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The diagnosis of amyotrophic lateral sclerosis (ALS) is based on evidence of upper and lower motor neuron degeneration in the bulbar, cervical, thoracic, and lumbar regions in a patient with progressive motor weakness, in the absence of differential diagnosis. Despite these well-defined criteria, ALS can be difficult to diagnose, given the wide variety of clinical phenotypes. Indeed, the central or peripheral location of the disease varies with a spectrum ranging from predominantly central to exclusively peripheral, symptoms can be extensive or limited to the limbs, bulbar area or respiratory muscles, and the duration of the disease may range from a few months to several decades. In the absence of a specific test, the diagnostic strategy relies on clinical, electrophysiological, biological and radiological investigations to confirm the disease and exclude ALS mimics. The main challenge is to establish a diagnosis based on robust clinical and paraclinical evidence without delaying treatment initiation by increasing the number of additional tests. This approach requires a thorough knowledge of the phenotypes of ALS and its main differential diagnoses.

The diagnosis of amyotrophic lateral sclerosis (ALS) is based on progressive degeneration of upper and lower motor neurons.ALS can be difficult to diagnose due to the wide range of clinical phenotypes (central/peripheral location, symptom distribution, disease duration).A thorough diagnostic strategy including clinical, electrophysiological, biological and radiological investigations is essential to confirm ALS and exclude differential diagnoses.

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BACKGROUND: C9orf72 gene repeat expansion (C9RE) is the most frequent gene variant associated with amyotrophic lateral sclerosis (ALS). We aimed to study the phenotype of motor neurone disease (MND) patients with C9RE in a Portuguese cohort. METHODS: Demographical and clinical data of MND patients with (C9RE+) and without C9RE were compared. ALS al Rating Scale-Revised (ALSFRS-R) and Edinburgh Cognitive and Behavioural ALS Screen (ECAS) were used to evaluate functional and cognitive performance, respectively. Survival analysis was performed using Kaplan Meier log-rank test and Cox proportional hazards model. RESULTS: We included 761 patients of whom 61 (8.0 %) were C9RE+. C9RE+ patients had a higher frequency of ALS (95.1 vs 78.4 %, p = 0.002), and lower frequency of progressive muscular atrophy (3.3 vs 16.7 %, p = 0.006). C9RE+ was associated with earlier age of onset (58.1 vs 62.6 years, p = 0.003) and more frequent MND family history (65.5 vs 11.4 %, p < 0.001). Gender, ethnicity, onset site, diagnostic delay, disease progression rate until diagnosis (ΔF), ALSFRS-R and time until non-invasive ventilation did not differ between groups. Cognitive/behavioural symptoms and ECAS did not differ between groups, except a worse visuospatial score in C9RE+ group (p = 0.035). Death rate was 1.8 and 1.6 times higher in C9RE+ patients with MND and ALS, respectively. Significant survival prognostic factors in C9RE+ group were diagnosis delay (HR = 0.96, 95 %CI 0.92-0.99, p = 0.008) and ΔF (HR = 1.93, 95 %CI 1.26-2.96, p = 0.002). CONCLUSION: Our study corroborates most previous cohorts' findings, but harbours some singularities regarding onset site, phenotype, and cognitive profile, that contribute to a better understanding of C9RE epidemiology.

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BACKGROUND: Edinburgh Cognitive and Behavioral ALS Screen (ECAS) is a validated assessment designed to screen cognitive functions and behavioral disorders in amyotrophic lateral sclerosis (ALS). Objective of this study is to determine the factors associated with ECAS impairment in a cohort of ALS patients without a co-morbid diagnosis of dementia, at the time of diagnosis. METHODS: We enrolled 71 non-demented ALS patient. We collected clinical and demographic data, ALS familiarity, analysis of the most commonly mutated genes in ALS, ALS Milano Torino Staging System and ALS Functional Rate Scale revised scores, progression rate; finally, we recorded whether symptoms onset involved spinal or bulbar area. The alteration of the ECAS was estimated based on age and education-adjusted-validated cut off for each of the items included in ECAS. A multivariable regression analysis was done. RESULTS: The significant determinants of ECAS alterations were: bulbar onset in both ALS-specific test and total ECAS score; bulbar onset and familiarity in ALS-non-specific test; finally, familiarity and diagnosis delay in ALS-behavioral test. All the subjects carrying C9orf72 mutations had alteration of both total ECAS score and ALS-specific tests. DISCUSSION: At diagnosis, bulbar-onset ALS, family history, diagnosis delay and C9orf72 hexanucleotide repeat expansion may contribute to impairment of ECAS.

