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  • Systematic Review
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6 June 2026

38 Pages

Circulating and Tissue Biomarkers Associated with Disease Severity and Progression in Adolescent Idiopathic Scoliosis: A Systematic Review

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1
Surgical Sciences and Technologies, IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy
2
Department of Spine Surgery, IRCCS Istituto Ortopedico Rizzoli, 40136 Bologna, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Cells2026, 15(12), 1044;https://doi.org/10.3390/cells15121044
This article belongs to the Special Issue Resolving Chronic Inflammation: Molecular Mechanisms, Cellular Signaling and Therapeutic Frontiers
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Highlights

What are the main findings?
  • Multiple inflammatory, epigenetic, metabolic, and bone-related biomarkers were consistently associated with AIS severity, particularly with higher Cobb angles and altered skeletal metabolism.
  • Only a limited number of longitudinal studies identified potential predictive biomarkers for curve progression, including specific circulating miRNA signatures and reduced spermidine levels.
What are the implications of the main findings?
  • Biomarkers may improve early risk stratification and support more personalized management of AIS by complementing current clinical and radiographic assessment tools.
  • Further large-scale prospective studies with standardized methodologies are required to validate clinically applicable biomarkers for predicting AIS progression and guiding treatment decisions.

Abstract

Adolescent idiopathic scoliosis (AIS) is a multifactorial spinal deformity with variable progression patterns, making early risk stratification challenging. Circulating and tissue biomarkers, including inflammatory, metabolic, endocrine, epigenetic, and bone-related markers, have recently been investigated as potential predictors of disease severity and progression. This systematic review evaluated the current evidence on circulating and tissue biomarkers associated with AIS severity and progression. PubMed, Scopus, and Web of Science were searched for studies published between April 2016 and April 2026. Studies assessing circulating or tissue-based inflammatory, metabolic, epigenetic, and bone-related biomarkers in AIS patients were included. Data on study design, biomarker type, analytical methods, and associations with curve severity or progression were extracted. Twenty-nine studies involving more than 4000 participants were included. Biomarkers identified included inflammatory cytokines, microRNAs, metabolic hormones, and bone metabolism markers. Most studies reported significant associations between biomarkers and curve severity, particularly for inflammatory mediators, epigenetic regulators, and bone-related markers. However, few studies evaluated longitudinal progression, and only a limited number of studies identified predictive biomarkers, including circulating miRNA panels and spermidine levels. ROBINS-I assessment showed substantial risk of bias, mainly related to confounding and selective reporting. Heterogeneity was observed across study designs and outcome definitions. Current evidence supports associations between biomarkers and AIS severity, but predictive value for progression remains limited.

