Category: 3. Business

Introduction

The Global Initiative for Chronic Obstructive Lung Disease (GOLD) releases an annual report outlining recommendations for diagnosis, management, and prevention of Chronic Obstructive Pulmonary Disease (COPD).¹ While the GOLD reports are formally classified as a strategy, they are commonly regarded as guidelines for prescription of inhaled medication. In the Netherlands, COPD care is guided by either the GOLD strategy or the national guideline, which largely reflect the GOLD recommendations regarding pharmacological treatment.^2,3 The 2023 edition of the GOLD strategy introduced significant updates to inhaled medication treatment.^4,5 These revised guidelines should have substantial implications for clinical practice. Some treatment recommendations remained consistent between the 2022 and 2023 strategy. Two treatment algorithms are used to select the appropriate follow-up inhaled therapy: the exacerbation algorithm for patients experiencing exacerbations and the dyspnoea algorithm for those with persistent dyspnoea. Furthermore, asthma as a comorbidity or history requires primary treatment for asthma, including the use of inhaled corticosteroids (ICS).^5,6 The 2023 strategy introduced three key changes compared with the 2022 recommendations: 1) the use of ICS in the absence of exacerbations, regardless of dyspnoea severity, is no longer recommended; 2) earlier escalation to triple therapy is advised for patients with elevated blood eosinophil counts (≥0.3 x10⁹/L), and 3) the combination of ICS and a long-acting beta-agonist (LABA) as a double therapy is no longer recommended. When ICS is indicated, triple therapy combining a LABA, a long-acting muscarinic antagonist (LAMA), and ICS is advised.⁵ This last recommendation was supported by two large trials that showed that triple therapy was more effective compared to LABA+ICS.^7,8 However, this change resulted in debate regarding patients who were clinically stable on LABA+ICS. To address this, the 2025 strategy introduced an important nuance: clinically stable patients already using LABA+ICS may continue this regimen.¹

There is considerable debate on the appropriate use of ICS in COPD. Although the GOLD strategy provides clear guidance on initiating ICS, it offers no recommendations on how or whether ICS should be withdrawn in (clinically stable) patients, aside from a brief note on discontinuation in case of side effects.^1,5,6 Overuse of ICS could potentially be harmful due to the increased risk for side effects (for example pneumonia). Conversely, there is an undesirable risk for undertreatment when withholding ICS in COPD patients with exacerbations and high eosinophil counts.⁹ Therefore, it is important to assess prescribing patterns in daily practice and to investigate clinicians’ adherence to the latest evidence-based recommendations. Clinicians may encounter barriers when implementing guideline recommendations in daily practice, for example due to resistance to modifying existing treatment regimens or frequent annual guideline updates.¹⁰ Identifying patients whose treatment more frequently deviates from the GOLD strategy may encourage clinicians to evaluate whether current treatment remains appropriate in these patients. Findings from this Dutch cohort study could provide valuable guidance for other healthcare systems facing similar challenges in implementing the GOLD recommendations.

Therefore, this descriptive study aims to evaluate clinicians’ adherence to the recommendations on inhaled medications of the GOLD strategy in daily practice and describe whether, and to what extent, the 2023 update has influenced prescribing patterns. Also, we aimed to identify subgroups of patients at risk for receiving inhaled medication not in line with GOLD recommendations. Furthermore, we explored ICS use and indication, specifically in the context of clinically stable patients. Lastly, barriers to implementation of the GOLD recommendations are discussed.

Methods

Study Design

In this retrospective observational cohort study, data of patients with COPD were collected from the Franciscus Gasthuis and Vlietland hospital, a large teaching hospital specializing in COPD and asthma care in Rotterdam, the Netherlands. First, data of patients with a COPD diagnosis in 2022 were automatically extracted from electronic patient records. Data on asthma as a comorbidity and lung function data that could not be automatically extracted were supplemented by the researchers manually. Secondly, to determine adherence to the guidelines, a cross-year comparison between 2022 and 2023 was conducted. Each patient’s treatment was assessed against the treatment algorithms in the 2022 GOLD strategy at the time it was last in effect. This assessment was repeated in November 2023, after the 2023 GOLD strategy had been in effect for one year. For adequate interpretation, it is important to note that the update of GOLD for the new year (eg “202x”) is routinely presented in the last months of the year before (202x-1). We assessed whether specific patient groups were less likely to receive treatment in accordance with the most recent recommendations. Finally, an in-depth evaluation of ICS use was performed. Ethics approval for this retrospective data study was waived by the Institutional Review Board of the Franciscus Gasthuis & Vlietland, Rotterdam, the Netherlands (identification number 2023–037). The study complies with the Declaration of Helsinki. This study was reported in line with the STROBE reporting checklist for observational studies.¹¹

Setting

Data were sourced from a secondary care hospital specializing in COPD and asthma. This hospital serves as a regional centre of expertise due to its coordination of care. Patients were referred to this hospital by their general practitioners or from other medical facilities, implying that initial therapy had likely been initiated elsewhere. Approximately 20 to 25 healthcare professionals, including treating physicians and specialized nurses, are responsible for the outpatient clinic care and therefore prescribe the inhaled medication.

Patient Selection

Data of all patients with an active COPD diagnosis and at least one inhaled medication prescription in the year before the release of the GOLD 2023 strategy (1–12-2021 until 01–12-2022) were included in the study. Patients were excluded from the study if essential data for objective clinical assessment were missing, such as eosinophil count or Clinical COPD Questionnaire (CCQ) score. Patients younger than 40 years were excluded to avoid including those with primarily asthma. Lastly, patients with a Forced Expiratory Volume in 1 second/Forced Vital Capacity (FEV1/FVC) ratio >0.7 were also excluded, as this criterion does not align with the GOLD definition of COPD.

Data Collection

Data on age, sex, inhaled medication, eosinophils, Clinical COPD Questionnaire (CCQ) score, exacerbations, hospitalizations, and the use of prednis(ol)one, azithromycin, or roflumilast, were automatically extracted from electronic patient records. An exacerbation was defined as a prescription of 30 or 40 mg prednis(ol)one daily for a duration of 3 to 14 days, which, according to COPD guidelines, is the primary treatment for COPD exacerbations in both primary and secondary care in the Netherlands. Use of other systemic corticosteroids is uncommon. The range of doses and number of days was chosen to capture potential variations in prescribing practices between physicians. Prednisone prescriptions were extracted from pharmacy records of our hospital and of the “Landelijk Schakel Punt (LSP)”. LSP is the national pharmacy data-sharing system in the Netherlands and includes the community prescriptions from general practitioners.¹² Antibiotics were excluded from the exacerbation definition as it is very uncommon in the Netherlands to treat COPD exacerbations with antibiotic monotherapy. Hospitalization was classified as any hospital stay linked to the COPD diagnosis, where the primary physician overseeing care was a pulmonologist. Asthma was considered a comorbidity next to COPD if the treating physician documented asthma, asthmatic characteristics, or ACO/ACOS in the medical history or conclusion in the patient record. However, if asthma was not mentioned or if only reversibility or bronchial hyperactivity were described, we considered this variable as negative. Most recent FEV1, FVC, and FEV1/FVC values before the visit to the pulmonologist were collected from the electronic patient record. If there was both a pre- and post-bronchodilator value known, the post value was used. Multiple eosinophil counts were extracted, including the most recent value, the highest count in the past year, the highest count over the past three years, and the highest count recorded ever. The CCQ is the widely used COPD questionnaire in the Netherlands, in contrast to COPD Assessment Test (CAT) or the Medical Research Council (MRC) dyspnoea scale. As the CCQ represents the same domains, these data were collected.¹³

Assessment of Adherence to Inhaled Medication Recommendations

We evaluated the inhaled medication at two time points: 1) the date of the most recent inhaled medication prescription before December 1, 2022, when the 2022 GOLD strategy applied; and 2) one year after the release of the 2023 strategy (November 14, 2023), Figure 1A. By that time, it could be assumed that patients had attended at least one follow-up visit since the new recommendations applied and, if necessary, had had the opportunity to have their inhaled medication adjusted. Our main outcome was the percentage of COPD patients with inhaled treatment according to the relevant GOLD strategy at each timepoint. The assessment of guideline adherence was based on the treatment algorithms in the GOLD strategies. Six objective parameters were used: (i) presence of asthma as comorbidity or in the past; (ii) number of exacerbations; (iii) number of hospitalizations; (iv) previous medication; (v) eosinophil count; and (vi) CCQ symptom score, Figure 1B-C. Guideline adherence was assessed by comparing the recommended treatment, as determined from the assessment shown in Figure 1, with the patient’s actual treatment. If these were identical, guideline adherence was scored as “adherent”; if not, it was scored as “nonadherent”. For the primary assessment of the treatment, we used the most recent CCQ symptom score, highest-ever eosinophil count, and the number of exacerbations and hospitalizations in the previous three years. The CCQ symptom score (the subdomain determined by the sum of question 1, 2, 5 and 6 dived by 4) was used as an objective parameter for dyspnoea. A threshold of 1.9 points was used for severe symptoms, based on a previous study.¹⁴ Our assessment followed the pharmacological follow-up protocols outlined in the GOLD strategies, rather than the initial treatment protocol.^5,6 Finally, while the follow-up inhaled medication protocol in the 2023 and currently applicable 2025 strategies is similar, the latter provides an additional nuance for stable patients on LABA/ICS. The 2025 strategy states that LABA/ICS could be continued if patients currently have no exacerbations, a low symptom load, and no features of asthma.¹ Therefore, we investigated how many patients in 2023 would have had treatment according to the guideline if the 2025 nuance was already applicable.

Figure 1 Overview of the 2022 and 2023 assessment. This figure provides an overview of the two assessments conducted in this study. (A) shows the timeline of the two assessments. (B) illustrates our assessment based on the follow-up treatment protocols in the 2022 GOLD strategy, while (C) illustrated the assessment based on the follow-up treatment protocols in the 2023 GOLD strategy. Abbreviations: LAMA, Long-Acting Muscarinic Antagonist; LABA, Long-Acting Beta-Agonist; ICS, Inhaled corticosteroids.

Differences Between Specific Patient Groups

To evaluate specific patient groups at higher risk of inadequate treatment, we used the 2023 strategy exclusively, since its recommendations are identical to those of the 2025 strategy currently in force. Guideline adherence was assessed in various subgroups: patients with low (<0.1 x10⁹/L), medium (0.1–0.3 x10⁹/L) and high (>0.3 x10⁹/L) eosinophil count; patients treated by the dyspnoea algorithm and those treated by the exacerbation algorithm; different GOLD classifications based on severity of airflow obstruction (I, II, III, IV); and patients on single inhaler triple therapy (SITT) or multiple inhaler triple therapy (MITT).

In-Depth Evaluation of ICS Use

To evaluate ICS use and its indications in greater depth, we first conducted a cross-sectional analysis at the second measurement point of this study (end of 2023), as the 2023 strategy recommendations for ICS are consistent with those of the 2025 strategy currently in effect. We then assessed whether adherence to the 2023 GOLD strategy changed when different timeframes for exacerbations and hospitalizations were applied (previous one, two, or three years). Additionally, we examined the impact of using either the most recent eosinophil count or the highest count recorded in the last year, last three years, or ever. Next, we compared the 2022 and 2023 strategy at the same historical time point: the end of 2022, which was the final moment the 2022 strategy was applicable. We determined ICS indications among ICS users according to the 2022 strategy and assessed what the indication would have been at that same time according to the 2023 strategy. Patients who had an indication under the 2022 strategy but not under the 2023 strategy represent those whose medication would need to be adjusted to align with the newest recommendations. These patients were then re-evaluated one year later, at the second measurement point of this study.

Statistical Analysis

Most outcomes are presented descriptively using means and standard deviations, median and interquartile ranges, or numbers and percentages, as appropriate. The primary outcome (guideline adherence at each time point) is presented as a percentage with the 95% confidence interval (CI). The McNemar test is used to test for statistical significant differences in guideline adherence between 2022 and 2023. The chi-square test was used to compare clinicians’ adherence to the GOLD strategy across different subgroups.

