The Pediatric Microbiome as a Diagnostic Biomarker: Cross-Disease Evidence and the Path to Clinical Translation

Abstract

Microbiome-derived signatures are increasingly investigated as diagnostic biomarkers in children, yet their clinical readiness remains uncertain because pediatric microbiomes vary substantially with age, development, diet, medication exposure, geography, and sampling context. This narrative review evaluates the diagnostic potential and translational maturity of pediatric microbiome biomarkers across disease domains. A prespecified PubMed search identified 124 curated records evaluating taxonomic, functional, genomic, metabolomic, or composite microbiome-derived signatures. Evidence was examined according to clinical task, biological matrix, analytical platform, comparator selection, modeling strategy, diagnostic performance, and validation maturity. Gastrointestinal and hepatobiliary disorders, particularly inflammatory bowel disease, showed the most developed diagnostic evidence, including clinically relevant comparators and independent validation. Promising signals were also reported in neurodevelopmental, respiratory, immune/allergic, oral, and metabolic disorders, although most studies remained at the discovery or internal-validation stage. High discrimination in case–healthy-control studies frequently weakened when models were tested against symptomatic controls or independent populations, highlighting spectrum bias, overfitting, developmental confounding, and limited transportability. Clinical translation therefore requires prespecified clinical use, leakage-controlled models, independent multicenter validation, comparison with standard care, and prospective evaluation of calibration, feasibility, clinical impact, and cost. Pediatric microbiome diagnostics should be judged by reproducibility and clinical utility rather than by discovery-cohort accuracy alone.

1. Introduction

Children require diagnostic tests that are accurate, reproducible, minimally invasive, and interpretable within the context of rapidly changing developmental physiology. This need is especially apparent in disorders for which diagnosis remains syndromic, requires invasive procedures, or relies on combinations of laboratory, imaging, endoscopic, histologic, and behavioral assessments. The microbiome is an attractive diagnostic source because microbial communities can be sampled repeatedly from stool, saliva, the upper airway, skin, and other accessible sites, while sequencing and mass-spectrometry platforms can resolve taxonomic, functional, genomic, and metabolite-level features. The central clinical question, however, is not whether disease is accompanied by dysbiosis, but whether a microbiome-derived signature can reproduce the same diagnostic decision across children, centers, regions, laboratories, and analytical platforms.

This issue is particularly important in pediatrics. The microbiome undergoes marked maturation during infancy and early childhood, and its composition and developmental trajectory vary with age and geography and are shaped by delivery mode, breastfeeding or formula exposure, diet, and antibiotic use [1,2,3,4]. These sources of variation can obscure disease-related signals and complicate the definition of stable diagnostic thresholds. They may also interact with disease itself: restricted dietary preferences, for example, may mediate apparent autism-microbiome associations rather than simply confound them [5]. Pediatric microbiome biomarkers therefore need to remain robust across relevant developmental and exposure-related variation, rather than merely achieve statistical separation within a single cohort.

The diagnostic literature has also evolved substantially. Early studies emphasized individual taxa and measures of alpha diversity (the diversity of microbes within a single sample) and beta diversity (differences in microbial community composition between samples). More recent work combines taxa into panels, interrogates microbial functions, reconstructs metagenome-assembled genomes (MAGs), profiles microbial structural variants and insertion/deletion variants (InDels), quantifies volatile or soluble metabolites, and integrates microbiome features with host or clinical variables. Machine learning is increasingly used to extract diagnostic signals from high-dimensional microbiome data and can yield high apparent discrimination. However, high dimensionality and analytical flexibility also increase susceptibility to overfitting, data leakage, batch effects, and model instability, producing performance estimates that may not reproduce in independent populations [6,7].

This narrative review evaluates the diagnostic potential and translational maturity of pediatric microbiome biomarkers across disease areas. Particular emphasis is placed on the intended clinical task, biological matrix, biomarker layer, comparator selection, analytical and modeling strategy, validation design, transportability, and incremental value over established care. The review further considers which microbial signatures have progressed from biological association to reproducible and clinically interpretable diagnostic tools, and what evidence is required for the remainder to advance toward clinical implementation. The overall diagnostic and translational pathway is summarized in Figure 1.

Figure 1. Framework for pediatric microbiome biomarker development and clinical translation. The framework summarizes the major steps in developing microbiome-derived biomarkers for pediatric diagnostic applications. The process begins with definition of the intended clinical use, including disease detection, differential diagnosis, and phenotyping or stratification, followed by selection of the biological matrix and control of pre-analytical factors. Subsequent stages include assay and biomarker-layer selection, leakage-controlled model development, assessment of clinical performance, and independent validation and implementation. Pediatric-specific determinants, including developmental stage, diet and environmental exposures, and medication or treatment, may influence biomarker performance throughout the workflow. Clinical evaluation should extend beyond discrimination to include clinically relevant comparators, calibration, incremental value over standard care, transportability, workflow feasibility, assay failure, cost, and clinical impact. AUC, area under the curve; ITS, internal transcribed spacer; MAG, metagenome-assembled genome; VOC, volatile organic compound.

2. Literature Search and Evidence Curation

This narrative review was informed by a prespecified PubMed search updated to 14 August 2026. PubMed was selected for its broad coverage of clinically relevant biomedical literature, although the absence of complementary searches in Embase, Scopus, or Web of Science may have limited the identification of relevant studies across certain disease domains. Search terms covered pediatric populations, the microbiome, and diagnostic performance, including microbiome- and microbiota-related terms together with terms for diagnosis, biomarkers, receiver operating characteristic (ROC) curves and area under the curve (AUC), classification, prediction, and machine learning. We focused on original studies evaluating microbiome-derived taxonomic, functional, genomic, metabolomic, or composite signatures in pediatric populations and reporting diagnostic discrimination, classification, or validation. Adult-only studies, Mendelian randomization and other etiological analyses, conventional microbiological diagnostic studies, purely methodological or in silico studies, and reports outside the diagnostic-biomarker scope were not included in the core synthesis. Prognostic, treatment-response, or association studies were considered when they also informed diagnostic or stratification questions or helped define the boundaries of the evidence base. Three authors (M.-S.W., Z.-J.H., and D.-Y.W.) contributed to the literature search, and four (P.W., Z.-J.H., D.-Y.W., and M.-S.W.) participated in evidence curation. Formal independent duplicate screening and inter-rater agreement assessment were not undertaken. The final curated dataset comprised 124 records (Supplementary Table S1). For each study, we considered the clinical task, population and comparator, biological matrix, analytical platform, biomarker layer, model-development strategy, diagnostic performance, and validation level. Clinical tasks and validation maturity were categorized pragmatically for narrative comparison. These records represent a curated evidence base rather than an exhaustive collection of eligible studies. As this review was designed as a narrative synthesis rather than a systematic review or meta-analysis, no pooled estimates were generated.

3. From Microbiome Associations to Diagnostic Biomarkers: Clinical Tasks and Evidence Maturity

Microbiome studies use the term “biomarker” for fundamentally different clinical tasks. Disease association denotes a reproducible group-level difference and can support mechanistic or feature discovery, but does not establish diagnostic utility. Disease detection asks whether a signature distinguishes affected from unaffected children, often using healthy controls. Differential diagnosis is more stringent and usually more clinically relevant because the comparator comprises children with similar symptoms, inflammatory states, or competing diagnoses. Phenotyping or stratification separates subtypes, severity states, or complications within an established disease. Prognosis concerns future outcomes, including disease progression or complications, whereas treatment-response prediction concerns subsequent response to therapy; neither should be conflated with cross-sectional diagnosis. Microbiome-derived signatures may also have prognostic or disease-monitoring value even when their specificity for initial diagnosis is limited, although such applications require longitudinal validation.

This taxonomy directly affects the interpretation of diagnostic performance. A classifier that separates severe, treatment-naive Crohn disease from healthy children can achieve a high AUC without demonstrating that it distinguishes inflammatory bowel disease (IBD) from irritable bowel syndrome, infection, or other causes of abdominal symptoms. In this context, symptomatic controls are children presenting with similar gastrointestinal symptoms in whom IBD has been excluded following appropriate clinical evaluation. Performance against clinically similar controls is generally more informative for clinical translation because case–control designs using highly selected cases and healthy controls can overestimate diagnostic accuracy. The pediatric IBD literature illustrates this contrast: microbiome discrimination can be very high against healthy controls but falls substantially against symptomatic gastrointestinal controls [8,9,10,11,12]. A lower AUC in this setting should therefore not automatically be interpreted as model failure; it may instead reflect a more clinically demanding and informative comparison.

