Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disorder characterized by the selective loss of upper and lower motor neurons. The absence of definitive diagnostic biomarkers and the heterogeneity of disease progression pose significant challenges for early diagnosis, clinical management, and therapeutic development[@benatar2012]. Biomarkers are objective measures of biological processes that can aid in diagnosis, track disease progression, predict outcomes, and monitor treatment responses. In ALS, biomarker research has expanded substantially over the past two decades, encompassing fluid biomarkers, neuroimaging markers, electrophysiological measures, and clinical endpoints[@vu2017].
Amyotrophic lateral sclerosis (ALS) is a rapidly progressive neurodegenerative disorder characterized by the selective loss of upper and lower motor neurons. The absence of definitive diagnostic biomarkers and the heterogeneity of disease progression pose significant challenges for early diagnosis, clinical management, and therapeutic development[@benatar2012]. Biomarkers are objective measures of biological processes that can aid in diagnosis, track disease progression, predict outcomes, and monitor treatment responses. In ALS, biomarker research has expanded substantially over the past two decades, encompassing fluid biomarkers, neuroimaging markers, electrophysiological measures, and clinical endpoints[@vu2017].
The identification of reliable biomarkers for ALS is critical for several reasons. First, diagnostic delay remains a significant problem, with the average time from symptom onset to diagnosis ranging from 8 to 14 months[@chio2013]. Biomarkers that can facilitate earlier diagnosis would enable earlier intervention and potentially improve outcomes. Second, the heterogeneous nature of ALS progression complicates clinical trial design, as patients progress at vastly different rates[@chio2014]. Biomarkers that can predict progression rates would enable better patient stratification. Third, the lack of objective measures of disease activity makes it difficult to assess treatment efficacy in clinical trials[@benatar2013]. Surrogate biomarkers that can track biological processes affected by therapies would accelerate drug development.
Cerebrospinal fluid (CSF) provides an accessible window into the central nervous system, reflecting biochemical changes occurring in the brain and spinal cord. Several CSF biomarkers have been investigated in ALS, with neurofilament proteins emerging as the most promising[@petzold2005].
Neurofilament light chain (NfL) and phosphorylated neurofilament heavy chain (pNfH) are structural proteins released into the CSF following axonal damage. Multiple studies have demonstrated elevated CSF NfL and pNfH levels in ALS patients compared to healthy controls and patients with ALS-mimicking conditions[@lu2015]. Importantly, neurofilament levels appear to correlate with disease progression rate and survival, making them potential prognostic biomarkers[@steinacker2016]. CSF NfL has also been shown to increase over time in individual patients, reflecting ongoing axonal degeneration, and may predict conversion from presymptomatic to symptomatic stages in individuals with genetic mutations[@benatar2018].
Tau protein and beta-amyloid have been studied as potential biomarkers of neurodegeneration in ALS. While some studies report reduced CSF tau levels in ALS, findings have been inconsistent[@sussmuth2004]. The relationship between CSF beta-amyloid and ALS remains unclear, though alterations may be more pronounced in patients with comorbid cognitive impairment[@wada2012].
Inflammatory biomarkers including cytokines and chemokines have been investigated in ALS CSF. Elevated levels of interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and various chemokines have been reported, suggesting ongoing neuroinflammation[@kuhle2009]. However, the specificity of these findings for ALS remains limited, as similar changes are observed in other neurological conditions.
S100B is a calcium-binding protein primarily expressed in astrocytes. Elevated CSF S100B levels have been reported in ALS and may reflect astrocyte activation or damage[@sussmuth2003]. Some studies suggest that S100B levels correlate with disease progression, though findings have not been consistently replicated.
Blood-based biomarkers offer practical advantages over CSF sampling, including ease of collection and the potential for repeated measurements. Neurofilament proteins can be reliably measured in blood using ultrasensitive immunoassays, and elevated serum or plasma NfL levels have been demonstrated in ALS patients[@gaiottino2013]. Blood NfL correlates with CSF NfL levels and shows similar prognostic value, making it a promising biomarker for clinical use[@bacioglu2016].
