Incremental Diagnostic Value of the Plasma Kynurenine/Tryptophan Ratio for Deep Infiltrating Endometriosis and Its Correlation With Eutopic Endometrial Matrix Metalloproteinase-9 Expression: A Single-Center Prospective Study

Background: Surgery for deep infiltrating endometriosis (DIE) is complex, and current clinical imaging has limited ability to identify and stratify atypical lesions. The kynurenine/tryptophan ratio (KTR) reflects immunometabolic activation, and matrix metalloproteinase- 9 (MMP-9) is associated with tissue invasion. Methods: This was a single-center prospective study, with pathology as the gold standard. Three analysis populations were defined. KTR was measured by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and MMP-9 by immunohistochemistry (IHC) derived histochemical score (H-score). Multivariable regression, DeLong comparison, cal- ibration and decision curves, and nested F test were used. Results: Compared with controls, KTR differed in the DIE and non-DIE groups, with β_diff = 0.28/0.15 (both p_adj < 0.05). Each 1 SD increase in natural log transformed KTR (lnKTR) was associated with higher ENZIAN stage (OR_perSD = 1.62, p < 0.001). After KTR was added to the baseline model, the area under the receiver operating characteristic curve (AUC) increased from 0.79 to 0.83 ( ∆AUC = 0.04, 95% CI: 0.02–0.07, p = 0.003). Net benefit increased across the 10%–30% threshold range, and calibration improved from α = –0.127, β = 0.861 to α = –0.039 and β = 0.946. Among imaging suspected but atypical subjects, net correct reclassification was +14 for events and +17 for non-events. KTR was independently and positively asso- ciated with MMP-9 (β_std = 0.29, 95% CI: 0.15–0.43, p < 0.001), with ∆R2 = 0.04 (F = 11.882,p < 0.001). Conclusions: KTR provides an independent and translatable diagnostic increment on current pathways and is associated with eutopic endometrial MMP-9, supporting coupling between systemic immunometabolism and local remodeling. These findings support its use for preoperative stratification and optimization of surgical planning.

deep infiltrating endometriosis; kynurenine/tryptophan ratio; diagnostic gain; MMP-9 1. Introduction Endometriosis is a common chronic disease in women of reproductive age, and the deep infiltrating subtype rep- resents a high burden phenotype with marked invasive- ness, increased complications, and greater surgical com- plexity [ 1,2]. Current diagnosis and treatment rely on symptoms, clinical examination, transvaginal ultrasound (TVUS), and magnetic resonance imaging (MRI). How- ever, detection of lesions in complex locations or with atypical features remains unstable, and serum carbohydrate antigen 125 (CA-125) shows limited discriminative abil- ity for phenotype and depth of infiltration [ 3]. Histolog- ically, matrix metalloproteinase-9 (MMP-9) mediates ex- tracellular matrix degradation and fibrotic remodeling and is considered with a marker of tissue infiltration [ 4]. In terms of immunometabolism, interferon-related pathways can activate the kynurenine pathway by directing trypto- phan through indoleamine 2,3-dioxygenase and tryptophan 2,3-dioxygenase. Kynurenine, together with aryl hydrocar- bon receptor (AhR) signaling, is hypothesized to contribute to tissue invasion [ 5]. The plasma kynurenine/tryptophan ratio (KTR) provides a systemic measure of this pathway has potential as an easily accessible biomarker [ 6]. Sero- logic studies targeting the deep infiltrating subtype mostly adopt cross-sectional designs and report receiver operating characteristic (ROC) curves for single biomarkers. They rarely evaluate the independent incremental value beyond real-world clinical and imaging pathways and generally lack evidence of clinical net benefit demonstrated by re- classification or decision curve analysis. A pervious study has inadequately controlled key confounders, such as men- strual cycle, exogenous hormones, inflammation, and renal function, and often provide incomplete pre-analytical and laboratory quality-control (QC) information, limiting their generalizability [ 7]. Key gaps remain regarding whether, under standardized procedures, kynurenine pathway mark- ers show gradient associations with phenotype and infil- tration load, whether they provide calibratable incremental value with net benefit beyond a baseline model composed of symptoms, signs, CA-125, and imaging [ 8], and whether systemic markers are independently associated with MMP9 in the eutopic endometrium at the individual level. These key gaps limit effective preoperative stratification and sur- gical planning [ 9]. In this single-center prospective study, we aimed to clarify the gradient association of the plasma KTR with the phenotype and infiltration load of deep in- filtrating endometriosis (DIE) based on existing clinical and imaging pathways, to evaluate its independent incre- mental contribution to diagnostic performance, and to ex- plore its relationship with MMP-9 expression in the eu- topic endometrium. Using rigorous pre-analytical proce- dures and laboratory validation, plasma KTR was quan- tified by liquid chromatography-tandem mass spectrome- try (LC-MS/MS). A baseline prediction model including symptoms, signs, CA-125, and imaging indicators was con- structed, and discrimination, calibration, reclassification, and decision curve analyses were used to evaluate the di- agnostic gain provided by KTR. MMP-9 expression in the eutopic endometrium was quantified by immunohistochem- istry (IHC), and multivariable regression together with ro- bustness checks was used to examine the association be- tween the systemic immunometabolic marker to local ma- trix remodeling. The results showed that KTR provided quantifiable diagnostic information with clinical net benefit on top of the existing pathway and was independently and positively associated with MMP-9 expression in the eutopic endometrium, providing an actionable biological basis for preoperative risk stratification and kynurenine pathway- targeted precision interventions. 