Category: 3. Business

Introduction

Inflammatory bowel diseases (IBD), including Crohn’s disease (CD) and ulcerative colitis (UC), result from interactions between the host, environmental factors, and gut microbiome.^1,2 A common feature of IBD is microbial community alteration, characterized by reduced microbial diversity, depletion of short-chain fatty acid–producing bacteria, enrichment of opportunistic taxa, and disrupted metabolic pathways that change with disease activity and therapeutic interventions.^3–5 Recent longitudinal and multi-omics studies have shown that these changes in compositional and functional shifts are closely related to host transcriptomic, proteomic, and metabolomic signatures, underscoring the centrality of host–microbe interactions in disease pathogenesis.^6–8

Despite these advances, significant clinical unmet needs remain. The diagnosis of IBD is often delayed because symptoms overlap with those of other gastrointestinal disorders,^9,10 and current biomarkers such as C-reactive protein and fecal calprotectin lack disease specificity and prognostic utility.^11–13 Reliable microbial or molecular biomarkers capable of predicting treatment response or relapse risk are still lacking, highlighting the need for integrative analytic frameworks that can bridge the gap between discovery and clinical application.^14,15 Traditional statistical analyses, including univariate (Linear discriminant analysis Effect Size, LEfSe) or multivariate regression models (Microbiome Multivariable Association with Linear Models, MaAsLin2), have been instrumental in identifying differentially abundant taxa and host factors.^16,17 However, these approaches are limited in capturing nonlinear interactions and complex dependencies within high-dimensional, multi-omics datasets.^18,19

These datasets are intrinsically high-dimensional, sparse, and heterogeneous across cohorts, sampling sites, sequencing platforms, and geographies, with outcomes that can vary considerably depending on the analytical methods applied. Longitudinal and cross-cohort studies have revealed significant inter-individual variability and nonlinear dependencies among microbes, host factors, and metabolites, which makes finding biomarkers more complicated.^7,20 Additionally, large-scale integration of metagenomic and metabolomic data has shown both the potential to identify disease-related features and the ongoing challenge of aligning results across studies. This emphasizes the need for strong computational methods to handle this complexity.^6,21

In this context, machine learning (ML) has emerged as a robust framework for characterizing gut microbial changes in IBD.²² ML approaches are particularly well-suited for (i) extracting predictive patterns from high-dimensional data, (ii) integrating heterogeneous inputs spanning microbial taxa, functional genes, host omics, and clinical metadata, and (iii) delivering measurable classification performance for diagnostic, prognostic, and therapeutic stratification.²³ Applications in IBD cohorts have demonstrated that ML models outperform traditional statistical methods in discriminating cases from controls, differentiating subtypes, and predicting treatment responses.^4,24 Simultaneously, benchmarking studies and recent state-of-the-art reviews have emphasized critical challenges—including external validation, model calibration, interpretability, and transparent reporting—that must be addressed before ML-driven microbiome biomarkers can be reliably translated into clinical settings.^18,23,25

Building on recent reviews that highlighted the importance of methodological robustness, reproducibility, and the need for clinical translation in AI-driven microbiome research,^26,27 this review extends these perspectives by incorporating frameworks such as time-series and causal inference modeling, multi-omics data integration, and foundation-model approaches to enhance translational relevance. We provide a comprehensive overview of recent applications of ML for characterizing gut microbiome alterations in IBD, emphasizing commonly used algorithms, analytical pipelines, and model evaluation strategies. Finally, we highlight the diagnostic and prognostic potential of ML-based approaches and discuss their strengths, limitations, and key considerations in translating microbiome-derived insights into clinically applicable tools for precision medicine.

Microbiome Dysbiosis in IBD: From Traditional Studies to Multi-Omics Analyses

Early studies on gut microbiome in IBD primarily relied on 16S rRNA gene sequencing, which enabled cost-effective profiling of microbial communities at the genus or family level. These studies provided the first evidence of disease-associated dysbiosis characterized by reduced alpha diversity, enrichment of pathobionts, and depletion of commensals.^3,5 Initial analytical approaches focused mainly on univariate comparisons of relative abundances, typically using methods such as LEfSe.¹⁷ Ma et al applied LEfSe analysis to compare patients with early-stage CD, advanced CD, and healthy controls, identifying enrichment of Parabacteroides and Lachnospiraceae incertae sedis in early CD, expansion of Escherichia-Shigella and Proteus in advanced CD, and preservation of short-chain fatty acid-producing taxa such as Roseburia and Butyricicoccus in healthy controls.²⁸ These findings demonstrate how LEfSe highlights disease-stage-specific microbial signatures while underscoring its reliance on univariate contrasts.

However, subsequent microbiome analyses have increasingly adopted multivariate analyses that incorporated metadata-based adjustments and advanced statistical modeling. Tools, such as MaAsLin2¹⁶/MaAsLin3²⁹ and ANCOM-BC³⁰/ANCOM-BC2,³¹ further enhance feature selection by integrating clinical metadata and complex covariates. Chen et al used MaAsLin2 to identify taxa whose relative abundances differed in quiescent CD (CD-R), independent of active inflammation and environmental or genetic confounders.³² They found that Faecalibacterium, Dorea, and Fusicatenibacter were significantly decreased in patients with CD-R compared to healthy first-degree relatives and non-relative healthy controls. Rosso et al applied ANCOM-BC to fecal bacterial profiles of patients with IBD (both UC and CD) vs non-IBD controls in Buenos Aires.³³ Among the bacterial genera that were differentially abundant in ANCOM-BC, Bifidobacterium was enriched in patients with UC compared to non-IBD controls, whereas in patients with CD, Bifidobacterium, Bacteroides, Lactobacillus, and Faecalibacterium were identified as differentially abundant taxa. Together, these approaches reduced the analytical bias and enabled a more reproducible investigation of disease-specific taxa, marking a substantial advancement from exploratory compositional analyses to clinically grounded biomarker discovery. In addition, other methods such as ALDEx2³⁴ and LinDA,³⁵ although not discussed in detail in this review, represent parallel efforts to further mitigate the biases inherent to differential abundance analysis.

The advent of high-throughput shotgun metagenomic sequencing has provided a technological leap, offering strain-level resolution and direct access to microbial functional repertoires, including genes and pathways relevant to host metabolism and immunity.³⁶ In a recent multibiome study, Akiyama et al applied shotgun metagenomic sequencing with MaAsLin2 across Japanese and external validation cohorts to characterize microbial signatures in IBD.³⁷ Both UC and CD are associated with the depletion of short-chain fatty acid-producing bacteria and the enrichment of Enterococcus faecium and Bifidobacterium species. Notably, Escherichia coli specifically increased only in patients with CD, highlighting its potential role as a disease-specific microbial marker.

Importantly, gut microbiome dysbiosis in patients with IBD is not limited to bacteria. Studies on the gut virome^38,39 have reported an increased abundance of bacteriophages (eg, Caudovirales) and decreased abundance of eukaryotic viruses, while mycobiome studies have identified altered fungal-bacterial interactions and increased abundance of Candida species.⁴⁰ Beyond the microbial taxa themselves, metabolomic and proteomic analyses revealed disease-associated alterations in bile acids, short-chain fatty acids, and amino acid derivatives, providing functional readouts that link microbial activity to host immune and epithelial responses.^41,42 Together, these multi-omics approaches provide a more comprehensive understanding of microbial changes and its systemic effects.

However, the integration of multiple data layers has resulted in significant analytical challenges. Microbiome and multi-omics datasets are intrinsically high-dimensional, sparse, and heterogeneous with marked variability across cohorts, sequencing platforms, and sample types.^18,19,43 Traditional univariate and linear statistical approaches are often insufficient for capturing nonlinear dependencies and cross-modal interactions. Although analytical tools such as LEfSe and MaAsLin2 are widely used for differential abundance and association testing, they are inherently sensitive to compositional bias, differences in sequencing depth, and batch effects.^19,44 LEfSe operates on relative abundances and may generate spurious associations when sample library sizes vary, whereas MaAsLin2 assumes linear relationships and is affected by zero inflation and normalization artifacts. These issues have been repeatedly highlighted in recent benchmarking studies, emphasizing the need for compositional data–aware methods and rigorous normalization to ensure cross-cohort reproducibility and biological validity.⁴⁵ Collectively, these challenges have motivated the adoption of ML and related artificial intelligence (AI) approaches, which are particularly well-suited for recognizing complex patterns in high-dimensional data and enabling multimodal integration.^46,47 Leveraging these advanced computational frameworks, researchers aim to move beyond traditional analyses toward more reproducible, predictive, and clinically interpretable models of gut microbiome dysbiosis in IBD.

Machine Learning Applications in IBD Microbiome Research

As the complexity of microbiome data in IBD has become more apparent, ML approaches have been increasingly adopted to address the limitations of conventional statistical analyses.^18,48 Unlike traditional differential abundance methods, ML algorithms can handle high-dimensional and heterogeneous data and discover subtle nonlinear patterns that may better capture disease-associated microbial biomarkers.^49,50 These shifts have established ML as a key tool for developing diagnostic and prognostic models of IBD.^51,52

Disease Classification and Subtype Diagnosis

One of the earliest and most widely explored applications of ML in microbiome research was disease diagnosis. Classifier models trained on microbial abundance profiles or functional gene features have been used to discriminate patients with IBD from healthy controls,²⁴ distinguish patients with CD from those with UC,⁵⁰ and predict clinical outcomes such as flare risk, therapeutic response, or remission status.^52,53 In many cases, ML classifiers have achieved significant performance as measured via receiver operating characteristic (ROC) curves, the area under the curves (AUCs), sensitivity, and specificity, often surpassing the predictive capacity of conventional biomarkers such as fecal calprotectin.²⁴ For example, Liñares-Blanco et al applied a panel of supervised learning algorithms to fecal microbiome data and developed a robust signature capable of classifying not only IBD versus controls but also differentiating CD from UC.⁵⁰ Their random forest (RF)-based model achieved high AUC values across cross-validation, and feature selection identified a reproducible set of bacterial taxa that contributed most strongly to classification. Similarly, Park et al reported that a fecal microbiome-based ML model accurately discriminated between CD and UC, further confirming that microbial signatures can serve as reliable classifiers of IBD subtypes.⁵¹ Recent large-scale studies have underscored the diagnostic power of microbiome-informed ML. Zheng et al’s multi-ethnic cohort analysis demonstrated that an ML classifier trained on microbial taxonomic features achieved AUCs exceeding 0.7~0.9, outperforming conventional biomarkers such as fecal calprotectin in identifying both UC and CD.²⁴ Similarly, Kim et al constructed an ML model using Korean fecal metagenomes that reliably differentiated patients with IBD from healthy patients, emphasizing the generalizability of microbiome signatures across populations.⁵⁴ Together, these studies highlight that microbiome-based ML frameworks may evolve into noninvasive diagnostic tools capable of complementing or reducing the need for invasive endoscopy.

Biomarker Discovery and Algorithmic Approaches

In addition to its classification capabilities, ML plays a key role in the discovery of disease-specific features and biomarkers.^55,56 By ranking microbial taxa, genes, or metabolites according to their predictive importance, algorithms not only improve model interpretability but also generate hypotheses about how microbes might contribute to diseases. This capability has enabled the development of candidate biomarker panels that integrate microbial and metabolic features with clinical metadata, thereby providing a more comprehensive view of IBD pathophysiology. Among the various algorithms employed, RF⁵⁷ is the most widely used in microbiome studies because of its robustness to noise, sparsity tolerance, and straightforward feature importance readouts.⁵⁸ Knights et al highlighted its stability and performance compared to traditional statistical approaches.⁵⁹ Kim et al further applied RF–based feature selection to identify microbial markers associated with IBD, distinguishing taxa such as Veillonella and Escherichia-Shigella between patients with UC and those with CD.⁵⁴ Manandhar et al also applied RF classifiers trained on gut microbiome features to diagnose IBD and differentiate CD from UC using the American Gut Project data comprising 729 patients with IBD and 700 healthy patients.⁴⁷ With 50 LEfSe-selected taxa, the RF model achieved a test AUCs of > 0.80 for IBD versus controls; using the top 500 high-variance operational taxonomic units (OTUs) further improved performance to an AUCs > 0.82. For subtype classification (331 CD vs 141 UC), RF models trained on either differential taxa or high-variance OTUs attained test AUCs > 0.90, underscoring their strong discriminatory power and potential clinical utility.

