Upper motor neuron signs in primary lateral sclerosis and hereditary spastic paraplegia.

INTRODUCTION/AIMS: 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). METHODS: 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. RESULTS: We included 34 PLS and 20 HSP patients, with a median symptom duration at first visit of 3.0 (interquartile range, IQR = 4.0) and 19.0 (IQR = 22.0) years, respectively. In PLS patients, hyperreflexia of upper (UL) (88.2%) and lower (LL) (91.2%) limbs, and LL spasticity (79.4%) were the most common findings. Spasticity of LL was significantly (p = .012) more frequent in LL-spinal onset subgroup, tongue spasticity in bulbar-onset subgroup (p = .021), and Hoffman sign in UL-spinal onset subgroup (p = .024). The PLS subgroup with shorter disease duration had a higher frequency of abnormal jaw jerk reflex (p = .037). Compared with HSP, PLS patients had a higher frequency of UL hyperreflexia (88.2% vs. 42.1%, p < .001) and UL spasticity (44.1% vs. 0.0%, p < .001). Asymmetric distribution of UMN signs was present in PLS and not in HSP. DISCUSSION: In PLS, UL UMN signs are nearly always present and UMN sign distribution appears to be associated with onset region. At first observation, bulbar involvement, asymmetrical distribution of UMN signs and UL spasticity may indicate PLS versus HSP.

Impact of diabetes mellitus on the respiratory function of amyotrophic lateral sclerosis patients.

BACKGROUND AND PURPOSE: Respiratory insufficiency and its complications are the main cause of death in amyotrophic lateral sclerosis (ALS). The impact of diabetes mellitus (DM) on respiratory function of ALS patients is uncertain. METHODS: A retrospective cohort study was carried out. From the 1710 patients with motor neuron disease followed in our unit, ALS and progressive muscular atrophy patients were included. We recorded demographic characteristics, functional ALS rating scale (Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised [ALSFRS-R]) and its subscores at first visit, respiratory function tests, arterial blood gases, phrenic nerve amplitude (PhrenAmpl), and mean nocturnal oxygen saturation (SpO2 mean). We excluded patients with other relevant diseases. Two subgroups were analysed: DIAB (patients with DM) and noDIAB (patients without DM). Independent t-test, χ2 , or Fisher exact test was applied. Binomial logistic regression analyses assessed DM effects. Kaplan-Meier analysis assessed survival. p < 0.05 was considered significant. RESULTS: We included 1639 patients (922 men, mean onset age = 62.5 ± 12.6 years, mean disease duration = 18.1 ± 22.0 months). Mean survival was 43.3 ± 40.7 months. More men had DM (p = 0.021). Disease duration was similar between groups (p = 0.063). Time to noninvasive ventilation (NIV) was shorter in DIAB (p = 0.004); total survival was similar. No differences were seen for ALSFRS-R or its decay rate. At entry, DIAB patients were older (p < 0.001), with lower forced vital capacity (p = 0.001), arterial oxygen pressure (p = 0.01), PhrenAmpl (p < 0.001), and SpO2 mean (p = 0.014). CONCLUSIONS: ALS patients with DM had increased risk of respiratory impairment and should be closely monitored. Early NIV allowed for similar survival rate between groups.

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.

BACKGROUND AND PURPOSE: Respiratory insufficiency and its complications are the main cause of death in amyotrophic lateral sclerosis (ALS). Respiratory symptoms are scored in questions Q10 (dyspnoea) and Q11 (orthopnoea) of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R). The association of respiratory test alterations with respiratory symptoms is unclear. METHODS: Patients with ALS and progressive muscular atrophy were included. We retrospectively recorded demographic data, ALSFRS-R, forced vital capacity (FVC), maximal inspiratory (MIP) and expiratory (MEP) pressures, mouth occlusion pressure at 100 ms, nocturnal oximetry (SpO2 mean), arterial blood gases, and phrenic nerve amplitude (PhrenAmpl). Three groups were categorized: G1, normal Q10 and Q11; G2, abnormal Q10; and G3, abnormal Q10 and Q11 or only abnormal Q11. A binary logistic regression model explored independent predictors. RESULTS: We included 276 patients (153 men, onset age = 62.6 ± 11.0 years, disease duration = 13.0 ± 9.6 months, spinal onset in 182) with mean survival of 40.1 ± 26.0 months. Gender, onset region, and disease duration were similar in G1 (n = 149), G2 (n = 78), and G3 (n = 49). Time to noninvasive ventilation (NIV) was shorter in G3 (p < 0.001), but survival was similar. ALSFRS-R subscores were significantly different (G1 > G2 > G3, p < 0.001), except for lower limb subscore (p = 0.077). G2 and G3 patients were older than G1 (p < 0.001), and had lower FVC, MIP, MEP, PhrenAmpl, and SpO2 mean. Independent predictors for G2 were MIP and SpO2 mean; for G3, the only independent predictor was PhrenAmpl. CONCLUSIONS: These three distinct ALS phenotypic respiratory categories represent progressive stages of ventilatory dysfunction, supporting ALSFRS-R clinical relevance. Orthopnoea is a severe symptom that should prompt NIV, phrenic nerve response being an independent predictor. Early NIV promotes similar survival for G2 and G3.

