Prognosis for patients with amyotrophic lateral sclerosis: development and validation of a personalised prediction model

Connectome-Based Propagation Model in Amyotrophic Lateral Sclerosis

OBJECTIVE

Clinical trials in amyotrophic lateral sclerosis (ALS) continue to rely on survival or functional scales as endpoints, despite the emergence of quantitative biomarkers. Neuroimaging-based biomarkers in ALS have been shown to detect ALS-associated pathology in vivo, although anatomical patterns of disease spread are poorly characterized. The objective of this study is to simulate disease propagation using network analyses of cerebral magnetic resonance imaging (MRI) data to predict disease progression.

METHODS

Using brain networks of ALS patients (n = 208) and matched controls across longitudinal time points, network-based statistics unraveled progressive network degeneration originating from the motor cortex and expanding in a spatiotemporal manner. We applied a computational model to the MRI scan of patients to simulate this progressive network degeneration. Simulated aggregation levels at the group and individual level were validated with empirical impairment observed at later time points of white matter and clinical decline using both internal and external datasets.

RESULTS

We observe that computer-simulated aggregation levels mimic true disease patterns in ALS patients. Simulated patterns of involvement across cortical areas show significant overlap with the patterns of empirically impaired brain regions on later scans, at both group and individual levels. These findings are validated using an external longitudinal dataset of 30 patients.

INTERPRETATION

Our results are in accordance with established pathological staging systems and may have implications for patient stratification in future clinical trials. Our results demonstrate the utility of computational models in ALS to predict disease progression and underscore their potential as a prognostic biomarker. ANN NEUROL 2020;87:725-738.

Associations between illness cognitions and health-related quality of life in the first year after diagnosis of amyotrophic lateral sclerosis

OBJECTIVE

To describe illness cognitions among patients with amyotrophic lateral sclerosis (ALS), to study cross-sectional associations between illness cognitions and health-related quality of life (HRQoL) and to study the predictive value of illness cognitions measured shortly after the diagnosis for HRQoL at follow-up.

METHODS

Prospective longitudinal design. We administered Self-report questionnaires at study onset (n = 72) and follow-up (n = 48). Median follow-up period was 10.0 months. At baseline median ALS Functional Rating Scale-Revised was 43, median time since onset of symptoms was 13.6 months, 79% of patients presented with spinal onset. Illness cognitions Helplessness, Acceptance and Disease Benefits were measured with the Illness Cognitions Questionnaire (ICQ) and HRQoL with the ALS Assessment Questionnaire (ALSAQ-40). Correlational and regression analyses were used.

RESULTS

Patients experienced more Helplessness at follow-up. We found no significant changes in Acceptance or Disease Benefits at follow-up. In cross-sectional analyses, Helplessness was independently related to worse HRQoL at baseline (β = 0.44; p = .001) and Acceptance and Disease Benefits were independently related to worse HRQoL at follow-up (β = -0.17, p = .045) and (β = -0.186, p = .03 respectively). Longitudinal analyses showed that, adjusted for disease severity at baseline, Helplessness at baseline was a predictor of worse HRQoL at follow-up (β = 0.43; p = .006). None of the illness cognitions were a significant predictor of HRQoL with adjustment for baseline HRQoL.

CONCLUSION

Helplessness was independently associated with HRQoL in the cross-sectional and longitudinal analyses. These results can help us identify patients shortly after diagnosis who might benefit from psychological interventions.

Serum phosphorylated neurofilament heavy-chain levels reflect phenotypic heterogeneity and are an independent predictor of survival in motor neuron disease

To investigate the prognostic role and the major determinants of serum phosphorylated neurofilament heavy -chain (pNfH) concentration across a large cohort of motor neuron disease (MND) phenotypes. Enzyme-linked immunosorbent assay (ELISA) was used to measure serum pNfH concentration in 219 MND patients consecutively enrolled in our tertiary MND clinic. A multifactorial analysis was carried out to investigate the major clinical determinants of serum pNfH. Kaplan–Meier survival curves and Cox regression analysis were performed to explore the prognostic value of serum pNfH. Serum pNfH levels were not homogenous among MND phenotypes; higher concentrations in pyramidal, bulbar, and classic phenotypes were observed. C9orf72-MND exhibited higher pNfH concentrations compared to non-C9orf72 MND. Multiple linear regression analysis revealed mean MEP/cMAP and disease progression rate as the two major predictors of serum pNfH levels (R² = 0.188; p ≤ 0.001). Kaplan–Meier curves showed a significant difference of survival among MND subgroups when divided into quartiles based on pNfH concentrations, log-rank X² = 53.0, p ≤ 0.0001. Our study evidenced that higher serum pNfH concentration is a negative independent prognostic factor for survival. In Cox multivariate model, pNfH concentration showed the highest hazard ratio compared to the other factors influencing survival included in the analysis. pNfH differs among the MND phenotypes and is an independent prognostic factor for survival. This study provides supporting evidence of the role of pNfH as useful prognostic biomarker for MND patients. Neurofilament measurements should be considered in the future prognostic models and in clinical trials for biomarker-based stratification, and to evaluate treatment response.

UK case control study of smoking and risk of amyotrophic lateral sclerosis

Introduction: Susceptibility to amyotrophic lateral sclerosis (ALS) is associated with smoking in some studies, but it is not clear which aspect of smoking behavior is related. Using detailed records of lifetime smoking we investigated the relationship between smoking and ALS in a UK population. Methods: In this retrospective case-control study, smoking status was collected using environmental questionnaires from people diagnosed with ALS between 2008 and 2013 and from age, sex and geographically matched controls. Categorical measures of smoking behavior were: smoking at the time of survey and smoking initiation; continuous measures were intensity (cigarettes per day), duration (years from starting to stopping or time of survey), cigarette pack years, and comprehensive smoking index (CSI), a measure of lifetime smoking. We used logistic regression to assess the risk of ALS with different combinations of smoking variables adjusted for age at survey, gender, level of education, smoking status and alcohol initiation, selecting the best model using the Akaike Information Criterion. Results: There were 388 records with full smoking history. The best-fitting model used CSI and smoking status at the time of survey. We found a weak association between current smoking and risk of ALS, OR 3.63 (95% CI 1.02-13.9) p value 0.05. Increase in CSI score did not increase risk of ALS: OR 0.81 (95% CI 0.58-1.11) p value 0.2.Conclusion: There is weak evidence of a positive effect of current smoking on the risk of ALS which does not show dose-dependence with higher levels of lifetime smoking and maybe a false positive result.

