Symptoms timeline and outcomes in amyotrophic lateral sclerosis using artificial intelligence

Rare Diseases, Spotlighting Amyotrophic Lateral Sclerosis, Huntington's Disease, and Myasthenia Gravis: Insights from Landscape Analysis of Current Research

Rare diseases are a diverse group of disorders that, despite each individual condition's rarity, collectively affect a significant portion of the global population. Currently approximately 10,000 rare diseases exist globally, with 80% of these diseases being identified as having genetic origins. In this Review, we examine data from the CAS Content Collection to summarize scientific progress in the area of rare diseases. We examine the publication landscape in the area in an effort to provide insights into current advances and developments. We then discuss the evolution of key concepts in the field, genetic associations, as well as the major technologies and development pipelines of rare disease treatments. We focus our attention on three specific rare diseases: (i) amyotrophic lateral sclerosis, a terminal neurodegenerative disease affecting the central nervous system resulting in progressive loss of motor neurons that control voluntary muscles; (ii) Huntington's disease, another terminal neurodegenerative disease that causes progressive degeneration of nerve cells in the brain, with a wide impact on a person's functional abilities; and (iii) myasthenia gravis, a chronic autoimmune synaptopathy leading to skeletal muscle weakness. While the pathogenesis of these rare diseases is being elucidated, there is neither a cure nor preventative treatment available, only symptomatic treatment. The objective of the paper is to provide a broad overview of the evolving landscape of current knowledge on rare diseases and specifically on the biology and genetics of the three spotlighted diseases, to outline challenges and evaluate growth opportunities, an aim to further efforts in solving the remaining challenges.

Amyotrophic Lateral Sclerosis (ALS) Type 8: A Narrative Review

Amyotrophic lateral sclerosis type 8 (ALS8) is a rare familial subtype of ALS caused by mutations in the vesicle-associated membrane protein-associated protein B (VAPB) gene, particularly the p.P56S mutation. It is distinguished by slower disease progression and an earlier onset compared to sporadic ALS forms, along with unique clinical features such as severe cramping, fasciculations, postural tremors, and cognitive and behavioral impairments. Although current pharmacological options, such as riluzole, edaravone, and sodium phenylbutyrate/taurursodiol, provide modest benefits, they fail to address the underlying genetic mechanisms of ALS8. Emerging gene therapies, RNA-based interventions, and stem cell approaches hold promise for precision-targeted treatments but face challenges in clinical application. Symptom management strategies, including respiratory, nutritional, and psychological support, are crucial for improving patient outcomes and quality of life. Despite significant progress in understanding the genetic and molecular pathogenesis of ALS8, its rarity, phenotypic variability, and limited clinical data pose challenges to therapeutic advancements. This narrative review highlights current therapeutic strategies, the unique clinical trajectory of ALS8, and potential pathways for innovative, subtype-specific interventions, emphasizing the need for multidisciplinary and targeted approaches to optimize care for this distinct ALS subtype.

Artificial intelligence for screening and diagnosis of amyotrophic lateral sclerosis: a systematic review and meta-analysis

INTRODUCTION

Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurological disease that leads to progressive motor function degeneration. Diagnosing ALS is challenging due to the absence of a specific detection test. The use of artificial intelligence (AI) can assist in the investigation and treatment of ALS.

METHODS

We searched seven databases for literature on the application of AI in the early diagnosis and screening of ALS in humans. The findings were summarized using random-effects summary receiver operating characteristic curve. The risk of bias (RoB) analysis was carried out using QUADAS-2 or QUADAS-C tools.

RESULTS

In the 34 analyzed studies, a meta-prevalence of 47% for ALS was noted. For ALS detection, the pooled sensitivity of AI models was 94.3% (95% CI - 63.2% to 99.4%) with a pooled specificity of 98.9% (95% CI - 92.4% to 99.9%). For ALS classification, the pooled sensitivity of AI models was 90.9% (95% CI - 86.5% to 93.9%) with a pooled specificity of 92.3% (95% CI - 84.8% to 96.3%). Based on type of input for classification, the pooled sensitivity of AI models for gait, electromyography, and magnetic resonance signals was 91.2%, 92.6%, and 82.2%, respectively. The pooled specificity for gait, electromyography, and magnetic resonance signals was 94.1%, 96.5%, and 77.3%, respectively.

CONCLUSIONS

Although AI can play a significant role in the screening and diagnosis of ALS due to its high sensitivities and specificities, concerns remain regarding quality of evidence reported in the literature.