Dynamic Bayesian networks for stratification of disease progression in amyotrophic lateral sclerosis.

BACKGROUND AND PURPOSE: Progression rate is quite variable in amyotrophic lateral sclerosis (ALS); thus, tools for profiling disease progression are essential for timely interventions. The objective was to apply dynamic Bayesian networks (DBNs) to establish the influence of clinical and demographic variables on disease progression rate. METHODS: In all, 664 ALS patients from our database were included stratified into slow (SP), average (AP) and fast (FP) progressors, according to the Amyotrophic Lateral Sclerosis Functional Rating Scale Revised (ALSFRS-R) rate of decay. The sdtDBN framework was used, a machine learning model which learnt optimal DBNs with both static (gender, age at onset, onset region, body mass index, disease duration at entry, familial history, revised El Escorial criteria and C9orf72) and dynamic (ALSFRS-R scores and sub-scores, forced vital capacity, maximum inspiratory pressure, maximum expiratory pressure and phrenic amplitude) variables. RESULTS: Disease duration and body mass index at diagnosis are the foremost influences amongst static variables. Disease duration is the variable that better discriminates the three groups. Maximum expiratory pressure is the respiratory test with prevalent influence on all groups. ALSFRS score has a higher influence on FP, but lower on AP and SP. The bulbar sub-score has considerable influence on FP but limited on SP. Limb function has a more decisive influence on AP and SP. The respiratory sub-score has little influence in all groups. ALSFRS-R questions 1 (speech) and 9 (climbing stairs) are the most influential in FP and SP, respectively. CONCLUSIONS: The sdtDBN analysis identified five variables, easily obtained during clinical evaluation, which are the most influential for each progression group. This insightful information may help to improve prognosis and care.

Learning prognostic models using a mixture of biclustering and triclustering: Predicting the need for non-invasive ventilation in Amyotrophic Lateral Sclerosis.

Longitudinal cohort studies to study disease progression generally combine temporal features produced under periodic assessments (clinical follow-up) with static features associated with single-time assessments, genetic, psychophysiological, and demographic profiles. Subspace clustering, including biclustering and triclustering stances, enables the discovery of local and discriminative patterns from such multidimensional cohort data. These patterns, highly interpretable, are relevant to identifying groups of patients with similar traits or progression patterns. Despite their potential, their use for improving predictive tasks in clinical domains remains unexplored. In this work, we propose to learn predictive models from static and temporal data using discriminative patterns, obtained via biclustering and triclustering, as features within a state-of-the-art classifier, thus enhancing model interpretation. triCluster is extended to find time-contiguous triclusters in temporal data (temporal patterns) and a biclustering algorithm to discover coherent patterns in static data. The transformed data space, composed of bicluster and tricluster features, capture local and cross-variable associations with discriminative power, yielding unique statistical properties of interest. As a case study, we applied our methodology to follow-up data from Portuguese patients with Amyotrophic Lateral Sclerosis (ALS) to predict the need for non-invasive ventilation (NIV) since the last appointment. The results showed that, in general, our methodology outperformed baseline results using the original features. Furthermore, the bicluster/tricluster-based patterns used by the classifier can be used by clinicians to understand the models by highlighting relevant prognostic patterns.

Thyroid dysfunction in Portuguese amyotrophic lateral sclerosis patients.