1. Introduction

Scoliosis is a three-dimensional deformity of the spine and trunk, defined by a lateral curvature with a major coronal curve measuring ≥10° using the Cobb method on radiographs [1]. Curve severity is commonly stratified into mild (<20°), moderate (20–40°), and severe (>40–50°), with the latter frequently requiring surgical correction [2,3]. In skeletally immature patients, curves exceeding 20° typically prompt closer surveillance and, when indicated, brace treatment to mitigate the risk of progression [3]. Notably, curve progression is highly variable and tends to accelerate during periods of rapid growth, representing a critical window for clinical management [1,3].
Adolescent idiopathic scoliosis (AIS) is the most prevalent form of structural spinal deformity, accounting for approximately 80% of cases [4]. It arises during the pubertal growth phase and may progress until skeletal maturity [1]. Epidemiological data indicate a global prevalence ranging from 1% to 4% among adolescents [1,5,6], with meta-analytic estimates of 1.34% for curves ≥10° [7]. Longitudinal evidence further suggests that up to 2.5% of adolescents develop measurable spinal curvature during growth [8].
Despite its high prevalence, the etiology of AIS remains incompletely understood and is widely considered multifactorial, involving genetic susceptibility alongside environmental, hormonal, metabolic, and biomechanical influences [6,7,8,9]. Disease expression is strongly modulated by sex and growth status, with females exhibiting a markedly higher risk of curve progression and severe deformity. Reported female-to-male ratios range from 1.5:1 to 11:1, depending on curve magnitude and study characteristics [10,11,12].
Current clinical management relies on physical examination and radiographic assessment, including quantification of the major coronal curve using the Cobb method, classification of curve patterns, and assessment of skeletal maturity (e.g., Risser stage or Sanders classification) [13,14]. However, while radiography remains the diagnostic gold standard, it offers limited insight into the biological mechanisms underlying disease progression and provides suboptimal prognostic accuracy [15]. This limitation is clinically relevant, as only a subset of patients will experience significant curve progression. Existing risk stratification approaches, based on age, skeletal maturity, and baseline curve magnitude, lack sufficient precision to reliably identify high-risk individuals at an early stage [16,17,18,19,20,21].
Consequently, treatment decisions are often based on currently available clinical risk estimates rather than individualized biological predictors. Repeated radiographic monitoring also exposes patients to cumulative radiation, while long-term bracing can negatively affect quality of life and psychological well-being [22]. Furthermore, progressive scoliosis may impose a substantial clinical and socioeconomic burden, including aesthetic deformity, chronic pain, functional limitations, reduced quality of life, and pulmonary complications [21,22,23]. Surgical correction, although effective for severe curves, is associated with considerable risks, including neurological, cardiopulmonary, gastrointestinal, and infectious complications, as well as implant-related issues and fusion failure [24,25,26,27].
These limitations highlight the need for objective, minimally invasive tools to improve early risk stratification and disease monitoring. In this context, circulating biomarkers measurable in accessible biological samples have emerged as promising candidates. Among these, inflammatory mediators, including cytokines, interleukins, and other immune-related factors, as well as metabolic and bone-related markers, are increasingly being investigated for their potential role in AIS pathophysiology and progression [28,29,30,31]. These biomarkers may reflect underlying biological processes such as low-grade inflammation, altered bone metabolism, and dysregulated energy balance, which have been associated with scoliosis onset and progression. Although a growing body of literature has explored the relationship between biological biomarkers and AIS, the available evidence remains heterogeneous in terms of study design, patient selection, biomarker assessment, and outcome measures. In particular, the distinction between biomarkers associated with disease severity and those predictive of progression is not always clearly defined [28]. Longitudinal studies provide stronger evidence for predictive value, whereas cross-sectional studies mainly contribute to understanding underlying biological mechanisms.
Therefore, this systematic review aims to comprehensively evaluate the current evidence on circulating and tissue biomarkers associated with disease severity and progression in AIS, highlighting their potential clinical utility and identifying gaps for future research.

2. Materials and Methods

2.1. PICOS and Eligibility Criteria

This systematic review was designed using the PICOS framework (Population, Intervention, Comparison, Outcomes, Study design) [32]. Specifically, studies were considered if they met the following criteria: P (Population): patients diagnosed with AIS, typically adolescents aged between 10 and 18 years, diagnosed according to standard clinical and radiographic criteria, including Cobb angle measurement; I (Intervention): the exposure of interest consisted of circulating and tissue biomarkers assessed in biological samples from AIS patients. These include circulating biomarkers measured in blood, tissue biomarkers, when available, and non-coding RNAs; C (Comparison): When available, comparisons included differences according to disease severity, curve progression, or non-progressive versus progressive within the AIS population. Comparisons with healthy controls were also considered when reported; O (Outcomes): the primary outcomes were disease severity, assessed through measurements of the major coronal curve using the Cobb method, defined as a significant increase in Cobb angle during follow-up (e.g., ≥5° or ≥10°). Secondary outcomes included the risk of progression, the need for surgical intervention, and associations with growth and skeletal maturity indicators, such as the Risser stage. S (Study design): Eligible studies included observational designs, such as prospective or retrospective cohort studies, case–control studies, and cross-sectional studies in human subjects. Case reports, reviews, editorials, and non-peer-reviewed articles were excluded. Animal and in vitro studies were not considered.

2.2. Information Sources and Search Strategy

A systematic search was conducted in March 2026 across three databases (PubMed®, Scopus, and Web of Science™), following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [33]. The search strategy is reported in the Supplementary Materials (Table S1).
Filters were used to restrict the results to studies published in English between 2016 and 2026. Duplicate records were removed using EndNote® 2025, and titles and abstracts were independently assessed by four authors (FV, FS, LB, and GT). Studies that did not satisfy the inclusion criteria were excluded. Any discrepancies were resolved through discussion or, when necessary, by consulting a fifth reviewer (DC).
Full-text versions of the remaining studies were then evaluated for eligibility, and their reference lists were also checked for additional relevant publications. Data extraction was carried out independently by FV and FS using a standardized form. The protocol was registered in PROSPERO (Registration number: CRD420261389875).