2. Materials and Methods 2.1 Study Design and Sample Size Estimation This study was a single-center, prospective, diagnos- tic gain evaluation with paired histological correlation. The primary objective was to assess the independent incremen- tal value of the plasma KTR for the diagnosis and preoper- ative stratification of DIE beyond a baseline pathway com- prising symptoms, clinical signs, CA-125, and imaging. The paired analysis of eutopic endometrial MMP-9 expres- sion aimed to provide evidence of biological plausibility for KTR as an auxiliary biomarker, rather than a systematic in- vestigation of pathogenesis. Participants were consecutively enrolled along the clinical pathway. All testing was completed before surgery and pathological determination, and the study team main- tained mutual blinding across all components. Ethical ap- proval was obtained prior to study initiation (Approval No. KT2022062), and all participants provided written in- formed consent. Participants were enrolled from January 1, 2023 to December 31, 2024, and the data were finalized on March 31, 2025. Sample size was pre-calculated based on two primary objectives. Objective 1 considered the differ- ence in the area under the receiver operating characteristic curve (AUC) for KTR added to the “baseline model” (De- Long method, two-sided α = 0.05, effect ∆AUC = 0.04, event proportion 0.35, correlation coefficient within the prediction population 0.60, power 0.80), requiring a total of 216 participants. Objective 2 assessed the standardized difference in KTR between DIE and non-DIE (Cohen’s d = 0.5, two-sided α = 0.05, power 0.90), requiring ≥85 partic- ipants per group. Considering both objectives and an antic- ipated 10% rate of loss to follow-up or unusable specimens, the planned total sample size was 270, with consecutive en- rollment stratified into three categories, DIE, non-DIE en- dometriosis, and non-endometriosis controls, targeting an approximately balanced distribution to preserve the power of the primary analyses; no fixed quotas were set. 2.2 Study Population The study population consisted of women of repro- ductive age scheduled to undergo laparoscopy or laparo- tomy. Inclusion criteria: age 18–50 years; planned deter- mination of the presence or absence of endometriosis in the current surgery; completion of symptom quantification, pelvic imaging, and blood sampling before surgery; and consent to obtain eutopic endometrial samples at surgery. Exclusion criteria: fever or active infection within the past four weeks; use of systemic glucocorticoids or immunosup- pressants within two weeks before surgery; chronic renal insufficiency [estimated glomerular filtration rate (eGFR) <60 mL·min−1·1.73 m−2]; pregnancy or lactation; history of malignancy; inability to complete follow-up; or inade- quate specimen handling. 2.3 Outcome Ascertainment and Disease Grading This study used surgical exploration combined with histopathological examination as the gold standard. Par- ticipants were classified by lesion type into three mutu- ally exclusive study groups: ¬ DIE group: suspicious le- sions were found during surgery in deep structures such as the uterosacral ligaments, rectovaginal septum, vaginal fornix, bladder, and bowel wall; histopathology confirmed the presence of endometrial-like glands and/or stroma, and the lesions involved subperitoneal tissue with an infiltra- tion depth ≥5 mm, consistent with the definition of DIE; ­ non-DIE endometriosis group: histopathology confirmed the presence of endometriosis, but the lesions were confined to ovarian endometriomas and/or superficial peritoneal le- sions, without involvement of the above deep structures or not meeting the DIE criterion of infiltration depth ≥5 mm; ® non-endometriosis control group: reproductive-age women scheduled to undergo laparoscopy or laparotomy for benign non-inflammatory pelvic conditions such as uter- ine fibroids or simple ovarian cysts. All patients underwent standardized TVUS preoperatively, and, when necessary, pelvic MRI within 90 days before surgery to assess for sus- picious signs of endometriosis. Intraoperatively, the lead surgeon systematically explored typical sites of involve- ment, including the pelvic peritoneum, ovaries, uterosacral ligaments, rectovaginal septum, vaginal fornix, bladder, 2 and bowel wall. Suspicious lesions, such as ovarian cyst walls and peritoneal pigmented or fibrotic areas, were rou- tinely excised or biopsied for pathology. Only when preop- erative imaging did not suggest endometriosis, no typical or suspicious endometriotic lesions were seen intraopera- tively, and all suspicious lesions were confirmed by pathol- ogy to show no endometriosis at any site, the participants were included in the non-endometriosis control group. All pathological slides were independently reviewed by two senior pathologists blinded to the KTR results, and any discrepancies were resolved by discussion to reach con- sensus. The primary outcome was the presence or absence of DIE (binary), which was used for diagnostic modeling and gain analyses. At the same time, based on surgical records and imaging data, trained investigators completed the ENZIAN classification, recording the involvement lev- els of zones A, B, C, and F (grades 0–3), as well as the total number of involved sites, and calculating the overall score [10], to quantify infiltration burden and disease severity. 2.4 Specimen Collection and Laboratory Testing 2.4.1 Plasma KTR Measurement Within 14 days before surgery, 5 mL of EDTA- anticoagulated blood was collected from the antecubital vein in the early morning under fasting conditions. Within 1 hour after collection, plasma was separated by centrifu- gation at 4 °C, 1500 g for 10 minutes, aliquoted into pre- cooled polypropylene tubes, stored at –80 °C, with a max- imum of one freeze-thaw cycle. The date of last men- strual period, menstrual cycle phase (early follicular phase prioritized for sampling), exogenous hormone use, inter- val from blood draw to surgery, hemolysis index, and as- say batch were recorded. A methodologically validated as- say was used to quantify kynurenine and tryptophan, us- ing stable isotope internal standards to correct for matrix effects and recovery. Calibration curves covered 0.5–20 µmol·L−1 for kynurenine and 20–200 µmol ·L−1 for tryp- tophan, with the lower limit of quantification and linear correlation coefficients pre-specified in the validation re- port and meeting QC acceptance [11]. Low-, medium-, and high-level QC materials were included in each batch, and both within-batch and between-batch coefficients of varia- tion were maintained at 10%. KTR was calculated as the ratio of kynurenine/tryptophan and natural log transformed (lnKTR) to improve distributional characteristics. Labora- tory personnel were blinded to clinical groupings and out- comes throughout the study. 