Accordingly, this review describes diagnostic maturity as a pragmatic progression from discovery and internal validation to independent validation, clinically relevant differential diagnosis, and prospective or real-world evaluation. Internal validation using resampling methods such as cross-validation or bootstrapping can quantify optimism during model development but does not establish transportability. A random held-out subset drawn from the same dataset remains an internal validation sample and is not necessarily stronger than resampling. External validation in independent participants, particularly across hospitals, regions, or clinical settings, tests whether performance generalizes beyond the development data. Prospective validation in clinically representative populations further evaluates performance under realistic disease prevalence, case mix, and workflow. TRIPOD + AI and PROBAST + AI provide contemporary frameworks for transparent reporting and for assessing the quality, applicability, and risk of bias of prediction models developed using regression or machine-learning methods [13,14]. These clinical tasks, their target populations, and their implications for diagnostic translation are summarized in Table 1 and applied consistently throughout this review. Representative studies illustrating differences in comparator selection, validation maturity, external transportability, and comparison with standard care are presented in Table 2.

Table 1. Clinical tasks in pediatric microbiome biomarker studies.

Clinical Task	Typical Comparison or Target Population	Primary Clinical Question	Implications for Diagnostic Translation
Disease association	Cases vs. controls; group-level differences in microbial composition, diversity, or function	Is there a reproducible group-level difference between affected and unaffected children?	Provides biological or mechanistic discovery evidence but does not establish individual-level diagnostic accuracy.
Disease detection	Children with the target disease vs. healthy or otherwise unaffected children	Can a microbial signature distinguish affected from unaffected children?	Provides initial evidence of discrimination. Comparisons with healthy controls may overestimate clinical performance because they provide greater biological contrast than the population in which the test would be applied and are susceptible to spectrum bias.
Differential diagnosis	Target disease vs. symptomatic children or those with competing or overlapping diagnoses	Can the signature distinguish the target disease from clinically similar alternatives?	More closely reflects real-world diagnostic decision-making. Lower discrimination than that observed against healthy controls may reflect a more clinically challenging and informative comparison.
Phenotyping/stratification	Subtype, severity state, or complication within an established diagnosis	Can the signature distinguish clinically meaningful subgroups among children with an established diagnosis?	Represents within-disease classification after diagnosis and should be distinguished from disease detection and differential diagnosis.
Prognosis/treatment-response prediction	Future clinical outcome or response to a specified therapy	Can baseline or longitudinal microbial features predict a future clinical outcome or treatment response?	Represents a distinct longitudinal prediction task and should be analyzed separately from cross-sectional diagnosis rather than considered diagnostic validation.

Note: Clinical tasks are categorized according to the intended use of the microbiome-derived marker. Diagnostic tasks are distinguished from prognostic and treatment-response prediction tasks.

Table 2. Representative pediatric microbiome diagnostic studies across stages of translational maturity.

Indication/Study	Clinical Task and Comparator	Matrix and Platform	Biomarker or Model	Development/Internal Performance	Independent, External, or Clinically Relevant Validation	Comparison with Standard Care	Translational Implication
Crohn disease—Shah et al. 2022 [15]	Differential diagnosis; treatment-naive Crohn disease vs. symptomatic non-IBD children across North American regions	Ileal and rectal biopsies; stool; 16S rRNA taxonomic profiling	Matrix-specific taxonomic random-forest classifiers	Northeast training region: AUC 0.89 (ileum), 0.87 (rectum), and 0.82 (stool)	Geographic holdout within the same multicenter parent cohort: Southeast 0.85/0.83/0.85 and West 0.91/0.76/0.74 (ileum/rectum/stool)	Not reported	Cross-regional transportability was demonstrated within the parent cohort, but performance depended on matrix and the holdout cohorts were not newly recruited external populations.
Crohn disease—Shen et al. 2025 [16]	Disease detection; discovery n = 534; independent D3 cohort n = 40 (7 Crohn disease); disease-specificity testing across five pediatric disease cohorts	Stool; shotgun metagenomics	19-InDel random-forest model; additional taxonomic, functional, structural-variant, and combined models	19-InDel model AUC 0.982; combined model 0.986	Independent D3 cohort: InDel AUC 0.996; combined model 0.874; additional disease-specificity stress testing across five pediatric disease cohorts	Not reported	Very high discrimination was observed, but the external cohort included only seven Crohn disease cases; prospective locked-model validation and comparison with established IBD diagnostics remain necessary.
Pediatric IBD—Vermeer et al. 2025 [8]	Differential diagnosis; IBD vs. healthy controls in development and vs. symptomatic gastrointestinal controls in independent validation	Stool; microbiota profiling plus targeted fecal amino acids	Composite of two microbial markers and four amino acids	AUC 0.94 vs. healthy controls	AUC 0.84 in an independent cohort with symptomatic gastrointestinal controls	Against symptomatic gastrointestinal controls, the six-marker panel achieved an AUC of 0.84, compared with 0.91 for a clinical model (age, sex, BMI, CRP, and FCP), 0.92 for FCP alone, and 0.95 for FCP plus leucine.	Directly illustrates spectrum effects: discrimination decreased when healthy controls were replaced by a clinically relevant symptomatic comparator in independent validation.
Pediatric IBD—Vermeer et al. 2026 [10]	Differential diagnosis; therapy-naive de novo IBD vs. healthy controls and symptomatic gastrointestinal controls	Stool; shotgun metagenomics	Machine-learning metagenomic signature	AUC 0.96 vs. healthy controls	AUC 0.71 vs. symptomatic gastrointestinal controls within the same study	Fecal calprotectin and C-reactive protein outperformed the microbiome-only model against symptomatic controls	Demonstrates why realistic comparators and direct benchmarking against established tests are essential when assessing clinical utility.
Primary sclerosing cholangitis—Iwasawa et al. 2018 [17]	Differential diagnosis; pediatric-onset primary sclerosing cholangitis vs. ulcerative colitis and healthy controls	Saliva; 16S rRNA oral microbiome profiling	Oral taxonomic random-forest signature	Genus-level AUC 0.742 vs. healthy controls	Genus-level AUC 0.876 vs. ulcerative colitis using internal cross-validation and a clinically relevant comparator	Not reported	An easily collected matrix showed stronger discrimination for a clinically relevant differential diagnosis than for the healthy-control comparison.
Celiac disease—Prendergast et al. 2026 [18]	Disease detection; pediatric celiac disease vs. controls	Stool; 16S rRNA sequencing plus targeted and untargeted metabolomics	Integrated microbe-metabolite machine-learning model	AUC 0.66–0.68; AUPRC 0.73–0.79	Internal validation only	Not reported	A deliberately modest/negative example; the source study did not support immediate diagnostic use, underscoring the value of reporting limited as well as high performance.
ASD—Manghi et al. 2024 [19]	Disease detection; ASD vs. neurotypical siblings; >7000 salivary samples	Saliva; large-scale oral metagenomics	Oral metagenomic composition classifier	AUC 0.66	Sibling-comparator design reduces some family-level environmental differences; no separate external cohort was reported	Not reported	A large family-based cohort with a stringent comparator yielded only modest discrimination, showing the strong influence of comparator and cohort structure.
ASD—Wan et al. 2024 [20]	Disease detection; ASD vs. typically developing children	Stool; shotgun metagenomics	Five bacterial taxa plus 44 bacterial/viral MAG markers	AUC 0.886	AUC 0.734 in an independent validation cohort	Not reported	Provides a clear example of performance attenuation during independent validation of a higher-resolution genomic model.
ASD—Su et al. 2024 [21]	Disease detection; ASD vs. typically developing children; 1627 children	Stool; multikingdom shotgun metagenomics	31-feature multikingdom and functional panel	AUC 0.91	AUCs 0.87, 0.89, and 0.78 in subsequent independent or external cohorts	Not reported	Multikingdom features improved discovery performance and showed partial transportability, but external performance remained population dependent.
ASD—Lou et al. 2022 [22]	Disease detection; ASD vs. typically developing children in a large multi-age cohort	Stool; 16S rRNA sequencing plus microbial functional profiling	Two OTUs plus 17 microbial functions	AUC 0.86	AUCs 0.78, 0.82, and 0.67 across three validation sets	Not reported	Multiple validation cohorts revealed substantial population-dependent variability that would be missed by reporting the discovery AUC alone.
Lower respiratory tract infection—Mick et al. 2023 [23]	Differential diagnosis; adjudicated LRTI vs. noninfectious respiratory failure in mechanically ventilated children; uncertain cases analyzed separately	Tracheal aspirate; integrated host transcriptomics and respiratory metagenomics	Integrated host-microbe classifier	Fivefold cross-validation: host classifier median AUC 0.967 (range 0.953–0.996); integrated classifier 0.986 (0.953–1.000)	No independent external validation; application to 94 diagnostically uncertain cases was exploratory because definitive reference labels were unavailable	Not reported	The integrated model addresses the limitation that pathogen detection alone does not establish causality, but evidence remains internally validated and the invasive matrix limits routine use.
Mycoplasma pneumoniae pneumonia—Wang et al. 2023 [24]	Differential diagnosis; 158 Mycoplasma pneumoniae pneumonia cases vs. 29 children with other bacterial or viral pneumonia	BALF; 16S rRNA respiratory microbiome profiling	Mycoplasma abundance threshold	AUC 0.994; sensitivity 97.5%; specificity 96.6%	Internal only, but the disease-control comparator was clinically relevant	Not reported	A disease-proximal etiologic marker achieved very high discrimination against clinically similar controls, although BALF collection limits routine application.
Atopic dermatitis—Sun et al. 2019 [25]	Disease detection; pediatric atopic dermatitis vs. healthy children across three cities	Skin swab; microbiome profiling	25-genus Microbial Index of Skin Health (MiSH)	Within-city 10-fold cross-validation: Beijing 0.97, Qingdao 0.95, Denver 0.78; pooled three-city AUC 0.90	Geographic transfer: Beijing-trained model to Qingdao 0.75 and Denver 0.70; Qingdao-trained model to Beijing 0.89 and Denver 0.73	Not reported	Cross-city transportability was feasible but attenuated after transfer; pooled multicity performance should not be interpreted as independent external-validation performance.
Early childhood caries—Zhang et al. 2021 [26]	Disease detection; caries vs. caries-free children	Saliva; microbiome sequencing and salivary electrolyte measurement in the same cohort	Salivary microbiome classifier vs. salivary electrolyte classifier	Microbiome AUC 0.70	Internal validation only	Salivary electrolytes achieved AUC 0.94, outperforming the microbiome classifier	A simpler non-sequencing assay outperformed the microbiome classifier in a direct comparison, demonstrating that incremental value cannot be assumed.
Type 1 diabetes—Yuan et al. 2022 [27]	Disease detection and disease-phase stratification; new-onset/chronic type 1 diabetes vs. healthy controls	Saliva; oral taxonomic profiling	Oral taxonomic classifier	AUC 0.933 for new-onset type 1 diabetes vs. healthy controls	Internal validation only; no independent cohort reported	No direct comparison with established biochemical or autoantibody diagnostics	A highly feasible noninvasive matrix showed strong case–control discrimination, but clinical relevance depends on incremental value over established diagnostic pathways.
PFAPA syndrome—Tejesvi et al. 2020 [28]	Disease detection; PFAPA vs. controls	Tonsil tissue; fungal mycobiome sequencing	Random-forest fungal-community signature	AUC 0.47	Internal validation only	Not reported	A biologically plausible, lesion-proximal signature failed to discriminate cases from controls, showing that anatomical proximity and plausibility do not guarantee diagnostic value.