2.3. Risk of Bias Assessment

The risk of bias of the included studies was evaluated using the ROBINS-I (Risk Of Bias In Non-randomized Studies of Interventions) tool [34]. This instrument assesses potential bias across seven domains covering different stages of the study process: bias due to confounding and selection of participants (pre-intervention), classification of interventions (at intervention), deviations from intended interventions and missing data (post-intervention), as well as measurement of outcomes and selection of the reported results. Each domain was judged as having low, moderate, or serious (high) risk of bias, according to the ROBINS-I guidance. Two independent reviewers (FV and FS) performed the assessment, and any discrepancies were resolved through discussion or consultation with a third reviewer (DC) when necessary. The overall risk of bias for each study was determined by the highest level of bias identified in any domain.

3. Results

3.1. Study Selection

The initial database search identified a total of 1099 articles, 118 from PubMed, 317 from Web of Science, and 664 from Scopus. After removing 316 duplicates, 783 studies were screened by title and abstract (Figure 1). Of these, 500 were excluded for not being inherent to the topic. The remaining 283 were assessed for their eligibility and 255 were further excluded for various reasons: 50 were review articles, 102 regarded ischemic stroke, 53 were in vitro studies, and 50 did not focus on AIS pathology. Ultimately, 28 studies met the inclusion criteria, and 1 additional study was identified from reference screening, resulting in a total of 29 studies included in this systematic review [35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63] (Figure 1).
Figure 1. PRISMA flowchart. Search strategy on PubMed, Web of Science and Scopus databases.

3.2. Data Synthesis

The included studies involved over 4000 participants, including both patients with AIS and health controls (Table 1 and Table 2). Study designs were heterogeneous and included cross-sectional [35,36,37,38,39,40,41], case–control [42,43,44,45,46,47,48,49,50,51,52,53,54], prospective [55,56,57], and translational [58,59] approaches. Sample sizes ranged from small exploratory cohorts to large multicenter populations, with most studies showing a predominance of female participants, consistent with the epidemiology of AIS.
Table 1. Study design and demographic characteristics of the included studies.
Table 2. Clinical characteristics of study populations included in the review.
A broad spectrum of biomarkers was analyzed across the studies, including inflammatory mediators, epigenetic regulators, metabolic and hormonal factors, and bone-related markers, assessed in serum, plasma, and tissue samples using a variety of analytical techniques (Table 3). Most studies investigated associations between biomarkers and disease severity, typically quantified by the Cobb angle, whereas only a limited number of studies evaluated longitudinal outcomes related to disease progression (Table 4 and Table 5).
Table 3. Summary of inflammatory and metabolic biomarkers associated with disease severity and progression in AIS.
Table 4. Association between inflammatory biomarkers and curve severity (Cobb Angle) in patients with AIS.
Table 5. Association between inflammatory biomarkers and risk of disease progression in AIS.
Overall, the available evidence is largely derived from cross-sectional analyses, and only a small subset of studies incorporated follow-up data to assess progression. Correlation analyses further highlighted complex interactions between biomarkers and clinical, anthropometric, and metabolic parameters (Table 6). However, substantial heterogeneity in study design, biomarker selection, and outcome definitions was observed across the included studies, limiting direct comparability of findings and the identification of clinically validated biomarkers.
Table 6. Correlations between inflammatory biomarkers and clinical, anthropometric, and metabolic parameters in AIS.

3.2.1. Inflammatory Biomarkers

Several studies investigated the role of inflammatory mediators in AIS, highlighting their association with disease severity. Increased expression of pro-inflammatory cytokines and matrix-degrading enzymes, including interleukin-1β (IL-1β), matrix metalloproteinase-3 (MMP-3), and MMP-13, was observed in association with higher Cobb angles, particularly in severe curves exceeding 70° [35]. Similarly, systemic inflammatory indices derived from routine blood parameters, such as the neutrophil-to-lymphocyte ratio (NLR) and the C-reactive protein/albumin ratio (CAR), were positively correlated with curve severity, indicating a potential role of low-grade systemic inflammation in more advanced deformities [60].
At the molecular level, inflammatory signaling pathways were also implicated. Activation of Toll-like receptor (TLRs) pathways and increased expression of cytokines, including IL-1, IL-6, IL-8, and Tumor Necrosis Factor alpha (TNF-α), were associated with enhanced osteoclastogenesis and tissue degeneration [61]. In addition, immune-related biomarkers such as CD23 and β2-microglobulin were reduced in AIS and showed associations with disease severity [49].
Postoperative increases in inflammatory markers, including IL-6 and C-reactive protein, were observed but were related to surgical stress rather than disease severity or progression [56].

