Salivary Biomarkers for Early Detection of Autism Spectrum Disorder: A Scoping Review

Abstract

Background: Autism spectrum disorder (ASD) represents a neurobiological disorder with a high prevalence in the children’s population. The aim of the present review was to assess the current evidence on the use of salivary biomarkers for the early diagnosis of ASD. Materials and methods: A search was conducted on the electronic databases PUBMED/Medline, Google Scholar and Scopus for the retrieval of articles concerning the study topic. Results: A total of 22 studies have been included in the present review considering 21 articles identified from databases and 1 article included using a manual search. A wide range of biomarkers have been proposed for early detection of ASD diseases including nonspecific inflammation markers like interleukin-1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8), tumor necrosis factor α (TNFα), oxidative stress markers like superoxide dismutase and glutathione peroxidase, hormones such as cortisol and oxytocin, various microRNAs including miR-21, miR-132 and miR-137, and exosomes. The techniques used for biomarke detection may vary according to molecule type and concentration. Conclusions: salivary biomarkers could represent a potential useful tool for the primary detection of several systemic diseases including ASD, taking advantage of non-invasiveness and cost-effective capability compared to other biofluid-based diagnostic techniques.

1. Introduction

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by social communication deficits, restricted interests, and repetitive behaviors [1]. ASD may affect how an individual perceives and interacts with the world [1,2,3]. This disorder is also considered a “spectrum” because it includes a wide range of characteristics and abilities, varying significantly from one person to another. ASD can manifest in different ways and to varying degrees [4,5]. Subjects with autism can have problems such as difficulty in understanding social cues, like body language, tone of voice, or facial expressions [4,5]. They might struggle with initiating or maintaining conversations and forming relationships. Changes in routine or environment can sometimes cause distress [6]. Many subjects with autism develop deep, intense interests in specific topics, often focusing on them in great detail, and may have heightened or diminished sensitivity to sensory inputs like lights, sounds, textures, or smells [7]. Moreover, they might be either very sensitive or indifferent to sensory experiences [7].

The exact causes of autism are not fully understood; a combination of genetic and environmental factors is believed to contribute [8,9]. It is important to recognize that autism is not a disease or mental illness but rather a natural variation in human development [8]. Each subject affected by autism can present unique characteristics, and many can lead successful and fulfilling lives with the right support systems in place [10].

Autism is typically diagnosed in early childhood, though some individuals may not be diagnosed until later in life [7]. There is no cure for autism, but early interventions such as speech therapy, behavioral therapy, and educational support can help individuals develop skills and cope with challenges [11]. The prevalence of ASD has risen in recent years, highlighting the urgent need for early diagnostic tools that can improve prognosis by facilitating timely intervention [12,13]. Current diagnostic methods primarily rely on behavioral assessments and clinician-based evaluations, which can often be subjective and time-consuming [12,13]. This limitation underscores the growing interest in identifying reliable biological markers that could serve as objective, non-invasive tools for early diagnosis [13].

Saliva, a biofluid with an easily accessible collection method, has gained significant attention in recent years as a potential source of biomarkers for ASD [14]. Compared to other biological samples, such as blood or cerebrospinal fluid, saliva collection is non-invasive, painless, and can be performed repeatedly without any significant risk, making it ideal for use in pediatric populations, including those at high risk of ASD [15]. Moreover, saliva contains a wide array of biomolecules, including proteins, hormones, RNA, and metabolites, which are indicative of physiological and pathological processes occurring in the body [16]. Thus, salivary biomarkers have the potential to serve as a diagnostic tool for ASD [13]. Salivary screening, despite limited sensitivity, can achieve a high specificity and can produce a leverage effect in terms of primary prevention; in this way, the healthcare and economic advantage is substantial. Salivary analysis has potential as a fast and cost-effective primary screening to identify individuals with possible conditions such as ASD. Despite this advantage, diagnosis with salivary tests still needs to be confirmed by a secondary test using standard and consolidated diagnostic methods. Early diagnosis plays a crucial role in enhancing the outcomes of individuals with ASD, as early interventions can significantly improve developmental trajectories [12]. Traditional diagnostic approaches rely on clinical evaluations with behavioral assessments, and parent reports, which can often result in delays in diagnosis, typically occurring after the age of three [17]. As an alternative method, studies suggested that exfoliated deciduous tooth-derived pulp stem cells and lines obtained by periapical cysts could represent a useful diagnostic strategy due to a mitochondrial dysfunction in dopaminergic neurons associated with ASD [18,19].