Note: Representative studies were selected to illustrate comparator choice, validation maturity, transportability, and comparison with standard care; the table is not intended to be exhaustive. Performance estimates are reported as in the source studies. Geographic holdout within a parent cohort is distinguished from validation in independently recruited participants. Abbreviations: ASD, autism spectrum disorder; AUPRC, area under the precision-recall curve; AUC, area under the receiver operating characteristic curve; BALF, bronchoalveolar lavage fluid; IBD, inflammatory bowel disease; InDel, insertion/deletion; LRTI, lower respiratory tract infection; MAG, metagenome-assembled genome; MiSH, Microbial Index of Skin Health; OTU, operational taxonomic unit; PFAPA, periodic fever, aphthous stomatitis, pharyngitis, and adenitis.

4. The Microbiome Diagnostic Pipeline

As outlined in Figure 1, the microbiome diagnostic pipeline encompasses matrix selection, pre-analytical processing, assay and biomarker selection, leakage-controlled modeling, clinical-performance evaluation, and independent validation.

4.1. Biological Matrices

The biological matrix determines both the available signal and the clinical workflow in which a test could be deployed. Stool dominates the curated literature and is especially prominent in IBD, autism spectrum disorder (ASD), type 1 diabetes, metabolic disease, and immune conditions. It is non-invasive, repeatable, and scalable, but fecal microbiome composition varies with host and environmental factors, including age, diet, medication exposure, stool consistency, and intestinal transit [29]. Sample collection, preservation, storage, and processing introduce additional technical variation [30]. Mucosal tissue provides a disease-proximal signal in intestinal disease and may reveal information not captured in stool, but biopsy-based markers are difficult to justify as stand-alone screening tests when endoscopy is already required. Their translational value is therefore more likely to lie in adjunctive classification or mechanistic anchoring of less invasive assays.

Oral and salivary samples are highly feasible in children and permit repeated collection without specialized infrastructure. They are particularly attractive for caries, oral disease, and emerging systemic applications. Their limitations are equally important: the oral microbiome is strongly structured by local ecological niches and oral health status, so salivary signatures proposed as systemic biomarkers may also reflect local oral conditions [31]. Respiratory specimens span a feasibility gradient from nasopharyngeal or oropharyngeal swabs to sputum, tracheal aspirates, and bronchoalveolar lavage fluid (BALF), with greater disease proximity generally accompanied by greater sampling burden. Skin swabs combine lesion proximity with low sampling burden in atopic dermatitis. Urine and blood are conceptually different because they often contain microbiome-derived metabolites or microbial deoxyribonucleic acid (DNA) rather than a directly sampled microbial community; such signals should therefore be described carefully as “microbiome-derived” biomarkers. The clinical trade-offs of the principal biological matrices—including anatomical proximity, collection burden, major sources of variation, technical risks, and plausible clinical roles—are summarized in Table 3.

Table 3. Clinical trade-offs of biological matrices for pediatric microbiome diagnostics.

Matrix	Representative Pediatric Indications	Signal and Anatomical Proximity	Collection Burden and Setting	Major Non-Disease Sources of Variation	Major Technical or Interpretive Risks	Most Plausible Clinical Role
Stool/gut	IBD, ASD, type 1 diabetes, and other metabolic or immune conditions	Direct sampling of the intestinal ecosystem; disease-proximal for intestinal disorders but anatomically remote from many extraintestinal target organs	Noninvasive, repeatable, and scalable; suitable for outpatient or home collection	Age, diet, stool consistency, intestinal transit, antibiotic exposure, and geography	Collection, preservation, storage, and DNA-extraction workflows can alter measured community structure; compositional data require appropriate analysis	Noninvasive detection or triage and repeated monitoring for intestinal disease; for extraintestinal disease, translation requires evidence that the signal is not primarily driven by non-disease exposures.
Saliva/oral	Early childhood caries, ASD, primary sclerosing cholangitis, type 1 diabetes, and obstructive sleep apnea	Direct sampling of the oral community; disease-proximal for oral disease but more remote and potentially confounded when used as a systemic marker	Very low burden and readily repeatable; suitable for outpatient settings without specialized infrastructure	Oral hygiene, recent food intake, dentition or eruption status, caries, and local inflammation	Strong oral-niche structure can cause a proposed systemic signature to reflect local oral conditions rather than the target disease	Disease-proximal detection or differential diagnosis for oral conditions; systemic applications require independent validation demonstrating robustness to local oral status.
Mucosal tissue	Crohn disease and ulcerative colitis	Lesion-proximal, biologically rich mucosa-associated signal that may capture information not represented in stool	Invasive; requires endoscopy or biopsy and is generally available only when the procedure is already clinically indicated	Biopsy site, disease activity, and the same host or exposure determinants that influence intestinal microbiota	Limited value as a stand-alone screening test when endoscopy is already required; tissue-processing workflows introduce additional technical variation	Adjunctive classification, mechanistic anchoring, or development of less invasive surrogate assays when endoscopy is clinically indicated.
Upper-airway samples	Respiratory infection and adenoid disease	Local upper-airway community signal that may reflect colonization rather than lower-airway pathology	Easy and minimally invasive; suitable for outpatient and emergency settings	Recent respiratory infection, age, season, local inflammation, and upper-airway exposures	Colonization does not necessarily indicate lower-airway disease; strong site specificity limits inference regarding deeper pathology	Characterization of upper-airway conditions and selected triage applications; lower-airway diagnosis requires evidence linking the upper-airway signal to the target condition.
Tracheal aspirate/BALF	Severe lower respiratory tract infection and Mycoplasma pneumoniae pneumonia	Highly disease-proximal lower-airway microbial and, in some studies, host or metabolite signal	Invasive; restricted mainly to hospitalized or mechanically ventilated children and bronchoscopy-dependent settings	Sample availability is determined by the clinical indication for invasive sampling and the severity of illness	Pathogen detection alone does not establish causality; findings from severely ill populations may not generalize to routine pediatric care	High etiologic value when the specimen is already clinically indicated; unsuitable for population-level screening.
Skin swab	Atopic dermatitis	Disease-proximal cutaneous community signal obtained directly from the affected compartment	Noninvasive and low burden; suitable for repeated outpatient sampling	Sampling site, topical therapy, bathing, and skin-care practices	Site selection and recent topical treatment can materially alter the measured community	Low-burden disease-proximal detection or phenotyping, provided cross-site and cross-center reproducibility is demonstrated.
Urine	Microbiome-derived metabolites in ASD and other disorders	Measures microbiome-derived metabolites rather than a directly sampled microbial community; some metabolites have mixed microbial, host, and dietary origins	Simple, noninvasive quantitative sampling suitable for outpatient or home collection	Diet, renal function, hydration, and host metabolic variation	Microbial specificity of analytes may be uncertain; prespecified thresholds and independent validation are especially important for commercially developed assays	Potentially scalable targeted testing if analytes, thresholds, and microbial specificity are independently validated; not yet routine in the current evidence base.
Blood	Microbial DNA or microbiome-derived metabolite signatures, including exploratory studies in type 1 diabetes	Measures low-biomass microbial DNA or circulating microbiome-derived metabolites rather than a directly sampled community	Compatible with standard clinical blood-draw workflows	Host physiology, diet, medication exposure, and other systemic factors	Low biomass increases susceptibility to reagent and cross-sample contamination; stringent negative controls and contamination safeguards are essential	Potential compatibility with existing clinical laboratory workflows, contingent on contamination control and demonstrated incremental value over established biochemical diagnostics.