Results

Patient Characteristics

Data of 2434 patients with an active COPD diagnosis in 2022 were automatically extracted from the electronic patient records. After excluding patients under 40 years of age (n=5), those with FEV1/FVC >0.7 (n=85), and those with missing data on eosinophils (n=350) or CCQ score (n=675), a total of 1318 patients were included in the study cohort, Figure 2. Within this cohort, 689 (52.3%) patients were female, and the median age was 70 [interquartile range (IQR) 64–76], Table 1. Median FEV1 post bronchodilator percent predicted was 47 [IQR 36–63]. Asthma was documented as a comorbidity or past diagnosis in 245 patients (18.6%). The majority of patients in the study cohort (n=962, 73.0%) received triple therapy (the combination of a LABA, LAMA and ICS). This proportion of triple therapy users was higher in the study cohort than in the exclusion cohort based on missing eosinophils (54.0%) and the exclusion cohort based on missing CCQ (48.1%), Table S1. Consequently, LABA/LAMA and LABA/ICS use was lower in the study cohort (12.6% and 9.4% respectively) compared to the exclusion sub cohorts with missing eosinophils (21.1% and 11.4% respectively) and the sub cohort with missing CCQ (23.7% and 18.1% respectively). Maintenance use of prednisone and azithromycin was higher in the study cohort (4.7% and 10.9% respectively) than in the exclusion cohorts (2.0–2.1% and 3.0–3.7% respectively).

Figure 2 Patient selection.

Table 1 Baseline Characteristics

Assessment of Adherence to Inhaled Medication Recommendations

During the first measurement point of this study (end of 2022), 680 patients received inhaled medication in line with the recommendations of the 2022 strategy (51.6% with 95% CI interval 48.9–54.3%). During the second measurement point of this study (end 2023), one year after the 2022 analysis, 1074 patients of the study cohort were still alive (81.4%). Of those, the inhaled medication regimen of 615 patients was adherent to the 2023 strategy (57.3% with 95% CI interval 54.3–60.2%). There was no difference in guideline adherence to the 2022 strategy between patients who were alive in 2023 and those who had died: 51.8% and 50.8%, respectively (p=0.789).

Among the patients alive at both time points (n=1074), the inhaled medication prescriptions of 454 patients (42.3%) adhered to both GOLD strategies, 102 (9.5%) adhered in 2022 but not in 2023, 161 (15.0%) did not adhere in 2022 but did in 2023, and the prescriptions of 357 patients (33.2%) did not adhere to the guideline at both time points, Table 2. In these patients, the adherence to the 2022 strategy in 2022 was statistically significantly different from the adherence to the 2023 strategy in 2023 (p < 0.001).

Table 2 Adherence to GOLD Strategy in 2022 and 2023

The vast majority of patients who had prescriptions in line with the GOLD strategy at both time points, as well as those who were deviating from GOLD strategies at both time points, did not have their medication adjusted during the study period: 447 out of 454 (98.5%) and 294 out of 357 (82.4%) respectively, Figure 3A and D.

Figure 3 Treatment patterns between 2022 and 2023. This figure illustrates the medication flows and treatment patterns between 2022 and 2023 among patients who were alive at both time points (n=1074): (A) Patients treated according to GOLD at both time points (n=454, 42.3%); (B) patients treated according to GOLD in 2022 but not in 2023 (n=102, 9.5%); (C) patients not treated according to GOLD in 2022 but are treated according to GOLD in 2023 (n=161, 15.0%); (D) patients not treated according to GOLD at either time point (n=357, 34.3%). Abbreviations: LAMA, Long-Acting Muscarinic Antagonist; LABA, Long-Acting Beta-Agonist; ICS, Inhaled corticosteroids.

Of the 102 patients whose treatment went from adherent to the 2022 strategy to non-adherent to the 2023 strategy, approximately half had no change in medication (n=52, 51.0%) and half had a change in medication (n=50, 49.0%), Figure 3B. For patients whose medication remained the same, changes should have been made according to the 2023 strategy due to the use of LABA/ICS (n=22) or the use of triple therapy due to high symptom burden, which is no longer a justification for ICS in the 2023 strategy (n=5). The treatment of the remaining patients (n=25) no longer met the GOLD recommendations due to differences in exacerbations, hospitalizations, eosinophil counts, or CCQ scores between the 2022 and 2023 assessments. For example, a patient on LABA/LAMA with exacerbations had no indication for ICS in 2022 due to low eosinophil levels at the time, but became eligible for ICS in 2023 when higher eosinophil counts were measured. The 50 patients whose treatment went from adherent in 2022 to nonadherent in 2023 by changes in their medication, showed the following reasons for nonadherence to the guideline: de-escalating triple therapy to LABA/ICS (n=25), withdrawal of ICS despite exacerbations or hospitalizations (n=15) or in the presence of asthma as comorbidity (n=2), start of ICS treatment despite lack of exacerbations (n=5) or in case of eosinophils <0.1 x10⁹/L (n=1), and escalating from LABA/ICS to triple therapy in absence of exacerbations (n=2).

One hundred sixty-one patients were not treated according to the GOLD 2022 strategy, but had treatment in line with the 2023 strategy. Of these, 50 (31.1%) had their medication adjusted, so actively conforming to the strategy, and the remaining 111 patients (68.9%) did not undergo any changes in their medication regimen, Figure 3C. The majority (n=76) of the treatments of these 111 patients were now adherent to the GOLD strategy due to the differences in the recommendations between the 2022 and 2023 strategy, which aligned their existing medications with the updated recommendations. Specifically, for 59 patients on triple therapy, GOLD 2022 recommended LABA/ICS but not triple therapy, whereas the GOLD 2023 now supports the triple therapy. Similarly, 17 patients previously on LABA or LABA/LAMA had an indication for ICS in 2022, but no longer required ICS following the GOLD 2023 revision. For the remaining 35 of the 111 patients whose medication was not changed, differences in exacerbations, hospitalizations, eosinophils, or CCQ scores between the 2022 and 2023 assessments led to a new recommendation in 2023, which coincidentally aligned with their existing medication regimen. For example, one patient experienced exacerbations in 2023, which justified the triple therapy he was already receiving.

Extra Nuance of the 2025 Strategy: Clinically Stable Patients on LABA/ICS

Five of the 120 patients still on LABA/ICS in 2023 had no exacerbations, a low symptom load, and no features of asthma (4.2%), meaning that their medication regimen would have aligned with the recommendations if this current nuance of the 2025 strategy would have been valid at that time.

Differences Between Specific Patient Groups

Certain patient groups were more often received inhaled medication not in accordance with the 2023 strategy, Figure 4. When categorized by eosinophil levels into low (<0.1 x10⁹/L), medium (0.1–0.3 x10⁹/L), and high (≥0.3 x10⁹/L), there were 214 (19.9%), 513 (47.8%), and 347 (32.3%) patients in each subgroup, respectively. In the subgroup with low eosinophils, 69.6% (95% CI 63.2–75.4%) of the patients were not treated according to the 2023 strategy, compared to 40.2% (95% CI 36.0–44.5%) in the medium, and 30.0% (95% CI interval 25.4–35.0%) in the high eosinophil group (p<0.001). Patients who were treated according to the dyspnoea algorithm (n=288) were more likely to receive treatment not in line with the 2023 recommendations compared to patients who were treated according to the exacerbation algorithm (n=786): 68.4% (95% CI interval 62.8–73.5%) and 33.3% (95% CI interval 30.1–36.7%) were treated incorrectly, respectively (p<0.001). Furthermore, nonadherence to the GOLD 2023 strategy was higher in patients with less severe GOLD classifications: 56.6% (95% CI interval 46.7–65.9%) in GOLD I, 43.6% (95% CI interval 38.9–48.5%) in GOLD II, 42.8% (95% CI interval 38.2–47.5%) in GOLD III, and 28.1% (95% CI interval 20.6–36.9%) in GOLD IV subgroup (p<0.001). Among triple therapy users (n=769), 358 (46.6%) used a single device (SITT), while 411 (53.4%) used multiple devices (MITT). Nonadherence to the 2023 strategy was similar: 31.6% (95% CI interval 27.0–36.6%) in SITT compared to 29.2% (95% CI interval 25.0–33.8%) in MITT (p=0.476).

Figure 4 Percentages of patients in different subgroups who are not treated according to the 2023 strategy. Patients are categorized four times: on the far left by eosinophil levels: low (<0.1 x109/L), medium (0.1–0.3 x109/L), and high (≥0.3 x109/L); second from the left those receiving treatment based on the dyspnoea algorithm and those treated according to the exacerbation algorithm in the GOLD strategy; second from the right by GOLD classification based on airflow obstruction severity; and on the far right, triple therapy users are divided into single-inhaler triple therapy (SITT) and multiple-inhaler triple therapy (MITT) users. The blue bars represent the proportion of ICS users, while the grey bars display those without ICS. The blue dotted lines shows the percentage (42.7%) of patients that is not treated according to the GOLD report in the total cohort (n=1074). *= statistically significant difference.

In-Depth Evaluation of ICS Use

The cross-sectional analysis in 2023 showed that, among the patients alive in 2023 (n=1074), 898 patients (83.6%) were using an ICS. Of the total cohort, 625 patients (58.2%) used an ICS appropriately, while 125 patients (11.6%) correctly did not receive ICS treatment. Overtreatment was more prevalent than undertreatment: 273 patients (25.4% with 95% CI interval 22.9–28.1%) received ICS inappropriately, whereas 51 patients (4.7% with 95% CI interval 3.6–6.2%) did not receive and ICS despite having an indication according to the 2023 strategy. The highest ever recorded eosinophil value was used for the primary assessments. When instead only the most recent eosinophil count was considered, the proportion of patients classified as appropriate ICS users decreased from 58.2% to 43.8%, Figure 5A. A similar trend was seen when different timeframes for exacerbations and hospitalizations were applied: 48.4% when using data from the past year, 58.2% for the past three years, and 61.9% for the past five years, Figure 5B. Of the 898 ICS users in 2023, 59 patients (6.6%) had no exacerbations or hospitalizations in the past five years and consistently had an eosinophil count of less than 0.3.

Figure 5 Assessment of ICS treatment using different time intervals. (A) displays the proportion of patients with correct ICS use (dark green), correct ICS absence (light green), incorrect ICS use (dark Orange), and incorrect ICS absence (light orange) across different time intervals of eosinophil count. (B) presents these proportions based on various time intervals for exacerbations and hospitalizations. The black box highlights the values used for the primary assessment of inhalation treatment in this study.

When applying the 2022 and 2023 strategy at the same time point, which was the first measurement time point just before the 2022 strategy was replaced by the 2023 strategy, 1099 patients (83.4%) of the total study cohort (n=1318) were using an ICS. Among them, 88 patients had an indication for ICS at that moment according to the 2022 strategy, but would no longer have an indication once the 2023 strategy became applicable, Table 3. At the second measurement time point of this study, which occurred one year after the implementation of the 2023 strategy, 24 of these 88 patients had died. Of the 64 patients who were still alive, ICS was continued in 55 patients despite still having no indication according to the 2023 strategy, ICS was continued in five patients who had acquired an indication during that year (eg due to an exacerbation or hospitalisation), and it was appropriately discontinued in four patients.

Table 3 Longitudinal Comparison in ICS Users (n=1099) from 2022 to 2023

Discussion

In a real-world Dutch secondary care setting, this study demonstrated that only a small majority of patients were treated according to the GOLD recommendations, with a slight increase from 51.6% in 2022 to 57.3% in 2023. Some patient subgroups were at higher risk of receiving treatment that did not align with the GOLD strategy: patients with low eosinophil counts, those without exacerbations, and those with a less severe GOLD classification. Our in-depth evaluation of ICS revealed that overtreatment with ICS was common and more prevalent than undertreatment.