Currently, recent advances in biosensor technology, particularly biodegradable sensors, offer promising opportunities for non-invasive, early detection of various conditions including ASD, through specific salivary biomarker detection [20,21,22]. However, there is a great lack of scientific reports, especially in the field of ASD, so the aim of this scoping review was to perform an overview of the most promising salivary biomarkers for early detection of ASD. For this purpose, the potential of novel biosensors in detecting salivary biomarkers related to ASD will be discussed, highlighting their development, mechanisms, applications, and challenges.

2. Materials and Methods

The present review was performed considering the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) checklist [21]. The present scoping review was registered on the Open Science Framework (OSF). The electronic search was conducted on PUBMED/Medline, Google scholars database and Scopus using the following search string: (autism OR autism spectrum disorder) AND (saliva*AND (biomarkers OR markers)).

The following inclusion criteria were considered: randomized clinical trial, cohort study, case control study, pilot study, retrospective study, prospective study, cross-sectional study.

The exclusion criteria were literature reviews, meta-analysis, editorials, proceedings, case reports, case series, original articles written in a non-English language, off-topic articles.

Screening, Eligibility and Synthesis Processes

The first-level screening was performed considering the title and the abstract of each publication identified after the duplicate removal. The eligibility assessment was conducted using the full text of the manuscript. The data synthesis included the recording of the manuscript reference, authors, journal, year of publication, the salivary biomarker and the assessment technique.

3. Results and Discussion

A total of 22 original articles were identified after the screening process, with 21 obtained from electronic databases and one item from a manual search (Figure 1). After the duplication removal, a total of 16 articles were considered for full text collecting and eligibility assessment. A total of eight studies were excluded for the following reasons: two literature review, one secondary variable/indirect evidence, five off-topic articles. A total of eight articles were considered for the data synthesis process.

3.1. Salivary Biosensors in ASD Diagnosis

ASD is a heterogeneous condition with varying degrees of severity. Early diagnosis is crucial to tailor interventions that improve social, cognitive, and language development. However, identifying biomarkers for autism has proven to be challenging due to the complexity and variability of the disorder [12]. Currently, diagnosis depends heavily on behavioral observation and caregiver input, which can lead to underdiagnosis or late diagnosis, especially in children with subtle manifestations of ASD [11].

Saliva has emerged as a promising biofluid for diagnostic purposes due to its non-invasive nature, ease of collection, and rich content of biomarkers [13]. Several studies have identified specific salivary biomarkers, including proteins, hormones, and microRNAs, that could be linked to ASD. For instance, elevated levels of certain proteins (e.g., S100B protein) and inflammatory markers have been found in the saliva of individuals with ASD, suggesting that saliva could serve as a valuable diagnostic medium [13]. Moreover, saliva collection can be performed without the need for specialized healthcare personnel, making it an ideal candidate for point-of-care testing.

Progress in the field of biodegradable materials offered a major opportunity to transform healthcare technologies [23,24,25]. The integration of biodegradable biosensors in the early diagnosis of various conditions is a rising innovation in recent years [22]. As a hot topic, chitosan-based sponge and biodegradable polymers were used for glucose and creatinine detection [26]. Chitosan-based sponges are characterized by high biocompatibility, and they represent an effective device for salivary sampling and collection in the patient’s mouth due to their capability to absorb fluids with no enzymatic alterations of proteins and biomarkers (including cortisol, dopamine and adrenaline) [26]. According to the opinion of the authors of this manuscript, a chitosan-based biosensor that detects dopamine levels in saliva might be used for real-time monitoring of ASD-associated dysregulation. However, currently, there is a lack of studies in the field of novel and early diagnostic methods for ASD, which can represent valuable alternatives to traditional methods. But, in the near future, such biosensors can represent great potential, although further scientific research is needed to confirm this statement.

Studies have investigated the validity of salivary cortisol levels as a potential marker for autism, as individuals with ASD often exhibit altered stress responses [11]. Similarly, cytokines and other inflammatory mediators have been shown to be elevated in ASD patients, further supporting the role of salivary biosensors in early detection. These biosensors were reported in the literature, as they were integrated into detectors or handheld devices, providing real-time, on-site diagnostic results [25,27]. Such devices are easy to use in clinics, schools, or even at home, facilitating widespread screening and reducing delays in diagnosis. Moreover, in the future, they might have the potential to allow for continuous monitoring of children at risk of ASD, enabling longitudinal tracking of biomarker levels as early as possible.