Note: Representative indications are illustrative rather than exhaustive. Disease proximity refers to the anatomical relationship between the sampled compartment and the target disease process; greater proximity does not necessarily imply greater diagnostic accuracy or clinical utility. Abbreviations: ASD, autism spectrum disorder; BALF, bronchoalveolar lavage fluid; DNA, deoxyribonucleic acid; IBD, inflammatory bowel disease.

4.2. Biomarker Layers

Taxonomic panels are the most intuitive biomarker layer and are often the easiest to translate into targeted assays. Their principal limitations are ecological resolution and data structure: a genus can contain functionally heterogeneous species and strains, while relative-abundance measurements are inherently compositional and require analytical approaches that account for this dependence [32]. Community-level diversity metrics summarize ecological structure but rarely provide stable clinical thresholds. Functional profiles can be more conserved across individuals than taxonomic composition, suggesting that gene- or pathway-level biomarkers may be more transferable in some settings; this potential advantage nevertheless requires external validation [33]. Genome-level markers such as MAGs, single-nucleotide polymorphisms, structural variants, and InDels offer greater resolution at the cost of dimensionality and analytical complexity. Microbiome-derived metabolites may bring assays closer to quantitative clinical chemistry, but their source is not always exclusively microbial. Multi-omics and composite models can combine complementary information and improve discrimination, but higher AUC alone does not justify greater cost, lower interpretability, or reduced transferability.

4.3. Analytical Platforms and Diagnostic Modeling

Platform choice defines the available feature space. Sequencing of the 16S ribosomal RNA (16S rRNA) gene, primarily used to characterize bacterial communities, and the internal transcribed spacer (ITS), a noncoding region within the ribosomal RNA gene cluster commonly used for fungal profiling, provides relatively cost-effective approaches to taxonomic profiling. However, results depend on target region, primer choice, and reference database. Shotgun metagenomics provides species-, strain-, gene-, and function-level resolution and enables MAG- and microbial-variant-based classifiers, but requires greater sequencing depth and computational standardization. Metabolomics captures downstream functional signals but introduces additional pre-analytical and instrument-specific variation. Multi-omics designs broaden biological coverage while also increasing opportunities for batch effects and analytical heterogeneity.

The modeling pipeline should keep feature discovery separate from unbiased performance estimation. Feature selection, normalization, imputation, and hyperparameter tuning should be confined to the training data and, where applicable, repeated within each resampling fold; performing these steps before cross-validation can leak information from the evaluation data and yield optimistically biased performance estimates [7]. Once finalized, a diagnostic classifier should be locked before independent validation. This separation is especially important for microbiome data, in which thousands of sparse, compositional features may be evaluated in cohorts of only tens to hundreds of children. Standardization studies have shown that DNA extraction and laboratory workflow can materially alter measured community structure [30,34], while sequencing experiments can contain systematic, protocol-dependent bias [35]. Without explicit safeguards, a classifier may therefore learn laboratory-specific signal rather than disease-related biology.

Diagnostic evaluation should extend beyond AUC. Sensitivity and specificity at clinically meaningful thresholds are required for interpretation, while calibration is essential when model outputs are used as probabilities. Decision-curve analysis, incremental value over existing tests, cost, turnaround time, and assay failure rate are also relevant to whether a model can change care. For multiclass or imbalanced problems, precision-recall performance may be more informative than ROC curves. The most credible studies predefine the clinical task, use clinically relevant comparators, maintain strict separation between training and validation, and report how performance changes under external transport.

5. Diagnostic Evidence Across Pediatric Diseases

The distribution of the 124 curated records across disease domains, biological matrices, and validation-maturity categories is shown in Figure 2. Gastrointestinal/hepatobiliary and neurodevelopmental/neurological disorders account for the largest proportions of the evidence base, while stool is the predominant matrix across multiple disease domains. Most records remain at the discovery or internal-validation stage, with relatively few reaching independent, external, prospective, or longitudinal evaluation. Selected studies illustrating the progression from discovery-cohort discrimination to clinically relevant or external validation are summarized in Table 2.

Figure 2. Evidence landscape of pediatric microbiome biomarkers across disease domains and biological matrices. (A) Distribution of the 124 curated records across pediatric disease domains and biological matrices. Cell values represent record counts, and row totals indicate the number of records within each disease domain. Stool/gut samples account for the largest proportion of the evidence base, particularly in gastrointestinal/hepatobiliary and neurodevelopmental/neurological disorders, whereas oral, respiratory, skin, mucosal, urine, and blood-derived signals contribute to selected disease areas. (B) Validation maturity of the same evidence base by disease domain. Records are grouped according to the validation categories used in this review, spanning discovery or association studies, internal or internally unclear validation, independent or external validation, prospective or longitudinal evaluation, and studies for which diagnostic validation was not applicable. Values represent counts rather than percentages. The complete characteristics and validation details of the 124 curated records are provided in Table S1.

5.1. Gastrointestinal and Hepatobiliary Diseases

The gastrointestinal and hepatobiliary domain contains the most mature pediatric microbiome diagnostic evidence in the curated literature (42 of 124 records). IBD is the most developed indication, supported by a biologically plausible mucosal-microbial basis, well-characterized treatment-naive pediatric cohorts, and studies using both healthy and clinically relevant gastrointestinal comparators [36,37,38,39]. Research has expanded beyond taxonomic dysbiosis and taxon-based classifiers [40,41,42,43] to include fungal signatures [44,45], functional and metabolite-based approaches [8,46,47,48], and microbial genomic variants [16], with some studies also evaluating cross-geographic transportability [15] (Supplementary Table S1). This broader evidence base also includes a large multicenter study of the treatment-naive mucosa-associated microbiome in new-onset pediatric Crohn disease [37] and by longitudinal multi-omics profiling of host and microbial activity in IBD [38].

Early and intermediate Crohn disease studies illustrate why both matrix and validation design matter. In a North American pediatric cohort using ileal, rectal, and fecal microbiota, ileal models achieved AUCs of 0.85–0.91 across geographic regions, whereas rectal and fecal models were somewhat lower and less consistent, particularly in the western region [15]. In one small study, fungal dysbiosis classified pediatric Crohn disease more accurately in feces than in mucosa (AUC approximately 0.85 versus 0.71) [44], and a Saudi bacterial study similarly reported higher performance in stool than in tissue (AUC approximately 0.97 versus 0.83) [40]. Together, these findings indicate that the apparent optimal matrix depends on population, microbial kingdom, specimen type, sample size, and analytical approach. More recent high-resolution metagenomic approaches have extended beyond taxonomic markers. Shen et al. analyzed 1175 fecal metagenomes across discovery, independent-validation, and disease-specificity cohorts. Their InDel-based random forest classifier achieved an AUC of 0.982 in the Crohn disease discovery cohort and 0.996 in an independent validation cohort, with additional disease-specificity testing across five other pediatric disease cohorts [16]. Despite these promising results, prospective validation and direct comparison with established IBD diagnostics remain necessary before clinical implementation.

The most clinically informative studies are not necessarily those with the highest AUC. Studies enrolling children with gastrointestinal symptoms show how performance changes under a more realistic diagnostic comparison. A fecal model combining two microbes with four amino acids achieved an AUC of 0.94 for pediatric IBD versus healthy controls in discovery, but 0.84 when validated against symptomatic gastrointestinal controls [8]. In a large treatment-naive shotgun metagenomics study, Vermeer et al. (2026) reported that microbiome classification reached an AUC of 0.96 against healthy controls but only 0.71 against symptomatic gastrointestinal controls, while a clinical model incorporating fecal calprotectin and C-reactive protein outperformed the microbiome-only model [10]. Fecal volatile organic compound profiling similarly achieved an AUC of 0.71 for IBD versus gastrointestinal-symptom controls [9]. Earlier work in children and young adults with IBD and clinically similar gastrointestinal disorders reported an AUC of 0.83 in development and 0.84 in an independent validation cohort, with high sensitivity but more modest specificity [11]. Collectively, these studies demonstrate the importance of spectrum effects: performance is generally less striking when the comparator more closely resembles the population in whom the test would be used.

