Recent Advances in Biomarkers for Early Diagnosis and Therapeutic Monitoring of Neurodegenerative Diseases: A Systematic Review

Neurodegenerative diseases comprise a heterogeneous group of disorders in which progressive neuronal dysfunction and neuronal loss produce cognitive, motor, behavioural or autonomic impairment. Alzheimer’s disease, Parkinson’s disease, Huntington’s disease and amyotrophic lateral sclerosis represent major clinical and biological phenotypes; they differ in their dominant pathological proteins, genetic determinants, anatomical vulnerability and rate of progression. Despite these differences, clinical manifestations may become evident only after disease-associated molecular and structural changes have accumulated. Biomarkers are central to modern disease characterisation and to the development of mechanism-based therapies. ^1,3-5

The term biomarker should be used precisely. The FDA-NIH BEST framework defines a biomarker as a defined characteristic measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention. Molecular, histologic, radiographic and physiologic measures can therefore qualify as biomarkers, whereas symptoms, functional status and survival are clinical outcomes rather than biomarkers. This distinction is particularly important in neurodegenerative disease research, where a measurable change in a biological marker may indicate target engagement or biological activity without necessarily proving clinical benefit. ²

Earlier biomarker work focused heavily on cerebrospinal fluid (CSF) proteins and genetic variants, and the field has expanded to include plasma proteins, cell-free nucleic acids, transcriptomic signatures, metabolomics, neuroimaging, electrophysiology and ultrasensitive assays. The most clinically consequential recent advances have been the emergence of high-performance blood biomarkers for AD, the development of α-synuclein seed amplification assays for PD, increasing validation of neurofilament light chain (NfL) across disorders, and the development of mutant huntingtin (mHTT) measures as pharmacodynamic biomarkers in HD. ^5,6,11

In neurodegenerative diseases, a marker may show biological association without being sufficiently qualified for diagnosis, prognosis, treatment monitoring or use as a surrogate endpoint. Accordingly, the intended context of use, assay performance, population, sampling time and relationship to clinical outcomes are to be specified before a biomarker is incorporated into a clinical study.

2. Biomarker Concepts and Classification

2.1 Biological Basis and Major Platforms

Biomarkers may be classified according to the biological layer measured. Genomic biomarkers include pathogenic variants, repeat expansions and other DNA-level characteristics. Transcriptomic biomarkers quantify RNA abundance or expression signatures. Proteomic biomarkers measure proteins, protein fragments or post-translationally modified species. Metabolomic biomarkers quantify metabolites or metabolic intermediates. In neurodegenerative disorders, these molecular layers are complemented by imaging, electrophysiological and physiologic measures. ^1,3-5

Table 1. Major biomarker platforms and their potential contexts of use

2.2 Biomarker Categories by Context of Use

For clinical and drug-development purposes, biomarkers should be linked to a predefined context of use. Diagnostic biomarkers identify or support the presence of a disease or pathological process. Prognostic biomarkers provide information about the likelihood or rate of a future clinical event or disease progression. Monitoring biomarkers are measured serially to assess disease status or change over time. Pharmacodynamic/response biomarkers indicate a biological response to an intervention and are particularly important for demonstrating target engagement. Predictive biomarkers identify individuals more likely to respond to a specific intervention, whereas safety biomarkers indicate toxicity or injury. ^1,2,5

Table 2. Biomarker categories and examples in neurodegenerative diseases

2.3 Type 0, Type 1 and Type 2 Biomarkers

The source manuscript also describes a Type 0–2 framework. In this terminology, Type 0 biomarkers follow the natural history of disease, Type 1 biomarkers reflect biological activity or intervention effects, and Type 2 biomarkers are surrogate measures intended to predict clinical benefit. The distinction is conceptually useful, but a biomarker should not be called a surrogate endpoint solely because it changes with treatment; surrogate status requires evidence linking biomarker change to clinical benefit. ^1,2

Table 3. Type 0–2 Biomarker framework

3. Practical Considerations in Biomarker Development

A useful biomarker must be analytically reliable, biologically plausible and clinically meaningful for its intended context of use. In neurodegenerative disease, additional challenges arise because the pathological process is spatially compartmentalised, often develops over many years, and may be accompanied by substantial biological heterogeneity. Blood biomarkers are attractive as they are relatively accessible and repeatable, but concentrations can be influenced by age, renal function, body composition, peripheral expression, assay platform and other comorbidities. ^1,3-5

Table 4. Advantages and practical concerns associated with biomarkers

3.1 Characteristics of an Ideal Neurodegenerative Biomarker

Biomarker development should address analytical validity, biological validity and clinical validity separately. A technically reproducible assay does not necessarily have sufficient disease specificity, and a statistically associated marker does not automatically provide clinically useful information. Fit-for-purpose validation is important when biomarkers are used for clinical trials or therapeutic decisions.