Over the past decade, researchers have focused on various molecular signatures in saliva that may be associated with ASD (Table 1), including proteins, peptides, and small molecules [11]. Several studies have identified altered levels of oxidative stress markers in the saliva of individuals with ASD. This includes higher levels of reactive oxygen species (ROS) and reduced antioxidant defense mechanisms. Oxidative stress is thought to contribute to neuroinflammation and neuronal damage, both of which have been implicated in the pathophysiology of ASD [28].

Regarding neurotransmitters and hormones, the dysregulation of neurotransmitters such as serotonin, dopamine, and gamma-aminobutyric acid (GABA) is well established in the context of ASD. Some studies have found altered levels of these neurotransmitters in the saliva of individuals with ASD, suggesting potential links between neurochemical imbalances and the disease [29]. Similarly, hormonal imbalances, including variations in cortisol and oxytocin levels, have been noted in ASD individuals and could be indicative of disruptions in the hypothalamic–pituitary–adrenal (HPA) axis or social bonding mechanisms, respectively [30].

Currently, several studies have demonstrated the feasibility of using biosensors to detect biomarkers in biological fluids and research has begun to focus on applying this technology to the detection of autism-related markers in saliva (Figure 2) [13]. Disposable, eco-friendly biosensors to measure these biomarkers in saliva can enable early and non-invasive diagnosis for children.

Chronic inflammation has been proposed as a central mechanism in the development of ASD. Various cytokines, such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α), have been found to be elevated in the saliva of ASD patients. These inflammatory markers may reflect neuroinflammatory processes within the brain and could potentially be used to identify individuals at risk of ASD in early childhood [15]. Also, genetic alterations have been frequently associated with ASD, and some studies have explored the presence of specific genetic material in saliva, such as microRNAs (miRNAs). miRNAs are small non-coding RNAs that regulate gene expression and have been shown to be dysregulated in various neurodevelopmental disorders, including ASD. The presence of specific miRNAs in saliva may serve as a diagnostic tool for identifying ASD at an early age [13]. For this purpose, advanced techniques such as mass spectrometry, microRNA analysis, and proteomics offer enormous potential for the identification of salivary biomarkers ofr the early detection of autism. However, some methodological and clinical challenges need to be addressed before these tests can be used in practice. Large-scale validation and standardization of techniques are essential to make these diagnostic tools reliable and accessible.

Table 1. Publications on the use of saliva for the diagnosis of autism, organized by title, authors, year, and brief description.

Authors	Journal	Years	Study Design	Sample Size	Age	Biomarker	Ref.
SS Mahmood	Cureus	2024	case-control study	96	5–12 yr	malondialdehyde [MDA] glutathione [GSH] uric acid	[31]
Z Kalemaj	Front Neurosci	2022	Pilot Study	10	3–7 yr	miRNA-199b-5p (inv. with AUTS2 gene) miRNA-199a- 3p (inv. with CB1 receptor (Cannabinoid receptor type 1) miRNA-4516 miRNA-1246 inv. With SYN II Synapsin II) miRNA-1246 miRNA-4516 miRNA-454-5p	[32]
MMD Souza	Rev. Odontol.	2021	cross-sectional	24	5–11 yr	Protein concentration uric acid Indirect carbonylation	[33]
Hicks SD	BMC Pediatr.	2016	cross-sectional	24	5–13 yr	miR-7-5p, miR-27a-3p, miR-140-3p, miR-191-5p, miR-2467-5p	[13]
Samborska-Mazur J	J. Clin. Med.	2020	cross-sectional	38	3–13 yr	interleukin-1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8), tumor necrosis factor α (TNFα), monocyte chemoattractant protein-1 (MCP-1), Regulated on Activation, Normal T-cell Expressed and Secreted (RANTES), Eotaxin	[15]
Beversdorf DQ	Front Psychiatry	2022	cohort study	898	18–73 months	miR-224-5p miR-27a-3p miR-27b-3p miR-151a-5p	[34]
Mota FSB	Int. J. Biol. Macromol.	2022	case-control study	75	35.47 ± 10.42 months	severe ASD vs. Healthy: Calmodulin-3, Plastin-2 and Protein S100-A7 present in the mild/moderate ASD in the control group; Cysteine-rich secretory protein 3, CRISP-3 and Neutrophil elastase (EC 3.4.21.37)	[35]
Abdulla AM	J. Clin. Pediatr. Dent.	2015	case-control study	100	6–12 yr	Cortisol	[36]

Table 1. Publications on the use of saliva for the diagnosis of autism, organized by title, authors, year, and brief description.