3.2.2. Epigenetic Biomarkers

Epigenetic regulators represented a major class of biomarkers in AIS. Multiple studies identified altered expression of circulating and tissue-specific microRNAs (miRNAs) associated with disease severity. Increased levels of miR-96-5p were associated with AIS and contributed to predictive models including clinical variables [43]. Similarly, elevated expression of miR-941, miR-151a-3p, and miR-148b-5p was observed in more severe cases [51].
Additional studies identified broader miRNA signatures associated with AIS diagnosis and severity. A four-miRNA panel demonstrated high diagnostic accuracy in distinguishing AIS patients from controls [45], while other studies identified multiple miRNA panels associated with severe phenotypes [37,55].
Epigenetic regulation at the DNA and chromatin level was also implicated. Increased estrogen receptor 1 (ESR1) methylation was positively associated with curve severity [36], while histone modifications involving SUV39H1 and H3K9me3 were linked to increased chondrocyte proliferation and AIS severity [46]. Furthermore, altered expression of regulatory genes involved in inflammatory signaling was reported, including reduced SOCS3 expression and genetic variants associated with increased curve severity [62].
Finally, circulating microRNAs such as miR-130b-3p were associated with reduced bone mass and increased disease severity, supporting a link between epigenetic regulation and skeletal metabolism [41].
In addition, extracellular vesicle-associated microRNAs were also shown to be significantly altered in AIS. In particular, members of the miR-30 family were upregulated in severe AIS and were associated with impaired osteogenic differentiation. Functional analyses demonstrated that extracellular vesicles enriched in these miRNAs reduced osteogenic marker expression and mineralization capacity, supporting a mechanistic link between circulating epigenetic factors and disease severity [63].

3.2.3. Metabolic and Hormonal Biomarkers

Metabolic and hormonal alterations were consistently reported across studies. Elevated leptin levels were positively associated with curve severity, while reduced levels of osteocalcin and N-terminal telopeptide indicated impaired bone turnover [38].
Alterations in leptin signaling were also observed, with reduced free leptin index and increased soluble leptin receptor levels in AIS patients [50]. Increased ghrelin levels were associated with disease severity and osteopenia [53,57], while adiponectin levels were linked to reduced bone mineral density [54].
Metabolic enzymes were also involved. Reduced Dipeptidyl peptidase-4 (DPP-4) activity was observed in AIS and showed associations with metabolic parameters, although its relationship with curve severity was inconsistent [44,48].
In addition, mitochondrial and nuclear circulating DNA levels were altered in AIS, although their predictive value for disease progression remained limited [47].

3.2.4. Bone Metabolism and Signaling Pathways

Markers of bone metabolism and osteogenesis were strongly associated with AIS severity. Reduced osteogenic activity, reflected by decreased RUNX2 expression and altered RANKL/OPG balance, was observed in more severe cases [53]. Increased osteoclast activity and bone resorption markers, such as TRAP5b, further supported a state of enhanced bone turnover [40]. Chen et al. [42] further supported the involvement of bone metabolism alterations in AIS. In this study, abnormal osteocyte lacuno-canalicular network structure was observed in AIS bone tissue, together with reduced canalicular number and length, increased lacunar volume and surface, and lower bone mechanical properties. In a parallel serological cohort, serum osteocalcin was negatively correlated with Cobb angle, supporting an association between impaired osteocyte function, altered bone formation, and curve severity.
At the signaling level, dysregulation of multiple pathways was reported. Activation of IL-6/STAT3 signaling was associated with cartilage degradation and increased MMP13 expression [52]. Similarly, Wnt/β-catenin signaling and associated regulators, including miR-145, were linked to impaired osteocyte function and disease severity [58].
Gene expression studies further highlighted alterations in pathways involved in extracellular matrix organization, muscle regulation, and inflammatory signaling, including Wnt-related genes and structural proteins [39]. These findings are consistent with evidence showing that epigenetic and extracellular vesicle-mediated mechanisms may directly impair osteogenic function and contribute to disease severity [63].