INTRODUCTION: Thyroid hormones influence neuromuscular function, and it has been thought that this might contribute to degeneration of motor neurons. METHODS: We used case-control methods to investigate the prevalence of thyroid dysfunction (hyperthyroidism and hypothyroidism) in ALS patients followed in our centre, between 2015 and 2020. Data from patients with neuromuscular disorders not derived from thyroid dysfunction, followed within the same time frame, were used as controls. Thyroid dysfunction was defined by previous thyroid replacement medication managed by an endocrinologist. We used odds ratios (OR) with a 95% confidence interval (CI) to compare 579 ALS patients and 415 age-gender-matched disease controls. Additionally, we provide a summarized review of the literature. RESULTS: Hypothyroidism (prevalence of 5.0 versus 8.6%; OR = 0.56, 95% CI 0.34-0.92, p = 0.023), hyperthyroidism (prevalence of 0.3 versus 1.2%; OR = 0.28, 95% CI 0.06-1.47, p = 0.134) and overall thyroid dysfunction (prevalence of 5.4 versus 9.9%; OR = 0.52, 95% CI 0.32-0.84, p = 0.015) were less prevalent in ALS patients than in controls, but similar to the national epidemiological data for thyroid disease. Our data are in line with the findings of most previous studies. CONCLUSIONS: We conclude that thyroid dysfunction is not associated with ALS.

Familial clustering of primary lateral sclerosis and amyotrophic lateral sclerosis: Supplementary evidence for a continuum.

BACKGROUND AND PURPOSE: Primary lateral sclerosis (PLS) is a motor neuron disorder characterized by a pure upper motor neuron degeneration in the bulbar and spinal regions. The key difference with amyotrophic lateral sclerosis (ALS) is the lower motor neuron system integrity. Despite important literature on this disease, the pathophysiology of PLS remains unknown, and the link with ALS still balances between a continuum and a separate entity from ALS. METHODS: We report nine families in which both PLS and ALS cases occurred, in general among first-degree relatives. RESULTS: The patients with PLS and ALS had a typical disease presentation. Genetic studies revealed mutations in SQSMT1, TBK1, and TREM2 genes in two PLS patients and one ALS patient. CONCLUSIONS: These results strongly support a phenotypic continuum between PLS and ALS.

Learning dynamic Bayesian networks from time-dependent and time-independent data: Unraveling disease progression in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease causing patients to quickly lose motor neurons. The disease is characterized by a fast functional impairment and ventilatory decline, leading most patients to die from respiratory failure. To estimate when patients should get ventilatory support, it is helpful to adequately profile the disease progression. For this purpose, we use dynamic Bayesian networks (DBNs), a machine learning model, that graphically represents the conditional dependencies among variables. However, the standard DBN framework only includes dynamic (time-dependent) variables, while most ALS datasets have dynamic and static (time-independent) observations. Therefore, we propose the sdtDBN framework, which learns optimal DBNs with static and dynamic variables. Besides learning DBNs from data, with polynomial-time complexity in the number of variables, the proposed framework enables the user to insert prior knowledge and to make inference in the learned DBNs. We use sdtDBNs to study the progression of 1214 patients from a Portuguese ALS dataset. First, we predict the values of every functional indicator in the patients' consultations, achieving results competitive with state-of-the-art studies. Then, we determine the influence of each variable in patients' decline before and after getting ventilatory support. This insightful information can lead clinicians to pay particular attention to specific variables when evaluating the patients, thus improving prognosis. The case study with ALS shows that sdtDBNs are a promising predictive and descriptive tool, which can also be applied to assess the progression of other diseases, given time-dependent and time-independent clinical observations.

Assessing disease progression in ALS: prognostic subgroups and outliers.

Background: 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. Methods: 1056 ALS patients were stratified into three progression categories based on their ΔFS: slow progressors (below 25th percentile), intermediate progressors (between 25th and 75th percentiles), and fast progressors (above 75th percentile). Survival outcomes were classified as short survivors (<2 years), average survivors (2-5 years), and long survivors (>5 years). Clinical and demographic characteristics within each subgroup were then analyzed. Results: ΔFS stratification yielded cutoff values of <0.29, 0.29-1.03, and >1.03 points/month. Long survivors comprised 26% and 21% were short survivors. Six percent of the fast progressors had a life expectancy of more than 5 years, and none of the clinical and demographic characteristics analyzed could fully explain this discrepancy. Conversely, 13% of intermediate progressors lived less than 2 years, according to a short-diagnostic delay in these patients. Discussion: 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.