2.4.2 Eutopic Endometrial Sampling and MMP-9 Expression Detection Eutopic endometrial sampling was performed at the start of surgery to avoid interference from intraoperative energy devices. Tissues were immediately fixed in 10% neutral buffered formalin for 24 hours, routinely dehy- drated and embedded, and sectioned at 4 µm. A laboratory- validated rabbit monoclonal anti-MMP-9 antibody was used with heat-induced antigen retrieval in a pH 9.0 buffer, a polymer detection system with 3,3 ′-diaminobenzidine (DAB) chromogen, and hematoxylin counterstaining. Each batch included both a negative control and a known pos- itive control. Two pathologists, blinded to KTR and out- comes, independently evaluated staining intensity and the percentage of positive cells in glandular epithelium and stromal areas. They calculated the H-score (range 0–300) for each sample. Slides with a difference of >30 points were jointly reviewed to reach a consensus value. The team assessed inter-rater consistency quarterly using duplicate samples and reported the intraclass correlation (ICC) coeffi- cient (target ≥0.80). For samples with suspected uncertain cycle phase, a third pathologist reviewed the endometrial phase to ensure consistency with the blood sampling time window. 2.5 Baseline Clinical and Imaging Information On the same day as blood sampling, symptom assess- ment was completed, including 10-point visual analog scale scores for dysmenorrhea, chronic pelvic pain, and dys- pareunia [ 12]. Cyclical bowel or urinary symptoms were recorded as binary variables using a structured question- naire. A senior physician performed a bimanual pelvic examination and recorded posterior fornix tenderness and palpable nodules. Serum CA-125 was measured using a chemiluminescent immunoassay platform, traceable to na- tional reference materials and subjected to daily QC. The imaging protocol followed a TVUS-first strategy performed by trained sonographers, with pelvic MRI supplemented within 90 days before surgery when necessary. Whether DIE was suggested and the total number of involved sites were recorded in a unified manner. The imaging readers and the surgical team were mutually blinded. 2.6 V ariable Definitions, Time-Window Alignment, and Confounding Control The primary exposure variable was lnKTR. The pri- mary outcome was pathology-confirmed DIE analyzed as a binary variable. Secondary outcomes included the EN- ZIAN grade and the count of involved sites. Pre-specified confounders included age, body mass index (BMI), smok- ing status, menstrual cycle phase or exogenous hormone use, high-sensitivity C-reactive protein, creatinine, and es- timated glomerular filtration rate. Time-window alignment requirements were as follows: symptom scales and physi- cal examination were completed on the same day as blood sampling; the interval between imaging and surgery did not exceed 90 days; the interval between blood sampling and surgery did not exceed 14 days; and the eutopic endome- trial sampling was performed concurrently with surgery. Records exceeding the time windows were not included in the primary analyses. 3 2.7 Statistical Analysis 2.7.1 Chain A: Between-Group Differences and Gradient Trend of KTR With DIE The analysis first compared differences in KTR among the three groups. Depending on the data distri- bution, analysis of variance or generalized linear models were used with inclusion of pre-specified confounders, and adjusted mean differences and 95% confidence intervals (CIs) were reported. An ordinal logistic regression model based on ENZIAN grade was constructed to test the gradi- ent trend of KTR. Stratified analyses were performed to as- sess the consistency of association direction by menstrual cycle phase and exogenous hormone use. Two sensitiv- ity analyses tested the robustness of the results: excluding those with high-sensitivity C-reactive protein >10 mg·L−1 and excluding those with an estimated glomerular filtra- tion rate <60 mL ·min−1·1.73 m −2 [13]. Effect sizes and p-values were two-sided, with a significance threshold of 0.05 (p < 0.05). 2.7.2 Chain B: Evaluation of Independent Diagnostic Gain A pre-specified baseline prediction model was con- structed, including symptom quantification, clinical signs, CA-125, and imaging binary indicator and site count. KTR was then incorporated into this model, the AUCs were compared using the DeLong method with optimism cor- rection by bootstrap, and the optimism-corrected AUC dif- ference was reported. Category-based net reclassification improvement and integrated discrimination improvement were calculated according to clinically relevant risk thresh- olds (10%, 20%, 30%) [ 14]. Decision curve analysis was used to evaluate changes in net benefit within the above threshold range. Calibration plots and Brier scores were provided, and calibration improvements after adding KTR were reported. For the imaging-suspicious but atypical sub- group, likelihood ratios and changes in pre- versus post-test probabilities were calculated to display the impact of KTR on clinical classification. 2.7.3 Chain C: Multivariable Correlation Between KTR and MMP-9 Expression in Eutopic Endometrium With MMP-9 H-score as the dependent variable and KTR as the main independent variable, a multiple linear regression model was built with inclusion of pre-specified confounders. Collinearity and residual distributions were examined, and H-score was log transformed or robust re- gression was used when necessary. Standardized regres- sion coefficients and 95% CIs were reported. Interaction terms were specified for different menstrual cycle phases and exogenous hormone use to evaluate effect modifica- tions. Measurement consistency was verified by the ICC coefficient between the two raters, and measurement error sensitivity analyses of the model were incorporated. 2.7.4 Internal V alidation and Sensitivity Analyses All discrimination models underwent internal valida- tion with 1000 bootstrap resamples to obtain optimism- corrected AUC, calibration slope, and intercept. Three types of sensitivity analyses were conducted for the primary