 Biomarkers can provide objective measurements, reduce some forms of reporting bias, permit quantitative longitudinal assessment and provide mechanistic information about disease processes. However, timing of sample collection is difficult, specimen storage affects analyte stability, laboratory error can influence measurements, normal reference ranges may be difficult to establish, and ethical responsibilities arise when biomarkers identify individuals at risk without providing a clear therapeutic option. ^1-5

4. Biomarkers Across Major Neurodegenerative Diseases

The four disorders considered in this review illustrate complementary biomarker strategies. AD has the most mature biological classification, with amyloid, tau and neurodegeneration domains now incorporated into disease-definition frameworks. PD is increasingly supported by direct detection of pathological α-synuclein. HD benefits from a definitive inherited genetic marker, while fluid markers such as NfL and mHTT add information about disease burden and therapeutic response. ALS has no single diagnostic laboratory test, but neurofilaments and other fluid measures are increasingly useful for prognosis and treatment development. ^6-10,18-22

Because pathological changes may precede recognisable clinical symptoms, biomarkers have potential value for early detection, differential diagnosis, disease staging, prognosis, monitoring and therapeutic development. Current evidence supports these concepts while also showing that biomarker performance must be defined according to the disease, assay and intended clinical use. ^6-10,13-22

The four prototypical disorders considered in the source manuscript illustrate distinct anatomical and biological patterns: AD involves prominent hippocampal and cortical neuronal loss with cognitive impairment; PD involves degeneration of the nigrostriatal dopaminergic system and motor impairment; HD involves basal-ganglia degeneration and abnormal involuntary movements; and ALS involves degeneration of spinal, bulbar and cortical motor neurons with progressive weakness and muscle atrophy. ^6,8,13,30,35

Neurodegenerative disorders are a heterogeneous group of diseases characterised by progressive dysfunction and loss of neurons in selectively vulnerable regions of the nervous system. The source manuscript highlighted genetic abnormalities, biochemical defects, chronic or environmental influences and incompletely understood causes as contributors to neurodegeneration. Clinical consequences may include progressive impairment of cognition, speech, vision, hearing, movement, feeding and other neurological functions.

5. Parkinson’s Disease

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterised clinically by parkinsonism and biologically by α-synuclein pathology, dopaminergic neuronal loss and broader multisystem involvement. Loss of dopaminergic neurons in the substantia nigra pars compacta contributes to motor impairment, while non-motor manifestations such as REM sleep behaviour disorder, olfactory dysfunction and autonomic abnormalities may precede or accompany motor disease.

5.1 Pathophysiology Relevant to Biomarkers

The principal pathological hallmark is accumulation and misfolding of α-synuclein, accompanied by neuronal dysfunction and loss. This biological complexity explains why conventional clinical scales, although indispensable for patient care, do not capture all aspects of disease biology. Earlier biomarker approaches examined dopamine metabolites, CSF proteins, inflammatory markers, oxidative-stress markers and neuroimaging of dopaminergic pathways, but many lacked sufficient specificity for routine diagnosis. ^18-20,30,33

5.2 Established and Emerging PD Biomarkers

α-Synuclein is the most disease-specific molecular target currently being developed for PD biomarker applications. Total α-synuclein concentration in CSF has shown group-level differences, but measurement of pathogenic aggregates using seed amplification assays has provided a more direct assessment of α-synuclein pathology. In the Parkinson’s Progression Markers Initiative, CSF α-synuclein seed amplification substantially separated PD from controls and also revealed biological heterogeneity within clinically defined PD cohorts. ^18-20

Other candidate biomarkers remain useful mainly as complementary measures. DJ-1, total tau, phosphorylated tau and Aβ-related measures have been investigated in CSF and blood, particularly when cognitive impairment or differential diagnosis is relevant. Metabolomic and proteomic panels, inflammatory cytokines, urate and RNA signatures have also been explored. However, these markers should not be presented as established standalone diagnostic tests without specifying the assay, population and context of use. ^15,18-20