Extracellular vesicle (EV)-associated biomarkers represent an emerging area of research. EVs are membrane-bound particles released by cells that can carry proteins, nucleic acids, and lipids reflecting their cell of origin. Studies have identified ALS-associated proteins and RNA species in neuronal-derived EVs from blood, including TDP-43, SOD1, and FUS mutations[@sproviero2022]. While technically challenging, EV-based approaches may provide disease-specific information about underlying pathogenic processes.
Genetic biomarkers are particularly relevant for familial ALS cases, where specific mutations can be identified. The most common genetic causes of ALS include expansions in C9orf72 and mutations in SOD1, FUS, and TARDBP[@ranganathan2022]. Genetic testing is now routinely incorporated into clinical practice for patients with familial ALS or early-onset disease. Additionally, the presence of C9orf72 expansions has been associated with cognitive and behavioral features, including frontotemporal dementia, which occurs in approximately 15% of ALS patients[@ferrari2011].
Conventional magnetic resonance imaging (MRI) is typically normal or shows nonspecific changes in ALS, but advanced MRI techniques can detect subtle abnormalities in motor pathways[@agosta2014]. Several MRI-based biomarkers have been investigated:
Diffusion tensor imaging (DTI) measures water diffusion in brain tissue, providing information about white matter microstructure. In ALS, DTI reveals increased diffusivity and reduced fractional anisotropy in the corticospinal tract, reflecting axonal degeneration and demyelination[@filippi2011]. These changes are typically bilateral but may be asymmetric, correlating with clinical weakness patterns.
Magnetic resonance spectroscopy (MRS) measures metabolite levels in brain tissue. Reduced N-acetylaspartate (NAA) levels, a marker of neuronal integrity, have been reported in the motor cortex of ALS patients[@pioro1999]. The NAA/creatine ratio shows promise as a marker of disease burden and may correlate with clinical progression.
Functional MRI (fMRI) assesses brain activity patterns. Studies have shown altered activation patterns in the motor cortex of ALS patients, including both hypoactivation related to neuronal loss and hyperactivation reflecting compensatory reorganization[@lul2007]. Resting-state fMRI has identified functional connectivity changes in motor and cognitive networks.
Quantitative MRI techniques, including T1-weighted imaging for cortical thickness measurement and susceptibility-weighted imaging for iron deposition, are being investigated to track disease progression[@kwong2012].
Positron emission tomography (PET) imaging provides information about brain metabolism and specific molecular targets. Fluorodeoxyglucose (FDG)-PET shows hypometabolism in the motor cortex and frontotemporal regions in ALS, with patterns that may distinguish ALS from ALS-FTD[@pagano2020]. PET ligands targeting specific pathological features, including microglial activation (PK11195) and tau pathology (flortaucipir), are being investigated to understand disease mechanisms[@van2021].
Nerve conduction studies (NCS) and needle electromyography (EMG) are established components of the ALS diagnostic workup. While not strictly biomarkers, electrophysiological findings support the diagnosis of diffuse motor neuron disease and help exclude mimic conditions[@costa2012].
Motor unit number estimation (MUNE) and motor unit number index (MUNIX) are quantitative electrophysiological techniques that estimate the number of functional motor units in a muscle. These measures show progressive decline in ALS and correlate with clinical weakness[@swash2021]. MUNIX may be more sensitive than clinical measures for detecting early changes and is being evaluated as a potential biomarker for clinical trials.
Electrical impedance myography (EIM) is a noninvasive technique that measures the electrical conductivity of muscle tissue. ALS causes characteristic changes in EIM parameters that correlate with disease progression[@rutkove2007].
