Discovering biomarkers for diagnosis, infection stage identification, treatment, and management of pediatric tuberculosis in the era of multi-omics: a narrative review

Introduction

Background

Tuberculosis (TB) is a chronic respiratory infectious disease caused by Mycobacterium tuberculosis (Mtb) infection. The World Health Organization’s 2024 Global tuberculosis Report estimated that there were about 1.30 million new TB cases and 0.19 million deaths in 2023 among children and young adolescents (0–14 years of age), which was regarded as one of the top ten causes of child death (1). Although albeit low in absolute numbers, an increasing trend of pediatric TB cases (aged <15 years) was notified in the European Union/ European Economic Area countries between 2015 and 2023 (2). Compared with adults, children with TB have the characteristics of low bacterial count, increased frequency of latent infection evolving into active TB, and atypical symptoms etc. (3-5).

The integration of clinical, radiographic, and microbiological data is used to diagnose TB [pulmonary TB (PTB), a patient with TB disease involving the lung parenchyma; extrapulmonary TB (EPTB), TB affecting extrapulmonary sites within the body exclusively or in combination with PTB] in children (6,7). The gold standard for diagnosing TB is through microbiological confirmation which supports the diagnosis of confirmed TB (8). Confirmed TB was defined as positive molecular or culture testing for Mtb from at least one respiratory sample. Unconfirmed TB has no microbiological confirmation, but had at least two signs and symptoms of TB (8). However, the sensitivity of microbiological detection methods inevitably correlates with the bacterial burden, especially in the testing for childhood TB. Most affected children have negative smear results, with a culture positivity rate of less than 40% (9). Tuberculin skin test (TST) and interferon gamma release assay (IGRA) are recognized by World Health Organization (WHO) for widespread latent tuberculosis infection (LTBI) screening (9). TST can’t distinguish between latent and active infection and is less sensitive in children (10). The specificity of IGRAs is superior to that of TST, however, IGRAs showed no better performance than TST in low income countries (11). Due to the atypical clinical symptoms of childhood TB, it is prone to misdiagnosis or delayed treatment. Timely and accurate diagnosis will effectively utilize drug resources and avoid inappropriate use of anti-TB drugs.

Rationale and knowledge gap

The development of high-throughput technology (HT) has promoted revolutions in multiple omics disciplines, including proteomics, transcriptomics, metabolomics, lipidomics and genomics. By integrating data from various omics approaches, uncharacterized biological pathways related to specific diseases or conditions can be identified. The accessibility of omics technologies had driven research into novel biomarkers for TB, such as diagnosis, distinguishing different stages of infection, and monitoring treatment responses (12,13). However, most reported omics biomarkers lack multicenter validation and show discrepancy. While previous study provided a general overview of omics technologies for pediatric TB diagnostics, their analysis only focused primarily on proof-of-concept studies and rarely evaluated biomarker reproducibility across populations (14). This review synthesized studies in recent ten years, identifying overlapping biomarkers and future challenges for biomarker validation.

Objectives

This review aimed to summarize existing literature on pediatric TB across omics, which identified overlapping biomarkers and related biological pathways. In addition, we also discussed themes and challenges within clinical and technological contexts in this field (Figure 1). We present this article in accordance with the Narrative Review reporting checklist (available at https://pm.amegroups.com/article/view/10.21037/pm-25-46/rc).

Figure 1 Typical work flow for biomarker discovery via omics approach. Different omics approaches correspond to specimen collection and platforms in order to obtain biomarkers of TB through analysis. ELISA, enzyme-linked immunosorbent assay; GC-MS, gas chromatography-mass spectrometry; LC-MS, liquid chromatography-mass spectrometry; MTB, Mycobacterium tuberculosis; NGS, next generation sequencing; NMR, nuclear magnetic resonance; RT-qPCR, reverse transcription-quantitative polymerase chain reaction; SOMA, slow off-rate modified aptamer; TB, tuberculosis; WGS, whole-genome sequencing.

Methods

In this review we searched PubMed for original articles published from 2013.01.01 to 2025.07.20, with the search terms (“pediatric tuberculosis” OR “children”) AND (“proteomics” OR “transcriptomic” OR “metabolomic” OR “lipidomics” OR “radiomic” OR “geonomics”), reporting the biomarkers for pediatric TB in diagnosis, distinguishing different stages of infection, and monitoring treatment responses. Table 1 showed the search strategy summary. The age range of children in the study was 0–14 years and the restriction of language was English.