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BACKGROUND: Multiple sclerosis (MS) is an inflammatory disease of the central nervous system that causes damage to the myelin and axons and is caused by genetic or environmental factors. Amyotrophic lateral sclerosis (ALS) is characterized by rapidly progressive degeneration of the motor neurons resulting in the presence of upper and lower motor-neuron signs and symptoms. CASE PRESENTATION: A 46-year-old female patient presented with symmetrical weakness of the lower limbs and numbness that developed over weeks. Magnetic resonance imaging (MRI) of the brain exhibited typical demyelination features, high signal abnormality involving the periventricular and subcortical white matter, and an oval-shaped lesion. The patient was diagnosed with MS based on the clinical presentation and radiological examination. However, there was rapid progression of the symptoms, involvement of bulbar dysfunction, and muscle atrophy. Furthermore, the patient did not respond to acute therapy and immunotherapy, which made the diagnosis of MS less likely or suggested that it could be associated with another diagnosis. Her neurophysiological test met the criteria of ALS, and she was started on riluzole. LITERATURE REVIEW: We reviewed all articles from 1986 to 2023, and there were 32 reported cases describing the co-occurrence of ALS and MS in different populations. Our case is the 33rd, and to our knowledge, it is the only case reported in the Middle East and specifically in Saudi Arabia. The main proposed mechanism according to postmortem examinations is a combination of degenerative and inflammatory processes with a cascade of production of reactive oxygen species and nitric oxide, which lead to cell death and apoptosis during concomitant ALS with MS. CONCLUSION: The co-occurrence of ALS and MS is extremely rare, but it can be explained by pathogenesis related to neurodegeneration, inflammation, or genetic susceptibility. Rapid progressive motor and bulbar symptoms could be red-flag symptoms, extensive evaluation might be needed for these patients.

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Experimental studies identified a role of neuroinflammation in the pathogenesis of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). However, the role of inflammatory molecules as diagnostic and prognostic biomarkers in patients with ALS is unclear. In this cross-sectional study, the cerebrospinal fluid (CSF) levels of a set of inflammatory cytokines and chemokines were analyzed in 56 newly diagnosed ALS patients and in 47 age- and sex-matched control patients without inflammatory or degenerative neurological disorders. The molecules analyzed included: interleukin (IL)-1ß, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12, IL-13, IL-17, granulocyte colony stimulating factor (GCSF), macrophage inflammatory protein (MIP)-1a, MIP-1b, tumor necrosis factors (TNF), eotaxin. Principal component analysis (PCA) was used to explore possible associations between CSF molecules and ALS diagnosis. In addition, we analyzed the association between CSF cytokine profiles and clinical characteristics, including the disease progression rate score, and peripheral inflammation assessed using the Neutrophil-to-lymphocyte ratio (NLR). PCA identified six principal components (PCs) explaining 70.67% of the total variance in the CSF cytokine set. The principal component (PC1) explained 26.8% of variance and showed a positive load with CSF levels of IL-9, IL-4, GCSF, IL-7, IL-17, IL-13, IL-6, IL-1ß, TNF, and IL-2. Logistic regression showed a significant association between PC1 and ALS diagnosis. In addition, in ALS patients, the same component was significantly associated with higher disease progression rate score and positively correlated with NLR. CSF inflammatory activation in present in ALS at the time of diagnosis and may characterize patients at higher risk for disease progression.

