Predicting disease progression in amyotrophic lateral sclerosis

Predicting amyotrophic lateral sclerosis (ALS) progression with machine learning

OBJECTIVE

To predict ALS progression with varying observation and prediction window lengths, using machine learning (ML).

METHODS

We used demographic, clinical, and laboratory parameters from 5030 patients in the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database to model ALS disease progression as fast (at least 1.5 points decline in ALS Functional Rating Scale-Revised (ALSFRS-R) per month) or non-fast, using Extreme Gradient Boosting (XGBoost) and Bayesian Long Short Term Memory (BLSTM). XGBoost identified predictors of progression while BLSTM provided a confidence level for each prediction.

RESULTS

ML models achieved area under receiver-operating-characteristics curve (AUROC) of 0.570-0.748 and were non-inferior to clinician assessments. Performance was similar with observation lengths of a single visit, 3, 6, or 12 months and on a holdout validation dataset, but was better for longer prediction lengths. 21 important predictors were identified, with the top 3 being days since disease onset, past ALSFRS-R and forced vital capacity. Nonstandard predictors included phosphorus, chloride and albumin. BLSTM demonstrated higher performance for the samples about which it was most confident. Patient screening by models may reduce hypothetical Phase II/III clinical trial sizes by 18.3%.

CONCLUSION

Similar accuracies across ML models using different observation lengths suggest that a clinical trial observation period could be shortened to a single visit and clinical trial sizes reduced. Confidence levels provided by BLSTM gave additional information on the trustworthiness of predictions, which could aid decision-making. The identified predictors of ALS progression are potential biomarkers and therapeutic targets for further research.

Detection of Amyotrophic Lateral Sclerosis (ALS) Comorbidity Trajectories Based on Principal Tree Model Analytics

The multifaceted nature and swift progression of Amyotrophic Lateral Sclerosis (ALS) pose considerable challenges to our understanding of its evolution and interplay with comorbid conditions. This study seeks to elucidate the temporal dynamics of ALS progression and its interaction with associated diseases. We employed a principal tree-based model to decipher patterns within clinical data derived from a population-based database in Taiwan. The disease progression was portrayed as branched trajectories, each path representing a series of distinct stages. Each stage embodied the cumulative occurrence of co-existing diseases, depicted as nodes on the tree, with edges symbolizing potential transitions between these linked nodes. Our model identified eight distinct ALS patient trajectories, unveiling unique patterns of disease associations at various stages of progression. These patterns may suggest underlying disease mechanisms or risk factors. This research re-conceptualizes ALS progression as a migration through diverse stages, instead of the perspective of a sequence of isolated events. This new approach illuminates patterns of disease association across different progression phases. The insights obtained from this study hold the potential to inform doctors regarding the development of personalized treatment strategies, ultimately enhancing patient prognosis and quality of life.

Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder characterised by the progressive loss of motor neurons in the brain and spinal cord. The fact that ALS's disease course is highly heterogeneous, and its determinants not fully known, combined with ALS's relatively low prevalence, renders the successful application of artificial intelligence (AI) techniques particularly arduous.

OBJECTIVE

This systematic review aims at identifying areas of agreement and unanswered questions regarding two notable applications of AI in ALS, namely the automatic, data-driven stratification of patients according to their phenotype, and the prediction of ALS progression. Differently from previous works, this review is focused on the methodological landscape of AI in ALS.

METHODS

We conducted a systematic search of the Scopus and PubMed databases, looking for studies on data-driven stratification methods based on unsupervised techniques resulting in (A) automatic group discovery or (B) a transformation of the feature space allowing patient subgroups to be identified; and for studies on internally or externally validated methods for the prediction of ALS progression. We described the selected studies according to the following characteristics, when applicable: variables used, methodology, splitting criteria and number of groups, prediction outcomes, validation schemes, and metrics.

RESULTS

Of the starting 1604 unique reports (2837 combined hits between Scopus and PubMed), 239 were selected for thorough screening, leading to the inclusion of 15 studies on patient stratification, 28 on prediction of ALS progression, and 6 on both stratification and prediction. In terms of variables used, most stratification and prediction studies included demographics and features derived from the ALSFRS or ALSFRS-R scores, which were also the main prediction targets. The most represented stratification methods were K-means, and hierarchical and expectation-maximisation clustering; while random forests, logistic regression, the Cox proportional hazard model, and various flavours of deep learning were the most widely used prediction methods. Predictive model validation was, albeit unexpectedly, quite rarely performed in absolute terms (leading to the exclusion of 78 eligible studies), with the overwhelming majority of included studies resorting to internal validation only.

CONCLUSION

This systematic review 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.

Quantitative Brain MRI Metrics Distinguish Four Different ALS Phenotypes: A Machine Learning Based Study

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease whose diagnosis depends on the presence of combined lower motor neuron (LMN) and upper motor neuron (UMN) degeneration. LMN degeneration assessment is aided by electromyography, whereas no equivalent exists to assess UMN dysfunction. Magnetic resonance imaging (MRI) is primarily used to exclude conditions that mimic ALS. We have identified four different clinical/radiological phenotypes of ALS patients. We hypothesize that these ALS phenotypes arise from distinct pathologic processes that result in unique MRI signatures. To our knowledge, no machine learning (ML)-based data analyses have been performed to stratify different ALS phenotypes using MRI measures. During routine clinical evaluation, we obtained T1-, T2-, PD-weighted, diffusion tensor (DT) brain MRI of 15 neurological controls and 91 ALS patients (UMN-predominant ALS with corticospinal tract CST) hyperintensity, n = 21; UMN-predominant ALS without CST hyperintensity, n = 26; classic ALS, n = 23; and ALS patients with frontotemporal dementia, n = 21). From these images, we obtained 101 white matter (WM) attributes (including DT measures, graph theory measures from DT and fractal dimension (FD) measures using T1-weighted), 10 grey matter (GM) attributes (including FD based measures from T1-weighted), and 10 non-imaging attributes (2 demographic and 8 clinical measures of ALS). We employed classification and regression tree, Random Forest (RF) and also artificial neural network for the classifications. RF algorithm provided the best accuracy (70-94%) in classifying four different phenotypes of ALS patients. WM metrics played a dominant role in classifying different phenotypes when compared to GM or clinical measures. Although WM measures from both right and left hemispheres need to be considered to identify ALS phenotypes, they appear to be differentially affected by the degenerative process. Longitudinal studies can confirm and extend our findings.

Implications of Artificial Intelligence Algorithms in the Diagnosis and Treatment of Motor Neuron Diseases-A Review

Motor neuron diseases (MNDs) are a group of chronic neurological disorders characterized by the progressive failure of the motor system. Currently, these disorders do not have a definitive treatment; therefore, it is of huge importance to propose new and more advanced diagnoses and treatment options for MNDs. Nowadays, artificial intelligence is being applied to solve several real-life problems in different areas, including healthcare. It has shown great potential to accelerate the understanding and management of many health disorders, including neurological ones. Therefore, the main objective of this work is to offer a review of the most important research that has been done on the application of artificial intelligence models for analyzing motor disorders. This review includes a general description of the most commonly used AI algorithms and their usage in MND diagnosis, prognosis, and treatment. Finally, we highlight the main issues that must be overcome to take full advantage of what AI can offer us when dealing with MNDs.

Leveraging process mining for modeling progression trajectories in amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease whose spreading and progression mechanisms are still unclear. The ability to predict ALS prognosis would improve the patients' quality of life and support clinicians in planning treatments. In this paper, we investigate ALS evolution trajectories using Process Mining (PM) techniques enriched to both easily mine processes and automatically reveal how the pathways differentiate according to patients' characteristics.

