinto, Mamede de Carvalho
The rate of disease progression, measured by the decline of ALS Functional Rating Scale-Revised (ALSFRS-R) from symptom onset to diagnosis (ΔFS) is a well-established prognostic biomarker for predicting survival. Objectives: This study aims to categorize a large patient cohort based on the initial ΔFS and subsequently investigate survival deviations from the expected prognosis defined by ΔFS. Our study reaffirms ΔFS as a prognostic biomarker for ALS. We disclosed outliers defying anticipated patterns. The observed shift in progression categories underscores the non-linear nature of disease progression. Genetic and unknown biological reasons may explain these deviations. Further research is needed to fully understand modulation of ALS survival.
Automated Pipeline for Denoising, Missing Data Processing, and Feature Extraction for Signals acquired via Wearable Devices in Multiple Sclerosis and Amyotrophic Lateral Sclerosis Applications
#Open access #Peer-reviewed #Journal
Cossu L, Cappon G, Facchinetti A
The incorporation of health-related sensors in wearable devices has increased their use as essential monitoring tools for a wide range of clinical applications. However, the signals obtained from these devices often present challenges such as artifacts, spikes, high-frequency noise, and data gaps, which impede their direct exploitation. Additionally, clinically relevant features are not always readily available. This problem is particularly critical within the H2020 BRAINTEASER project, funded by the European Community, which aims at developing models for the progression of Multiple Sclerosis (MS) and Amyotrophic Lateral Sclerosis (ALS) using data from wearable devices. The performance and effectiveness of the proposed automated pipeline have been evaluated through pivotal beta testing sessions, which demonstrated the ability of the pipeline to improve the data quality and extract features from the data. Further clinical validation of the extracted features will be performed in the upcoming steps of the BRAINTEASER project.
Adaptive and self-learning Bayesian filtering algorithm to statistically characterize and improve signal-to-noise ratio of heart-rate data in wearable devices
#Open access #Peer-reviewed #Journal
Cossu L, Cappon G, Facchinetti A
The use of wearable sensors to monitor vital signs is increasingly important in assessing individual health. However, their accuracy often falls short of that of dedicated medical devices, limiting their usefulness in a clinical setting. This study introduces a new Bayesian filtering (BF) algorithm that is designed to learn the statistical characteristics of signal and noise, allowing for optimal smoothing. The results show that BF accurately captures SNR variability, reducing the root mean square error from 2.84 bpm to 1.21 bpm and the mean absolute relative error from 3.46% to 1.36%. These findings highlight the potential of BF as a preprocessing tool to enhance signal quality from wearable sensors, particularly in HR data, thereby expanding their applications in clinical and research settings.
An extensible and unifying approach to retrospective clinical data modeling: the BrainTeaser Ontology
#Open access #Peer-reviewed #Journal
Faggioli, G., Menotti, L., Marchesin, S., Chiò, A., Dagliati, A., de Carvalho, M., Gromicho, M., Manera, U., Tavazzi, E., Di Nunzio, G. M., Silvello, G., and Ferro, N.
Automatic disease progression prediction models require large amounts of training data, which are seldom available, especially when it comes to rare diseases. A possible solution is to integrate data from different medical centres. Nevertheless, various centres often follow diverse data collection procedures and assign different semantics to collected data. Ontologies, used as schemas for interoperable knowledge bases, represent a state-of-the-art solution to homologate the semantics and foster data integration from various sources. This work presents the BrainTeaser Ontology (BTO), an ontology that models the clinical data associated with two brain-related rare diseases (ALS and MS) in a comprehensive and modular manner.
High Frequency of Cognitive and Behavioral Impairment in Amyotrophic Lateral Sclerosis Patients with SOD1 Pathogenic Variants
#Open access #Peer-reviewed #Journal
Calvo A, Moglia C, Canosa A, Manera U, Vasta R, Grassano M, Daviddi M, De Mattei F, Matteoni E, Gallone S, Brunetti M, Sbaiz L, Cabras S, Peotta L, Palumbo F, Iazzolino B, Mora G, Chiò A
While the cognitive-behavioral characteristics of amyotrophic lateral sclerosis (ALS) patients carrying C9orf72 pathological repeat expansion have been extensively studied, our understanding of those carrying SOD1 variants is mostly based on case reports. The aim of this paper is to extensively explore the cognitive-behavioral characteristics of a cohort of ALS patients carrying pathogenetic variants of SOD1 gene, comparing them to patients without pathogenetic variants of 46 ALS-related genes (wild-type [WT]-ALS) and healthy controls.
Disentangling the relationship between social cognition, executive functions and behaviour changes in amyotrophic lateral sclerosis.