Discussion

Proteomics

Proteomics encompasses various techniques focused on studying proteins expressed in cells or organisms. It primarily investigates their composition, structure, function, interactions, expression profiles, and modifications. Proteomics is an innovative approach used to identify new biomarkers for diagnosing, prognosing, and understanding the pathophysiological mechanisms related to diseases (15). This methodology can be used to study various biological fluids such as saliva, urine, and blood, with a particular focus on changes in protein abundance that may occur with disease, age, or treatment response. Over the past decade, proteomics technology has been applied in various biomedical contexts, including detecting diagnostic markers, understanding disease mechanisms, tracking variations in proteomic profiles triggered by internal or external signals, and mapping signaling cascades in disease-specific mechanisms (16).

Several different techniques have been used to analyze proteins in biological samples. The enzyme-linked immunosorbent assay (ELISA) method is primarily used for the detection of specific known proteins, widely applied but with limited detection capabilities. With recent advancements in high-throughput proteomics technology, researchers can simultaneously study hundreds to thousands of proteins (17). For non-targeted discovery studies, mass spectrometry (MS) is a critical methodology for protein analysis, capable of identifying and quantifying complex proteomes at the sub-nanogram level. This technology has been widely used to detect biomarkers for TB diagnosis (18,19).

Due to the molecules secreted during the host immune response process after Mtb infection, such as cytokines, which are mainly transmitted through the bloodstream and easily collected, most proteomic biomarker studies for TB diagnosis are based on blood samples. Researchers began to focus on the immune response of the host to Mtb at the beginning of the 21st century, identifying various cytokines involved in Mtb immune responses. Cytokines such as interferon (IFN)-γ and tumor necrosis factor-α (TNF-α) were identified as candidate biomarkers for TB (20-22). Similarly, interferon-gamma-induced protein 10 (IP-10), also known as CXCL10, is an inflammatory-mediating chemokine secreted by various immune cells such as monocytes, neutrophils, macrophages, and endothelial cells. It was upregulated in both proteomics and transcriptomics in pediatric TB which has been widely evaluated as a diagnostic biomarker for active TB (ATB) and LTBI (23-26).

Compared to adults, there is less research on children, mainly aimed at exploring the diagnostic potential of proteomic biomarkers to identify different disease states (active TB versus health, LTBI, or other diseases) and monitor treatment responses for TB (Table 2). These proteins are critically involved in cytokine signaling, complement activation, and innate immunity pathways, such as cytokines, chemokines, and acute phase proteins. Despite significant statistical methodological differences between studies, these proteins are identified relatively frequently, suggesting they are either general markers of infection, interacting with other proteins characteristic of TB, or specific proteins of TB. According to Togun’s study, estimated sensitivity, specificity and area under the curve (AUC) from different logistic discriminant models in the training and test datasets are all between 0.70 and 0.75 (25). Fossati et al. aimed to construct a parsimonious biosignature for pediatric TB disease and finally found the 6-protein model achieved the best performance with 96.7% sensitivity at 70% specificity [95% confidence interval (CI): 0.83–0.99] on test data (27).

IP-10 (CXCL10) is an easily detectable cytokine evaluated as a diagnostic biomarker for ATB. However, the high polymorphism of these proteins in the entire population may limit their widespread application in biological characteristics. From a technical perspective, plasma sampling, sample preparation and data collection need a standardized process to mitigate bias.

Transcriptomic

Transcriptomics focuses on comprehensively analyzing all RNA molecules in cells or tissues under specific spatiotemporal conditions, quantitatively revealing dynamic patterns of gene expression, variable splicing events, and transcriptional regulatory networks through high-throughput sequencing techniques or microarray platforms. It encompasses both coding and non-coding RNA, and gene expression profiles can be used to discover biomarkers, diagnose diseases, predict progression, and monitor responses to treatment (28). Due to improved sample processing methods, whole blood transcriptomics has led the way in the discovery of diagnostic biomarkers for TB, surpassing proteomics and metabolomics. This has resulted in the development of rapid sample input-response multiplex polymerase chain reaction (PCR) platforms (29). In total cellular RNA, 80% consists of ribosomal RNA (rRNA), 15% is transfer RNA (tRNA), leaving only 5% for mRNA and all other forms of RNA. Analysis of host-encoded RNA can be used to study the gene expression patterns throughout the entire disease process and may exhibit different profiles. Non-coding RNA, which does not encode any proteins, is typically associated with regulatory functions and may undergo changes in different disease states.