Although RF remains the most commonly used approach, other algorithms, including support vector machines (SVMs) and boosting frameworks (eg, XGBoost and AdaBoost), are also widely applied.^60,61 In an extensive multiomics study of CD location subtypes, Gonzalez et al systematically evaluated ten different algorithms (including RFs, extra trees, decision trees, SVMs, multilayer perceptron classifiers, voting classifiers, naïve Bayes, k-nearest neighbors, logistic regression, and AdaBoost) for their ability to classify disease subtypes. The authors identified the best-performing models and applied them to downstream analyses, revealing distinct associations according to disease location. Specifically, colonic CD exhibited a greater similarity to ulcerative colitis and showed stronger associations with Bacteroides vulgatus and neutrophil activity, whereas ileal CD was more closely linked to bile acid metabolism and demonstrated marked alterations in Faecalibacterium prausnitzii. These findings underscore how both the model choice and feature modality substantially influence predictive performance.⁶²

Besides single-algorithm approaches, ensemble methods are also advantageous. Ha et al trained eight ML algorithms, including RF, deep neural networks (DNNs), logistic regression, k-nearest neighbors, decision trees, gradient boosting, and SVMs, on multi-cohort pediatric IBD data. An ensemble model combining three top-performing classifiers (DNN, logistic regression, and SVMs) achieved the highest accuracy in predicting future remission, outperforming any single algorithm.⁵² The characteristics of each ML/DL algorithm are summarized in Table 1.

Table 1 Comparative Characteristics and Performance Trends of ML Algorithms in IBD Microbiome Research

Challenges and Limitations of ML in IBD Microbiome Research

However, despite these advances, several challenges remain unresolved. Overfitting is a recurrent issue due to the imbalance between high-dimensional microbial features and relatively small sample sizes. A recent review by Dudek et al emphasized the risks of data leakage and overfitting in microbiome ML workflows, calling for more rigorous cross-validation and model-locking strategies.⁶⁹ In addition to overfitting, performance metrics such as AUCs or accuracy often rely solely on internal cross-validation and may overestimate true generalizability. Robust evaluation requires nested cross-validation, bootstrapping, and, ideally, independent external validation to ensure fair model assessment and prevent information leakage.^48,70 As demonstrated by Kubinski et al, model performance varies markedly across preprocessing choices and cohorts, underscoring the difficulty of achieving cross-cohort reproducibility.⁷¹ Moreover, upstream sources of technical noise—including differences in normalization procedures, zero-inflated feature distributions, and the inherent sparsity and compositionality of microbial data—can substantially distort model behavior and feature importance rankings, further complicating reproducibility and external validation.^71–73 Furthermore, as highlighted by Papoutsoglou et al, the lack of standardized preprocessing pipelines and benchmarking datasets undermines reproducibility and complicates comparisons across studies.⁴⁸ Collectively, these findings demonstrate that while ML has brought substantial progress beyond traditional statistical approaches in IBD microbiome research, persistent barriers, including overfitting, limited generalizability, and lack of methodological standardization, must be addressed. Rigorous external validation, harmonized data pipelines, and the integration of interpretable ML frameworks are essential to ensure that microbiome-based models can transition into reliable clinical applications.⁷⁴

Emerging Approaches Beyond Traditional ML

While traditional ML approaches such as RF and SVMs have shown strong utility in IBD microbiome research, recent years have seen the rise of deep learning (DL) and next-generation AI frameworks that aim to capture even more complex and nonlinear relationships within multi-omics data.^66,75 Unlike traditional methods that often rely on predefined or engineered features, DL methods use representation learning to automatically extract latent patterns from raw or minimally processed inputs.^75,76 Oh and Zhang reported DeepMicro as a notable early framework that employed autoencoders to compress high-dimensional microbiome profiles into compact latent representations, thereby enhancing the disease classification performance. Building on this foundation, subsequent DL applications have explored a range of architectures: autoencoders for dimensionality reduction and feature learning,⁷⁵ and convolutional neural networks (CNNs) to identify structured patterns in microbial abundance matrices.⁷⁷ More advanced models, such as the Multimodal Variational Information Bottleneck (MVIB), further integrate multi-omics layers and clinical metadata, highlighting how DL can support more accurate and clinically relevant predictions.⁷⁸ Collectively, these approaches emphasize the potential of DL to surpass conventional ML, offering scalable and flexible tools for predictive microbiome research.

Recently, attention has shifted toward the development of foundation models that leverage large-scale pretraining on biological sequences to generate generalized representations. The Microbial General Model (MGM) was one of the first attempts to construct a microbiome-focused foundation model trained on more than 260,000 microbiome samples using transformer-based pretraining.⁷⁹ Although still in its early stages, the MGM demonstrates how models tailored to microbial communities can eventually classify community types, identify keystone taxa, and capture longitudinal dynamics, thereby providing a scalable framework for studying microbiome-driven processes in IBD. In parallel, host-centric genome foundation models, such as AlphaGenome⁸⁰ and Nucleotide Transformer,⁸¹ have been developed to annotate genetic variants and predict their regulatory effects in human genomics. Although not originally designed for microbial applications, these host-oriented models provide transferable embeddings that may help bridge host genetic information with microbial-community dynamics. Together, microbiome- and host-focused foundation models highlight a forward-looking trajectory in which unified representational spaces may enable multi-omics integration and deepen our understanding of host–microbiome interactions in IBD.

In parallel, recent multi-omics studies have begun to adopt time-series and causal inference models that capture longitudinal microbiome–host interactions and reveal potential drivers of disease dynamics.^82,83 Dynamic Bayesian networks, Granger-causality analysis, and structural causal modeling frameworks allow researchers to infer temporal dependencies and directionality in microbial and metabolic shifts, providing insights that static cross-sectional analyses cannot capture.⁸⁴ These temporal and causal modeling approaches are crucial for understanding how microbial community states evolve during inflammation, remission, and therapeutic intervention in IBD. In addition, multi-omics data-fusion frameworks have rapidly advanced predictive microbiome analytics by integrating heterogeneous data layers within shared latent spaces. Representative examples include Multi-Omics Factor Analysis (MOFA+), which applies probabilistic factor modeling to uncover coordinated variance across omics layers;⁸⁵ DIABLO, a multivariate approach for discriminative integration of multi-omic modalities;⁸⁶ and deep representation learning frameworks, such as variational autoencoder–based or multimodal transformer architectures, which embed genomic, transcriptomic, and metabolomic profiles into unified representations for improved disease prediction and mechanistic interpretation.⁷⁵ More recently, the MintTea (Multi-omics Integration through Temporal Embedding and Attention) framework has extended this concept by integrating longitudinal multi-omics data through attention-based deep learning, enabling the identification of dynamic, disease-associated molecular modules across time and modalities.⁸⁷ These integrative frameworks exemplify the shift from isolated single-omic analysis toward data-fusion models that bridge microbial, metabolic, and host dimensions of IBD.

As these models grow in complexity, concerns regarding their interpretability have become more pressing. To ensure clinical relevance and acceptance, the integration of explainable AI (XAI)⁸⁸ methods, such as SHapley Additive exPlanation (SHAP) values, attention weight visualization, and concept bottleneck models, has been proposed.⁸⁹ For instance, Novielli et al applied SHAP values to microbiome classification tasks and showed that taxa such as Fusobacterium and Parvimonas were key drivers of colorectal cancer predictions, thereby providing transparent links between features and model outputs.⁹⁰ Similarly, Onwuka et al demonstrated that the SHAP-based prioritization of fecal and plasma metabolites could highlight IBD-associated signatures, reinforcing the role of XAI in biomarker discovery and clinician application.⁹¹ In line with these advances, a recent review by Kim et al emphasized that the field is shifting from descriptive profiling of dysbiosis toward more predictive and analytic (including prescriptive) applications, while highlighting ongoing challenges such as biological and technical heterogeneity, limited generalizability across cohorts, and the need for methodological standardization and external validation.⁹²

Clinical Translation and Challenges

The application of ML and DL to the IBD microbiome data has generated promising diagnostic models and candidate biomarker panels.^93,94 However, the path from computational discovery to clinical implementation remains complex and requires careful consideration of several limitations.^95,96 While traditional statistical analyses and ML/DL-based approaches have successfully identified bacterial taxa associated with disease states, causal or mechanistic links between these microbes and host physiology have been established only for a limited subset. Most computational analyses cannot by themselves determine whether these microbial signatures drive, result from, or merely correlate with inflammation. For example, several genera including Faecalibacterium frequently identified by differential abundant taxa analysis are known to produce short-chain fatty acids that modulate intestinal immune responses, and their depletion may contribute to mucosal inflammation and barrier dysfunction in IBD.^97,98 Conversely, enrichment of Escherichia–Shigella or Enterococcus may reflect pro-inflammatory activity through lipopolysaccharide-mediated TLR signaling.^99,100 To validate and interpret these associations, further experimental confirmation through in vivo or ex vivo studies is required, as well as the use of next-generation computational frameworks—such as foundation-model-based simulation or causal network modeling—to infer and test host–microbe interaction dynamics.¹⁰¹

Standardization and Reproducibility of Microbiome Assays

A key challenge is the standardization of microbiome assays. Pre-analytical variables such as sample types (feces or biopsies), stool collection methods, preservation techniques, sequencing platforms, and library preparation introduce substantial variability that directly affects downstream feature profiles.

In particular, differences in sequencing chemistry, read length, and bioinformatic preprocessing pipelines can introduce systematic biases in taxonomic resolution and relative abundance estimation.^102,103 Batch effects arising from DNA extraction kits, primer selection, and library preparation protocols have been shown to exceed inter-individual biological variability in some cases, thereby confounding disease-associated microbial signatures across studies.¹⁰⁴

Together, these technical factors interact with sampling- and host-related sources of variability, underscoring the importance of methodological harmonization. Kim et al compared the gut microbiome profiles obtained from the stool, luminal contents, and mucosal biopsies of patients with UC and healthy controls.¹⁰⁵ They found significant differences in both alpha and beta diversities across sample types, with biopsies yielding higher numbers of observed OTUs than stool or lavage samples. Community structures showed a correlation between stool and luminal contents, whereas biopsy samples did not correlate with either. Importantly, UC-control differences were evident only in stool samples, whereas lavage and biopsy samples did not show significant separation, partly due to the limited sample size. Classification analysis achieved AUCs of 0.85 for stool and 0.81 for lavage, underscoring that the sampling strategy critically influences microbiome readouts and diagnostic performance. Kruger et al systematically assessed intra- and inter-individual variability in gut health markers in healthy adults using an optimized fecal sampling and processing workflow.¹⁰⁶ They showed that the variability differed by marker, with some exhibiting substantial within-person variations. Importantly, they demonstrated that optimized pre-processing methods, such as mill homogenization, could significantly reduce technical variability, highlighting that methodological choices can obscure biological signals if not standardized. This underscores the need for assay harmonization to ensure reliable biomarker discovery and provide a robust foundation for ML applications in clinical contexts. Similarly, Clooney et al performed a large longitudinal multicenter study across two continents in patients with IBD and controls, revealing that the geographic site and temporal factors explained a significant share of the microbiome variance, often rivaling or exceeding disease effects.¹⁰⁷ They further observed that temporal stability was reduced in IBD, and that model performance improved when consecutive time points were considered. These findings illustrate how cross-site and temporal heterogeneity can mask disease-associated signatures, and emphasize the importance of standardized methodologies and rigorous external validation in the development of ML-based diagnostics and prognostics for IBD. Unless these sources of heterogeneity are rigorously controlled, models trained in one context are unlikely to be generalized to other clinical settings.¹⁰⁸ This challenge becomes even more pronounced when integrating multi-omics layers, such as metabolomic, transcriptomic, and proteomic data, which adds further complexity to model development and validation.

Model Interpretability and Explainable AI

Another major concern was the interpretability of the model. Algorithms such as RF, boosting algorithms, and neural networks often achieve high predictive performance but can act as “black boxes”.⁹⁶ In clinical practice, the opacity of AI models can undermine the trust of clinicians and complicate regulatory approval. A systematic review by Rosenbacke et al reported that explainable AI can either enhance or diminish trust depending on how explanations are presented.¹⁰⁹ Explanations that are clear, concise, and consistent with clinical reasoning tend to increase clinicians’ confidence in AI systems. Conversely, explanations that are overly technical, complex, inconsistent with clinical logic, or misleading can reduce it. Thus, providing transparent and clinically relevant explanations is essential for successfully integrating AI into healthcare and securing the trust of medical professionals. Rynazal et al applied XAI methods (notably SHAP for local explanations) to colorectal cancer (CRC) classification using gut microbiome data.¹¹⁰ They demonstrated that using local (patient-specific) feature attributes revealed which microbial taxa were most influential for individual predictions and enabled the stratification of patients with CRC into subgroups, thereby improving interpretability and potentially pointing toward mechanistic hypotheses. Yu et al developed a 10-species microbial signature for IBD using an XGBoost-based model (termed XGB-IBD10) and performed external validation.¹¹¹ Importantly, they applied SHAP analysis to explain model predictions, showing how individual taxa influenced the classification decisions. External validation confirmed the robustness of the identified microbial panel, demonstrating that explainability could enhance trust in model outputs and provide mechanistic insights into disease-associated microbes.