Other gastrointestinal and hepatobiliary indications range from clinically meaningful differential diagnosis to exploratory association. In a small pediatric study, fecal volatile organic compounds differentiated irritable bowel syndrome (IBS)/functional abdominal pain from IBD with an AUC of 0.94, whereas performance for IBS/functional abdominal pain versus healthy controls was only 0.59 [49]. A multidisease shotgun metagenomic classifier including acute pancreatitis, Crohn disease, IBS, and healthy controls reported one-vs-all AUCs of 0.83–0.97 [39]. In pediatric-onset primary sclerosing cholangitis, salivary microbiota showed stronger discrimination from ulcerative colitis (AUC 0.876) than from healthy controls (AUC 0.742), supporting the potential value of an easily collected matrix for differential diagnosis [17]. Biliary atresia studies included disease controls and a separate clinical cohort; in the initial database analysis, the Streptococcus/Bacteroides ratio achieved an AUC of 0.904, although the best-performing ratio differed in the subsequent clinical cohort [50]. By contrast, a recent celiac multi-omics study yielded only modest ROC-AUC values of 0.66–0.68 and precision-recall AUC values of 0.73–0.79 and explicitly did not support immediate diagnostic use [18]. Such moderate or negative findings are informative because they define the limits of microbiome-based discrimination and temper interpretation of highly separable discovery cohorts.

The remaining gastrointestinal and hepatobiliary studies—including celiac disease, IBS, functional constipation, metabolic dysfunction-associated steatotic liver disease/nonalcoholic fatty liver disease, pancreatitis, cholestasis, feeding intolerance, and related conditions (Supplementary Table S1) [17,18,39,42,47,48,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64]—span a broad but uneven discovery landscape. Some are primarily association studies that subsequently evaluate ROC or classifier performance, whereas others incorporate disease controls, internal cross-validation, or independent cohorts. Overall, IBD most closely approximates a pediatric microbiome diagnostic-development pipeline; most hepatobiliary and functional gastrointestinal applications remain at discovery or internal-validation stages, with selected exceptions that include independent validation or clinically relevant disease controls.

5.2. Neurodevelopmental and Neurological Disorders

ASD is the largest single disease category in the curated evidence base (23 studies; Supplementary Table S1) and most clearly illustrates the gap between research volume and diagnostic maturity [19,20,21,22,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83]. Reported approaches span stool, saliva, dental plaque, urinary microbially derived metabolites, taxonomic panels, volatile organic compounds (VOCs), MAGs, and microbial genomic variants. The most informative studies, however, are those that challenge a classifier with sibling comparators, external cohorts, developmental heterogeneity, or explicit assessment of confounding.

In a family-based oral metagenomic study involving 7812 participants from 2025 families, including 2154 children with ASD and 1646 neurotypical siblings, the oral microbiome distinguished children with ASD from their neurotypical siblings with an AUC of 0.66 [19]. This modest discrimination is informative because sibling comparators reduce some family-level environmental differences relative to unrelated healthy controls. Smaller oral studies have reported stronger performance: a salivary metatranscriptomic study of 346 children reported hold-out AUCs of 0.796 for ASD versus typically developing children and 0.765 for ASD versus non-autistic developmental delay [65]; a 2018 saliva/plaque study reported a salivary AUC of 0.963 [66]; and a 2025 dental-plaque random-forest model in 55 preschool children reported a test-set AUC of 0.937, sensitivity of 0.778, and specificity of 0.857, with leave-one-out cross-validation used during model development [67]. These contrasting results show how comparator choice, cohort structure, analytical approach, and validation design can materially influence apparent diagnostic performance.

Fecal microbiome studies have achieved higher biological resolution but continue to show substantial variation across validation cohorts. A combined panel of five bacterial taxa and 44 bacterial/viral MAG markers achieved an AUC of 0.886 in discovery and 0.734 in an independent validation cohort [20]. A large multikingdom metagenomic study of 1627 children integrated archaeal, bacterial, fungal, viral, gene, and pathway features; a 31-feature panel achieved an AUC of 0.91 in development, outperforming single-kingdom panels, and AUCs of 0.87, 0.89, and 0.78 in subsequent independent or external cohorts [21]. A large 16S-based developmental study similarly achieved an AUC of 0.86 in discovery but 0.78, 0.82, and 0.67 across three validation sets [22]. These results are more informative for translation than a single discovery AUC because they reveal how performance varies across populations.

Phenotype definition is equally important. A support-vector machine model distinguishing children aged 3–6 years with ASD and gastrointestinal symptoms from typically developing children without gastrointestinal symptoms achieved a validation-set AUC of 0.88 [78], whereas a fivefold cross-validated random-forest model distinguishing constipated ASD from typically developing controls achieved an AUC of 0.924 [77]. These findings apply to selected ASD phenotypes and should not be generalized to ASD as a whole. Diet is a major concern: a large independent Cell study found negligible direct associations between ASD diagnosis and the gut microbiome, while identifying stronger associations with dietary variables, stool consistency, and age, supporting a model in which ASD-related dietary behavior contributes to observed microbial differences [5]. Accordingly, studies restricted to gastrointestinally defined ASD phenotypes cannot exclude the possibility that classifiers capture diet- or gastrointestinal-related signals in addition to ASD-related variation.

Urinary metabolites linked to the microbiota-gut–brain axis provide another potentially scalable assay route. A targeted urinary metabolomics study reported a seven-indicator model with an AUC of 0.943, sensitivity of 92.6%, and specificity of 93.7% [68]. A 2026 multisite study of children aged 2–11 years reported 90% sensitivity and 100% specificity when one or more urinary microbially derived metabolites exceeded the reference range defined by typically developing controls [69]. These findings require independent replication using prespecified thresholds, particularly because some urinary metabolites may have microbial, host, and dietary sources. The latter diagnostic program is also associated with commercial testing and patent-pending intellectual property, further increasing the importance of transparent independent validation [69]. Earlier urinary aromatic-metabolite studies [70] and more recent structural-variant and broader microbial genomic-variant models [71,74] further illustrate the shift from simple taxonomic signatures toward metabolic function and microbial genome architecture.

Neurological conditions outside ASD remain at an earlier stage. Pediatric myasthenia gravis provides one of the stronger examples: a five-species model achieved a cross-validation AUC of 0.94 and an independent-validation AUC of 0.84 [84]. Other retained neurological and neuropsychiatric records cover migraine, depression, tic disorders, attention-deficit/hyperactivity disorder-related differential classification, viral encephalitis/meningitis, and epilepsy; these studies are generally smaller, more heterogeneous, or partly focused on phenotyping (Supplementary Table S1) [84,85,86,87,88,89,90,91,92]. In a prospective study involving 126 children with migraine and 50 healthy controls, Fan et al. identified altered gut microbial profiles, including reduced Bifidobacterium longum abundance, and investigated its therapeutic potential through animal experiments and a small pediatric probiotic intervention study [93]. These findings suggest a potential role for gut–brain interactions in pediatric migraine but do not establish a validated diagnostic classifier or demonstrate a causal contribution of the observed microbial alterations to disease pathogenesis.

5.3. Respiratory Diseases

Respiratory microbiome diagnostics involve a distinctive trade-off between anatomical proximity and sampling feasibility. Upper-airway samples are easy to obtain but may reflect colonization or upper-airway detection rather than lower-airway infection. Tracheal aspirates and BALF are more disease-proximal and can support etiologic or differential diagnosis, but collection is restricted to selected clinical populations and is unsuitable for population-scale screening. The most clinically relevant studies therefore ask whether microbial or host-microbial signals distinguish infection from carriage, competing infections, or noninfectious respiratory failure rather than simply from healthy children [23,24,94,95,96].

For Mycoplasma pneumoniae pneumonia, a BALF study of 158 cases and 29 children with other bacterial or viral pneumonia reported an AUC of 0.994, sensitivity of 97.5%, and specificity of 96.6% using Mycoplasma abundance [24]. The disease-control comparator is clinically relevant, although the invasiveness of BALF limits routine use. In mechanically ventilated children, an earlier integrated host/microbe metagenomic study distinguished established lower respiratory tract infection (LRTI) from noninfectious respiratory failure and evaluated diagnostically uncertain cases [23], whereas a subsequent host-microbe metatranscriptomic study explicitly separated adjudicated LRTI from incidental pathogen carriage and noninfectious respiratory failure [94]. These integrated approaches address the clinically important limitation that pathogen detection alone does not reliably establish causality.