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Neurodegenerative diseases (NDDs), which are caused by the degeneration of neurons and their functions, affect a significant part of the world's population. Although gait disorders are one of the critical and common markers to determine the presence of NDDs, diagnosing which NDD the patients have among a group of NDDs using gait data is still a significant challenge to be addressed. In this study, we addressed the multi-class classification of NDDs and aim to diagnose Parkinson's disease (PD), Amyotrophic lateral sclerosis disease (AD), and Huntington's disease (HD) from a group containing NDDs and healthy control subjects. We also examined the impact of disease-specific identified features derived from VGRF signals. Detrended Fluctuation Analysis (DFA), Dynamic Time Warping (DTW) and Autocorrelation (AC) were used for feature extraction on Vertical Ground Reaction Force (VGRF) signals. To compare the performance of the features, we employed Support Vector Machines, K-Nearest Neighbors, and Neural Networks as classifiers. In three-class problem addressing the classification of AD, PD and HD 93.3% accuracy rate was achieved, while in the four classes case, in which NDDs and HC groups were considered together, 93.5% accuracy rate was yielded. Considering the disease-specific impact of features, it is revealed that while DFA based features diagnose patients with AD with the highest accuracy, DTW has been shown to be more successful in diagnosing PD. AC based features provided the highest accuracy in diagnosing HD. Although gait disorder is common for NDDs, each disease may have its own distinctive gait rhythms; therefore, it is important to identify disease-specific patterns and parameters for the diagnosis of each disease. To increase the diagnostic accuracy, it is necessary to use a combination of features, which were effective for each disease diagnosis. Determining a limited number of disease-specific features would provide NDD diagnostic systems suitable to be deployed in edge-computing environments.

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BACKGROUND: Neurodegenerative diseases (NDDs) pose significant challenges due to their debilitating nature and limited therapeutic options. Accurate and timely diagnosis is crucial for optimizing patient care and treatment strategies. Gait analysis, utilizing wearable sensors, has shown promise in assessing motor abnormalities associated with NDDs. RESEARCH QUESTION: Research Question 1 To what extent can analyzing the interaction of both limbs in the time-frequency domain serve as a suitable methodology for accurately classifying NDDs? Research Question 2 How effective is the utilization of color-coded images, in conjunction with deep transfer learning models, for the classification of NDDs? METHODS: GaitNDD database was used, comprising recordings from patients with Huntington's disease, amyotrophic lateral sclerosis, Parkinson's disease, and healthy controls. The gait signals underwent signal preparation, wavelet coherence analysis, and principal component analysis for feature enhancement. Deep transfer learning models (AlexNet, GoogLeNet, SqueezeNet) were employed for classification. Performance metrics, including accuracy, sensitivity, specificity, precision, and F1 score, were evaluated using 5-fold cross-validation. RESULTS: The classification performance of the models varied depending on the time window used. For 5-second gait signal segments, AlexNet achieved an accuracy of 95.91 %, while GoogLeNet and SqueezeNet achieved accuracies of 96.49 % and 92.73 %, respectively. For 10-second segments, AlexNet outperformed other models with an accuracy of 99.20 %, while GoogLeNet and SqueezeNet achieved accuracies of 96.75 % and 95.00 %, respectively. Statistical tests confirmed the significance of the extracted features, indicating their discriminative power for classification. SIGNIFICANCE: The proposed method demonstrated superior performance compared to previous studies, offering a non-invasive and cost-effective approach for the automated diagnosis of NDDs. By analyzing the interaction between both legs during walking using wavelet coherence, and utilizing deep transfer learning models, accurate classification of NDDs was achieved.

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BACKGROUND: Motor neuron disease (MND) is a chronic and progressive neurodegenerative disorder with an unknown cause. The development of amyotrophic lateral sclerosis (ALS) is believed to be linked to an immune response. Monocytes/macrophages and T cells are key players in the disease's advancement. Monitoring levels of cytokines in the blood can help forecast patient outcomes, while immunotherapy shows promise in alleviating symptoms for certain individuals. CASE PRESENTATION: A 56-year-old male patient was admitted to the hospital due to progressive limb weakness persisting for eight months. The neurological examination revealed impairments in both upper and lower motor neurons, as well as sensory anomalies, without corresponding signs. Electrophysiological examination results indicated extensive neuronal damage and multiple peripheral nerve impairments, thereby the diagnosis was ALS. One month ago, the patient began experiencing symptoms of dry mouth and a bitter taste. Following tests for rheumatic immune-related antibodies and a lip gland biopsy, a diagnosis of Sjögren's syndrome (SS) was proposed. Despite treatment with medications such as hormones (methylprednisolone), immunosuppressants (hydroxychloroquine sulfate), and riluzole, the symptoms did not significantly improve, but also did not worsen. CONCLUSION: It is recommended to include screening for SS in the standard assessment of ALS. Furthermore, research should focus on understanding the immune mechanisms involved in ALS, providing new insights for the diagnosis and treatment of ALS in conjunction with SS.

