Impact of therapeutic agents on serum leucine-rich alpha-2 glycoprotein for monitoring endoscopically remitted ulcerative colitis

Impact of therapeutic agents on serum leucine-rich alpha-2 glycoprotein for monitoring endoscopically remitted ulcerative colitis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of therapeutic agents on serum leucine-rich alpha-2 glycoprotein for monitoring endoscopically remitted ulcerative colitis Junnosuke Hayasaka, Akira Matsui, Daisuke Kikuchi, Shu Hoteya This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6142072/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Serum leucine-rich alpha-2 glycoprotein (LRG) levels are measured to monitor ulcerative colitis (UC); however, the impact of concomitant medications on LRG remains unclear. This exploratory study aimed to determine the effects of various agents on serum LRG levels. Methods We conducted a single-center, retrospective study using medical records at our hospital from October 1, 2020, to June 30, 2023. Patients who underwent lower gastrointestinal endoscopy within 1 year before or after LRG measurement and had confirmed mucosal healing were included. The effects of medication on LRG levels were assessed using multiple regression analysis following multiple imputations. The analyzed agents included 5-aminosalicylic acid (5-ASA), immunomodulators, corticosteroids, calcineurin inhibitors, Janus kinase inhibitors, vedolizumab, interleukin-23 receptor antagonists, and anti-TNF-α agents. Results A total of 214 patients (351 measurements) were included. The median LRG was 11.2 µg/ml. Among patients, 63.2 had a Mayo Endoscopic Subscore of 0, while 36.8% had a score of 1. The frequency of medication use was as follows: 5-ASA (88.9%), immunomodulators (13.1%), corticosteroids (2.6%), calcineurin inhibitors (0.9%), Janus kinase inhibitors (5.7%), vedolizumab (3.4%), interleukin-23 receptor antagonists (1.7%), and anti-TNF-α agents (7.4%). Corticosteroids, calcineurin inhibitors, Janus kinase inhibitors, and anti-TNF-α agents were negatively associated with LRG (β = -3.42, -10.4, -2.34, and − 3.01, respectively). Conversely, vedolizumab and interleukin-23 receptor antagonists were positively associated with LRG. (β = 1.83 and 4.69, respectively). Conclusion LRG levels are influenced by medications, even in patients with mucosal healing. These effects should be considered when using LRG to monitor UC. leucine-rich alpha-2 glycoprotein ulcerative colitis endoscopy biomarkers Introduction Inflammatory bowel diseases (IBD) are prevalent in North America and Europe, with incidence rates remaining stable [ 1 ]. However, the incidence of IBD has been rising in Africa, Asia, and South America [ 1 ]. In Japan, cases of IBD—particularly ulcerative colitis (UC)—have been increasing, with over 220,000 reported cases [ 2 ]. In recent years, the STRIDE-II strategy has been used to treat IBD, emphasizing endoscopic mucosal healing as a long-term goal [ 3 ]. Even in clinical remission, one-third of cases exhibit endoscopic inflammation [ 4 ]. Achieving endoscopic mucosal healing is associated with a favorable prognosis in IBD [ 5 – 7 ]. When endoscopic activity is detected, treatment modifications are recommended [ 3 ]. As a result, colonoscopy remains the gold standard for assessing IBD [ 8 ]. However, frequent colonoscopic monitoring is challenging due to cost, time constraints, invasiveness, and manpower requirements. Serum biomarkers, on the other hand, offer a relatively noninvasive and easily repeatable alternative for monitoring disease activity. Therefore, identifying serum biomarkers that accurately reflect endoscopic activity is crucial. Leucine-rich α-2-glycoprotein (LRG) is a novel serum biomarker increasingly used to monitor UC. LRG is a 50-kDa glycoprotein with eight leucine-rich repeat domains and is induced by IL-22, tumor necrosis factor (TNF)-α, and IL-1β in an IL6-independent manner [ 9 , 10 ]. LRG is considered useful for monitoring UC because it reflects disease activity even in CRP-negative patients and correlates strongly with endoscopic activity [ 11 , 12 ]. However, LRG is a nonspecific biomarker and does not necessarily reflect UC activity. Elevated serum LRG levels have also been observed in other conditions, including rheumatoid arthritis [ 13 ], infections, and malignant diseases [ 14 ]. Currently, a wide range of therapeutic options for UC exists, including 5-aminosalicylic acid (5-ASA), immunosuppressive agents, small-molecule compounds, and biologics. Since LRG is influenced by cytokines, it may also be affected by immunosuppressive agents and biologics. However, it remains unclear whether LRG levels are impacted by these agents when used for UC monitoring. To address this gap, we conducted an exploratory study to evaluate the effects of various agents on LRG levels in the context of UC monitoring. Method This retrospective study utilized electronic medical records at our hospital. The study included patients with UC whose LRG levels were measured between October 1, 2020, and June 30, 2023. For endoscopic monitoring, patients who underwent lower gastrointestinal endoscopy within 1 year before or after LRG measurement and had confirmed mucosal healing were included. Patients were excluded if they were undergoing remission induction therapy, experienced treatment modifications, had symptoms that changed after colorectal surgery, or exhibited symptom variation between examinations. In addition, one case in which LRG levels fell below the sensitivity threshold was excluded. Ultimately, 214 patients were included, with a total of 351 measurements: 65 patients tested twice, 22 patients three times, eight patients four times, and one patient five times. Data extraction method In Japan, LRG testing is covered by insurance for patients with UC and Crohn’s disease. Therefore, we selected patients who were registered with either condition and had undergone LRG measurement during the study period. All cases were reviewed retrospectively using electronic medical records, and patients diagnosed with UC based on endoscopic, radiologic, histologic, and clinical criteria [ 15 , 16 ] were included. This study was conducted in accordance with the 1975 Declaration of Helsinki (6th Edition, 2008) and was approved by the hospital’s Ethics Committee (approval number 2202). Written informed consent was not required due to the retrospective study design, but patients were given the opportunity to opt out. Evaluation criteria We collected the following data: age, sex, disease location, disease duration, intervals between blood sampling and colonoscopy, and comorbidities (cancer, infectious disease, autoimmune disorder). Additionally, we recorded the partial Mayo score (PMS), Mayo endoscopic subscore (MES), medication use (5-aminosalicycic acid, immunomodulators, corticosteroids, calcineurin inhibitors, Janus kinase inhibitors, vedolizumab, interleukin-23 receptor antagonists, anti-TNF-α agents), and laboratory parameters (LRG, C-reactive protein, white blood cell count, hemoglobin, platelet count, erythrocyte sedimentation rate). Cancer was defined as any malignancy diagnosed within the past 5 years that was either not cured or remained under follow-up. Infections were classified as acute infections present at the time of LRG measurement. PMS was assessed at the time of LRG measurement. Concomitant medications included those prescribed for UC as well as those used to manage comorbid conditions. Statistical analyses Univariate analysis was performed as follows. Continuous variables were expressed as medians and interquartile ranges, while categorical variables were presented as numbers and percentages. The effect on LRG was evaluated using multiple regression analysis. The explanatory variables included age, sex, disease location, disease duration, comorbidities (cancer, infectious disease, autoimmune disorder), medications (5-ASA, immunomodulators, corticosteroids, calcineurin