The ALS Functional Rating Scale-Revised (ALSFRS-R) is the primary clinical outcome measure in ALS clinical trials and clinical practice[@cedarbaum1999]. This 12-item questionnaire assesses respiratory function, bulbar function, fine motor function, and gross motor function, with scores ranging from 0 (worst) to 48 (normal). The rate of ALSFRS-R decline is a key prognostic indicator and is used to assess treatment efficacy in clinical trials.
Forced vital capacity (FVC) is a measure of respiratory function that declines progressively in ALS. FVC is a critical predictor of survival and is used to monitor disease progression and inform clinical decisions regarding respiratory support[@schmidt2008].
Several clinical features are associated with prognosis in ALS. Age at onset is a strong predictor, with younger patients having longer survival. Bulbar onset is associated with shorter survival compared to limb onset, particularly in older patients. Time to diagnosis reflects disease aggressiveness. Weight loss and hypermetabolism are associated with more rapid progression[@chio2009].
The King's College staging system and the MiToS (Milano-Torino staging) system provide frameworks for stratifying patients based on disease burden and functional involvement[@kimura2021]. These staging systems may facilitate patient stratification in clinical trials.
Biomarkers play essential roles in ALS clinical trial design and execution. Prognostic biomarkers can identify patients likely to progress rapidly or slowly, enabling enrichment strategies that reduce trial size and duration[@benatar2020]. Predictive biomarkers may identify patients most likely to respond to specific therapies, supporting personalized treatment approaches. Pharmacodynamic biomarkers can demonstrate target engagement and biological activity of investigational therapies[@baker2021].
The CNTN4 biomarker network and other consortia are working to standardize biomarker measurement approaches and validate biomarkers for clinical trial use[^35]. Multidisciplinary collaborations are essential for advancing biomarker development in this challenging disease.
Peripheral blood gene expression studies have identified dysregulated pathways in ALS, including inflammation, oxidative stress, and mitochondrial function[@lillo2012]. Specific gene expression signatures may have biomarker potential, though findings have been inconsistent across studies.
MicroRNA (miRNA) profiles in blood and CSF are being investigated as potential biomarkers. Specific miRNAs have been associated with ALS diagnosis and progression, though their clinical utility remains to be established[@toivonen2014].
High-throughput proteomic and metabolomic platforms are identifying novel biomarker candidates in ALS. Studies have identified disease-associated proteins and metabolic alterations that may reflect underlying pathogenic processes[@blasco2017]. These unbiased approaches may reveal unexpected biomarkers that would not be identified through hypothesis-driven research.
Emerging digital health technologies offer potential for continuous, objective monitoring of ALS patients. Wearable devices can track motor activity, gait, and speech patterns[@lonardi2021]. Voice analysis using smartphone applications may detect early changes in bulbar function[@green2013]. These digital biomarkers could complement traditional clinical measures and enable remote monitoring.
The translation of promising biomarker candidates into clinically useful tests requires rigorous validation. Biomarkers must demonstrate analytical validity (reliable measurement), clinical validity (association with clinical outcomes), and clinical utility (improvement in patient outcomes)[@sawcer2008]. The Awaji criteria and other consensus guidelines provide frameworks for integrating biomarkers into ALS diagnostic criteria[@de2008].
Standardization of biomarker measurement is essential for comparing results across studies and enabling clinical implementation. Quality control programs and reference materials are being developed for neurofilament testing[@shaw2020]. International consortia, including the Biomarkers in ALS (BREAD) consortium, are working to advance biomarker validation and standardization.
The future of ALS biomarker research lies in multi-modal approaches that combine fluid, imaging, electrophysiological, and clinical biomarkers. Machine learning and artificial intelligence techniques may identify complex biomarker patterns that improve diagnostic accuracy and prognostic predictions[@grollemund2020]. Personalized biomarker approaches that account for genetic and clinical heterogeneity may enable precision medicine in ALS.
Integration of biomarker assessment into clinical practice will require development of standardized, accessible assays and establishment of reference ranges. As biomarker-guided approaches mature, they have the potential to transform ALS diagnosis, monitoring, and treatment.