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INTRODUCTION: Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease affecting upper and lower motor neurons, leading to death resulting mainly from respiratory failure, for which there is currently no curative treatment. Underlying pathological mechanisms for the development of ALS are diverse and have yet to be elucidated. Non-invasive testing to isolate circulating molecules including microRNA to diagnose ALS has been reported, but circulating extracellular vesicle (EV)-derived microRNA has not been fully studied in the ALS population. METHODS: A systematic literature review to explore studies investigating the profile of microRNAs in EVs from blood samples of ALS patients was carried out according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline. RESULTS: Eleven studies including a total of 263 patients with ALS were included in the present systematic review. The majority of patients had sporadic ALS, though a small number of patients with ALS having genetic mutations were included. Seven studies used plasma-derived EVs, and the remaining four studies used serum-derived EVs. RNA sequencing or microarrays were used in eight studies, and quantitative PCR was used in eight studies, of which five studies used RNA sequencing or microarrays for screening and quantitative PCR for validation. There was overlap of miR-199a-3p and miR-199a-5p in three studies. CONCLUSIONS: Overall, the systematic review addressed the current advances in the profiling of microRNAs in circulating EVs of ALS patients. Blood samples, isolation of EVs, and microRNA analysis were diverse. Although there was an overlap of miR-199a-3p and miR-199a-5p, collection of further evidence is warranted.

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OBJECTIVE: Motor Unit Number Estimation (MUNE) techniques are crucial in assessing lower motor neuron loss. MScanFit MUNE (MScanFit) is a novel tool which estimates MUNE values from compound muscle action potential (CMAP) scans by considering the probabilistic nature of motor unit firing. We conducted a prospective study to evaluate the diagnostic utility of MScanFit compared to quantitative electromyography (qEMG) in ALS patients. METHODS: We enrolled 35 patients diagnosed with amyotrophic lateral sclerosis (ALS) and 14 healthy controls, assessing qEMG and MScanFit MUNE in abductor pollicis brevis, abductor digiti minimi and tibialis anterior muscles. RESULTS: We found higher sensitivity of qEMG in detecting abnormalities compared to MScanFit, with a high concordance rate between the two techniques. Notably, a few muscles exhibited abnormal MUNE but normal qEMG findings, suggesting a potential complementary role for MScanFit in ALS diagnosis. Neurophysiological parameters from MScanFit showed good correlations with qEMG measures. Subclinical neurophysiological involvement was observed in muscles with normal strength, emphasizing the importance of sensitive diagnostic tools. CONCLUSION: MScanFit demonstrated validity in distinguishing ALS patients from healthy subjects and correlated well with qEMG parameters. SIGNIFICANCE: Our study confirmed the diagnostic utility of MScanFit MUNE in ALS, highlighting its role as a supplementary diagnostic tool.

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OBJECTIVES: To document the utility of decremental responses in the repetitive nerve stimulation test (RNS) and spontaneous activities in needle electromyography (EMG) in the trapezius muscle for the diagnosis of amyotrophic lateral sclerosis. METHODS: Subjects were retrospectively identified from our EMG database. Cervical spondylosis was represented as a disease control group. We investigated the sensitivity and specificity of RNS and EMG in the trapezius muscle and those of diagnostic criteria including the Gold Coast criteria (GCC). RESULTS: We reviewed 120 patients with amyotrophic lateral sclerosis and 17 patients with cervical spondylosis. "RNS or EMG" achieved the highest sensitivity (85%). The specificity was the highest for RNS (94%). Addition of RNS of the deltoid muscle achieved 98% sensitivity in the upper-limb onset amyotrophic lateral sclerosis. The sensitivity of the GCC was very high (88%). CONCLUSIONS: Neurophysiological parameters investigated in this study having close to 100% specificities or sensitivities are useful as complements to the GCC.