Table 5. Biomarkers investigated in Parkinson’s disease

Soluble, aggregated and post-translationally modified forms of α-synuclein, as well as DJ-1, dopamine-related metabolites, microRNAs, ST13 RNA, inflammatory markers, oxidative-stress products, total tau, phosphorylated tau, β-amyloid and serum urate are the markers which reflect different processes, including synaptic pathology, oxidative stress, inflammation, neuronal injury and metabolic alterations. Their usefulness varies by specimen and clinical context, and most should not be interpreted as established standalone diagnostic tests. ^15,18-20

Clinical and neurobehavioral measures investigated as early markers include REM sleep behaviour disorder, olfactory loss and oculomotor abnormalities. Neuroimaging approaches include SPECT and PET techniques for assessing dopaminergic pathways and metabolic alterations. Physiological measures and molecular analyses of CSF, plasma and other biofluids have also been investigated. These approaches are best considered complementary because none, by itself, captures the complete biological spectrum of PD. ^7,18-20

PD is frequently difficult to recognise at an early stage because there may be a prolonged interval between initial dopaminergic neuronal injury and the appearance of clinical motor symptoms. The major pathological hallmarks are loss of neurons in the substantia nigra pars compacta and the presence of intracellular α-synuclein aggregates associated with Lewy pathology. ^18-20,30,33

6. Alzheimer’s Disease

Alzheimer’s disease (AD) is the most common cause of dementia and is characterised pathologically by amyloid-β plaques and abnormal tau accumulation, together with progressive neurodegeneration. The biological definition of AD has increasingly shifted from a purely syndromal diagnosis toward biomarker-supported identification of underlying pathology. ^6,11-17

6.1 Amyloid, Tau and Neurodegeneration

The NIA-AA AT(N) framework organises biomarkers according to amyloid deposition (A), pathological tau (T), and neurodegeneration or neuronal injury (N). This framework separates the biological disease process from its clinical expression and has been highly influential in research and therapeutic trials. The 2024 revised criteria further emphasise that AD can be diagnosed biologically by biomarkers of amyloid and tau pathology, while acknowledging that clinical implementation requires appropriate validation and context. ^6,13

6.2 Fluid Biomarkers

CSF Aβ42, Aβ42/40 ratio, total tau and phosphorylated tau have long been central AD biomarkers. More recently, plasma p-tau217 and related phosphorylated tau measures have demonstrated high accuracy for detecting AD pathology in well-characterized cohorts. In a 2024 multicohort study, plasma percentage p-tau217 showed performance comparable to clinically used CSF assays for classifying amyloid PET status and strong performance for tau PET status. Subsequent primary- and secondary-care validation has further supported the clinical potential of automated plasma p-tau217 assays. ^6,11-17

Blood-based biomarkers are nevertheless not interchangeable with clinical diagnosis in every setting. Kidney dysfunction, cardiovascular and metabolic comorbidities, age and other factors can affect biomarker concentrations. Current evidence therefore supports a fit-for-purpose approach in which biomarker results are interpreted alongside clinical phenotype and, where appropriate, confirmatory CSF or PET testing. ^5,11-17,29

6.3 Additional Biomarkers

As potential AD biomarkers, BACE1, soluble APP (sAPP), and anti-Aβ autoantibodies are still biologically significant but have not replaced the more reliable Aβ and phosphorylated-tau measurements. While data on sAPP and anti-Aβ antibody levels have been inconsistent across various studies, elevated levels and activity of BACE1 are associated with the production of amyloid. As a result, it is preferable to present them as experimental rather than proven clinical biomarkers.. ^6,13-17

Table 6. Alzheimer’s disease biomarkers and their current contexts of use

BACE1 is involved in amyloidogenic processing of APP, while sAPP and anti-Aβ antibody measurements have been investigated as indicators of altered amyloid metabolism or immune responses. These measures remain biologically relevant but are less clinically established than Aβ42/40 and phosphorylated tau biomarkers. ^6,13-17

The biological cascade described in the source material includes abnormal amyloid precursor protein processing, β-amyloid accumulation and fibrillar plaque formation, followed by tau-related neurodegeneration. These processes provide the rationale for biomarker development based on amyloid, tau and neuronal injury. Modern biomarker frameworks have subsequently strengthened this biological interpretation by distinguishing amyloid pathology, pathological tau and neurodegeneration. ^6,13-17

Alzheimer’s disease is the most common neurodegenerative cause of dementia and is characterised by progressive cognitive impairment associated with synaptic dysfunction, neuronal loss and characteristic amyloid and tau pathology. The source manuscript described early-onset and late-onset disease, age-related cerebral atrophy, hippocampal and cortical involvement, and the contribution of genetic and non-genetic factors to disease susceptibility. ^6,11-17

7. Huntington’s Disease

Huntington’s disease (HD) is an autosomal-dominant neurodegenerative disorder caused by expansion of a CAG trinucleotide repeat in the HTT gene. The pathogenic expansion produces mutant huntingtin (mHTT), and repeat length is associated with age at onset and disease trajectory, although it does not determine an exact clinical course for an individual.