Taken together, these studies illustrate that XAI approaches, including feature importance ranking, SHAP values, and attention-based visualization, are critical not only for identifying which microbial taxa, metabolic pathways, or host features drive predictions but also for facilitating clinical adoption and guiding biological hypothesis generation. This process can provide a reliable output for clinical deployment.

Demonstrating Clinical Utility and Path to Adoption

Finally, the demonstration of its clinical utility remains a missing link. Current studies primarily focus on metrics such as the AUC, sensitivity, and specificity. Although these benchmarks are essential for the initial validation, they fail to capture whether such models can improve patient care. To achieve regulatory approval and widespread clinical uptake, predictive frameworks must demonstrate tangible benefits, such as reducing unnecessary colonoscopies, expediting the time to therapy initiation, and lowering healthcare costs. Ultimately, prospective validation and interventional studies are required to bridge the gap between computational promise and real-world clinical impact.

Recent longitudinal research has underscored this need. Al Radi et al investigated the predictive value of gut microbiome signatures for therapy intensification in IBD using a 10-year follow-up cohort.¹¹² Their findings demonstrated that baseline microbial features could predict the need for treatment escalation, highlighting the importance of long-term monitoring and the value of microbiome-informed risk stratification for guiding clinical decision making. This study illustrated how longitudinal datasets provide a rigorous framework for evaluating the durability and clinical relevance of microbiome-derived predictors. In parallel, Massaro et al presented the design of the OPTIMIST study, a prospective, longitudinal, observational pilot study conducted in Canada to investigate gut microbiome predictors of advanced therapy response in CD.¹¹³ The study enrolled patients initiating biological treatment, as well as non-IBD controls, to link baseline microbiome composition and temporal dynamics to therapeutic outcomes. By prospectively monitoring patients over time, the OPTIMIST study established a framework for evaluating microbiome-based predictors in real-world settings and supporting their eventual clinical translation.

In summary, ML and DL hold considerable promise for translating microbiome research into diagnostic and prognostic tools for IBD. However, clinical translation will require overcoming persistent challenges related to reproducibility across cohorts, generalizability to diverse patient populations, interpretability of results, and addressing data-sharing constraints. Progress in explainable AI, development of standardized pipelines, and adherence to rigorous reporting and evaluation guidelines will be crucial in bridging the gap between proof-of-concept studies and real-world patient care.

Conclusion and Future Perspectives

ML and DL have transformed IBD microbiome research from descriptive community profiling to predictive modeling. Alongside DA analyses, these approaches have enabled the classification of disease versus health, the differentiation of CD from UC, and the identification of microbial and metabolic biomarkers with diagnostic and prognostic significance (Figure 1). Studies employing RF, boosting frameworks, and ensemble models have demonstrated that microbiome-informed classifiers can approach or even surpass traditional biomarkers, and explainable AI methods are beginning to provide clearer insights into the microbial and functional drivers of diseases.

Figure 1 Evolution of analytical approaches for characterizing gut microbiota alterations in inflammatory bowel disease (IBD). An overview of analytical strategies for IBD illustrates the progression from traditional statistics to next-generation artificial intelligence (AI) frameworks. Early differential abundance (DA) analyses using 16S rRNA or metagenomic data identified disease-associated taxa; however, these were often limited by univariate contrasts and reproducibility issues. With the increasing data complexity of multi-omics approaches, machine learning (ML) approaches such as random forests, support vector machines (SVMs), and boosting algorithms have enabled robust disease classification, biomarker panel identification, and performance evaluation through receiver operating characteristic (ROC) curves, cross-validation, and feature importance scores. Recently, deep learning (DL) frameworks have been applied to capture nonlinear relationships in high-dimensional multi-omics datasets, providing latent feature embeddings and integrative predictive models. Looking ahead, foundation models trained on large-scale host and microbial genomic resources (eg, Microbial General Model for microbiome and AlphaGenome for host genetics) are emerging as transferable backbones that can be fine-tuned for diverse downstream tasks, from diagnosis and subtype prediction to therapy response modeling and prognosis.

Despite this progress, significant barriers remain before clinical adoption can be achieved. Variability in stool collection, sequencing platforms, and preprocessing pipelines continues to hinder reproducibility, and external validation often reveals a decline in model performance when applied across independent cohorts. Dependence on complex “black-box” algorithms also makes it more difficult for clinicians to trust and regulators to approve. Furthermore, most studies are retrospective, with few prospective or interventional studies directly evaluating whether microbiome-based ML models can improve patient outcomes. These issues emphasize the need to connect computational advances with the development of practical tools for precision medicine.

Looking ahead, progress will be fastest if efforts focus on: (1) rigorous validation, prioritizing pre-registered pipelines, locked models, and multi-site external testing; (2) strong benchmarking, with standardized preprocessing, shared baselines, and challenge datasets covering stool and mucosal samples, platforms, and regions; (3) biologically based interpretability, pairing explanation methods with wet-lab or orthogonal assays to verify mechanistic plausibility; (4) longitudinal and multimodal integration, combining metagenomics, metabolomics, proteomics, and host features to model disease trajectories rather than single time points; and (5) next-generation AI, where emerging genome foundation models—both microbiome-focused and host-centric—are developed under strict evaluation and compared directly to robust classical baselines. These steps will help ML move from promising prototypes to dependable clinical tools that guide diagnosis, monitoring, and treatment decisions for IBD.

Data Sharing Statement

Data sharing not applicable – no new data generated.

Acknowledgments

The figure was generated using BioRender by June-Young Lee (2025) https://BioRender.com/je7gx57. We would like to thank Editage for the English language editing.

Author Contributions

• June-Young Lee (J.Y. Lee): Conceptualization; Methodology; Formal analysis; Software; Data curation; Investigation; Resources; Visualization; Funding acquisition; Writing – original draft; Writing – review & editing.
• Dong Hyun Kim (D.H. Kim): Formal analysis; Software; Validation; Data curation; Visualization; Writing – review & editing.
• Jee-Won Choi (J.W. Choi): Data curation; Investigation; Visualization; Writing – review & editing.
• Minho Shong (M. Shong): Resources; Investigation; Writing – review & editing.
• Chang Kyun Lee (C.K. Lee): Conceptualization; Methodology; Supervision; Project administration; Funding acquisition; Writing – review & editing.

All authors contributed substantially to the work, critically revised the manuscript, and agree to be accountable for all aspects of the work. All authors gave final approval of the version to be published. June-Young Lee and Dong Hyun Kim contributed equally to this work and share first authorship.

Funding

This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea (grant number: RS-2023-KH135855) and the InnoCORE program of the Ministry of Science and ICT, Republic of Korea (grant number: N10250153).

Disclosure

The authors declare that they have no competing interests.

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Baker McKenzie has advised SEFE Securing Energy for Europe GmbH (“SEFE”), an international energy company, on the sale of SEFE Mobility. The buyer is Münster-based biogeen GmbH. The transaction will be completed once all necessary approvals have been obtained.

Holger Engelkamp, lead partner of the transaction, said:
We were able to support our client in successfully underwriting this important transaction. This sale enables our client to sharpen its strategic focus and optimally align its portfolio. With biogeen, we found a buyer whose vision for sustainable energy is perfectly aligned with SEFE Mobility’s focus.

SEFE, based in Berlin, is active along the entire energy value chain — from procurement and trading to sales, transportation and storage. With an annual sales volume of 200 terawatt-hours of gas and electricity, SEFE is one of the most important suppliers of industrial customers in Europe and supplies more than 50,000 customers, from small companies to municipal utilities and multinational corporations. SEFE employs around 2,000 people worldwide and is a federally owned company.

SEFE Mobility, based in Berlin, operates more than 50 filling stations for renewable biofuels in Germany and the Czech Republic. The company employs eight people and generated a turnover of around EUR 12 million in 2024.

biogeen is one of the leading biomethane producers in Germany and operates modern biogas plants to generate climate-friendly, regional and reliably available energy. biogeen is a portfolio company of Partners Group, one of the largest companies in the global private markets industry.

Baker McKenzie’s Corporate/M&A practice regularly advises on national and international transactions. Most recently, Baker McKenzie advised the following companies, among others: Thoma Bravo on the acquisition of Boeing’s Digital Aviation Solutions business; Trane Technologies on the acquisition of a stake in Kieback&Peter; Copeland on the acquisition of SPH Sustainable Process Heat; ResInvest on the acquisition of Onyx Power; AURELIUS on the acquisition of Landis+Gyr’s EMEA business; Knorr-Bremse on the acquisition of duagon Group; Cheyne Capital on the refinancing of Kaffee Partner; JD.com on the acquisition of Ceconomy; VINCI Energies on the acquisition of the R + S Group and the Zimmer & Hälbig Group; Bristol Myers Squibb on the transfer of Juno Therapeutics to TQ Therapeutics; Georg Fischer on the acquisition of the VAG Group; and AURELIUS on the acquisition of Teijin Automotive Technologies.

Legal adviser to SEFE:
Baker McKenzie

Lead:
Corporate/M&A: Holger Engelkamp (partner, Berlin)

Team:
Corporate/M&A: Ben Boi Beetz (associate, Berlin)
Tax: Thomas Gierath (partner), Tim Edelhoff (associate, both Munich)

Introduction

Hypertrophic scars (HS) typically occur after burns, surgeries, and extensive injuries, and are a common skin condition caused by abnormal wound healing. They are also a major source of health issues following skin damage.^1–3 The overall prevalence of HS is approximately 32%-72%, with up to 70% of burn patients developing HS, which can significantly impact patients’ mental and social well-being and their ability to function, leading to physical issues (such as pain, itching, and loss of joint mobility) and psychological disorders, thereby reducing patients’ quality of life and hindering their reintegration into society.^1,4,5 We still do not really understand how HS forms, and currently, there’s no one-size-fits-all treatment that works perfectly. Prevention remains the best approach to reduce abnormal scar formation.^4,6,7 Current clinical treatment options include local corticosteroid injections, radiotherapy, laser therapy, and surgical excision,^5,7–9 but the complicated ways HS forms make these treatments less effective in real-life situations.^5,6,10 Additionally, doctors usually diagnose HS just by looking at the symptoms; however, malignant tumors such as dermatofibrosarcoma protuberans may be misdiagnosed as HS, and biopsies are pretty invasive and might affect how HS or tumors develop.¹¹ In summary, we really need more research to figure out how HS forms in order to develop new treatment and diagnostic methods, identify molecular targets for potential therapies, and achieve the goals of prevention, diagnosis, reduction, and even reversal of HS formation.

Mitochondria are organelles in eukaryotic cells that produce energy, participate in metabolism and signal transduction, and regulate cell growth, differentiation, aging, and death.¹² Previous studies have suggested that the characteristics of HS include ongoing local inflammation, overgrowth of fibroblasts, and excessive deposition of collagen.^13,14 Among these factors, less apoptosis and more fibroblast overgrowth, along with collagen buildup, are linked to changes in mitochondrial function, and directly or indirectly intervening in mitochondrial function could be a potential treatment strategy for HS.^15–19 Many studies on the molecular pathogenesis of HS indicate that chemokines, cytokines, or growth factors play a crucial role in the formation of HS.⁷ Mitochondria are key players in infection and inflammation, central to pro-inflammatory responses, participating in the triggering and control of inflammation in diseases, and regulating the function of inflammatory cells and the release of inflammatory factors.^20,21 Additionally, some related mitochondrial genes are also believed to be involved in the process of HS treatment.¹⁵ These studies provide the possibility of targeting mitochondria for HS treatment and show that mitochondrial therapy might be a new option for HS. Although the key role of mitochondria in the pathogenesis of HS has been recognized, specific mitochondrial-related genes and their effects on HS are still not well understood.

Mendelian Randomization (MR) serves as a widely adopted causal inference technique, utilizing genetic variations as instrumental variables (IVs) to handle reverse causation, lessen bias, and enhance the strength of causal inference in studies. These genetic variations are established at conception and are not influenced by later outcomes, diseases, or confounding factors such as socioeconomic conditions, behaviors, or health status.^22–24 The growing data from Genome-Wide Association Studies (GWAS) and molecular quantitative trait loci (QTL) provides a strong foundation for MR research. GWAS utilizes genetic associations based on single nucleotide polymorphisms (SNPs) and traits, which allows researchers to integrate GWAS data with gene expression and methylation data, identifying expression quantitative trait loci (eQTL), methylation quantitative trait loci (mQTL), protein quantitative trait loci (pQTL).^25,26

Although the role of mitochondria in HS pathology has been noted,^7,20,21 there’s still no causal evidence from large-scale population genetic data pointing to specific mitochondrial genes; furthermore, previous studies have rarely integrated methylation, expression, and protein quantitative trait loci (m/e/pQTL) and combined colocalization to improve causal inference and steer clear of linkage and pleiotropy. To address this gap, we are using two-sample Mendelian randomization and combining three layers of QTL evidence with colocalization analysis to better assess the potential causal link between mitochondrial-related genes and HS, providing new avenues for future HS research and possible targets for treatment and diagnosis.