Less invasive respiratory, salivary, or gut signals are attractive, but many have been evaluated in case-healthy-control designs that are particularly susceptible to spectrum bias. A nasopharyngeal study including invasive pneumococcal disease, mild viral upper-respiratory infection, and healthy children achieved AUCs of 0.90, 0.86, and 0.86 for invasive pneumococcal disease versus viral upper-respiratory infection, healthy controls, and all controls, respectively [95]. Studies of recurrent respiratory infection and sleep-disordered breathing have also used gut or salivary signatures; a nested cross-validated salivary random-forest model in 36 children with obstructive sleep apnea (OSA) and 22 controls achieved an AUC of 0.94 [97], whereas a gut model in 43 children with OSA/hypopnea and 45 controls achieved an AUC of 0.794 [98]. Adenoid hypertrophy is particularly instructive: an eight-operational taxonomic unit gut model achieved an AUC of 0.985 in discovery and 0.978 in a geographically distinct validation cohort [99]. However, both cohorts compared adenoid hypertrophy with healthy controls, so geographic reproducibility should not be equated with disease-specific performance in symptomatic children. Other retained respiratory syncytial virus, recurrent-infection, and adenoid studies remain predominantly at discovery or internal-validation stages (Supplementary Table S1). Overall, these findings support prospective testing with clinically relevant disease controls. Some respiratory studies [24,95,96] already incorporate such comparators, whereas [99] provides geographic external validation but still lacks symptomatic disease controls.

5.4. Immune and Allergic Diseases

Immune and allergic diseases provide a useful contrast between lesion-proximal and remote gut biomarkers. In pediatric atopic dermatitis, a 25-genus Microbial Index of Skin Health yielded a pooled three-city AUC of 0.90 and an accuracy of 86.4%. Geographic model transfer was weaker: a Beijing-trained model achieved AUCs of 0.75 in Qingdao and 0.70 in Denver, whereas a Qingdao-trained model achieved AUCs of 0.89 in Beijing and 0.73 in Denver [25]. These findings illustrate that strong pooled discrimination does not necessarily translate into consistent performance across geographic populations. The skin microbiome thus represents a promising source of minimally invasive, disease-proximal biomarkers for pediatric atopic dermatitis, although further validation against clinically relevant disease controls and in independent populations is needed. A six-feature fecal microbiota model achieved a cross-validated AUC of 0.941 and an accuracy of 84.6% for pediatric atopic dermatitis versus healthy controls [100]; however, its single-center healthy-control design and lack of external validation leave its incremental clinical value uncertain. A six-species fecal panel distinguished children with atopic dermatitis with versus without food allergy with an AUC of 0.83, sensitivity of 0.77, and specificity of 0.80 [101]; this is a phenotyping task rather than initial disease diagnosis.

Beyond atopic dermatitis, microbiome diagnostic studies in food allergy, cow’s milk protein allergy, immunoglobulin A (IgA) vasculitis, juvenile idiopathic arthritis, and periodic fever, aphthous stomatitis, pharyngitis, and adenitis (PFAPA) remain at relatively early translational stages (Supplementary Table S1) [25,28,100,101,102,103,104,105,106,107,108]. A six-taxon fecal panel for cow’s milk protein allergy achieved an AUC of 0.871 [105], whereas a single-family Burkholderiaceae signal distinguished children with IgA vasculitis from healthy controls with an AUC of 0.766 [103]. The PFAPA tonsil mycobiome study provides a valuable negative example: a cross-validated random-forest classifier achieved an AUC of 0.47 [28], demonstrating that biological plausibility and lesion proximity do not guarantee diagnostic discrimination. A personalized gut-microbiota-targeted therapy study in pediatric atopic dermatitis [109] addressed treatment response and prognostic associations rather than disease diagnosis and is therefore best retained as contextual evidence rather than counted as core diagnostic validation.

5.5. Oral Diseases

Childhood caries is a favorable test case for microbiome diagnostics because saliva and dental plaque are readily accessible and anatomically close to the disease process. Ten studies in the curated list examined saliva and/or dental plaque [26,110,111,112,113,114,115,116,117,118]. Several reported multispecies classifiers with internal, cross-geographic, or longitudinal validation. A 14-species salivary model achieved AUCs of 0.910 in Qingdao, 0.862 in Guangzhou, and 0.922 across the two cities; models trained in one city and tested in the other achieved AUCs of 0.87–0.89 [110]. In another study, the salivary microbiome achieved an AUC of 0.70 and was outperformed by salivary electrolytes (AUC 0.94) [26]. This comparison is particularly relevant to translation: a microbiome classifier should be judged not only by discrimination but also by whether it adds value beyond simpler conventional assays.

Longitudinal design is an additional strength of the caries literature. Cross-sectional classifiers use microbiome features measured at a single time point to distinguish children with existing dental caries from those without, whereas longitudinal models assess whether baseline features predict subsequent caries development. Spatiotemporal oral microbiome studies have examined both current disease classification and prospective prediction of incident caries [115,118]. This distinction matters because strong classification of prevalent disease does not necessarily imply accurate prediction of incident disease. Recent ensemble models have reported perfect subgroup discrimination in selected settings [113], but these results require cautious interpretation given small subgroup sizes, high-dimensional feature spaces, repeated random train-test splitting, and the absence of fully independent external validation. Overall, caries represents a plausible translational niche for disease-proximal microbial panels while also showing why conventional salivary measures and prospectively defined endpoints remain essential comparators.

5.6. Metabolic, Endocrine, Cardiovascular, and Other Emerging Applications

The remaining records cover type 1 diabetes, growth disorders, obesity, hypertension, pediatric sepsis, leukemia, neonatal jaundice, retinopathy of prematurity, congenital heart disease-associated pulmonary arterial hypertension, and other rare indications, forming a heterogeneous mix of diagnostic, prognostic, and association studies (Supplementary Table S1) [27,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138]. In type 1 diabetes, oral microbiome models have reported AUCs of 0.933–0.94 in selected case-healthy cohorts [27,121], while a blood-microbiome random-forest model achieved an AUC of 0.82 [124]. In stool, an integrated nine-species/nine-metabolite model achieved AUCs of 0.976 in discovery and 0.809 in an independent validation cohort [122], whereas a separate 16S-based four-taxon model achieved an AUC of 0.830 [123]. These findings are promising but remain dominated by comparisons with healthy or non-diabetic controls and have not established incremental value over standard biochemical and autoantibody-based diagnostic pathways.

Other case–control studies report microbiome classifiers, with metabolomic integration in some, for central precocious puberty, idiopathic short stature, adolescent obesity, childhood undernutrition, and pediatric hypertension, with reported AUCs of approximately 0.83–1.00 across different evaluation settings [119,120,125,126,127,128]. However, these estimates include training-set performance, notably the AUC of 1.00 reported by Huang et al. for central precocious puberty [125], and should not be interpreted as uniformly validated diagnostic performance. Most models distinguish affected children from healthy or normal controls, although the pediatric hypertension study additionally evaluated improvement beyond body mass index [128]. High discrimination alone therefore does not establish clinical utility; relevant endpoints include incremental value over established diagnostic criteria, reduced invasiveness, earlier detection, or clinically useful phenotyping. Obesity may also confound microbiome-based studies of other pediatric disorders, as differences in adiposity, dietary patterns, and metabolic status can influence microbial profiles independently of the target disease. Studies should therefore assess whether diagnostic performance is maintained after accounting for relevant anthropometric and metabolic factors, using appropriately matched or stratified cohorts and multivariable adjustment where appropriate. The use of obesity-matched controls in pediatric metabolic dysfunction-associated steatotic liver disease [55] and the assessment of incremental value beyond body mass index in pediatric hypertension [128] illustrate these approaches.

The tail of the evidence base underscores the importance of maintaining clear diagnostic boundaries. A pediatric heart-failure study reported group-level gut microbiota and bilirubin-metabolism associations but no diagnostic classifier [139]. A longitudinal study detecting bloodstream-infection pathogens in the gut before bacteremia in children with leukemia/lymphoma represents an early-warning or surveillance application rather than cross-sectional diagnosis [133]. A neonatal-jaundice Mendelian-randomization study used adult-derived gut microbiome instruments and addressed causal associations rather than individual diagnosis [135]. A pediatric kidney-transplant diarrhea study used conventional culture, viral antigen testing, and cytomegalovirus testing rather than a microbiome classifier [137]. A two-sample Mendelian-randomization analysis of pediatric asthma and food allergy similarly evaluated genetically predicted associations rather than diagnostic performance [138]. These five studies were excluded from the 124-record core evidence set during scope refinement and are discussed here only to clarify the boundary between diagnostic biomarker studies and adjacent research questions; they do not support claims of pediatric microbiome diagnostic accuracy.