METHODS

We consider data collected in two distinct data sources, namely the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) dataset and a real-world clinical register (ALS-BS) including data of patients followed up in two tertiary clinical centers of Brescia (Italy). With a focus on the functional abilities progressively impaired as the disease progresses, we use two Process Discovery methods, namely the Directly-Follows Graph and the CareFlow Miner, to mine the population disease trajectories on the PRO-ACT dataset. We characterize the impairment trajectories in terms of patterns, timing, and probabilities, and investigate the effect of some patients' characteristics at onset on the followed paths. Finally, we perform a comparative study of the impairment trajectories mined in PRO-ACT versus ALS-BS.

RESULTS

We delineate the progression pathways on PRO-ACT, identifying the predominant disabilities at different stages of the disease: for instance, 85% of patients enter the trials without disabilities, and 48% of them experience the impairment of Walking/Self-care abilities first. We then test how a spinal onset increases the risk of experiencing the loss of Walking/Self-care ability as first impairment (52% vs. 27% of patients develop it as the first impairment in the spinal vs. the bulbar cohorts, respectively), as well as how an older age at onset corresponds to a more rapid progression to death. When compared, the PRO-ACT and the ALS-BS patient populations present some similarities in terms of natural progression of the disease, as well as some differences in terms of observed trajectories plausibly due to the trial scheduling and recruitment criteria.

CONCLUSIONS

We exploited PM to provide an overview of the evolution scenarios of an ALS trial population and to preliminary compare it to the progression observed in a clinical cohort. Future work will focus on further improving the understanding of the disease progression mechanisms, by including additional real-world subjects as well as by extending the set of events considered in the impairment trajectories.

Status of ALS Treatment, Insights into Therapeutic Challenges and Dilemmas

Amyotrophic lateral sclerosis (ALS) is an extremely heterogeneous disease of motor neurons that eventually leads to death. Despite impressive advances in understanding the genetic, molecular, and pathological mechanisms of the disease, the only drug approved to date by both the FDA and EMA is riluzole, with a modest effect on survival. In this opinion view paper, we will discuss how to address some challenges for drug development in ALS at the conceptual, technological, and methodological levels. In addition, socioeconomic and ethical issues related to the legitimate need of patients to benefit quickly from new treatments will also be addressed. In conclusion, this brief review takes a more optimistic view, given the recent approval of two new drugs in some countries and the development of targeted gene therapies.

Identifying patterns in amyotrophic lateral sclerosis progression from sparse longitudinal data

The clinical presentation of amyotrophic lateral sclerosis (ALS), a fatal neurodegenerative disease, varies widely across patients, making it challenging to determine if potential therapeutics slow progression. We sought to determine whether there were common patterns of disease progression that could aid in the design and analysis of clinical trials. We developed an approach based on a mixture of Gaussian processes to identify clusters of patients sharing similar disease progression patterns, modeling their average trajectories and the variability in each cluster. We show that ALS progression is frequently nonlinear, with periods of stable disease preceded or followed by rapid decline. We also show that our approach can be extended to Alzheimer's and Parkinson's diseases. Our results advance the characterization of disease progression of ALS and provide a flexible modeling approach that can be applied to other progressive diseases.

Considerations for Amyotrophic Lateral Sclerosis (ALS) Clinical Trial Design

Thoughtful clinical trial design is critical for efficient therapeutic development, particularly in the field of amyotrophic lateral sclerosis (ALS), where trials often aim to detect modest treatment effects among a population with heterogeneous disease progression. Appropriate outcome measure selection is necessary for trials to provide decisive and informative results. Investigators must consider the outcome measure's reliability, responsiveness to detect change when change has actually occurred, clinical relevance, and psychometric performance. ALS clinical trials can also be performed more efficiently by utilizing statistical enrichment techniques. Innovations in ALS prediction models allow for selection of participants with less heterogeneity in disease progression rates without requiring a lead-in period, or participants can be stratified according to predicted progression. Statistical enrichment can reduce the needed sample size and improve study power, but investigators must find a balance between optimizing statistical efficiency and retaining generalizability of study findings to the broader ALS population. Additional progress is still needed for biomarker development and validation to confirm target engagement in ALS treatment trials. Selection of an appropriate biofluid biomarker depends on the treatment mechanism of interest, and biomarker studies should be incorporated into early phase trials. Inclusion of patients with ALS as advisors and advocates can strengthen clinical trial design and study retention, but more engagement efforts are needed to improve diversity and equity in ALS research studies. Another challenge for ALS therapeutic development is identifying ways to respect patient autonomy and improve access to experimental treatment, something that is strongly desired by many patients with ALS and ALS advocacy organizations. Expanded access programs that run concurrently to well-designed and adequately powered randomized controlled trials may provide an opportunity to broaden access to promising therapeutics without compromising scientific integrity or rushing regulatory approval of therapies without adequate proof of efficacy.

A phase II open label clinical study of the safety, tolerability and efficacy of ILB® for Amyotrophic Lateral Sclerosis

Introduction

Amyotrophic lateral sclerosis (ALS) is an invariably lethal progressive disease, causing degeneration of neurons and muscle. No current treatment halts or reverses disease advance. This single arm, open label, clinical trial in patients with ALS investigated the safety and tolerability of a novel modified low molecular weight dextran sulphate (LMW-DS, named ILB^®) previously proven safe for use in healthy volunteers and shown to exert potent neurotrophic effects in pre-clinical studies. Secondary endpoints relate to efficacy and exploratory biomarkers.

Methods

Thirteen patients with ALS were treated with 5 weekly subcutaneous injections of ILB^®. Safety and efficacy outcome measures were recorded weekly during treatment and at regular intervals for a further 70 days. Functional and laboratory biomarkers were assessed before, during and after treatment.

Results

No deaths, serious adverse events or participant withdrawals occurred during or after ILB^® treatment and no significant drug-related changes in blood safety markers were evident, demonstrating safety and tolerability of the drug in this cohort of patients with ALS. The PK of ILB^® in patients with ALS was similar to that seen in healthy controls. The ILB^® injection elicited a transient elevation of plasma Hepatocyte Growth Factor, a neurotrophic and myogenic growth factor. Following the ILB^® injections patients reported increased vitality, decreased spasticity and increased mobility. The ALSFRS-R rating improved from 36.31 ± 6.66 to 38.77 ± 6.44 and the Norris rating also improved from 70.61 ± 13.91 to 77.85 ± 14.24 by Day 36. The improvement of functions was associated with a decrease in muscle atrophy biomarkers. These therapeutic benefits decreased 3–4 weeks after the last dosage.

Conclusions

This pilot clinical study demonstrates safety and tolerability of ILB^® in patients with ALS. The exploratory biomarker and functional measures must be cautiously interpreted but suggest clinical benefit and have a bearing on the mechanism of action of ILB^®. The results support the drug’s potential as the first disease modifying treatment for patients with ALS.

Trial registration

EudraCT 2017-005065-47.