Trends in Hospital Admissions for Patients with Amyotrophic Lateral Sclerosis: Insights from a Retrospective Cohort Study in a Province in Northern Italy

ALS is characterized by a highly heterogeneous course, ranging from slow and uncomplicated to rapid progression with severe extra-motor manifestations. This study investigated ALS-related hospitalizations and their connection to clinical aspects, comorbidities, and prognosis. We performed a retrospective cohort study including patients residing in Modena, Italy, newly diagnosed between 2007 and 2017 and followed up until 31 December 2022. Data were obtained from the Emilia Romagna ALS registry, regional hospitals, and medical records. Among the 249 patients, there were 492 hospital admissions, excluding those for diagnostic purposes; 63% of the patients had at least one hospitalization post-diagnosis, with an average stay of 19.90 ± 23.68 days. Younger patients were more likely to be hospitalized multiple times and experienced longer stays (44.23 ± 51.71 days if <65 years; 26.46 ± 36.02 days if older, p < 0.001). Patients who were hospitalized at least once more frequently underwent gastrostomy (64.97%) or non-invasive (66.24%) and invasive (46.50%) ventilation compared to those never hospitalized (21.74%, 31.52%, 13.04%, respectively, p < 0.001 for all). Emergency procedures led to longer hospitalizations (62.84 ± 48.91 days for non-invasive ventilation in emergencies vs. 39.88 ± 46.46 days electively, p = 0.012). Tracheostomy-free survival was not affected by hospitalizations. In conclusion, younger ALS patients undergo frequent and prolonged hospitalizations, especially after emergency interventions, although these do not correlate with reduced survival.

Advancements and challenges in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) continues to pose a significant challenge due to the disease complexity and heterogeneous manifestations. Despite recent drug approvals, there remains a critical need for the development of more effective therapies. This review explores the underlying mechanisms involved; including neuroinflammation, glutamate mediated excitotoxicity, mitochondrial dysfunction, and hypermetabolism, and how researchers are trying to develop novel drugs to target these pathways. While progress has been made, the unmet need of ALS patients highlights the urgency for continued research and resource allocation in the pursuit of effective treatments.

Cognitive function in amyotrophic lateral sclerosis: a cross-sectional and prospective pragmatic clinical study with review of the literature

BACKGROUND

Amyotrophic lateral sclerosis (ALS) can present with either bulbar or spinal symptoms, and in some cases, both types of symptoms may be present. In addition, cognitive impairment has been observed in ALS. The study aimed to evaluate the frontal and general cognitive performance in ALS not only cross-sectionally but also longitudinally.

METHODS AND MATERIALS

The Frontal Assessment Battery (FAB) and the Montreal Cognitive Assessment (MoCA) were employed to assess cognitive function in 52 adults with ALS and 52 cognitively healthy individuals. The statistical analyses encompassed the Pearson Chi square test, the Skillings-Mack test, the Spearman's rank correlation coefficient, and the Proportional Odds Logistic Regression Model (POLR).

RESULTS

Cross-sectionally, lower cognitive performance was associated with ALS diagnosis, older age, and motor functional decline. The cognitive impairment of individuals with bulbar and spinal-bulbar symptoms showed faster deterioration compared to those with spinal symptoms. The spinal subgroup consistently performed worst in delayed recall and attention, while the spinal-bulbar and bulbar subgroups exhibited inferior scores in delayed recall, attention, visuospatial skills, orientation, and verbal fluency.

CONCLUSION

The incorporation of cognitive screening in the diagnostic workup of ALS may be beneficial, as early detection can enhance symptom management and improve the quality of life for both individuals with ALS and their care partners.

An electronic health record cohort of Veterans with amyotrophic lateral sclerosis

Objective: To assemble and characterize an electronic health record (EHR) dataset for a large cohort of US military Veterans diagnosed with ALS (Amyotrophic Lateral Sclerosis). Methods: An EHR dataset for 19,662 Veterans diagnosed with ALS between January 1, 2000 to December 31, 2020 was compiled from the Veterans Health Administration (VHA) EHR database by a query for ICD9 diagnosis (335.20) or ICD10 diagnosis (G12.21) for Amyotrophic Lateral Sclerosis. Results: The cohort is predominantly male (98.94%) and white (72.37%) with a median age at disease onset of 68 years and median survival from the date of diagnosis of 590 days. With the designation of ALS as a compensable illness in 2009, there was a subsequent increase in the number of Veterans diagnosed per year in the VHA, but no change in median survival. The cohort included a greater-than-expected proportion of individuals whose branch of service at the time of separation was the Army. Conclusions: The composition of the cohort reflects the VHA population who are at greatest risk for ALS. The greater than expected proportion of individuals whose branch of service at the time of separation was the Army suggests the possibility of a branch-specific risk factor for ALS.