In addition to discovering biomarkers, interpreting differential expression results based on pathophysiological cascades or regulatory circuitry is a critical outcome of transcriptomic analysis. Studies indicate that blood transcriptome changes can appear before conventional diagnosis of TB (30). Measuring simplified gene expression profiles in clinical settings burdened by TB can aid in diagnosis, prognosis, and treatment decisions in pediatric TB. Sweeney et al. conducted a meta-analysis using 14 publicly available datasets comprising over 2,500 samples from 10 countries. Their findings showed that a composite score (TB score) based on the mRNA levels of three differentially expressed genes in blood (GBP5, DUSP3, and KLF2) can distinguish TB from other diseases (31). A prototype assay kit for Mtb host response (Mtb-HR), based on gene-xpert real-time polymerase chain reaction (RT-PCR) detection, has been developed to quantify relative mRNA levels of three gene markers in whole blood samples from patients (32). Olbrich et al. conducted the first prospective evaluation of pediatric Mtb-HR, using a recommended TB score (GBP5, DUSP3, and KLF2) of 1.5, achieving an AUC of 0.85. The diagnostic accuracy was similar across different age groups and among children with malnutrition, human immunodeficiency virus (HIV), and EPTB (33).

Since 2013, 17 studies have explored transcriptional responses in pediatric TB, identifying diagnostic, prognostic, and treatment response biomarkers through gene expression analysis (Table 3). Several successful studies involved cohorts of hundreds of cases, employing standardized sample processing methods and accurate patient identification. The majority aimed to distinguish ATB from health or other disease states using differential gene expression levels, statistical filters, and hierarchical clustering in training sets [ATB vs. LTBI and healthy controls (HCs)]. These genes are primarily enriched in NF-κB signaling pathways, cytokine-cytokine receptor interactions, and various infection response pathways (including responses to TB).

GBP5 was present in the biomarkers identified by three studies, which was found increased in pediatric TB (33,35,41,48). It was also presented increased in young adults (12–18 years) with TB which supports the importance of type-1 interferon signaling in pediatric TB disease (30). Matrix metalloproteinase-9 (MMP-9) was also significantly upregulated in TB cases compared with HC (39,41). In addition, Azikin et al. found the expression levels of MMP-9 in the group of exposed and Mtb infected children have no differences (49).

The expression of MMP-9 was analysed in many studies in the pathophysiology of TB and its induction is regulated by receptor-mediated signalling pathways (50,51). MMPs exhibit endopeptidase activity, leading to inflammatory tissue damage in TB patients. Scientists are exploring ways to reduce the destruction of Mtb related matrix and reduce the pulmonary inflammation caused by TB by inhibiting the activity of MMP, so as to enhance the treatment of TB (52). On the contrary, CD3E and TGFBR2 were part of a biosignature separating TB cases from asymptomatic HC which showed a decreased likelihood of TB disease (39,41). MicroRNA (miRNA) is a kind of endogenous non coding small RNA molecule with a length of about 18–25 nucleotides, which precisely regulates gene expression through post transcriptional regulation mechanism (53). MiRNAs regulate many important physiological processes, such as cell proliferation and differentiation, organismal metabolism, and host immunity (54,55). Many studies indicate that miRNAs can be identified as biomarkers for the rapid diagnosis of TB (56,57). Some miRNAs actively regulate immune responses to clearMtb, while certain miRNAs are upregulated to inhibit the expression of immune-related genes, preventing TB clearance. This can be suggested that targeting miRNAs could improve immune system function for treating TB (58). MiR-31, conflicting reports (upregulated in Wang-2015 vs. downregulated in Zhou-2016) were presented which may be influenced by disease stage or age differences. It regulates NF-κB and Wnt signaling and modulates T-cell differentiation and macrophage polarization (59).

Further validation in a large multi-center cohort is essential. In addition, an RT-PCR-based validation step is best provided for transcriptomic study due to RNA instability in tropical temperatures.