#Open access #Peer-reviewed #Journal
Palumbo F, Iazzolino B, Callegaro S, Canosa A, Manera U, Vasta R, Grassano M, Matteoni E, Cabras S, Pellegrino G, Salamone P, Peotta L, Casale F, Fuda G, Moglia C, Chio A, Calvo A.
Social cognition (SC) deficits are included in the amyotrophic lateral sclerosis-frontotemporal spectrum disorder (ALS-FTDS) revised diagnostic criteria. However, the impact of SC assessment on cognitive classification and the cognitive–behavioural correlates of SC remain unclear. This cross-sectional study aimed to assess the impact of SC assessment on ALS-FTDS categorisation and explore the relationship of SC with executive functions (EF) and behaviour changes in a cohort of ALS patients.
Creatine kinase and respiratory decline in amyotrophic lateral sclerosis
#Open access #Peer-reviewed #Journal
Correia JP, Gromicho M, Pronto-Laborinho AC, Oliveira Santos M, de Carvalho M.
Respiratory problems are a key feature of amyotrophic lateral sclerosis (ALS). Elevated creatine kinase (CK) levels have been observed in many ALS patients, but their connection to disease progression is unclear. This study investigates whether CK can predict respiratory decline in ALS. Researchers analyzed data from 319 patients, including CK levels, respiratory function, and clinical outcomes like survival and time to non-invasive ventilation (NIV).
MUSE-XAE: MUtational Signature Extraction with eXplainable AutoEncoder enhances tumour types classification
#Open access #Peer-reviewed #Journal
Corrado Pancotti, Cesare Rollo, Francesco Codicè , Giovanni Birolo, Piero Fariselli, Tiziana Sanavia
Mutational signatures are a critical component in deciphering the genetic alterations that underlie cancer development and have become a valuable resource to understand the genomic changes during tumorigenesis. Therefore, it is essential to employ precise and accurate methods for their extraction to ensure that the underlying patterns are reliably identified and can be effectively utilized in new strategies for diagnosis, prognosis, and treatment of cancer patients.
Upper motor neuron signs in primary lateral sclerosis and hereditary spastic paraplegia
#Open access #Journal
Santos Silva C, Correia Rodrigues C, Fortuna Baptista M, Oliveira Santos M, Gromicho M, Carvalho V, Correia Guedes L, de Carvalho M
The frequency and distribution of upper motor neuron (UMN) signs in primary lateral sclerosis (PLS) are unknown. We aimed to study the spectrum of UMN signs in PLS and compare it with hereditary spastic paraplegia (HSP). We retrospectively analyzed the frequency of different UMN signs, including hyperreflexia (limbs and jaw), limb and tongue spasticity, Babinski, and Hoffman signs, in PLS patients at first observation and compared this respect to onset region and symptom duration. We also compared PLS versus HSP patients.
SYNDSURV: A simple framework for survival analysis with data distributed across multiple institutions
#Open access #Peer-reviewed #Journal
Cesare Rollo, Corrado Pancotti, Giovanni Birolo, Ivan Rossi, Tiziana Sanavia, Piero Fariselli
Data sharing among different institutions represents one of the major challenges in developing distributed machine learning approaches. Federated learning is a possible solution, but requires fast communications and flawless security. Here, we propose SYNDSURV (SYNthetic Distributed SURVival), an alternative approach that simplifies the current state-of-the-art paradigm by allowing different centres to generate local simulated instances from real data and then gather them into a centralised hub, where an Artificial Intelligence (AI) model can learn in a standard way. The main advantage of this procedure is that it is model-agnostic, therefore prediction models can be directly applied in distributed applications without requiring particular adaptations as the current federated approaches do.
Cognitive and Behavioral Features of Patients With Amyotrophic Lateral Sclerosis Who Are Carriers of the TARDBP Pathogenic Variant
#Open access #Peer-reviewed #Journal
Moglia C, Calvo A, Canosa A, Manera U, Vasta R, Di Pede F, Daviddi M, Matteoni E, Brunetti M, Sbaiz L, Cabras S, Gallone S, Grassano M, Peotta L, Palumbo F, Mora G, Iazzolino B, Chio A
This study focuses on understanding cognitive and behavioral changes in patients with amyotrophic lateral sclerosis (ALS) who carry specific genetic variants in the TARDBP gene. While these patients are believed to be more likely to experience cognitive issues, no systematic studies have explored this in detail. This research aimed to provide a comprehensive overview of the cognitive and behavioral traits of TARDBP ALS patients, using data from individuals followed at a specialised ALS referral center.
