Although loss of TAR DNA-binding protein 43 kDa (TDP-43) splicing repression is well documented in postmortem tissues of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), whether this abnormality occurs during early-stage disease remains unresolved. Cryptic exon inclusion reflects loss of function of TDP-43, and thus detection of proteins containing cryptic exon-encoded neoepitopes in cerebrospinal fluid (CSF) or blood could reveal the earliest stages of TDP-43 dysregulation in patients. Here we use a newly characterized monoclonal antibody specific to a TDP-43-dependent cryptic epitope (encoded by the cryptic exon found in HDGFL2) to show that loss of TDP-43 splicing repression occurs in ALS–FTD, including in presymptomatic C9orf72 mutation carriers. Cryptic hepatoma-derived growth factor-like protein 2 (HDGFL2) accumulates in CSF at significantly higher levels in familial ALS–FTD and sporadic ALS compared with controls and is elevated earlier than neurofilament light and phosphorylated neurofilament heavy chain protein levels in familial disease. Cryptic HDGFL2 can also be detected in blood of individuals with ALS–FTD, including in presymptomatic C9orf72 mutation carriers, and accumulates at levels highly correlated with those in CSF. Our findings indicate that loss of TDP-43 cryptic splicing repression occurs early in disease progression, even presymptomatically, and that detection of the HDGFL2 cryptic neoepitope serves as a potential diagnostic biomarker for ALS, which should facilitate patient recruitment and measurement of target engagement in clinical trials.

Objective: The amyotrophic lateral sclerosis (ALS) trial outcome measures are clinic based. Active and passive smartphone data can provide important longitudinal information about ALS progression outside the clinic.Methods: We used Beiwe, a research platform for smartphone‐based digital phenotyping, to collect active (self‐report ALSFRS‐R surveys and speech recordings) and passive (phone sensors and logs) data from patients with ALS for approximately 24 weeks. In clinics, at baseline and every 3 months, we collected vital capacity, ALSFRS‐R, and ALS‐CBS at enrollment, week 12, and week 24. We also collected ALSFRS‐R by telephone at week 6.Results: Baseline in‐clinic ALSFRS‐R and smartphone self‐report correlation was 0.93 (P < 0.001). ALSFRS‐R slopes were equivalent and within‐subject standard deviation was smaller for smartphone‐based self‐report (0.26 vs. 0.56). Use of Beiwe afforded weekly collection of speech samples amenable to a variety of analyses, and we found mean pause time to increase by 0.02 sec per month across the sample.Interpretation: Smartphone‐based digital phenotyping in people with ALS is feasible and informative. Self‐administered smartphone ALSFRS‐R scores correlate highly with clinic‐based ALSFRS‐R scores, have low variability, and could be used in clinical trials. More research is required to fully analyze speech recordings and passive data, and to identify optimal digital markers for use in future ALS clinical trials.

Objective: This study aimed to determine the diagnostic utility of clinician speech ratings and patient self-report for detecting early bulbar changes associated with amyotrophic lateral sclerosis (ALS), compared to instrumentation-based speech measures. Methods: Thirty-six individuals with ALS and 17 healthy control participants were included. Patients’ awareness of early bulbar motor involvement was assessed using self-reported scores on the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R). Clinicians’ detection of early bulbar motor involvement was assessed through perceptual speech ratings by two experienced speech-language pathologists. Participants with ALS were grouped as ‘bulbar pre-symptomatic’ or ‘bulbar symptomatic’ based on self-report and clinician ratings, and compared to healthy controls on six instrumentation-based speech measures. ROC analysis was used to compare the sensitivity and specificity of perceptual and instrumentation-based measures for detecting bulbar changes in pre-symptomatic individuals. Results: Early bulbar changes that were documented using instrumentation-based measures were undetected by both patients and clinicians. ROC analyses indicated that instrumentation-based measures outperformed clinicians’ scaled severity ratings, and that percent pause time was the best measure for differentiating healthy controls from bulbar pre-symptomatic individuals with ALS. Conclusions: Findings suggested that instrumentation-based measures of speech may be necessary for early detection of bulbar changes due to ALS.

Amyotrophic lateral sclerosis (ALS) therapeutic development has largely relied on staff-administered functional rating scales to determine treatment efficacy. We sought to determine if mobile applications (apps) and wearable devices can be used to quantify ALS disease progression through active (surveys) and passive (sensors) data collection. Forty ambulatory adults with ALS were followed for 6-months. The Beiwe app was used to administer the self-entry ALS functional rating scale-revised (ALSFRS-RSE) and the Rasch Overall ALS Disability Scale (ROADS) surveys every 2–4 weeks. Each participant used a wrist-worn activity monitor (ActiGraph Insight Watch) or an ankle-worn activity monitor (Modus StepWatch) continuously. Wearable device wear and app survey compliance were adequate. ALSFRS-R highly correlated with ALSFRS-RSE. Several wearable data daily physical activity measures demonstrated statistically significant change over time and associations with ALSFRS-RSE and ROADS. Active and passive digital data collection hold promise for novel ALS trial outcome measure development.

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.