3.2.5. Biomarkers Associated with Disease Progression

Only a limited number of studies directly assessed disease progression using longitudinal or follow-up data. Among these, lower circulating spermidine levels were associated with increased risk of progression, defined as a significant increase in Cobb angle or progression to severe curves [59]. Similarly, specific circulating miRNA panels demonstrated high predictive accuracy for progression to severe AIS, with some models achieving high sensitivity and specificity [37,55].
However, most studies relied on surrogate measures such as baseline Cobb angle or bone metabolic status to infer progression risk, rather than directly measuring longitudinal changes. Other investigations focused on disease presence, molecular mechanisms, or postoperative inflammatory responses without evaluating progression [47,56].
As a result, no biomarker has yet been consistently validated as a reliable predictor of AIS progression, highlighting the need for further longitudinal studies.

3.3. Risk of Bias Assessment

The risk of bias assessment using the ROBINS-I tool revealed substantial variability across domains (Figure 2). A high risk of bias was predominantly observed in pre-intervention domains, particularly due to confounding (97%) and selection of participants (86%), with only a small proportion of studies rated as moderate risk in these areas (respectively 3% and 14%). In contrast, most studies showed a low risk of bias in domains related to intervention classification (97%) and deviations from intended interventions (83%), with a minority rated as moderate (respectively 3% and 17%). Missing data was consistently rated as moderate risk across all studies (100%). Post-intervention domains generally demonstrated favorable assessments, with low risk of bias reported for outcome measurement (93%) and a small proportion of moderate risk (7%). However, bias in the selection of reported results remained a concern, with over half of the studies (52%) rated at high risk and the remainder (48%) at moderate risk. Overall, the findings indicate that while post-intervention methodological quality was generally acceptable, significant concerns persist in pre-intervention domains and selective reporting.
Figure 2. Risk of bias assessment of the included studies according to the ROBINS-I tool. The figure summarizes the proportion of studies rated as low, moderate, or serious risk of bias across the seven ROBINS-I domains, including confounding, participant selection, intervention classification, deviations from intended interventions, missing data, outcome measurement, and selection of reported results.