Authors	Journal	Years	Study Design	Sample Size	Age	Biomarker	Ref.
SS Mahmood	Cureus	2024	case-control study	96	5–12 yr	malondialdehyde [MDA] glutathione [GSH] uric acid	[31]
Z Kalemaj	Front Neurosci	2022	Pilot Study	10	3–7 yr	miRNA-199b-5p (inv. with AUTS2 gene) miRNA-199a- 3p (inv. with CB1 receptor (Cannabinoid receptor type 1) miRNA-4516 miRNA-1246 inv. With SYN II Synapsin II) miRNA-1246 miRNA-4516 miRNA-454-5p	[32]
MMD Souza	Rev. Odontol.	2021	cross-sectional	24	5–11 yr	Protein concentration uric acid Indirect carbonylation	[33]
Hicks SD	BMC Pediatr.	2016	cross-sectional	24	5–13 yr	miR-7-5p, miR-27a-3p, miR-140-3p, miR-191-5p, miR-2467-5p	[13]
Samborska-Mazur J	J. Clin. Med.	2020	cross-sectional	38	3–13 yr	interleukin-1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8), tumor necrosis factor α (TNFα), monocyte chemoattractant protein-1 (MCP-1), Regulated on Activation, Normal T-cell Expressed and Secreted (RANTES), Eotaxin	[15]
Beversdorf DQ	Front Psychiatry	2022	cohort study	898	18–73 months	miR-224-5p miR-27a-3p miR-27b-3p miR-151a-5p	[34]
Mota FSB	Int. J. Biol. Macromol.	2022	case-control study	75	35.47 ± 10.42 months	severe ASD vs. Healthy: Calmodulin-3, Plastin-2 and Protein S100-A7 present in the mild/moderate ASD in the control group; Cysteine-rich secretory protein 3, CRISP-3 and Neutrophil elastase (EC 3.4.21.37)	[35]
Abdulla AM	J. Clin. Pediatr. Dent.	2015	case-control study	100	6–12 yr	Cortisol	[36]

Mass spectroscopy, quantitative PCR (qPCR), and ELISA tests still remain the most useful methods for an effective detection of disease-related biomarkers (Figure 3). Mass spectrometry is a useful analytical technique that allows us to identify and quantify biological molecules based on their mass and structure [37]. This technique was also used to analyze proteins and other molecules that were detected in saliva [38]. This technique consists of different phases [39]:

• Ionization: The molecules present in the saliva sample are ionized, i.e., transformed into ions (electric charges).
• Separation: Ions are separated according to their mass/charge ratio (m/z).
• Detection: Spectrometry measures the abundance of each separate ion, creating a spectrum that represents the molecular composition of the sample [40].

Furthermore, mass spectrometry can analyze metabolites and smaller molecules such as sugars and fatty acids, which might be altered in patients with ASD [41]. Today, data complexity is one of the main challenges, as saliva contains a wide variety of molecules.

Figure 3. Categories of proteins, metabolites, miRNAs and hormones that are sensitive in ASD.

Figure 3. Categories of proteins, metabolites, miRNAs and hormones that are sensitive in ASD.

3.2. MicroRNA (miRNA) Profiling

MicroRNAs are small non-coding RNA molecules that regulate gene expression [13,34,41,42]. The miRNAs are involved in numerous biological processes, including the development of the nervous system and they are very stable in biological fluids such as saliva and [13,34]. For this reason, they may represent promising biomarkers in future for diagnosis of autism. This procedure consists of different steps [13,34,43,44]:

• RNA extraction: The first step is to extract RNA from saliva. This can be done using RNA-specific extraction techniques.
• MiRNA analysis: Once the RNA has been extracted, the microRNA profile can be analyzed through techniques such as quantitative PCR or high-capacity sequencing.
• qPCR (quantitative PCR): Uses specific primers to amplify the microRNAs of interest and measure their abundance.
• Next Generation Sequencing (NGS): Allows to obtain a detailed profile of all the microRNAs present in the sample.