A machine-learning based objective measure for ALS disease severity

Amyotrophic Lateral Sclerosis (ALS) disease severity is usually measured using the subjective, questionnaire-based revised ALS Functional Rating Scale (ALSFRS-R). Objective measures of disease severity would be powerful tools for evaluating real-world drug effectiveness, efficacy in clinical trials, and for identifying participants for cohort studies. We developed a machine learning (ML) based objective measure for ALS disease severity based on voice samples and accelerometer measurements from a four-year longitudinal dataset. 584 people living with ALS consented and carried out prescribed speaking and limb-based tasks. 542 participants contributed 5814 voice recordings, and 350 contributed 13,009 accelerometer samples, while simultaneously measuring ALSFRS-R scores. Using these data, we trained ML models to predict bulbar-related and limb-related ALSFRS-R scores. On the test set (n = 109 participants) the voice models achieved a multiclass AUC of 0.86 (95% CI, 0.85–0.88) on speech ALSFRS-R prediction, whereas the accelerometer models achieved a median multiclass AUC of 0.73 on 6 limb-related functions. The correlations across functions observed in self-reported ALSFRS-R scores were preserved in ML-derived scores. We used these models and self-reported ALSFRS-R scores to evaluate the real-world effects of edaravone, a drug approved for use in ALS. In the cohort of 54 test participants who received edaravone as part of their usual care, the ML-derived scores were consistent with the self-reported ALSFRS-R scores. At the individual level, the continuous ML-derived score can capture gradual changes that are absent in the integer ALSFRS-R scores. This demonstrates the value of these tools for assessing disease severity and, potentially, drug effects.

Predicting functional impairment trajectories in amyotrophic lateral sclerosis: a probabilistic, multifactorial model of disease progression

Objective

To employ Artificial Intelligence to model, predict and simulate the amyotrophic lateral sclerosis (ALS) progression over time in terms of variable interactions, functional impairments, and survival.

Methods

We employed demographic and clinical variables, including functional scores and the utilisation of support interventions, of 3940 ALS patients from four Italian and two Israeli registers to develop a new approach based on Dynamic Bayesian Networks (DBNs) that models the ALS evolution over time, in two distinct scenarios of variable availability. The method allows to simulate patients’ disease trajectories and predict the probability of functional impairment and survival at different time points.

Results

DBNs explicitly represent the relationships between the variables and the pathways along which they influence the disease progression. Several notable inter-dependencies were identified and validated by comparison with literature. Moreover, the implemented tool allows the assessment of the effect of different markers on the disease course, reproducing the probabilistically expected clinical progressions. The tool shows high concordance in terms of predicted and real prognosis, assessed as time to functional impairments and survival (integral of the AU-ROC in the first 36 months between 0.80–0.93 and 0.84–0.89 for the two scenarios, respectively).

Conclusions

Provided only with measurements commonly collected during the first visit, our models can predict time to the loss of independence in walking, breathing, swallowing, communicating, and survival and it can be used to generate in silico patient cohorts with specific characteristics. Our tool provides a comprehensive framework to support physicians in treatment planning and clinical decision-making.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-022-11022-0.

Neurofilament light and heterogeneity of disease progression in amyotrophic lateral sclerosis: development and validation of a prediction model to improve interventional trials

Background

Interventional trials in amyotrophic lateral sclerosis (ALS) suffer from the heterogeneity of the disease as it considerably reduces statistical power. We asked if blood neurofilament light chains (NfL) could be used to anticipate disease progression and increase trial power.

Methods

In 125 patients with ALS from three independent prospective studies—one observational study and two interventional trials—we developed and externally validated a multivariate linear model for predicting disease progression, measured by the monthly decrease of the ALS Functional Rating Scale Revised (ALSFRS-R) score. We trained the prediction model in the observational study and tested the predictive value of the following parameters assessed at diagnosis: NfL levels, sex, age, site of onset, body mass index, disease duration, ALSFRS-R score, and monthly ALSFRS-R score decrease since disease onset. We then applied the resulting model in the other two study cohorts to assess the actual utility for interventional trials. We analyzed the impact on trial power in mixed-effects models and compared the performance of the NfL model with two currently used predictive approaches, which anticipate disease progression using the ALSFRS-R decrease during a three-month observational period (lead-in) or since disease onset (ΔFRS).

Results

Among the parameters provided, the NfL levels (P < 0.001) and the interaction with site of onset (P < 0.01) contributed significantly to the prediction, forming a robust NfL prediction model (R = 0.67). Model application in the trial cohorts confirmed its applicability and revealed superiority over lead-in and ΔFRS-based approaches. The NfL model improved statistical power by 61% and 22% (95% confidence intervals: 54%–66%, 7%–29%).

Conclusion

The use of the NfL-based prediction model to compensate for clinical heterogeneity in ALS could significantly increase the trial power.

NCT00868166, registered March 23, 2009; NCT02306590, registered December 2, 2014.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40035-021-00257-y.

Mapping of critical events in disease progression through binary classification: Application to amyotrophic lateral sclerosis

BACKGROUND

The progression of many degenerative diseases is tracked periodically using scales evaluating functionality in daily activities. Although estimating the timing of critical events (i.e., disease tollgates) during degenerative disease progression is desirable, the necessary data may not be readily available in scale records. Further, analysis of disease progression poses data challenges, such as censoring and misclassification errors, which need to be addressed to provide meaningful research findings and inform patients.

METHODS

We developed a novel binary classification approach to map scale scores into disease tollgates to describe disease progression leveraging standard/modified Kaplan-Meier analyses. The approach is demonstrated by estimating progression pathways in amyotrophic lateral sclerosis (ALS). Tollgate-based ALS Staging System (TASS) specifies the critical events (i.e., tollgates) in ALS progression. We first developed a binary classification predicting whether each TASS tollgate was passed given the itemized ALSFRS-R scores using 514 ALS patients' data from Mayo Clinic-Rochester. Then, we utilized the binary classification to translate/map the ALSFRS-R data of 3,264 patients from the PRO-ACT database into TASS. We derived the time trajectories of ALS progression through tollgates from the augmented PRO-ACT data using Kaplan-Meier analyses. The effects of misclassification errors, condition-dependent dropouts, and censored data in trajectory estimations were evaluated with Interval Censored Kaplan Meier Analysis and Multistate Model for Panel Data.

RESULTS

The approach using Mayo Clinic data accurately estimated tollgate-passed states of patients given their itemized ALSFRS-R scores (AUCs > 0.90). The tollgate time trajectories derived from the augmented PRO-ACT dataset provide valuable insights; we predicted that the majority of the ALS patients would have modified arm function (67%) and require assistive devices for walking (53%) by the second year after ALS onset. By the third year, most (74%) ALS patients would occasionally use a wheelchair, while 48% of the ALS patients would be wheelchair-dependent by the fourth year. Assistive speech devices and feeding tubes were needed in 49% and 30% of the patients by the third year after ALS onset, respectively. The onset body region alters some tollgate passage time estimations by 1-2 years.

CONCLUSIONS

The estimated tollgate-based time trajectories inform patients and clinicians about prospective assistive device needs and life changes. More research is needed to personalize these estimations according to prognostic factors. Further, the approach can be leveraged in the progression of other diseases.

Decoding distinctive features of plasma extracellular vesicles in amyotrophic lateral sclerosis

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a multifactorial, multisystem motor neuron disease for which currently there is no effective treatment. There is an urgent need to identify biomarkers to tackle the disease's complexity and help in early diagnosis, prognosis, and therapy. Extracellular vesicles (EVs) are nanostructures released by any cell type into body fluids. Their biophysical and biochemical characteristics vary with the parent cell's physiological and pathological state and make them an attractive source of multidimensional data for patient classification and stratification.

METHODS

We analyzed plasma-derived EVs of ALS patients (n = 106) and controls (n = 96), and SOD1^G93A and TDP-43^Q331K mouse models of ALS. We purified plasma EVs by nickel-based isolation, characterized their EV size distribution and morphology respectively by nanotracking analysis and transmission electron microscopy, and analyzed EV markers and protein cargos by Western blot and proteomics. We used machine learning techniques to predict diagnosis and prognosis.