Circulating MicroRNAs as Diagnostic Biomarkers for Motor Neuron Disease

Motor neuron disease (MND) is a kind of neurodegenerative disease that selectively invades spinal cord anterior horn cells, brainstem motor neurons, cortical pyramidal cells and the pyramidal tract. The main clinical features are the symptoms and signs of impaired upper and lower motor neurons, manifested as muscle weakness, atrophy and pyramidal tract signs. Histopathology has shown the disappearance of pyramidal cells in the motor cortex, loss of motor neurons in the anterior horn of the spinal cord and brainstem, and degeneration of the corticospinal tract. Due to the lack of effective treatment methods, the prognosis is generally poor, so it is of great significance to confirm the diagnosis early by various means. However, the current diagnosis of MND mainly relies on the combination of clinical manifestations and neurophysiological examinations, lacking effective means of early diagnosis. Circulating microRNA (CmiRNA) is a kind of stable miRNA molecule in serum, plasma and other body fluids, which has the characteristics of distinct differential expression, sensitive detection and convenient sample collection. As a possible new biomarker of MND, CmiRNA can not only reveal the pathophysiological process of MND, but also monitor disease progression and response to drug therapy. With the development of miRNA detection technology, more and more CmiRNAs as biomarkers with potential diagnostic value have been investigated. In this review, we explored the possibility of circulating samples as different sources of biomarkers for the diagnosis of MND, analyzing the progress of CmiRNA detection techniques, and presenting potential diagnostic MND biomarkers that have been reported.

ALS is a multistep process in South Korean, Japanese, and Australian patients

OBJECTIVE

To establish whether amyotrophic lateral sclerosis (ALS) is a multistep process in South Korean and Japanese populations when compared to Australian cohorts.

METHODS

We generated incident data by age and sex for Japanese (collected between April 2009 and March 2010) and South Korean patients with ALS (collected between January 2011 and December 2015). Mortality rates were provided for Australian patients with ALS (collected between 2007 and 2016). We regressed the log of age-specific incidence against the log of age with least squares regression for each ALS population.

RESULTS

We identified 11,834 cases of ALS from the 3 populations, including 6,524 Australian, 2,264 Japanese, and 3,049 South Korean ALS cases. We established a linear relation between the log incidence and log age in the 3 populations: Australia r 2 = 0.99, Japan r 2 = 0.99, South Korea r 2 = 0.99. The estimate slopes were similar across the 3 populations, being 5.4 (95% confidence interval [CI], 4.8-5.5) in Japanese, 5.4 (95% CI, 5.2-5.7) in Australian, and 4.4 (95% CI, 4.2-4.8) in South Korean patients.

CONCLUSIONS

The linear relationship between log age and log incidence is consistent with a multistage model of disease, with slope estimated suggesting that 6 steps were required in Japanese and Australian patients with ALS while 5 steps were needed in South Korean patients. Identification of these steps could identify novel therapeutic strategies.

Diaphragmatic CMAP amplitude from phrenic nerve stimulation predicts functional decline in ALS

OBJECTIVE

To evaluate phrenic nerve motor amplitude (PhrenicAmp) as an independent predictor of functional decline in amyotrophic lateral sclerosis (ALS). We also assessed both PhrenicAmp and forced vital capacity (FVC) as predictors of functional loss in patients with bulbar dysfunction.

METHODS

We included consecutive ALS patients with PhrenicAmp and FVC at baseline. Participants were evaluated with the revised ALS Functional Rating Scale (ALSFRS-R) at inclusion and at, at least, one subsequent follow-up visit. The outcome measure of functional decline was the percentage reduction in ALSFRS-R from baseline. Bulbar dysfunction was defined by the presence of any relevant symptom on the ALSFRS-R bulbar sub-score. Correlations and mixed-effects regressions were used to study the relationship between functional decline and both PhrenicAmp and FVC baseline evaluations.

RESULTS

A total of 249 ALS patients were included; 64.2% of these had bulbar dysfunction. At inclusion, significant correlations were found between PhrenicAmp and FVC (p < 0.001), as well as between each respiratory measure and ALSFRS-R (all p < 0.001). The functional decline at first (median 3 months) and second (median 6 months) follow-up visits was significantly correlated with baseline values of both respiratory evaluations (all p < 0.01) in the entire ALS population, but only with baseline PhrenicAmp (all p < 0.05) in bulbar dysfunction cases. Regression analysis revealed that PhrenicAmp (all p < 0.05), but not FVC, was a significant independent predictor of functional decline in ALS patients and in those with bulbar dysfunction.

CONCLUSION

Baseline PhrenicAmp is an independent predictor of functional decline in ALS, whether or not bulbar dysfunction is present.

Improvements in the multidisciplinary care are beneficial for survival in amyotrophic lateral sclerosis (ALS): experience from a tertiary ALS center

Objective: The Ljubljana ALS Centre, established in 2002, is the only tertiary center for amyotrophic lateral sclerosis (ALS) in Slovenia. The aim of our study was to evaluate the impact of therapeutic interventions and improvements in the multidisciplinary care on the survival of our patients.Methods: All patients diagnosed with ALS at our center during years 2003-2005 (early group) and 2011-2012 (late group) were included in this retrospective cohort study (n = 124). Kaplan-Meier survival analysis and multiple regression analysis with Cox proportional hazards model were performed to compare survival and to evaluate the differences between the two cohorts.Results: Median survival from the time of diagnosis was 13.0 (95% CI 10.2-15.8) months in the early group and 21.8 (95% CI 17.2-26.4) months in the late group (p = 0.005). In the Cox proportional hazards analysis, the late group of patients was associated with better survival independently of all other prognostic factors (hazard ratio (HR)=0.51, 95% CI = 0.32-0.81, p = 0.004). Survival was also associated with patients' age, use of noninvasive ventilation (NIV) and gastrostomy. The model fit significantly improved when the interaction between the NIV use and the observed time period was added to the model (HR = 0.34, 95% CI = 0.12-0.96, p = 0.041).Conclusions: Our findings suggest that improvements in the multidisciplinary care were beneficial for survival of our patients with ALS. The survival benefit in the late group of our patients could be partially explained by the improvements in the NIV use at our center.

Regional spreading of symptoms at diagnosis as a prognostic marker in amyotrophic lateral sclerosis: a population-based study

OBJECTIVE

The lack of prognostic biomarkers in patients with amyotrophic lateral sclerosis (ALS) induced researchers to develop clinical evaluation tools for stratification and survival prediction. We assessed the correlation between patterns of functional involvement, considered as a cumulative number of body regions involved, and overall survival in a population-based series of patients with ALS (PARALS).

METHODS

We derived the functional involvement of four body regions at diagnosis using ALSFRS-R subscores for bulbar, upper limbs, lower limbs and respiratory/thoracic regions. We analysed the effect of number of body regions involved (NBRI) at diagnosis on overall survival, adjusting for age at onset, sex, site of onset, diagnostic delay, forced vital capacity, body mass index, mutational status, cognition and comparing it with King's staging system.

RESULTS

The NBRI was strongly related to survival, with a progressive increase of death/tracheostomy risk among groups (two body regions HR=1.24, 95% CI 1.06 to 1.45, p=0007; three body regions HR=1.65, 95% CI 1.38 to 1.98, p<0.001; four body regions HR=2.68, 95% CI 2.11 to 3.39, p<0.001). Using ALSFRS-R score, the consistency between the number of regions involved and King's clinical stage at diagnosis was very high (81%). The evaluation of respiratory/thoracic region and cognition allowed to subdivide patients into different prognostic categories. Regional spreading of the disease is associated with survival, independently from the initial region involved.