The adherence rate of 57% is not particularly high. One previous study assessed adherence to the GOLD strategy since the introduction of the updated treatment algorithms in the 2023 report. This cohort study of 3477 patients from 54 hospitals in South Korea reported lower adherence rates than our findings: 31.3% for the GOLD 2019 strategy and 28.0% for the GOLD 2023 strategy. Interestingly, clinicians’ adherence to the 2018 Korean national guideline was higher and more comparable (56.9%), indicating that it likely plays a more prominent role in guiding clinical practice in South Korea than the GOLD strategy.¹⁵ Other studies investigating adherence to earlier versions of the GOLD strategy reported adherence rates ranging from 36% to 60.5%.^16–19 Consistent with our findings, several studies have shown that guideline nonadherence by health care providers is more prevalent in lower-risk groups and in patients without exacerbations, in both primary and secondary care settings.^17–21 Additionally, similar to our results, overtreatment with ICS was frequently reported.^19–22

It is important to consider the barriers to implementing the GOLD recommendations in clinical practice. For example, clinicians could be reluctant to change a winning combination when patients are clinically stable Patients may also have strong preferences regarding specific inhaled medications. Furthermore, as will be discussed in more detail later in this discussion, the criteria for stopping ICS treatment are unclear. The annual release of new GOLD strategies could pose a challenge for clinicians to stay up to date if recommendations change too frequently. Davis et al investigated the knowledge and application of the GOLD recommendations through a survey among primary care and secondary care physicians in 12 countries. Primary care physicians were less familiar with the GOLD strategies than respiratory specialists (58% vs 93%). Surprisingly, the respiratory physicians did not demonstrate more guideline-concordant prescribing practices for inhaled medication than the primary care physicians, suggesting that awareness does not necessarily result in greater adherence to guidelines.¹⁰ Physicians may have followed national guidelines, which can differ from the GOLD recommendations.¹⁵ For instance, a COPD questionnaire survey in Germany showed that most (51.4%) pulmonary specialists preferred national guidelines compared to the GOLD guidelines (40.2%).²³ It could be that not all physicians are always completely at ease with the GOLD strategy. After all, the complete strategy has been compiled by experts, but changes frequently, and, to our knowledge, has never been compared to other possible strategies.

Although we observed that some patients’ inhaled medication had been appropriately adjusted in line with current guidelines, many patients without exacerbations or with low eosinophil counts were still receiving triple therapy, and others remained on LABA/ICS. The 2025 strategy offers additional nuance for the latter group, recommending the continuation of LABA/ICS for clinically stable patients without asthma, no exacerbations, and low symptom burden. However, only a small percentage (4.2%) of LABA/ICS users in our study cohort would have received the correct medication regimen if the 2025 nuances had been in effect at the time of the 2023 assessment. Adherence to the strategy changed between 2022 and 2023 in 263 patients, with some treatments going from adhering to the 2022 strategy to nonadhering to the 2023 strategy (n=102) and vice versa (n=161). Although we found that the guideline adherence rate improved slightly from 51.6% in 2022 to 57.3% in 2023, some of this improvement may be due to other factors than better adherence to the strategy per se. Not all changes in adherence could be attributed to intentional medication adjustments in response to updated recommendations, nor to the revised guidelines (in)validating existing treatment regimens. Furthermore, in a substantial proportion (n=60), changes between adherence to the 2022 and 2023 strategy were driven by variations in key clinical variables between the two assessment periods. For instance, a hospitalisation in 2019 could justify the prescription of an ICS in the 2022 assessment, as it occurred within the previous three years. However, since this hospitalisation occurred more than three years prior to the 2023 assessment, it no longer justified ICS use according to the 2023 GOLD strategy. The importance of the specific timeframes used for assessing appropriate medication was also demonstrated in our detailed assessment of ICS use within the study population.

The vast majority of our study population (83.6%) was treated with an ICS in 2023, with a substantial proportion seemingly being overtreated with ICS (25.4%), putting them at risk of side effects while probably having minimal or no benefit from the ICS. The GOLD strategies provide treatment algorithms and criteria for initiating ICS treatment, either as initial treatment or when escalation is required due to persistent exacerbations.^5,6 However, it is not specified in the GOLD strategy which eosinophil count to use for determination of treatment (eg most recent or highest value known), despite previous studies having demonstrated variability in eosinophil counts over time within individual COPD patients.^24–26 Furthermore, the GOLD strategies do not specify the timeframe for patients who are currently exacerbation-free on ICS treatment. In other words: after how many years without exacerbations is ICS treatment still justified? This absence of clear guidance regarding which eosinophil count and which timeframe for exacerbations should be applied for ICS indication is a key limitation of the current GOLD strategy. As shown by our study, increasing the timeframes for eosinophils and exacerbations increases the proportion of patients with an indication for ICS. It is possible that patients classified in our study as overtreated with ICS due to lack of recent exacerbations, originally had valid reasons for initiating ICS therapy, such as a history of multiple exacerbations at that time.

Currently, there is no conclusive evidence, and consequently no consensus, on how to manage ICS treatment once a patient becomes stable It is unclear after how many years of clinical stability without exacerbations ICS withdrawal should be considered, and whether this even should be considered if the initial indication for ICS was valid. Studies investigating the withdrawal of ICS in patients with COPD showed contradicting results.²⁷ Some studies showed that stopping ICS abruptly resulted in more exacerbations, a decline in lung function, and reduced quality of life. This was seen both in studies that compared ICS continuations to placebo,^27–29 as in one study that continued long-acting bronchodilators in both groups.³⁰ In contrast, other studies that continued long-acting bronchodilators reported no statistically significant differences in exacerbation rates between the ICS continuation and withdrawal group.^31–34 However, these results should be interpreted with caution due to the limited follow-up periods of 3 to 6 months,^31–34 observed trends toward increased exacerbations in patients who discontinued ICS,³⁴ and a higher risk of exacerbations in patients with elevated eosinophil counts.³³ The WISDOM trial is the largest trial to date investigating ICS withdrawal in COPD patients with prior exacerbations.³⁵ In contrast to the previous mentioned trials, this study first tapered the dose of ICS, rather than stopping abruptly. This approach aligns with the GINA guideline recommendations for ICS treatment in stable patients with asthma.³⁶ The WISDOM trial found no significant increase in exacerbations in the ICS withdrawal group (hazard ratio (HR) 1.06; 95% CI: 0.94–1.19). Post-hoc analysis of triple therapy users prior to enrolment showed similar results. However, the follow-up of less than one year and the observed trends towards more severe exacerbations (HR 1.20; 95% CI: 0.98–1.48), greater FEV₁ decline (−38 mL), and worse St. George’s Respiratory Questionnaire (SGRQ) scores (+0.55 vs −0.42) in the withdrawal group raise potential concerns.

The GOLD strategy lacks a recommendation regarding the withdrawal of ICS in clinically stable patients with COPD. GOLD only recommends considering ICS de-escalation in cases of side effects such as pneumonia, with the sidenote that discontinuing ICS in patients with eosinophil counts above 0.3 x10⁹/L could lead to more exacerbations.¹ Other guidelines provide more specific guidance on when ICS could be discontinued if patients remain free of exacerbations. For instance, the most recent Dutch COPD guidelines for both primary and secondary care suggest considering abrupt withdrawal of ICS after two years without exacerbations,^2,3 even though it is acknowledged in the latter that this recommendation is not supported by any evidence.³ Since one of the primary reasons for initiating ICS is to reduce exacerbations, stopping treatment when it has been successful (ie no exacerbations) seems contradictory. In our study, only a small proportion of patients (6.6%) consistently had eosinophil counts below 0.3 x10⁹/L and remained free of exacerbations and hospitalisations for at least five years. Furthermore, it should be noted that a recent systematic review and meta-analysis showed a reduction in all-cause mortality in COPD patients with ICS treatment.³⁷ The effect of discontinuing ICS on this mortality benefit is unknown. The lack of evidence on ICS withdrawal in stable patients may make clinicians reluctant to stop ICS therapy. This is reflected in our study by the small proportion of ICS users whose indication under the 2022 strategy no longer applied in 2023, and who actually had their ICS discontinued (4 out of 59). Conducting a prospective study with long-term follow-up to evaluate the impact of ICS discontinuation in clinically stable COPD patients could help fill this evidence gap.

Our study has some limitations. The data were extracted from a single secondary care centre in the Netherlands, yet including multiple prescribers. As shown in the baseline characteristics, our study population includes a relatively high proportion of triple therapy users, suggesting that our hospital may have a more severe COPD population, as indicated by the mortality rate. Although the hospital serves as a regional centre of expertise due to its coordination of care, its COPD population is comparable to those of general hospitals. In the Netherlands, COPD care is guided by either the GOLD strategy or the national guideline, which largely align with GOLD recommendations on pharmacological treatment.^2,3 Therefore, we expect that the findings of this study are also applicable to other countries where COPD care is guided by the GOLD strategy. This is supported by the similarity of our results to those of previous studies in a secondary care setting in other countries.^16–20,22 Consistent with previous studies, our study showed that clinicians were less likely to adhere to the GOLD recommendations regarding inhaled medication when treating low-risk patients.^17–21 Moreover, Rodrigues et al showed in a Dutch primary care setting that 36.4% of the COPD patients did not receive any inhaled medication within the first three months after diagnosis.³⁸ As most COPD patients in the Netherlands are treated in primary care, overall adherence to the GOLD strategy may be even lower in the general population than in our study cohort. Furthermore, patients with missing CCQ or eosinophil data were excluded from the study cohort. This may have introduced some selection bias towards patients with more severe COPD, since these values are more likely to be missing in patients with stable, less severe disease. This is also reflected in treatment patterns, with the excluded cohort receiving triple therapy less frequently. One might argue that in most patients, according to the latest recommendations by GOLD, it is not possible to determine whether treatment is appropriate without knowing the eosinophil count and CCQ score or another disease burden questionnaire, such as the Medical Research Council (MRC) or COPD Assessment Test (CAT). Physicians should be encouraged to assess the eosinophil count and disease burden in all COPD patients, including those with less severe and more clinically stable disease, to determine appropriate treatment. Due to the design of the study, it was not possible to account for the potential use of systemic corticosteroids prior to eosinophil testing. Similarly, appropriate de-escalation of ICS treatment based on the presence of pneumonia or other side effects could not be assessed. Given that, according to our assessment, only a small number of patients were undertreated, we expect that this did not occur often. Also, prednis(ol)one prescriptions were used as a surrogate for exacerbations. Exacerbations could have been missed if pharmacy records were not available in the “Landelijk Schakelpunt (LSP)” or if exacerbations were not treated with prednis(ol)one. However, given that most pharmacies participate in the LSP (95%) and most patients granted consent (90%), we assume that the number of prednisone prescriptions not recorded in our dataset is limited.¹² Furthermore, based on our clinical experience and in line with GOLD and national guidelines, we anticipate that it is very uncommon for patients with COPD exacerbations to be treated with antibiotics without the use of prednisone. Due to the limitations of this retrospective study, our database did not contain all factors that could influence adherence to the guidelines at patient-, prescriber- or time-related levels. These factors, for instance, include patient preferences, side-effects, and previous ICS response. As a next step following this descriptive study, a prospective study incorporating these factors in a multivariate regression analysis or an in depth qualitative study would help clarify what drives guideline non-adherence.

Conclusion

A substantial proportion of patients with COPD are not treated in accordance with prevailing recommendations in the GOLD strategy, particularly those with a low eosinophil count, those without exacerbations or hospitalisations, and those with a less severe GOLD classification. This study demonstrates that even after the 2023 updates to inhaled medication recommendations, guideline adherence improved only slightly and few had their medication actively adjusted, highlighting the real-world challenges of implementing guideline recommendations in daily practice. While there may be valid reasons for deviating from the guidelines, these findings should prompt treating physicians to reconsider the appropriateness of the current treatment for their patients. ICS overtreatment was frequent, posing an increased risk of unwanted short and long term side effects. This study highlighted key limitations of the current GOLD strategy, including the lack of consensus on whether to continue ICS treatment in stable COPD patients who initially had an appropriate indication, and the absence of clear guidance on which eosinophil count and which exacerbation timeframe should be use to guide ICS continuation. Conducting additional consensus studies on ICS tapering could provide clearer guidance for clinicians on which patients can safely discontinue ICS and which cannot, potentially leading to greater adherence to guidelines in clinical practice.

Data Sharing Statement

All data relevant to the study are included in the article or uploaded as supplementary information. The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Ethics Approval and Consent to Participate

Ethics approval for this study was waived by the Institutional Research Board of the Franciscus Gasthuis & Vlietland, Rotterdam, the Netherlands (identification number 2023-037). This research was declared outside the scope of the Medical Research Involving Human Subjects Act, as it used routinely collected healthcare data that had been pseudonymized. The Institutional Research Board of Franciscus Gasthuis & Vlietland waived the requirement to obtain informed consent, because the data is collected in a coded (pseudonymized) form, and obtaining consent is not feasible due to the large number of patients.

