LONgitudinal and Integrated Evaluation of Biomarkers in reLation to phenotYpe in ALS

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder affecting upper and lower motor neurons and leading to muscle weakness, paralysis, and premature death. Although ALS is uniformly fatal, it is characterized by substantial clinical and biological heterogeneity, including differences in site of onset, rate of disease progression, cognitive and behavioral involvement, and survival. This heterogeneity poses major challenges for prognostication, patient counseling, and the design and interpretation of clinical trials. In recent years, neurochemical biomarkers have gained increasing relevance in ALS research. Neurofilaments, particularly neurofilament light chain (NfL), are currently the most widely studied biomarkers and are considered indicators of axonal degeneration. Elevated NfL levels in cerebrospinal fluid (CSF) and blood have been consistently observed in ALS and are associated with disease severity and survival. However, several key questions remain unresolved, including the relative contribution of upper versus lower motor neuron degeneration to NfL release, the extent to which NfL reflects disease aggressiveness versus anatomical disease burden, and the factors influencing its distribution between CSF and blood. Other biomarkers may capture complementary aspects of ALS pathophysiology. Glial fibrillary acidic protein (GFAP), a marker of astrocytic activation, has been reported to be increased in ALS and may be related to extra-motor and cognitive features. Phosphorylated tau (p-tau181), widely used as a biomarker of Alzheimer's disease, has recently been proposed as a potential ALS biomarker, possibly reflecting lower motor neuron degeneration. In addition, classic Alzheimer's disease biomarkers such as Aβ42 and Aβ40 may provide insights into cognitive impairment and overlapping neurodegenerative mechanisms in ALS. The relationships among these biomarkers, their longitudinal evolution, and their associations with clinical phenotype and disease progression remain insufficiently characterized. The LONELYALS study (LONgitudinal and integrated Evaluation of biomarkers in reLation to phenotYpe in ALS) is an ongoing, monocentric, observational clinical-epidemiological study focusing on biological material. The study adopts a prospective cohort design with a case-control component and is conducted at a single specialized ALS center. A total of 140 adult patients with ALS, aged 18 to 90 years, are being enrolled and followed longitudinally for up to 36 months. Patients are recruited during inpatient admissions, and all participants provide biological samples and clinical data according to standardized procedures. The study involves the collection and analysis of CSF and blood samples, including genetic material, alongside comprehensive clinical, neurological, and neuropsychological assessments. Biomarkers of interest include established and emerging neurochemical markers related to axonal degeneration, astroglial activation, tau pathology, and amyloid metabolism. Longitudinal sampling allows evaluation of biomarker trajectories over time and their relationship with disease progression. The primary objectives of the study are to explore the associations between baseline biomarker levels and ALS phenotype, to assess the relationships between CSF and blood biomarker concentrations, and to evaluate the prognostic value of biomarkers with respect to longitudinal clinical evolution. Additional aims include investigating the influence of physiological and pathophysiological factors-such as age, sex, body mass index, and renal function-on biomarker levels; exploring interrelationships among different biomarkers; and identifying novel CSF biomarkers relevant to ALS. By integrating longitudinal biomarker data with detailed phenotypic characterization, the LONELYALS study seeks to improve understanding of ALS pathophysiology and heterogeneity. The results are expected to support biomarker-based patient stratification, facilitate more accurate prognostication, and contribute to the development of personalized clinical management strategies. In the longer term, improved biomarker characterization may also enhance patient selection and outcome assessment in future interventional trials.