CONCLUSIONS

The evaluation of NBRI, with the inclusion of initial respiratory/thoracic involvement and cognition, can be useful in many research fields, improving the stratification of patients. Our findings highlight the importance of the spatial spreading of functional impairment in the prediction of ALS outcome.

Diagnostic and prognostic values of PBMC proteins in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal motor neuron disease for which there are no validated biomarkers. Previous exploratory studies have identified a panel of candidate protein biomarkers in peripheral blood mononuclear cells (PBMCs) that include peptidyl-prolyl cis-trans isomerase A (PPIA), heat shock cognate protein 71 kDa (HSC70), heterogeneous nuclear ribonucleoprotein A2/B1 (hnRNPA2B1) and TDP-43. It has also been found that PPIA plays a key role in the assembly and dynamics of ribonucleoprotein (RNP) complexes and interacts with TDP-43. Its absence accelerates disease progression in a SOD1 mouse model of ALS, and low levels of PPIA in PBMCs are associated with early-onset ALS. However, the diagnostic and prognostic values of PPIA and the other candidate protein biomarkers have not been established. We analyzed the PBMC proteins in a well-characterized cohort of ALS patients (n=93), healthy individuals (n=104) and disease controls (n=111). We used a highly controlled sample processing procedure that implies two-step differential detergent fractionation. We found that the levels of the selected PBMC proteins in the soluble and insoluble fraction, combined, have a high discriminatory power for distinguishing ALS from controls, with PPIA, hnRNPA2B1 and TDP-43 being the proteins most closely associated with ALS. We also found a shift toward increased protein partitioning in the insoluble fraction in ALS and this correlated with a worse disease phenotype. In particular, low PPIA soluble levels were associated with six months earlier death. In conclusion, PPIA is a disease modifier with prognostic potential. PBMC proteins indicative of alterations in protein and RNA homeostasis are promising biomarkers of ALS, for diagnosis, prognosis and patient stratification.

Spectroscopic markers of neurodegeneration in the mesial prefrontal cortex predict survival in ALS

Background and objective: N-acetylaspartate (NAA) and myo-inositol (mIns) are spectroscopic markers of neuronal integrity and astrogliosis, respectively. We performed a survival analysis to determine the prognostic value of the NAA/mIns metabolite ratio in ALS after a period of two and five years. Methods: Twenty-four patients with ALS (two with ALS-FTD) were recruited to participate in a high-field MR spectroscopy study of the mesial prefrontal cortex. Univariate and multivariate Cox proportional hazards analyses were used to assess NAA/mIns as a predictor of survival alongside other demographic and clinical measures. Census dates were set at two and five years after the time of MR scan for each patient. Survival curves were calculated using the Kaplan-Meier method. Results: After a five-year observation period, 19 patients had died and five were still alive. Median survival time from date of scan was 1.95 years. Univariate and multivariate Cox analysis showed NAA/mIns to be a significant independent predictor of survival at two years after scanning, but not at five years. Conclusion: Cerebral degeneration in the mesial prefrontal cortex as detected by the NAA/mIns metabolite ratio is predictive of survival in ALS in a time-dependent manner.

A multicenter random forest model for effective prognosis prediction in collaborative clinical research network

BACKGROUND

The accuracy of a prognostic prediction model has become an essential aspect of the quality and reliability of the health-related decisions made by clinicians in modern medicine. Unfortunately, individual institutions often lack sufficient samples, which might not provide sufficient statistical power for models. One mitigation is to expand data collection from a single institution to multiple centers to collectively increase the sample size. However, sharing sensitive biomedical data for research involves complicated issues. Machine learning models such as random forests (RF), though they are commonly used and achieve good performances for prognostic prediction, usually suffer worse performance under multicenter privacy-preserving data mining scenarios compared to a centrally trained version.

METHODS AND MATERIALS

In this study, a multicenter random forest prognosis prediction model is proposed that enables federated clinical data mining from horizontally partitioned datasets. By using a novel data enhancement approach based on a differentially private generative adversarial network customized to clinical prognosis data, the proposed model is able to provide a multicenter RF model with performances on par with-or even better than-centrally trained RF but without the need to aggregate the raw data. Moreover, our model also incorporates an importance ranking step designed for feature selection without sharing patient-level information.

RESULT

The proposed model was evaluated on colorectal cancer datasets from the US and China. Two groups of datasets with different levels of heterogeneity within the collaborative research network were selected. First, we compare the performance of the distributed random forest model under different privacy parameters with different percentages of enhancement datasets and validate the effectiveness and plausibility of our approach. Then, we compare the discrimination and calibration ability of the proposed multicenter random forest with a centrally trained random forest model and other tree-based classifiers as well as some commonly used machine learning methods. The results show that the proposed model can provide better prediction performance in terms of discrimination and calibration ability than the centrally trained RF model or the other candidate models while following the privacy-preserving rules in both groups. Additionally, good discrimination and calibration ability are shown on the simplified model based on the feature importance ranking in the proposed approach.

CONCLUSION

The proposed random forest model exhibits ideal prediction capability using multicenter clinical data and overcomes the performance limitation arising from privacy guarantees. It can also provide feature importance ranking across institutions without pooling the data at a central site. This study offers a practical solution for building a prognosis prediction model in the collaborative clinical research network and solves practical issues in real-world applications of medical artificial intelligence.

High neutrophil-to-lymphocyte ratio predicts short survival duration in amyotrophic lateral sclerosis

The present study aimed to investigate the prognostic importance of the neutrophil-to-lymphocyte ratio (NLR) in patients with amyotrophic lateral sclerosis (ALS). Among 322 patients diagnosed as having definite, probable, or possible ALS at a single tertiary hospital, 194 patients were included in the final analysis. Patients were divided into three groups (T1, T2, and T3) according to the tertile of their NLR. Survival rate was significantly lower in T3 compared to the other groups (log-rank test; T1 vs. T3, p = 0.009; T2 vs. T3, p = 0.008). Median survival duration was 37.0 (24.0–56.0), 32.5 (19.5–51.2), and 22.0 (17.0–38.0) months in T1, T2, and T3, respectively. In a multivariable Cox proportional hazards regression analysis, the hazard ratio of age at onset, bulbar-onset, and NLR (T3/T1) was 1.04 (1.02–1.06, p < 0.001), 1.68 (1.10–2.57, p = 0.015), and 1.60 (1.01–2.51, p = 0.041), respectively. A high baseline NLR may serve as a useful indicator for short survival duration in patients with ALS.

