Amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig’s disease, is a progressive neurological disorder that leads to the deterioration of nerve cells in the brain and spinal cord, ultimately causing muscle weakness and paralysis. ALS has an incidence rate of 2 to 3 per 100,000 worldwide, with a higher prevalence of 6–7 per 100,000 in Europe (Gao et al., 2021). Tragically, most patients survive just 2–5 years after symptom onset, primarily due to complications from respiratory muscle deterioration (Barberio et al., 2023). ALS exhibits marked clinical and genetic heterogeneity, with survival ranging from 2 to over 30 years, and over 30 genes implicated in pathogenesis. This variability highlights the need for biomarkers that address distinct molecular subtypes. For instance, Mutations in SOD1 lead to misfolded protein aggregation in the cytoplasm, contributing to mitochondrial dysfunction (Tafuri et al., 2015), whereas C9orf72 hexanucleotide repeat expansions drive RNA toxicity via dipeptide repeat protein (DPR) accumulation and nucleocytoplasmic transport defects (Requardt et al., 2021; Schmitz et al., 2021). Typically, initial symptoms manifest between the ages of 55 and 65; however, the disease may also present in elderly individuals or younger populations (Lin et al., 2021). The lack of early diagnostic tools and the average delay of 12–15 months before diagnosis significantly hinder timely intervention. This diagnostic bottleneck highlights the need to integrate biomarkers into clinical workflows.

ALS presents in two primary forms: familial and sporadic (Ajroud-Driss and Siddique, 2015). Familial cases, accounting for about 5%–10%, are associated with inherited genetic mutations, whereas the vast majority, 90%–95%, occur randomly without a clear hereditary pattern (Barberio et al., 2023). Riluzole was historically the sole approved drug, but additional therapies like edaravone and AMX0035 have since been approved in certain regions (Hinchcliffe and Smith, 2017). Non-invasive ventilation is an accepted intervention that enhances the quality of life and survival rates in patients with ALS (Morrison, 2011). Gastrostomy, sialorrhea management, multidisciplinary care, and assistive communication tools effectively improve the quality of life of patients (Ng and Khan, 2012; Garuti et al., 2019). These therapies have limited effectiveness, highlighting the urgent need for biomarkers to enable early diagnosis, guide treatment, and monitor disease progression (De Marchi et al., 2025).

Significant advancements have been made in identifying potential biomarkers for ALS that could support early diagnosis, provide prognostic insights, and deepen understanding of disease progression (Huang et al., 2020). Various targeted approaches, guided by known disease pathways, have been employed to uncover dysregulated molecular signatures. Meanwhile, untargeted approaches, such as large-scale proteomic and lipidomic profiling, have also yielded insights. Similar goals have been achieved by applying targeted methods that systematically examine biomolecules across various domains, including genomics, proteomics, glycomics, and lipidomics techniques (Turner et al., 2009). The biological substances used in the investigation included tissues from patients, encompassing biofluids such as blood, cerebrospinal fluid (CSF), and urine, as well as samples acquired from necropsy, specifically brain and spinal cord tissues. In vivo and in vitro models of ALS, based on identified mutations linked to the disease, have served as valuable sources for analysis. Different researchers have thoroughly examined various facets of biomarker research for ALS (Turner et al., 2013; Califf, 2018; Xu and Xu, 2024).

Biomarkers typically have multiple, often overlapping purposes (Califf, 2018). Diagnostic biomarkers provide a tool to distinguish between genuine disease and false positives, and have great potential to predict the presentation of disease (Xu and Xu, 2024). In contrast, predictive or prognostic biomarkers can assess the risk of disease progression in patients and the likelihood of patient survival (Sun et al., 2020). Moreover, categorical biomarkers can characterize disease subsets and shed light on disease mechanisms. Clinical pharmacodynamic biomarkers that demonstrate therapeutic efficacy may reduce the need for placebo groups, thereby lowering participant numbers and the duration of clinical trials (Shefner et al., 2022). Recent advances in antisense oligonucleotide (ASO) therapies, such as tofersen (targeting SOD1) and BIIB105 (targeting C9orf72), underscore the critical role of biomarkers in enabling precision medicine (Moriyama and Yokota, 2024). For instance, NfL reduction in CSF correlates with tofersen’s pharmacodynamic effects in SOD1-ALS (Everett and Bucelli, 2024), while poly-glycine-proline (poly-GP) DPR levels serve as target engagement biomarkers in C9orf72 trials (Wilson et al., 2022). However, widespread clinical adoption of these biomarkers requires harmonized assay standardization, cross-platform validation, and multicenter reproducibility studies to address variability in detection methodologies and ensure regulatory compliance (Steinbach et al., 2018; Shefner et al., 2022). In this review, we have examined biomarkers for ALS in the context of emerging technologies and their potential implications for the treatment and management of ALS. Although no biomarker technology has been fully implemented as a primary outcome measure in clinical trials, several are now emerging as valuable adjunct tools, offering complementary support within clinical research and practice.