Methods

Study Design

This study is based on a two-sample MR method, using mitochondrial-related genes as exposure variables and HS as an outcome variable. SNPs were used as IVs to explore the causal association between mitochondrial genes and HS based on GWAS data through three levels of analysis: DNA methylation, gene expression, protein abundance. Subsequently, colocalization was used to strengthen causal inference, and Steiger filtering analysis was used to test the direction of causality. Finally, multi-omics analysis was conducted to combine the results from mitochondrial mQTL, eQTL, and pQTL to get a complete picture of how mitochondrial-related gene regulation is linked to HS at various levels (Figure 1). The MR analysis methods used in this study followed the three main assumptions of MR research²⁷ and the STRIOBE-MR guidelines (Supplementary Table S1).²⁸

Figure 1 Flow chart. Abbreviations: QTL, quantitative trait loci; SNP, single nucleotide polymorphisms; PPH4, posterior probability of H4.

Data Sources

The mitochondrial-related genes are sourced from the MitoCarta3.0 database, which comprises an updated list of 1136 human mitochondrial genes.²⁹ The SNP-CpG associations found in blood samples were obtained by McRae et al from DNA mQTLs data in 1980 European individuals.²⁶ The blood eQTLs dataset was obtained from the eQTLGen consortium (https://www.eqtlgen.org/phase1.html), which comprises 31,684 individuals. The pQTLs were derived from the UK Biobank pharmacoproteomics project (UKB-ppp: https://registry.opendata.aws/ukbppp/) involving 54,219 UKB participants, and a GWAS study (Decode) that measured plasma protein levels in 35,559 Icelanders using 4907 aptamers conducted by Egil Ferkingstad.³⁰ The genetic association with HS was derived from the FinnGen database (https://www.finngen.fi/fi) (1866 cases and 423,041 controls, dataset number L12_HYPETROPHICSCAR). The website was last accessed on July 15, 2024. The data collected was sourced from European populations (Table 1). The original studies obtained informed consent from the participants, so this part of the study does not need ethics committee approval.

Table 1 Information of Included Studies and Consortia

SNPs Selection Criteria

IVs were selected from mitochondrial gene mQTLs, eQTLs, and pQTLs, with a screening criterion of P < 5×10⁻⁸, and an F-statistic greater than 10 as an indicator to exclude weak instrumental variables. The Linkage disequilibrium coefficient (r2) was set at 0.3, the linkage disequilibrium region width was set at 500kb and a minor allele frequency (MAF) > 0.01 was set, to ensure the SNPs were independent and to eliminate the impact of linkage disequilibrium on the results. LDtrait (https://ldlink.nih.gov/?tab=ldtrait) was used to exclude SNPs related to confounding factors and outcomes,¹¹ and SNPs located within ±1000kb of the cis-regulatory regions of mitochondrial genes were extracted. Relevant IVs were extracted from the mQTLs, eQTLs, and pQTLs data for mitochondrial genes. Relevant SNPs were extracted from the GWAS summary data of the outcome variable (HS), excluding any palindromic SNPs, removing SNPs directly related to the outcome variable (HS) (P < 5×10⁻⁸). MR-PRESSO was used to identify and exclude outlier SNPs.

Cis-mQTLs, Cis-eQTLs, and Cis-pQTLs in the Whole Mitochondrial Genome and MR Analysis for HS

The MR-Egger regression, inverse variance weighted method (IVW), weighted median method, weighted mode, and simple mode were five regression models applied in two-sample MR analysis to assess the potential causal relationship between mitochondrial genes and HS risk. IVW served as the primary method for causal estimation, while the other methods were used as supplementary analyses. When SNPs≤3, the effect of a single SNP on the outcome was assessed using the Wald ratio method in conjunction with fixed-effect IVW. When SNPs>3, random-effects IVW was used. In IVW, when estimating causal effects using multiple SNPs as IVs, the inverse of the variance (R²) for each locus was used as a weight, and the causal effect estimates for each locus were weighted and summed, resulting in the final estimate as the causal effect estimate of the IVW method. MR-Egger essentially relied on a weaker assumption (InSIDE) than IVW to perform causal effect estimation by introducing a regression intercept to detect and correct biases caused by the pleiotropy of IVs. MR-Egger’s results were referenced when horizontal pleiotropy was present. The false discovery rate (FDR) served as an indicator of the error rate, and was calculated for P-value correction. The formula for calculating FDR is: .

Cochran’s Q test and I² were used to assess the heterogeneity of SNPs. P < 0.05 in the Cochran’s Q test indicates heterogeneity. The value of I² ranges from 0% to 100%, and I² >50% indicates a certain degree of heterogeneity in IVW results. The calculation formula is . The MR-Egger method’s intercept was used for analyzing pleiotropy, and Leave-one-out was used for sensitivity analysis. The MR-Egger regression intercept and MR-PRESSO were used to detect pleiotropy of SNPs. When the MR-Egger regression intercept and 0 showed no statistical significance (P > 0.05) and the MR-PRESSO level pleiotropy test results were not significant (P > 0.05), it indicated that SNPs do not have a pleiotropic effect. In the Leave-one-out analysis, each SNP was removed one at a time to observe the impact of each SNP on the results. All MR analyses were conducted using the TwoSample MR package in R version 4.1.0, with a significance level of α = 0.05.

Steiger Filtering Analysis

Steiger filtering test was used to check the direction of the causal relationship between genotypes, intermediate variables, and final outcomes. This method is based on the random distribution of genetic variation, looking at how IVs affect intermediate variables and final outcomes, and calculating the correlation between the two to see if the causal direction of the genotype matches for both. This study calculated how much variance was explained by instrumental variable SNPs on mitochondrial gene CpG sites/gene expressions/protein levels and the variance of HS. We also checked whether the variance of HS was smaller than that of the mitochondrial gene CpG sites/gene expressions/protein levels. In the MR Steiger results, if HS’s variance is smaller than that of the mitochondrial gene CpG sites/ gene expressions/ protein levels, it’s marked as “TRUE”, which means the causal relationship lines up with what we expected, while a “FALSE” result means the causal relationship goes against what we expected.

Colocalization Analysis

The colocalization analysis was employed to ascertain whether two phenotypes were influenced by the same causal variant within a designated genomic region, thereby reinforcing the evidence for their association. In this investigation, the underlying assumption of colocalization analysis posited that each trait could have a maximum of one true causal variant in the specified region. Five mutually exclusive model assumptions (H0-H4) delineated all conceivable association scenarios. H0: Phenotype 1 (GWAS) and Phenotype 2 (QTL or GWAS) do not exhibit significant associations with any SNPs in that genomic region. H1/H2: Either Phenotype 1 or Phenotype 2 demonstrates a significant association with SNPs in the specified genomic region. H3: Both Phenotype 1 and Phenotype 2 show significant associations with SNPs in that genomic area, but these associations arise from distinct causal variant loci. H4: Both Phenotype 1 and Phenotype 2 are significantly associated with SNPs within that genomic region and are driven by the same causal variant locus. In the course of the colocalization analysis, posterior probabilities (PP.H0-PP.H4) were computed for each of the five models, with the cumulative sum of these probabilities equating to 1. A higher posterior probability indicates a greater likelihood that the model assumption is accurate based on the observed data. This study supports the H4 assumption, as the H4 model suggests that both traits are governed by the same causal variant. Generally, a PP.H4 value exceeding 0.5 is regarded as validation of the H4 model assumption.³¹ The R package “coloc” was utilized to identify SNPs located within ±1000kb of the cis-regulatory region of mitochondrial genes for the purpose of colocalization analysis. A PP.H4 value greater than 0.7 is interpreted as strong evidence of colocalization between the two traits in the specified region, while a value between 0.5 and 0.7 indicates moderate evidence for their colocalization in that area.

Integrating Results from Multi-Omics Level of Evidence

To comprehensively understand the relationship between mitochondrial-related gene regulation and HS at different levels, this study used multi-omics analysis to integrate results from three different levels of gene regulation. We categorized candidate genes into three tiers: ①Tier 1 genes are those linked to HS at the levels of methylation, gene expression, and protein abundance (FDR<0.05), with gene expression and protein abundance showing the same causal direction with HS; ②Tier 2 genes are those linked to HS at any two of the three levels (FDR<0.05); ③Tier 3 genes are those for which any one of the three levels meets the criteria of being linked to HS (FDR<0.05) and having a colocalization PP.H4>0.5.

Ethical Statement and Human Samples

All human samples were collected after written informed consent was obtained, and the research protocol was approved by the Ethics Committee of Fujian Medical University Union Hospital (approval number: 2025KY428). Hypertrophic scar samples were obtained from patients who underwent scar excision in Fujian Medical University Union Hospital. Normal skin samples were collected from patients who underwent limb amputation in Fujian Medical University Union Hospital.

Cell Isolation and Culture

Following the removal of excess subcutaneous adipose tissue from the skin, the dermal sections were finely minced and subjected to tissue block explant culture to obtain Hypertrophic Scar Fibroblasts (HSFs) and Normal Skin Fibroblasts (NSFs). The fibroblasts were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (Gibco, Grand Island, NY, USA) enriched with 10% Fetal Bovine Serum (FBS) (Corning, USA), along with 100 U/mL of penicillin and 100 μg/mL of streptomycin. These cultures were maintained in a humidified incubator set to 37°C with an atmosphere of 5% (v/v) CO2. For subsequent experiments, fibroblasts in the third to fifth passage were utilized.

qRT-PCR

Total RNA was obtained from the dermal layers of both normal skin and hypertrophic scar tissues through the application of Trizol reagent (R0016, Beyotime, Shanghai, China). The synthesis of the first-strand complementary DNA (cDNA) was carried out utilizing HiScript II Reverse Transcriptase (R223-01, Vazyme, Nanjing, China). Subsequently, the mRNA expression levels were assessed via quantitative reverse transcription polymerase chain reaction (qRT-PCR) employing ChamQ SYBR qPCR Master Mix (Q311-02, Vazyme, Nanjing, China). The quantification of mRNA expression was standardized against β-actin RNA. Each reaction was conducted in triplicate on an ABI PRISM 7500 StepOnePlus platform, with data analysis performed following the comparative CT method. The primer sequences used are listed below: HTATIP2-F (5’-TCGTCTTTCAAAGTAATCCTTGAGT-3’) and HTATIP2-R (5’-GCGATAATCACAATAGGCAAA-3’).

Western Blot

Cell or tissue proteins were extracted using RIPA buffer (NCM Biotech, Suzhou, Jiangsu, China) at a temperature of 4 °C for a duration of 30 minutes. Following this, samples were subjected to centrifugation at 12,000 × g for 15 minutes to facilitate the separation of cellular debris. The concentration of proteins in the resulting supernatant was quantified utilizing a BCA assay kit (Thermo Fisher Scientific, Carlsbad, CA, USA). Subsequently, the proteins within the lysates were resolved via sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and subsequently transferred onto polyvinylidene difluoride (PVDF) membranes (Millipore, Billerica, MA, USA). The membranes were then subjected to a blocking step using QuickBlock Western Blocking Solution (Beyotime, Shanghai, China) for 10 minutes, followed by an overnight incubation with primary antibodies at 4°C. Afterward, the membranes were incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies at room temperature for 1 hour. The primary antibody used was rabbit anti-HTATIP2 (1:1000; Cat. No. ab177961, Abcam, Cambridge, UK), and the corresponding secondary antibody was goat anti-rabbit IgG (Cat. No. ab288151, Abcam, Cambridge, UK). Imaging of the protein bands was conducted using a GE Amersham Imager 600 (GE Healthcare, Little Chalfont, USA), with β-actin serving as an internal control. All experimental procedures were conducted in triplicate to ensure reliability of the results.

Cell Immunofluorescence

The expression levels of HTATIP2 in fibroblasts were assessed via immunofluorescence techniques. Fibroblasts derived from both normal skin and hypertrophic scar tissues were cultured on glass coverslips for a duration of 24 hours. Subsequently, the cells underwent fixation using a 4% paraformaldehyde solution at room temperature (25 °C) for 15 minutes. Following fixation, a blocking step was performed utilizing goat serum at room temperature for 1 hour. The cells were then treated with primary antibodies targeting HTATIP2 (dilution 1:100, Cat. No. ab177961, Abcam, Cambridge, UK) and incubated overnight at 4°C. After that, a secondary antibody (Cat. No. ab150077, Abcam, Cambridge, UK) was applied at room temperature for 2 hours. Lastly, the cells were counterstained with DAPI at room temperature for 5 minutes, and the outcomes were analyzed using a fluorescence microscope.