Calculated Maximal Volume Ventilation (cMVV) as a Marker of Early Respiratory Failure in Amyotrophic Lateral Sclerosis (ALS)
#Symposium
Manera U, Torrieri MC, Moglia C, Canosa A, Vasta R, Palumbo F, Matteoni E, Cabras S, Grassano M, Bombaci A, Mattei A, Bellocchia M, Tabbia G, Ribolla F, Chiò A, Calvo A
Understanding respiratory failure in amyotrophic lateral sclerosis (ALS) is complex because it affects patients in different ways. One test, called maximal voluntary ventilation (MVV), shows potential for detecting and monitoring early breathing changes linked to neuromuscular disorders. This study examined whether a calculated version of MVV (cMVV) could help predict how ALS progresses, using data from patients in the Piemonte and Valle d’Aosta ALS registry (PARALS).
Impact of diabetes mellitus on the respiratory function of amyotrophic lateral sclerosis patients
#Open access # Peer-reviewed #Journal
Pinto S, Oliveira Santos M, Gromicho M, Swash M, de Carvalho M
Breathing problems and related complications are the leading cause of death in people with ALS. This study aimed to understand how diabetes might impact breathing function in ALS patients.
Longitudinal Missing Data Imputation for Predicting Disability Stage of Patients with Multiple Sclerosis
#Conference
Mahin Vazifehdan, Pietro Bosoni, Daniele Pala, Eleonora Tavazzi, Roberto Bergamaschi, Riccardo Bellazzi and Arianna Dagliati
Multiple Sclerosis (MS) is a chronic disease characterized by progressive or alternate impairment of neurological functions (motor, sensory, visual, and cognitive). Predicting disease progression with a probabilistic and time-dependent approach might help in suggesting interventions that can delay the progression of the disease. This study aimed at i) exploring different methodologies for imputing missing FS sub-scores, and ii) predicting the EDSS score using complete clinical data. Results show that Exponential Weighted Moving Average achieved the lowest error rate in the missing data imputation task; furthermore, the combination of Classification and Regression Trees for the imputation and SVM for the prediction task obtained the best accuracy.
Predicting Clinical Outcomes of amyotrophic lateral sclerosis Progression using Logistic Regression and Deep-Learning Multilayer Perceptron Approaches
#Conference
Guazzo A, Atzeni M, Idi E, Trescato I, Tavazzi E, Longato E, Manera U, Chiò A, Gromicho M, Alves I, de Carvalho M, Vettoretti M, Di Camillo B
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that results in death within a short time span (3-5 years). One of the major challenges in treating ALS is its highly heterogeneous disease progression and the lack of effective prognostic tools to forecast it. The main aim of this study was, then, to test the feasibility of predicting relevant clinical outcomes that characterize the progression of ALS with a two-year prediction horizon via artificial intelligence techniques using routine visits data.
Investigating the impact of environmental data on ALS prognosis with survival analysis
#Open access #Peer-reviewed #Journal
Ruben Branco, Diogo F. Soares, Andreia S. Martins, Joana Valente, Eduardo N. Castanho, Sara C. Madeira, Helena Aidos
Amyotrophic lateral sclerosis (ALS) is characterized by rapid motor neuron degeneration and subsequent loss of motor function, typically leading to death by respiratory failure. As evidence of environmental pollutants playing a role on ALS incidence surfaces, iDPP @ CLEF 2023 challenge sought to evaluatethe predictive power of these pollutants on prognosis.
Survival analysis for multiple sclerosis: predicting risk of disease worsening
#Workshop
Ruben Branco, Joana Valente, Andreia S. Martins, Diogo F. Soares, Eduardo N. Castanho, Sara C. Madeira, Helena Aidos
Multiple sclerosis (MS) is a chronic neurodegenerative disease with a wide range of clinical manifestations and disease courses. Prognosis prediction is therefore an important tool for clinical decision-making and treatment administration. As proposed by the iDPP @ CLEF 2023 challenge, we have explored several survival prediction models to rank MS patients according to the risk of worsening.
Baseline Machine Learning Approaches To Predict Multiple Sclerosis Disease Progression
#Workshop
Guazzo A, Trescato I, Longato E, Tavazzi E, Vettoretti M, Di Camillo B
Developed in the context of the iDPP@CLEF 2023 challenge, this work aims at developing different machine-learning approaches to predict a worsening in patient disability caused by MS using a shared dataset provided by the challenge organisers.