Electrodiagnostic testing and clinical observation are the primary methods for diagnosing ALS (de Carvalho, 2020). Patients experience a decline in strength and functional capacity over time. A conclusive diagnosis requires clinical evidence of both lower and upper motor neuron (LMN and UMN) involvement to exclude disorders that mimic ALS (Štětkářová and Ehler, 2021). The LMN signs include muscular atrophy, weakness, and superficial fasciculations, while the UMN signs include rapid tendon reflexes, spasticity, and extensor plantar response (Burke, 2021). The presence of fasciculation potentials (e.g., spontaneous electrical activity in muscle fibers) is also essential (Pugdahl et al., 2021). Transcranial magnetic stimulation (TMS) enables the evaluation of UMN involvement (Vucic et al., 2013). Although not yet widely used due to the need for specialized software, threshold tracking TMS has shown promise as a diagnostic tool in the past decade for detecting early signs of increased cortical excitability (Al-Sultan et al., 2019). Advanced neuroimaging techniques are increasingly used to identify reliable and sensitive indicators of upper motor neuron (UMN) lesions in ALS (Bede and Hardiman, 2014). Despite the abundance of possibilities, including magnetic resonance imaging (MRI), spectroscopy, volumetry, functional MRI (fMRI), diffusion-weighted imaging (DWI), and positron emission tomography, further progress is needed to establish a method that is effective for everyday clinical practice. Recent diagnostic innovations include digital biomarkers (e.g., voice/speech analysis, wearable sensors) and AI-driven MRI protocols to detect upper motor neuron involvement (Straczkiewicz et al., 2024).

Genetic contributions are central to ALS development, with multiple genes identified in association with both familial and sporadic forms of the condition (Goutman et al., 2022). Table 1 summarizes the key genes associated with ALS susceptibility and progression (Chia et al., 2018; Mathis et al., 2019). The first gene identified in connection with ALS was superoxide dismutase 1 (SOD1), which encodes a copper-zinc-binding antioxidant enzyme (Wang et al., 2024). It accounts for approximately 20% of familial cases, although there is considerable variation across nations. Notably, disease-causing mutations have been identified throughout the SOD1 protein, not only the metal-binding site (Sirangelo and Iannuzzi, 2017). To date, over 170 unique pathogenic variants of SOD1 have been reported (http://ghr.nlm.nih.gov/gene/SOD1). Such genetic mutations frequently lead to aberrant protein folding and the formation of insoluble SOD1 aggregates, which predominantly accumulate in the cytoplasm of motor neurons (Kaur et al., 2016). These aggregates disrupt normal cellular homeostasis by impairing essential processes such as mitochondrial function, axonal transport, and proteasomal degradation (Magrané and Manfredi, 2009). The resulting cellular stress contributes to motor neuron dysfunction and eventual cell death, hallmark features of ALS pathology (Barber and Shaw, 2010). Beyond SOD1, several other genes linked to RNA and DNA metabolism have emerged as critical in ALS etiology. These genes include TARDBP, FUS, and SETX (also known as Senataxin). The TARDBP gene encodes the TAR DNA-binding protein 43 (TDP-43), which is involved in the majority of sporadic ALS cases (Tziortzouda et al., 2021; Kortazar-Zubizarreta et al., 2023). The (GGGGCC)n repeat expansions (G4C2) in C9orf72 exert multifaceted effects on cell homeostasis, including disruption of nucleocytoplasmic transport, sequestration of RNA-binding proteins, and translation into DPRs that aggregate and contribute to neurotoxicity (Koppers et al., 2015; McGoldrick and Robertson, 2023). Beyond these genes, various other genetic polymorphisms have been linked to an increased susceptibility to ALS (Table 1).