Future technological advancements may simplify and normalizing, making these signatures such as GBP5 suitable for translation into a point of-care test.

Metabolomic

Metabolomic seeks to comprehensively profile endogenous biochemical entities present in clinical specimens (such as lipids, fatty acids, sugars, amino acids, nucleotides), with surpassing 20,000 distinct metabolic signatures in human samples (60). Nuclear magnetic resonance (NMR) and MS are two primary analytical platforms used to measure metabolite changes, identifying metabolites with characteristic differential expression. These metabolites can serve as biomarkers for diagnosis, disease differentiation, and monitoring drug therapy progress (Table 4). Typical samples studied in metabolomics include biological fluids such as blood, urine, sputum, cerebrospinal fluid (CSF), feces, and bacterial cultures.

TB affects multiple metabolic pathways in the host, including those involving nitric oxide, amino acids, glucose, and lipid metabolism (45). The pro-inflammatory and anti-inflammatory responses in ATB and LTBI patients lead to dysregulation of cytokines and related metabolites involved in relevant metabolic pathways. Glutamine, the most abundant amino acid in the bloodstream, is converted to tricarboxylic acid (TCA) cycle metabolites like glutamate through the activity of various enzymes. Some studies have reported significantly reduced levels of Gln in active TB patients, while children infected with Mtb also show lower Gln levels, reflecting the adaptive mechanisms of Mtb and host immune responses (62,68). Dutta et al.’s study indicated that n-acetylneuraminic acid had a potential diagnostic value with an AUC of 0.66. After one month of quinolinic acid ester treatment, the AUC was 0.77, and after successful pyrazinic acid ester treatment, the AUC was 0.87. Ultimately, the treatment response was determined by four metabolites (γ-glutamyl asparagine, γ-glutamyl glycine, glutamine, and pyrazinic acid), yielding an AUC of 0.86 (45). Through analysis, it was found that n-acetylneuraminic acid interacts immunoregulatorily between lymphocytes and non-lymphocytes. Pyrazinic acid salts are associated with metabolic genes regulated by p53 and mitochondrial translation. Additionally, numerous differential metabolites are related to changes in energy metabolism, immune physiology, pathogen cell wall damage, and repair (69).

The well-known association between HIV and TB coinfection in children was not assessed in the metabolomic study. The role of nutrition in driving changes in metabolomic and lipidomic profiles in children with TB has not been clearly explained. In addition, the number of bacteriologically confirmed pediatric TB cases was small. More children with PTB, as well as children with EPTB and immunocompromised children are needed to be included in future research to validate the potential of proposed metabolites as diagnostic biomarkers for pediatric TB.

Lipidomics

Lipidomics is a branch of metabolomics that primarily studies lipid metabolites. Traditional metabolomics focuses on molecules soluble in the cytoplasmic solute, whereas lipidomics primarily examines membrane-bound, water-insoluble molecules. Compared to cytoplasmic metabolites, lipids often contain more hydrocarbons, are larger in size, less polar, and have slower turnover rates (70). Mtb is one of the most complex lipid-coated organisms in nature, forming a barrier between the pathogen and the host. It possesses extensive lipid biosynthetic capabilities within its genome (71). Due to the limited similarity between the lipid structures of Mtb and human lipids, they are potential candidates for diagnostic and therapeutic biomarkers.

High-throughput lipidomics analysis has confirmed that the regulation of toxic lipids in Mtb occurs through metabolic coupling (72). Similarly, lipidomics analysis reveals the relationship between iron acquisition, phospholipid homeostasis, and the virulence of Mtb (73). The virulence factors including mycolic acids, trehalose dimycolates (TDMs), phthiocerol dimycocerosates (PDIMs), and phosphatidylinositol (PI)-based phosphatidylinositol mannosides (PIMs), lipomannan (LM), lipoarabinomannan (LAM), and mannose-capped LAM (ManLAM) are most essential for Mtb’growth and virulence. LAM and its structural variants can be recognized by and activate human CD1b-restricted T cells, and antibodies against LAM can modulate the immune response to Mtb (74). LAM is the only WHO-endorsed TB biomarker that can be detected in urine, an easily collected sample (75). FujiLAM could potentially add value to the rapid diagnosis of TB in children by comparing accuracy of LAM urine tests for diagnosis of PTB in children, with sensitivity of 42–64.9% and specificity of 60–83.8% (76,77). The potential utility of LAM urine testing was also found in HIV-negative children with severe acute malnutrition (SAM) (78). Pal and colleagues conducted lipid analysis of drug-susceptible (DS) and drug-resistant (DR) strains of Mtb, analyzing the lipid composition of clinical isolates. They confirmed that DR Mtb strains have distinct lipid signatures compared to DS strains (79). Lipidomics technology has also elucidated the specific connections between rifampicin resistance and lipid factors, providing targets for diagnosis and treatment (80). Lahiri et al. validate a new organism-wide lipidomic analysis platform for drug-resistant mycobacteria (80). Rifampin resistance mutations lead to altered concentrations of mycobactin siderophores and acylated sulfoglycolipids. Changes in mycobacteria, carboxymybacteriacins, and sulfolipids may be characteristic of mutations at rifampicin binding sites. Enzymes involved in key-steps of FA and cholesterol uptake, catabolism and storage may be feasible targets to treat mycobacterial infections (80).