Intelligent Disease Progression Prediction: Overview of iDPP@CLEF 2023
#Open access #Peer-reviewed #Journal
Erica Tavazzi, Enrico Longato, Martina Vettoretti, Helena Aidos, Isotta Trescato, Chiara Roversi, Andreia S Martins, Eduardo N Castanho, Ruben Branco, Diogo F Soares, Alessandro Guazzo, Giovanni Birolo, Daniele Pala, Pietro Bosoni, Adriano Chiò, Umberto Manera, Mamede de Carvalho, Bruno Miranda, Marta Gromicho, Inês Alves, Riccardo Bellazzi, Arianna Dagliati, Piero Fariselli, Sara C Madeira, Barbara Di Camillo
iDPP@CLEF aims at developing an evaluation infrastructure for AI algorithms to describe ALS and MS mechanisms, stratify patients based on their phenotype, and predict disease progression in a probabilistic, time-dependent manner.
iDPP@CLEF 2022 ran as a pilot lab in CLEF 2022, with tasks related to predicting ALS progression and explainable AI algorithms for prediction. iDPP@CLEF 2023 continued in CLEF 2023, with a focus on predicting MS progression and exploring whether pollution and environmental data can improve the prediction of ALS progression.
Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review
#Open access #Peer-reviewed #Journal
Erica Tavazzi, Enrico Longato, Martina Vettoretti, Helena Aidos, Isotta Trescato, Chiara Roversi, Andreia S Martins, Eduardo N Castanho, Ruben Branco, Diogo F Soares, Alessandro Guazzo, Giovanni Birolo, Daniele Pala, Pietro Bosoni, Adriano Chiò, Umberto Manera, Mamede de Carvalho, Bruno Miranda, Marta Gromicho, Inês Alves, Riccardo Bellazzi, Arianna Dagliati, Piero Fariselli, Sara C Madeira, Barbara Di Camillo
This systematic review examines artificial intelligence’s methodological landscape in ALS, focusing on patient stratification and disease progression prediction.
Out of 1604 reports, we identified 15 studies on patient stratification, 28 on ALS progression prediction, and 6 on both. We highlighted a general agreement in terms of input variable selection for both stratification and prediction of ALS progression, and in terms of prediction targets. A striking lack of validated models emerged, as well as a general difficulty in reproducing many published studies, mainly due to the absence of the corresponding parameter lists. While deep learning seems promising for prediction applications, its superiority with respect to traditional methods has not been established; there is, instead, ample room for its application in the subfield of patient stratification. Finally, an open question remains on the role of new environmental and behavioural variables collected via novel, real-time sensors.
Presymptomatic geographical distribution of ALS patients suggests the involvement of environmental factors in the disease pathogenesis
#Open access # Peer-reviewed #Journal
Vasta R, Callegaro S, Sgambetterra S, Cabras S, Di Pede F, De Mattei F, Matteoni E, Grassano M, Bombaci A, De Marco G, Fuda G, Marchese G, Palumbo F, Canosa A, Mazzini L, De Marchi F, Moglia C, Manera U, Chiò A, Calvo A.
Since the underlying process of ALS starts long before symptoms appear, studying where patients have lived over time could help identify potential risk factors for the disease. This study examined the residential locations of a large group of ALS patients over the 50 years leading up to their diagnosis.
BRAINTEASER Architecture for Integration of AI Models and Interactive Tools for Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) Progression Prediction and Management
#Conference
Vladimir Urošević, Nikola Vojičić, Aleksandar Jovanović, Borko Kostić, Sergio Gonzalez-Martinez, María Fernanda Cabrera-Umpiérrez, Manuel Ottaviano, Luca Cossu, Andrea Facchinetti & Giacomo Cappon
The presented platform architecture and deployed implementation in real-life clinical and home care settings on four Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) study sites, integrates the novel working tools for improved disease management with the initial releases of the AI models for disease monitoring. The described robust industry-standard scalable platform is to be a referent example of the integration approach based on loose coupling APIs and industry open standard human-readable and language-independent interface specifications, and its successful baseline implementation for further upcoming releases of additional and more advanced AI models and supporting pipelines (such as for ALS and MS progression prediction, patient stratification, and ambiental exposure modelling) in the following development.
Serum chloride as a respiratory failure marker in amyotrophic lateral sclerosis
#Open access #Peer-reviewed #Journal
Manera U, Grassano M, Matteoni E, Bombaci A, Vasta R, Palumbo F, Torrieri MC, Cugnasco P, Moglia C, Canosa A, Chiò A, Calvo A.
To early recognise respiratory failure signs in ALS patients is of outstanding importance to precocious treat it with non-invasive ventilation. Many tests can be used to evaluate respiratory function in early phases: no one of them is considered unfailing, so a combination of them is usually performed in clinical practice. We identified than serum chloride, measured by a simple venous blood sampling, can add useful information by identifying patients that are starting to compensate acidosis due to nocturnal hypoventilation and the consequent increase of blood carbon dioxide, by increasing blood carbonate and expelling chloride in urines. Serum chloride is a cheap and widely diffuse that should be added to the initial standard examination in ALS patients.