The FUS gene produces a protein that binds to both RNA and DNA, involved in transcriptional regulation, RNA splicing, and stress granule formation, all vital functions potentially linked to neuronal degeneration in ALS (Deng et al., 2014). The mouse model that expresses the human FUS protein, similar to the one that expresses the TDP-43 protein, exhibits highly aggressive behavior and has a short lifespan (Motaln et al., 2023). The recent identification of a repeated pattern of GGGGCC expansion in a non-coding region of the C9ORF72 gene has led to its implication in ALS. This could be due to three processes: RNA-binding protein sequestration, dipeptide synthesis (repetition), or haplo-insufficiency (Koppers et al., 2015). Additionally, changes in the VAPB gene have been documented in ALS (Borgese et al., 2021). VAPB is involved in vesicle-mediated transport, facilitating the transfer of misfolded proteins during the unfolded protein response (UPR), as well as regulating lipid metabolism and shuttling lipids from the endoplasmic reticulum to other cellular organelles. A Brazilian family was found to carry the VAPB-P56S mutation that binds to the wild-type protein and disrupts its ability to activate the UPR pathway (Kabashi et al., 2013). This dominant-negative effect impairs ER stress responses, contributing to neuronal vulnerability. Additionally, reduced expression of VAPB has been observed in the spinal cords of ALS patients (Anagnostou et al., 2010; Leoni et al., 2022). Other genes involved include CHMP2B, OPTN, DCTN1, ANG, ATXN2, UBQLN2, PFN1, and SQSTM1 (Table 1).

Extensive research has established that the pathogenesis of ALS arises from a multifactorial interplay of molecular and systemic mechanisms (Taylor et al., 2016; Madji Hounoum et al., 2017) (Figure 1). These include genetic mutations driving RNA misregulation and protein misfolding (e.g., TDP-43, SOD1 aggregates), mitochondrial dysfunction with reactive oxygen species (ROS) overproduction, and impaired autophagy-lysosomal clearance (Jiang and Xu, 2025). Neuroinflammation, mediated by microglial activation and cytokine release, synergizes with these processes to exacerbate neuronal damage. Mitochondrial dysfunction arising from impaired axonal transport deprives motor neurons of ATP, exacerbating ROS accumulation and energy deficits in degenerating axons (Mandal and Drerup, 2019). Concurrently, gut dysbiosis disrupts microbial metabolite production (e.g., short-chain fatty acids), which compromises intestinal barrier integrity and promotes systemic inflammation via gut-derived endotoxin leakage into circulation (Lee et al., 2024). This systemic inflammation exacerbates neuroinflammation through vagus nerve signaling and blood-brain barrier disruption (Anderson, 2022). While mechanistically distinct, these pathways converge to amplify oxidative stress and inflammatory cascades, creating a self-reinforcing cycle of neurodegeneration. Together, these findings highlight the genetic and mechanistic heterogeneity of ALS.

The table summarizes major ALS-linked genes and their chromosomal localizations, as curated from the ALS Online Database (http://alsod.iop.kcl.ac.uk/). The associated proteins and their functional roles in cellular processes relevant to ALS pathophysiology are also listed. UPR: Unfolded Protein Response.

Biomarkers are quantifiable indicators that reflect normal biological functions, the presence of a disease, or the pharmacological response to therapeutic interventions (Califf, 2018). Multiple categories of biomarkers have been recognized, encompassing molecular, radiographic, and physiological markers (Califf, 2018). The NIH-FDA grouped biomarkers into seven separate types, each associated with the BEST (Biomarkers, Endpoints, and other Tools) resources, with an individual role in the pathway to identify biomarkers. These include: (i) susceptibility/risk biomarker, an indicator of a statistical association between a biological characteristic and disease susceptibility; (ii) diagnostic biomarker, an objectively measurable feature reflecting normal function, disease state, or therapeutic response; (iii) monitoring biomarker, a measurable feature used to assess the effectiveness or potential risk of a therapeutic intervention over time; (iv) prognostic biomarker, a characteristic used as a basis for stratified patient therapy to identify those likely to benefit from an intervention; (v) predictive biomarker, a characteristic associated with a defined response; (vi) pharmacodynamic, or response, biomarker, a direct evidence that a biological response has occurred when using an intervention; and (vii) safety biomarker, an indicator of the probability of occurrence, presence, or extent of a toxicity associated with the administration of a therapeutic to a subject. These classifications are integral to advancing therapeutic development, as each provides critical insights that guide the creation of targeted treatment strategies. Neurofilaments, particularly NfL, are among the most clinically advanced biomarkers, validated in multiple ALS cohorts and currently used as secondary endpoints in clinical trials. In contrast, cryptic exon-derived peptides remain in the discovery phase.