including only those sampled in the early follic- ular phase; excluding those with elevated high-sensitivity C-reactive protein and with renal insufficiency; and strati- fication by imaging modality. Consistency of results was judged by the direction of effects and the magnitude of change in effect sizes. 2.7.5 Data Management and QC An electronic case report form was established, with dual independent data entry along with range and logic checks. After source data verification, outliers were cor- rected. Samples were tracked throughout with barcodes and three-level QC samples. Duplicate samples were ran- domly interleaved within assay batches to monitor batch effects. Bidirectional blinding was implemented among the clinical team, laboratory, and pathology, and unblind- ing was performed only after data lock. When the propor- tion of missing data was 5%, multiple imputation was performed for covariates and baseline predictors (10 imputed datasets, chained equations), and the exposure and outcome were not imputed. Statistical analyses were performed using R software (version 4.3 or later; R Foundation for Statistical Computing, Vienna, Austria). The main packages included stable versions for ROC comparison, reclassification, and decision curve analysis. All code and analysis logs were archived for future reference after data lock. 3. Results 3.1 Participant Characteristics and Assay QC A total of 356 candidate participants were assessed, of whom 272 enrolled. Of these, 257 provided samples suitable for pathological evaluation, including 94 DIE, 87 non-DIE endometriosis, and 76 non-endometriosis con- trols. The study identified three distinct analytical cohorts (Fig. 1). Analysis population A comprised 252 partici- pants with valid KTR (92/85/75). Analysis population B included 234 cases were used to determine diagnostic gain (85/79/70). Analysis population C included 213 cases for the KTR–MMP-9 correlation (79/71/63) (Fig. 1). Kruskal– Wallis and Pearson χ2 tests showed no statistically signif- icant differences among the three groups in baseline de- mographics, cycle/hormones, renal function, and smoking status (all p > 0.05). Levels of high-sensitivity C-reactive protein (hsCRP) and CA-125 differed significantly among the three groups (both p < 0.001). Similarly, the imag- ing results for suspected DIE and the total count of sus- pected involved sites showed significant differences (both p < 0.001) (Table 1). Method validation and descriptive 4 Fig. 1. Flowchart of participant screening, inclusion, and exclusion. DIE, deep infiltrating endometriosis; KTR, kynure- nine/tryptophan ratio; IHC, immunohistochemistry; LC-MS/MS, liquid chromatography-tandem mass spectrometry; CA-125, carbo- hydrate antigen 125. Table 1. Baseline characteristics of the study population (n, %)/M [IQR]. V ariable DIE group (n = 94) Non-DIE endometriosis group (n = 87) Non-endometriosis control group (n = 76) Statistic p-value Demographic and behavioral Age (years) 33.7 [28.9–38.6] 32.1 [27.6–37.7] 31.4 [26.5–36.2] H = 2.713 0.258 BMI (kg·m−2) 22.8 [21.1–25.0] 22.6 [21.0–24.3] 22.7 [21.1–24.6] H = 0.534 0.766 Smoking status (yes) 13 (13.83%) 10 (11.49%) 9 (11.84%) χ2 = 0.594 0.743 Smoking status (no) 81 (86.17%) 77 (88.51%) 67 (88.16%) — — Cycle and hormones Menstrual cycle phase: follicu- lar phase 52 (55.32%) 46 (52.87%) 40 (52.63%) χ2 = 0.847 0.932 Menstrual cycle phase: early se- cretory 24 (25.53%) 22 (25.29%) 18 (23.68%) — — Menstrual cycle phase: late se- cretory 18 (19.15%) 19 (21.84%) 18 (23.68%) — — Exogenous hormone use (yes) 22 (23.40%) 18 (20.69%) 13 (17.11%) χ2 = 2.506 0.286 Exogenous hormone use (no) 72 (76.60%) 69 (79.31%) 63 (82.89%) — — Inflammation and renal function hsCRP (mg·L−1) 1.68 [0.92–3.21] 1.22 [0.74–2.15] 0.98 [0.61–1.62] H = 16.427 <0.001 Creatinine (µmol·L−1) 67 [60–73] 65 [59–72] 65 [58–71] H = 1.057 0.589 eGFR (mL·min−1ꞏ1.73 m−2) 105 [97–114] 106 [98–114] 107 [99–115] H = 1.833 0.400 Diagnostic pathway variables CA-125 (U·mL−1) 29.5 [17.6–46.9] 18.7 [12.5–27.9] 13.0 [8.5–18.1] H = 35.761 <0.001 Imaging modality: TVUS only 51 (54.26%) 46 (52.87%) 49 (64.47%) χ2 = 6.537 0.162 Imaging modality: MRI only 12 (12.77%) 11 (12.64%) 7 (9.21%) — — Imaging modality: both 31 (32.98%) 30 (34.48%) 20 (26.32%) — — Imaging suggesting DIE (yes) 72 (76.60%) 27 (31.03%) 4 (5.26%) χ2 = 136.984 <0.001 Imaging suggesting DIE (no) 22 (23.40%) 60 (68.97%) 72 (94.74%) — — Count of suspected involved sites on imaging 2 [1–3] 1 [0–1] 0 [0–0] H = 91.538 <0.001 M, median; IQR, interquartile range; BMI, body mass index; hsCRP , high-sensitivity C-reactive protein; eGFR, estimated glomerular filtration rate; TVUS, transvaginal ultrasound; MRI, magnetic resonance imaging. statistics, and an ICC two-way random-effects consistency model were used. The LC-MS/MS calibration range cov- ered 0.50–200.00 µmol/L, with a lower limit of quantifica- tion (LLOQ) of 0.50 and 20.00, Bias ranged from –2.27% to –1.83%, while within-run and between-run CVs remained between 3.29–6.18%. The QC pass rate was ≥97.22% (Ta- ble 2A). The IHC scoring demonstrated good to excellent consistency, with overall H-score ICC = 0.892 (95% CI 5 Table 2A. LC-MS/MS performance. Parameter Kynurenine Tryptophan Calibration range (µmol/L) 0.50–20.00 20.00–200.00 LLOQ (µmol·L−1) 0.5 20 LOD (µmol·L−1) 0.14 6.37 Accuracy (%Bias) –1.83 –2.27 Within-run CV (%) 3.29 4.06 Between-run CV (%) 4.91 6.18 QC pass rate (%) 98.06 97.22 Hemolysis index range 0–31 0–31 Freeze-thaw cycles (times) 0–1 0–1 LLOQ, lower limit of quantification; LOD, limit of detection; CV , coefficient of variation. Table 2B. IHC scoring consistency (MMP-9 H-score). Region Rater 1 mean H-score Rater 2 mean H-score ICC (95% CI) Glandular epithelium 148.32 ± 39.14 151.07 ± 38.22 0.887 (0.851–0.916) Stromal area 134.26 ± 35.79 136.11 ± 34.88 0.874 (0.835–0.906) Overall H-score 141.59 ± 36.42 144.05 ± 35.76 0.892 (0.858–0.920) MMP-9, matrix metalloproteinase-9; ICC, intraclass correlation. 