Acknowledgments

We are grateful to dr. Simone Rauh for her help in processing the data.

Author Contributions

LC and JiV initiated this study. 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

The faculty received an unrestricted grant from Chiesi Pharmaceuticals B.V. Furthermore, the funding includes contributions from the research group in the Franciscus Gasthuis & Vlietland hospital and transformation funds from health insurance companies.

Disclosure

LC: the faculty has received an unrestricted grant from Chiesi Pharmaceuticals B.V (outside the scope of this work). JvB: has received grants and/or consultancy fees from ALK-Abello, AstraZeneca, Chiesi, European Commission COST (COST Action 19132), GSK, Novartis, Pfizer, Teva, Trudell Medical, and Vertex, outside the submitted work and all paid to his institution (UMCG). HK: has received research/educational grants and served on advisory boards for Boehringer Ingelheim, GSK and Novartis, and has served on advisory boards for AstraZeneca and Chiesi. JiV: reports unrestricted faculty research grants from GSK, Teva, AZ, Chiesi, Sanofi, and speaker fees from AZ, GSK, Sanofi, Chiesi, Stichting RoLeX and Health Investment. The authors report no other conflicts of interest in this work.

References

1. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease (2025 report). Global Initiative for Chronic Obstructive Lung Disease.

2. Bischoff EBM, Broekhuizen L, Donkers J, et al. 1NHG-standaard COPD. Nederlands Huisartsen Genootschap. 2021. Available from: https://richtlijnen.nhg.org/standaarden/copd. Accessed November 20, 2025.

3. Federatie Medisch Specialisten (FMS) richtlijn COPD. Nederlandse Verenigingvan Artsen voor Longziektenen Tuberculose (NVALT). Available from: https://richtlijnendatabase.nl/richtlijn/copd/startpagina_-_copd.html. Accessed November 20, 2025.; 2023.

4. Agusti A, Celli BR, Criner GJ, et al. Global Initiative for chronic obstructive lung disease 2023 report: GOLD executive summary. Eur Respir J. 2023;61(4):2300239. doi:10.1183/13993003.00239-2023

5. Global strategy for prevention, diagnosis and management of: 2023 report COPD. Available from: https://goldcopd.org/2023-gold-report-2/2023. Accessed November 20, 2025.

6. 2022 Global Strategy for Prevention, Diagnosis and Management of COPD. Available from: https://goldcopd.org/2022-gold-reports/2022. Accessed November 20, 2025.

7. Lipson DA, Barnhart F, Brealey N, et al. Once-daily single-inhaler triple versus dual therapy in patients with COPD. N Engl J Med. 2018;378(18):1671–1680. doi:10.1056/NEJMoa1713901

8. Rabe KF, Martinez FJ, Ferguson GT, et al. Triple inhaled therapy at two glucocorticoid doses in moderate-to-very-severe COPD. N Engl J Med. 2020;383(1):35–48. doi:10.1056/NEJMoa1916046

9. Quint JK, Ariel A, Barnes PJ. Rational use of inhaled corticosteroids for the treatment of COPD. NPJ Prim Care Respir Med. 2023;33(1):27. doi:10.1038/s41533-023-00347-6

10. Davis KJ, Landis SH, Oh YM, et al. Continuing to Confront COPD International Physician Survey: physician knowledge and application of COPD management guidelines in 12 countries. Int J Chron Obstruct Pulmon Dis. 2015;10:39–55. doi:10.2147/COPD.S70162

11. von Elm E, Altman DG, Egger M, et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61(4):344–349. doi:10.1016/j.jclinepi.2007.11.008

12. Samenvatting en Conclusie Apotheken, RIVM Onderzoek ICT in de Zorg. Rijksinstituut voor Volksgezondheid en Milieu (RIVM). 2016. Available from: https://www.rivm.nl/documenten/samenvatting-resultaten-apotheken. Accessed, 2025.

13. Sundh J, Stallberg B, Lisspers K, Kampe M, Janson C, Montgomery S. Comparison of the COPD assessment test (CAT) and the Clinical COPD questionnaire (CCQ) in a clinical population. COPD. 2016;13(1):57–65. doi:10.3109/15412555.2015.1043426

14. Smid DE, Franssen FME, Gonik M, et al. Redefining cut-points for high symptom burden of the global initiative for chronic obstructive lung disease classification in 18,577 patients with chronic obstructive pulmonary disease. J Am Med Dir Assoc. 2017;18(12):1097e11–e24. doi:10.1016/j.jamda.2017.09.003

15. Han SM, Kim HS, Park SY, et al. Adherence to pharmacological management guidelines for stable chronic obstructive lung disease. Tuberc Respir Dis. 2025;88(2):310–321. doi:10.4046/trd.2024.0130

16. Mannino DM, Yu TC, Zhou H, Higuchi K. Effects of GOLD-adherent prescribing on COPD symptom burden, exacerbations, and health care utilization in a real-world setting. Chronic Obstr Pulm Dis. 2015;2(3):223–235. doi:10.15326/jcopdf.2.3.2014.0151

17. Grewe FA, Sievi NA, Bradicich M, et al. Compliance of pharmacotherapy with GOLD guidelines: a longitudinal study in patients with COPD. Int J Chron Obstruct Pulmon Dis. 2020;15:627–635. doi:10.2147/COPD.S240444

18. Palmiotti GA, Lacedonia D, Liotino V, et al. Adherence to GOLD guidelines in real-life COPD management in the Puglia region of Italy. Int J Chron Obstruct Pulmon Dis. 2018;13:2455–2462. doi:10.2147/COPD.S157779

19. Scalone G, Nava S, Ventrella F, et al. Pharmacological approach and adherence to treatment recommendations in frequently and non-frequently exacerbating COPD patients from Italy: MISTRAL – The prospective cohort, observational study. Pulm Pharmacol Ther. 2018;53:68–77. doi:10.1016/j.pupt.2018.09.001

20. Alabi FO, Alkhateeb HA, Zibanayi MT, et al. The adherence to and utility of the Global Initiative for Chronic Obstructive Lung Disease guidelines for treating COPD among pulmonary specialists: a retrospective analysis. BMC Pulm Med. 2023;23(1):216. doi:10.1186/s12890-023-02503-7

21. Mangold V, Boesing M, Berset C, et al. Adherence to the GOLD guidelines in primary care: data from the Swiss COPD cohort. J Clin Med. 2023;12(20):6636. doi:10.3390/jcm12206636

22. Larsson K, Ekberg-Jansson A, Stridsman C, Hanno M, Vanfleteren L. Adherence to treatment recommendations for chronic obstructive pulmonary disease – results from the swedish national airway register. Int J Chron Obstruct Pulmon Dis. 2021;16:909–918. doi:10.2147/COPD.S300299

23. Glaab T, Vogelmeier C, Hellmann A, Buhl R. Guideline-based survey of outpatient COPD management by pulmonary specialists in Germany. Int J Chron Obstruct Pulmon Dis. 2012;7:101–108. doi:10.2147/COPD.S27887

24. Singh D, Kolsum U, Brightling CE, et al. Eosinophilic inflammation in COPD: prevalence and clinical characteristics. Eur Respir J. 2014;44(6):1697–1700. doi:10.1183/09031936.00162414

25. Kwok WC, Chau CH, Tam TCC, Lam FM, Ho JCM. Variability of blood eosinophil count at stable-state in predicting exacerbation risk of chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis. 2023;18:1145–1153. doi:10.2147/COPD.S401357

26. Bafadhel M, Pavord ID, Russell REK. Eosinophils in COPD: just another biomarker? Lancet Respir Med. 2017;5(9):747–759. doi:10.1016/S2213-2600(17)30217-5

27. Georgiou A, Ramesh R, Schofield P, White P, Harries TH. Withdrawal of inhaled corticosteroids from patients with COPD; effect on exacerbation frequency and lung function: a systematic review. Int J Chron Obstruct Pulmon Dis. 2024;19:1403–1419. doi:10.2147/COPD.S436525

28. van der Valk P, Monninkhof E, van der Palen J, Zielhuis G, van Herwaarden C. Effect of discontinuation of inhaled corticosteroids in patients with chronic obstructive pulmonary disease: the COPE study. Am J Respir Crit Care Med. 2002;166(10):1358–1363. doi:10.1164/rccm.200206-512OC

29. Choudhury AB, Dawson CM, Kilvington HE, et al. Withdrawal of inhaled corticosteroids in people with COPD in primary care: a randomised controlled trial. Respir Res. 2007;8(1):93. doi:10.1186/1465-9921-8-93

30. Wouters EF, Postma DS, Fokkens B, et al. Withdrawal of fluticasone propionate from combined salmeterol/fluticasone treatment in patients with COPD causes immediate and sustained disease deterioration: a randomised controlled trial. Thorax. 2005;60(6):480–487. doi:10.1136/thx.2004.034280

31. Rossi A, van der Molen T, Del Olmo R, et al. INSTEAD: a randomised switch trial of indacaterol versus salmeterol/fluticasone in moderate COPD. Eur Respir J. 2014;44(6):1548–1556. doi:10.1183/09031936.00126814

32. Frith PA, Ashmawi S, Krishnamurthy S, et al. Efficacy and safety of the direct switch to indacaterol/glycopyrronium from salmeterol/fluticasone in non-frequently exacerbating COPD patients: the FLASH randomized controlled trial. Respirology. 2018;23(12):1152–1159. doi:10.1111/resp.13374

33. Chapman KR, Hurst JR, Frent SM, et al. Long-term triple therapy de-escalation to indacaterol/glycopyrronium in patients with chronic obstructive pulmonary disease (SUNSET): a randomized, double-blind, triple-dummy clinical trial. Am J Respir Crit Care Med. 2018;198(3):329–339. doi:10.1164/rccm.201803-0405OC

34. Harries TH, Gilworth G, Corrigan CJ, et al. Withdrawal of inhaled corticosteroids from patients with COPD with mild or moderate airflow limitation in primary care: a feasibility randomised trial. BMJ Open Respir Res. 2022;9(1). doi:10.1136/bmjresp-2022-001311.

35. Magnussen H, Disse B, Rodriguez-Roisin R, et al. Withdrawal of inhaled glucocorticoids and exacerbations of COPD. N Engl J Med. 2014;371(14):1285–1294. doi:10.1056/NEJMoa1407154

36. Global Strategy for Asthma Management and Prevention. Global Initiative for Asthma; 2024. Available from: https://ginasthma.org/2024-report/. Accessed November 20, 2025.

37. Rogliani P, Manzetti GM, Gholamalishahi S, Bafadhel M, Calzetta L. Inhaled corticosteroids in chronic obstructive pulmonary disease: a systematic review and meta-analysis on mortality protection – making a long story short. Expert Rev Respir Med. 2025;1–11. doi:10.1080/17476348.2025.2465853

38. Rodrigues G, Antao J, Deng Q, et al. Trends in initial pharmacological COPD treatment in primary care (2010-2021): a population-based study using the PHARMO Data Network. Respir Res. 2024;25(1):447. doi:10.1186/s12931-024-03073-w

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.