Current ALS therapies, including riluzole, edaravone, and AMX0035, provide modest survival or functional benefits (Nikitin et al., 2023). Recent advances in ASOs, such as tofersen (SOD1-ALS) and BIIB105 (C9orf72-ALS), highlight the critical role of genetic biomarkers in patient stratification (Xie et al., 2025). These therapies demonstrate how biomarkers, such as NfL levels and DPR proteins, can be used to track target engagement and predict therapeutic efficacy in real time. Gene-editing approaches (e.g., CRISPR-Cas9 targeting C9orf72 hexanucleotide repeats) and small-molecule modifiers of TDP-43 aggregation are under preclinical investigation (Nowak et al., 2024). Reliable biomarkers are necessary to serve as surrogate endpoints in trials, thereby accelerating drug approval. Immunomodulatory strategies, like low-dose IL-2 to expand regulatory T cells (Tregs), have shown promise in reducing neuroinflammatory biomarkers (e.g., CHIT1, GFAP) and slowing progression in subsets of patients (De Marchi et al., 2023).

Metabolic interventions are also under investigation, including creatine supplementation, NAD + precursors, and agents modulating mitochondrial dysfunction (Lundt and Ding, 2024). These treatments address cellular energy deficits, redox imbalance, and oxidative stress, key contributors to ALS pathogenesis (Sharma et al., 2025). Increasingly, advanced imaging tools such as PET-MRI, sodium imaging, and quantitative susceptibility mapping are being incorporated alongside biofluid biomarkers (e.g., NfL, pNfH, cryptic peptides) to provide multidimensional monitoring of disease progression (Juengling et al., 2022). Integration of imaging and molecular data facilitates precision trial design by enabling real-time stratification of patients into molecular subtypes (Kitaoka et al., 2025).

Initiatives like PRECISION ALS, the ALS CARE Database and the ENCALS (European Network for the Cure of ALS) consortium underscore the importance of large-scale biomarker datasets (McFarlane et al., 2023). AI-driven algorithms now leverage these datasets to identify hidden patient subgroups, optimize trial recruitment, and predict therapeutic response (Kitaoka et al., 2025). Open-access platforms integrating multi-omics data will accelerate the transition from biomarker discovery to clinical translation. Such platforms also enhance reproducibility, enable global collaboration, and reduce the risk of overfitting in biomarker validation. A convergence of high-throughput transcriptomics, proteomics, and digital biomarkers (e.g., voice monitoring, wearable sensors) may yield composite signatures that enable earlier diagnosis and personalized therapy allocation (Kitaoka et al., 2025; Liu and Song, 2025). Combining these emerging strategies with rigorous clinical validation will ultimately be key to transforming ALS management from generalized care to precision neurology.

Despite recent advances in identifying numerous candidate biomarkers, none have yet been adopted into standard clinical practice. This reflects the broader understanding that ALS is a heterogeneous disorder, clinically, genetically, and pathophysiologically, making it improbable for any single biomarker to represent the entire disease landscape. Biomarker research has flourished over the past decade, but validation in more extensive clinical datasets, incorporating patient-reported outcomes, remains essential. Detailed clinical phenotyping has brought us closer to elucidate prognostic features, enabling accurate assessment of survival at the group level. Standardized neuropsychological assessments have enabled classification of patients according to whether they exhibit progressive cognitive decline. Concomitantly, genomic studies have begun to characterize disease subtypes by molecular pathway. Still, the earliest diagnosis is a significant problem, often being delayed by approximately 15 months. Despite the limited number of tools for tracking disease progression, scales like ALSFRS-R continue to be crucial for evaluating both early and advanced stages of ALS. The next few years are likely to amplify the paramount importance of understanding ALS variability and, in turn, the biology that underlies it (or vice versa). This will most likely include a combination of imaging, fluid-based, and neurophysiological biomarkers. New approaches, such as quantitative EEG, imaging data, and biomarkers derived from cryptic splicing, may improve diagnostic specificity, especially during the presymptomatic stage. The potential for fluid-based markers, such as NfL, in conjunction with advanced neuroimaging and genomic profiling, to facilitate smaller, more focused populations and shorter trial durations could be a game-changer. Additionally, ALS-directed gene product measurements offer pharmacodynamic insights into target engagement within this complex background. Translating these biomarkers into clinical practice will also require standardized assays, regulatory approvals, and infrastructure for routine biomarker testing across healthcare settings. Validated biomarkers could transform ALS research into a precision medicine-driven field, enabling earlier interventions and personalized care. Such a precision approach promises earlier intervention, streamlined trials, and personalized ALS care.