6. What Determines Diagnostic Performance Across Diseases?

6.1. Sample Proximity Versus Accessibility

Disease-proximal samples can provide more direct information about the affected compartment—for example, dental plaque in caries, skin swabs in atopic dermatitis, intestinal mucosa in IBD, and lower-airway specimens in pneumonia—but greater anatomical proximity does not necessarily yield higher diagnostic accuracy. Sampling burden also varies substantially by matrix: stool and saliva are readily repeatable in outpatient settings, whereas mucosal biopsies, tracheal aspirates, and BALF are restricted to selected clinical contexts. Accessible matrices, in turn, may be more susceptible to age, diet, stool consistency, oral hygiene, and other exposure-related influences. The optimal matrix is therefore not simply the one with the highest discovery AUC, but the least burdensome specimen that retains sufficient disease-specific information for the intended clinical task. When an invasive specimen is already obtained for clinical care, microbiome profiling may add etiologic or stratification information; for screening, surveillance, or triage, noninvasive sampling is generally preferable.

6.2. Single Taxa Versus Composite Signatures

Single-taxon markers are relatively simple to interpret and measure, but taxonomic abundance can vary with host, environmental, geographic, and technical factors. Composite and higher-resolution signatures may improve discrimination by integrating complementary information. In pediatric Crohn disease and ASD, models incorporating microbial genomic variants, MAGs, multikingdom and functional markers, or metabolomic features have outperformed simpler taxonomic or single-layer models in specific cohorts [16,20,21,74]. These findings support the potential value of higher-resolution feature layers but do not establish a universal hierarchy in which greater complexity is always superior. Indeed, a combined taxa-MAG ASD classifier declined from an AUC of 0.886 in discovery to 0.734 in independent validation [20]. Additional feature layers may also increase assay complexity, calibration requirements, missing-data problems, and platform-standardization burden. A more defensible translational goal is therefore a parsimonious, prespecified feature set that retains external performance and demonstrates added clinical value.

6.3. Healthy Controls Versus Clinically Relevant Controls

Comparator selection can strongly influence apparent diagnostic performance. Healthy controls are useful for discovery but often provide greater biological contrast than the symptomatic populations in which a test would actually be used. In one large pediatric IBD study, microbiome AUC was 0.96 against healthy controls but 0.71 against symptomatic gastrointestinal controls [10]. A separate six-marker microbiota-amino-acid panel achieved an AUC of 0.94 in a healthy-control discovery cohort and 0.84 in an independent symptomatic-control validation cohort [8], although this difference reflects both a change in comparator and independent validation. Clinically relevant differential-diagnosis designs include fecal VOC profiling of IBD versus IBS/functional abdominal pain [49], primary sclerosing cholangitis versus ulcerative colitis [17], biliary atresia versus other cholestatic diseases [50], and ASD versus non-autistic developmental delay [65]. Lower-airway studies have likewise distinguished LRTI from adjudicated incidental pathogen carriage and noninfectious respiratory failure [94], or from established noninfectious respiratory failure with subsequent application to diagnostically uncertain cases [23]. Such comparisons may be more clinically informative than case-healthy-control discrimination even when numerical performance is lower.

6.4. Internal Versus External Validation

Cross-validation estimates performance within resampled development data but does not establish transportability to a new center or population. External validation can reveal performance changes that are invisible within the development cohort. The combined taxa-MAG ASD classifier declined from an AUC of 0.886 in development to 0.734 in an independent validation cohort [20], while the developmental ASD model achieved AUCs of 0.78, 0.82, and 0.67 across three validation sets [22]. These external results are therefore more informative for transportability than discovery-only performance. Conversely, caries models retained AUCs of 0.87–0.89 when transferred between Qingdao and Guangzhou [110], an adenoid-hypertrophy classifier achieved an AUC of 0.978 in a geographically distinct validation cohort [99], and an ileal Crohn disease classifier achieved AUCs of 0.85–0.91 across North American regions [15]. These findings support geographic transportability within the settings studied, although the clinical relevance of the comparators varied. Representative examples of performance retention or decline under external validation are provided in Table 2.

7. Why Promising Microbiome Biomarkers Fail to Translate

Translation can fail when biological, technical, or statistical variation obscures the disease-specific signal. Pediatric biological heterogeneity is a fundamental challenge. The microbiome changes substantially during early childhood, and its developmental trajectory is shaped by age, feeding, birth mode, diet, and antibiotic exposure. Longitudinal pediatric studies show that early-life microbiome development is dynamic and sensitive to these factors [1,3,4]. A classifier that does not account for relevant developmental and exposure-related variables may therefore capture these signals alongside disease-related variation. As summarized in Figure 3, developmental factors, dynamic exposures, disease-related changes, and clinical care can alter the measured microbiome and thereby contribute to confounding, miscalibration, reduced reproducibility, and impaired transportability. Interpretation is further complicated by the relationship between microbiome alterations and disease. Disease-associated changes may reflect downstream consequences rather than causal contributors, while shared processes such as inflammation, dietary changes, and gastrointestinal dysfunction may generate overlapping microbial signatures across disorders, limiting diagnostic specificity. In neurodevelopmental disorders, potentially bidirectional gut–brain interactions further complicate causal interpretation [5]. Conversely, early-life microbial exposures may actively shape immune development. Henrick et al. reported that Bifidobacterium infantis supplementation in breastfed infants was associated with altered intestinal immune responses, including reduced Th2- and Th17-associated cytokine signaling, and identified a potential immunoregulatory role for microbial indole-3-lactic acid [140]. Human milk bioactive components, including human milk oligosaccharides, lactoferrin, and immunoglobulins, together with milk-associated microbiota, may further shape early microbial colonization and mucosal immune development [141]. These findings support the biological plausibility of microbiome-mediated immune imprinting, although the persistence of these effects and their implications for later disease susceptibility remain uncertain. Thus, infant microbiome signatures may reflect nutritional exposures and host–microbe immune interactions rather than disease-specific processes alone. Dietary variation is also an important source of microbiome heterogeneity beyond infancy. In an adult randomized trial, Rinott et al. reported changes in gut microbiome composition and cardiometabolic markers associated with Mediterranean dietary interventions, particularly a plant-enriched Green-Mediterranean diet [142]. Although these findings cannot be directly extrapolated to children, they underscore the importance of accounting for dietary exposures when developing and validating pediatric microbiome-based diagnostic signatures, particularly across populations with different dietary patterns.

Figure 3. Pediatric determinants of microbiome biomarker reproducibility and diagnostic performance. Pediatric microbiome biomarkers are influenced by multiple developmental, environmental, clinical, and treatment-related factors that can alter the measured microbiome independently of the target disease. Baseline and developmental factors include age, developmental stage, delivery mode, feeding history, and puberty, whereas dynamic exposures include diet, antibiotics, probiotics, medication or disease treatment, geography, and household environment. These determinants can affect taxonomic composition, microbial functions and pathways, metabolites, temporal stability, and body-site-specific signals. As a result, diagnostic models may capture non-disease-related variation, leading to confounding, reduced reproducibility, subgroup miscalibration, impaired transportability, and false-positive or false-negative classification. Reverse causation and mediation further complicate interpretation because disease manifestations, disease-related behaviors, and clinical care can themselves modify microbiome profiles or lie on the pathway between disease and the measured microbial signal.

Microbiome-targeted interventions, including probiotics and prebiotics, may provide complementary insights into the biological relevance of disease-associated microbial alterations. However, limited or inconsistent therapeutic benefits do not necessarily indicate that these alterations are downstream consequences of disease, as intervention efficacy depends on formulation, dose, timing, host context, and the biological mechanisms targeted. Conversely, clinical improvement following microbiome modulation does not, by itself, establish that the original microbial signature contributed causally to disease. Longitudinal and mechanistic studies are therefore needed to distinguish causal contributions from downstream effects. Nevertheless, microbiome-derived signatures may retain diagnostic or prognostic value regardless of their causal role in disease pathogenesis. Pre-analytical variability compounds this problem. Sample collection, preservation, storage, DNA extraction, and related laboratory procedures can alter measured microbial composition. Standardization studies show that DNA extraction can be a major source of technical variation and that laboratory-specific effects may be substantial [30,34]. Contamination is an additional concern, particularly for low-biomass samples [30,34]. These effects are especially problematic when technical processing is confounded with case–control status.

Analytical variability adds a further layer. Differences in amplification protocols, primer or variable-region choice, sequencing workflows, and bioinformatic processing can alter measured community profiles [34,35]. Sequencing-based microbiome measurements are also subject to systematic, protocol-dependent bias that can distort abundance estimates [35]. A classifier may therefore capture protocol-specific as well as biological signal, making cross-platform validation an important test of translational robustness.

Model-related bias is another major barrier. Microbiome datasets often contain many candidate features relative to the number of participants. Feature selection and model tuning should therefore be confined to training data, with preprocessing and model specification kept separate from unbiased performance evaluation. Repeated observations or clustering by child, household, or site should also be handled explicitly. Even well-conducted internal resampling cannot substitute for independent external validation when the goal is to establish transportability.