4. Discussion

This systematic review provides a comprehensive synthesis of current evidence on circulating and tissue biomarkers associated with disease severity and progression in AIS. Overall, the findings support a multifactorial biological framework in which inflammatory, epigenetic, metabolic, and bone-related markers may be involved in biological processes associated with disease severity (Figure 3). However, only a limited subset of studies has evaluated their role in predicting disease progression. An additional challenge in interpreting progression-related biomarkers is the lack of standardization in progression definitions across studies. Thresholds for progression varied, commonly including increases of ≥5° or ≥10° in the major curve, and were often assessed over different follow-up durations. These differences are particularly relevant given the inherent variability of radiographic measurements and the influence of skeletal maturity, growth velocity, and treatment interventions on curve evolution. Consequently, heterogeneity in progression endpoints may have affected the comparability of findings and the identification of reliable predictive biomarkers.
Figure 3. Schematic model illustrates circulating and tissue biomarkers, their underlying mechanisms, and their contribution to structural alterations and clinical outcomes in AIS, as identified in this review. The figure was created by the authors using Microsoft PowerPoint.
In particular, a consistent association was observed between several biomarkers, particularly inflammatory cytokines [37,41,43,45,46,51,55,62,63], miRNAs [36,37,41,43,45,46,51,55,62,63], markers of bone metabolism [39,40,42,52,53,58,63], and curve severity, typically measured by the Cobb angle. These results support the hypothesis that AIS is not solely a structural deformity but also involves systemic biological alterations, including low-grade inflammation, dysregulated osteogenesis, and altered energy metabolism.
In contrast, evidence supporting their predictive value for longitudinal progression remains scarce [37,47,55,56,59]. This discrepancy reflects the predominance of cross-sectional study designs and represents a major limitation in the current literature.
The consistent association between inflammatory mediators and higher Cobb angles suggests a potential role of low-grade systemic inflammation in structural remodeling processes observed in AIS, particularly through cartilage degradation and extracellular matrix breakdown [64,65,66].
At the molecular level, the involvement of pathways such as TLR signaling and IL-6/STAT3 activation further strengthens the biological plausibility of inflammation-driven skeletal alterations [44,52,61]. These pathways are known to regulate osteoclastogenesis and tissue degeneration, suggesting that similar mechanisms may be involved in curve progression in AIS [67,68]. However, the distinction between disease-related inflammation and secondary responses remains critical, as postoperative elevations in inflammatory markers likely reflect acute physiological stress rather than intrinsic disease activity [56,69].
Similarly, epigenetic regulators, particularly circulating miRNAs, emerged as promising biomarkers, with several studies identifying specific signatures associated with disease severity [37,41,43,45,51,55,63] and, in some cases, progression risk [37,55].
Epigenetic regulation emerges as a particularly promising area, providing a potential link between genetic susceptibility and environmental influences. The identification of miRNA signatures associated with disease severity suggests that post-transcriptional regulation may play a key role in modulating skeletal growth and remodeling [70,71,72]. Notably, functional evidence from studies on extracellular vesicle-associated miRNAs indicates that these molecules have been shown in experimental models to influence osteogenic differentiation, providing biological plausibility for their association with AIS severity [73,74]. Nevertheless, the lack of external validation and the predominance of exploratory analyses currently limit their clinical applicability.
In parallel, metabolic and hormonal alterations, such as changes in leptin, ghrelin, and adipokines, highlight the interplay between energy balance and skeletal development in AIS [38,44,47,48,50,53,54,57]. This perspective aligns with emerging evidence linking AIS to broader metabolic phenotypes.
Bone metabolism markers further support this concept [39,40,42,52,53,58,63], suggesting that impaired osteogenic activity and increased bone resorption may be associated with curve progression [75,76]. Consistent evidence of altered bone metabolism reinforces the hypothesis of intrinsic skeletal fragility in AIS [77]. Indeed, impaired osteogenic activity, increased bone resorption, and structural abnormalities in the osteocyte network suggest that defective bone quality, rather than bone quantity alone, may be associated with greater disease severity. Dysregulation of key signaling pathways, including Wnt/β-catenin, provides a plausible mechanistic framework that may link molecular alterations to macroscopic deformity [78,79].
Despite growing interest in biomarker-based prediction, only a limited number of studies directly evaluated disease progression using longitudinal designs. Only a small subset of studies employed longitudinal designs, and among these, few identified biomarkers with potential prognostic value, such as specific miRNA panels or spermidine levels [37,55,59]. However, most studies relied on cross-sectional analyses, which, while useful for understanding biological mechanisms, do not allow causal inference or reliable prediction of disease trajectory. This represents a critical gap in the literature, as the clinical utility of biomarkers in AIS lies primarily in their ability to predict progression and guide early intervention.
The interpretation of these findings must also consider the methodological limitations identified through the risk of bias assessment. The ROBINS-I analysis revealed substantial concerns, particularly in pre-intervention domains. A high risk of bias due to confounding and participant selection was observed in most studies, reflecting the observational nature of the evidence and the frequent lack of adjustment for key clinical variables such as age, sex, skeletal maturity, and baseline curve severity. Furthermore, although ROBINS-I provided a standardized framework for assessing methodological quality across the heterogeneous study designs included in this review, it was originally developed for non-randomized intervention studies and may not fully capture all sources of bias relevant to observational biomarker research. Therefore, the risk-of-bias findings should be interpreted considering this methodological limitation. These factors are well-known determinants of AIS progression and may have influenced the reported associations, thereby limiting the internal validity of the findings.
In contrast, domains related to intervention classification and post-intervention processes generally showed lower risk of bias, suggesting that biomarker measurement and outcome assessment were relatively consistent and methodologically sound across studies. However, the presence of a moderate risk of bias due to missing data in all studies indicates potential issues with incomplete datasets and follow-up, which may affect the robustness of the results. Furthermore, the high proportion of studies with a serious risk of bias in the selection of reported results raises concerns about selective reporting and publication bias, potentially leading to an overrepresentation of statistically significant findings.
To provide a more structured synthesis of the available evidence, biomarker categories were classified according to the consistency and replication of findings reported across the included studies (Table 7). Overall, inflammatory cytokines, miRNAs, and bone metabolism markers showed the most consistent associations with AIS severity, whereas evidence for progression-related biomarkers remains limited and largely exploratory. Most candidate biomarkers still require independent validation before they can be considered clinically useful predictors of disease progression.
Table 7. Summary of evidence strength for biomarker categories associated with AIS severity and progression.
Several limitations should be considered when interpreting these findings. First, the predominance of cross-sectional designs limits the ability to establish causal relationships or predictive value. Second, substantial heterogeneity was observed in study design, patient populations, biomarker selection, and analytical methods. Third, sample sizes were often small, particularly in translational and tissue-based studies, reducing statistical power. Finally, differences in outcome definitions, especially regarding disease progression, further limit comparability across studies.
The identification of reliable biomarkers for AIS progression remains a key unmet clinical need. An additional emerging area of interest involves biomarkers of skeletal growth and maturity. Although not included in the present systematic review because they did not meet the predefined eligibility criteria, recent studies have investigated circulating biomarkers associated with residual growth potential rather than curve severity or progression. For example, Welborn et al. identified Collagen X Biomarker (CXM) as a promising indicator of longitudinal bone growth, demonstrating strong correlations with established measures of skeletal maturity, including Sanders score, Risser stage, bone age, and peak height velocity [80]. Such biomarkers may complement traditional radiographic assessments and could potentially contribute to more individualized prediction of progression risk in AIS. However, further studies are needed to clarify their direct relationship with curve progression and clinical outcomes. Ideally, such biomarkers should be measurable in accessible biological samples, reflect underlying disease mechanisms, and enable early and accurate risk stratification.
Future research should prioritize large-scale, longitudinal studies integrating multi-omics approaches, including transcriptomics, metabolomics, and proteomics. Standardization of study design, biomarker assessment, and outcome definitions will be essential to facilitate comparison across studies and enable clinical translation. In addition, combining biomarkers with clinical and radiographic parameters may improve predictive models and support personalized management strategies in AIS.