inhibitors, Janus kinase inhibitors, vedolizumab, interleukin-23 receptor antagonists, anti-TNF-α agents), and laboratory parameters (C-reactive protein, white blood cell count, hemoglobin, platelet count, and erythrocyte sedimentation rate). Missing data were present in 42.2% (148/351) of all cases. To address this, we used multiple imputations (MI) to replace missing values with statistically plausible estimates, reducing potential bias. MI is a method that generates multiple datasets by imputing missing values based on observed data, often producing more reliable results than complete case analysis [ 17 ]. MI was performed using the chained-equation method under a missing-at-random assumption [ 18 ]. All clinical data except for the interval between blood sampling and colonoscopy were included in the imputation models, and 20 imputed datasets were generated. The results from each dataset were combined using Rubin’s rules [ 19 ]. Additionally, two sensitivity analyses were conducted. The first was a multiple regression analysis that included MES as an explanatory variable since mucosal healing encompassed both MES 0 and MES 1. The second was a complete case analysis to validate the MI method. Statistical significance was set at P < 0.05. All analyses were performed using R software (version 4.1.3, The R Foundation for Statistical Computing, Vienna, Austria). MI was performed using the mice package (v3.13.0). Result The baseline characteristics of the participants are presented in Table 1 . The median patient age was 55 years, and 64.1% of the patients were male. Approximately half of the patients had extensive colitis, with a median disease duration of 12 years. The most commonly used agent was 5-ASA in 88.9% of cases. Immunomodulators were used in 13.1%, corticosteroids in 2.6%, calcineurin inhibitors in 0.9%, Janus kinase inhibitors in 5.7%, vedolizumab in 3.4%, interleukin-23 receptor antagonists in 1.7%, and anti-TNF-α agents in 7.4%. The median LRG level was 11.2 µg/mL, while the median PMS was 0. A total of 63.2% of patients had MES 0, and 80% had MES 1. The median interval between blood sampling and colonoscopy was 23 days. The maximum missing value was 42.2% for PMS, followed by 10.2% for ESR; all other values had less than 5% missing data. Table 2 shows the results of the multiple regression analysis following multiple imputations. Age, sex, disease location, disease duration, and comorbidities were not significantly associated with LRG. Table 1 Characteristics of patients with ulcerative colitis N = 351 Missing data Age (years), median [IQR] 55.0 [43.0, 66.0] 0 Sex, male, N [%] 225 (64.1) 0 Disease location, N [%] 0 Proctitis 95 (27.1) Left-sided 62 (17.7) Extensive 194 (55.3) Disease duration (years), median [IQR] 12.0 [4.0, 23.0] 3 (0.85) Comorbidities, N [%] Cancer 23 (6.6) 0 Infectious disease 3 (0.9) 0 Autoimmune disorder 12 (3.4) 0 Medication, N [%] 5-aminosalicycic acid 312 (88.9) 0 Immunomodulators 46 (13.1) 0 Corticosteroids 9 (2.6) 0 Calcineurin inhibitors 3 (0.9) 0 Janus kinase inhibitor 20 (5.7) 0 Vedolizumab 12 (3.4) 0 Interleukin-12/23 receptor antagonists 6 (1.7) 0 Anti-TNF agents 26 (7.4) 0 Laboratory data median [IQR] Leucine-rich alpha-2 glycoprotein (µg/ml) 11.2 [9.8, 13.6] 0 Albumin (g/dL) 4.4 [4.2, 4.6] 1 (0.28) C-reactive protein (mg/dL) 0.05 [0.02, 0.12] 10 (2.85) White blood cell (/µL) 5500 [4700, 6500] 2 (0.57) Hemoglobin (g/dL) 14.3 [13.2, 15.2] 2 (0.57) Platelet count (10 3 /µl) 253 [217, 293] 2 (0.57) Erythrocyte sedimentation rate (mm) 5.0 [3.0, 11.5] 36 (10.3) Partial Mayo score, median [IQR] 0.0 [0.0, 1.0] 148 (42.2) Mayo Endoscopic Subscore 0 0 222 (63.2) 1 129 (36.8) Intervals between blood sampling and colonoscopy (days) median [IQR] 23 [0, 119] 0 *Note. Values are presented as number (%) or median [interquartile range]. Immunomodulators included azathioprine and methotrexate. Calcineurin inhibitors included tacrolimus and cyclosporine. Janus kinase inhibitor included tofacitinib, upadacitinib, and filgotinib. Interleukin-23 receptor antagonists included ustekinumab and risankizumab. Anti-TNF-α agents included infliximab, adalimumab, and golimumab. Cancers included lung cancer, breast cancer, colorectal cancer, basal cell carcinoma, prostate cancer, vaginal cancer, mucosa-associated lymphoid tissue lymphoma, and neuroendocrine tumors. Infectious diseases include diverticulitis, herpes zoster, and the common cold. Autoimmune disorders included remitting seronegative symmetrical synovitis with pitting edema, polyarteritis nodosa, psoriasis, primary sclerosing cholangitis, and autoimmune hepatitis. Abbreviations: IQR, interquartile range; TNF-α, Tumor Necrosis Factor-alpha Table 2 Clinical factors affecting LRG analyzed using multiple regression analysis with multiple imputations β 95% confidence interval P value Age (years) 0.004 -0.02 0.03 0.77 Sex, male -0.74 -1.48 0.01 0.066 Disease location Proctitis - Left-sided 0.11 -0.78 0.99 0.81 Extensive 0.36 -0.36 1.08 0.34 Disease duration (years) -0.008 -0.04 0.02 0.58 Comorbidities Cancer 0.14 -1.08 1.36 0.84 Infectious disease 0.93 -2.15 4.01 0.56 Autoimmune disorder -1.37 -3.41 0.67 0.20 Medication 5-aminosalicycic acid -0.34 -1.28 0.59 0.49 Immunomodulators 0.54 -0.37 1.45 0.25 Corticosteroid -3.42 -5.94 -0.90 0.017 Calcineurin inhibitors -10.4 -15.2 -5.46 < 0.001 Janus kinase inhibitor -2.34 -3.58 -1.09 < 0.001 Vedolizumab 1.83 0.19 3.47 0.030 Interleukin-23 receptor antagonists 4.69 2.22 7.16 < 0.001 Anti-TNF-α agents -3.01 -4.16 -1.85 < 0.001 Laboratory data Albumin (g/dL) -1.59 -2.79 -0.40 0.013 C-reactive protein (mg/dL) 1.56 0.94 2.18 < 0.001 White blood cell (/10 3 µL) 0.25 < 3000 0.37 -1.95 2.69 0.77 3000–9999 - ≥ 10000 1.73 -0.34 3.80 0.14 Hemoglobin (g/dL) 0.36 0.08 0.64 0.013 Platelet (/10 5 µL) < 150 0.63 -1.17 2.42 0.62 150–349 - ≥ 350 1.98 0.81 3.14 0.002 Erythrocyte sedimentation rate (mm) 0.30 0.26 0.34 < 0.001 Immunomodulators included azathioprine and methotrexate. Calcineurin inhibitors included tacrolimus and cyclosporine. Janus kinase inhibitor included tofacitinib, upadacitinib, and filgotinib. Interleukin-23 receptor antagonists included ustekinumab and risankizumab. Anti-TNF-α agents included infliximab, adalimumab, and golimumab. Cancers included lung cancer, breast cancer, colorectal cancer, basal cell carcinoma, prostate cancer, vaginal cancer, mucosa-associated lymphoid tissue lymphoma, and neuroendocrine tumor. Infectious diseases included diverticulitis, herpes zoster, and the common cold. Autoimmune disorders included remitting seronegative symmetrical synovitis with pitting edema, polyarteritis nodosa, psoriasis, primary sclerosing cholangitis, and autoimmune hepatitis Abbreviations: TNF-α, Tumor Necrosis Factor-alpha; LRG, leucine-rich alpha-2 glycoprotein Corticosteroids, calcineurin inhibitors, Janus kinase inhibitors, and anti-TNF-α agents were negatively associated with LRG, whereas interleukin-23 receptor antagonists and vedolizumab were positively associated with LRG. 5-ASA and immunomodulators showed no significant association with LRG. Additionally, C-reactive protein, hemoglobin, platelet count, and ESR were positively associated with LRG, while albumin levels were negatively associated with LRG. Sensitivity analysis The results of the multiple regression analysis, with MES, included as an explanatory variable, and the complete case analysis are presented in Table 3 . In the complete case analysis, 121 patients (178 measurements; 30 patients tested twice, six patients tested three times, and five patients tested four times) were included. In both sensitivity analyses, Janus kinase inhibitors and anti-TNF-α agents were consistently negatively associated with LRG, whereas vedolizumab was consistently positively associated with LRG. Table 3 Two sensitivity analyses for agents affecting LRG β 95% confidence interval P value Add MES as an explanatory variable 5-aminosalicycic acid -0.17 -1.10 