Association of Copresence of Pathogenic Variants Related to Amyotrophic Lateral Sclerosis and Prognosis
#Open access #Peer-reviewed #Journal
Chio A, Moglia C, Canosa A, Manera U, Grassano M, Vasta R, Palumbo F, Gallone S, Brunetti M, Barberis M, De Marchi F, Dalgard C, Chia R, Mora G, Iazzolino B, Peotta L, Traynor BJ, Corrado L, Dalfonso S, Mazzini L, Calvo A.
Despite recent progress, it’s still unclear how different genes or genetic variations linked to amyotrophic lateral sclerosis (ALS) influence the symptoms and progression of the disease when they occur together. This study aimed to explore whether having multiple ALS-related genetic variants affects how the disease develops
Clinical characteristics in amyotrophic lateral sclerosis with Sub-Saharan Africa ancestry – A Portuguese hospital-based cohort study
#Peer-reviewed #Journal
Miguel Oliveira Santos, Marta Gromicho, Susana Pinto, Ana Pronto-Laborinho and Mamede de Carvalho
This research paper explores the characteristics of patients with amyotrophic lateral sclerosis (ALS) who originate from Sub-Saharan Africa (SSA) and are diagnosed and followed in a specialized ALS clinic in Portugal. Sub-Saharan African Amyotrophic Lateral Sclerosis (SAALS) is a distinct population with a younger onset age and a longer disease duration, but total survival is independent of SA ancestry. The researchers believe that the specialized ALS clinic in Portugal, with its access to respiratory care and riluzole, could explain the observed differences between their results and previous data from African ALS patients followed in their own countries. Improving access to healthcare for SAALS patients could help to improve their survival rates.
Triclustering-based classification of longitudinal data for prognostic prediction: targeting relevant clinical endpoints in amyotrophic lateral sclerosis
#Open access #Peer-reviewed #Journal
Diogo F. Soares, Rui Henriques, Marta Gromicho, Mamede de Carvalho, Sara C. Madeira
This research introduces a new type of understandable models for predicting how diseases progress over time, focusing on a specific group of patients with certain characteristics. We developed a new method called TCtriCluster, which looks for meaningful patterns in data collected over time from these patients, helping us understand the progression of the disease better. By using these patterns, we improved the accuracy of predicting important events in ALS, such as when patients might need breathing assistance or other types of support. This method performed better than existing ones, with high accuracy in predicting when certain interventions might be needed. This approach was tested on a large group of ALS patients in Portugal, providing valuable insights for healthcare professionals in managing the disease and its various stages.
iDPP@CLEF 2023: The Intelligent Disease Progression Prediction Challenge
#Workshop
Helena Aidos, Roberto Bergamaschi, Paola Cavalla, Adriano Chiò, Arianna Dagliati, Barbara Di Camillo, Mamede Alves de Carvalho, Nicola Ferro, Piero Fariselli, Jose Manuel García Dominguez, Sara C. Madeira & Eleonora Tavazzi
Amyotrophic Lateral Sclerosis (ALS) and Multiple Sclerosis (MS) are chronic diseases characterized by progressive or alternate impairment of neurological functions (motor, sensory, visual, cognitive). The goal of iDPP@CLEF Open Evaluation Challenge is to design and develop an evaluation infrastructure for AI algorithms able to: better describe disease mechanisms; stratify patients according to their phenotype assessed all over the disease evolution; predict disease progression in a probabilistic, time dependent fashion. iDPP@CLEF will continue in CLEF 2023, focusing on the prediction of MS progression and exploring whether pollution and environmental data can improve the prediction of ALS progression.
Demographic changes in a large motor neuron disease cohort in Portugal: a 27 year experience
#Open access #Peer-reviewed #Journal
Inês Alves, Marta Gromicho, Miguel Oliveira Oliveira Santos, Susana Pinto, Ana Pronto-Laborinho, Michael Swash & Mamede De Carvalho
This research paper investigated the clinical and demographic characteristics of motor neuron disease (MND) patients over a 27-year period. The researchers found that the average age at onset of MND was increasing, diagnostic delay was decreasing, and the proportion of patients using respiratory support with noninvasive ventilation (NIV) was increasing. They also found that median survival was increasing for ALS patients with spinal onset.
The researchers suggest that these changes are likely due to improved comprehensive care for MND patients. They believe that early diagnosis and treatment, including the use of NIV, are contributing to the better outcomes seen in recent years.
The researchers’ findings are important for future studies exploring the impact of new treatments on MND patients.