0.858–0.920), and ICCs of 0.887 and 0.874 for glandular epithelium and stromal areas, respectively (Table 2B). 3.2 Phenotypic Differences and Gradient of KTR With DIE (Chain A) In Analysis population A, the raw distributions of lnKTR overlapped significantly across the three diagnos- tic groups, with the median and upper quartile tending to be higher in the DIE group (Fig. 2). Using multivariable lin- ear regression (ANCOV A) and ordinal logistic regression, after adjustment for age, BMI, smoking status, menstrual cycle phase/exogenous hormones, hsCRP and eGFR, the adjusted geometric means of KTR in the DIE and non-DIE endometriosis groups were significantly higher than in the non-endometriosis control group ( β_diff = 0.28/0.15; both p_adj < 0.05) (Table 3A). Moreover, each 1 standard de- viation (SD) increase in lnKTR was associated with an in- crease in ENZIAN grade (OR_perSD = 1.62, p < 0.001) (Table 3B). 3.3 Independent Diagnostic Gain of KTR (Chain B) Logistic regression modeling was employed for Anal- ysis population B. AUC values were compared using the DeLong method, while the net reclassification improve- ment (NRI), integrated discrimination improvement (IDI), and their 95% CIs were obtained by 1000 bootstrap resam- ples. Calibration was assessed with the Brier score accom- panied by calibration plots. After adding KTR, AUC in- creased from 0.79 to 0.83 ( ∆AUC = 0.04, 95% CI: 0.02– 0.07, p = 0.003). At the pre-specified 20% primary thresh- old, NRI_category = 0.16 ( p = 0.004), and these results were consistent at the 10% and 30% thresholds. The Brier score decreased from 0.185 to 0.173 ( ∆–0.012), indicat- ing improvements in discrimination and overall error (Ta- ble 4). Decision curve analysis was performed, with 95% CIs obtained by 1000 bootstrap resamples. Within Pt = 0.10–0.30, the “Baseline + KTR” curve lay above “Base- line” across the entire range, showing a sustained and stable magnitude increase in net benefit (Fig. 3A). A logistic cal- ibration model with locally estimated scatterplot smooth- ing (LOESS) smoothing was used to display predicted– observed agreement. The calibration intercept and slope of the baseline model were α = –0.127 and β = 0.861, indicat- ing slight underestimation; after adding KTR,α approached 0 (–0.039) and β approached 1 (0.946), with calibration markedly improved (Fig. 3B). A risk-band reclassification matrix, a signed-rank test, and the Wilson method were ap- plied (threshold 20%). In the imaging-suspicious but atyp- ical subgroup, after adding KTR the net correct reclassi- fication was +14 for events and +17 for non-events (Ta- ble 5A). Pre- versus post-test probability changes showed an increase for events and a decrease for non-events, both p < 0.001 (Table 5B). Based on the 20% threshold, the likelihood ratios were positive likelihood ratio (LR+) 3.14 (95% CI 2.07–4.76) and LR– 0.30 (95% CI: 0.17–0.54) (Ta- ble 5C). 3.4 Independent Association Between KTR and MMP-9 in Eutopic Endometrium (Chain C) Analysis population C (n = 213) underwent multiple linear regression. MMP-9 H-score was defined as depen- dent variable, standardized the lnKTR as z-score, log trans- formed the hsCRP levels, and applied a nested F test to eval- uate incremental explained variance. lnKTR was positively associated with MMP-9 in eutopic endometrium ( β_std = 0.29, 95% CI: 0.15–0.43, p < 0.001). Among covariates, 6 Fig. 2. Raw distribution of plasma KTR across the three diagnostic groups. lnKTR, log transformed KTR. Table 3A. Adjusted differences in plasma KTR across the three diagnostic groups. Group KTR (×10−3) (95% CI) β_diff (ln ratio) (95% CI) Wald z p_adj DIE 40.21 (37.88–42.69) 0.28 (0.16–0.41) 4.39 <0.001 Non-DIE endometriosis 35.22 (33.01–37.59) 0.15 (0.05–0.26) 2.8 0.01 Non-endometriosis controls (reference) 30.31 (28.19–32.52) — — — Note: Geometric means are back-transformations of model marginal means of lnKTR and are presented as ( ×10−3); β_diff denotes the difference in log means relative to the non-endometriosis control group. Table 3B. ENZIAN grade trend analysis. Predictor OR_perSD (95% CI) Wald z p-value Brant test χ2 Brant test p-value lnKTR (z) 1.62 (1.35–1.94) 4.883 <0.001 4.183 0.523 Note: OR_perSD denotes the odds ratio for a 1 SD increase in lnKTR corresponding to a higher ENZIAN grade. only ENZIAN total score (β_std = 0.18), hsCRP (ln) (β_std = 0.13), and late secretory relative to follicular phase (β_std = 0.11) were significant (all p < 0.05). Model yielded R 2 = 0.32, after adding lnKTR, ∆R2 = 0.04 (F = 11.882, p < 0.001) (Table 6). The scatter plot, colored by menstrual cy- cle phase, and the partial regression plot was controlled for all covariates. The overall partial regression line was con- sistent with the previously described direction, and the point clouds largely overlapped across all phases (Fig. 4). 3.5 Stratified Analyses and Missing Data Sensitivity Analyses ANCOV A, the DeLong method with NRI_category (20% primary threshold), and multiple linear regression consistent with the main analyses were used. In stratifi- cations of “early follicular only”, excluding hsCRP >10 mg·L⁻1/eGFR <60 mL ·min⁻1·1.73 m⁻ 2, and by imaging modality (TVUS only versus MRI only), β_diff in Chain A, ∆AUC and NRI in Chain B, and β_std in Chain C were all positive and significant (all p < 0.05). The directions and magnitudes of effects were consistent with the main analy- ses, indicating robustness (Table 7). 