0.75 0.72 Immunomodulators 0.43 -0.47 1.33 0.35 Corticosteroid -3.21 -5.70 -0.72 0.023 Calcineurin inhibitors -9.87 -14.7 -5.05 < 0.001 Janus kinase inhibitor -2.11 -3.35 -0.88 0.001 Vedolizumab 2.13 0.50 3.75 0.011 Interleukin-23 receptor antagonists 4.75 2.32 7.18 < 0.001 Anti-TNF-α agents -2.72 -3.87 -1.57 < 0.001 Complete case analysis 5-aminosalicycic acid -0.96 -2.29 0.36 0.16 Immunomodulators 0.71 -0.34 1.76 0.18 Corticosteroid -2.20 -6.24 -1.83 0.29 Calcineurin inhibitors -1.27 -7.43 4.90 0.69 Janus kinase inhibitor -1.81 -3.47 -0.16 0.033 Vedolizumab 2.92 1.12 4.72 0.002 Interleukin-23 receptor antagonists 3.39 -0.46 7.24 0.087 Anti-TNF-α agents -2.68 -3.94 -1.43 < 0.001 Immunomodulators included azathioprine and methotrexate. Calcineurin inhibitors included tacrolimus and cyclosporine. Janus kinase inhibitor included tofacitinib, upadacitinib, and filgotinib. Interleukin-23 receptor antagonists included ustekinumab and risankizumab. Anti-TNF-α agents included infliximab, adalimumab, and golimumab. Abbreviations: MES, mayo endoscopic subscore; TNF-α, Tumor Necrosis Factor-alpha; LRG, leucine-rich alpha-2 glycoprotein Corticosteroids and calcineurin inhibitors were negatively associated with LRG in the multiple regression analysis, with MES added as an explanatory variable; however, no negative association was observed in the complete case analysis. Similarly, interleukin-23 receptor antagonists were positively associated with LRG in the multiple regression analysis with MES included but showed no positive association in the complete case analysis. 5-ASA and immunomodulators were not significantly associated with LRG in either sensitivity analysis. Discussion This retrospective study analyzed the effects of various agents on LRG using MI. These findings revealed that LRG levels were negatively affected by corticosteroids, calcineurin inhibitors, Janus kinase inhibitors, and anti-TNF-α agents, while they were positively influenced by vedolizumab and interleukin-23 receptor antagonists. In contrast, 5-ASA and immunosuppressive agents had no apparent effect. These results suggest that LRGs are differentially affected by agents targeting distinct cytokines. To the best of our knowledge, this is the first study to evaluate the effects of agents on LRG after adjusting for clinical variables. Corticosteroids induce anti-inflammatory proteins via glucocorticoid receptor (GR)α and inhibit the production of various inflammatory proteins. When GRα enters the nucleus, it induces IκB kinase, a nuclear factor kappa B (NF-κB) inhibitory protein, via the glucocorticoid response element. GRα also interacts with transcription factors such as activator protein 1 (AP-1) and NF-κB via protein-protein interactions, inhibiting their DNA binding and transcriptional activity [ 20 – 23 ]. NF-κB regulates the expression of multiple cytokines, including TNF-α, IL-1, IL-6, and IL-8 [ 24 ]. Therefore, the suppression of these cytokines may contribute to the negative effect of corticosteroids on LRG. When T cells recognize antigens via the TCR, signals are transmitted to the nucleus, and cytokines such as IL-1β, IL-2, IL-4, IL-6, TNF-α, and IFN-γ are produced. Calcineurin inhibitors form a complex with FK506 binding protein, which binds to calcineurin and inhibits its activity [ 25 , 26 ]. Consequently, the nuclear translocation of the nuclear factor of activated T cells (NFAT) is blocked, preventing the transcription of NFAT-dependent genes, such as IL-2, and suppressing cytokine production via T cell activation [ 27 , 28 ]. Given its inhibitory effects on cytokines that induce LRG, calcineurin inhibition likely contributes to lower LRG levels. In addition, because IL-2 plays a role in activating the JAK-STAT pathway, its suppression may further reduce LRG levels through this mechanism. There are four types of JAKs—JAK1, JAK2, JAK3, and TYK2—which pair to facilitate intracellular signaling [ 29 ]. Filgotinib is highly selective for JAK1 and has a weaker impact on homeostatic immune function than tofacitinib and Upadacitinib [ 30 ]. Tofacitinib, a pan-JAK inhibitor, primarily targets JAK1 and JAK3 but also inhibits JAK2 and TYK2 [ 31 ]. Upadacitinib is also highly selective for JAK1 [ 32 ], but has some effects on JAK2 as well [ 33 ]. Despite differences in JAK selectivity, all JAK inhibitors likely contribute to lower LRG levels by suppressing multiple cytokines. However, JAK inhibitors do not inhibit TNF-α, IL-17, or IL-1, among other cytokines [ 29 ]. Therefore, they likely affect LRG through a different pathway than anti-TNF-α agents or agents that target those cytokines. TNF-α binds to TNF receptors, inducing cell death via caspase-8 and caspase-3, as well as triggering inflammatory responses via NF-κB, mitogen-activated protein kinase, and AP-1 pathways. It is speculated that by neutralizing TNF-α and suppressing the secretion of proinflammatory cytokines, anti-TNF-α agents contribute to lower LRG levels. Furthermore, even during mucosal healing, corticosteroids, calcineurin inhibitors, JAK inhibitors, and anti-TNF-α agents likely suppress cytokines that induce LRG, albeit through distinct pharmacologic mechanisms. In contrast, vedolizumab and interleukin-23 receptor antagonists were positively associated with LRG. Vedolizumab inhibits lymphocyte migration into the intestinal tract, exerting an anti-inflammatory effect [ 34 ]. The α4β7 integrin binds to mucosal adressin cell adhesion molecule-1 (MAdCAM-1), which is specifically expressed on vascular endothelial cells in intestinal tissue, facilitating lymphocyte recruitment to the gut. Vedolizumab, a humanized IgG1 monoclonal antibody targeting the α4β7 integrin of human lymphocytes, inhibits this interaction, thereby suppressing lymphocyte migration in an intestine-specific manner [ 35 ]. Unlike other agents, vedolizumab does not directly alter cytokine levels, which may explain its relatively higher LRG levels. This aligns with a recent study reporting that LRG levels were higher in patients receiving vedolizumab compared to other therapies [ 36 ]. Interleukin-23 receptor antagonists examined in this study included ustekinumab and risankizumab. Ustekinumab targets the p40 subunit shared by IL-12 and IL-23 [ 37 ], while risankizumab selectively binds the IL-23 p19 subunit, blocking IL-23 receptor signaling [ 38 ]. IL-12 promotes Th1 differentiation, stimulating IFN-γ production from NK cells and cytotoxic T cells while enhancing type 1 innate lymphocyte cytotoxicity [ 39 , 40 ]. IL-23 has been reported to support helper T cell proliferation and maintenance, favoring the production of IL-17A, IL-17F, and IL-22 while also inducing IFN-γ secretion from activated T cells [ 39 , 41 ]. As a result, IL-22 is suppressed during anti-IL-12/23p40 antibody therapy, but other LRG-inducing cytokines may not be similarly inhibited, even in the context of mucosal healing. IL-22 activates the JAK/STAT pathway, primarily via STAT3 [ 42 ]. Therefore, Interleukin-23 receptor antagonists act by inhibiting IL-12, IL-22, and IL-23, which may contribute to lower LRG levels, mainly by modulating the JAK-STAT pathway via JAK2 and TYK2. However, this finding contradicts the results of this study. Specifically, ustekinumab was associated with lower serum trough levels of ustekinumab and lower LRG at remission induction in the high-LRG group, whereas the low-LRG group exhibited higher serum trough levels of Ustekinumab [ 43 ]. Therefore, in patients receiving ustekinumab, lower agent concentrations may have been associated with higher LRG levels. Similarly, in cases involving interleukin-23 receptor antagonists, agent concentrations may have influenced LRG values. Sensitivity analysis yielded different results. However, when endoscopic activity in mucosal healing was considered, the findings remained consistent with those of the main analysis, indicating the robustness of the primary results. The similarity in LRG levels may be