Classifying Patients with Amyotrophic Lateral Sclerosis by Changes in FVC. A Group-based Trajectory Analysis

Rationale: A model for stratifying progression of respiratory muscle weakness in amyotrophic lateral sclerosis (ALS) would identify disease mechanisms and phenotypes suitable for future investigations. This study sought to categorize progression of FVC after presentation to an outpatient ALS clinic.Objectives: To identify clinical phenotypes of ALS respiratory progression based on FVC trajectories over time.Methods: We derived a group-based trajectory model from a single-center cohort of 837 patients with ALS who presented between 2006 and 2015. We applied our model to the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database with 7,461 patients with ALS. Baseline characteristics at first visit were used as predictors of trajectory group membership. The primary outcome was trajectory of FVC over time in months.Measurements and Main Results: We found three trajectories of FVC over time, termed "stable low," "rapid progressor," and "slow progressor." Compared with the slow progressors, the rapid progressors had shorter diagnosis delay, more bulbar-onset disease, and a lower ALS Functional Rating Scale-Revised (ALSFRS-R) total score at baseline. The stable low group had a shorter diagnosis delay, lower body mass index, more bulbar-onset disease, lower ALSFRS-R total score, and were more likely to have an ALSFRS-R orthopnea score lower than 4 compared with the slow progressors. We found that projected group membership predicted respiratory insufficiency in the PRO-ACT cohort (concordance statistic = 0.78, 95% CI, 0.76-0.79).Conclusions: We derived a group-based trajectory model for FVC progression in ALS, which validated against the outcome of respiratory insufficiency in an external cohort. Future studies may focus on patients predicted to be rapid progressors.

Spinal Cord Metabolic Signatures in Models of Fast- and Slow-Progressing SOD1G93A Amyotrophic Lateral Sclerosis

The rate of disease progression in amyotrophic lateral sclerosis (ALS) is highly variable, even between patients with the same genetic mutations. Metabolic alterations may affect disease course variability in ALS patients, but challenges in identifying the preclinical and early phases of the disease limit our understanding of molecular mechanisms underlying differences in the rate of disease progression. We examined effects of SOD1^G93A on thoracic and lumbar spinal cord metabolites in two mouse ALS models with different rates of disease progression: the transgenic SOD1^G93A-C57BL/6JOlaHsd (C57-G93A, slow progression) and transgenic SOD1^G93A-129SvHsd (129S-G93A, fast progression) strains. Samples from three timepoints (presymptomatic, disease onset, and late stage disease) were analyzed using Gas Chromatography-Mass Spectrometry metabolomics. Tissue metabolome differences in the lumbar spinal cord were driven primarily by mouse genetic background, although larger responses were observed in metabolic trajectories after the onset of symptoms. The significantly affected lumbar spinal cord metabolites were involved in energy and lipid metabolism. In the thoracic spinal cord, metabolic differences related to genetic background, background-SOD1 genotype interactions, and longitudinal SOD1^G93A effects. The largest responses in thoracic spinal cord metabolic trajectories related to SOD1^G93A effects before onset of visible symptoms. More metabolites were significantly affected in the thoracic segment, which were involved in energy homeostasis, neurotransmitter synthesis and utilization, and the oxidative stress response. We find evidence that initial metabolic alterations in SOD1^G93A mice confer disadvantages for maintaining neuronal viability under ALS-related stressors, with slow-progressing C57-G93A mice potentially having more favorable spinal cord bioenergetic profiles than 129S-G93A. These genetic background-associated metabolic differences together with the different early metabolic responses underscore the need to better characterize the impact of germline genetic variation on cellular responses to ALS gene mutations both before and after the onset of symptoms in order to understand their impact on disease development.

C9orf72 and intracerebral hemorrhage

The chromosome 9 open reading frame 72 (C9orf72) GGGGCC repeat expansion has been associated with several diseases, including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. It has also been associated with increased white matter changes in frontotemporal dementia and risk of cognitive impairment in ALS. Dementia is common both before and after intracerebral hemorrhage (ICH). Because the mechanisms of cognitive impairment in patients with ICH are uncertain, we investigated whether C9orf72 could influence dementia risk in this patient group. Therefore, we genotyped 1010 clinically characterized ICH cases and 2147 population controls in comparison with prior data of dementia and ALS cases. We did not find any association between C9orf72 repeat expansion and repeat size with ICH compared with controls or with dementia when assessing ICH patients only. The frequency of C9orf72 expansions in our series of individuals born in 1946 (2/2147) and other U.K. controls was age dependent, decreasing with increasing age, highlighting the high age-dependent penetrance of this expansion.

CMS-01 Genetic testing for familial amyotrophic lateral sclerosis (ALS): insights and challenges

Background: Pathogenic variants in ALS genes are known to be present in up to 70% of familial and 10% of apparently sporadic ALS cases, and can be associated with risks for ALS only, or risks for other neurodegenerative diseases (eg. frontotemporal dementia). While there are no changes to medical management for patients confirmed as pathogenic variant carriers, genetic testing may be important for future drug trials. Confirmation of a pathogenic variant also provides relatives with the opportunity to consider predictive and/or reproductive genetic testing. Genetic counselling is an important aspect of testing decision-making as it enables individuals to make informed decisions about genetic testing while minimising adverse psychological, ethical and legal outcomes. Few studies have explored how individuals decide whether to pursue testing, nor the needs and experiences of familial ALS families.Objective: To identify factors that influence patient and family member decision-making about genetic testing for ALS genes, assess the impact of familial disease on the patient and their family, and identify information and support needs.Methods: In-depth, semi-structured interviews with individuals from Australian ALS families with known pathogenic gene variants explored experiences of familial ALS, and factors that influenced genetic testing decision-making. Interviews were analysed using an inductive approach.Results: Thirty-four individuals from 24 families were interviewed and included patients (n = 4), spouses (n = 4), and asymptomatic at-risk relatives (n = 26). Life stage, experience of disease, costs, research opportunities, and attitudes to familial ALS and/or reproductive options influenced decision-making. Some patients and relatives experienced difficulty gaining accurate information from their health professionals about the costs and implications of genetic counselling or testing, resulting in a reluctance to proceed.Discussion and conclusion: This study provides new insight into the Australian experience of genetic testing and counselling for familial ALS. It highlights the need to work together with other health professionals to ensure the complexities of genetic testing decision-making, and referral pathways are better understood.

Safety and tolerability of Triumeq in amyotrophic lateral sclerosis: the Lighthouse trial

Background: Neuroinflammation and human endogenous retroviruses (HERV) are thought to have a role in the pathophysiology of amyotrophic lateral sclerosis (ALS). Therapy directed against endogenous retroviruses has demonstrated positive effects during in vitro and biomarker studies. Consequently, the present study was undertaken to assess the safety and tolerability of long-term antiretroviral therapy (ART), Triumeq (abacavir, lamivudine, and dolutegravir) exposure in patients with ALS, and efficacy against biomarkers of disease progression. Methods: Patients were observed during a 10-week lead-in period before receiving Triumeq treatment for 24 weeks at four specialist ALS centers. The primary outcomes were safety and tolerability. Secondary outcomes included HERV-K expression levels, urinary p75ECD levels, neurophysiological parameters, and clinical indicators. The ENCALS prediction model was applied to provide an estimate of the cohort survival. The trial was registered (NCT02868580). Findings: 40 patients with ALS received Triumeq and 35 (88%) completed treatment. There were no drug-related serious adverse events; one patient was withdrawn from the study due to a drug-associated increase in liver enzymes. A favorable response on HERV-K expression levels was observed, accompanied by a decline in ALSFRS-R progression rate of 21.8% (95% CI -4.8%-48.6%) and the amount of urinary p75ECD measured. One patient died five months after stopping treatment, while five were expected to have died during the treatment period (interquartile range 2-8). Interpretation: Long-term Triumeq exposure was safe and well tolerated in this cohort. There was suggestive indication for a possible biological response in some pharmacodynamic and clinical biomarkers. A larger international phase 3 trial will be deployed to assess the effect of Triumeq on overall survival and disease progression. Funding: Funding was provided by the FightMND Foundation; MND Research Institute of Australia; MND Association, United Kingdom, and GSK. ViiV Healthcare provided the Triumeq.