7 Fig. 3. Clinical benefit and calibration assessment of the models. (A) Decision curve analysis: net benefit after adding KTR. (B) Calibration plot: predicted vs. observed (Baseline vs. Baseline + KTR). Table 4. Comparison of diagnostic performance between the baseline model and “Baseline + KTR”. Metric Baseline (symptoms + signs + CA-125 + imaging) Baseline + KTR AUC (95% CI) 0.79 (0.74–0.84) 0.83 (0.79–0.87) ∆AUC (95% CI) — 0.04 (0.02–0.07) p_DeLong — 0.003 Brier score 0.185 0.173 ΔBrier — –0.012 NRI_category (10%/20%/30%, estimate [95% CI]) — 0.18 (0.07–0.29)/0.16 (0.05–0.28)/0.14 (0.03–0.26) p_NRI (10%/20%/30%) — 0.001/0.004/0.009 IDI (95% CI) — 0.04 (0.02–0.07) p_IDI — 0.001 AUC (optimism-corrected) 0.78 0.82 Note: Thresholds were defined as 10%, 20%, and 30%; all comparisons were based on the same Analysis population B and covariate set. AUC, area under the receiver operating characteristic curve; NRI, net reclassification improvement; IDI, integrated discrimination improve- ment. Table 5A. Risk-band reclassification matrix. Risk category before adding KTR/Risk category after adding KTR <10% 10– <20% 20– <30% ≥30% Subtotal (events/non-events) <10% 1/15 3/5 1/2 0/0 5/22 10–<20% 0/12 3/10 7/6 2/0 12/28 20–<30% 0/2 2/8 6/7 5/2 13/19 ≥30% 0/2 0/7 2/1 8/2 10/12 Subtotal (events/non-events) 1/31 8/30 16/16 15/4 40/81 Net correct reclassification numbers: events +14 (up-classified 18, down-classified 4); non-events +17 (down-classified 32, up-classified 15). Note: Rows indicate risk categories before adding KTR, columns indicate risk categories after adding KTR, and each cell is presented as events/non-events. Table 5B. Change in predicted probability before and after adding KTR ( ∆p = after adding KTR – before adding KTR). Group ∆p M[IQR] Z-value p-value Events (n = 40) +0.07 [+0.03, +0.13] 4.086 <0.001 Non-events (n = 81) –0.05 [–0.10, –0.02] –4.732 <0.001 8 Table 5C. Likelihood ratios based on the 20% threshold (after adding KTR). Metric Point estimate 95% CI (Wilson) LR+ 3.14 2.07–4.76 LR– 0.30 0.17–0.54 Note: Risk-band thresholds were the same as in Table 4 (<10%, 10– <20%, 20– <30%, ≥30%). Table 5C was based on post-KTR binary classification (threshold 20%): sensitivity = 31/40, specificity = 61/81. LR, likelihood ratio. Table 6. Multivariable association between KTR and MMP-9 expression in eutopic endometrium. Independent variable β_std (95% CI) SE t value p-value lnKTR (z) 0.29 (0.15–0.43) 0.07 4.143 <0.001 Age 0.06 (–0.04–0.16) 0.05 1.200 0.232 BMI –0.04 (–0.14–0.06) 0.05 –0.800 0.424 Smoking (yes = 1) 0.05 (–0.07–0.17) 0.06 0.833 0.406 Phase: early secretory (vs. follicular) 0.08 (–0.02–0.18) 0.05 1.600 0.111 Phase: late secretory (vs. follicular) 0.11 (0.01–0.21) 0.05 2.200 0.029 Exogenous hormones (yes = 1) –0.07 (–0.17–0.03) 0.05 –1.400 0.162 hsCRP (ln) 0.13 (0.03–0.23) 0.05 2.600 0.010 eGFR –0.03 (–0.13–0.07) 0.05 –0.600 0.547 ENZIAN total score 0.18 (0.06–0.30) 0.06 3.000 0.003 Note: Model R 2 (with KTR): 0.32; ∆R2 (increment when adding lnKTR to the baseline model without KTR): 0.04; nested model F test (without KTR vs. with KTR): F = 11.882, p < 0.001. Fig. 4. Scatter plot of KTR (log) versus MMP-9 H-score and partial regression line. 9 Table 7. Summary of robustness in stratified and sensitivity analyses. Analysis scenario Chain A: adjusted β_diff of lnKTR for DIE vs. controls (95% CI), p Chain B: ΔAUC (95% CI), p_DeLong Chain B: NRI_category (95% CI), p Chain C: β_std (lnKTR→MMP- 9) (95% CI), p Early follicular only 0.30 (0.16–0.45), p = 0.001 0.05 (0.02–0.08), p = 0.002 0.19 (0.07–0.31), p = 0.002 0.27 (0.12–0.42), p = 0.001 Excluding hsCRP >10 mg·L−1 and eGFR <60 mL·min−1·1.73 m−2 0.27 (0.14–0.41), p = 0.001 0.04 (0.01–0.07), p = 0.010 0.17 (0.05–0.28), p = 0.006 0.28 (0.14–0.41), p < 0.001 By imaging modality: TVUS only 0.26 (0.11–0.40), p = 0.001 0.03 (0.003–0.06), p = 0.047 0.14 (0.02–0.26), p = 0.022 0.27 (0.11–0.41), p = 0.001 By imaging modality: MRI only 0.31 (0.16–0.47), p = 0.001 0.05 (0.02–0.09), p = 0.004 0.21 (0.06–0.35), p = 0.006 0.30 (0.13–0.46), p = 0.001 Table 8. Variable missingness and imputation settings (Analysis population B, n = 234). V ariable Missing count (n) Missing rate (%) Imputation method (MICE) Age (years) 2 0.85 PMM BMI (kgꞏm−2) 3 1.28 PMM Smoking status (yes/no) 5 2.14 Binary logistic Menstrual cycle phase (follicular/early secretory/late secretory) 8 3.42 Multinomial logistic Exogenous hormone use (yes/no) 4 1.71 Binary logistic Dysmenorrhea V AS (0–10) 7 2.99 PMM Dyspareunia V AS (0–10) 9 3.85 PMM Posterior fornix tenderness (yes/no) 6 2.56 Binary logistic hsCRP (mg·L−1, ln used for modeling) 12 5.13 PMM (on ln values) eGFR (mL·min−1·1.73 m−2) 6 2.56 PMM CA-125 (U·mL−1) 10 4.27 PMM Imaging suggesting DIE (binary) 1 0.43 Binary logistic Count of suspected involved sites on imaging 3 1.28 PMM Note: Little’s MCAR test: χ2 = 18.742, p = 0.282. Because the missing rate of hsCRP was >5%, the diagnostic model used multiple imputation (MICE, m = 10, chained equations; including all covariates and imaging indicators, with exposure KTR and outcome DIE not imputed but entered as predictors in the imputation equations). V AS, visual analog scale; PMM, predictive mean matching. 10 Table 9. Comparison of sensitivity between complete-case analysis and multiple imputation (Analysis Population B, n = 234). Metric Complete cases (CC, n = 206) Multiple imputation (MI pooled, n = 234; m = 10) lnKTR (per ↑1 SD) → DIE, OR (95% CI), p 1.57 (1.23–2.04), p = 0.001 1.62 (1.29–2.05), p = 0.001 AUC (Baseline model) 0.79 (0.73–0.84) 0.79 (0.74–0.84) AUC (Baseline + KTR) 0.83 (0.78–0.87) 0.83 (0.79–0.87) ΔAUC (DeLong 95% CI), p_DeLong 0.04 (0.01–0.07), p = 0.009 0.04 (0.02–0.07), p = 0.003 Brier score (Baseline → Baseline + KTR) 0.186 → 0.174 0.185 → 0.173 Calibration intercept α (Baseline → Baseline + KTR) –0.121 → –0.043 –0.127 → –0.039 Calibration slope β (Baseline → Baseline + KTR) 0.865 → 0.944 0.861 → 0.946 CC, complete-case analysis; MI, multiple imputation. Little’s MCAR test and MICE multiple imputation (m = 10) was assessed. Within Analysis population B, the missing data rates for each covariate remained <5%, with the exception of hsCRP , and the missingness pattern was consistent with the MCAR assumption ( χ2 = 18.742, p = 0.282) (Table 8). Using logistic regression and the DeLong test, the odds ratio (OR) for DIE per 1 SD increase in lnKTR was similar between complete case and imputed analyses (1.57 vs. 1.62). Moreover, the AUC increased from 0.79 to 0.83, and the directions of improvement in the Brier score and calibration were consistent, indicating that multiple im- putation did not alter the main conclusions (Table 9). 