attributed to their limited discriminatory power between MES 0 and MES 1. However, the number of complete cases was approximately half of the total cases. The differences observed in the sensitivity analysis compared to the main analysis may have resulted from the reduced number of agents included and potential overfitting in the multivariate analysis. We anticipate that the robustness of the findings will be further validated as more data are accumulated in future studies. This study had several limitations. First, it was a single-center, retrospective study, making some degree of bias unavoidable. Second, not all biologics and JAK inhibitors could be evaluated individually. Finally, the study focused on patients undergoing maintenance therapy, and it remains unclear whether these findings apply to remission induction therapy. In conclusion, LRG levels are influenced differently by various agent concentrations, even in the presence of mucosal healing. Therefore, when using LRG to monitor UC, the specific medications being administered should be taken into account. Declarations Data availability: If requested, access to the data of this study can be reviewed through the corresponding author, although this data is not available to the public due to privacy and ethical restrictions. Funding : None. Conflict of interest: All authors have no conflict of interest to disclose. Conflict of interest disclosure: All authors have no conflict of interest to disclose. References Ng SC, Shi HY, Hamidi N, Underwood FE, Tang W, Benchimol EI, Panaccione R, Ghosh S, Wu JCY, Chan FKL, Sung JJY, Kaplan GG (2017) Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies. Lancet 390:2769-2778. https://doi.org/10.1016/S0140-6736(17)32448-0. Murakami Y, Nishiwaki Y, Oba MS, Asakura K, Ohfuji S, Fukushima W, Suzuki Y, Nakamura Y (2019) Estimated prevalence of ulcerative colitis and Crohn's disease in Japan in 2014: an analysis of a nationwide survey. 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Amano T, Yoshihara T, Shinzaki S, Sakakibara Y, Yamada T, Osugi N, Hiyama S, Murayama Y, Nagaike K, Ogiyama H, Yamaguchi T, Arimoto Y, Kobayashi I, Kawai S, Egawa S, Kizu T, Komori M, Tsujii Y, Asakura A, Tashiro T, Tani M, Otake-Kasamoto Y, Uema R, Kato M, Tsujii Y, Inoue T, Yamada T, Kitamura T, Yonezawa A, Iijima H, Hayashi Y, Takehara T (2024) Selection of anti-cytokine biologics by pretreatment levels of serum leucine-rich alpha-2 glycoprotein in patients with inflammatory bowel disease. Sci Rep 14:29755. https://doi.org/10.1038/s41598-024-80285-6. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6142072","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":423220227,"identity":"a59d17f2-f871-4aff-9843-dbaa0f6d0c16","order_by":0,"name":"Junnosuke Hayasaka","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA70lEQVRIiWNgGAWjYBACAyCWgDATgLgCiJmZG0jRcgakhZEULYxtIAYBLebsZw/e5qmplTNnT3724OO82mj+dqCWHxXbcGqx7MlLtuY5dtzYsueZueHMbcdzZxxmbGDsOXMbt8MO5JhJ57AdS9xwI8FMmnfbsdwGoBZmxjY8Ws6/AWr5B9KS/k3675xjufMJarkBtCW3rQaoBchgbKjJ3UBIi+WMN8bWf/sOGBuceVMm2XPsQO5GoJaD+Pxizp9jeHPGtzo5g+Pp2yR+1NTlzjt/+OCDHxW4tUDBYVTGAULqgaAOgzEKRsEoGAWjAA4Ahnhg2sCdoBMAAAAASUVORK5CYII=","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":true,"prefix":"","firstName":"Junnosuke","middleName":"","lastName":"Hayasaka","suffix":""},{"id":423220228,"identity":"2001d05c-7b1b-4f32-8532-e2c1e641fafc","order_by":1,"name":"Akira Matsui","email":"","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":false,"prefix":"","firstName":"Akira","middleName":"","lastName":"Matsui","suffix":""},{"id":423220229,"identity":"ef143e21-45c9-4249-b2de-f1502a2cca11","order_by":2,"name":"Daisuke Kikuchi","email":"","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":false,"prefix":"","firstName":"Daisuke","middleName":"","lastName":"Kikuchi","suffix":""},{"id":423220230,"identity":"eef90671-5eed-43db-8c0c-9bcc6bfff9fa","order_by":3,"name":"Shu Hoteya","email":"","orcid":"","institution":"Toranomon Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shu","middleName":"","lastName":"Hoteya","suffix":""}],"badges":[],"createdAt":"2025-03-03 02:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6142072/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6142072/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77739522,"identity":"526f9ad0-2937-4e24-969f-05e5630c66f2","added_by":"auto","created_at":"2025-03-05 04:53:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":812904,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6142072/v1/4e28efce-e4b2-4493-ad59-e75a00c45f64.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of therapeutic agents on serum leucine-rich alpha-2 glycoprotein for monitoring endoscopically remitted ulcerative colitis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInflammatory bowel diseases (IBD) are prevalent in North America and Europe, with incidence rates remaining stable [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, the incidence of IBD has been rising in Africa, Asia, and South America [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In Japan, cases of IBD\u0026mdash;particularly ulcerative colitis (UC)\u0026mdash;have been increasing, with over 220,000 reported cases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In recent years, the STRIDE-II strategy has been used to treat IBD, emphasizing endoscopic mucosal healing as a long-term goal [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Even in clinical remission, one-third of cases exhibit endoscopic inflammation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Achieving endoscopic mucosal healing is associated with a favorable prognosis in IBD [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. When endoscopic activity is detected, treatment modifications are recommended [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. As a result, colonoscopy remains the gold standard for assessing IBD [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, frequent colonoscopic monitoring is challenging due to cost, time constraints, invasiveness, and manpower requirements.\u003c/p\u003e \u003cp\u003eSerum biomarkers, on the other hand, offer a relatively noninvasive and easily repeatable alternative for monitoring disease activity. Therefore, identifying serum biomarkers that accurately reflect endoscopic activity is crucial. Leucine-rich α-2-glycoprotein (LRG) is a novel serum biomarker increasingly used to monitor UC. LRG is a 50-kDa glycoprotein with eight leucine-rich repeat domains and is induced by IL-22, tumor necrosis factor (TNF)-α, and IL-1β in an IL6-independent manner [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. LRG is considered useful for monitoring UC because it reflects disease activity even in CRP-negative patients and correlates strongly with endoscopic activity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, LRG is a nonspecific biomarker and does not necessarily reflect UC activity. Elevated serum LRG levels have also been observed in other conditions, including rheumatoid arthritis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], infections, and malignant diseases [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCurrently, a wide range of therapeutic options for UC exists, including 5-aminosalicylic acid (5-ASA), immunosuppressive agents, small-molecule compounds, and biologics. Since LRG is influenced by cytokines, it may also be affected by immunosuppressive agents and biologics. However, it remains unclear whether LRG levels are impacted by these agents when used for UC monitoring. To address this gap, we conducted an exploratory study to evaluate the effects of various agents on LRG levels in the context of UC monitoring.