RESULTS

Our procedure resulted in high-yield isolation of intact and polydisperse plasma EVs, with minimal lipoprotein contamination. EVs in the plasma of ALS patients and the two mouse models of ALS had a distinctive size distribution and lower HSP90 levels compared to the controls. In terms of disease progression, the levels of cyclophilin A with the EV size distribution distinguished fast and slow disease progressors, a possibly new means for patient stratification. Immuno-electron microscopy also suggested that phosphorylated TDP-43 is not an intravesicular cargo of plasma-derived EVs.

CONCLUSIONS

Our analysis unmasked features in plasma EVs of ALS patients with potential straightforward clinical application. We conceived an innovative mathematical model based on machine learning which, by integrating EV size distribution data with protein cargoes, gave very high prediction rates for disease diagnosis and prognosis.

Modeling and Bioinformatics Identify Responders to G-CSF in Patients With Amyotrophic Lateral Sclerosis

Objective: Developing an integrative approach to early treatment response classification using survival modeling and bioinformatics with various biomarkers for early assessment of filgrastim (granulocyte colony stimulating factor) treatment effects in amyotrophic lateral sclerosis (ALS) patients. Filgrastim, a hematopoietic growth factor with excellent safety, routinely applied in oncology and stem cell mobilization, had shown preliminary efficacy in ALS. Methods: We conducted individualized long-term filgrastim treatment in 36 ALS patients. The PRO-ACT database, with outcome data from 23 international clinical ALS trials, served as historical control and mathematical reference for survival modeling. Imaging data as well as cytokine and cellular data from stem cell analysis were processed as biomarkers in a non-linear principal component analysis (NLPCA) to identify individual response. Results: Cox proportional hazard and matched-pair analyses revealed a significant survival benefit for filgrastim-treated patients over PRO-ACT comparators. We generated a model for survival estimation based on patients in the PRO-ACT database and then applied the model to filgrastim-treated patients. Model-identified filgrastim responders displayed less functional decline and impressively longer survival than non-responders. Multimodal biomarkers were then analyzed by PCA in the context of model-defined treatment response, allowing identification of subsequent treatment response as early as within 3 months of therapy. Strong treatment response with a median survival of 3.8 years after start of therapy was associated with younger age, increased hematopoietic stem cell mobilization, less aggressive inflammatory cytokine plasma profiles, and preserved pattern of fractional anisotropy as determined by magnetic resonance diffusion tensor imaging (DTI-MRI). Conclusion: Long-term filgrastim is safe, is well-tolerated, and has significant positive effects on disease progression and survival in a small cohort of ALS patients. Developing and applying a model-based biomarker response classification allows use of multimodal biomarker patterns in full potential. This can identify strong individual treatment responders (here: filgrastim) at a very early stage of therapy and may pave the way to an effective individualized treatment option.

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.

Amyotrophic Lateral Sclerosis (ALS) prediction model derived from plasma and CSF biomarkers

Amyotrophic Lateral Sclerosis (ALS) is a degenerative disorder of motor neurons which leads to complete loss of movement in patients. The only FDA approved drug Riluzole provides only symptomatic relief to patients. Early Diagnosis of the disease warrants the importance of diagnostic and prognostic models for predicting disease and disease progression respectively. In the present study we represent the predictive statistical model for ALS using plasma and CSF biomarkers. Forward stepwise (Binary likelihood) Logistic regression model is developed for prediction of ALS. The model has been shown to have excellent validity (94%) with good sensitivity (98%) and specificity (93%). The area under the ROC curve is 99.3%. Along with age and BMI, VEGF (Vascular Endothelial Growth Factor), VEGFR2 (Vascular Endothelial Growth Factor Receptor 2) and TDP43 (TAR DNA Binding Protein 43) in CSF and VEGFR2 and OPTN (Optineurin) in plasma are good predictors of ALS.

Melatonin may slow disease progression in amyotrophic lateral sclerosis: Findings from the Pooled Resource Open-Access ALS Clinic Trials database

INTRODUCTION

We utilized the Pooled Resource Open-Access Clinical Trials (PRO-ACT) database to investigate whether melatonin use among patients with amyotrophic lateral sclerosis (ALS) was associated with slower disease progression and prolonged survival.

METHODS

This retrospective analysis of the PRO-ACT database addresses the impact of melatonin on progression and overall survival of ALS. A Cox proportional hazards ratio model was performed to investigate the effect that melatonin had on time to death. For secondary outcome measures, linear mixed effects regression models were used to ascertain the effect of melatonin on change in standardized ALS Functional Rating Scale (sALSFRS) and percentage predicted forced vital capacity (FVC) scores.

RESULTS

Melatonin users had a significantly decreased annualized hazard death rate compared with the non-melatonin users (hazard ratio, 0.241; 95% confidence interval, 0.088-0.659; P = .0056). The melatonin users also had a slower rate of decline in sALSFRS score (t = 2.71; P = .0069) and change in percent predicted FVC score (t = 2.94; P = .0035) compared with the non-melatonin users.

DISCUSSION

Our findings suggest that melatonin may be beneficial for patients with ALS. Due to the nature of this database, our results are solely intended to be hypothesis-generating and no strong associations can be made. Given the low cost and favorable safety profile of melatonin, the hypotheses generated warrant further investigation.

Neck flexor weakness at diagnosis predicts respiratory impairment in amyotrophic lateral sclerosis

BACKGROUND AND PURPOSE

The purpose was to assess the prognostic role of neck muscle weakness at diagnosis in amyotrophic lateral sclerosis (ALS) patients with respect to survival and respiratory impairment.

METHODS

A retrospective cohort study was conducted. All ALS patients seen in the Turin ALS Centre from 2007 to 2014 were included. Muscle strength at diagnosis was evaluated using the Medical Research Council (MRC) scale. Survival was considered as the time from diagnosis to death or tracheostomy; time to respiratory impairment was considered as the interval from diagnosis to the first event amongst an ALS Functional Rating Scale revised item 10 <4, forced vital capacity <70%, start of non-invasive ventilation or tracheostomy. Time from diagnosis to dysarthria, dysphagia and walking impairment were considered as secondary outcomes. Cox proportional hazard regression models adjusted for sex, age at diagnosis, diagnostic delay, onset site, genetics status and the MRC scores of other muscle groups were used to assess the prognostic role of neck muscles.

RESULTS

A total of 370 patients were included in the study. Fifty-nine (15.9%) patients showed neck flexor weakness at diagnosis; MRC values were mostly in agreement for neck extensors. Neck flexors were the only muscles able to predict survival (hazard ratio 0.49, 95% confidence interval 0.28-0.86; p = 0.01). Furthermore, neck flexor normal strength decreased the risk of respiratory impairment (hazard ratio 0.46, 95% confidence interval 0.22-0.96; p = 0.04) but did not influence any secondary outcomes.

DISCUSSION

Neck flexor weakness at diagnosis predicts survival and respiratory impairment in ALS. This result could be valuable for both planning of patients' interventions and clinical trials' design.