Shivakoti et al. identified various baseline lipid levels associated with the development of treatment failure in TB. In patients with treatment failure, baseline levels of cholesterol esters, oxylipins, 15,16-dihydroxyeicosatrienoic acid [an oxygenated derivative of α-linolenic acid (ALA)], and certain phosphatidylcholines were lower. Conversely, higher levels were observed in lipid families such as ceramides, diacylglycerols, and various triglycerides. Specific cholesterol esters were highlighted as the best predictive factors for treatment failure in TB (81). Some researches have found that lipid metabolism is related to the occurrence and increase risk of anti-TB drug-induced liver injury (ATB-DILI) (82-84). Wang et al. conducted the first analysis of plasma lipidomics in patients with ATB-DILI. By analyzing the lipid metabolism spectrum, potential lipid biomarkers involved in DILI were identified (85).

Radiomic

Radiomics is defined as extracting and analyzing a large number of high-dimensional imaging features from quantitative medical images. These radiomic parameters describe multi-dimensional features of imaging data, detecting Quantify differences that are not visible to the naked eye (86). Radiomics involves several steps: first, acquiring medical images; next, trained radiologists identify regions of interest (ROIs) and annotate or segment them. Once ROIs are defined, applying imaging informatics and machine learning algorithms can increase efficiency and accuracy. Simultaneously, this non-invasive technology can assist clinicians in making preliminary assessments in cases where disease progression or effective sample collection is challenging.

Currently, radiomics features have been repeatedly used to establish diagnostic, prognostic, and treatment response prediction models for pediatric diseases (87-89). Studies have shown that imaging omics has demonstrated clinical utility in the application of PTB, for example, in adult TB populations, it has been used to differentiate between pulmonary tumors and non-tuberculous mycobacterial pneumonia (90), differentiating between drug-sensitive and drug-resistant PTB (91). According to reports, the non-symptomatic presentations of PTB often share significant phenotypic similarities with pediatric respiratory infections, notably CAP (92). It is one of the most common reasons for pediatric hospitalizations in developed countries and a major cause of death among children in developing countries (6,93,94). Clinical diagnosis of community-acquired pneumonia (CAP) is challenging due to varying symptoms with age, often lacking specificity in young children. Numerous studies indicate that clinical, laboratory, and chest X-ray findings are not reliably distinguishing between bacterial and viral etiologies in children with CAP (95,96). Current pathogen detection methods commonly used suffer from drawbacks such as long detection periods, false positives, false negatives, making it difficult to achieve rapid pathogen diagnosis (97). In terms of imaging, there are many similarities in the radiological manifestations between pediatric TB and CAP, such as consolidations, nodular shadows, and ground-glass opacities (98).

Up to now, only one study on the differential diagnosis of childhood PTB and CAP has been retrieved. Wang et al. (99) extracted lesion characteristics from computed tomography (CT) images of 53 clinically diagnosed pediatric TB patients and 62 CAP patients, 5 and 6 key features respectively were selected to establish radiological characteristics for lung consolidation areas and lymph node areas. Ultimately, these radiological features were combined with clinical factors (duration of fever) to construct a predictive model. The results showed that the classification performance of the combined model (AUC =0.971, 95% CI: 0.912–1) was superior to that of the clinical model alone (AUC =0.832, 95% CI: 0.677–0.987) and the imaging model alone (AUC =0.957, 95% CI: 0.889–1).