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eThis retrospective study utilized electronic medical records at our hospital. The study included patients with UC whose LRG levels were measured between October 1, 2020, and June 30, 2023. For endoscopic monitoring, patients who underwent lower gastrointestinal endoscopy within 1 year before or after LRG measurement and had confirmed mucosal healing were included. Patients were excluded if they were undergoing remission induction therapy, experienced treatment modifications, had symptoms that changed after colorectal surgery, or exhibited symptom variation between examinations. In addition, one case in which LRG levels fell below the sensitivity threshold was excluded. Ultimately, 214 patients were included, with a total of 351 measurements: 65 patients tested twice, 22 patients three times, eight patients four times, and one patient five times.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData extraction method\u003c/h2\u003e \u003cp\u003eIn Japan, LRG testing is covered by insurance for patients with UC and Crohn\u0026rsquo;s disease. Therefore, we selected patients who were registered with either condition and had undergone LRG measurement during the study period. All cases were reviewed retrospectively using electronic medical records, and patients diagnosed with UC based on endoscopic, radiologic, histologic, and clinical criteria [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] were included.\u003c/p\u003e \u003cp\u003e This study was conducted in accordance with the 1975 Declaration of Helsinki (6th Edition, 2008) and was approved by the hospital\u0026rsquo;s Ethics Committee (approval number 2202). Written informed consent was not required due to the retrospective study design, but patients were given the opportunity to opt out.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEvaluation criteria\u003c/h3\u003e\n\u003cp\u003eWe collected the following data: age, sex, disease location, disease duration, intervals between blood sampling and colonoscopy, and comorbidities (cancer, infectious disease, autoimmune disorder). Additionally, we recorded the partial Mayo score (PMS), Mayo endoscopic subscore (MES), medication use (5-aminosalicycic acid, immunomodulators, corticosteroids, calcineurin inhibitors, Janus kinase inhibitors, vedolizumab, interleukin-23 receptor antagonists, anti-TNF-α agents), and laboratory parameters (LRG, C-reactive protein, white blood cell count, hemoglobin, platelet count, erythrocyte sedimentation rate). Cancer was defined as any malignancy diagnosed within the past 5 years that was either not cured or remained under follow-up. Infections were classified as acute infections present at the time of LRG measurement. PMS was assessed at the time of LRG measurement. Concomitant medications included those prescribed for UC as well as those used to manage comorbid conditions.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eUnivariate analysis was performed as follows. Continuous variables were expressed as medians and interquartile ranges, while categorical variables were presented as numbers and percentages. The effect on LRG was evaluated using multiple regression analysis. The explanatory variables included age, sex, disease location, disease duration, comorbidities (cancer, infectious disease, autoimmune disorder), medications (5-ASA, immunomodulators, corticosteroids, calcineurin inhibitors, Janus kinase inhibitors, vedolizumab, interleukin-23 receptor antagonists, anti-TNF-α agents), and laboratory parameters (C-reactive protein, white blood cell count, hemoglobin, platelet count, and erythrocyte sedimentation rate). Missing data were present in 42.2% (148/351) of all cases. To address this, we used multiple imputations (MI) to replace missing values with statistically plausible estimates, reducing potential bias. MI is a method that generates multiple datasets by imputing missing values based on observed data, often producing more reliable results than complete case analysis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. MI was performed using the chained-equation method under a missing-at-random assumption [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. All clinical data except for the interval between blood sampling and colonoscopy were included in the imputation models, and 20 imputed datasets were generated. The results from each dataset were combined using Rubin\u0026rsquo;s rules [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Additionally, two sensitivity analyses were conducted. The first was a multiple regression analysis that included MES as an explanatory variable since mucosal healing encompassed both MES 0 and MES 1. The second was a complete case analysis to validate the MI method.\u003c/p\u003e \u003cp\u003eStatistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All analyses were performed using R software (version 4.1.3, The R Foundation for Statistical Computing, Vienna, Austria). MI was performed using the \u003cem\u003emice\u003c/em\u003e package (v3.13.0).\u003c/p\u003e"},{"header":"Result","content":"\u003cp\u003eThe baseline characteristics of the participants are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median patient age was 55 years, and 64.1% of the patients were male. Approximately half of the patients had extensive colitis, with a median disease duration of 12 years. The most commonly used agent was 5-ASA in 88.9% of cases. Immunomodulators were used in 13.1%, corticosteroids in 2.6%, calcineurin inhibitors in 0.9%, Janus kinase inhibitors in 5.7%, vedolizumab in 3.4%, interleukin-23 receptor antagonists in 1.7%, and anti-TNF-α agents in 7.4%. The median LRG level was 11.2 \u0026micro;g/mL, while the median PMS was 0. A total of 63.2% of patients had MES 0, and 80% had MES 1. The median interval between blood sampling and colonoscopy was 23 days. The maximum missing value was 42.2% for PMS, followed by 10.2% for ESR; all other values had less than 5% missing data. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of the multiple regression analysis following multiple imputations. Age, sex, disease location, disease duration, and comorbidities were not significantly associated with LRG.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of patients with ulcerative colitis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;351\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMissing data\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.0 [43.0, 66.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male, N [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e225 (64.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease location, N [%]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProctitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft-sided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62 (17.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e194 (55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration (years), median [IQR]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.0 [4.0, 23.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (0.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities, N\u0026nbsp;[%]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutoimmune disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication, N\u0026nbsp;[%]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-aminosalicycic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e312 (88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunomodulators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (13.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosteroids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcineurin inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (0.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanus kinase inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (5.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVedolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-12/23 receptor antagonists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-TNF agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory data median [IQR]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeucine-rich alpha-2 glycoprotein (\u0026micro;g/ml)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.2 [9.8, 13.