4. Discussion Although the distributions of lnKTR overlapped among the three groups, participants with the deep infil- trating phenotype showed higher median and upper quar- tile levels when surgical pathology, quantified by ENZIAN, served as the gold standard. After simultaneous adjustment for age, BMI, smoking, menstrual cycle phase, exogenous hormones, hsCRP , and eGFR, this difference remained. The non-DIE endometriosis group also showed higher lnKTR levels than the non-endometriosis controls, and each standard deviation increase in lnKTR corresponded to a higher ENZIAN grade, suggesting a clear dose–response relationship. Stratified and sensitivity analyses showed no reversal of effect direction, and the linearity, precision, and QC pass rate of LC-MS/MS assay, as well as the consis- tency of IHC scoring, provided technical credibility for the inference. From an immunometabolic perspective, inter- feron signaling in the inflammatory microenvironment ac- celerates tryptophan metabolism through indoleamine 2,3- dioxygenase and tryptophan 2,3-dioxygenase pathways, leading to tryptophan depletion and kynurenine accumula- tion, with KTR rising as a systemic readout of pathway ac- tivation [15,16]. As a ligand of AhR, kynurenine can syn- ergize with proinflammatory transcriptional networks, up- regulate MMP expression, and enhance matrix degradation and fibrotic remodeling, conferring stronger adhesion and invasive capacity to lesions [17]. Accordingly, systemic el- evation of KTR is consistent with the gradient of deeper tis- sue infiltration and broader involvement. Previous reports have mostly observed abnormalities of the tryptophan path- way in mixed endometriosis populations [18], but evidence for discrimination of the deep infiltrating subtype and for quantification of anatomical burden has been insufficient, and control of menstrual cycle and hormonal exposure has often been inadequate. In contrast, the present prospective data, obtained under rigorous preanalytical procedures and multivariable adjustment, demonstrate both phenotypic dif- ferences and grading trends, indicating that KTR relates not only to the presence of disease but also to the intensity of the pathological phenotype, providing a testable framework for perioperative stratification and for subsequent mechanistic studies targeting immunometabolism. After adding KTR to the baseline model composed of symptoms, signs, CA-125, and imaging, discrimination improved, prediction error decreased, probability calibra- tion approached the ideal, and sustained net benefit ap- peared within the clinically relevant risk range of 0.10– 0.30; ∆AUC = 0.04. Although this represents a numer- ically moderate increment that commonly appears with a strong baseline model, under the premise that symp- toms, signs, and imaging information already integrate into the model, this level of improvement usually corresponds to more substantive risk re-ranking among intermediate- risk patients, especially in preoperative stratification sce- narios around the 10%–30% decision thresholds. In the imaging-suspicious but atypical population, addition of KTR achieved net correct reclassification for both events and non-events, shifted individual post-test probabilities in the appropriate direction, and improved likelihood ra- tios at the 20% threshold. These findings suggest reduc- tions in unnecessary interventions and missed diagnoses in preoperative stratification and management pathways. All these improvements remained robust after internal boot- strap correction. The immunometabolic dimension repre- sented by KTR provides a biological basis for the above phenomena. The conversion of tryptophan to kynurenine through indoleamine 2,3-dioxygenase and tryptophan 2,3- dioxygenase pathways is regulated by inflammatory sig- nals, and an elevated KTR reflects systemic pathway ac- tivation [19]. Its information content does not overlap with the structural features captured by imaging or the humoral characteristics of CA-125. When incorporated into the model, it can correct risk ranking and probability calibra- 11 tion, allowing individuals at intermediate risk near deci- sion thresholds to be adjusted more accurately upward or downward, thereby translating into net benefit within clin- ically relevant trade-off ranges [ 20]. High-quality preana- lytical control and laboratory validation provided method- ological assurance for this gain. A pervious serologic study has largely remained at the level of ROC curves for single indicators, have seldom reported calibration metrics and de- cision curves concurrently, and have lacked evidence of re- classification and likelihood ratios in imaging-atypical sce- narios [ 21]. The present results, evaluated at prespecified thresholds, show concordant improvements in discrimina- tion, calibration, and decision performance, with clinical interpretability presented by the reclassification matrix and likelihood ratios. These findings fill key gaps in the prior evidence chain, indicating that KTR, as an effective supple- ment to the traditional pathway, has clear clinical translata- bility. With surgical pathology as the reference and after ad- justment for age, BMI, smoking, menstrual cycle phase, ex- ogenous hormones, high-sensitivity C-reactive protein, es- timated glomerular filtration rate, and lesion burden, lnKTR showed an independent positive association with MMP-9 H-score in eutopic endometrium. Partial regression plots showed a stable slope, with point clouds across cycle strata that largely overlapped and stratified, as well as sensitiv- ity analyses pointing in the same direction, suggesting that this association was not driven by