Spectrum bias is pervasive across the evidence base. Healthy controls can differ from symptomatic controls in multiple clinical and exposure-related characteristics beyond the target disease, so high discrimination against healthy children may partly reflect a broad illness-wellness contrast rather than disease-specific information. This effect is particularly clear in pediatric IBD: a microbiota-amino-acid panel declined from an AUC of 0.94 against healthy controls to 0.84 in an independent symptomatic-control cohort [8], while a separate metagenomic model achieved an AUC of 0.96 against healthy controls but only 0.71 against children with gastrointestinal symptoms [10]. In ASD, the hold-out AUC was slightly lower for ASD versus non-autistic developmental delay (0.765) than for ASD versus typically developing children (0.796) [65], while a large sibling-comparator study yielded a more modest AUC of 0.66 [19]. Comparator selection should therefore be prespecified to reflect the population in whom the test would actually be used.

Geographic transportability and the validation gap are closely linked. Gut microbiomes vary across populations with different geographic, cultural, and dietary contexts [2], while technical site effects introduce an additional source of variation [30,34]. Several pediatric studies have deliberately tested geographic transportability, including cross-regional Crohn disease [15], adenoid hypertrophy [99], and cross-city caries classifiers [110]. These studies show that regional transportability is achievable, but they do not establish robustness across laboratories. External validation should therefore document whether sampling, extraction, sequencing, and preprocessing were standardized or intentionally varied, because transportability across populations under a common laboratory workflow addresses a different question from reproducibility across routine clinical laboratories.

A final and more consequential gap is clinical utility. AUC, sensitivity, and specificity alone do not show that a microbiome test improves clinical decision-making, and reporting of calibration, decision-analytic performance, incremental value over standard biomarkers, operational characteristics, cost, and patient impact remains limited across the studies reviewed. In pediatric IBD, a clinical model driven largely by fecal calprotectin and C-reactive protein outperformed the microbiome-only model against symptomatic gastrointestinal controls [10]. This does not argue against microbiome diagnostics; rather, it illustrates the comparison required for translation. A new assay should either provide clinically useful information beyond existing tests or deliver comparable information with lower burden, earlier availability, greater accessibility, or additional phenotypic value.

8. A Roadmap Toward Clinically Actionable Pediatric Microbiome Diagnostics

A seven-stage framework for translating pediatric microbiome biomarkers from biological discovery to clinical impact is presented in Figure 4. Stage 1 is biological discovery, in which disease-associated taxa, genes, pathways, genomic variants, or metabolites are identified with transparent control of pediatric covariates. Stage 2 is a reproducible signature, in which direction and effect persist in an independent sample or technical replication. Stage 3 is a locked diagnostic classifier, with feature definitions, preprocessing, algorithm, and threshold fixed before validation. Stage 4 is independent multicenter validation across regions and, ideally, laboratories or platforms. Stage 5 is comparison with the current standard of care, including incremental discrimination, calibration, and clinically interpretable thresholds. Stage 6 is prospective real-world validation in consecutively recruited children with realistic disease prevalence and competing diagnoses. Stage 7 is clinical impact and cost-effectiveness, demonstrating that use of the test changes decisions, outcomes, workflow, or resource use. The principal evidence threshold and common failure mode at each stage are also illustrated in Figure 4.

Figure 4. Seven-stage framework from diagnostic discrimination to clinical utility. The proposed framework describes seven stages in the translation of pediatric microbiome biomarkers from biological discovery to clinical utility. Stage 1 identifies candidate microbial, functional, genomic, or metabolite signals. Stage 2 establishes a reproducible and internally validated signature, followed by development of a locked classifier with prespecified features, preprocessing, algorithm, and threshold at Stage 3. Stage 4 requires independent multicenter validation across relevant populations, regions, laboratories, or platforms. Stage 5 evaluates incremental value and calibration against the current standard of care. Stage 6 tests performance prospectively under real-world conditions, including realistic disease prevalence, competing diagnoses, missing samples, and assay failures. Stage 7 establishes clinical impact and cost-effectiveness through demonstrable improvements in decision-making, outcomes, workflow, accessibility, or resource use. As evidence matures, evaluation should progress from discrimination metrics toward external performance, calibration, transportability, net clinical benefit, operational performance, and cost-effectiveness. AUC, area under the curve.

Representative studies illustrate progress along this pathway while highlighting remaining translational gaps. Early taxonomic studies in pediatric Crohn disease identified disease-associated microbial signatures [37,41], while subsequent work evaluated the geographic transportability of microbiome-based classification [15]. More recent approaches have incorporated microbial genomic variants into diagnostic classifiers [16], although further prospective validation is needed to establish their performance across clinical settings. In pediatric IBD, independent validation against symptomatic gastrointestinal controls [8,11] and comparison with established clinical biomarkers [10] represent further progress toward clinically relevant evaluation. Nevertheless, these advances do not establish completion of the later translational stages. Prospective validation of locked classifiers in representative clinical populations, followed by evaluation of clinical impact and cost-effectiveness, remains necessary.

Three design priorities should be minimum expectations for the next generation of studies. First, recruitment should be anchored to a prespecified clinical decision rather than convenient case-healthy sampling. Second, data architecture should prevent leakage: child-level splitting, site-aware validation, nested feature selection, and locked preprocessing should be explicit. Third, pediatric metadata should be measured rather than assumed away. Age, developmental stage, diet, recent antibiotics, probiotic exposure, stool consistency, treatment status, and key local exposures should be incorporated into study design and analysis as appropriate. Where possible, harmonized protocols and shared reference materials should be used to distinguish biological transportability from laboratory reproducibility.

The most promising near-term applications are those in which the microbiome addresses an unmet clinical decision using a feasible biological matrix. Pediatric IBD has the strongest cross-study foundation and is progressing toward disease-control comparisons and independent validation. Oral diagnostics for caries and selected salivary applications benefit from simple repeated sampling. Disease-proximal respiratory or skin signatures may add value when specimens are already collected or can be obtained noninvasively. ASD research is biologically rich and technologically advanced, but clinical translation depends on demonstrating robustness to diet, development, gastrointestinal phenotype, family environment, and geography. Across domains, the central challenge is no longer whether microbial signatures can distinguish pediatric diseases in discovery cohorts, but whether they remain accurate across children, centers, populations, platforms, and real diagnostic settings. An important complementary research direction is to determine whether longitudinal microbiome profiles can predict disease progression, complications, or treatment response and provide incremental value beyond established clinical predictors. Such studies should define prognostic endpoints and prediction horizons and account for disease severity and treatment-related changes.

9. Limitations of This Review

Several limitations of this review warrant acknowledgment. First, the literature search was restricted to PubMed, and evidence selection followed a narrative rather than a systematic-review approach. Relevant studies indexed elsewhere or not captured by the search strategy may therefore have been missed. Second, formal independent duplicate screening and study-level risk-of-bias assessment were not undertaken, limiting the reproducibility of study selection and the consistency of methodological appraisal. Selection and publication biases cannot be excluded, particularly if studies reporting favorable diagnostic findings were more likely to be identified or published. Language-related selection bias is also possible because language eligibility criteria were not explicitly documented. Finally, the 124 curated records represent a selected cross-disease evidence base rather than an exhaustive survey of the literature. Comparisons of evidence maturity across disease domains should therefore be interpreted as descriptive rather than definitive.

10. Conclusions

Pediatric microbiome diagnostics has moved beyond descriptive dysbiosis, but the evidence remains uneven. IBD currently provides the most mature diagnostic pipeline, with studies that include treatment-naive populations, clinically relevant gastrointestinal controls, independent cohorts, and increasingly high-resolution functional or genomic biomarkers. Caries, selected respiratory applications, skin-based atopic dermatitis classifiers, and some oral or metabolic models occupy plausible translational niches. ASD has the largest biomarker literature outside IBD and includes sophisticated multikingdom and genome-level models, but it also provides the clearest warning that discovery performance can decline substantially under independent validation and that diet, development, gastrointestinal symptoms, and geography can compete with diagnosis as sources of signal.

The field should therefore move beyond the highest AUC as the primary marker of progress. Strong pediatric diagnostic evidence requires clinically relevant comparators, prespecified and locked analyses, independent multicenter validation, transparent handling of age and exposure-related heterogeneity, and direct comparison with standard care. Calibration, decision consequences, implementation burden, and cost should be evaluated before claims of clinical actionability. Etiologic, prognostic, treatment-response, adult, and conventional-microbiology studies remain informative but should not be counted as diagnostic validation. A reproducible pediatric microbiome test is more likely to emerge from well-defined clinical questions, rigorous study design, and deliberate testing of transportability than from ever larger feature spaces alone.