Various miRNAs are key regulators during brain development, and alterations in these miRNAs could be indicative of neurological dysfunctions such as those seen in autism [13,34]. Preliminary studies have suggested that the miRNA profile may be different in children with autism than in neurotypical children, offering a potential diagnostic biomarker [13,34]. The identification of autism-specific miRNAs is still in its early stages, and large-scale studies are needed to confirm which microRNAs are truly associated with autism [13,34]. In diagnosis, standardization of extraction and analysis techniques is critical to ensuring reproducible results. As an example, the miR-137 was found to be associated in neurogenesis and brain development, and changes in miR-137 expression may be associated with ASD [13,34]. Additionally, miR-132 is a key regulator of synaptic plasticity and neuronal function, and altered expression of miR-132 could indicate disruptions in neural signaling pathways in individuals with ASD. miR-21 was also evaluated and found to be involved in neuroinflammation, which is often seen in ASD. Its levels in saliva could potentially correlate with the presence of a neuroinflammatory marker [13,34].

Moreover, quantitative PCR (qPCR) is commonly used for miRNA profiling, allowing the amplification and quantification of specific miRNAs [44]. Moreover, next-generation sequencing (NGS) provides a comprehensive analysis of all miRNAs present in a sample, giving a broad view of miRNA expression profiles [44].

3.3. Proteomics

Proteomics as a method to evaluate all the proteins present in a sample [33,35,36,40,45]. At this point, salivary proteomics for autism focuses on the analysis of proteins that might be altered in patients with ASD [33,35,36,40]. The methodological process is characterized by different phases:

• Protein identification: Using techniques such as mass spectrometry (which we have discussed) or Western blot, it is possible to identify the proteins present in saliva and determine their levels.
• Quantification: The identified proteins are then quantified to determine if there are any significant differences between children with autism and those with neurotypical condition.

Proteins such as brain-derived neurotrophic factor (BDNF) have been associated with brain development and may be altered in children with autism [33,35,36,40,46]. Salivary proteomics could therefore help identify diagnostic markers. Because inflammation is often seen in individuals with autism, proteomics can help identify proteins linked to the immune response that are altered in patients with ASD. However, the protein complexity of saliva is a major challenge, since there are too many proteins, and some are at very low concentrations, which makes it difficult to identify specific biomarkers [33,35,36,40,46]. As with other techniques, clinical validation is still needed to ensure that the identified biomarkers are useful for early diagnosis.

Proteins in saliva can reflect various neurobiological processes, such as neuroinflammation, oxidative stress, and brain development. Research suggests certain proteins may be altered in individuals with ASD. Brain-Derived Neurotrophic Factor (BDNF) [46] plays a critical role in brain development and synaptic plasticity. Altered levels of BDNF have been observed in individuals with ASD, suggesting it may be a useful biomarker for early diagnosis. Cytokines and chemokines [15] such as IL-6 (interleukin-6) and TNF-α (tumor necrosis factor-alpha) are involved in inflammation. Elevated levels of these inflammatory markers in saliva have been associated with neuroinflammatory responses observed in ASD. Proteins associated with oxidative stress [30] like superoxide dismutase (SOD) and glutathione peroxidase could be altered in ASD. Higher oxidative stress in the brain may contribute to neurodevelopmental disruptions in ASD.

Salivary hormones are another category of biomarkers being investigated for ASD diagnosis [47]. Hormones such as cortisol (the stress hormone) and oxytocin (a hormone related to social bonding) may provide insight into the neuroendocrine abnormalities associated with ASD [36]. Cortisol levels in response to stress are commonly observed in children with ASD [36]. Abnormal stress responses might be linked to sensory processing issues and social behavior impairment associated with ASD. Oxytocin [48] plays a key role in social behavior and bonding. Research has shown that individuals with ASD may have lower levels of oxytocin, which could contribute to difficulties in social interaction and communication. Other emerging biomarkers could be represented by salivary peptides that are small protein fragments that might be related to brain function and immune responses [49].