CSF chitinase proteins in amyotrophic lateral sclerosis

OBJECTIVE

To evaluate the classifier performance, clinical and biochemical correlations of cerebrospinal fluid (CSF) levels of the chitinase proteins Chitotriosidase-1 (CHIT1), Chitinase-3-like protein 1 (CHI3L1) and Chitinase-3-like protein 2 (CHI3L2) in amyotrophic lateral sclerosis (ALS).

METHODS

CSF levels of CHIT1, CHI3L1, CHI3L2, phosphorylated neurofilament heavy chain (pNFH) and C-reactive protein were measured by ELISA in a longitudinal cohort of patients with ALS (n=82), primary lateral sclerosis (PLS, n=10), ALS-mimic conditions (n=12), healthy controls (n=25) and asymptomatic carriers of ALS-causing genetic mutations (AGC; n=5).

RESULTS

CSF CHIT1, CHI3L1 and CHI3L2 were elevated in patients with ALS compared with healthy controls (p<0.001) and ALS-mimics (CHIT1, p<0.001; CHI3L1, p=0.017; CHI3L2, p<0.001). CHIT1 and CHI3L2 were elevated in ALS compared with PLS (CHIT1, p=0.021; CHI3L1, p=0.417; CHI3L2, p<0.001). Chitinase levels were similar in AGCs and healthy controls. Chitinase proteins distinguished ALS from healthy controls (area under the curve (AUC): CHIT1 0.92; CHI3L1 0.80; CHI3L2 0.90), mimics (AUC: CHIT1 0.84; CHI3L1 0.73; CHI3L2 0.88) and, to a lesser extent, PLS (AUC: CHIT 0.73; CHI3L1 0.51; CHI3L2 0.82) but did not outperform pNFH. CHIT1 and CHI3L2 correlated with disease progression rate (Pearson's r=0.49, p<0.001; r=0.42, p<0.001, respectively). CHI3L1 correlated with degree of cognitive dysfunction (r=-0.25, p=0.038). All chitinases correlated with pNFH. CHIT1 levels were associated with survival in multivariate models. Chitinase levels were longitudinally stable.

CONCLUSIONS

CSF chitinase proteins may have limited value as independent diagnostic and stratification biomarkers in ALS, but offer a window into non-autonomous mechanisms of motor neuronal loss in ALS, specifically in assessing response to therapies targeting neuroinflammatory pathways.

Pharmacogenetic interactions in amyotrophic lateral sclerosis: a step closer to a cure?

Genetic mutations related to amyotrophic lateral sclerosis (ALS) act through distinct pathophysiological pathways, which may lead to varying treatment responses. Here we assess the genetic interaction between C9orf72, UNC13A, and MOBP with creatine and valproic acid treatment in two clinical trials. Genotypic data was available for 309 of the 338 participants (91.4%). The UNC13A genotype affected mortality (p = 0.012), whereas C9orf72 repeat-expansion carriers exhibited a faster rate of decline in overall (p = 0.051) and bulbar functioning (p = 0.005). A dose-response pharmacogenetic interaction was identified between creatine and the A allele of the MOBP genotype (p = 0.027), suggesting a qualitative interaction in a recessive model (HR 3.96, p = 0.015). Not taking genetic information into account may mask evidence of response to treatment or be an unrecognized source of bias. Incorporating genetic data could help investigators to identify critical treatment clues in patients with ALS.

Serum neurofilament heavy chains as early marker of motor neuron degeneration

Objective

To determine whether serum phosphorylated neurofilament heavy chain (pNfH) levels are elevated before patients were diagnosed with sporadic or familial ALS, and what the prognostic value of these prediagnostic pNfH levels is.

Methods

pNfH was measured via ELISA in leftovers of serum drawn for routine purposes before the time of diagnosis. These prediagnostic samples were retrieved from the biobank of the University Hospitals Leuven for 95 patients who in follow‐up received a diagnosis of ALS. Additionally, 35 patients with mild cognitive impairment (MCI) and 85 healthy controls (HC) were included in this retrospective study.

Results

The median disease duration (range) from onset to prediagnostic sampling and from onset to diagnosis was 6.5 (−71.9–36.1) and 9.9 (2.0–40.7) months, respectively. Fifty‐eight percent of the prediagnostic samples had serum pNfH levels above the 95th percentile of pNfH levels measured in HC. Serum pNfH levels (median (range)) were elevated up to 18 months before the diagnosis of ALS (91 pg/mL (6–342 pg/mL)) in comparison with HC (30 pg/mL (6–146 pg/mL); P = 0.05), and increased during the prediagnostic stage, which was not observed in patients with MCI. Furthermore, prediagnostic pNfH levels were a univariate predictor of survival in ALS (hazard ratio (95% CI): 2.16 (1.20–3.87); P = 0.01).

Interpretation

Our findings demonstrate that serum pNfH is elevated well before the time of diagnosis in mainly sporadic ALS patients. These results encourage to prospectively explore if pNfH has an added value to shorten the diagnostic delay in ALS.

Cross-sectional and longitudinal assessment of the upper cervical spinal cord in motor neuron disease

Background

Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease characterized by both upper and lower motor neuron degeneration. While neuroimaging studies of the brain can detect upper motor neuron degeneration, these brain MRI scans also include the upper part of the cervical spinal cord, which offers the possibility to expand the focus also towards lower motor neuron degeneration. Here, we set out to investigate cross-sectional and longitudinal disease effects in the upper cervical spinal cord in patients with ALS, progressive muscular atrophy (PMA: primarily lower motor neuron involvement) and primary lateral sclerosis (PLS: primarily upper motor neuron involvement), and their relation to disease severity and grey and white matter brain measurements.

Methods

We enrolled 108 ALS patients without C9orf72 repeat expansion (ALS C9–), 26 ALS patients with C9orf72 repeat expansion (ALS C9+), 28 PLS patients, 56 PMA patients and 114 controls. During up to five visits, longitudinal T1-weighted brain MRI data were acquired and used to segment the upper cervical spinal cord (UCSC, up to C3) and individual cervical segments (C1 to C4) to calculate cross-sectional areas (CSA). Using linear (mixed-effects) models, the CSA differences were assessed between groups and correlated with disease severity. Furthermore, a relationship between CSA and brain measurements was examined in terms of cortical thickness of the precentral gyrus and white matter integrity of the corticospinal tract.