References

1. Ni J, Wu GD, Albenberg L, Tomov VT. Gut microbiota and IBD: causation or correlation? Nat Rev Gastroenterol Hepatol. 2017;14(10):573–584. doi:10.1038/nrgastro.2017.88

2. Kostic AD, Xavier RJ, Gevers D. The microbiome in inflammatory bowel disease: current status and the future ahead. Gastroenterology. 2014;146(6):1489–1499. doi:10.1053/j.gastro.2014.02.009

3. Shan Y, Lee M, Chang EB. The gut microbiome and inflammatory bowel diseases. Annual Review of Medicine. 2022;73:455–468. doi:10.1146/annurev-med-042320-021020

4. Lloyd-Price J, Arze C, Ananthakrishnan AN, et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature. 2019;569(7758):655–662. doi:10.1038/s41586-019-1237-9

5. Morgan XC, Tickle TL, Sokol H, et al. Dysfunction of the intestinal microbiome in inflammatory bowel disease and treatment. Genome Biol. 2012;13(9):R79. doi:10.1186/gb-2012-13-9-r79

6. Ning L, Zhou Y-L, Sun H, et al. Microbiome and metabolome features in inflammatory bowel disease via multi-omics integration analyses across cohorts. Nat Commun. 2023;14(1):7135. doi:10.1038/s41467-023-42788-0

7. Zhang Y, Thomas JP, Korcsmaros T, Gul L. Integrating multi-omics to unravel host-microbiome interactions in inflammatory bowel disease. Cell Rep Med. 2024;5(9):101738. doi:10.1016/j.xcrm.2024.101738

8. Hu S, Bourgonje AR, Gacesa R, et al. Mucosal host-microbe interactions associate with clinical phenotypes in inflammatory bowel disease. Nat Commun. 2024;15(1):1470. doi:10.1038/s41467-024-45855-2

9. Jayasooriya N, Baillie S, Blackwell J, et al. Systematic review with meta-analysis: time to diagnosis and the impact of delayed diagnosis on clinical outcomes in inflammatory bowel disease. Aliment. Pharmacol. Ther. 2023;57(6):635–652. doi:10.1111/apt.17370

10. Zhang SY, Lin Y. Addressing diagnostic delays in inflammatory bowel diseases in Germany. World J Gastroenterol. 2024;30(41):4484–4489. doi:10.3748/wjg.v30.i41.4484

11. Henriksen M, Jahnsen J, Lygren I, et al. C-reactive protein: a predictive factor and marker of inflammation in inflammatory bowel disease. Results from a prospective population-based study. Gut. 2008;57(11):1518–1523. doi:10.1136/gut.2007.146357

12. Walsham NE, Sherwood RA. Fecal calprotectin in inflammatory bowel disease. Clin Exp Gastroenterol. 2016;9:21–29. doi:10.2147/ceg.S51902

13. Clough J, Colwill M, Poullis A, Pollok R, Patel K, Honap S. Biomarkers in inflammatory bowel disease: a practical guide. Ther Adv Gastroenterol. 2024;17:17562848241251600. doi:10.1177/17562848241251600

14. Verstockt B, Noor NM, Marigorta UM, Pavlidis P, Deepak P, Ungaro RC. Results of the seventh scientific workshop of ECCO: precision medicine in IBD-disease outcome and response to therapy. J Crohns Colitis. 2021;15(9):1431–1442. doi:10.1093/ecco-jcc/jjab050

15. Chen L, Zhang C, Niu R, et al. Multi-omics biomarkers for predicting efficacy of biologic and small-molecule therapies in adults with inflammatory bowel disease: a systematic review. United Eur Gastroenterol J. 2025;13(4):517–530. doi:10.1002/ueg2.12720

16. Mallick H, Rahnavard A, McIver LJ, et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol. 2021;17(11):e1009442. doi:10.1371/journal.pcbi.1009442

17. Segata N, Izard J, Waldron L, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12(6):R60. doi:10.1186/gb-2011-12-6-r60

18. Hernández Medina R, Kutuzova S, Nielsen KN, et al. Machine learning and deep learning applications in microbiome research. ISME Commun. 2022;2(1):98. doi:10.1038/s43705-022-00182-9

19. Nearing JT, Douglas GM, Hayes MG, et al. Microbiome differential abundance methods produce different results across 38 datasets. Nat Commun. 2022;13(1):342. doi:10.1038/s41467-022-28034-z

20. Halfvarson J, Brislawn CJ, Lamendella R, et al. Dynamics of the human gut microbiome in inflammatory bowel disease. Nat Microbiol. 2017;2(5):17004. doi:10.1038/nmicrobiol.2017.4

21. Franzosa EA, Sirota-Madi A, Avila-Pacheco J, et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat Microbiol. 2019;4(2):293–305. doi:10.1038/s41564-018-0306-4

22. Tarca AL, Carey VJ, X-w C, Romero R, Drăghici S. Machine Learning and Its Applications to Biology. PLoS Comput Biol. 2007;3(6):e116. doi:10.1371/journal.pcbi.0030116

23. Marcos-Zambrano LJ, Karaduzovic-Hadziabdic K, Loncar Turukalo T, et al. Applications of machine learning in human microbiome studies: a review on feature selection, biomarker identification, disease prediction and treatment. Review. Front Microbiol. 2021;Volume 12. doi:10.3389/fmicb.2021.634511

24. Zheng J, Sun Q, Zhang M, et al. Noninvasive, microbiome-based diagnosis of inflammatory bowel disease. Nat Med. 2024;30(12):3555–3567. doi:10.1038/s41591-024-03280-4

25. Khachatryan L, Xiang Y, Ivanov A, et al. Results and lessons learned from the sbv IMPROVER metagenomics diagnostics for inflammatory bowel disease challenge. Sci Rep. 2023;13(1):6303. doi:10.1038/s41598-023-33050-0

26. Caminero A, Tropini C, Valles-Colomer M, et al. Credible inferences in microbiome research: ensuring rigour, reproducibility and relevance in the era of AI. Nat Rev Gastroenterol Hepatol. 2025;22(11):788–803. doi:10.1038/s41575-025-01100-9

27. Gilbert JA, Azad MB, Bäckhed F, et al. Clinical translation of microbiome research. Nat Med. 2025;31(4):1099–1113. doi:10.1038/s41591-025-03615-9

28. Ma X, Lu X, Zhang W, et al. Gut microbiota in the early stage of Crohn’s disease has unique characteristics. Gut Pathog. 2022;14(1):46. doi:10.1186/s13099-022-00521-0

29. Nickols WA, Kuntz T, Shen J, et al. MaAsLin 3: refining and extending generalized multivariable linear models for meta-omic association discovery. bioRxiv. 2024:2024.12.13.628459. doi:10.1101/2024.12.13.628459

30. Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11(1):3514. doi:10.1038/s41467-020-17041-7

31. Lin H, Peddada SD. Multigroup analysis of compositions of microbiomes with covariate adjustments and repeated measures. Nat Method. 2024;21(1):83–91. doi:10.1038/s41592-023-02092-7

32. Chen W, Li Y, Wang W, et al. Enhanced microbiota profiling in patients with quiescent Crohn’s disease through comparison with paired healthy first-degree relatives. Cell Rep Med. 2024;5(7):101624. doi:10.1016/j.xcrm.2024.101624

33. Rosso AD, Aguilera P, Quesada S, et al. Comprehensive phenotyping in inflammatory bowel disease: search for biomarker algorithms in the transkingdom interactions context. Microorganisms. 2022;10(11):2190. doi:10.3390/microorganisms10112190

34. Gloor GB. ALDEx 2: ANOVA-Like Differential Expression tool for compositional data. 2016.

35. Zhou H, He K, Chen J, Zhang X. LinDA: linear models for differential abundance analysis of microbiome compositional data. Genome Biol. 2022;23(1):95. doi:10.1186/s13059-022-02655-5

36. Vich Vila A, Imhann F, Collij V, et al. Gut microbiota composition and functional changes in inflammatory bowel disease and irritable bowel syndrome. Sci Transl Med. 2018;10(472). doi:10.1126/scitranslmed.aap8914

37. Akiyama S, Nishijima S, Kojima Y, et al. Multi-biome analysis identifies distinct gut microbial signatures and their crosstalk in ulcerative colitis and Crohn’s disease. Nat Commun. 2024;15(1):10291. doi:10.1038/s41467-024-54797-8

38. Tun HM, Peng Y, Massimino L, et al. Gut virome in inflammatory bowel disease and beyond. Gut. 2024;73(2):350–360. doi:10.1136/gutjnl-2023-330001

39. Norman Jason M, Handley Scott A, Baldridge Megan T, et al. Disease-specific alterations in the enteric virome in inflammatory bowel disease. Cell. 2015;160(3):447–460. doi:10.1016/j.cell.2015.01.002

40. Hsu C, Ghannoum M, Cominelli F, Martino LD. Mycobiome and inflammatory bowel disease: role in disease pathogenesis, current approaches and novel nutritional-based therapies. Inflamm Bowel Dis. 2023;29(3):470–479. doi:10.1093/ibd/izac156

41. Zhang X, Deeke SA, Ning Z, et al. Metaproteomics reveals associations between microbiome and intestinal extracellular vesicle proteins in pediatric inflammatory bowel disease. Nat Commun. 2018;9(1):2873. doi:10.1038/s41467-018-05357-4

42. Li M, Yang L, Mu C, et al. Gut microbial metabolome in inflammatory bowel disease: from association to therapeutic perspectives. Comput Struct Biotechnol J. 2022;20:2402–2414. doi:10.1016/j.csbj.2022.03.038

43. Liu L, Wang G, Safo SE. Multivariate sparse functional linear discriminant analysis: an application to inflammatory bowel disease classification. arXiv preprint, arXiv. 2025.

44. Gloor GB, Macklaim JM, Pawlowsky-Glahn V, Egozcue JJ. Microbiome datasets are compositional: and this is not optional. Mini review. Front Microbiol. 2017;8. doi:10.3389/fmicb.2017.02224

45. Silverman JD, Washburne AD, Mukherjee S, David LA. A phylogenetic transform enhances analysis of compositional microbiota data. eLife. 2017;6:e21887. doi:10.7554/eLife.21887

46. Hodgkiss R, Acharjee A. Unravelling metabolite-microbiome interactions in inflammatory bowel disease through AI and interaction-based modelling. Biochimica et Biophysica Acta. 2025;1871(3):167618. doi:10.1016/j.bbadis.2024.167618

47. Manandhar I, Alimadadi A, Aryal S, Munroe PB, Joe B, Cheng X. Gut microbiome-based supervised machine learning for clinical diagnosis of inflammatory bowel diseases. Am J Physiol Gastrointest Liver Physiol. 2021;320(3):G328–g337. doi:10.1152/ajpgi.00360.2020

48. Papoutsoglou G, Tarazona S, Lopes MB, et al. Machine learning approaches in microbiome research: challenges and best practices. Review. Front Microbiol. 2023;14. doi:10.3389/fmicb.2023.1261889

49. Lee S, Lee I. Comprehensive assessment of machine learning methods for diagnosing gastrointestinal diseases through whole metagenome sequencing data. Gut Microbes. 2024;16(1):2375679. doi:10.1080/19490976.2024.2375679

50. Liñares-Blanco J, Fernandez-Lozano C, Seoane JA, López-Campos G. Machine learning based microbiome signature to predict inflammatory bowel disease subtypes. original research. Front Microbiol. 2022;13. doi:10.3389/fmicb.2022.872671

51. Kim H, Na JE, Kim S, et al. A machine learning-based diagnostic model for crohn’s disease and ulcerative colitis utilizing fecal microbiome analysis. Microorganisms. 2023;12(1):36. doi:10.3390/microorganisms12010036

52. Ha SM, Lee K, Kim GH, Hurych J, Cinek O, Shim JO. Gut-microbiota-based ensemble model predicts prognosis of pediatric inflammatory bowel disease. Iscience. 2024;27(12):111442. doi:10.1016/j.isci.2024.111442

53. Jacobs JP, Sauk JS, Ahdoot AI, et al. Microbial and metabolite signatures of stress reactivity in ulcerative colitis patients in clinical remission predict clinical flare risk. Inflamm Bowel Dis. 2023;30(3):336–346. doi:10.1093/ibd/izad185

54. Kim HS, Oh SJ, Kim BK, et al. Dysbiotic signatures and diagnostic potential of gut microbial markers for inflammatory bowel disease in Korean population. Sci Rep. 2024;14(1):23701. doi:10.1038/s41598-024-74002-6

55. Lee Y, Cappellato M, Di Camillo B. Machine learning-based feature selection to search stable microbial biomarkers: application to inflammatory bowel disease. Gigascience. 2022;12. doi:10.1093/gigascience/giad083

56. Abbas M, Matta J, Le T, et al. Biomarker discovery in inflammatory bowel diseases using network-based feature selection. PLoS One. 2019;14(11):e0225382. doi:10.1371/journal.pone.0225382