3.4. Metabolomics

Metabolomics focuses on small molecules (metabolites) that are involved in metabolic processes [36,50]. Metabolite profiles in saliva may provide insights into metabolic dysregulation that could be linked to ASD [51,52]. Altered levels of amino acids such as glutamate and glycine have been found in individuals with ASD [36,50]. These metabolites play a role in neurotransmission and may reflect abnormalities in neural communication. Short-chain fatty acids (SCFAs) [14] are produced by gut microbiota and have been linked to neuroinflammation. Given the growing interest in the gut–brain axis in ASD, salivary levels of SCFAs could potentially serve as a marker for early detection. Several methods of detection have been described, including Nuclear Magnetic Resonance (NMR) spectroscopy and Gas Chromatography-Mass Spectrometry (GC-MS), that are commonly used techniques to identify and quantify metabolites in saliva. Exosomes are 30–150 nm small extracellular vesicles (sEVs) secreted by cells and are being studied for their potential role in autism spectrum disorders (ASDs). Research suggests that exosomes may be involved in intercellular communication in the brain and may impair neurological development and functioning, factors potentially related to autism. In this way, the using exosomal biomarkers can represent a new frontier for the early detection of neuropsychiatric diseases and ASD [52]. Recent studies seem to suggest that an altered lipid profile could be linked with ASD, due to disturbances in the synergy between cholesterol and gangliosides that are implicated in ASD. Low cholesterol levels and unusual ganglioside levels have been observed in ASD [53,54,55]. Moreover, reduced levels of long-chain polyunsaturated fatty acids (PUFAs), such as those found in fish oil, eggs, and meat, have been linked to ASD. These are vital for brain development and function [50,51,52]. In addition, PUFA imbalances and their metabolism into bioactive prostaglandins are significant topics of research, since elevated prostaglandin E2 (PGE2), a membrane-derived lipid molecule, has been linked to ASD [53,54,55].

3.5. Limits and Future Directions

While the identification of salivary biomarkers for early ASD diagnosis is promising, several challenges remain validation and standardization. The biomarkers need to be validated in large, diverse clinical populations to ensure their reliability and specificity. Moreover, sensitivity and specificity represent another emerging challenge. Despite the limited studies in this field, e salivary biomarker detection can be promising as an early diagnostic tool for several diseases including ASD with potentially advantageous effects related to chair-side wide-scale primary screening. As alternatives, new biomarkers have been identified in recent years that can accurately differentiate between ASD and other neurodevelopmental or psychological disorders. Variability in sample collection, handling, and storage can affect biomarker levels in saliva, but in cases of simple identification protocol for a target molecule, independent of its concentration, can be suggestive of a possible condition.

Salivary biomarkers hold significant potential for the early diagnosis of ASD, especially in children who may be too young to undergo more invasive procedures [46,47,48,49]. They may offer a non-invasive, accessible, and potentially cost-effective alternative to more demanding diagnostic methods. Ongoing research is critical to refine these biomarkers and ensure their clinical applicability in early ASD screening. While the potential of salivary biomarkers for early ASD diagnosis is promising, several challenges must be addressed before these biomarkers can be incorporated into clinical practice. One of the main obstacles is the lack of standardized protocols for saliva collection and processing. Variability in how saliva samples are collected, stored, and analyzed can lead to inconsistent results across studies, making it difficult to establish reliable diagnostic criteria. Furthermore, the identification of a single definitive biomarker for ASD remains elusive, as the disorder is highly heterogeneous, with multiple genetic, environmental, and neurological factors contributing to its development. Therefore, a combination of biomarkers rather than a single marker may be necessary to improve diagnostic accuracy. To overcome these challenges, future research should focus on large-scale, multi-center studies that standardize protocols and include diverse ASD populations. The development of high-throughput technologies for analyzing salivary biomarkers can also enhance the sensitivity and specificity of these tests. Additionally, longitudinal studies are needed to investigate how salivary biomarkers change over time, particularly during critical windows of neurodevelopment. The integration of genetic, environmental, and clinical data can help in the creation of a comprehensive biomarker profile that allows personalized early diagnosis for ASD. Limitations of this manuscript mostly depend on the lack of scientific reports in the literature to assess and underline the accuracy of salivary biomarkers and biosensors for early detection of ASD; for this reason, further research is needed and critical in this field.

4. Conclusions

Saliva-based biomarkers can offer an exciting frontier for the early diagnosis of autism spectrum disorder, allowing a fast and cost-effective first-level screening. Despite the challenges in standardization and the need for further validation, the non-invasive nature of saliva collection and its potential to reflect underlying biological processes make it an attractive alternative to traditional diagnostic methods. Currently, there is a very limited number of reports and continuing research and innovation in this area might serve to pave the way for more accessible, accurate, and timely diagnosis of ASD, ultimately improving outcomes for affected individuals and their families.