Results

Results for SNPs Screening

This study screened out 16,091 SNPs associated with 2489 mitochondrial-related DNA methylation CpG sites from cis-mQTL (Supplementary Table S2). From cis-eQTL, 25,928 SNPs linked to the expression of 893 mitochondrial-related transcripts were obtained (Supplementary Table S3). From two proteomic cohort studies’ cis-pQTL, 3791 SNPs associated with the expression of 170 mitochondrial-related proteins (137 unique proteins) were identified (Supplementary Table S4).

Mitochondrial Gene Methylation and HS

cis-mQTLs were used as genetic tools for MR analysis to systematically assess the causal effects of mitochondrial genes on HS. Among the 376 CpG sites found to be associated with HS (Supplementary Figure S1), 180 served as protective factors, while 196 were risk factors. The IVW results showed that cg25628542 (AADAT) was a protective factor for HS (OR=0.920, 95% CI: 0.877–0.965, P<0.001, FDR=0.015). cg17372223 (NT5DC2) was also identified as a protective factor for HS (OR=0.937, 95% CI:0.902–0.973, P<0.001, FDR=0.015). In contrast, cg11728787 (HEMK1) was identified as a risk factor for HS (OR=1.185, 95% CI:1.093–1.285, P<0.001, FDR=0.002).cg07434944 (MRPL23) was identified as a risk factor for HS (OR=1.319, 95% CI:1.170–1.488, P<0.001, FDR<0.001).

When considering the conditions of P<0.05 and I²>50% in the heterogeneity analysis, it was indicated that cg23811061 from the IMMT gene (with I²=55%, Cochran’s Q=35.528, P=0.003) and cg18676053 from the STOM gene (with I²=53%, Cochran’s Q=35.832, P=0.005) showed significant heterogeneity. Except for these two, other CpG sites showed no significant heterogeneity.

Based on the analysis using the MR Egger regression intercept, 9 CpG sites near 5 genes were found to be significantly different from 0 (P<0.05), specifically those of MRSA (cg26077133, cg02319733, cg12940923, cg16038868), IMMT (cg06002975, cg23811061), MRPL23 (cg07578618), HIBADH (cg00453374), and CPT1B (cg17137457). By MR-PRESSO analysis, 5 CpG sites near 4 genes revealed significant horizontal pleiotropy: IMMT (cg23811061, P=0.003), YBEY (cg14065109, P=0.006), STOM (cg18676053, P=0.009) and STOM (cg14215970, P=0.049), and PDK1 (cg04033559, P=0.030). Therefore, excluding these 14 CpG sites, a total of 362 HS-related CpG sites did not show any horizontal pleiotropy, suggesting the MR results from this study were robust (Supplementary Table S5).

Steiger filtering test results showed that all the mitochondrial gene CpG sites in the HS dataset were “TRUE”, suggesting that the causal relationship between the mitochondrial gene CpG sites and the outcome aligned with what we expected (Supplementary Table S5).

The colocalization analysis of mQTLs for HS and mitochondrial genes revealed that 4 CpG sites near 2 genes significantly shared causal variants (PP.H4>0.7), including MRPL23 (cg23135908, PP.H4=0.929) (cg06498964, PP.H4=0.861), (cg07578618, PP.H4=0.712), and USP30 (cg12535380, PP.H4=0.743) (Supplementary Table S5).

Mitochondrial Gene Expression and HS

cis-eQTLs were used as genetic tools for MR analysis, and 233 mitochondrial genes were found associated with HS (Supplementary Figure S2). AMT was identified as a protective factor for HS (OR=0.900, 95% CI:0.845–0.958, P<0.001, FDR=0.020). GPD2 was also a protective factor for HS (OR=0.867, 95% CI:0.805–0.934, P<0.001, FDR=0.005). In contrast, ATP5MC1 posed a risk for HS (OR=1.349, 95% CI:1.215–1.498, P<0.001, FDR<0.001). Similarly, BAX was a risk factor for HS (OR=1.294, 95% CI:1.152–1.452, P<0.001, FDR<0.001).

When considering the conditions of P<0.05 and I²>50% in the heterogeneity analysis, it was indicated that 2 genes displayed significant heterogeneity: LYPLAL1 (I²=57%, Cochran’s Q=71.312, P<0.001) and MECR (I²=59%, Cochran’s Q=9.849, P=0.043). The other gene loci did not show any significant heterogeneity.

The results of MR Egger regression and MR-PRESSO analysis indicated that three genes, namely MPV17L2 (with P _Egger=0.010 and P _PRESSO=0.034), GLYCTK (with P _Egger=0.023 and P _PRESSO=0.049), and NT5DC3 (with P _Egger=0.028 and P _PRESSO=0.039), had significant horizontal pleiotropy. The other 230 genes linked to HS did not show significant horizontal pleiotropy, suggesting that the MR results of this study were reliable (Supplementary Table S6).

Steiger filtering test results showed that the direction of mitochondrial gene expression in the HS dataset was “TRUE”, suggesting mitochondrial genes related to the outcome aligned with what we expected (Supplementary Table S6).

Colocalization analysis of eQTLs for HS and mitochondrial genes showed that the PP.H4 value for MRPS23 is 0.602>0.5, suggesting significant shared causal variants in this region (Supplementary Table S6).

Mitochondrial Protein and HS

cis-pQTLs were used as genetic tools for MR analysis, and 34 proteins (including 31 unique ones) were identified associated with HS (Figure 2). The IVW results indicated that 17 proteins acted as protective factors for HS, while 17 were identified as risk factors. RTN4IP1 is a protective factor for HS (OR= 0.354, 95% CI: 0.203–0.617, P < 0.001, FDR= 0.008); CASP9 is a protective factor for HS (OR= 0.459, 95% CI: 0.298–0.707, P < 0.001, FDR= 0.011); CAT is a risk factor for HS (OR= 1.462, 95% CI: 1.203–1.775, P < 0.001, FDR= 0.005); and FABP1 is also a risk factor for HS (OR= 1.367, 95% CI: 1.171–1.596, P < 0.001, FDR= 0.003).

Figure 2 Associations of genetically predicted mitochondrial gene encoded protein with HS in Mendelian randomization analysis. Abbreviations: OR, odds ratio; FDR, false discovery rate; PPH4, posterior probability of H4.

For P<0.05 and I²>50%, heterogeneity analysis revealed that two proteins showed heterogeneity in the IVW analysis: ACAA1 (I²=36%, Cochran’s Q=76.565, P= 0.007) and PDK1 (I²=50%, Cochran’s Q= 33.801, P= 0.009). The remaining ones did not show heterogeneity.

Horizontal pleiotropy analysis showed that there were two proteins with significant MR Egger regression intercepts (P<0.05), namely, FABP1 (P=0.026) and EFHD1 (P=0.043). The MR-PRESSO analysis indicated that there were two proteins with significant horizontal pleiotropy, specifically: PDK1 (P=0.005) and ACAA1 (P=0.006). The remaining 30 proteins associated with HS did not exhibit horizontal pleiotropy, suggesting that the MR results of this study were robust (Supplementary Table S7).

Steiger filtering test showed that the direction of mitochondrial proteins in the HS dataset is “TRUE”, indicating that the causal relationship between mitochondrial proteins and the outcome aligned with what we expected (Supplementary Table S7).

Colocalization analysis of pQTLs for HS and mitochondrial genes indicated that there was no evidence of colocalization between the mitochondrial gene pQTLs and HS (Supplementary Table S7).

Integrating Evidence from Multi-Omics Levels

After integrating multiple omics evidence, this study identified two genes associated with HS that have strong multi-omics evidence: HTATIP2 and PDK1. The FDR values for these two genes in methylation, gene expression, and protein levels were all below 0.05. Moreover, cg01397325 (HTATIP2) methylation was linked to a lower risk of HS (OR= 0.925, P<0.001), and cg24426391 (HTATIP2) methylation was also linked to a lower risk of HS (OR= 0.961, P=0.002). The gene expression (OR=1.171, P<0.001) and protein abundance (OR=1.110, P<0.001) of HTATIP2 were both associated with a higher risk of HS. Methylation of PDK1’s CpG site cg04033559 (OR=0.937, P<0.001) was linked to a lower risk of HS, whereas methylation of PDK1’s CpG sites cg10165864 (OR=1.298, P<0.001) and cg17679246 (OR=1.188, P<0.001) was linked to a higher HS risk. The gene expression (OR=0.759, P<0.001) and protein abundance (OR=0.625, P<0.001) of PDK1 were both negatively correlated with the risk of HS (Figure 3 and Table 2).

Figure 3 Grading results for HS candidate genes. Abbreviations: HS, hypertrophic scars; QTL, quantitative trait loci.

Table 2 Genetically Predicted Methylation, Expression, and Protein of Tier 1 Candidate Gene with HS in Mendelian Randomization

Sixteen genes were identified tier 2 genes associated with HS, including CARS2, CASP9, COMT, DGUOK, MMAB, MRPL21, MSRA, NDUFS2, NSUN2, NT5DC2, NT5DC3, PRDX5, RDH13, RIDA, SND1, and TRAP1. The FDR for all these genes was less than 0.05. The following CpG sites were associated with risk factors for HS: cg11716795 of MRPL21 (OR=1.097, P< 0.001), cg17964305 of MSRA (OR=1.463, P=0.001), cg23400122 of MSRA (OR=1.238, P<0.001), cg26077133 of MSRA (OR=1.045, P<0.001), cg26420013 of NSUN2 (OR=1.172, P=0.002), cg22710716 of NT5DC2 (OR=1.051, P=0.002), cg21863207 of NT5DC3 (OR=1.102, P=0.001), cg01708924 of PRDX5 (OR=1.371, P=0.001), cg02592727 of RDH13 (OR=1.096, P=0.004), cg07598936 of RDH13 (OR=1.138, P<0.001), cg08278892 of RDH13 (OR=1.146, P=0.003), cg00893242 of SND1 (OR=1.121, P<0.001), cg06714981 of SND1 (OR=1.055, P=0.002), and cg11539674 of SND1 (OR=1.093, P<0.001). The gene expression of CASP9 (OR=0.849, P<0.001), COMT (OR=0.837, P<0.002), MMAB (OR=0.711, P<0.001), MRPL21 (OR=0.903, P<0.001), NDUFS2 (OR=0.685, P<0.001), NSUN2 (OR=0.930, P<0.002), NT5DC3 (OR=0.903, P<0.001), RDH13 (OR=0.907, P<0.001), RIDA (OR=0.559, P<0.001), SND1 (OR=0.679, P<0.001), and TRAP1 (OR=0.890, P<0.001) were protective factors for HS. The protein levels of CASP9 (OR=0.459, P<0.001), MMAB (OR=0.827, P<0.001), and RIDA (OR=0.591, P<0.001) also served as protective factors for HS (Figure 3 and Table 3).

Table 3 Genetically Predicted Methylation, Expression, and Protein of Tier 2 Candidate Gene with HS in Mendelian Randomization

IMMT, MRPL23, and USP30 genes were identified as tier 3 genes associated with HS. Among the tier 3 genes, the methylation of cg07610327 in IMMT was a risk factor for HS (OR=1.191, P<0.001), while cg06498964 (OR=0.829, P<0.001) and cg07578618 (OR=0.820, P<0.001) in the MRPL23 were protective factors for HS. Methylation of cg03124318 (OR=0.828, P<0.001) and cg12535380 (OR=0.863, P<0.001) in USP30 was also a protective factor for HS (Figure 3 and Table 4).

Table 4 Genetically Predicted Methylation, Expression, and Protein of Tier 3 Candidate Gene with HS in Mendelian Randomization

Tissue-Specific Verification

The levels of HTATIP2 expression in normal skin (NS) and hypertrophic scar tissue (HS) were assessed by qRT-PCR, Western blot analysis, and immunofluorescence techniques. Notably, both mRNA and protein concentrations of HTATIP2 exhibited a significant elevation in the HS cohort in comparison to the NS group (Figure 4A and B). Furthermore, HSF and NSF were isolated from HS and NS tissues, respectively. Subsequently, cell immunofluorescence was conducted to measure the intracellular expression levels of HTATIP2. The results indicated that HTATIP2 intensity was substantially greater in the HSF group relative to the NSF group, as depicted in Figure 4C.