Results

Compared to controls, CSAs at baseline showed significantly thinner UCSC in all groups in the MND spectrum. Over time, ALS C9– patients demonstrated significant thinning of the UCSC and, more specifically, of segment C3 compared to controls. Progressive thinning over time was also observed in C1 of PMA patients, while ALS C9+ and PLS patients did not show any longitudinal changes. Longitudinal spinal cord measurements showed a significant relationship with disease severity and we found a significant correlation between spinal cord and motor cortex thickness or corticospinal tract integrity for PLS and PMA, but not for ALS patients.

Discussion

Our findings demonstrate atrophy of the upper cervical spinal cord in the motor neuron disease spectrum, which was progressive over time for all but PLS patients. Cervical spinal cord imaging in ALS seems to capture different disease effects than brain neuroimaging. Atrophy of the cervical spinal cord is therefore a promising additional biomarker for both diagnosis and disease progression and could help in the monitoring of treatment effects in future clinical trials.

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Atrophy of upper cervical spinal cord is shown in the motor neuron disease spectrum.

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Progressive cervical spinal cord thinning occurs over time for all but PLS patients.

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Cervical spinal cord imaging is a potential biomarker for disease progression in ALS.

Prognostic analysis of amyotrophic lateral sclerosis based on clinical features and plasma surface-enhanced Raman spectroscopy

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with a wide range of survival times. We aimed to explore prognostic factors related to short survival based on clinical features and plasma metabolic signatures using surface-enhanced Raman spectroscopy (SERS). One hundred and thirty-eight sporadic ALS cases were enrolled serially, including 62 for the short-duration group (≤3 years) and 76 for the long-duration group (>3 years). Multivariate analysis showed that an older age of onset (>60 years; odds ratio [OR] = 3.98, 95% CI: 1.09-14.53), lower body mass index (BMI) (<18.5; OR = 6.80, 95% CI: 1.36-33.92), and lower ALSFRS-R score (<35; OR = 6.03, 95% CI: 1.42-25.63) were associated with higher odds of tracheotomy or death, while a higher uric acid (UA) level showed a protective effect (>356.36 μmol/L; OR = 0.19, 95% CI: 0.05-0.73). SERS analysis showed significant differences between the two groups, and pathway analysis highlighted five main metabolic pathways, including metabolisms of glutathione, pyrimidine, phenylalanine, galactose, and phenylalanine-tyrosine-tryptophan biosynthesis. In conclusion, age of onset, BMI, ALSFRS-R score and UA, together with dysregulation of glucose, amino acid, nucleic acid, and antioxidant metabolism contributed to disease progression, and are therefore potential therapeutic targets for ALS.

The prognostic value of spirometric tests in Amyotrophic Lateral Sclerosis patients

OBJECTIVE

Amyotrophic lateral sclerosis (ALS) patients tend to develop progressive respiratory muscle weakness, leading to ventilatory failure and ineffective cough, principal causes of morbidity and mortality. Since patients are usually unaware of these symptoms, these are generally not noticed until the advanced stages and are associated with poor prognosis. The monitoring of respiratory function on a regular basis is therefore of great importance. Despite the availability of several pulmonary function tests, none of them was found to be the best indicator of the disease progression throughout the course of this condition. The main aim of our work was to evaluate the prognostic value of these respiratory measures evaluated in a brief period of observation and their correlation with motor functional impairments in an ALS cohort.

PATIENTS AND METHODS

Patients with ALS who had respiratory assessments performed and functional motor scales administered at baseline and six months later were included. All patients were assessed with forced vital capacity, both in seated and supine position (FVC; sFVC), peak expiratory flow (PEF), peak expiratory cough flow (PCEF), the revised ALS functional rating scale (ALSFRS-R), at baseline and after six months, and their disease progression rate (ΔFS) was obtained.

RESULTS

We included 73 patients with probable or definite ALS according to El-Escorial revised Criteria. At baseline, PCEF and PEF significantly correlated with ALSFRS-R total, bulbar and spinal subscores and ΔFS, while FVC% significantly correlated with ΔFS. After 6 months all the respiratory parameters significantly correlated with ALSFRS-R and all its subscores. Longitudinally, FVC%, sFVC% and PCEF significantly correlated with ΔFS and some of ALSFRS-R subscores. As concerns the survival analysis, monthly declines of FVC% and sFVC%, significantly correlated with the survival. The worse prognosis in terms of survival was finally found in those whose FVC% and sFVC% dropped below their respective cut-offs.

CONCLUSION

Throughout the course of ALS disease, the monitoring of several respiratory markers, namely FVC, sFVC, PEF and PCEF, plays a critical role in predicting the prognosis of these subjects, both in terms of survival and functional ability. The implementation of monthly cut-offs in the evaluation of FVC and sFVC may allow a faster recognition of those patients with worse prognosis and therefore an optimized tailored clinical care, as well as a better stratification in clinical trials.

Evidence synthesis in prognosis research

Over the past few years, evidence synthesis has become essential to investigate and improve the generalizability of medical research findings. This strategy often involves a meta-analysis to formally summarize quantities of interest, such as relative treatment effect estimates. The use of meta-analysis methods is, however, less straightforward in prognosis research because substantial variation exists in research objectives, analysis methods and the level of reported evidence. We present a gentle overview of statistical methods that can be used to summarize data of prognostic factor and prognostic model studies. We discuss how aggregate data, individual participant data, or a combination thereof can be combined through meta-analysis methods. Recent examples are provided throughout to illustrate the various methods.

Implications of spirometric reference values for amyotrophic lateral sclerosis

Objective: Spirometry is commonly used as screening tool for respiratory insufficiency in neuromuscular diseases. Despite the well-known effects of reference standards on spirometric outcomes, its standardization is overlooked in current guidelines. We aim to illustrate the effect of spirometric reference values on prognostication, medical decision-making, and trial eligibility in the applied setting of amyotrophic lateral sclerosis (ALS). Methods: We selected 4,651 patients with 32,022 FVC measurements from the PRO-ACT dataset. The FVC estimates were standardized according to five reference standards: Knudson '76, Knudson '83, ECSC, NHANES III, and GLI-2012. (Generalized) linear mixed-effects and Cox proportional hazard models were used to evaluate longitudinal patterns and time-to-event outcomes. Results: The mean population %predicted FVC varied between 78.5% (95% CI 78.0-79.1) and 88.5% (95% CI 87.9-89.1). The unstandardized liters provided the worst fit on the survival data (AIC 20573, c-index 0.760), whereas the GLI provided the best fit (AIC 20374, c-index 0.780, p < 0.001). The mean population rate of decline in %predicted FVC could vary as much as 11.4% between reference standards. The median time-to-50% predicted FVC differed by 2.9 months between recent (14.5 months, 95% CI 14.4-16.1) and early reference standards (17.4 months, 95% CI 16.1-18.2). Conclusion: Independent of technique, device, or evaluator, spirometric reference values affect the utility of spirometry in ALS. Standardization of reference values is of the utmost importance to optimize clinical decision-making, improve prognostication, enhance between-center comparison and unify patient selection for clinical trials.