Creatine Kinase and Respiratory Decline in Amyotrophic Lateral Sclerosis.

Respiratory dysfunction is an important hallmark of amyotrophic lateral sclerosis (ALS). Elevation of creatine kinase (CK) has been reported in 23-75% of ALS patients, but the underlying mechanisms remain unknown. This work aims to enlighten the role of CK as a prognostic factor of respiratory dysfunction in ALS. A retrospective analysis of demographic and clinical variables, CK, functional decline per month (ΔFS), forced vital capacity (%FVC), and mean amplitude of the phrenic nerve compound motor action potential (pCMAP) in 319 ALS patients was conducted. These measurements were evaluated at study entry, and patients were followed from the moment of first observation until death or last follow-up visit. High CK values were defined as above the 90th percentile (CK ≥ P90) adjusted to sex. We analyzed survival and time to non-invasive ventilation (NIV) as proxies for respiratory impairment. Linear regression analysis revealed that high CK was associated with male sex (p < 0.001), spinal onset (p = 0.018), and FVC ≥ 80% (p = 0.038). CK was 23.4% higher in spinal-onset ALS patients (p < 0.001). High CK levels were not linked with an increased risk of death (p = 0.334) in Cox multivariate regression analysis. CK ≥ P90 (HR = 1.001, p = 0.038), shorter disease duration (HR = 0.937, p < 0.001), lower pCMAP (HR = 0.082, p < 0.001), and higher ΔFS (HR = 1.968, p < 0.001) were risk factors for respiratory failure. The association between high CK levels and poorer respiratory outcomes could derive from cellular metabolic stress or a specific phenotype associated with faster respiratory decline. Our study suggests that CK measurement at diagnosis should be more extensively investigated as a possible marker of poor respiratory outcome in future studies, including a larger population of patients.

Cognitive reserve as a modulator of cognitive decline and of behavioral symptoms in patients with amyotrophic lateral sclerosis.

Introduction: 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. Methods: A prospective sample of 162 ALS patients and 61 controls were evaluated with the Edinburgh Cognitive and Behavioral ALS Screen (ECAS) (dependent variable), a Cognitive Reserve Index questionnaire (CRIq) and demographic data (age and sex), and, for patients, clinical variables: disease duration, site of onset, the ALS Functional Rating Scale (ALSFRS), forced vital capacity (FVC), and gene mutation chromosome 9 open reading frame 72 (C9orf72) (independent variables). Multiple regression and mediation analyses were performed to predict cognitive and behavioral symptoms. Results: For the ALS group, the statistical model explained 38.8% of variance in ECAS total (p < 0.001), 59.4% of executive functions (p < 0.001), and 55% of behavioral symptoms (p < 0.001). For controls, it accounted for 52.8% of variance in ECAS total (p < 0.001). Interaction effects and mediation analysis showed CR is an ECAS total modulator, with a differential effect within groups (p < 0.001). Verbal fluency was the single best cognitive score to differentiate patients from controls (p = 0.004), and the gene mutation C9orf72 was found to be a behavioral symptom' predictor in patients (p = 0.009). Conclusion: This study supports the proposed concept that CR acts as a cognitive modulator in ALS patients and healthy individuals. Moreover, CR also modulates behavioral manifestations in ALS.

Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns.

Identifying groups of patients with similar disease progression patterns is key to understand disease heterogeneity, guide clinical decisions and improve patient care. In this paper, we propose a data-driven temporal stratification approach, ClusTric, combining triclustering and hierarchical clustering. The proposed approach enables the discovery of complex disease progression patterns not found by univariate temporal analyses. As a case study, we use Amyotrophic Lateral Sclerosis (ALS), a neurodegenerative disease with a non-linear and heterogeneous disease progression. In this context, we applied ClusTric to stratify a hospital-based population (Lisbon ALS Clinic dataset) and validate it in a clinical trial population. The results unravelled four clinically relevant disease progression groups: slow progressors, moderate bulbar and spinal progressors, and fast progressors. We compared ClusTric with a state-of-the-art method, showing its effectiveness in capturing the heterogeneity of ALS disease progression in a lower number of clinically relevant progression groups.

