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.

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.

Sporadic Spinal-Onset Amyotrophic Lateral Sclerosis Associated with Myopathy in Three Unrelated Portuguese Patients.

Amyotrophic lateral sclerosis (ALS) and myopathy have been already described as part of a common genetic syndrome called multisystem proteinopathy. They may occur together or not, and can be associated with other clinical features such as frontotemporal dementia and Paget's bone disease. In addition, primary skeletal muscle involvement has been also reported in inherited forms of lower motor neuron disease, in spinal-bulbar muscular atrophy and in spinal muscular atrophy. We aim to characterize three sporadic, spinal-onset ALS patients, one with a concurrent non-specific myopathy, and two with a previous diagnosis of myopathy before upper and lower motor neuron signs emerged. Perhaps our sporadic ALS cases associated with myopathy share a common, but still unknown, pathogenic background. These cases raise the paradigm of a possible interplay between skeletal muscle degeneration and motor neuron damage.

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.

Respiratory onset in amyotrophic lateral sclerosis: clinical features and spreading pattern.

Objective To describe the clinical features and progression of patients with respiratory onset amyotrophic lateral sclerosis (ALS). Methods: We analyzed the clinical features, including respiratory tests, functional score, noninvasive ventilation (NIV) time and survival of ALS patients with respiratory-onset in our database consisting of 1688 patients. In a subset of 625 ALS patients we analyzed the spreading pattern to other bodily regions. Results: We included 1579 patients with ALS. Sixty-three patients (4%) presented respiratory-onset (79.4% men, mean onset-age 67.7 ± 8.9yrs). All had predominant LMN involvement, and significant weight loss (>10%) was identified in 38.9%. The respiratory tests were abnormal in these respiratory-onset patients (p < 0.001). ALSFRS-R respiratory subscore was lower in this population (p < 0.001). NIV was adapted in 84.1%, sooner than in the larger group of ALS patients (p < 0.001), and survival from disease onset was shorter (p < 0.001). Respiratory-onset was a predictor of time to NIV (X2=42.0, p < 0.001) and of survival (X2=7.1, p = 0.008). The spreading pattern was studied in 18 patients with isolated respiratory-onset. The progression interval to the 2nd region was 4.7 ± 5.7mo and to a 3rd region 6.1 ± 8.7mo. Different patterns of spread had no impact on survival. Conclusions: This phenotype is typically seen in emaciated older men with predominant lower motor neuron involvement, and is associated with diaphragm paresis and central respiratory involvement. NIV adaptation is rapid but total survival is shorter than in the other patients. Spreading pattern did not affect time to NIV adaptation or total survival, as NIV support is a modifying treatment in the course of ALS.

Triclustering-based classification of longitudinal data for prognostic prediction: targeting relevant clinical endpoints in amyotrophic lateral sclerosis.

This work proposes a new class of explainable prognostic models for longitudinal data classification using triclusters. A new temporally constrained triclustering algorithm, termed TCtriCluster, is proposed to comprehensively find informative temporal patterns common to a subset of patients in a subset of features (triclusters), and use them as discriminative features within a state-of-the-art classifier with guarantees of interpretability. The proposed approach further enhances prediction with the potentialities of model explainability by revealing clinically relevant disease progression patterns underlying prognostics, describing features used for classification. The proposed methodology is used in the Amyotrophic Lateral Sclerosis (ALS) Portuguese cohort (N = 1321), providing the first comprehensive assessment of the prognostic limits of five notable clinical endpoints: need for non-invasive ventilation (NIV); need for an auxiliary communication device; need for percutaneous endoscopic gastrostomy (PEG); need for a caregiver; and need for a wheelchair. Triclustering-based predictors outperform state-of-the-art alternatives, being able to predict the need for auxiliary communication device (within 180 days) and the need for PEG (within 90 days) with an AUC above 90%. The approach was validated in clinical practice, supporting healthcare professionals in understanding the link between the highly heterogeneous patterns of ALS disease progression and the prognosis.

Differential Expression of miRNAs in Amyotrophic Lateral Sclerosis Patients.

Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease that affects nerve cells in the brain and spinal cord, causing loss of muscle control, muscle atrophy and in later stages, death. Diagnosis has an average delay of 1 year after symptoms onset, which impairs early management. The identification of a specific disease biomarker could help decrease the diagnostic delay. MicroRNA (miRNA) expression levels have been proposed as ALS biomarkers, and altered function has been reported in ALS pathogenesis. The aim of this study was to assess the differential expression of plasma miRNAs in ALS patients and two control populations (healthy controls and ALS-mimic disorders). For that, 16 samples from each group were pooled, and then 1008 miRNAs were assessed through reverse transcription-quantitative polymerase chain reaction (RT-qPCR). From these, ten candidate miRNAs were selected and validated in 35 ALS patients, 16 ALS-mimic disorders controls and 15 healthy controls. We also assessed the same miRNAs in two different time points of disease progression. Although we were unable to determine a miRNA signature to use as disease or condition marker, we found that miR-7-2-3p, miR-26a-1-3p, miR-224-5p and miR-206 are good study candidates to understand the pathophysiology of ALS.