Figure 4 The expression of HTATIP2 in skin tissues of patients with HS. (A) The mRNA levels of HTATIP2 in normal skin and hypertrophic scar tissues were detected by qRT-PCR. (B) The protein levels of HTATIP2 in normal skin and hypertrophic scar tissues were detected by Western blot. (C) The HTATIP2 expression NSF and HSF was detected by cell immunofluorescence. Scale bar: 50μm. ***p < 0.001 using unpaired two-tailed Student’s t-test. Data are presented as mean ± SD. Abbreviations: NS, Normal Skin; HS, Hypertrophic Scar; NSF, Normal Skin Fibroblast; HSF, Hypertrophic Scar:Fibroblast.

A schematic summary of the multi-omics integration process was in Figure 5.

Figure 5 A schematic diagram of the multi-omics integration process. Figure created by Figdraw.

Discussion

This study focused on the causal relationship between mitochondrial-related genes and HS. At present, there is a consensus on the mechanisms of HS formation, which relate to the release of different growth factors, persistent inflammation, overproduction of fibroblasts (with less apoptosis and more proliferation), and excessive deposition of collagen. Additionally, the formation of new blood vessels also plays a key role in HS development.^13,14 There are five main signaling pathways involved in scar formation: Wnt/β-catenin, TGF-β, Notch, Sonic Hedgehog, and Ras/MEK/ERK signaling pathways.³² Previous studies showed that mitochondrial function was closely linked to cell growth, apoptosis, and oxidative stress.^20,21 Consequently, alterations in mitochondrial genes could affect the chances of developing HS. These links lay the groundwork for this study.

Previous studies on mitochondria and HS mainly focused on molecules sourced from research on tumors and myocardial infarction, such as those affecting tumor proliferation, apoptosis, and ROS generation, to check their effects on scars and scar fibroblasts.³³ There has not been much research identifying key genes through large-scale, high-throughput data screening, and even when it’s done, it typically focused on the RNA expression of differentially expressed genes.^34,35 Such research paid less attention to a comprehensive look at gene transcription, translation, and regulation and lacked a solid scientific basis for multi-omics screening of target molecules. This study used data from several cohorts related to mitochondrial genes, screening HS-related genes by looking at three aspects: DNA methylation, RNA expression, and protein abundance. After a series of validity checks, the reliability of the screening results was confirmed, which meant it has broader applications for finding potential candidate genes for HS.

HTATIP2, also known as TIP30 or CC3, is a redox enzyme essential for tumor suppression, primarily involved in nuclear transport and the regulation of angiogenesis, and playing a role in regulating programmed cell death.³⁶ Previous studies found that gene methylation negatively correlates with RNA and protein expression. Our multi-omics results showed that both DNA methylation sites of HTATIP2 were negatively correlated with HS, while HTATIP2 gene expression and protein levels were positively correlated with HS formation and development. Some researchers found that methylation of HTATIP2 led to downregulation of gene expression in uterine leiomyoma, which aligned with our multi-omics results.³⁷ Therefore, the relationship between HTATIP2 methylation and RNA expression/protein abundance in relation to HS matched what previous studies have reported.

However, there were few reports on the relationship between HTATIP2 and HS. Previous studies revealed that the formation of uncontrolled blood vessel growth could lead to persistent inflammation and ultimately result in HS.³⁸ Additionally, blocking the growth of new blood vessels in endothelial cells effectively reduced HS formation.³⁹ Research has shown that higher levels of HTATIP2 reduced the ability to form new blood vessels in patients with chronic limb-threatening ischemia (CLTI), while downregulating its expression enhanced the factors in macrophages isolated from CLTI patients that regulate blood vessel growth, such as Neuropilin-1 and Angiopoietin-1, promoting blood vessel growth (endothelial tube formation assays) and arterial formation (smooth muscle proliferation).⁴⁰ Studies on liver cancer also confirmed that HTATIP2 promoted cell death and inhibited blood vessel growth.^41,42

The formation of HS is closely related to increased proliferation of fibroblasts and reduced apoptosis. Apoptosis is a physiological process of cell self-destruction carried out in a programmed way.⁴³ Previous literature has reported that increasing the proliferation capacity of fibroblasts and inhibiting their apoptosis could promote scar formation.^43–45 In lab experiments, HTATIP2 was found to make fibroblasts more sensitive to apoptosis.³⁶ This goes against our findings that the gene expression and protein levels of HTATIP2 were risk factors for HS.

One of the characteristics of HS is the excessive proliferation of fibroblasts and the excessive deposition of extracellular matrix (ECM).^46,47 In the dysregulated wound healing process, specialized myofibroblasts proliferate excessively, leading to abnormal accumulation of ECM.⁴⁸ The Wnt pathway plays an important role in the pathological mechanisms of keloids and HS.⁴⁹ Some research indicated that Interferon-α2b could inhibit the proliferation and migration of HS fibroblasts by suppressing the Wnt pathway.⁵⁰ In interaction models of fibroblasts and endothelial cells, some researchers found that in both in vitro and in vivo models, the Wnt pathway could regulate the cellular activities of fibroblasts.⁵¹ In a review on chronic kidney disease, some scholars also mentioned that the Wnt pathway could activate myofibroblasts, leading to excessive ECM deposition.⁵² An integrative analysis of the DNA methylome and transcriptome in uterine leiomyoma showed that HTATIP2 was related to the ECM deposition and the dysregulation of the Wnt/β-catenin pathway.³⁷ These studies indicated that the Wnt pathway could promote excessive deposition of ECM by activating myofibroblasts, which could lead to HS. And previous studies also revealed that HTATIP2 could interfere with the Wnt pathway, leading to pathway dysregulation, which contradicted our findings that HTATIP2 gene expression and protein levels were risk factors for HS.

The TGF-β pathway and Notch pathway play important roles in the formation of HS. The TGF-β pathway is the most well-known signaling pathway regulating the biological behavior of fibroblasts and collagen formation. Continuous activation of this pathway leads to excessive activation of fibroblasts, ultimately causing excessive collagen deposition and the formation of HS.⁵³ During the wound healing process, timely intervention in TGF-β effectively reduces the formation of HS.⁵⁴ Blocking Notch signaling in macrophages could reduce inflammation in the wound tissue through intercellular signaling and inhibit collagen synthesis by fibroblasts.⁵⁵ Moreover, the Notch signaling pathway promotes HS formation by regulating the subtypes of epidermal cells.⁵⁶ In pancreatic cancer research, researchers found that inhibiting HTATIP2 enhanced miR-10b’s role in promoting TGF-β, enhancing EGFR signaling, facilitating EGF-TGF-β cross-talk and promoting the expression of EMT-promoting genes.⁵⁷ In multiple sclerosis (MS) research, HTATIP2 acts as a pro-apoptotic factor, inhibiting nuclear transport and, consequently, Notch1-mediated oligodendrocyte differentiation and remyelination.⁵⁸ These research results suggested that HTATIP2 might inhibit HS formation through the TGF-β pathway and Notch pathway, which contradicted our findings that HTATIP2 gene expression and protein abundance were positively correlated with HS.

Previous studies have shown that HTATIP2 may have a negative effect on HS, which is at odds with our results. The reasons for this are as follows. First, the diseases targeted by different studies varied, and most studies focused on tumors. Tumor microenvironments differ from those of wounds or scars,⁵⁹ which means the same gene might work differently in various environments, and might even do the opposite. Second, previous studies mainly focused on the regulatory level of a single gene, while our research integrated multi-omics results of the methylation, gene expression and protein levels in mitochondrial related genes. So earlier studies might not give the full picture. Our multi-omics findings suggested that the HTATIP2 gene could play a role in how HS develops, which needs more research in the future.

Pyruvate dehydrogenase (PDH) is a mitochondrial multienzyme complex that catalyzes the oxidative decarboxylation of pyruvate and is one of the key enzymes responsible for regulating carbohydrate fuel homeostasis in mammals. Its activity is regulated by a cycle of phosphorylation and dephosphorylation. PDK1 (Pyruvate Dehydrogenase Kinase 1) catalyzes the phosphorylation of pyruvate dehydrogenase, leading to its inactivation, playing a key role in regulating glucose and fatty acid metabolism and overall metabolic homeostasis.^58,60 It downregulates aerobic respiration and inhibits the conversion of pyruvate to acetyl-CoA by regulating the metabolic flux of tricarboxylic acid cycle metabolites, playing an important role in the cellular response to hypoxia, which is vital for cell proliferation in low-oxygen conditions, protecting cells from apoptosis caused by hypoxia and oxidative stress.⁶¹ Research shows that low-oxygen conditions boost glycolysis while weakening mitochondrial function, resulting in increased proliferation and migration in scar fibroblasts, increased collagen synthesis, and inhibition of apoptosis. So, the molecular changes in glucose metabolism and mitochondrial function due to hypoxia could be potential targets for treating HS and keloids.³⁵

The direct clinical relationship between PDK1 and scars has not been reported. A search on the cell biological behaviors related to scars found that in cases of pulmonary hypertension, the DNMT1-HIF-1α-PDK pathway could reduce fibroblast proliferation and collagen production.⁶² In terms of cellular senescence, some researchers found that inhibiting PDK1 could alleviate cellular senescence and skin atrophy, which specifically promotes fibroblast proliferation and collagen deposition when PDK1 was inhibited.⁶³ Interestingly, some researchers noted that in the fibrosis of various diseases, inhibiting PDK1 led to fibroblast apoptosis and reduced collagen production.⁶⁴ Previous studies suggested that PDK1 could both promote and inhibit the development of HS. This might be due to different methylation sites of PDK1, which still needs to be tested experimentally.

The repair process of wounds constantly requires the involvement of inflammatory responses.⁶⁵ HS are consequences of a dysregulated wound healing process, which involves inflammatory responses in their occurrence and development.¹³ T cells, as key cells in the inflammatory response, through their production, development, and differentiation, play a crucial role in the direction of the inflammatory response and the emergence of different clinical outcomes. T cells are linked to scar formation, and those with different differentiation have varying effects on HS. IL-10-producing T lymphocytes regulate inflammatory cell cytokine expression to promote hyaluronan-rich ECM deposition and attenuate fibrosis. Promoting IL-10-producing lymphocytes in wounds may be a therapeutic target to promote regenerative or scarless wound healing.⁶⁶ Previous studies showed that PDK1 was crucial for T cell development, differentiation, and homeostasis, and “knockout of PDK1 in lymphocytes hindered T cell development in the thymus and exhibited a substantial influence on immune cell homeostasis in the spleen and lymph nodes”.⁶⁷ Lack of PDK1 could disrupt T cell-mediated inflammatory responses, leading to inflammatory skin diseases.⁶⁸ Research on scarring pathways found that PDK1 is linked to the Wnt/β-Catenin, Notch, and Ras/ERK/MEK pathways.^69–71 It’s important to note that PDK1 regulates Notch-induced T cell development.⁷² PDK1 also helps promote the differentiation of T follicular helper cells via the PI3K/AKT pathway.⁷³ In summary, this study revealed that PDK1 likely regulated T cell development through the Notch and PI3K/AKT pathways, shaping the inflammatory response and ultimately impacting HS formation.

In the search for scar-related studies on tier 2 and tier 3 genes identified through multi-omics integration, three previously reported genes associated with HS formation were noted: COMT, SND1, and TRAP1. COMT (Catechol-O-methyltransferase) catalyzes the transfer of a methyl group from S-adenosylmethionine to catecholamines, including the neurotransmitters dopamine, epinephrine, and norepinephrine. This O-methylation is one of the primary degradation pathways for catecholamine neurotransmitters.⁷⁴ Previous studies have reported that catecholamines are related to scar formation. For example, dopamine could regulate inflammatory responses and scar formation, which is why many wound healing materials include dopamine to help reduce scarring.^75,76 A prospective cohort study found that a specific variant of the COMT gene (AA rs4680 genotype) may protect against scarring and itching after burns.⁷⁷ SND1 is known to be an oncogene in several tumors, highly expressed in burned skin tissue, and could promote the formation of HS through the ERK/JNK signaling pathway, which makes it a risk factor for HS formation.⁷⁸ TRAP1, as a molecular chaperone of Smad4, could bring Smad4 into the vicinity of the TGF-β receptor complex and mediate downstream signaling, regulating the formation and contraction of HS.⁷⁹ These three genes are relevant to our findings and deserve further investigation. However, there are not many reports on the other 18 genes linked to HS. Nevertheless, these genes, although not previously reported, still hold value for research and could help fill gaps in understanding the pathogenesis of HS.