Respiratory phenotypes in amyotrophic lateral sclerosis as determined by respiratory questions on the Amyotrophic Lateral Sclerosis Functional Rating Scale–Revised and their relation to respiratory tests
#Open access #Journal
Susana Pinto, Miguel Oliveira Santos, Marta Gromicho, Michael Swash, Mamede de Carvalho
This research paper examined the association between respiratory symptoms and respiratory test results in patients with amyotrophic lateral sclerosis (ALS). The researchers found that there are three distinct groups of ALS patients based on their respiratory symptoms and test results. Patients with the most severe respiratory problems were more likely to need mechanical ventilation. Measuring the phrenic nerve amplitude may be helpful in identifying patients who are at risk of developing severe respiratory problems. Orthopnea is a severe symptom that should prompt NIV and early NIV promotes survival.
Amyotrophic lateral sclerosis regional progression intervals change according to time of involvement of different body regions
#Open access #Journal
Manera U, D’Ovidio F, Cabras S, Torrieri MC, Canosa A, Vasta R, Palumbo F, Grassano M, De Marchi F, Mazzini L, Mora G, Moglia C, Calvo A, Chiò A.
ALS is one of the more heterogeneous neurological disease and the prediction of its progression is one of the most important topic in ALS research. Moreover, no study have previously evaluated the timing of progression in the different body regions that can be involved by the disease. To do that we collected a huge dataset comprehending all the ALS functional rating scale (ALSFRS-R) scores and, by performing different analysis, we discovered that functional impairment runs faster in the body regions involved later, suggesting a snowball-like behaviour of the disease.
Sporadic Spinal-Onset Amyotrophic Lateral Sclerosis Associated with Myopathy in Three Unrelated Portuguese Patients
#Open access #Peer-reviewed #Journal
Miguel Oliveira Santos, Marta Gromicho, Ana Pronto-Laborinho and Mamede de Carvalho
This research paper explores the possible connection between amyotrophic lateral sclerosis (ALS), a progressive neurodegenerative disease that affects muscle control, and myopathy, a muscular condition that weakens muscles. The researchers describe three cases of ALS patients who also had myopathy, suggesting that there may be a link between these two conditions and that they may share some common causes.
Trends in the diagnostic delay and pathway for amyotrophic lateral sclerosis patients across different countries
#Open access #Peer-reviewed #Journal
Catarina Falcão de Campos, Marta Gromicho, Hilmi Uysal, Julian Grosskreutz, Magdalena Kuzma-Kozakiewicz, Miguel Oliveira Santos, Susana Pinto, Susanne Petri, Michael Swash and Mamede de Carvalho
This paper investigated how long it takes to diagnose amyotrophic lateral sclerosis (ALS). The researchers examined data from more than 1,400 ALS patients from four different countries and discovered that the median diagnostic delay was 11 months. This means that it took people an average of 11 months from the time they noticed symptoms to being officially diagnosed with ALS.
The researchers discovered that several factors contributed to the diagnostic delay, including:
Symptom presentation: In the early stages of ALS, symptoms are frequently mild and nonspecific, making diagnosis difficult.
Referral to specialists: Many people with ALS are not initially referred to a neurologist, who is the specialist who can make a definitive diagnosis of ALS.
Testing: Electromyography (EMG) is a test that is used to measure electrical activity in muscles. EMG is very important for diagnosing ALS, but it is not always available or accessible.
The researchers propose that a specific diagnostic test for ALS, such as a biomarker, could help to shorten the diagnostic time. They also emphasize the importance of referring patients to a neurologist and performing an EMG to diagnose ALS early on.
The flail-arm syndrome: the influence of phenotypic features
#Open access #Peer-reviewed #Journal
Marta Gromicho, Miguel Oliveira Oliveira Santos, Susana Pinto, Michael Swash & Mamede De Carvalho
This research paper looked at a specific type of ALS called flail arm syndrome (FAS). FAS is characterized by weakness in the upper limbs, and it typically progresses slowly. The researchers studied the clinical features, progression, and survival of FAS patients, and they found that the presence or absence of upper motor neuron (UMN) signs at diagnosis was the most important factor in determining how long a patient would live.
The researchers found that the location of the weakness, whether it was in the distal muscles of the arms (the muscles closest to the fingers) or the proximal muscles of the arms (the muscles closest to the shoulders), did not affect the prognosis of FAS patients. This suggests that the most important factor in determining the prognosis of FAS is the severity of the disease, as indicated by the presence or absence of UMN signs.
Mild Dysphagia Does Not Influence Survival in Ventilated Amyotrophic Lateral Sclerosis Patients
#Peer-reviewed #Journal
Miguel Oliveira Santos, Marta Gromicho, Susana Pinto, Michael Swash, Mamede de Carvalho
It is unknown if mild dysphagia affects survival of patients with amyotrophic lateral sclerosis (ALS) on continuous non-invasive ventilation (NIV). We often must discuss the pros and cons of gastrostomy in patients with NIV, but there is a lack of information on the issue. We, therefore, studied dysphagia as a survival predictor factor in patients with ALS already adapted to continuous NIV, using our collected data.