A multistationary loop model of ALS unveils critical molecular interactions involving mitochondria and glucose metabolism

Amyotrophic lateral sclerosis (ALS) is a poor-prognosis disease with puzzling pathogenesis and inconclusive treatments. We develop a mathematical model of ALS based on a system of interactive feedback loops, focusing on the mutant SOD1G93A mouse. Misfolded mutant SOD1 aggregates in motor neuron (MN) mitochondria and triggers a first loop characterized by oxidative phosphorylation impairment, AMP kinase over-activation, 6-phosphofructo-2-kinase (PFK3) rise, glucose metabolism shift from pentose phosphate pathway (PPP) to glycolysis, cell redox unbalance, and further worsening of mitochondrial dysfunction. Oxidative stress then triggers a second loop, involving the excitotoxic glutamatergic cascade, with cytosolic Ca2+ overload, increase of PFK3 expression, and further metabolic shift from PPP to glycolysis. Finally, cytosolic Ca2+ rise is also detrimental to mitochondria and oxidative phosphorylation, thus closing a third loop. These three loops are overlapped and positive (including an even number of inhibitory steps), hence they form a candidate multistationary (bistable) system. To describe the system dynamics, we model the interactions among the functional agents with differential equations. The system turns out to admit two stable equilibria: the healthy state, with high oxidative phosphorylation and preferential PPP, and the pathological state, with AMP kinase activation, PFK3 over expression, oxidative stress, excitotoxicity and MN degeneration. We demonstrate that the loop system is monotone: all functional agents consistently act toward the healthy or pathological condition, depending on low or high mutant SOD1 input. We also highlight that molecular interactions involving PFK3 are crucial, as their deletion disrupts the system's bistability leading to a single healthy equilibrium point. Hence, our mathematical model unveils that promising ALS management strategies should be targeted to mechanisms that keep low PFK3 expression and activity within MNs.

Long-term survival of participants in the CENTAUR trial of sodium phenylbutyrate-taurursodiol in amyotrophic lateral sclerosis

An orally administered, fixed‐dose coformulation of sodium phenylbutyrate‐taurursodiol (PB‐TURSO) significantly slowed functional decline in a randomized, placebo‐controlled, phase 2 trial in ALS (CENTAUR). Herein we report results of a long‐term survival analysis of participants in CENTAUR. In CENTAUR, adults with ALS were randomized 2:1 to PB‐TURSO or placebo. Participants completing the 6‐month (24‐week) randomized phase were eligible to receive PB‐TURSO in the open‐label extension. An all‐cause mortality analysis (35‐month maximum follow‐up post‐randomization) incorporated all randomized participants. Participants and site investigators were blinded to treatment assignments through the duration of follow‐up of this analysis. Vital status was obtained for 135 of 137 participants originally randomized in CENTAUR. Median overall survival was 25.0 months among participants originally randomized to PB‐TURSO and 18.5 months among those originally randomized to placebo (hazard ratio, 0.56; 95% confidence interval, 0.34‐0.92; P = .023). Initiation of PB‐TURSO treatment at baseline resulted in a 6.5‐month longer median survival as compared with placebo. Combined with results from CENTAUR, these results suggest that PB‐TURSO has both functional and survival benefits in ALS.

Umbilical Cord Mesenchymal Stem Cells in Amyotrophic Lateral Sclerosis: an Original Study

OBJECTIVE

Amyotrophic lateral sclerosis (ALS) is still incurable. Although different therapies can affect the health and survival of patients. Our aim is to evaluate the effect of umbilical mesenchymal stem cells administrated intrathecally to patients with amyotrophic lateral sclerosis on disability development and survival.

METHODS

This case-control study involved 67 patients treated with Wharton's jelly mesenchymal stem cells (WJ-MSC). The treated patients were paired with 67 reference patients from the PRO-ACT database which contains patient records from 23 ALS clinical studies (phase 2/3). Patients in the treatment and reference groups were fully matched in terms of race, sex, onset of symptoms (bulbar/spinal), FT9 disease stage at the beginning of therapy and concomitant amyotrophic lateral sclerosis medications. Progression rates prior to treatment varied within a range of ± 0.5 points. All patients received three intrathecal injections of Wharton's jelly-derived mesenchymal stem cells every two months at a dose of 30 × 106 cells. Patients were assessed using the ALSFRS-R scale. Survival times were followed-up until March 2020.

RESULTS

Median survival time increased two-fold in all groups. In terms of progression, three response types measured in ALSFRS-R were observed: decreased progression rate (n = 21, 31.3%), no change in progression rate (n = 33, 49.3%) and increased progression rate (n = 13, 19.4%). Risk-benefit ratios were favorable in all groups. No serious adverse drug reactions were observed.

INTERPRETATION

Wharton's jelly-derived mesenchymal stem cells therapy is safe and effective in some ALS patients, regardless of the clinical features and demographic factors excluding sex. The female sex and a good therapeutic response to the first administration are significant predictors of efficacy following further administrations. Graphical Abstract Medical therapeutic experiment with retrospective case-control analyses.

Manifold learning for amyotrophic lateral sclerosis functional loss assessment : Development and validation of a prognosis model

Amyotrophic lateral sclerosis (ALS) is an inexorably progressive neurodegenerative condition with no effective disease-modifying therapy at present. Given the striking clinical heterogeneity of the condition, the development and validation of reliable prognostic models is a recognised research priority. We present a prognostic model for functional decline in ALS where outcome uncertainty is taken into account. Patient data were reduced and projected onto a 2D space using Uniform Manifold Approximation and Projection (UMAP), a novel non-linear dimension reduction technique. Information from 3756 patients was included. Development data were sourced from past clinical trials. Real-world population data were used as validation data. Predictors included age, gender, region of onset, symptom duration, weight at baseline, functional impairment, and estimated rate of functional loss. UMAP projection of patients showed an informative 2D data distribution. As limited data availability precluded complex model designs, the projection was divided into three zones defined by a functional impairment range probability. Zone membership allowed individual patient prediction. Patients belonging to the first zone had a probability of [Formula: see text] (± [Formula: see text]) to have an ALSFRS score over 20 at 1-year follow-up. Patients within the second zone had a probability of [Formula: see text] (± [Formula: see text]) to have an ALSFRS score between 10 and 30 at 1 year follow-up. Finally, patients within the third zone had a probability of [Formula: see text] (± [Formula: see text]) to have an ALSFRS score lower than 20 at 1 year follow-up. This approach requires a limited set of features, is easily updated, improves with additional patient data, and accounts for results uncertainty. This method could therefore be used in a clinical setting for patient stratification and outcome projection.

Trial of Sodium Phenylbutyrate-Taurursodiol for Amyotrophic Lateral Sclerosis

BACKGROUND

Sodium phenylbutyrate and taurursodiol have been found to reduce neuronal death in experimental models. The efficacy and safety of a combination of the two compounds in persons with amyotrophic lateral sclerosis (ALS) are not known.

METHODS

In this multicenter, randomized, double-blind trial, we enrolled participants with definite ALS who had had an onset of symptoms within the previous 18 months. Participants were randomly assigned in a 2:1 ratio to receive sodium phenylbutyrate-taurursodiol (3 g of sodium phenylbutyrate and 1 g of taurursodiol, administered once a day for 3 weeks and then twice a day) or placebo. The primary outcome was the rate of decline in the total score on the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R; range, 0 to 48, with higher scores indicating better function) through 24 weeks. Secondary outcomes were the rates of decline in isometric muscle strength, plasma phosphorylated axonal neurofilament H subunit levels, and the slow vital capacity; the time to death, tracheostomy, or permanent ventilation; and the time to death, tracheostomy, permanent ventilation, or hospitalization.

RESULTS

A total of 177 persons with ALS were screened for eligibility, and 137 were randomly assigned to receive sodium phenylbutyrate-taurursodiol (89 participants) or placebo (48 participants). In a modified intention-to-treat analysis, the mean rate of change in the ALSFRS-R score was -1.24 points per month with the active drug and -1.66 points per month with placebo (difference, 0.42 points per month; 95% confidence interval, 0.03 to 0.81; P = 0.03). Secondary outcomes did not differ significantly between the two groups. Adverse events with the active drug were mainly gastrointestinal.