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.

Pattern of lung function decline in patients with amyotrophic lateral sclerosis: implications for timing of noninvasive ventilation

BACKGROUND

The course of lung function decline in amyotrophic lateral sclerosis (ALS) and the effect of noninvasive positive-pressure ventilation (NIPPV) on that decline are uncertain. We sought to model lung function decline, determine when NIPPV is initiated along that course, and assess its impact on the course of decline.

METHODS

An observed sigmoid pattern of forced vital capacity decline was reproduced with a four-parameter nonlinear mixed-effects logistic model.

RESULTS

Analyses were performed on 507 patients overall and in 353 patients for whom a determination of adherence to NIPPV was ascertained. A sigmoid bi-asymptotic model provided a statistical fit of the data and showed a period of stable vital capacity, followed by an accelerated decline, an inflection point, then a slowing in decline to a plateau. By the time NIPPV was initiated in accordance with reimbursement guidelines, vital capacity had declined by ≥85% of the total range. Nearly half of the total loss of vital capacity occurred over 6.2 months centred at an inflection point occurring 17 months after disease onset and 5.2 months before initiation of NIPPV at a vital capacity of about 60%. Fewer bulbar symptoms and a faster rate of decline of lung function predicted adherence to NIPPV, but the intervention had no impact on final vital capacity.

CONCLUSIONS

In patients with ALS, vital capacity decline is rapid but slows after an inflection point regardless of NIPPV. Initiating NIPPV along reimbursement guidelines occurs after ≥85% of vital capacity loss has already occurred.

Accelerometry for remote monitoring of physical activity in amyotrophic lateral sclerosis: a longitudinal cohort study

Background

The extensive heterogeneity between patients with amyotrophic lateral sclerosis (ALS) complicates the quantification of disease progression. In this study, we determine the value of remote, accelerometer-based monitoring of physical activity in patients with ALS.

Methods

This longitudinal cohort study was conducted in a home-based setting; all study materials were sent by mail. Patients wore the ActiGraph during waking hours for 7 days every 2–3 months and provided information regarding their daily functioning (ALSFRS-R). We defined four accelerometer-based endpoints that either reflect the average daily activity or quantify the patient’s physical capacity.

Results

A total of 42 patients participated; the total valid monitoring period was 9288 h with a 93.0% adherence rate. At baseline, patients were active 27.9% (range 11.6–52.4%) of their time; this declined by 0.64% (95% 0.43–0.86, p < 0.001) per month. Accelerometer-based endpoints were strongly associated with the ALSFRS-R (r 0.78, 95% CI 0.63–0.92, p < 0.001), but showed less variability over time than the ALSFRS-R (coefficient of variation 0.64–0.81 vs. 1.06, respectively). Accelerometer-based endpoints could reduce sample size by 30.3% for 12-month trials and 44.6% for 18-month trials; for trials lasting less than 9 months, the ALSFRS-R resulted in smaller sample sizes.

Conclusion

Accelerometry is an objective method for quantifying disease progression, which could obtain real-world insights in the patient’s physical functioning and may personalize the delivery of care. In addition, remote monitoring provides patients with the opportunity to participate in clinical trials from home, paving the way to a patient-centric clinical trial model.

ALS blood expression profiling identifies new biomarkers, patient subgroups, and evidence for neutrophilia and hypoxia

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a debilitating disease with few treatment options. Progress towards new therapies requires validated disease biomarkers, but there is no consensus on which fluid-based measures are most informative.

METHODS

This study analyzed microarray data derived from blood samples of patients with ALS (n = 396), ALS mimic diseases (n = 75), and healthy controls (n = 645). Goals were to provide in-depth analysis of differentially expressed genes (DEGs), characterize patient-to-patient heterogeneity, and identify candidate biomarkers.

RESULTS

We identified 752 ALS-increased and 764 ALS-decreased DEGs (FDR < 0.10 with > 10% expression change). Gene expression shifts in ALS blood broadly resembled acute high altitude stress responses. ALS-increased DEGs had high exosome expression, were neutrophil-specific, associated with translation, and overlapped significantly with genes near ALS susceptibility loci (e.g., IFRD1, TBK1, CREB5). ALS-decreased DEGs, in contrast, had low exosome expression, were erythroid lineage-specific, and associated with anemia and blood disorders. Genes encoding neurofilament proteins (NEFH, NEFL) had poor diagnostic accuracy (50-53%). However, support vector machines distinguished ALS patients from ALS mimics and controls with 87% accuracy (sensitivity: 86%, specificity: 87%). Expression profiles were heterogeneous among patients and we identified two subgroups: (i) patients with higher expression of IL6R and myeloid lineage-specific genes and (ii) patients with higher expression of IL23A and lymphoid-specific genes. The gene encoding copper chaperone for superoxide dismutase (CCS) was most strongly associated with survival (HR = 0.77; P = 1.84e-05) and other survival-associated genes were linked to mitochondrial respiration. We identify a 61 gene signature that significantly improves survival prediction when added to Cox proportional hazard models with baseline clinical data (i.e., age at onset, site of onset and sex). Predicted median survival differed 2-fold between patients with favorable and risk-associated gene expression signatures.

CONCLUSIONS

Peripheral blood analysis informs our understanding of ALS disease mechanisms and genetic association signals. Our findings are consistent with low-grade neutrophilia and hypoxia as ALS phenotypes, with heterogeneity among patients partly driven by differences in myeloid and lymphoid cell abundance. Biomarkers identified in this study require further validation but may provide new tools for research and clinical practice.

Survival prediction models in motor neuron disease

BACKGROUND AND PURPOSE

This study aimed to assess the predictive value of multimodal brain magnetic resonance imaging (MRI) on survival in a large cohort of patients with motor neuron disease (MND), in combination with clinical and cognitive features.

METHODS

Two hundred MND patients were followed up prospectively for a median of 4.13 years. At baseline, subjects underwent neurological examination, cognitive assessment and brain MRI. Grey matter volumes of cortical and subcortical structures and diffusion tensor MRI metrics of white matter tracts were obtained. A multivariable Royston-Parmar survival model was created using clinical and cognitive variables. The increase of survival prediction accuracy provided by MRI variables was assessed.

RESULTS

The multivariable clinical model included predominant upper or lower motor neuron presentations and diagnostic delay as significant prognostic predictors, reaching an area under the receiver operating characteristic curve (AUC) of a 4-year survival prediction of 0.79. The combined clinical and MRI model including selected grey matter fronto-temporal volumes and diffusion tensor MRI metrics of the corticospinal and extra-motor tracts reached an AUC of 0.89. Considering amyotrophic lateral sclerosis patients only, the clinical model including diagnostic delay and semantic fluency scores provided an AUC of 0.62, whereas the combined clinical and MRI model reached an AUC of 0.77.

CONCLUSION

Our study demonstrated that brain MRI measures of motor and extra-motor structural damage, when combined with clinical and cognitive features, are useful predictors of survival in patients with MND, particularly when a diagnosis of amyotrophic lateral sclerosis is made.