cycle differences or co- variates such as inflammation or renal function. At the level of immunometabolism, inflammatory signals induce activation of indoleamine 2,3-dioxygenase and tryptophan 2,3-dioxygenase, increasing the flux of tryptophan toward kynurenine [22]. Kynurenine, as a ligand of AhR, can acti- vate downstream transcriptional networks, enhance MMP- 9 transcription and secretion, and promote changes in cell adhesion, extracellular matrix degradation, and stromal re- modeling, conferring stronger invasive and fibrotic capac- ity to lesions [ 23]. The concordant changes in systemic KTR elevation and local MMP-9 enhancement can be in- terpreted as a coupling marker between immunometabolic signaling and tissue microenvironmental activity [ 24], sup- porting the discriminative value of the plasma biomarker for the infiltrative phenotype and providing a measurable bridging index for perioperative risk stratification and po- tential targeted research. Previous multi-source disease models have suggested that kynurenine and AhR upregu- late MMP-9, but population-level evidence in human eu- topic endometrium remains scarce [ 25]. Paired blood– tissue data, combined with consistent robustness checks, fill this key gap by enabling the systemic marker and histologic processes to be compared and verified within the same sub- jects.

To avoid overstating the conclusions, several limita- tions should be acknowledged. The single-center prospec- tive cohort may be affected by referral and spectrum bias, lacks external and temporal validation, and the stability of risk thresholds has not yet been prospectively tested. NRI, IDI, and decision curves are sensitive to sample size and threshold selection, and their CIs carry inherent un- certainty. KTR is influenced by concomitant inflamma- tion, diet, and metabolic status. Although adjustments were made for hsCRP and renal function, residual con- founding may remain. MMP-9 was semi-quantified by IHC, and despite good ICC, batch effects and reader drift may occur. The maximum 90-day interval from imag- ing to surgery may lead to disease progression. Future work should conduct multicenter, pre-registered external and temporal validation; establish cross-platform standard- ization of LC-MS/MS and IHC with reference materials; prospectively confirm thresholds according to clinical use scenarios; evaluate decision impact and cost-effectiveness in imaging-atypical and preoperative stratification path- ways; combine plasma markers, symptoms, and structural imaging to develop deployable multimodal models; refine preanalytical standard operating procedures (SOPs) and inter-laboratory consistency; and, within the cohort, track longitudinal changes and modifiability of the kynurenine– AhR–MMP-9 pathway to clarify causal associations with clinical outcomes. 5. Conclusions This study demonstrates that plasma KTR provides in- dependent and quantifiable diagnostic value beyond symp- toms, signs, CA-125, and routine imaging. It improves dis- crimination, calibration, and clinical net benefit, and is par- ticularly useful for preoperative stratification in imaging- suspicious but atypical cases. KTR shows an indepen- dent positive association with MMP-9 expression in eu- topic endometrium, suggesting an association between sys- temic immunometabolic activation and local matrix remod- eling with clear biological plausibility. Overall, KTR can serve as a practical auxiliary biomarker for DIE to optimize referral and surgical planning and provides a foundation for kynurenine pathway–related precision interventions and multimodal diagnostic models. Availability of Data and Materials The datasets used and analyzed during the current study are available from the corresponding author on rea- sonable request, subject to institutional data sharing policies and ethical approval. Author Contributions HX: Conceived and designed the study, participated in patient enrollment and data collection, performed pre- 12 liminary data analysis, and drafted the first version of the manuscript. HJ: Assisted in study design, performed statistical analysis and data verification, prepared figures and tables, and contributed to revising and polishing the manuscript. CM: Conceived and supervised the overall study, coordinated patient recruitment and clinical data in- terpretation, provided critical revision of the manuscript for important intellectual content, and serves as the corre- sponding author. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work. Ethics Approval and Consent to Participate This study was conducted in accordance with the Dec- laration of Helsinki. The protocol was reviewed and ap- proved by the Ethics Committee of Zhejiang Provincial People’s Hospital, Hangzhou, Zhejiang, China (approval No. KT2022062). Written informed consent was obtained from all participants before enrolment in the study. Acknowledgment The authors sincerely thank the clinicians, sonogra- phers, and nursing staff of the Department of Obstetrics, Y ongkang Hospital of Traditional Chinese Medicine, for their support in participant recruitment, perioperative man- agement, and imaging examinations. We are also grateful to the pathology and laboratory teams for their assistance with specimen processing, LC-MS/MS assays, and immunohis- tochemical evaluations, as well as to all women who gen- erously participated in this study. Funding This research received no external funding. Conflicts of Interest The authors declare no conflicts of interest. Declaration of AI and AI-Assisted Technologies in the Writing Process During the preparation of this manuscript, the authors used ChatGPT to assist with language editing (including grammar, wording, and style). The authors carefully re- viewed and revised all AI-assisted suggestions and take full responsibility. No AI tools were used for data analysis, data generation, or scientific interpretation. Supplementary Material Supplementary material associated with this article can be found, in the online version, at https://doi.org/10. 31083/CEOG47790.

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