57. Breiman L. Random Forests. Mach Learn. 2001;45(1):5–32. doi:10.1023/A:1010933404324

58. Statnikov A, Henaff M, Narendra V, et al. A comprehensive evaluation of multicategory classification methods for microbiomic data. Microbiome. 2013;1(1):11. doi:10.1186/2049-2618-1-11

59. Knights D, Costello EK, Knight R. Supervised classification of human microbiota. FEMS Microbiol Rev. 2011;35(2):343–359. doi:10.1111/j.1574-6976.2010.00251.x

60. Li L, Deng X, Wang S, Huang T. Integrating traditional omics and machine learning approaches to identify microbial biomarkers and therapeutic targets in pediatric inflammatory bowel disease. Front Pharmacol. 2025;16:1545392. doi:10.3389/fphar.2025.1545392

61. Li L, Liu Z, Qin J, et al. Constructing inflammatory bowel disease diagnostic models based on k-mer and machine learning. Front Microbiol. 2025;16:1578005. doi:10.3389/fmicb.2025.1578005

62. Gonzalez CG, Mills RH, Zhu Q, et al. Location-specific signatures of Crohn’s disease at a multi-omics scale. Microbiome. 2022;10(1):133. doi:10.1186/s40168-022-01331-x

63. Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–297. doi:10.1007/BF00994018

64. Jiang Y, Luo J, Huang D, Liu Y, Li DD. Machine learning advances in microbiology: a review of methods and applications. Front Microbiol. 2022;13:925454. doi:10.3389/fmicb.2022.925454

65. Jerome HF. Greedy function approximation: a gradient boosting machine. Ann Stat. 2001;29(5):1189–1232. doi:10.1214/aos/1013203451

66. Przymus P, Rykaczewski K, Martín-Segura A, et al. Deep learning in microbiome analysis: a comprehensive review of neural network models. Rev Front Microbiol. 2025;15. doi:10.3389/fmicb.2024.1516667

67. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444. doi:10.1038/nature14539

68. Polikar R. Ensemble based systems in decision making. IEEE Circuits Syst Mag. 2006;6(3):21–45. doi:10.1109/MCAS.2006.1688199

69. Dudek NK, Chakhvadze M, Kobakhidze S, Kantidze O, Gankin Y. Supervised machine learning for microbiomics: bridging the gap between current and best practices. Mach Learn Appl. 2024;18:100607. doi:10.1016/j.mlwa.2024.100607

70. Topçuoğlu BD, Lesniak NA, MTt R, Wiens J, Schloss PD. A framework for effective application of machine learning to microbiome-based classification problems. mBio. 2020;11(3). doi:10.1128/mBio.00434-20

71. Kubinski R, Djamen-Kepaou JY, Zhanabaev T, et al. Benchmark of data processing methods and machine learning models for gut microbiome-based diagnosis of inflammatory bowel disease. Front Genet. 2022;13:784397. doi:10.3389/fgene.2022.784397

72. Lin H, Peddada SD. Analysis of microbial compositions: a review of normalization and differential abundance analysis. Npj Biofilms Microbi. 2020;6(1):60. doi:10.1038/s41522-020-00160-w

73. Pan AY. Statistical analysis of microbiome data: the challenge of sparsity. Curr. Opin. Endocr. Metab. Res. 2021;19:35–40. doi:10.1016/j.coemr.2021.05.005

74. Zand A, Stokes Z, Sharma A, van Deen WK, Hommes D. Artificial intelligence for Inflammatory Bowel Diseases (IBD); accurately predicting adverse outcomes using machine learning. Dig Dis Sci. 2022;67(10):4874–4885. doi:10.1007/s10620-022-07506-8

75. Oh M, Zhang L. DeepMicro: deep representation learning for disease prediction based on microbiome data. Sci Rep. 2020;10(1):6026. doi:10.1038/s41598-020-63159-5

76. Jain S, Safo SE. DeepIDA-GRU: a deep learning pipeline for integrative discriminant analysis of cross-sectional and longitudinal multiview data with applications to inflammatory bowel disease classification. Briefings Bioinf 2024;25(4). doi:10.1093/bib/bbae339

77. Reiman D, Metwally A, Yang D. Using convolutional neural networks to explore the microbiome. Annu Int Conf IEEE Eng Med Biol Soc. 2017;2017:4269–4272. doi:10.1109/embc.2017.8037799

78. Grazioli F, Siarheyeu R, Alqassem I, Henschel A, Pileggi G, Meiser A. Microbiome-based disease prediction with multimodal variational information bottlenecks. PLoS Comput Biol. 2022;18(4):e1010050. doi:10.1371/journal.pcbi.1010050

79. Zhang H, Zhang Y, Kang Z, Song L, Yang R, Ning K. MGM as a large-scale pretrained foundation model for microbiome analyses in diverse contexts. bioRxiv. 2025. doi:10.1101/2024.12.30.630825

80. Avsec Ž, Latysheva N, Cheng J, et al. AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model. bioRxiv. 2025. doi:10.1101/2025.06.25.661532

81. Dalla-Torre H, Gonzalez L, Mendoza-Revilla J, et al. Nucleotide transformer: building and evaluating robust foundation models for human genomics. Nat Method. 2025;22(2):287–297. doi:10.1038/s41592-024-02523-z

82. Sazal M, Stebliankin V, Mathee K, Yoo C, Narasimhan G. Causal effects in microbiomes using interventional calculus. Sci Rep. 2021;11(1):5724. doi:10.1038/s41598-021-84905-3

83. Lyu R, Qu Y, Divaris K, Wu D. Methodological considerations in longitudinal analyses of microbiome data: a comprehensive review. Genes. 2024;15(1):51. doi:10.3390/genes15010051

84. Ruiz-Perez D, Lugo-Martinez J, Bourguignon N, et al. Dynamic bayesian networks for integrating multi-omics time series microbiome data. Msystems. 2021;6(2). doi:10.1128/mSystems.01105-20

85. Argelaguet R, Velten B, Arnol D, et al. Multi-omics factor analysis-a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol. 2018;14(6):e8124. doi:10.15252/msb.20178124

86. Singh A, Shannon CP, Gautier B, et al. DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays. Bioinformatics. 2019;35(17):3055–3062. doi:10.1093/bioinformatics/bty1054

87. Muller E, Shiryan I, Borenstein E. Multi-omic integration of microbiome data for identifying disease-associated modules. Nat Commun. 2024;15(1):2621. doi:10.1038/s41467-024-46888-3

88. Sharma C, Sharma S, Sharma K, Sethi GK, Chen H-Y. Exploring explainable AI: a bibliometric analysis. Discov Appl Sci. 2024;6(11):615. doi:10.1007/s42452-024-06324-z

89. Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. presented at: Proceedings of the 31st International Conference on Neural Information Processing Systems; 2017; Long Beach, California, USA.

90. Novielli P, Romano D, Magarelli M, et al. Explainable artificial intelligence for microbiome data analysis in colorectal cancer biomarker identification. Original Research. Front Microbiol. 2024;15. doi:10.3389/fmicb.2024.1348974

91. Onwuka S, Bravo-Merodio L, Gkoutos GV, Acharjee A. Explainable AI-prioritized plasma and fecal metabolites in inflammatory bowel disease and their dietary associations. Iscience. 2024;27(7):110298. doi:10.1016/j.isci.2024.110298

92. Kim M, Gim D, Kim S, et al. From dysbiosis to prediction: AI-powered microbiome insights into IBD and CRC. Gastroenterol Insights. 2025;16(3):34. doi:10.3390/gastroent16030034

93. Stafford IS, Gosink MM, Mossotto E, Ennis S, Hauben M. A systematic review of artificial intelligence and machine learning applications to inflammatory bowel disease, with practical guidelines for interpretation. Inflamm Bowel Dis. 2022;28(10):1573–1583. doi:10.1093/ibd/izac115

94. Tariq R, Afzali A. Artificial intelligence in inflammatory bowel disease: innovations in diagnosis, monitoring, and personalized care. Therap Adv Gastroenterol. 2025;18:17562848251357407. doi:10.1177/17562848251357407

95. De Deo D, Dal Buono A, Gabbiadini R, et al. Digital biomarkers and artificial intelligence: a new frontier in personalized management of inflammatory bowel disease. Rev Front Immunol. 2025;16. doi:10.3389/fimmu.2025.1637159

96. Tourab H, López Pérez L, Arroyo-Gallego P, et al. The use of machine learning and explainable artificial intelligence in gut microbiome research: a scoping review. 2024.

97. Lenoir M, Martín R, Torres-Maravilla E, et al. Butyrate mediates anti-inflammatory effects of Faecalibacterium prausnitzii in intestinal epithelial cells through Dact3. Gut Microbes. 2020;12(1):1–16. doi:10.1080/19490976.2020.1826748

98. Zhang Z, Zhang H, Chen T, Shi L, Wang D, Tang D. Regulatory role of short-chain fatty acids in inflammatory bowel disease. Cell Commun Signal. 2022;20(1):64. doi:10.1186/s12964-022-00869-5

99. Mirsepasi-Lauridsen HC, Vallance BA, Krogfelt KA, Petersen AM. Escherichia coli pathobionts associated with inflammatory bowel disease. Clin Microbiol Rev. 2019;32(2). doi:10.1128/cmr.00060-18

100. Giambra V, Pagliari D, Rio P, et al. Gut microbiota, inflammatory bowel disease, and cancer: the role of guardians of innate immunity. Cells. 2023;12(22):2654. doi:10.3390/cells12222654

101. Jansma J, El Aidy S. Understanding the host-microbe interactions using metabolic modeling. Microbiome. 2021;9(1):16. doi:10.1186/s40168-020-00955-1

102. Nearing JT, Comeau AM, Langille MGI. Identifying biases and their potential solutions in human microbiome studies. Microbiome. 2021;9(1):113. doi:10.1186/s40168-021-01059-0

103. Allali I, Arnold JW, Roach J, et al. A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome. BMC Microbiol. 2017;17(1):194. doi:10.1186/s12866-017-1101-8

104. Xu Z, Yeoh YK, Tun HM, et al. Variation in the metagenomic analysis of fecal microbiome composition calls for a standardized operating approach. Microbiol Spectr. 2024;12(12):e0151624. doi:10.1128/spectrum.01516-24

105. Kim D, Jung J-Y, Oh H-S, et al. Comparison of sampling methods in assessing the microbiome from patients with ulcerative colitis. BMC Gastroenterol. 2021;21(1):396. doi:10.1186/s12876-021-01975-3

106. Kruger K, Myeonghyun Y, van der Wielen N, et al. Evaluation of inter- and intra-variability in gut health markers in healthy adults using an optimised faecal sampling and processing method. Sci Rep. 2024;14(1):24580. doi:10.1038/s41598-024-75477-z

107. Clooney AG, Eckenberger J, Laserna-Mendieta E, et al. Ranking microbiome variance in inflammatory bowel disease: a large longitudinal intercontinental study. Gut. 2021;70(3):499–510. doi:10.1136/gutjnl-2020-321106

108. Huang J, Zhu X, Ma Y, et al. Machine learning in the differential diagnosis of ulcerative colitis and Crohn’s disease: a systematic review. Translat Gastroenterol Hepatol. 2025;10:10. doi:10.21037/tgh-24-87

109. Rosenbacke R, Å M, McKee M, Stuckler D. How explainable artificial intelligence can increase or decrease clinicians’ trust in AI applications in health care: systematic review. JMIR AI. 2024;3:e53207. doi:10.2196/53207

110. Rynazal R, Fujisawa K, Shiroma H, et al. Leveraging explainable AI for gut microbiome-based colorectal cancer classification. Genome Biol. 2023;24(1):21. doi:10.1186/s13059-023-02858-4

111. Yu S, Li J, Ye Z, et al. Identification of a 10-species microbial signature of inflammatory bowel disease by machine learning and external validation. Cell Regen. 2025;14(1):32. doi:10.1186/s13619-025-00246-w

112. Al Radi ZMA, Prins FM, Collij V, et al. Exploring the predictive value of gut microbiome signatures for therapy intensification in patients with inflammatory bowel disease: a 10-year follow-up study. Inflamm Bowel Dis. 2024;30(10):1642–1653. doi:10.1093/ibd/izae064

113. Massaro CA, Meade S, Lemarié FL, et al. Gut microbiome predictors of advanced therapy response in Crohn’s disease: protocol for the OPTIMIST prospective, longitudinal, observational pilot study in Canada. BMJ Open. 2025;15(3):e094280. doi:10.1136/bmjopen-2024-094280

About NVIDIA
NVIDIA (NASDAQ: NVDA) is the world leader in AI and accelerated computing.