Development of a prognostic model of respiratory insufficiency or death in amyotrophic lateral sclerosis

A clinically useful model to prognose onset of respiratory insufficiency in amyotrophic lateral sclerosis (ALS) would inform disease interventions, communication and clinical trial design. We aimed to derive and validate a clinical prognostic model for respiratory insufficiency within 6 months of presentation to an outpatient ALS clinic.We used multivariable logistic regression and internal cross-validation to derive a clinical prognostic model using a single-centre cohort of 765 ALS patients who presented between 2006 and 2015. External validation was performed using the multicentre Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database with 7083 ALS patients. Predictors included baseline characteristics at first outpatient visit. The primary outcome was respiratory insufficiency within 6 months, defined by initiation of noninvasive ventilation, forced vital capacity (FVC) <50% predicted, tracheostomy, or death.Of 765 patients in our centre, 300 (39%) had respiratory insufficiency or death within 6 months. Six baseline characteristics (diagnosis age, delay between symptom onset and diagnosis, FVC, symptom onset site, amyotrophic lateral sclerosis functional rating scale-revised (ALSFRS-R) total score and ALSFRS-R dyspnoea score) were used to prognose the risk of the primary outcome. The derivation cohort c-statistic was 0.86 (95% CI 0.84-0.89) and internal cross-validation produced a c-statistic of 0.86 (95% CI 0.85-0.87). External validation of the model using the PRO-ACT cohort produced a c-statistic of 0.74 (95% CI 0.72-0.75).We derived and externally validated a clinical prognostic rule for respiratory insufficiency in ALS. Future studies should investigate interventions on equivalent high-risk patients.

Spinal Cord Imaging in Amyotrophic Lateral Sclerosis: Historical Concepts-Novel Techniques

Amyotrophic lateral sclerosis (ALS) is the most common adult onset motor neuron disease with no effective disease modifying therapies at present. Spinal cord degeneration is a hallmark feature of ALS, highlighted in the earliest descriptions of the disease by Lockhart Clarke and Jean-Martin Charcot. The anterior horns and corticospinal tracts are invariably affected in ALS, but up to recently it has been notoriously challenging to detect and characterize spinal pathology in vivo. With recent technological advances, spinal imaging now offers unique opportunities to appraise lower motor neuron degeneration, sensory involvement, metabolic alterations, and interneuron pathology in ALS. Quantitative spinal imaging in ALS has now been used in cross-sectional and longitudinal study designs, applied to presymptomatic mutation carriers, and utilized in machine learning applications. Despite its enormous clinical and academic potential, a number of physiological, technological, and methodological challenges limit the routine use of computational spinal imaging in ALS. In this review, we provide a comprehensive overview of emerging spinal cord imaging methods and discuss their advantages, drawbacks, and biomarker potential in clinical applications, clinical trial settings, monitoring, and prognostic roles.

Tracking a Fast-Moving Disease: Longitudinal Markers, Monitoring, and Clinical Trial Endpoints in ALS

Amyotrophic lateral sclerosis (ALS) encompasses a heterogeneous group of phenotypes with different progression rates, varying degree of extra-motor involvement and divergent progression patterns. The natural history of ALS is increasingly evaluated by large, multi-time point longitudinal studies, many of which now incorporate presymptomatic and post-mortem assessments. These studies not only have the potential to characterize patterns of anatomical propagation, molecular mechanisms of disease spread, but also to identify pragmatic monitoring markers. Sensitive markers of progressive neurodegenerative change are indispensable for clinical trials and individualized patient care. Biofluid markers, neuroimaging indices, electrophysiological markers, rating scales, questionnaires, and other disease-specific instruments have divergent sensitivity profiles. The discussion of candidate monitoring markers in ALS has a dual academic and clinical relevance, and is particularly timely given the increasing number of pharmacological trials. The objective of this paper is to provide a comprehensive and critical review of longitudinal studies in ALS, focusing on the sensitivity profile of established and emerging monitoring markers.

Revised Airlie House consensus guidelines for design and implementation of ALS clinical trials

OBJECTIVE

To revise the 1999 Airlie House consensus guidelines for the design and implementation of preclinical therapeutic studies and clinical trials in amyotrophic lateral sclerosis (ALS).

METHODS

A consensus committee comprising 140 key members of the international ALS community (ALS researchers, clinicians, patient representatives, research funding representatives, industry, and regulatory agencies) addressed 9 areas of need within ALS research: (1) preclinical studies; (2) biological and phenotypic heterogeneity; (3) outcome measures; (4) disease-modifying and symptomatic interventions; (5) recruitment and retention; (6) biomarkers; (7) clinical trial phases; (8) beyond traditional trial designs; and (9) statistical considerations. Assigned to 1 of 8 sections, committee members generated a draft set of guidelines based on a "background" of developing a (pre)clinical question and a "rationale" outlining the evidence and expert opinion. Following a 2-day, face-to-face workshop at the Airlie House Conference Center, a modified Delphi process was used to develop draft consensus research guidelines, which were subsequently reviewed and modified based on comments from the public. Statistical experts drafted a separate document of statistical considerations (section 9).

RESULTS

In this report, we summarize 112 guidelines and their associated backgrounds and rationales. The full list of guidelines, the statistical considerations, and a glossary of terms can be found in data available from Dryad (appendices e-3-e-5, doi.org/10.5061/dryad.32q9q5d). The authors prioritized 15 guidelines with the greatest potential to improve ALS clinical research.

CONCLUSION

The revised Airlie House ALS Clinical Trials Consensus Guidelines should serve to improve clinical trial design and accelerate the development of effective treatments for patients with ALS.

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.

A ferroptosis-based panel of prognostic biomarkers for Amyotrophic Lateral Sclerosis

Accurate patient stratification into prognostic categories and targeting Amyotrophic Lateral Sclerosis (ALS)-associated pathways may pave the way for promising trials. We evaluated blood-based prognostic indicators using an array of pathological markers. Plasma samples were collected as part of a large, phase III clinical trial (Mitotarget/TRO19622) at months 1, 6, 12 and 18. The ALSFRS-r score was used as a proxy of disease progression to assess the predictive value of candidate biological indicators. First, established clinical predictors were evaluated in all 512 patients. Subsequently, pathologic markers, such as proxies of neuronal integrity (Neurofilament light chain and phosphorylated heavy chain), DNA oxidation (8-oxo-2'-desoxyguanosine), lipid peroxidation (4-hydroxy-2-nonenal, isoprostane), inflammation (interleukin-6) and iron status (ferritin, hepcidin, transferrin) were assessed in a subset of 109 patients that represented the whole cohort. Markers of neuronal integrity, DNA and lipid oxidation, as well as iron status at baseline are accurate predictors of disability at 18-month follow-up. The composite scores of these markers in association with established clinical predictors enable the accurate forecasting of functional decline. The identified four biomarkers are all closely associated with 'ferroptosis', a recently discovered form of programmed cell death with promising therapeutic targets. The predictive potential of these pathophysiology-based indicators may offer superior patient stratification for future trials, individualised patient care and resource allocation.