CONCLUSIONS

Sodium phenylbutyrate-taurursodiol resulted in slower functional decline than placebo as measured by the ALSFRS-R score over a period of 24 weeks. Secondary outcomes were not significantly different between the two groups. Longer and larger trials are necessary to evaluate the efficacy and safety of sodium phenylbutyrate-taurursodiol in persons with ALS. (Funded by Amylyx Pharmaceuticals and others; CENTAUR ClinicalTrials.gov number, NCT03127514.).

Does including machine learning predictions in ALS clinical trial analysis improve statistical power?

Objective

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease which leads to progressive muscle weakness and eventually death. The increasing availability of large ALS clinical trial datasets have generated much interest in developing predictive models for disease progression. However, the utility of predictive modeling on clinical trial analysis has not been thoroughly evaluated.

Methods

We evaluated a predictive modeling approach for ALS disease progression measured by ALSFRS‐R using the PRO‐ACT database and validated our findings in a novel test set from a former clinical trial. We examined clinical trial scenarios where model predictions could improve statistical power for detecting treatment effects with simulated clinical trials.

Results

Models constructed with imputed PRO‐ACT data have better external validation results than those fitted with complete observations. When fitted with imputed data, super learner (R² = 0.71, MSPE = 19.7) and random forest (R² = 0.70, MSPE = 19.6) have similar performance in the external validation and slightly outperform the linear mixed effects model (R² = 0.69, MSPE = 20.5). Simulation studies suggest including machine learning predictions as a covariate in the analysis model of a 12‐month clinical study can increase the trial's effective sample size by 16% when there is a hypothetical treatment effect of 25% reduction in ALSFRS‐R mean rate of change.

Interpretation

Predictive modeling approaches for ALSFRS‐R are able to explain a moderate amount of variability in longitudinal change, which is improved by robust missing data handling for baseline characteristics. Including ALSFRS‐R post‐baseline model prediction results as a covariate in the model for primary analysis may increase power under moderate treatment effects.

Exploiting mutual information for the imputation of static and dynamic mixed-type clinical data with an adaptive k-nearest neighbours approach

Background

Clinical registers constitute an invaluable resource in the medical data-driven decision making context. Accurate machine learning and data mining approaches on these data can lead to faster diagnosis, definition of tailored interventions, and improved outcome prediction. A typical issue when implementing such approaches is the almost unavoidable presence of missing values in the collected data. In this work, we propose an imputation algorithm based on a mutual information-weighted k-nearest neighbours approach, able to handle the simultaneous presence of missing information in different types of variables. We developed and validated the method on a clinical register, constituted by the information collected over subsequent screening visits of a cohort of patients affected by amyotrophic lateral sclerosis.

Methods

For each subject with missing data to be imputed, we create a feature vector constituted by the information collected over his/her first three months of visits. This vector is used as sample in a k-nearest neighbours procedure, in order to select, among the other patients, the ones with the most similar temporal evolution of the disease over time. An ad hoc similarity metric was implemented for the sample comparison, capable of handling the different nature of the data, the presence of multiple missing values and include the cross-information among features captured by the mutual information statistic.

Results

We validated the proposed imputation method on an independent test set, comparing its performance with those of three state-of-the-art competitors, resulting in better performance. We further assessed the validity of our algorithm by comparing the performance of a survival classifier built on the data imputed with our method versus the one built on the data imputed with the best-performing competitor.

Conclusions

Imputation of missing data is a crucial –and often mandatory– step when working with real-world datasets. The algorithm proposed in this work could effectively impute an amyotrophic lateral sclerosis clinical dataset, by handling the temporal and the mixed-type nature of the data and by exploiting the cross-information among features. We also showed how the imputation quality can affect a machine learning task.

Development and validation of a 1-year survival prognosis estimation model for Amyotrophic Lateral Sclerosis using manifold learning algorithm UMAP

Amyotrophic Lateral Sclerosis (ALS) is an inexorably progressive neurodegenerative condition with no effective disease modifying therapies. The development and validation of reliable prognostic models is a recognised research priority. We present a prognostic model for survival in ALS where result uncertainty is taken into account. Patient data were reduced and projected onto a 2D space using Uniform Manifold Approximation and Projection (UMAP), a novel non-linear dimension reduction technique. Information from 5,220 patients was included as development data originating from past clinical trials, and real-world population data as validation data. Predictors included age, gender, region of onset, symptom duration, weight at baseline, functional impairment, and estimated rate of functional loss. UMAP projection of patients shows an informative 2D data distribution. As limited data availability precluded complex model designs, the projection was divided into three zones with relevant survival rates. These rates were defined using confidence bounds: high, intermediate, and low 1-year survival rates at respectively [Formula: see text] ([Formula: see text]), [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]). Predicted 1-year survival was estimated using zone membership. This approach requires a limited set of features, is easily updated, improves with additional patient data, and accounts for results uncertainty.

Neuroinflammation: An overview of neurodegenerative and metabolic diseases and of biotechnological studies

Neuroinflammation is an important factor contributing to cognitive impairment and neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), Amyotrophic lateral sclerosis (ALS), ischemic injury, and multiple sclerosis (MS). These diseases are characterized by inexorable progressive injury of neuron cells, and loss of motor or cognitive functions. Microglia, which are the resident macrophages in the brain, play an important role in both physiological and pathological conditions. In this review, we provide an updated discussion on the role of ROS and metabolic disease in the pathological mechanisms of activation of the microglial cells and release of cytotoxins, leading to the neurodegenerative process. In addition, we also discuss in vivo models, such as zebrafish and Caenorhabditis elegans, and provide new insights into therapeutics bioinspired by neuropeptides from venomous animals, supporting high throughput drug screening in the near future, searching for a complementary approach to elucidating crucial mechanisms associated with neurodegenerative disorders.

Addressing heterogeneity in amyotrophic lateral sclerosis CLINICAL TRIALS

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative disorder with complex biology and significant clinical heterogeneity. Many preclinical and early phase ALS clinical trials have yielded promising results that could not be replicated in larger phase 3 confirmatory trials. One reason for the lack of reproducibility may be ALS biological and clinical heterogeneity. Therefore, in this review, we explore sources of ALS heterogeneity that may reduce statistical power to evaluate efficacy in ALS trials. We also review efforts to manage clinical heterogeneity, including use of validated disease outcome measures, predictive biomarkers of disease progression, and individual clinical risk stratification. We propose that personalized prognostic models with use of predictive biomarkers may identify patients with ALS for whom a specific therapeutic strategy may be expected to be more successful. Finally, the rapid application of emerging clinical and biomarker strategies may reduce heterogeneity, increase trial efficiency, and, in turn, accelerate ALS drug development.

A case of motor neuron involvement in Gaucher disease

Gaucher disease (GD) is a genetic disorder characterized by an accumulation of glucosylceramide in cells in the monocyte-macrophage system. We describe a case of a 33-year-old man with a previous diagnosis of type 3 GD who displayed a progressive weakening of the limbs followed by upper motor neuron involvement. A diagnosis of definite Amyotrophic Lateral Sclerosis was made. This is the first reported case of concurrent Gaucher disease and the ALS phenotype in the same patient.