The inclusion of multiple validation steps-heterogeneity testing, pleiotropy correction, and colocalization enhances robustness in this study. The PP.H4>0.7 threshold has been widely used in recent multi-omics integration and causal inference studies to see if two traits are driven by the same causal variant.^80,81 These studies all back up using PP.H4>0.7 as a well-accepted empirical threshold for colocalization analysis today. We also recognize that the interpretation of PP.H4 should be considered alongside the specific research context and biological plausibility, instead of just relying on one threshold. Therefore, in this study, in addition to sharing the PP.H4 results, we also incorporated the Steiger direction test (to check if the causal direction is consistent), tests for heterogeneity (Cochran’s Q and I²), and MR-PRESSO to detect pleiotropy. This helps us assess the robustness of the analysis results from different angles. These tests can be seen as sensitivity analyses for our study, helping to reduce the instability of results caused by different model assumptions or biases from single instrumental variables.

One of the primary limitations of this study is the lack of wet lab validation to corroborate the bioinformatics findings. Our analysis mainly shows a potential causal link between genetic regulation at the systemic level and the risk of hypertrophic scars, but we still need to validate the specific mechanisms in future studies that look into the functional research on local tissues. Additionally, the study population is predominantly of European descent, which may limit the generalizability of the findings to other ethnic groups. This demographic constraint necessitates further research involving diverse populations to ensure broader applicability. These limitations highlight the need for comprehensive follow-up studies to validate and extend our findings.

This study found 21 mitochondrial genes that could be potential targets, with HTATIP2 and PDK1 being the most promising for clinical use. This research lays a solid foundation for further exploration into how HS forms at the molecular level and gives us some hopeful targets for HS management.

Data Sharing Statement

Data supporting the findings of this study are available from the corresponding author upon reasonable request.

Ethics Approval and Consent to Participate

This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Fujian Medical University Union Hospital (2025KY428). Written informed consent was obtained from all the participants.

Acknowledgment

We acknowledge the editors and reviewers for their helpful comments on this paper.

Author Contributions

Conceptualization: Teng Gong, Zhaohong Chen, Minjuan Wu.

Data curation: Teng Gong, Jiansheng Zheng.

Formal analysis: Teng Gong, Minjuan Wu.

Investigation: Teng Gong, Minjuan Wu.

Methodology: Teng Gong, Jiansheng Zheng.

Project administration: Minjuan Wu, Zhaohong Chen.

Supervision: Teng Gong, Zhaohong Chen.

Funding acquisition: Teng Gong.

Writing – original draft: Teng Gong, Jiansheng Zheng, Zhaohong Chen, Minjuan Wu.

Writing – review & editing: Teng Gong, Jiansheng Zheng, Zhaohong Chen, Minjuan Wu.

Teng Gong, Minjuan Wu, Jiansheng Zheng are co-first authors. All authors took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This study was supported by the National Natural Science Foundation of China (32071186), Fujian Provincial Natural Science Foundation of China (2022J01243), Joint Funds for the innovation of Science and Technology, Fujian Province (2023Y9213), High-level Hospital and Clinical Specialty Discipline Construction Program for Fujian Medical Development, China ([2021]76), and Fujian Provincial Key Laboratory of Burn and Trauma, China.

Disclosure

The authors declare that they have no competing interests.

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The electrification of Brazil’s heavy-duty trucking sector represents a critical opportunity to reduce greenhouse gas (GHG) emissions, improve air quality, and enhance public health. While Brazil’s long-standing reliance on biofuels has leveraged domestic resources and infrastructure, this strategy is best viewed as a transitional measure that may not fully meet the country’s long-term climate and economic objectives. 

Electrification offers a strategic pathway to decarbonize road freight, drawing on international experience from jurisdictions such as the European Union and the United States. It aligns with Brazil’s national sustainability commitments and can deliver substantial co-benefits for the environment, public health, and economic resilience.

Realizing this transition will require coordinated action across government, industry, and civil society. Public sector leadership—through robust regulatory frameworks, targeted incentives, and investment in infrastructure—will be essential. Energy providers, logistics operators, municipalities, and vehicle manufacturers all have complementary roles in developing a supportive ecosystem for zero-emission freight. Manufacturers remain key stakeholders by contributing to innovation and adapting vehicle offerings to the Brazilian context. However, the success of the transition hinges on whole-of-sector collaboration, guided by coherent policies and supported by public and private investment.

This report presents international case studies, quantifies environmental and economic impacts, and outlines actionable recommendations to inform decision-making. It aims to support policymakers, industry leaders, and other stakeholders in accelerating the electrification of Brazil’s heavy-duty vehicle fleet, in line with national goals to reduce emissions and improve public health.

Introduction

Bloodstream infections (BSIs) are a major global health problem in the elderly as age-related immune defects, multiple comorbid conditions and diminished physiological reserves predispose these patients to high morbidity, prolonged hospitalization and increased mortality.^1,2 Although Escherichia coli (E.coli) is the predominant pathogen causing BSIs, non-E.coli organisms such as Pseudomonas aeruginosa and Klebsiella pneumoniae associated with BSIs are known to be more severe, and result in higher mortality and complex treatment requirements necessitating a longer hospital stay.³ The elderly are at particular risk for complicated clinical courses due to late diagnosis which may limit prompt initiation of appropriate treatment, thereby emphasizing the need for pathogen-specific risk stratification approach.

Neutrophil-to-platelet ratio (NPAR) also reflects both systemic inflammation and thrombotic risk.⁴ Elevated NPAR is associated with poor prognosis in sepsis and other bacterial infections, but the role of NPAR in predicting E.coli bloodstream infection (BSI) among elderly patients was not well studied.⁵ With development of microfluidics and novel phage-based detection systems, combining NPAR with pathogen specific diagnostic tools holds promise in the early management of E.coli BSI. Though evidence suggests that patients experience higher mortality in non-E.coli compared to E.coli infection causing BSIs, there is currently no study that has directly compared risk of mortality between E.coli and non- E.coli BSIs among elderly population. Furthermore, the utility of NPAR in predicting E.coli BSI among elderly patients remains unexplored.

This study aimed to compare mortality between E.coli versus non-E.coli BSIs among elderly inpatients, explore potential utility of NPAR as a diagnostic biomarker to predict E.coli BSI and its prognostic implications among them.

Methods

Study Design and Participants

This single-center, retrospective cohort study encompassed 527 elderly patients diagnosed with BSIs between December 2011 and February 2024 at the Second Medical Center of the Chinese PLA General Hospital through the hospital’s infection information system. The inclusion criteria were: (1) age greater than 65 years; and (2) availability of complete medical records. The exclusion criteria were as follows: (1) Incomplete medical records. The study protocol was reviewed and approved by the Chinese PLA Hospital Ethical Committee (Approval No.NO. S2024-359-02) and complied with the Declaration of Helsinki. Due to the retrospective design, informed consent was waived.

Data Collection

Data on NPAR levels and other covariates, including demographic and clinical factors, were collected at baseline. NPAR was measured as both a continuous variable and categorized into tertiles (T1, T2, and T3). The outcome of interest was the occurrence of E.coli BSI, which was confirmed by blood culture. A BACT/ALERT 3D automatic blood culture instrument (bioMérieux, France) was used for blood culture, a Vitek2 Compact automatic microbiological identification and antimicrobial susceptibility analysis system (bioMérieux, France) was used for strain identification and antimicrobial susceptibility testing. Baseline data were collected on a range of demographic and clinical characteristics, including age, gender, department, smoking status, comorbidities (eg, diabetes mellitus, hypertension, coronary disease), and clinical interventions (eg, number of operations, use of ventilator, central venous catheter, urinary catheter, chemotherapy, radiotherapy, blood transfusion, polypharmacy regimens). The hospitalization duration was also recorded as a continuous variable.

Statistical Analysis

Descriptive statistics were used to summarize the baseline characteristics of the participants. Continuous variables were expressed as means with standard deviations, and categorical variables were reported as frequencies and percentages. Categorical variables were compared using the chi-square or Fisher’s exact test; continuous variables were analyzed using the Student’s t-test, Mann–Whitney U-test or Kruskal–Wallis test as appropriate; logistic regression was used to assess the predictive value of NPAR for E. coli BSI; Cox proportional hazards models were applied for survival analysis; and the Kaplan–Meier method with Log rank test was used to compare mortality between groups. To evaluate the association between NPAR and E. coli BSI, we constructed three models in our analysis: Model 1: Unadjusted crude model. Model 2: Adjusted for age and sex. Model 3: Based on Model 2, we further adjusted for variables that showed statistical significance in the univariate analysis, as well as through reverse adjustment, including department, coronary disease, and combination of drug. The odds ratios (ORs) and 95% confidence intervals (CIs) were calculated for both continuous NPAR and NPAR tertiles (T1 as the reference group). The dose-response relationship for continuous NPAR was assessed, and the trend across NPAR tertiles was evaluated using a likelihood ratio test. The non-linearity of the dose-response relationship was examined using a non-linear regression model. The p-value for trend was calculated to assess the monotonicity of the dose-response across tertiles of NPAR. Statistical significance was set at P < 0.05 for all analyses. All statistical analyses were performed using R (version 3.6.3) statistical software.

Results

Baseline Characteristics

A cohort of 510 elderly participants (mean age 89.9 ± 8.5 years) meeting inclusion and exclusion criteria was stratified into E.coli BSI (n=92, 18.2%) and non-E.coli BSIs groups (n=418, 81.8%) based on the causative agent of bloodstream infection. The baseline characteristics of the overall cohort and the two groups are summarized in Table 1. No intergroup differences in age (P=0.978), gender (P=0.229), smoking status (P=0.193), diabetes (P=0.614), or hypertension (P=1.0). Higher coronary disease prevalence in non-E.coli group (57.2% vs 32.3%, P<0.001) and comparable surgical frequency (P=0.441).

Table 1 Baseline Clinical Characteristic of Enrolled Bloodstream Infection Patients

Clinical Interventions and Outcomes

As shown in Table 2, 49.1% (n = 510) patients did not require ventilator support, with a higher proportion of patients in the E. coli group (61.3%) compared to the non-E.coli group (46.4%) (P = 0.045). Similarly, the duration of central venous catheter use was significantly longer in the E.coli group, with 25.8% of patients requiring it for more than 90 days, compared to 14.1% in the non-E.coli group (P = 0.023). Blood transfusion was more frequently administered in the E.coli group (73.1% vs 58.1%, P = 0.01). The length of hospital stay did not differ significantly between the two groups (P = 0.563), with a median length of stay of 90.0 days (IQR: 65.4, 96.0) in the overall cohort.

Table 2 Clinical Interventions and Outcomes of Enrolled Bloodstream Infection Patients

Departments and Infection Source Distribution

There was a statistically significant difference between E.coli group and non-E.coli groups regarding the departments in which patients were hospitalized (P = 0.046) (Table 1). The distribution of departments of patients with E.coli bloodstream infections were as follows: Cardiology (16.30%), Endocrinology (3.26%), Gastroenterology (35.87%), Hematology (1.09%), ICU (4.35%), Nephrology (4.35%), Neurology (5.43%), Oncology (2.17%), and Respiratory Medicine (10.87%) (Figure 1). The distribution of infection sources among patients with E.coli bloodstream infections were as follows: Biliary infection source (28.26%), non-biliary intra-abdominal infection (3.26%), Pulmonary infection source (16.30%), Unidentified infection source (23.92%), and Urinary tract infection source (28.26%) (Figure 1). Also, we found that non-E. coli pathogens were the primary contributors. Among these, Staphylococcus species were the most prevalent, accounting for 125 isolates out of 510 total samples (Supplementary Table 1).

Figure 1 Distribution of infection sources and departments in patients with Escherichia coli bloodstream infections. (A) Distribution of infection sources in patients with Escherichia coli bloodstream infections. (B) Distribution of infection sources in internal medicine bloodstream Infection patients with Escherichia coli. (C) Distribution of infection sources in surgeon bloodstream infection patients with Escherichia coli. (D) Distribution of departments of patients with Escherichia coli bloodstream infections.

NPAR Associations with E.coli BSI

The association between NPAR and E.coli BSI was evaluated using three models (Table 3). All models demonstrated a statistically significant inverse relationship between continuous NPAR and E.coli BSI risk: Model 1, OR=0.88 (95% CI: 0.84, 0.93; P < 0.001), Model 2, OR =0.88 (95% CI: 0.84, 0.92; P < 0.001), and in Model 3, OR=0.89 (95% CI: 0.84, 0.94; P < 0.001). Using Tertile 1 (T1) as the reference group, in Tertile 2 (T2), the odds of infection were significantly reduced, with Model 1(OR=0.53; 95% CI: 0.32, 0.89; P = 0.017), Model 2(OR= 0.50; 95% CI: 0.29, 0.84; P = 0.01) and Model 3(OR =0.46; 95% CI: 0.26, 0.81; P = 0.008). In Tertile 3 (T3), the odds of infection were even more substantially reduced, with Model 1(OR =0.21; 95% CI: 0.11, 0.40; P < 0.001), Model 2 (OR=0.21; 95% CI: 0.11, 0.39; P < 0.001), and Model 3 (OR= 0.23; 95% CI: 0.11, 0.46; P < 0.001). Furthermore, a statistically significant trend towards decreasing odds of infection across increasing tertiles of NPAR was observed in all models (P for trend < 0.001). These findings suggest that higher NPAR levels correlate with a reduced likelihood of E.coli BSI, with the risk decreasing progressively across tertiles. This aligns with NPAR’s role as a composite inflammatory marker, where elevated values may reflect an attenuated susceptibility to systemic infection.