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4 [4.2, 4.6]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05 [0.02, 0.12]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (2.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell (/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5500 [4700, 6500]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.3 [13.2, 15.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet count (10\u003csup\u003e3\u003c/sup\u003e/\u0026micro;l)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e253 [217, 293]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0.57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErythrocyte sedimentation rate (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0 [3.0, 11.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36 (10.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePartial Mayo score, median [IQR]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0 [0.0, 1.0]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e148 (42.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMayo Endoscopic Subscore\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e222 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntervals between blood sampling and colonoscopy (days) median [IQR]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 [0, 119]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e*Note. Values are presented as number (%) or median [interquartile range].\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eImmunomodulators included azathioprine and methotrexate. Calcineurin inhibitors included tacrolimus and cyclosporine. Janus kinase inhibitor included tofacitinib, upadacitinib, and filgotinib. Interleukin-23 receptor antagonists included ustekinumab and risankizumab. Anti-TNF-α agents included infliximab, adalimumab, and golimumab. Cancers included lung cancer, breast cancer, colorectal cancer, basal cell carcinoma, prostate cancer, vaginal cancer, mucosa-associated lymphoid tissue lymphoma, and neuroendocrine tumors. Infectious diseases include diverticulitis, herpes zoster, and the common cold. Autoimmune disorders included remitting seronegative symmetrical synovitis with pitting edema, polyarteritis nodosa, psoriasis, primary sclerosing cholangitis, and autoimmune hepatitis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eAbbreviations: IQR, interquartile range; TNF-α, Tumor Necrosis Factor-alpha\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical factors affecting LRG analyzed using multiple regression analysis with multiple imputations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% confidence interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, male\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDisease location\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProctitis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft-sided\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComorbidities\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutoimmune disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMedication\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-aminosalicycic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunomodulators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosteroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcineurin inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-15.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanus kinase inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVedolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-23 receptor antagonists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-TNF-α agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-4.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein (mg/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cell (/10\u003csup\u003e3\u003c/sup\u003e\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3000\u0026ndash;9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;10000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet (/10\u003csup\u003e5\u003c/sup\u003e\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e150\u0026ndash;349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErythrocyte sedimentation rate (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eImmunomodulators included azathioprine and methotrexate. Calcineurin inhibitors included tacrolimus and cyclosporine. Janus kinase inhibitor included tofacitinib, upadacitinib, and filgotinib. Interleukin-23 receptor antagonists included ustekinumab and risankizumab. Anti-TNF-α agents included infliximab, adalimumab, and golimumab. Cancers included lung cancer, breast cancer, colorectal cancer, basal cell carcinoma, prostate cancer, vaginal cancer, mucosa-associated lymphoid tissue lymphoma, and neuroendocrine tumor. Infectious diseases included diverticulitis, herpes zoster, and the common cold. Autoimmune disorders included remitting seronegative symmetrical synovitis with pitting edema, polyarteritis nodosa, psoriasis, primary sclerosing cholangitis, and autoimmune hepatitis\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: TNF-α, Tumor Necrosis Factor-alpha; LRG, leucine-rich alpha-2 glycoprotein\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCorticosteroids, calcineurin inhibitors, Janus kinase inhibitors, and anti-TNF-α agents were negatively associated with LRG, whereas interleukin-23 receptor antagonists and vedolizumab were positively associated with LRG. 5-ASA and immunomodulators showed no significant association with LRG. Additionally, C-reactive protein, hemoglobin, platelet count, and ESR were positively associated with LRG, while albumin levels were negatively associated with LRG.\u003c/p\u003e\n\u003ch3\u003eSensitivity analysis\u003c/h3\u003e\n\u003cp\u003eThe results of the multiple regression analysis, with MES, included as an explanatory variable, and the complete case analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In the complete case analysis, 121 patients (178 measurements; 30 patients tested twice, six patients tested three times, and five patients tested four times) were included. In both sensitivity analyses, Janus kinase inhibitors and anti-TNF-α agents were consistently negatively associated with LRG, whereas vedolizumab was consistently positively associated with LRG.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTwo sensitivity analyses for agents affecting LRG\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e95% confidence interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAdd MES as an explanatory variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-aminosalicycic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunomodulators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosteroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-3.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcineurin inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-14.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-5.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanus kinase inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVedolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-23 receptor antagonists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-TNF-α agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eComplete case analysis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5-aminosalicycic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunomodulators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosteroid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCalcineurin inhibitors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-7.