5. Conclusions

Taken together, the current evidence suggests that multiple circulating and tissue biomarkers are associated with AIS severity, but their role in predicting disease progression remains insufficiently established. Future research should prioritize well-designed prospective longitudinal studies with standardized definitions of progression, adequate control of confounding variables, and rigorous methodological approaches. The integration of multi-omics strategies and advanced analytical models may further improve the identification of robust biomarker signatures. Ultimately, improving study quality and reducing bias will be essential to translate biomarker research into clinically useful tools for early risk stratification and personalized management of AIS.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cells15121044/s1, Table S1: Combination of free-vocabulary and/or Medical Subject Headings (MeSH) terms for the identification of studies.

Author Contributions

Conceptualization, F.S., F.V.; methodology, F.S., F.V., D.C.; validation, L.B., G.T., C.G., A.G.; formal analysis, F.S., F.V., D.C., G.G.; investigation, F.S., F.V., D.C.; data curation, F.S., F.V., D.C.; writing—original draft preparation, F.S., F.V., D.C.; writing—review and editing, F.S., F.V., D.C., G.C., C.G., G.G.; visualization, F.S., F.V., D.C., G.C., L.B., G.T., C.G., A.G., G.G.; supervision, A.G., G.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the 5 × 1000 project 2023 (redditi 2022) 5M-2023-23687106 “Nuove Prospettive Traslazionali per la Diagnosi, Prevenzione e Trattamento delle Malattie Muscoloscheletriche Rare e Complesse”.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding author(s).

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
AISAdolescent idiopathic scoliosis
PICOSPopulation, Intervention, Comparison, Outcomes, Study design
PRISMAPreferred Reporting Items for Systematic Reviews and Meta-Analyses
MeSHMedical Subject Headings
ROBINS-IRisk Of Bias In Non-randomized Studies of Interventions
ILinterleukin
MMPmatrix metalloproteinase
NLRneutrophil-to-lymphocyte ratio
CARC-reactive protein/albumin ratio
TNF-αTumor Necrosis Factor alpha
DPP-4Dipeptidyl peptidase-4
TLRsToll-like receptors
miRNAsmicroRNAs
ESR1estrogen receptor 1
SUV39H1Histone-lysine N-methyltransferase
H3K9me3Histone H3 Lysine 9 trimethylation
SOCS3Suppressor of Cytokine Signaling 3
RUNX2Runt-related transcription factor 2
RANKLReceptor Activator of Nuclear Factor kappa-B Ligand
OPGOsteoprotegerin
TRAP5bTartrate-resistant acid phosphatase 5b
STAT3Signal Transducer and Activator of Transcription 3

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