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OBJECTIVE: We sought to characterize the clinical prognostic factors in veterans with amyotrophic lateral sclerosis (ALS) followed in our ALS clinic. BACKGROUND: ALS is a rare, progressive neurodegenerative condition associated with decreased survival compared to that in the normal population. METHOD: The electronic medical records of 105 veterans diagnosed with ALS who are followed in our ALS clinic between 2010 and 2021 were reviewed. Approval from the institutional review board was obtained from the study protocol. Demographic and clinical variables included age at symptom onset, age at initial evaluation, survival (from symptom onset to death), gender, site of onset (appendicular, bulbar, and respiratory), initial amyotrophic lateral sclerosis functional-related score-revised (ALSFRS-R), total functional independence measure (TFIM) scores, initial forced vital capacity (FVC), and interventions (Riluzole, gastrostomy, noninvasive ventilation [NIV], and tracheostomy). Normally distributed data was expressed as mean ± standard deviation. Fischer's exact analysis of the distribution differences of categorical data. The Kaplan-Meier plot analyzed the time-to-event. RESULTS: The mean (SD) age at symptom onset was 62.0 (11.1) years, age at diagnosis was 65 (11) years, with 72% of the patients being over 60 years at diagnosis. The median survival time from symptom onset was 4.12 (3) years. Limb-onset ALS (appendicular) was the most frequent (52%) followed by bulbar-onset ALS (43%). The mean ALSFRS-R and TFIM scores were 31 (8) and 91 (25), respectively. Family history (familial), bulbar, and respiratory presentation at diagnosis were associated with shorter survival times. CONCLUSION: This study suggests that of the clinical prognostic factors veterans with familial ALS, bulbar, and respiratory onset at presentations had shorter survival. The presence of Agent Orange, PEG placement, and NIV did not affect survival.

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Background: Vulpian-Bernhardt syndrome is an atypical form of the motor neuron disease described since the 19th century. The importance of a timely diagnosis lies in the increased survival present in this variant. Due to the clinical rarity and complex diagnosis we report a clinical case of this disease, which is why we describe the typical clinical presentation, the diagnostic approach, and we make a bibliographic review of this neurodegenerative disorder as well. Clinical case: Latin American man whose clinical case onset was characterized by thoracic asymmetric and increasing limb weakness, showing affection from distal to proximal upper limbs area. Subsequently, symptoms worsened to the point of limiting day-to-day activities and conditioning patient's physical independence. Physical examination was consistent with motor neuron disease. Nerve conduction studies were performed and confirmed findings compatible with motor neuron involvement limited to thoracic limbs. Conclusion: Vulpian-Bernhardt syndrome is an uncommon form of motor neuron disease. Due to the rarity of its presentation, it is frequent to confuse clinical profile even for trained physicians. The importance of electrodiagnosis relies in identifying the neurogenic origin of the disease, as well as the active denervation and reinnervation data. Considering that with this syndrome patients have a longer survival than with the classic form of amyotrophic lateral sclerosis, it is important to have a clear diagnosis approach in order to provide a better quality of life and supportive treatment.

Introducción: el síndrome de Vulpian-Bernhardt es una forma atípica de la enfermedad de la motoneurona descrita desde el siglo XIX. La importancia de un diagnóstico oportuno radica en la mayor supervivencia que presenta esta variante. Debido a la rareza clínica y al diagnóstico complejo presentamos un caso clínico de esta enfermedad, por lo que describimos el cuadro clínico típico, el abordaje diagnóstico y hacemos una revisión bibliográfica de este trastorno neurodegenerativo. Caso clínico: hombre de origen latinoamericano que comenzó su padecimiento con debilidad de miembros torácicos, asimétrica y progresiva de distal a proximal. Los síntomas progresaron hasta limitar sus actividades de la vida diaria y su independencia física. La exploración física fue compatible con enfermedad de motoneurona. Se hicieron estudios de extensión y neuroconducción que confirmaron hallazgos compatibles con afectación en motoneurona limitada a miembros torácicos. Conclusión: el síndrome Vulpian-Bernhardt es una forma clínica poco común. Debido a su rareza, es fácil confundir el cuadro clínico, incluso por parte de experimentados. La importancia del electrodiagnóstico radica en identificar el origen neurogénico de la enfermedad, los datos de denervación activa y reinervación. Al ser una forma en la que se presenta una supervivencia mayor que en la forma clásica, es importante el diagnóstico claro para dar una mejor calidad de vida y tratamiento de soporte.