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanus kinase inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVedolizumab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInterleukin-23 receptor antagonists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-TNF-α agents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eImmunomodulators included azathioprine and methotrexate. Calcineurin inhibitors included tacrolimus and cyclosporine. Janus kinase inhibitor included tofacitinib, upadacitinib, and filgotinib. Interleukin-23 receptor antagonists included ustekinumab and risankizumab. Anti-TNF-α agents included infliximab, adalimumab, and golimumab.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: MES, mayo endoscopic subscore; TNF-α, Tumor Necrosis Factor-alpha; LRG, leucine-rich alpha-2 glycoprotein\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCorticosteroids and calcineurin inhibitors were negatively associated with LRG in the multiple regression analysis, with MES added as an explanatory variable; however, no negative association was observed in the complete case analysis. Similarly, interleukin-23 receptor antagonists were positively associated with LRG in the multiple regression analysis with MES included but showed no positive association in the complete case analysis. 5-ASA and immunomodulators were not significantly associated with LRG in either sensitivity analysis.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis retrospective study analyzed the effects of various agents on LRG using MI. These findings revealed that LRG levels were negatively affected by corticosteroids, calcineurin inhibitors, Janus kinase inhibitors, and anti-TNF-α agents, while they were positively influenced by vedolizumab and interleukin-23 receptor antagonists. In contrast, 5-ASA and immunosuppressive agents had no apparent effect. These results suggest that LRGs are differentially affected by agents targeting distinct cytokines. To the best of our knowledge, this is the first study to evaluate the effects of agents on LRG after adjusting for clinical variables.\u003c/p\u003e \u003cp\u003eCorticosteroids induce anti-inflammatory proteins via glucocorticoid receptor (GR)α and inhibit the production of various inflammatory proteins. When GRα enters the nucleus, it induces IκB kinase, a nuclear factor kappa B (NF-κB) inhibitory protein, via the glucocorticoid response element. GRα also interacts with transcription factors such as activator protein 1 (AP-1) and NF-κB via protein-protein interactions, inhibiting their DNA binding and transcriptional activity [\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. NF-κB regulates the expression of multiple cytokines, including TNF-α, IL-1, IL-6, and IL-8 [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, the suppression of these cytokines may contribute to the negative effect of corticosteroids on LRG.\u003c/p\u003e \u003cp\u003eWhen T cells recognize antigens via the TCR, signals are transmitted to the nucleus, and cytokines such as IL-1β, IL-2, IL-4, IL-6, TNF-α, and IFN-γ are produced. Calcineurin inhibitors form a complex with FK506 binding protein, which binds to calcineurin and inhibits its activity [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Consequently, the nuclear translocation of the nuclear factor of activated T cells (NFAT) is blocked, preventing the transcription of NFAT-dependent genes, such as IL-2, and suppressing cytokine production via T cell activation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Given its inhibitory effects on cytokines that induce LRG, calcineurin inhibition likely contributes to lower LRG levels. In addition, because IL-2 plays a role in activating the JAK-STAT pathway, its suppression may further reduce LRG levels through this mechanism.\u003c/p\u003e \u003cp\u003eThere are four types of JAKs\u0026mdash;JAK1, JAK2, JAK3, and TYK2\u0026mdash;which pair to facilitate intracellular signaling [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Filgotinib is highly selective for JAK1 and has a weaker impact on homeostatic immune function than tofacitinib and Upadacitinib [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Tofacitinib, a pan-JAK inhibitor, primarily targets JAK1 and JAK3 but also inhibits JAK2 and TYK2 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Upadacitinib is also highly selective for JAK1 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], but has some effects on JAK2 as well [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Despite differences in JAK selectivity, all JAK inhibitors likely contribute to lower LRG levels by suppressing multiple cytokines.\u003c/p\u003e \u003cp\u003eHowever, JAK inhibitors do not inhibit TNF-α, IL-17, or IL-1, among other cytokines [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Therefore, they likely affect LRG through a different pathway than anti-TNF-α agents or agents that target those cytokines.\u003c/p\u003e \u003cp\u003eTNF-α binds to TNF receptors, inducing cell death via caspase-8 and caspase-3, as well as triggering inflammatory responses via NF-κB, mitogen-activated protein kinase, and AP-1 pathways. It is speculated that by neutralizing TNF-α and suppressing the secretion of proinflammatory cytokines, anti-TNF-α agents contribute to lower LRG levels. Furthermore, even during mucosal healing, corticosteroids, calcineurin inhibitors, JAK inhibitors, and anti-TNF-α agents likely suppress cytokines that induce LRG, albeit through distinct pharmacologic mechanisms.\u003c/p\u003e \u003cp\u003eIn contrast, vedolizumab and interleukin-23 receptor antagonists were positively associated with LRG.\u003c/p\u003e \u003cp\u003eVedolizumab inhibits lymphocyte migration into the intestinal tract, exerting an anti-inflammatory effect [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The α4β7 integrin binds to mucosal adressin cell adhesion molecule-1 (MAdCAM-1), which is specifically expressed on vascular endothelial cells in intestinal tissue, facilitating lymphocyte recruitment to the gut. Vedolizumab, a humanized IgG1 monoclonal antibody targeting the α4β7 integrin of human lymphocytes, inhibits this interaction, thereby suppressing lymphocyte migration in an intestine-specific manner [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Unlike other agents, vedolizumab does not directly alter cytokine levels, which may explain its relatively higher LRG levels. This aligns with a recent study reporting that LRG levels were higher in patients receiving vedolizumab compared to other therapies [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterleukin-23 receptor antagonists examined in this study included ustekinumab and risankizumab.\u003c/p\u003e \u003cp\u003eUstekinumab targets the p40 subunit shared by IL-12 and IL-23 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], while risankizumab selectively binds the IL-23 p19 subunit, blocking IL-23 receptor signaling [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. IL-12 promotes Th1 differentiation, stimulating IFN-γ production from NK cells and cytotoxic T cells while enhancing type 1 innate lymphocyte cytotoxicity [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. IL-23 has been reported to support helper T cell proliferation and maintenance, favoring the production of IL-17A, IL-17F, and IL-22 while also inducing IFN-γ secretion from activated T cells [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. As a result, IL-22 is suppressed during anti-IL-12/23p40 antibody therapy, but other LRG-inducing cytokines may not be similarly inhibited, even in the context of mucosal healing. IL-22 activates the JAK/STAT pathway, primarily via STAT3 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Therefore, Interleukin-23 receptor antagonists act by inhibiting IL-12, IL-22, and IL-23, which may contribute to lower LRG levels, mainly by modulating the JAK-STAT pathway via JAK2 and TYK2. However, this finding contradicts the results of this study. Specifically, ustekinumab was associated with lower serum trough levels of ustekinumab and lower LRG at remission induction in the high-LRG group, whereas the low-LRG group exhibited higher serum trough levels of Ustekinumab [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Therefore, in patients receiving ustekinumab, lower agent concentrations may have been associated with higher LRG levels. Similarly, in cases involving interleukin-23 receptor antagonists, agent concentrations may have influenced LRG values.