CLT-01 The NEALS Consortium - a collaborative research organization

Background: NEALS is a non-profit organization that aims to successfully promote both research and awareness of ALS, encouraging collaborations among clinicians, researchers, and industry through a commitment to open scientific communication. The efforts of NEALS members are recognized by disease foundations, research groups, and industry, generating critical support through awards and project funding.Objectives: Bringing together a group of distinguished scientists and researchers over the nearly quarter century since its inception, NEALS has developed novel research approaches and recruited a number of new investigators into the field of ALS research in an effort to accelerate ALS clinical trials through academic input and partnership.Methods: The NEALS Consortium adheres to an inclusive policy of adding clinical sites wherever there is interest and expertise, improving access to trials in regions where trial participation was not previously available. NEALS sites include 127 academic ALS trial centers in the United States, Canada, Mexico, Lebanon, Italy, Australia and Israel.Education and Participation of the PALS Community in Clinical ResearchAnnual Clinical Research Learning InstitutePALS & CALS as Research AmbassadorsAdvocacy efforts by Research AmbassadorsNEALS website & clinical trial database: http://www.
neals.orgResults: The numerous trials run by NEALS-affiliated Principal Investigators and/or Centers is used as a broad benchmark of success for the Consortium. Additionally, the growth of participation in studies by people with ALS has been used as a similar outcome measure when assessing the success of the organization. The research done by NEALS members, paired with the membership's commitment to a coordinated effort, give the NEALS Consortium a unique role, one with top credibility in the community of people with ALS (PALS). The strong relationship between the Consortium's research members and PALS is substantiated by the increasing participation of PALS in the organization's research efforts. Since 1999, 125,286 PALS have participated in NEALS studies. In 2018 alone, 4,173 people with ALS participated in NEALS trials, up significantly from 1999's participation (n=101).Discussion and conclusions: It is more critical than ever for healthcare professionals to underscore the importance of clinical research into the causes and treatments of ALS and associated conditions. In this vision for the future, by involving more people in ALS research and ensuring that they and those around them are properly informed about the disease, public knowledge regarding rare disease research grows, allowing the research community to give back to the patient population through science breakthroughs and increased disease knowledge.

Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering

Amyotrophic lateral sclerosis (ALS) is a complex progressive neurodegenerative disorder with an estimated prevalence of about 5 per 100,000 people in the United States. In this study, the ALS disease progression is measured by the change of Amyotrophic Lateral Sclerosis Functional Rating Scale (ALSFRS) score over time. The study aims to provide clinical decision support for timely forecasting of the ALS trajectory as well as accurate and reproducible computable phenotypic clustering of participants. Patient data are extracted from DREAM-Phil Bowen ALS Prediction Prize4Life Challenge data, most of which are from the Pooled Resource Open-Access ALS Clinical Trials Database (PRO-ACT) archive. We employed model-based and model-free machine-learning methods to predict the change of the ALSFRS score over time. Using training and testing data we quantified and compared the performance of different techniques. We also used unsupervised machine learning methods to cluster the patients into separate computable phenotypes and interpret the derived subcohorts. Direct prediction of univariate clinical outcomes based on model-based (linear models) or model-free (machine learning based techniques - random forest and Bayesian adaptive regression trees) was only moderately successful. The correlation coefficients between clinically observed changes in ALSFRS scores relative to the model-based/model-free predicted counterparts were 0.427 (random forest) and 0.545(BART). The reliability of these results were assessed using internal statistical cross validation and well as external data validation. Unsupervised clustering generated very reliable and consistent partitions of the patient cohort into four computable phenotypic subgroups. These clusters were explicated by identifying specific salient clinical features included in the PRO-ACT archive that discriminate between the derived subcohorts. There are differences between alternative analytical methods in forecasting specific clinical phenotypes. Although predicting univariate clinical outcomes may be challenging, our results suggest that modern data science strategies are useful in clustering patients and generating evidence-based ALS hypotheses about complex interactions of multivariate factors. Predicting univariate clinical outcomes using the PRO-ACT data yields only marginal accuracy (about 70%). However, unsupervised clustering of participants into sub-groups generates stable, reliable and consistent (exceeding 95%) computable phenotypes whose explication requires interpretation of multivariate sets of features. HIGHLIGHTS: • Used a large ALS data archive of 8,000 patients consisting of 3 million records, including 200 clinical features tracked over 12 months. • Employed model-based and model-free methods to predict ALSFRS changes over time, cluster patients into cohorts, and derive computable phenotypes. • Research findings include stable, reliable, and consistent (95%) patient stratification into computable phenotypes. However, clinical explication of the results requires interpretation of multivariate information. Graphical Abstract ᅟ.

Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions

Background: Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressive neurodegenerative condition with limited therapeutic options at present. Survival from symptom onset ranges from 3 to 5 years depending on genetic, demographic, and phenotypic factors. Despite tireless research efforts, the core etiology of the disease remains elusive and drug development efforts are confounded by the lack of accurate monitoring markers. Disease heterogeneity, late-stage recruitment into pharmaceutical trials, and inclusion of phenotypically admixed patient cohorts are some of the key barriers to successful clinical trials. Machine Learning (ML) models and large international data sets offer unprecedented opportunities to appraise candidate diagnostic, monitoring, and prognostic markers. Accurate patient stratification into well-defined prognostic categories is another aspiration of emerging classification and staging systems. Methods: The objective of this paper is the comprehensive, systematic, and critical review of ML initiatives in ALS to date and their potential in research, clinical, and pharmacological applications. The focus of this review is to provide a dual, clinical-mathematical perspective on recent advances and future directions of the field. Another objective of the paper is the frank discussion of the pitfalls and drawbacks of specific models, highlighting the shortcomings of existing studies and to provide methodological recommendations for future study designs. Results: Despite considerable sample size limitations, ML techniques have already been successfully applied to ALS data sets and a number of promising diagnosis models have been proposed. Prognostic models have been tested using core clinical variables, biological, and neuroimaging data. These models also offer patient stratification opportunities for future clinical trials. Despite the enormous potential of ML in ALS research, statistical assumptions are often violated, the choice of specific statistical models is seldom justified, and the constraints of ML models are rarely enunciated. Conclusions: From a mathematical perspective, the main barrier to the development of validated diagnostic, prognostic, and monitoring indicators stem from limited sample sizes. The combination of multiple clinical, biofluid, and imaging biomarkers is likely to increase the accuracy of mathematical modeling and contribute to optimized clinical trial designs.

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease where substantial heterogeneity in clinical presentation urgently requires a better stratification of patients for the development of drug trials and clinical care. In this study we explored stratification through a crowdsourcing approach, the DREAM Prize4Life ALS Stratification Challenge. Using data from >10,000 patients from ALS clinical trials and 1479 patients from community-based patient registers, more than 30 teams developed new approaches for machine learning and clustering, outperforming the best current predictions of disease outcome. We propose a new method to integrate and analyze patient clusters across methods, showing a clear pattern of consistent and clinically relevant sub-groups of patients that also enabled the reliable classification of new patients. Our analyses reveal novel insights in ALS and describe for the first time the potential of a crowdsourcing to uncover hidden patient sub-populations, and to accelerate disease understanding and therapeutic development.

Pilot trial of inosine to elevate urate levels in amyotrophic lateral sclerosis

OBJECTIVE

To test the safety, tolerability, and urate-elevating capability of the urate precursor inosine taken orally or by feeding tube in people with amyotrophic lateral sclerosis (ALS).

METHODS

This was a pilot, open-label trial in 25 participants with ALS. Treatment duration was 12 weeks. The dose of inosine was titrated at pre-specified time points to elevate serum urate levels to 7-8 mg/dL. Primary outcomes were safety (as assessed by the occurrence of adverse events [AEs]) and tolerability (defined as the ability to complete the 12-week study on study drug). Secondary outcomes included biomarkers of oxidative stress and damage. As an exploratory analysis, observed outcomes were compared with a virtual control arm built using prediction algorithms to estimate ALSFRS-R scores.