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BACKGROUND: Several single-center studies proposed utility of vagus nerve (VN) ultrasound for detecting disease severity, autonomic dysfunction, and bulbar phenotype in amyotrophic lateral sclerosis (ALS). However, the resulting body of literature shows opposing results, leaving considerable uncertainty on the clinical benefits of VN ultrasound in ALS. METHODS: Relevant studies were identified up to 04/2024 and individual patient data (IPD) obtained from the respective authors were pooled with a so far unpublished cohort (from Munich). An IPD meta-analysis of 109 patients with probable or definite ALS (El Escorial criteria) and available VN cross-sectional area (CSA) was performed, with age, sex, ALS Functional Rating Scale-revised (ALSFRS-R), disease duration, and bulbar phenotype as independent variables. RESULTS: Mean age was 65 years (± 12) and 47% of patients (± 12) had bulbar ALS. Mean ALSFRS-R was 38 (± 7), and mean duration was 18 months (± 18). VN atrophy was highly prevalent [left: 67% (± 5), mean CSA 1.6mm2 (± 0.6); right: 78% (± 21), mean CSA 1.8 mm2 (± 0.7)]. VN CSA correlated with disease duration (mean slope: left - 0.01; right - 0.01), but not with ALSFRS-R (mean slope: left 0.004; mean slope: right - 0.002). Test accuracy for phenotyping bulbar vs. non-bulbar ALS was poor (summary receiver operating characteristic area under the curve: left 0.496; right 0.572). CONCLUSION: VN atrophy in ALS is highly prevalent and correlates with disease duration, but not with ALSFRS-R. VN CSA is insufficient to differentiate bulbar from non-bulbar ALS phenotypes. Further studies are warranted to analyze the link between VN atrophy, autonomic impairment, and survival in ALS.

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ALS (Amyotrophic Lateral Sclerosis) is a neurodegenerative disorder causing profound physical disability that severely impairs a patient's life expectancy and quality of life. It also leads to muscular atrophy and progressive weakness of muscles due to insufficient nutrition in the body. At present, there are no disease-modifying therapies to cure ALS, and there is a lack of preventive tools. The general clinical assessments are based on symptom reports, neurophysiological tests, neurological examinations, and neuroimaging. But, these techniques possess various limitations of low reliability, lack of standardized protocols, and lack of sensitivity, especially in the early stages of disease. So, effective methods are required to detect the progression of the disease and minimize the suffering of patients. Extensive studies concentrated on investigating the causes of neurological disease, which creates a barrier to precise identification and classification of genes accompanied with ALS disease. Hence, the proposed system implements a deep RSFFNNCNN (Resemble Single Feed Forward Neural Network-Convolutional Neural Network) algorithm to effectively classify the clinical associations of ALS. It involves the addition of custom weights to the kernel initializer and neutralizer 'k' parameter to each hidden layer in the network. This is done to increase the stability and learning ability of the classifier. Additionally, the comparison of the proposed approach is performed with SFNN (Single Feed NN) and ML (Machine Learning) based algorithms, namely, NB (Naïve Bayes), XGBoost (Extreme Gradient Boosting) and RF (Random Forest), to estimate the efficacy of the proposed model. The reliability of the proposed algorithm is measured by deploying performance metrics such as precision, recall, F1 score, and accuracy.

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PURPOSE OF REVIEW: Amyotrophic lateral sclerosis (ALS) has a strong genetic basis, but the genetic landscape of ALS appears to be complex. The purpose of this article is to review recent developments in the genetics of ALS. RECENT FINDINGS: Large-scale genetic studies have uncovered more than 40 genes contributing to ALS susceptibility. Both rare variants with variable effect size and more common variants with small effect size have been identified. The most common ALS genes are C9orf72 , SOD1 , TARDBP and FUS . Some of the causative genes of ALS are shared with frontotemporal dementia, confirming the molecular link between both diseases. Access to diagnostic gene testing for ALS has to improve, as effective gene silencing therapies for some genetic subtypes of ALS are emerging, but there is no consensus about which genes to test for. SUMMARY: Our knowledge about the genetic basis of ALS has improved and the first effective gene silencing therapies for specific genetic subtypes of ALS are underway. These therapeutic advances underline the need for better access to gene testing for people with ALS. Further research is needed to further map the genetic heterogeneity of ALS and to establish the best strategy for gene testing in a clinical setting.