Our findings are relevant for decision-making processes concerning gastrostomy in this particular population.
Systematic evaluation of genetic mutations in ALS: a population-based study
#Open access #Peer-reviewed #Journal
Grassano M, Calvo A, Moglia C, Sbaiz L, Brunetti M, Barberis M, Casale F, Manera U, Vasta R, Canosa A, D’Alfonso S, Corrado L, Mazzini L, Dalgard C, Karra R, Chia R, Traynor B, Chiò A
ALS is probably caused by an interplay between environmental and genetic factors. Several descriptions of patients carrying more than one pathogenic mutation are present in scientific literature, but few papers systematically evaluated the presence of mutations in ALS-related genes in population-based cohorts. In this study, we highlighted that the presence of more than one mutation is more common than expected. Patients with more than one mutation resulted to have more aggressive phenotypes, confirming that disease features can be due to the malfunction of several neuronal pathways.
Exploring the phenotype of Italian patients with ALS with intermediate ATXN2 polyQ repeats
#Open access #Peer-reviewed #Journal
Chio A, Moglia C, Canosa A, Manera U, Grassano M, Vasta R, Palumbo F, Gallone S, Brunetti M, Barberis M, De Marchi F, Dalgard C, Chia R, Mora G, Iazzolino B, Peotta L, Traynor B, Corrado L, D’Alfonso S, Mazzini L, Calvo A
ATXN2 is a gene related to a neurodegenerative disease called Spinocerebellar Ataxia type 2, but it is also a recognised risk factor for ALS. In this study, we explored the role of the so-called intermediate repeated expansion (a specific type of mutations characterised by the wrong repetition of short sequences of DNA that produces altered amminoacidic sequences called polyQ) in the determination of ALS patients’ phenotype. We discovered that patients with these alterations showed an early age at onset and a higher progression rate. We confirmed that ATXN2 can be a potential target for new therapeutic approaches.
Exposure to electromagnetic fields does not modify neither the age of onset nor the disease progression in ALS patients
#Open access #Peer-reviewed #Journal
Vasta R, Callegaro S, Grassano M, Canosa A, Cabras S, Di Pede F, Matteoni E, De Mattei F, Casale F, Salamone P, Mazzini L, De Marchi F, Moglia C, Calvo A, Chiò A, Manera U
In this paper, we evaluate the effect of exposure to electromagnetic fields on ALS onset age and progression rate. We studied the association of patients’ residency with the distribution of power lines and repeater antennas. We did not find any significant relationship between electromagnetic fields and ALS phenotype or progression.
Learning prognostic models using a mixture of biclustering and triclustering: Predicting the need for non-invasive ventilation in Amyotrophic Lateral Sclerosis
#Open access #Peer-reviewed #Journal
Diogo F. Soares, Rui Henriques, Marta Gromicho, Mamede de Carvalho, Sara C. Madeira
This paper wants to improve the best features in methods used for predicting outcomes and deciding when patients with Amyotrophic Lateral Sclerosis (ALS) need non-invasive breathing assistance. It achieves this by studying clear patterns of how the disease progresses. The discoveries made can also be useful for understanding other aspects of ALS and similar diseases.
Baseline Machine Learning Approaches To Predict Amyotrophic Lateral Sclerosis Disease Progression
#Workshop
Trescato I., Guazzo A., Longato E., Hazizaj E., Roversi C., Tavazzi E., Vettoretti M., Di Camillo B.
The main goal of this study was to compare different methods for predicting the occurrence and timing of specific events in ALS, including the need for non-invasive ventilation, percutaneous endoscopic gastrostomy, and death. The study took place during the CLEF Challenge 2022, and the organizers provided two versions of datasets: the first comprised information collected only at the first visit after ALS diagnosis, while the second included all information collected during a 6-month follow-up starting from the first visit. Notably, regardless of the outcome predicted, the models including dynamic features improved model performance, highlighting the significance of the first 6 months of data for accurate predictions in fast-progressing diseases like ALS.
Deep learning methods to predict amyotrophic lateral sclerosis disease progression
#Open access #Journal
Corrado Pancotti, Giovanni Birolo, Cesare Rollo, Tiziana Sanavia, Barbara Di Camillo, Umberto Manera, Adriano Chiò & Piero Fariselli
Amyotrophic lateral sclerosis (ALS) is a complex disease that weakens muscles by attacking nerve cells. Because ALS patients face shortened lifespans, understanding the disease’s progression quickly is crucial for improving treatments. Scientists are turning to computer models to forecast how ALS develops over time.