7.1 Genetic and Clinical Biomarkers

Genetic testing for the pathogenic HTT CAG expansion provides a definitive molecular basis for diagnosis in appropriate clinical settings and identifies individuals at genetic risk. However, genotype alone is not a dynamic measure of disease progression. Clinical motor, cognitive and psychiatric measures therefore remain important endpoints, while MRI measures of striatal and other brain-region atrophy can detect structural changes before substantial clinical decline.

7.2 Fluid Biomarkers

NfL has emerged as one of the most consistently supported fluid biomarkers in HD. CSF and plasma NfL concentrations rise with disease burden and are associated with clinical and imaging measures. mHTT can be quantified in CSF and has particular value for assessing target engagement of therapies designed to lower huntingtin expression. Recent evidence continues to support NfL as a prognostic biomarker, while mHTT is particularly informative for pharmacodynamic applications. ^8,9,21,22

 H2AFY, inflammatory markers such as IL-8 and TNF-α, and imaging of phosphodiesterase-10 (PDE10) remain of research interest, but their clinical use is less established than genetic testing, MRI, NfL and mHTT. ^21,22

Table 7. Huntington’s disease biomarker landscape

Mutant huntingtin is particularly important because it is the pathogenic protein produced by the expanded HTT allele. Its detection in CSF, blood and saliva and its potential as a prognostic and pharmacodynamic biomarker, supports CSF mHTT as a target-engagement measure in studies of huntingtin-lowering therapies. ^8,21,22

A histone-related protein called H2AFY was identified as having higher levels in HD and being responsive to sodium phenylbutyrate in the early stages of the illness. Although this observation has pharmacodynamic significance, it is still under investigation and shouldn't be used as a reliable clinical biomarker. Similarly, since transcriptional dysregulation and epigenetic mechanisms play a role in the pathophysiology of HD, histone deacetylase inhibition has been investigated experimentally.^21,22

The source manuscript described biomarker domains including clinical motor testing, anti-saccade error rate, digitomotography, cognitive testing such as the Symbol Digit Modalities Test, structural MRI, PDE10 imaging with [18F]MNI-659 PET, immune markers including IL-8 and TNF-α, H2AFY and mutant huntingtin. These measures interrogate different components of HD biology and disease progression. ^21,22

CAG-repeat length is associated with age at onset, with larger expansions generally associated with earlier disease onset. These values should be presented as broad clinical ranges rather than absolute predictors because repeat length does not determine an individual's exact phenotype or age at onset. ³⁵

Huntington’s disease is an inherited neurodegenerative disorder caused by a pathogenic CAG-repeat expansion in the HTT gene on chromosome 4. The major clinical domains are motor abnormalities, cognitive impairment and emotional or psychiatric changes, including involuntary movements, impaired coordination, difficulties with swallowing and speech, impaired concentration, memory and judgement, impulsivity, anxiety, depression and loss of initiative. ³⁵

8. Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a progressive motor-neuron disease involving upper and lower motor neurons and leading to progressive weakness, muscle atrophy, bulbar dysfunction and, in many patients, respiratory failure. ALS is genetically heterogeneous. Pathogenic variants in genes including C9orf72, SOD1, TARDBP and FUS account for a substantial proportion of familial disease, while many sporadic cases remain genetically unresolved.

8.1 Pathophysiology Relevant to Biomarkers

ALS pathology includes motor-neuron degeneration, axonal loss, reactive gliosis, neuroinflammation and disturbances in protein homeostasis, RNA metabolism, excitotoxicity and mitochondrial function. Mutant SOD1 has been linked to mitochondrial abnormalities, oxidative stress and impaired cellular energetics, while no single biomarker represents the complete disease process.

8.2 Diagnostic, Prognostic and Monitoring Biomarkers

Diagnosis remains primarily clinical and electrophysiological, supported by neurological examination and electromyography and by exclusion of alternative diagnoses. The major unmet need is a biomarker that can shorten diagnostic delay and distinguish ALS from its mimics. Neurofilament light chain and phosphorylated neurofilament heavy chain are currently among the best-supported fluid biomarkers, with particularly strong evidence for prognostic use and increasing support for treatment-response assessment.

