ALS is characterized by significant genetic, clinical, and biological heterogeneity. The heritability of ALS is approximately 50%, and variants in more than 200 genes have been associated with the disease. Clinical features are highly variable for most variants, likely due to interactions with other modifier genes and environmental factors. Mutations in groups of genes belonging to specific ALS pathomechanisms may be associated with distinct phenotypes, but better correlations with clinical and biomarker profiles are still needed. Clinically, patients show significant variability in disease onset and progression, as well as in motor and cognitive phenotypes. Several clinical, neurophysiological, neuropsychological, and neuroradiological measures have been developed to account for this variability, but neurochemical biomarkers may represent an ideal tool to identify homogeneous patient subgroups.

The most extensively studied neurochemical biomarkers in ALS are neurofilaments, which are released from degenerating motor neurons into biological fluids and have diagnostic and prognostic value. Other potential biomarkers of neuronal damage in ALS include tau (associated with shorter survival), UCHL1, and TDP-43 (both elevated in ALS patients). Microglial and astrocytic involvement in ALS pathogenesis can be investigated by measuring MCP-1 and GFAP, respectively.

Considering the growing evidence implicating IFN-alpha involvement in ALS pathogenesis, we aim to comprehensively profile cytokines, neuroinflammatory markers, and analytes related to neurodegeneration in the plasma and cerebrospinal fluid (CSF) of clinically characterized ALS patients and matched healthy controls.

This study will support the validation of IFN-alpha pathway activation as a therapeutic target and explore its association with disease phenotype and progression. Furthermore, correlations between biomarker levels and available clinical data will provide insights into potential diagnostic and prognostic biomarkers for ALS, thereby facilitating future therapeutic development.

Inclusion criteria for ALS patients:

• Age equal or over 18 years old
• ALS patients, diagnosed accordingly to the revised El Escorial Criteria Disease duration <24 months from symptom onset.

Inclusion criteria for controls

• Age equal or over 18 years old
• Subjects without a diagnosis of neurodegenerative disease or neuromuscular disorder.

Exclusion criteria for ALS patients:

• FVC <60%;
• nutritional or respiratory failure; Significant hepatic or chronic renal failure or any intervening infective (pneumonia, flu-like syndromes, urinary tract infections) or metabolic (acute renal failure, hyperosmolar hyperglycaemic state, severe hyponatremia) conditions present at the time of assessment that could potentially affect biomarker levels.
• ALS patients exhibiting any of these conditions at the time of biofluid sampling as per the Case Report From (CRF) will be excluded from sample selection.

Exclusion criteria for controls:

• Significant hepatic or chronic renal failure or any intervening infective (pneumonia, flu-like syndromes, urinary tract infections) or metabolic (acute renal failure, hyperosmolar hyperglycaemic state, severe hyponatremia) conditions present at the time of assessment that could potentially affect biomarker levels.
• Controls exhibiting any of these conditions at the time of biofluid sampling as per the Case Report From (CRF) will be excluded from sample selection.

1. 30 ALS patients - 30 healthy controls
2. > 18 years old
3. Age- and sex-matched controls
4. Inclusion/Exclusion criteria (see above)
5. ALS patients have been recruited and blood samples collected by Angelo Quattrini's team (UO neurology, UO Experimental pathology-INSPE)
6. Healthy control samples were purchased from BioIVT.

Individual data will not be shared because it is not relevant to the interpretation or consideration of the data. The controls and ALS patients were fully age- and gender-matched and so the data can be considered without the need to return to patient ID's or demographic/clinical data.