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OBJECTIVE: To report initial results from the Amyotrophic Lateral Sclerosis (ALS) Identified genetic testing (GT) program on characteristics of individuals tested and frequency of reported disease-causing variants. METHODS: ALS Identified used the Invitae Amyotrophic Lateral Sclerosis panel (Invitae, San Francisco, CA, USA) to assay 22 ALS-associated genes. Sponsored by Biogen (Cambridge, MA, USA), the program was launched in June 2021 and was available at no charge to individuals ≥18 years in the United States and Puerto Rico with an ALS diagnosis or a known family history of ALS. Deidentified data were available to Biogen. RESULTS: As of 26 October 2023, 998 healthcare professionals ordered the panel at 681 unique care sites. Of 8054 individuals examined, 7483 (92.9%) were reported to have a clinical diagnosis of ALS, while 571 (7.1%) were asymptomatic relatives. Of the individuals with a clinical ALS diagnosis, 57.7% were male (n = 4319) and 42.3% female (n = 3164). Mean (SD) age at diagnosis is 62 (13) years. Out of the 7483 clinically diagnosed individuals, 1810 (24.2%) showed genetic variations in ALS-associated genes. Among these, 865 individuals (47.8%) carried pathogenic variants, and 44 (2.4%) had likely pathogenic variants, totaling 12.1% of the clinically diagnosed population. INTERPRETATION: Since 2021 there has been robust uptake and sustained use of the ALS Identified program, one of the largest samples of people with ALS to date across the United States, demonstrating the interest and need for genetic ALS testing.

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Amyotrophic lateral sclerosis (ALS) is a neurological disease without effective treatment. No pathognomonic test can diagnose ALS in sporadic cases. Routine investigation in suspected cases includes neurological examination, imaging of the brain and spine and electromyography supported by blood and cerebrospinal fluid (CSF) analyses. The ALS diagnosis is made by clinical judgement and results from examinations. We aimed to study if the CSF biomarkers neurofilament light protein (NFL), glial fibrillary acidic protein (GFAP), YKL-40, soluble amyloid precursor protein (sAPP) α and ß, and soluble triggering receptor expressed on myeloid cells 2 (sTREM2) were associated with ALS diagnosis and could predict disease progression. Eighty-one patients with suspected ALS were included after referral to the neurological clinic at Sahlgrenska University Hospital. Fifty-nine patients were diagnosed having ALS, while 22 patients were given alternative diagnoses and labeled ALS mimics. Finally, 25 age-matched neurologically intact individuals were used as controls. ALS patients had significantly higher CSF levels of NFL than controls and mimics. Levels of YKL-40 and GFAP were significantly higher in ALS patients compared with controls. No difference was found between study groups when comparing levels of sAPPα, sAPPß and sTREM2. Further, elevated levels of NFL and YKL-40 were associated with an increased hazard of death and the annual decline in ALSFRS-R. We also found that patients with elevated levels of both NFL and YKL-40 had a particularly poor prognosis. The results demonstrate the usefulness of CSF biomarkers in the diagnosis and prognostication of ALS.

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BACKGROUND: In amyotrophic lateral sclerosis (ALS) prognosis is poor due to progressive weakening of the respiratory muscles. Survival and quality of life can be improved by noninvasive ventilation (NIV), which is initially applied while sleeping. The indication for NIV is based on pulmonary function testing (PFT) and polysomnography (PSG) with capnography (tCO2). While it is desirable to predict nocturnal ventilation by waking PFT in ALS, the parameters suited for reliable predictions remain elusive. METHODS: We retrospectively analyzed parameters derived from PFT (spirometry, body plethysmography, diffusion capacity, respiratory muscle testing) and blood gas analysis, PSG and tCO2 in 42 patients with ALS (27 men, 15 women, age 69 ± 12.1 years) and performed Spearman's correlation analysis of daytime waking parameters and nighttime sleep parameters. RESULTS: 28 patients (66.7%) showed restrictive impairment of ventilation and 15 patients (48.3%) showed insufficiency of the respiratory musculature. There was no obstructive impairment of ventilation. We did not observe any significant correlations between any single daytime PFT parameter with nocturnal pCO2. However, there were significant correlations between the ratios PIF/PEF, MEF50/MIF50, DLCO/VA as well as FEV1/FVC and nocturnal pCO2. Highly normal FEV1/FVC and Krogh-Factor (DLCOc/VA) indicated nocturnal hypercapnia. Furthermore, waking hypercapnia, concentrations of bicarbonate and base excess were each positively correlated with nocturnal hypercapnia. CONCLUSIONS: Waking PFT is not a good predictor of nocturnal ventilation. Inspiratory parameters as well as the ratios FEV1/FVC and DLCO/VA performed best and should be included in the interpretation. Our analyses confirm the relevance of inspiratory muscle weakness in ALS. PSG and tCO2 remain the gold standard for the assessment of nocturnal ventilation.

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