A blood gene-expression study involving 396 ALS patients, 75 patients with ALS-mimic disorders and 645 healthy controls reported 752 ALS-increased and 764 ALS-decreased differentially expressed genes. Individual neurofilament-gene measures (NEFH and NEFL) showed limited diagnostic accuracy in isolation, whereas a support-vector-machine approach using gene-expression features reportedly achieved 87% accuracy, with 86% sensitivity and 87% specificity. These findings are important as an example of a multigene approach, but they should not be interpreted as a clinically validated diagnostic test without independent replication and prospective validation.

Table 8. Biomarkers and outcome measures in amyotrophic lateral sclerosis

For diagnosis, the current benchmark remains neurological history and examination supported by electromyography and established clinical criteria. Prognostic biomarkers are intended to identify progression patterns and improve stratification in therapeutic trials, while monitoring biomarkers may help identify ineffective interventions earlier. ALSFRS-R remains a key clinical outcome measure, and electrical impedance myography is an emerging physiological measure rather than a fully established standalone biomarker. ^10,28,32

Glutamate excitotoxicity and mitochondrial abnormalities are associated with mutant SOD1. Mutant SOD1 can localise to mitochondrial compartments and is associated with altered mitochondrial membrane potential, impaired electron-transport activity, calcium and cytochrome-c dysregulation and increased reactive oxygen species. These mechanisms are relevant to interpret oxidative-stress and neuronal-injury biomarkers. ^25,26,27

Corticospinal and bulbospinal motor-neuron degeneration, reactive gliosis, microglial and astrocytic responses, loss of large myelinated motor fibres, denervation and reinnervation of muscle, and pathological changes in the motor cortex, brainstem and spinal cord provide a biological basis for electrophysiological and neurofilament biomarkers. ^25,26

Familial ALS is genetically heterogeneous. C9orf72, SOD1, TARDBP and FUS among major disease-associated genes are particularly useful for molecular classification and, in selected cases, treatment selection, but many ALS cases remain genetically unexplained. ^25,26

Amyotrophic lateral sclerosis is a progressive motor-neuron disease involving upper and lower motor neurons, progressive muscle weakness, muscle atrophy, bulbar dysfunction and, in later stages, respiratory involvement. Frontotemporal dementia can occur in association with ALS, producing additional frontal and temporal cortical pathology. ^25,26

8.3 ALS Pathology, Genetics and Biomarker Development

These findings illustrate the potential value of multigene signatures and cell-composition information, but the reported performance should be interpreted as evidence from the cited study rather than as a clinically validated diagnostic assay. Independent replication, prospective validation, assay standardisation, and evaluation in real-world diagnostic populations are required before such signatures can be adopted clinically.

Neurofilament-related genes including NEFH and NEFL were examined as candidate markers. The source manuscript reported individual-gene diagnostic accuracy of approximately 50–53%. A support-vector-machine approach using gene-expression information reportedly distinguished ALS from mimic diseases and healthy controls with 87% accuracy, 86% sensitivity and 87% specificity.

The investigators reported 752 ALS-increased and 764 ALS-decreased differentially expressed genes. Increased genes showed higher exosome-related expression, neutrophil-specific expression and associations with translation-related processes, and overlapped with genes near ALS susceptibility loci including IFRD1, TBK1 and CREB5. Decreased genes showed lower exosome expression, erythroid-lineage specificity and associations with anaemia and blood disorders.

A blood-based gene-expression study designed to identify differentially expressed genes, characterise patient-to-patient heterogeneity and identify candidate biomarkers. The study included 396 patients with ALS, 75 patients with ALS-mimic diseases and 645 healthy controls.

9. Comparative Clinical Utility of Biomarkers

The four diseases demonstrate that biomarker maturity depends strongly on disease biology and context of use. A pathogenic genetic variant can be diagnostic in HD and in selected familial forms of ALS or AD, whereas protein or imaging biomarkers are needed to characterise sporadic disease biology. AD currently has the most developed multimodal biomarker framework, whereas PD is rapidly moving toward pathology-based molecular diagnosis through α-synuclein seed amplification. HD and ALS demonstrate the value of NfL as a cross-disease measure of neuro-axonal injury, but its lack of disease specificity means that it is better suited to prognosis, disease activity and treatment monitoring than to standalone diagnosis. ^{5-10,18-22,28}

Table 9. Cross-disease comparison of biomarker utility