NVIDIA Forward-Looking Statements
Certain statements in this press release including, but not limited to, statements as to: CUDA GPU-accelerated computing revolutionizing design — enabling simulation at unprecedented scale, from atoms to transistors, from chips to complete systems, creating fully functional digital twins inside the computer; NVIDIA’s partnership with Synopsys harnessing the power of NVIDIA accelerated computing and AI to reimagine engineering and design — empowering engineers to explore more ideas, simulate faster, and invent the extraordinary products that will shape the future; expectations with respect to growth, performance and benefits of NVIDIA’s products, services, and technologies, and related trends and drivers; expectations with respect to supply and demand for NVIDIA’s products, services, and technologies; expectations with respect to NVIDIA’s third party arrangements, including with its collaborators and partners; expectations with respect to technology developments and related trends and drivers; expectations with respect to market growth and trends; expectations with respect to AI and related industries; and other statements that are not historical facts are forward-looking statements within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended, which are subject to the “safe harbor” created by those sections based on management’s beliefs and assumptions and on information currently available to management and are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic and political conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners’ products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; and changes in applicable laws and regulations, as well as other factors detailed from time to time in the most recent reports NVIDIA files with the Securities and Exchange Commission, or SEC, including, but not limited to, its annual report on Form 10-K and quarterly reports on Form 10-Q. Copies of reports filed with the SEC are posted on the company’s website and are available from NVIDIA without charge. These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances.

© 2025 NVIDIA Corporation. All rights reserved. NVIDIA, the NVIDIA logo, CUDA-X, NVIDIA Cosmos, NVIDIA NeMo, Nemotron, NVIDIA NIM and NVIDIA Omniverse are trademarks and/or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated. Features, pricing, availability and specifications are subject to change without notice.

About Synopsys
Synopsys, Inc. (Nasdaq: SNPS) is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. We deliver industry-leading silicon design, IP, simulation and analysis solutions, and design services. We partner closely with our customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow. Learn more at www.synopsys.com.

© 2025 Synopsys, Inc. All rights reserved. Synopsys, Ansys, the Synopsys and Ansys logos, and other Synopsys trademarks are available at https://www.synopsys.com/company/legal/trademarks-brands.html. Other company or product names may be trademarks of their respective owners.

Synopsys Forward-Looking Statements
This press release includes certain forward-looking statements regarding the demand and market outlook, products, business, strategies and opportunities of Synopsys and NVIDIA, including the benefits, impact and performance of NVIDIA’s AI and accelerated computing platform and Synopsys’ engineering solutions; Synopsys and NVIDIA’s collaboration efforts to redesign engineering and design across industries; expectations regarding the anticipated benefits of the multi-year partnership and specific initiatives, including for engineering teams and customers; expectations regarding the ability to harness AI efficiencies; and expectations with respect to technology developments and trends. These statements involve risks, uncertainties and other factors that could cause actual results, time frames or achievements to differ materially from those expressed or implied in such forward-looking statements. Such risks, uncertainties and factors include but are not limited to macroeconomic environment and global economic conditions; NVIDIA’s reliance on third parties to manufacture, assemble, package and test its products; the impact of technological development and competition; development of new products and technologies or enhancements to Synopsys’ and NVIDIA’s existing product and technologies; market acceptance of Synopsys’, NVIDIA’s or their partners’ products; changes in consumer preferences or demands; changes in industry standards and interfaces; and changes in applicable laws and regulations, including the impact of China export control restrictions; and the risks more fully described in filings Synopsys and NVIDIA make with the SEC from time to time, including in the “Risk Factors” section of their respective Annual Reports on Form 10-K, Quarterly Reports on Form 10-Q and other documents filed by either of them from time to time with the SEC. The information provided herein is as of the date hereof. Synopsys and NVIDIA assume no obligation and do not intend to update or revise any forward-looking statement, whether as a result of new information, future events or otherwise, unless required by law. Neither Synopsys nor NVIDIA gives any assurance that either Synopsys or NVIDIA will achieve its expectations.

Many of the features and products described herein remain in various stages and will be offered on a when-and-if-available basis. The statements above are not intended to be, and should not be interpreted as a commitment, promise, or legal obligation, and the development, release, and timing of any features or functionalities described are subject to change and remains at the sole discretion of Synopsys. Synopsys will have no liability for failure to deliver or delay in the delivery of any of the products, features or functions set forth herein.

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.

References

1. Wang J, Zhao M, Zhang H, et al. KLF4 alleviates hypertrophic scar fibrosis by directly activating BMP4 transcription. Int J Biol Sci. 2022;18(8):3324–3336. doi:10.7150/ijbs.71167

2. Leszczynski R, da Silva CA, Pinto A, Kuczynski U, da Silva EM. Laser therapy for treating hypertrophic and keloid scars. Cochrane Database Syst Rev. 2022;9(9):CD011642. doi:10.1002/14651858.CD011642.pub2

3. Yin J, Zhang S, Yang C, et al. Mechanotransduction in skin wound healing and scar formation: potential therapeutic targets for controlling hypertrophic scarring. Front Immunol. 2022;13:1028410. doi:10.3389/fimmu.2022.1028410

4. Mony MP, Harmon KA, Hess R, Dorafshar AH, Shafikhani SH. An updated review of hypertrophic scarring. Cells. 2023;12(5):678. doi:10.3390/cells12050678

5. Frech FS, Hernandez L, Urbonas R, Zaken GA, Dreyfuss I, Nouri K. Hypertrophic scars and keloids: advances in treatment and review of established therapies. Am J Clin Dermatol. 2023;24(2):225–245. doi:10.1007/s40257-022-00744-6

6. Yuan B, Upton Z, Leavesley D, Fan C, Wang XQ. Vascular and collagen target: a rational approach to hypertrophic scar management. Adv Wound Care. 2023;12(1):38–55. doi:10.1089/wound.2020.1348

7. Cui HS, Joo SY, Lee SY, Cho YS, Kim DH, Seo CH. Effect of hypertrophic scar fibroblast-derived exosomes on keratinocytes of normal human skin. Int J Mol Sci. 2023;24(7):6132. doi:10.3390/ijms24076132

8. Fernández-Guarino M, Bacci S, Pérez González LA, Bermejo-Martínez M, Cecilia-Matilla A, Hernández-Bule ML. The role of physical therapies in wound healing and assisted scarring. Int J Mol Sci. 2023;24(8):7487. doi:10.3390/ijms24087487

9. Lin TR, Chou FH, Wang HH, Wang RH. Effects of scar massage on burn scars: a systematic review and meta-analysis. J Clin Nurs. 2023;32(13–14):3144–3154. doi:10.1111/jocn.16420

10. He J, Fang B, Shan S, et al. Mechanical stretch promotes hypertrophic scar formation through mechanically activated cation channel Piezo1. Cell Death Dis. 2021;12(3):226. doi:10.1038/s41419-021-03481-6

11. Ogawa R. The most current algorithms for the treatment and prevention of hypertrophic scars and keloids: a 2020 update of the algorithms published 10 years ago. Plast Reconstr Surg. 2022;149(1):79e–94e. doi:10.1097/PRS.0000000000008667

12. Monzel AS, Enríquez JA, Picard M. Multifaceted mitochondria: moving mitochondrial science beyond function and dysfunction. Nat Metab. 2023;5(4):546–562. doi:10.1038/s42255-023-00783-1

13. Wang ZC, Zhao WY, Cao Y, et al. The roles of inflammation in keloid and hypertrophic scars. Front Immunol. 2020;11:603187. doi:10.3389/fimmu.2020.603187

14. Li J, Yin Y, Zhang E, Gui M, Chen L, Li J. Peptide deregulated in hypertrophic scar-1 alleviates hypertrophic scar fibrosis by targeting focal adhesion kinase and pyruvate kinase M2 and remodeling the metabolic landscape. Int J Biol Macromol. 2023;235:123809. doi:10.1016/j.ijbiomac.2023.123809

15. Liu BH, Chen L, Li SR, Wang ZX, Cheng WG. Smac/DIABLO regulates the apoptosis of hypertrophic scar fibroblasts. Int J Mol Med. 2013;32(3):615–622. doi:10.3892/ijmm.2013.1442

16. Xiao Y, Xu D, Song H, et al. Cuprous oxide nanoparticles reduces hypertrophic scarring by inducing fibroblast apoptosis. Int J Nanomed. 2019;14:5989–6000. doi:10.2147/IJN.S196794

17. Hsieh SC, Wu CC, Hsu SL, Yen JH. Molecular mechanisms of gallic acid-induced growth inhibition, apoptosis, and necrosis in hypertrophic scar fibroblasts. Life Sci. 2017;179:130–138. doi:10.1016/j.lfs.2016.08.006

18. Chen JY, Zhang L, Zhang H, Su L, Qin LP. Triggering of p38 MAPK and JNK signaling is important for oleanolic acid-induced apoptosis via the mitochondrial death pathway in hypertrophic scar fibroblasts. Phytother Res. 2014;28(10):1468–1478. doi:10.1002/ptr.5150

19. Xia W, Wang Q, Lin S, et al. A high-salt diet promotes hypertrophic scarring through TRPC3-mediated mitochondrial Ca2+ homeostasis dysfunction. Heliyon. 2023;9(8):e18629. doi:10.1016/j.heliyon.2023.e18629

20. Andrieux P, Chevillard C, Cunha-Neto E, Nunes J. Mitochondria as a cellular hub in infection and inflammation. Int J Mol Sci. 2021;22(21):11338. doi:10.3390/ijms222111338

21. Vringer E, Tait S. Mitochondria and cell death-associated inflammation. Cell Death Differ. 2023;30(2):304–312. doi:10.1038/s41418-022-01094-w

22. Birney E. Mendelian Randomization. Cold Spring Harb Perspect Med. 2022;12(4). doi:10.1101/cshperspect.a041302

23. Ference BA, Holmes MV, Smith GD. Using Mendelian randomization to improve the design of randomized trials. Cold Spring Harb Perspect Med. 2021;11(7):a040980. doi:10.1101/cshperspect.a040980

24. Ference BA. Interpreting the clinical implications of drug-target Mendelian randomization studies. J Am Coll Cardiol. 2022;80(7):663–665. doi:10.1016/j.jacc.2022.06.007

25. Zhu Z, Zhang F, Hu H, et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. 2016;48(5):481–487. doi:10.1038/ng.3538

26. McRae AF, Marioni RE, Shah S, et al. Identification of 55,000 replicated DNA methylation QTL. Sci Rep. 2018;8(1):17605. doi:10.1038/s41598-018-35871-w

27. Gagliano taliun SA, Evans DM. Ten simple rules for conducting a mendelian randomization study. PLoS Comput Biol. 2021;17(8):e1009238. doi:10.1371/journal.pcbi.1009238

28. Skrivankova VW, Richmond RC, Woolf B, et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomization: the STROBE-MR statement. JAMA. 2021;326(16):1614–1621. doi:10.1001/jama.2021.18236

29. Rath S, Sharma R, Gupta R, et al. MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations. Nucleic Acids Res. 2021;49(D1):D1541–D1547. doi:10.1093/nar/gkaa1011

30. Ferkingstad E, Sulem P, Atlason BA, et al. Large-scale integration of the plasma proteome with genetics and disease. Nat Genet. 2021;53(12):1712–1721. doi:10.1038/s41588-021-00978-w

31. Li X, Liang Z. Causal effect of gut microbiota on pancreatic cancer: a Mendelian randomization and colocalization study. J Cell Mol Med. 2024;28(8):e18255. doi:10.1111/jcmm.18255

32. Finnerty CC, Jeschke MG, Branski LK, Barret JP, Dziewulski P, Herndon DN. Hypertrophic scarring: the greatest unmet challenge after burn injury. Lancet. 2016;388(10052):1427–1436. doi:10.1016/S0140-6736(16)31406-4