Relative to adults, there is a relative lack of imaging omics research in the field of pediatric PTB, limited mainly to studies on differential diagnosis between TB and pneumonia. Several factors contribute to this difference. Firstly, there are significantly fewer pediatric TB cases compared to adults. Secondly, the incidence of pulmonary tumors and non-tuberculous mycobacterial pneumonia is very low in children, resulting in limited and insufficient data available to establish radiological models. Multiple center cases are needed in the future to construct and validate radiomics models to rapid detect pediatric TB.

Genomics

Genomic approaches elucidate pediatric TB through dual lenses: host genetic susceptibility and pathogen variation biomarkers. Several studies identified child-specific susceptibility loci, as shown in Table 5. IL-4 gene SNP rs2243268 and rs2243274 showed a reduced risk of developing EPTB and severe TB in children (100). Toll-like receptor 1 (TLR1) Gene SNP rs5743618 genotypes showed a decreased level of TNF-α and CXCL10 production (101).

In the past decade, advancements in routine whole-genome sequencing (WGS) technology have transformed biomedical research. WGS analysis of Mtb is playing an increasingly vital role in diagnosis, predicting drug sensitivity, epidemiological investigations, host-pathogen interactions, pathogenesis, and global epidemiological surveys (104,105). The WGS of the first strain of Mtb (the widely used laboratory strain H37Rv) marked a milestone in TB research (106). Over time, WGS has been used to analyze clinical isolates of Mtb with varying drug susceptibility profiles, as well as laboratory-derived drug-resistant mutants (107-109). Rifampin resistance is conferred by mutation of its target, the subunit of the RNA polymerase (RpoB) (110). Furthermore, using WGS to definitively distinguish between relapse and reinfection, and for testing experimental drug regimens, is of significant importance (111).

Currently, there is limited research on the genetic susceptibility of children with TB which is easily influenced by population and regional distribution. More systematic research were needed about Mtb variation biomarkers in the Tb bacilli isolated from children with TB.

Conclusions

Due to the atypical clinical symptoms of pediatric TB and the similarity between PTB and pneumonia symptoms, it is a longstanding challenge for clinicians to promptly confirm and initiate correct antimicrobial treatment before pathogen detection. Unlike traditional detection methods, biomarkers play a crucial role in accurate diagnosis and prognosis of TB, identification of LTBI, and prediction of the risk of progression to ATB. Additionally, biomarkers can monitor treatment progress, evaluate treatment efficacy, and contribute to understanding drug resistance mechanisms and new drug development. Multi-omics integration can reveal interconnected networks of molecular cross-talk mechanisms, providing a network-based framework to decode pathophysiological cascades in TB and propel biomarker-driven therapeutic optimization.

Through cross-omics integration, we identified these biomarkers recurrently validated across independent cohorts and omics layers. IP-10 (CXCL10) was upregulated in both proteomics and transcriptomics in pediatric TB, involving in chemokine signaling, recruitment of T cells and monocytes. GBP5 was identified in multiple studies as a key diagnostic and prognostic biomarker which was part of the interferon-inducible GTPases, involved in cell-autonomous immunity against intracellular pathogens. MMP-9 was also significantly upregulated in TB cases compared with HC but had no differences in the group of exposed and Mtb infected children. Reducing the inhibition of MMP activity can reduce pulmonary inflammation and enhance the treatment of TB. On the contrary, CD3E and TGFBR2 were part of a biosignature separating TB cases from asymptomatic HC which showed a decreased likelihood of TB disease. Glutamine was consistently reduced in pediatric TB which reflects immune cell metabolic reprogramming (glutaminolysis for energy). It correlates with increased TCA cycle activity and oxidative stress. TB-LAM has good diagnostic value for children with TB, especially in HIV co-infected and severe acute malnutrition (SAM) children.

However, despite extensive research by investigators into pediatric TB across multiple omics disciplines, there was minimal overlap of biomarkers between studies. This is due to several factors including differences in study cohorts, specimen collection, criteria for TB diagnosis, and diversity in data analysis methods. Moving forward, it is imperative to expand sample sets, differentiate more detailed subgroups, and employ advanced machine learning techniques for further validation in prospective studies.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://pm.amegroups.com/article/view/10.21037/pm-25-46/rc

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