Table 3 Associations of NPAR with Escherichia coli Bloodstream Infections in Elder Patients

Dose-Response Relationship Between NPAR and E.coli BSI

A linear relationship between NPAR and E.coli BSI was statistically significant (P < 0.05), while a non-linear relationship was not evident (P = 0.424, Cutoff value =19.4) (Figure 2). Therefore, as NPAR increases, there is a linear decrease in E.coli BSI risk. Hence NPAR may serve as a continuous protective marker rather than having a threshold at a particular level.

Figure 2 The dose-response relationship between NPAR and Escherichia coli bloodstream infection. (NAPR: neutrophil-to-platelet ratio).

Survival Analysis

The Kaplan-Meier survival plots of patients with E.coli and non-E.coli BSIs are shown in Figure 3, which revealed higher survival probability of patients with E coli compared with non-E coli counterparts (HR=0.43; 95% CI 0.21, 0.88, P=0.021). Notably, the survival probability for patients with non-E.coli infections dropped more rapidly over time, whereas the E.coli group exhibited a more gradual decline in survival.

Figure 3 Kaplan-Meier curve analysis of patients in Escherichia coli and non-Escherichia coli bloodstream infection groups.

Antimicrobial Resistance Pattern

As shown in Table 4 and Figure 4, almost 97.1% of E.coli isolates were resistant to Ampicillin (AMP), 77.1% were resistant to Ciprofloxacin (CIP), 74.3% were resistant to Cefazolin (CEZ), 72.9% were resistant to Levofloxacin (LVX), and 72.7% were resistant to Ampicillin/Sulbactam (AMP/SUL). Overall, E.coli isolates exhibited no resistance to the following antibiotics: Amikacin (AMK), Ertapenem (ETP), Tigecycline (TGC), and Cefotetan (CET). The antibiotic with the highest resistance rate in E.coli isolates from bloodstream infection patients in the respiratory department were AMP (100%), Ceftriaxone (CTX, 100%), CEZ (100%). AMP (100%), Doxycycline (DXY, 100%), Gentamicin (GEN, 100%). In contrast, the general surgery department had a lower resistance rate for antibiotics like Ampicillin/Sulbactam (60.0%) and Gentamicin (66.7%). Notably, antibiotics like Amikacin, Ertapenem, and Tigecycline showed a resistance rate of 0% across all departments. Resistance to Cefotetan was absent across all departments tested.

Table 4 Antibiotic Resistance Rate of Escherichia coli in Bloodstream Infection Patients

Figure 4 Overall antibiotic resistance rate of Escherichia coli.

Discussion

Our study provides several important implications to be addressed clinically for BSIs in extremely elderly patients. First, we demonstrated that non-E. coli BSIs had a significantly higher risk of mortality compared to E. coli BSI in extremely elderly inpatients with the mean age of 89.9 ± 8.5 years, indicating the clinical importance of identifying the pathogen causing infection. We also provided that low NPAR was inversely associated with the presence of E. coli BSI which may be useful early identification and risk stratification in elderly, leading to a more tailored and early intervention to be initiated.

Among 30,923 cases of E.coli bloodstream infections, 2961 cases of 30-day mortality were observed, resulting in an overall 30-day mortality of 9.6% (2961/30,923).⁶ Hospital-acquired or third-generation-cephalosporin-resistant E.coli BSI showed significantly higher mortality rates compared to community-acquired or third-generation-cephalosporin-susceptible E.coli BSI.⁶ In our cohort of elderly patients, non-E.coli BSIs were associated with higher mortality compared to E.coli BSIs, even after adjustment for demographics and clinical factors, which is consistent with previous studies. Various studies have reported that the reasons why high mortality is associated with non-E.coli infections include difficulty in diagnosis, limited treatment options, and increased infection severity due to multidrug-resistant organisms or high virulence.^6,7 As elderly patients have weak immunity and often suffer from multiple underlying diseases, there would be a greater concern about their course and response to treatment.^8,9 Our findings indicate that it is vital to early detection and proper management for non-E.coli BSIs. Previous studies have reported that E.coli BSI in elderly patients predominantly originate from urinary tract infections,¹⁰ which is consistent with the findings of our study, where urinary and biliary tract infections were identified as the leading sources. These infections typically elicit a relatively mild systemic inflammatory response, characterized by only a modest elevation in neutrophil counts and minimal alterations in platelet levels. In contrast, non-E. coli BSIs – such as those caused by Staphylococcus aureus, Klebsiella pneumoniae, or Enterococcus faecalis – are more often associated with catheter-related infections, non-biliary intra-abdominal infections, and respiratory tract infections, which usually trigger a more severe systemic inflammatory response.¹¹ It is reported that thrombocytopenia was independently associated with mortality among patients with BSIs.¹² This aligns with prior studies showing NPAR and related indices (eg, neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio) as strong, independent predictors of sepsis severity and mortality.¹³ A high NPAR captures a dual-risk profile: amplified inflammatory response and impaired hemostatic balance, both of which have been independently linked to increased mortality in bloodstream infections.¹⁴ In our multivariable Cox regression models, high NPAR remained significantly associated with mortality even after adjusting for age, comorbidities, and infection source, indicating its robust prognostic value. Clinically, low NPAR may serve as an early indicator of E. coli BSI and help clinicians stratify patients who are likely to have more benign infection courses, potentially guiding early empirical therapy decisions and resource allocation. Conversely, persistently elevated NPAR should prompt vigilance for non-E. coli pathogens or complicated infection sources.

The prevalence of E.coli producing extended-spectrum beta-lactamases (ESBL) among BSI patients was 40.98%. E.coli isolates were generally sensitive to carbapenems and β-lactam/β-lactamase inhibitor combinations. Hospital-acquired infections, biliary tract infections, gastric tube insertion procedures, and prior cephalosporin administration were identified as independent risk factors for the isolation of ESBL-producing strains. ESBL positivity, hospital-acquired infections, and cancer were independent risk factors for mortality.¹⁵ Meta-analysis results indicate that it is necessary to shift current treatment practices from antibiotic escalation strategies that delay appropriate therapy to early, relatively aggressive, and comprehensive antibiotic treatment, especially in patients with BSIs caused by Klebsiella pneumoniae or E.coli.¹⁶ Choi et al found that E.coli is the most common pathogenic microorganism in BSIs, accounting for 32.3%, and the adjusted hazard ratio (aHR) for 30-day mortality and subsequent medical costs for E.coli BSI was lower compared to other microorganisms causing BSI. E.coli-BSI resulted in lower mortality rates during the first 7 days and from days 8 to 30 compared to BSIs caused by other microorganisms.¹⁷ Our study found that the proportion of internal medicine patients was higher in the non-E.coli group, while E.coli infections were more common in the surgical department. The incidence of coronary artery disease was lower in the E.coli group, whereas it was higher in the non-E.coli group. There were significant differences in the duration of ventilator use and central venous catheter use between the E.coli and non-E.coli groups, with patients in the non-E.coli group having a longer durations of use.

NPAR is a simple, readily accessible measure derived from routine blood tests, and it has been proposed as a marker for various infectious and inflammatory conditions.^18–20 As shown in Figure 2, the OR for E.coli BSI decreases with increasing NPAR values. The analysis indicates a significant overall association between NPAR and the risk of E.coli bloodstream infection. Overall P-value: <0.001, indicating a strong statistical significance for the association between NPAR and the risk of E.coli bloodstream infection. In our analysis, the non-linear regression yielded a p-value of 0.424, indicating no significant evidence of a non-linear relationship between NPAR and infection risk. Therefore, we conclude that the relationship is best modeled as linear, suggesting a consistent, proportional association between NPAR and infection risk. OR (95% CI): The odds ratio decreases progressively with higher NPAR levels, approaching 1, indicating that higher NPAR values are associated with a reduced risk of E.coli bloodstream infection. Our findings suggest that low NPAR values are strongly associated with an increased risk of E.coli infection, with patients in the lowest NPAR tertile having substantially higher odds of having an E.coli infection compared to those in the highest tertile. This association remained consistent across various analytical models, further reinforcing the evidence for NPAR as a predictor of E.coli infections. Although the precise mechanisms linking NPAR to infection risk are not fully understood, it is believed that NPAR reflects the balance between inflammatory and immune responses.^21–23 During E. coli BSI, lipopolysaccharides (LPS) derived from the bacterial cell wall activate macrophages and other immune cells via Toll-like receptor 4 (TLR4) and related signaling pathways.²⁴ This activation triggers a cascade release of proinflammatory cytokines, including interleukin (IL)-6, IL-1, and TNF-α. Among these, IL-6 plays a central role in the IL-6–liver axis by markedly stimulating hepatic synthesis of thrombopoietin (TPO), the key regulator of megakaryocyte proliferation and differentiation.²⁵ Elevated TPO levels subsequently enhance platelet production, resulting in reactive thrombocytosis.²⁵ In addition, several inflammatory cytokines, such as IL-6, IL-11, and granulocyte-macrophage colony-stimulating factor (GM-CSF), may directly or indirectly act on hematopoietic stem and progenitor cells to promote megakaryocyte maturation and platelet release. In contrast, non-E. coli BSIs like S. aureus can induce platelet aggregation and clearance through α-toxin- and ClfA-mediated mechanisms, thereby promoting thrombocytopenia and contributing to an elevated NPAR.²⁶ Together, these inflammatory responses provide a plausible explanation for the increased platelet counts frequently observed in E. coli BSI and may partially underlie the association between a low NPAR and disease progression.

The antibiotic resistance profiles of E.coli isolates in this study reveal significant variability across different clinical departments. High resistance rates to Ampicillin, Ampicillin/Sulbactam, and Cefazolin are consistent with previous reports of widespread beta-lactam resistance.^27,28 However, the absence of resistance to Amikacin and Ertapenem is encouraging, as these antibiotics are vital for treating multidrug-resistant infections. The elevated resistance rates to Ciprofloxacin and Levofloxacin in the respiratory and cardiology departments may be linked to the frequent use of these antibiotics in those specialties. The lack of resistance to Tigecycline and Ertapenem across all departments suggests that these antibiotics could serve as effective treatment options for E.coli bloodstream infections. The relatively low resistance rates to Imipenem/Cilastatin and Meropenem in most departments further highlight the importance of carbapenems in managing severe E.coli infections. The significant resistance to Ticarcillin/Clavulanate, particularly in the general surgery department, underscores the need for more judicious use of this antibiotic to prevent the development of further resistance. Doctors should emphasize careful monitoring of antibiotic use, restricting the use of broad-spectrum antibiotics, and promoting individualized treatment guided by sensitivity testing to reduce the spread of resistance.^29,30

Conclusion

The NPAR, as demonstrated in our study, holds significant potential as a simple, cost-effective, and globally applicable biomarker for early identifying and targeted managing E. coli BSI in elderly patients. Low NPAR is associated with an increased likelihood of E. coli BSI and can help clinicians identify high-risk patients who may benefit from early therapeutic interventions. In future research, the role of NPAR as a predictive and prognostic biomarker for E.coli BSI could be further extended to populations across different age groups, with subsequent studies needed to explore longitudinal NPAR trends during treatment as a monitoring biomarker, and further elucidate the underlying biological mechanisms linking NPAR with infection susceptibility and clinical outcomes in E. coli BSI.

Data Confidentiality Statement

All patient data were handled in strict compliance with confidentiality regulations. The data were anonymized prior to analysis, and no identifiable personal information was disclosed or shared outside the research team.

Data Sharing Statement

The data are available from the corresponding author on reasonable request.

Consent to Participate

This research was approved and waived the consent by the Ethics Committee of Chinese PLA General Hospital (NO. S2020-25601). Informed consent was not required due to the retrospective nature of the study design. All authors confirm this study adheres to the Declaration of Helsinki.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This retrospective study was supported by National Clinical Research Center for Geriatric Diseases Open Project: NCRCG-PLAGH-2022012 and NCRCG-PLAGH-2023004; Beijing Natural Science Foundation: L222014.

Disclosure

All authors in this study declare no competing conflicts.

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