Erythrocytes' surface properties and stiffness predict survival and functional decline in ALS patients.

Erythrocytes play a fundamental role in oxygen delivery to tissues and binding to inflammatory mediators. Evidences suggest that dysregulated erythrocyte function could contribute to the pathophysiology of several neurodegenerative diseases. We aimed to evaluate changes in morphological, biomechanical, and biophysical properties of erythrocytes from amyotrophic lateral sclerosis (ALS) patients, as new areas of study in this disease. Blood samples were collected from ALS patients, comparing with healthy volunteers. Erythrocytes were assessed using atomic force microscopy (AFM) and zeta potential analysis. The patients' motor and respiratory functions were evaluated using the revised ALS Functional Rating Scale (ALSFRS-R) and percentage of forced vital capacity (%FVC). Patient survival was also assessed. Erythrocyte surface roughness was significantly smoother in ALS patients, and this parameter was a predictor of faster decline in ALSFRS-R scores. ALS patients exhibited higher erythrocyte stiffness, as indicated by reduced AFM tip penetration depth, which predicted a faster ALSFRS-R score and respiratory subscore decay. A lower negative charge on the erythrocyte membrane was predictor of a faster ALSFRS-R and FVC decline. Additionally, a larger erythrocyte surface area was an independent predictor of lower survival. These changes in morphological and biophysical membrane properties of ALS patients' erythrocytes, lead to increased cell stiffness and morphological variations. We speculate that these changes might precipitate motoneurons dysfunction and accelerate disease progression. Further studies should explore the molecular alterations related to these observations. Our findings may contribute to dissect the complex interplay between respiratory function, tissue hypoxia, progression rate, and survival in ALS.

An extensible and unifying approach to retrospective clinical data modeling: the BrainTeaser Ontology.

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. BTO assists in organizing and standardizing the data collected during patient follow-up. It was created by harmonizing schemas currently used by multiple medical centers into a common ontology, following a bottom-up approach. As a result, BTO effectively addresses the practical data collection needs of various real-world situations and promotes data portability and interoperability. BTO captures various clinical occurrences, such as disease onset, symptoms, diagnostic and therapeutic procedures, and relapses, using an event-based approach. Developed in collaboration with medical partners and domain experts, BTO offers a holistic view of ALS and MS for supporting the representation of retrospective and prospective data. Furthermore, BTO adheres to Open Science and FAIR (Findable, Accessible, Interoperable, and Reusable) principles, making it a reliable framework for developing predictive tools to aid in medical decision-making and patient care. Although BTO is designed for ALS and MS, its modular structure makes it easily extendable to other brain-related diseases, showcasing its potential for broader applicability.Database URL  https://zenodo.org/records/7886998 .

The flail-arm syndrome: the influence of phenotypic features.

Objective: The flail-arm syndrome (FAS), one of the Amyotrophic lateral sclerosis (ALS) phenotypes, is characterized by slow progression and predominantly lower motor neuron (LMN) involvement with proximal upper limb (UL) weakness. We aim to characterize the clinical features, progression and survival of FAS associated with distal or proximal onset and presence or absence of upper motor neuron signs (UMN) signs at diagnosis. Methods: Data from 704 ALS patients was analyzed. Of the 190 patients with UL onset; 134 were excluded as not respecting the published criteria for FAS. The included patients were divided into four groups according to distal/proximal onset and presence/absence of UMN signs. Results: 56 FAS patients (8% of the population), median age at onset 59.9 years (Q1/Q3, 50.3-68.1), 75% men, were studied. Distal onset with UMN signs occurred in 37.5%, distal onset without UMN signs in 28.6%, proximal onset with UMN signs in 8.9% and proximal onset without UMN signs in 25%. Age of onset, sex, fasciculations at onset, diagnostic delay, progression rate, time to respiratory involvement and survival were similar among the four groups. Sex ratio was more balanced in patients with UMN signs (p = 0.032) and survival was shorter (69.5 months, 95% CI: 55.4-110.4 vs 152.6 months, 95% CI: 69.0-177.3; p = 0.035). The Cox regression identified rate of progression (p < 0.001) and UMN signs (p = 0.003) as independent predictors of shorter survival. Conclusions: Distal or proximal onset had no influence on clinical characteristics and prognosis but UMN signs at diagnosis are a negative prognostic predictor.