33. Cai S, Zhao M, Zhou B, et al. Mitochondrial dysfunction in macrophages promotes inflammation and suppresses repair after myocardial infarction. J Clin Invest. 2023;133(4). doi:10.1172/JCI159498

34. Wei T, Xu Z. The diagnostic value and associated molecular mechanism study for fibroblast-related mitochondrial genes on keloid. Skin Res Technol. 2024;30(9):e70024. doi:10.1111/srt.70024

35. Wang Q, Wang P, Qin Z, et al. Altered glucose metabolism and cell function in keloid fibroblasts under hypoxia. Redox Biol. 2021;38:101815. doi:10.1016/j.redox.2020.101815

36. Xiao H, Palhan V, Yang Y, Roeder RG. TIP30 has an intrinsic kinase activity required for up-regulation of a subset of apoptotic genes. EMBO J. 2000;19(5):956–963. doi:10.1093/emboj/19.5.956

37. Carbajo-García MC, Corachán A, Juárez-Barber E, et al. Integrative analysis of the DNA methylome and transcriptome in uterine leiomyoma shows altered regulation of genes involved in metabolism, proliferation, extracellular matrix, and vesicles. J Pathol. 2022;257(5):663–673. doi:10.1002/path.5920

38. Korntner S, Lehner C, Gehwolf R, et al. Limiting angiogenesis to modulate scar formation. Adv Drug Deliv Rev. 2019;146:170–189. doi:10.1016/j.addr.2018.02.010

39. Zhang J, Liu Z, Cao W, et al. Amentoflavone inhibits angiogenesis of endothelial cells and stimulates apoptosis in hypertrophic scar fibroblasts. Burns. 2014;40(5):922–929. doi:10.1016/j.burns.2013.10.012

40. Patel AS, Ludwinski FE, Mondragon A, et al. HTATIP2 regulates arteriogenic activity in monocytes from patients with limb ischemia. JCI Insight. 2023;8(24). doi:10.1172/jci.insight.131419

41. Ito M, Jiang C, Krumm K, et al. TIP30 deficiency increases susceptibility to tumorigenesis. Cancer Res. 2003;63(24):8763–8767.

42. NicAmhlaoibh R, Shtivelman E. Metastasis suppressor CC3 inhibits angiogenic properties of tumor cells in vitro. Oncogene. 2001;20(2):270–275. doi:10.1038/sj.onc.1204075

43. Hinz B, Lagares D. Evasion of apoptosis by myofibroblasts: a hallmark of fibrotic diseases. Nat Rev Rheumatol. 2020;16(1):11–31. doi:10.1038/s41584-019-0324-5

44. Li Q, Zhang B, Lu J, et al. SNHG1 functions as a ceRNA in hypertrophic scar fibroblast proliferation and apoptosis through miR-320b/CTNNB1 axis. Arch Dermatol Res. 2023;315(6):1593–1601. doi:10.1007/s00403-022-02516-y

45. Shi J, Xiao H, Li J, et al. Wild-type p53-modulated autophagy and autophagic fibroblast apoptosis inhibit hypertrophic scar formation. Lab Invest. 2018;98(11):1423–1437. doi:10.1038/s41374-018-0099-3

46. Wynn TA, Ramalingam TR. Mechanisms of fibrosis: therapeutic translation for fibrotic disease. Nat Med. 2012;18(7):1028–1040. doi:10.1038/nm.2807

47. Zhao X, Kwan J, Yip K, Liu PP, Liu FF. Targeting metabolic dysregulation for fibrosis therapy. Nat Rev Drug Discov. 2020;19(1):57–75. doi:10.1038/s41573-019-0040-5

48. Moretti L, Stalfort J, Barker TH, Abebayehu D. The interplay of fibroblasts, the extracellular matrix, and inflammation in scar formation. J Biol Chem. 2022;298(2):101530. doi:10.1016/j.jbc.2021.101530

49. Igota S, Tosa M, Murakami M, et al. Identification and characterization of Wnt signaling pathway in keloid pathogenesis. Int J Med Sci. 2013;10(4):344–354. doi:10.7150/ijms.5349

50. Meng X, Gao X, Shi K, et al. Interferon-α2b-induced RARRES3 upregulation inhibits hypertrophic scar fibroblasts’ proliferation and migration through Wnt/β-catenin pathway suppression. J Interferon Cytokine Res. 2023;43(1):23–34. doi:10.1089/jir.2022.0183

51. Tan Y, Zhang M, Kong Y, et al. Fibroblasts and endothelial cells interplay drives hypertrophic scar formation: insights from in vitro and in vivo models. Bioeng Transl Med. 2024;9(2):e10630. doi:10.1002/btm2.10630

52. Yuan Q, Tang B, Zhang C. Signaling pathways of chronic kidney diseases, implications for therapeutics. Signal Transduct Target Ther. 2022;7(1):182. doi:10.1038/s41392-022-01036-5

53. Zhang T, Wang XF, Wang ZC, et al. Current potential therapeutic strategies targeting the TGF-β/Smad signaling pathway to attenuate keloid and hypertrophic scar formation. Biomed Pharmacother. 2020;129:110287. doi:10.1016/j.biopha.2020.110287

54. Xiaojie W, Banda J, Qi H, et al. Scarless wound healing: current insights from the perspectives of TGF-β, KGF-1, and KGF-2. Cytokine Growth Factor Rev. 2022;66:26–37. doi:10.1016/j.cytogfr.2022.03.001

55. He T, Bai X, Jing J, et al. Notch signal deficiency alleviates hypertrophic scar formation after wound healing through the inhibition of inflammation. Arch Biochem Biophys. 2020;682:108286. doi:10.1016/j.abb.2020.108286

56. Li B, Gao C, Diao JS, et al. Aberrant Notch signalling contributes to hypertrophic scar formation by modulating the phenotype of keratinocytes. Exp Dermatol. 2016;25(2):137–142. doi:10.1111/exd.12897

57. Ouyang H, Gore J, Deitz S, Korc M. microRNA-10b enhances pancreatic cancer cell invasion by suppressing TIP30 expression and promoting EGF and TGF-β actions. Oncogene. 2014;33(38):4664–4674. doi:10.1038/onc.2013.405

58. Brosnan CF, John GR. Revisiting Notch in remyelination of multiple sclerosis lesions. J Clin Invest. 2009;119(1):10–13. doi:10.1172/JCI37786

59. Hua Y, Bergers G. Tumors vs. chronic wounds: an immune cell’s perspective. Front Immunol. 2019;10:2178. doi:10.3389/fimmu.2019.02178

60. Zheng N, Wei J, Wu D, Xu Y, Guo J. Master kinase PDK1 in tumorigenesis. Biochim Biophys Acta Rev Cancer. 2023;1878(6):188971. doi:10.1016/j.bbcan.2023.188971

61. Kim JW, Tchernyshyov I, Semenza GL, Dang CV. HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia. Cell Metab. 2006;3(3):177–185. doi:10.1016/j.cmet.2006.02.002

62. Tian L, Wu D, Dasgupta A, et al. Epigenetic metabolic reprogramming of right ventricular fibroblasts in pulmonary arterial hypertension: a pyruvate dehydrogenase kinase-dependent shift in mitochondrial metabolism promotes right ventricular fibrosis. Circ Res. 2020;126(12):1723–1745. doi:10.1161/CIRCRESAHA.120.316443

63. Kim J, Kim HS, Choi DH, et al. Kaempferol tetrasaccharides restore skin atrophy via PDK1 inhibition in human skin cells and tissues: bench and clinical studies. Biomed Pharmacother. 2022;156:113864. doi:10.1016/j.biopha.2022.113864

64. Jia S, Agarwal M, Yang J, Horowitz JC, White ES, Kim KK. Discoidin domain receptor 2 signaling regulates fibroblast apoptosis through PDK1/Akt. Am J Respir Cell Mol Biol. 2018;59(3):295–305. doi:10.1165/rcmb.2017-0419OC

65. Eming SA, Wynn TA, Martin P. Inflammation and metabolism in tissue repair and regeneration. Science. 2017;356(6342):1026–1030. doi:10.1126/science.aam7928

66. Short WD, Wang X, Li H, et al. Interleukin-10 producing T lymphocytes attenuate dermal scarring. Ann Surg. 2021;274(4):627–636. doi:10.1097/SLA.0000000000004984

67. Huang Y, Feng Q, Zhang Y, et al. The effect of PDK1 in maintaining immune cell development and function. Biochem Biophys Res Commun. 2024;721:150106. doi:10.1016/j.bbrc.2024.150106

68. Yu M, Owens DM, Ghosh S, Farber DL. Conditional PDK1 ablation promotes epidermal and T-cell-mediated dysfunctions leading to inflammatory skin disease. J Invest Dermatol. 2015;135(11):2688–2696. doi:10.1038/jid.2015.232

69. Zuo Q, He J, Zhang S, et al. PPARγ coactivator-1α suppresses metastasis of hepatocellular carcinoma by inhibiting warburg effect by PPARγ-Dependent WNT/β-Catenin/pyruvate dehydrogenase kinase isozyme 1 axis. Hepatology. 2021;73(2):644–660. doi:10.1002/hep.31280

70. Lee M, Chen GT, Puttock E, et al. Mathematical modeling links Wnt signaling to emergent patterns of metabolism in colon cancer. Mol Syst Biol. 2017;13(2):912. doi:10.15252/msb.20167386

71. Han X, Wei Y, Wu X, Gao J, Yang Z, Zhao C. PDK1 regulates transition period of apical progenitors to basal progenitors by controlling asymmetric cell division. Cereb Cortex. 2020;30(1):406–420. doi:10.1093/cercor/bhz146

72. Kelly AP, Finlay DK, Hinton HJ, et al. Notch-induced T cell development requires phosphoinositide-dependent kinase 1. EMBO J. 2007;26(14):3441–3450. doi:10.1038/sj.emboj.7601761

73. Sun Z, Yao Y, You M, et al. The kinase PDK1 is critical for promoting T follicular helper cell differentiation. Elife. 2021;10. doi:10.7554/eLife.61406

74. Macdonald IA, Bennett T, Fellows IW. Catecholamines and the control of metabolism in man. Clin Sci. 1985;68(6):613–619. doi:10.1042/cs0680613

75. Zhao X, Luo J, Huang Y, et al. Injectable antiswelling and high-strength bioactive hydrogels with a wet adhesion and rapid gelling process to promote sutureless wound closure and scar-free repair of infectious wounds. ACS Nano. 2023;17(21):22015–22034. doi:10.1021/acsnano.3c08625

76. Sha Q, Wang Y, Zhu Z, et al. A hyaluronic acid/silk fibroin/poly-dopamine-coated biomimetic hydrogel scaffold with incorporated neurotrophin-3 for spinal cord injury repair. Acta Biomater. 2023;167:219–233. doi:10.1016/j.actbio.2023.05.044

77. Oh J, Fernando A, Muffley L, Honari S, Gibran NS. Correlation between the warrior/worrier gene on post burn pruritus and scarring: a prospective cohort study. Ann Surg. 2022;275(5):1002–1005. doi:10.1097/SLA.0000000000004235

78. Qin G, Sun Y, Guo Y, Song Y. PAX5 activates telomerase activity and proliferation in keloid fibroblasts by transcriptional regulation of SND1, thus promoting keloid growth in burn-injured skin. Inflamm Res. 2021;70(4):459–472. doi:10.1007/s00011-021-01444-3

79. Wang X, Chu J, Wen CJ, et al. Functional characterization of TRAP1-like protein involved in modulating fibrotic processes mediated by TGF-β/Smad signaling in hypertrophic scar fibroblasts. Exp Cell Res. 2015;332(2):202–211. doi:10.1016/j.yexcr.2015.01.015

80. Mustafa R, Mens MMJ, Van Hilten A, et al. A comprehensive study of genetic regulation and disease associations of plasma circulatory microRNAs using population-level data, Genome. Biol. 2024;25(1):276.

81. Chen Y, Zhang Z, Chen Y, et al. Investigating the shared genetic links between hypothyroidism and psychiatric disorders: a large-scale genomewide cross-trait analysis. J Affect Disord. 2025;369:312–320. doi:10.1016/j.jad.2024.08.202

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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.