RESULTS

Twenty-four out of 25 participants (96%) completed 12 weeks of study drug treatment. One participant was unable to comply with study visits and was lost to follow-up. Serum urate rose to target levels in 6 weeks. No serious AEs attributed to study drug and no AEs of special concern, such as urolithiasis and gout, occurred. Selected biomarkers of oxidative stress and damage had significant changes during the study period. Observed changes in ALSFRS-R did not differ from baseline predictions.

INTERPRETATION

Inosine appeared safe, well tolerated, and effective in raising serum urate levels in people with ALS. These findings, together with epidemiological observations and preclinical data supporting a neuroprotective role of urate in ALS models, provide the rationale for larger clinical trials testing inosine as a potential disease-modifying therapy for ALS.

Initial Identification of a Blood-Based Chromosome Conformation Signature for Aiding in the Diagnosis of Amyotrophic Lateral Sclerosis

BACKGROUND

The identification of blood-based biomarkers specific to the diagnosis of amyotrophic lateral sclerosis (ALS) is an active field of academic and clinical research. While inheritance studies have advanced the field, a majority of patients do not have a known genetic link to the disease, making direct sequence-based genetic testing for ALS difficult. The ability to detect biofluid-based epigenetic changes in ALS would expand the relevance of using genomic information for disease diagnosis.

METHODS

Assessing differences in chromosomal conformations (i.e. how they are positioned in 3-dimensions) represents one approach for assessing epigenetic changes. In this study, we used an industrial platform, EpiSwitch™, to compare the genomic architecture of healthy and diseased patient samples (blood and tissue) to discover a chromosomal conformation signature (CCS) with diagnostic potential in ALS. A three-step biomarker selection process yielded a distinct CCS for ALS, comprised of conformation changes in eight genomic loci and detectable in blood.

FINDINGS

We applied the ALS CCS to determine a diagnosis for 74 unblinded patient samples and subsequently conducted a blinded diagnostic study of 16 samples. Sensitivity and specificity for ALS detection in the 74 unblinded patient samples were 83∙33% (CI 51∙59 to 97∙91%) and 76∙92% (46∙19 to 94∙96%), respectively. In the blinded cohort, sensitivity reached 87∙50% (CI 47∙35 to 99∙68%) and specificity was 75∙0% (34∙91 to 96∙81%).

INTERPRETATIONS

The sensitivity and specificity values achieved using the ALS CCS identified and validated in this study provide an indication that the detection of chromosome conformation signatures is a promising approach to disease diagnosis and can potentially augment current strategies for diagnosing ALS. FUND: This research was funded by Oxford BioDynamics and Innovate UK. Work in the Oxford MND Care and Research Centre is supported by grants from the Motor Neurone Disease Association and the Medical Research Council. Additional support was provided by the Northeast ALS Consortium (NEALS).

Astaxanthin protects against kainic acid-induced seizures and pathological consequences

Excitotoxic damage caused by increased glutamate levels is involved in the pathogenesis of neurodegenerative diseases. Astaxanthin, a natural carotenoid with multiple health benefits, inhibits glutamate release from the brain tissue; however, whether it possesses the ability to affect glutamate-induced brain injury is unknown. The present study investigated the neuroprotective effects of astaxanthin on kainic acid (KA)-induced excitotoxicity in rats and the possible underlying intracellular signaling pathway. The rats were orally administrated with astaxanthin (50 or 100 mg/kg) for 7 days (once a day), and KA (15 mg/kg) was administered intraperitoneally at 1 h after the final administration. The results revealed that KA induced seizures, increased the hippocampal glutamate levels, caused considerable neuronal death and microglial activation in the hippocampal CA3 regions, and increased the production of proinflammatory cytokines. Astaxanthin pretreatment prevented these changes. Furthermore, astaxanthin pretreatment increased the expression of neuronal cell survival-related factors, including phosphorylated Akt, phosphorylated glycogen synthase kinase-3β, and Bcl-2 in the hippocampus of KA-injected rats. These results suggested that astaxanthin can attenuate seizures, mitigate inflammation, augment survival signals, and prevent hippocampal neuronal damage in the animal model of KA-induced excitotoxicity.

Membrane cholesterol depletion in cortical neurons highlights altered NMDA receptor functionality in a mouse model of amyotrophic lateral sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a chronic neurodegenerative disease affecting upper and lower motor neurons, with unknown aetiology. Lipid rafts, cholesterol enriched microdomains of the plasma membrane, have been linked to neurodegenerative disorders like ALS. The NMDA-receptor subcellular localization in lipid rafts is known to play many roles, from modulating memory strength to neurotoxicity. In this study, performed on the widely used G93A mouse model of ALS, we have shown an equal content of total membrane cholesterol in Control and G93A cortical cultures. Moreover, by electrophysiological studies, we have recorded NMDA- and AMPA-evoked currents which were not significantly different between the two neuronal populations. To study the role of membrane cholesterol on glutamate receptor functionality, we have analysed NMDA and AMPA receptors following cholesterol membrane depletion by methyl-β-cyclodextrin (MβCD). Interestingly, MβCD chronic treatment has provoked a significant reduction of NMDA-evoked currents in both cellular populations which was dose- and time-dependent but significantly higher in ALS neurons compared to Control. The different MβCD effect on NMDA-evoked currents was not due to a different membrane receptor subunit composition but seemed to cause in both neuronal populations a NMDA receptor membrane redistribution. MβCD treatment effect was receptor-specific since no alterations in the two neuronal populations were detected on AMPA receptors. These results lead us to speculate for an altered proteomic composition of lipid rafts in cortical mutated neurons and suggest the need for further studies on the lipid rafts composition and on their interaction with membrane receptors in ALS cortices.

The Dual Role of Microglia in ALS: Mechanisms and Therapeutic Approaches

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by a non-cell autonomous motor neuron loss. While it is generally believed that the disease onset takes place inside motor neurons, different cell types mediating neuroinflammatory processes are considered deeply involved in the progression of the disease. On these grounds, many treatments have been tested on ALS animals with the aim of inhibiting or reducing the pro-inflammatory action of microglia and astrocytes and counteract the progression of the disease. Unfortunately, these anti-inflammatory therapies have been only modestly successful. The non-univocal role played by microglia during stress and injuries might explain this failure. Indeed, it is now well recognized that, during ALS, microglia displays different phenotypes, from surveillant in early stages, to activated states, M1 and M2, characterized by the expression of respectively harmful and protective genes in later phases of the disease. Consistently, the inhibition of microglial function seems to be a valid strategy only if the different stages of microglia polarization are taken into account, interfering with the reactivity of microglia specifically targeting only the harmful pathways and/or potentiating the trophic ones. In this review article, we will analyze the features and timing of microglia activation in the light of M1/M2 phenotypes in the main mice models of ALS. Moreover, we will also revise the results obtained by different anti-inflammatory therapies aimed to unbalance the M1/M2 ratio, shifting it towards a protective outcome.

What causes amyotrophic lateral sclerosis?

Amyotrophic lateral sclerosis is a neurodegenerative disease predominantly affecting upper and lower motor neurons, resulting in progressive paralysis and death from respiratory failure within 2 to 3 years. The peak age of onset is 55 to 70 years, with a male predominance. The causes of amyotrophic lateral sclerosis are only partly known, but they include some environmental risk factors as well as several genes that have been identified as harbouring disease-associated variation. Here we review the nature, epidemiology, genetic associations, and environmental exposures associated with amyotrophic lateral sclerosis.