\u003c/p\u003e \u003cp\u003eSensitivity analysis yielded different results. However, when endoscopic activity in mucosal healing was considered, the findings remained consistent with those of the main analysis, indicating the robustness of the primary results. The similarity in LRG levels may be attributed to their limited discriminatory power between MES 0 and MES 1. However, the number of complete cases was approximately half of the total cases. The differences observed in the sensitivity analysis compared to the main analysis may have resulted from the reduced number of agents included and potential overfitting in the multivariate analysis. We anticipate that the robustness of the findings will be further validated as more data are accumulated in future studies.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, it was a single-center, retrospective study, making some degree of bias unavoidable. Second, not all biologics and JAK inhibitors could be evaluated individually. Finally, the study focused on patients undergoing maintenance therapy, and it remains unclear whether these findings apply to remission induction therapy.\u003c/p\u003e \u003cp\u003eIn conclusion, LRG levels are influenced differently by various agent concentrations, even in the presence of mucosal healing. Therefore, when using LRG to monitor UC, the specific medications being administered should be taken into account.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e If requested, access to the data of this study can be reviewed through the corresponding author, although this data is not available to the public due to privacy and ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e All authors have no conflict of interest to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosure:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have no conflict of interest to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNg SC, Shi HY, Hamidi N, Underwood FE, Tang W, Benchimol EI, Panaccione R, Ghosh S, Wu JCY, Chan FKL, Sung JJY, Kaplan GG (2017) Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies. 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Dig Dis 43:11-18. https://doi.org/10.1159/000542062.\u003c/li\u003e\n\u003cli\u003eSands BE, Sandborn WJ, Panaccione R, O\u0026apos;Brien CD, Zhang H, Johanns J, Adedokun OJ, Li K, Peyrin-Biroulet L, Van Assche G, Danese S, Targan S, Abreu MT, Hisamatsu T, Szapary P, Marano C; UNIFI Study Group (2019) Ustekinumab as Induction and Maintenance Therapy for Ulcerative Colitis. N Engl J Med 381:1201-1214. https://doi.org/10.1056/NEJMoa1900750.\u003c/li\u003e\n\u003cli\u003ePang Y, D\u0026apos;Cunha R, Winzenborg I, Veldman G, Pivorunas V, Wallace K (2024) Risankizumab: Mechanism of action, clinical and translational science. Clin Transl Sci 17:e13706. https://doi.org/10.1111/cts.13706.\u003c/li\u003e\n\u003cli\u003eMoschen AR, Tilg H, Raine T (2019) IL-12, IL-23 and IL-17 in IBD: immunobiology and therapeutic targeting. Nat Rev Gastroenterol Hepatol 16:185-196. https://doi.org/10.1038/s41575-018-0084-8.\u003c/li\u003e\n\u003cli\u003eWatford WT, Moriguchi M, Morinobu A, O\u0026apos;Shea JJ (2003) The biology of IL-12: coordinating innate and adaptive immune responses. Cytokine Growth Factor Rev 14:361-368. https://doi.org/10.1016/s1359-6101(03)00043-1.\u003c/li\u003e\n\u003cli\u003eNeurath MF (2019) IL-23 in inflammatory bowel diseases and colon cancer. Cytokine Growth Factor Rev 45:1-8. https://doi.org/10.1016/j.cytogfr.2018.12.002.\u003c/li\u003e\n\u003cli\u003ePickert G, Neufert C, Leppkes M, Zheng Y, Wittkopf N, Warntjen M, Lehr HA, Hirth S, Weigmann B, Wirtz S, Ouyang W, Neurath MF, Becker C (2009) STAT3 links IL-22 signaling in intestinal epithelial cells to mucosal wound healing. J Exp Med 206:1465-1472. https://doi.org/10.1084/jem.20082683.\u003c/li\u003e\n\u003cli\u003eAmano T, Yoshihara T, Shinzaki S, Sakakibara Y, Yamada T, Osugi N, Hiyama S, Murayama Y, Nagaike K, Ogiyama H, Yamaguchi T, Arimoto Y, Kobayashi I, Kawai S, Egawa S, Kizu T, Komori M, Tsujii Y, Asakura A, Tashiro T, Tani M, Otake-Kasamoto Y, Uema R, Kato M, Tsujii Y, Inoue T, Yamada T, Kitamura T, Yonezawa A, Iijima H, Hayashi Y, Takehara T (2024) Selection of anti-cytokine biologics by pretreatment levels of serum leucine-rich alpha-2 glycoprotein in patients with inflammatory bowel disease. Sci Rep 14:29755. https://doi.org/10.1038/s41598-024-80285-6.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"leucine-rich alpha-2 glycoprotein, ulcerative colitis, endoscopy, biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-6142072/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6142072/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eSerum leucine-rich alpha-2 glycoprotein (LRG) levels are measured to monitor ulcerative colitis (UC); however, the impact of concomitant medications on LRG remains unclear. This exploratory study aimed to determine the effects of various agents on serum LRG levels.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a single-center, retrospective study using medical records at our hospital from October 1, 2020, to June 30, 2023. Patients who underwent lower gastrointestinal endoscopy within 1 year before or after LRG measurement and had confirmed mucosal healing were included. The effects of medication on LRG levels were assessed using multiple regression analysis following multiple imputations. The analyzed agents included 5-aminosalicylic acid (5-ASA), immunomodulators, corticosteroids, calcineurin inhibitors, Janus kinase inhibitors, vedolizumab, interleukin-23 receptor antagonists, and anti-TNF-α agents.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 214 patients (351 measurements) were included. The median LRG was 11.2 \u0026micro;g/ml. Among patients, 63.2 had a Mayo Endoscopic Subscore of 0, while 36.8% had a score of 1. The frequency of medication use was as follows: 5-ASA (88.9%), immunomodulators (13.1%), corticosteroids (2.6%), calcineurin inhibitors (0.9%), Janus kinase inhibitors (5.7%), vedolizumab (3.4%), interleukin-23 receptor antagonists (1.7%), and anti-TNF-α agents (7.4%). Corticosteroids, calcineurin inhibitors, Janus kinase inhibitors, and anti-TNF-α agents were negatively associated with LRG (β = -3.42, -10.4, -2.34, and \u0026minus;\u0026thinsp;3.01, respectively). Conversely, vedolizumab and interleukin-23 receptor antagonists were positively associated with LRG. (β\u0026thinsp;=\u0026thinsp;1.83 and 4.69, respectively).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eLRG levels are influenced by medications, even in patients with mucosal healing. These effects should be considered when using LRG to monitor UC.\u003c/p\u003e","manuscriptTitle":"Impact of therapeutic agents on serum leucine-rich alpha-2 glycoprotein for monitoring endoscopically remitted ulcerative colitis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-04 11:29:13","doi":"10.21203/rs.3.rs-6142072/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3709474e-0d59-40a1-bef5-b44047945b00","owner":[],"postedDate":"March 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-05T04:53:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-04 11:29:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6142072","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6142072","identity":"rs-6142072","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}