One major data source aiding this research is the PRO-ACT repository. In 2015, a competition challenged developers to create programs predicting ALS progression using this data.
Our study dives into this challenge, applying advanced Deep Learning techniques.
Our findings suggest that Deep Learning could offer a fresh approach to forecasting ALS progression, offering hope for more effective treatment strategies.
Thyroid dysfunction in Portuguese amyotrophic lateral sclerosis patients
#Peer-reviewed #Journal
Santos Silva C, Gromicho M, Oliveira Santos M, Pinto S, Swash M, de Carvalho
This study looked into whether thyroid dysfunction or an imbalance in the hormones produced by the thyroid gland, is linked to amyotrophic lateral sclerosis (ALS).
The researchers compared the prevalence of thyroid dysfunction in ALS patients to that of people with non-thyroid-related neuromuscular disorders. They discovered that ALS patients had a lower prevalence of thyroid dysfunction than the control group, indicating that thyroid dysfunction is not a major contributor to ALS development.
The researchers also reviewed previous studies on thyroid dysfunction and ALS and discovered that their findings were consistent with those of other studies.
Overall, this study suggests that thyroid dysfunction does not pose a significant risk for ALS.
Respiratory onset in amyotrophic lateral sclerosis: clinical features and spreading pattern
#Open access #Peer-reviewed #Journal
Susana Pinto, Marta Gromicho, Miguel Oliveira Oliveira Santos, Michael Swash & Mamede De Carvalho
This study focused on persons with a rare type of ALS known as respiratory onset ALS. This type of ALS begins with breathing problems and affects the diaphragm muscle, which is essential for respiration.
The study discovered that most persons with respiratory onset ALS are elderly men who are underweight. They must begin utilizing non-invasive ventilation (NIV), which helps them breathe, earlier than patients with other types of ALS.
Despite NIV therapy, persons with respiratory onset ALS have a worse life expectancy than those with other types of ALS. The study also found that the pattern of how ALS spreads throughout the body does not affect how quickly people need NIV or how long they live.
Social cognition deficits in amyotrophic lateral sclerosis: A pilot cross-sectional population-based study
#Open access #Peer-reviewed #Journal
Palumbo F, Iazzolino B, Peotta L, Canosa A, Manera U, Grassano M, Casale F, Pellegrino G, Rizzone MG, Vasta R, Moglia C, Chiò A, Calvo A
ALS is mainly a neurodegenerative disease involving motor system, but also some aspects of cognition can be impaired. Social cognition is one of the most important brain function in humans, being responsible for our social living and well-being. We prospectively studied a cohort of ALS patients belonging to Piedmont ALS registry using neuropsychological tests specifically designed for assess social cognition. ALS patients showed significant lower scores than healthy controls, confirming a social cognition deficit than needs to be taken into account in patients management.
Motor Neuron Disease in Three Asymptomatic pVal50Met TTR Gene Carriers
#Open access #Journal
Cláudia Santos Silva, Miguel Oliveira Santos, Marta Gromicho, Ana Pronto-Laborinho, Isabel Conceição & Mamede de Carvalho
This research paper explores the potential link between transthyretin (TTR) gene mutations and motor neuron disease (MND). TTR is a protein that plays a crucial role in protecting nerve cells from damage. Mutations in the TTR gene can cause the protein to malfunction and form amyloid deposits, which can lead to nerve damage and a condition called amyloidosis.
The researchers studied three patients with MND who were also found to carry the pVal50Met mutation of the TTR gene. This mutation is associated with a rare form of amyloidosis called hereditary amyloid transthyretin (ATTRv) amyloidosis. The researchers examined the patients’ clinical symptoms, imaging scans, cerebrospinal fluid tests, nerve conduction studies, and small fiber tests.
The researchers’ findings suggest that he TTR gene mutations may contribute to the development of MND in some patients.
Delayed Diagnosis and Diagnostic Pathway of Amyotrophic Lateral Sclerosis Patients in Portugal: Where Can We Improve?
#Open access #Peer-reviewed #Journal
Catarina Falcão de Campos, Marta Gromicho, Hilmi Uysal, Julian Grosskreutz, Magdalena Kuzma-Kozakiewicz, Miguel Oliveira Santos, Susana Pinto, Susanne Petri, Michael Swash, Mamede de Carvalho
This paper discusses the challenges of diagnosing amyotrophic lateral sclerosis (ALS). The researchers found that people with spinal onset ALS, slower disease progression, cognitive symptoms at onset, and lower income were more likely to experience delays in diagnosis.
The authors also identified the referral process from non-neurologists to neurologists as a potential factor contributing to these delays. They suggest that early referral to a neurologist could help to expedite the diagnosis and improve outcomes for ALS patients.