Invest Clin 67(1): 57 - 72, 2026 https://doi.org/10.54817/IC.v67n1a05

Corresponding author: Zhangning Zhou. Department of Gastroenterology, Shulan (Hangzhou) Hospital, Shulan

International Medical College, Zhejiang Shuren University, Hangzhou 310022, P.R. China.

Email: zjsrxy2024@hotmail.com

Study on the predictive model of response

of patients with inflammatory bowel disease

to infliximab treatment.

Ru Ding1, Mengdi Fan2, Juanjuan Gu1 and Zhangning Zhou1

1Department of Gastroenterology, Shulan (Hangzhou) Hospital, Shulan International

Medical College, Zhejiang Shuren University, Hangzhou, P.R. China.

2Department of General Practice, Shulan (Hangzhou) Hospital, Shulan International

Medical College, Zhejiang Shuren University, Hangzhou, P.R, China.

Keywords: Inflammatory Bowel Diseases; Infliximab; Drug Therapy; Response; Disease

forecasting models.

Abstract. This study aimed to develop a predictive model for how patients

with inflammatory bowel disease (IBD) respond to infliximab (IFX) treatment.

One hundred adult IBD patients admitted to Shulan (Hangzhou) Hospital from

August 2023 to November 2024 were included and divided into response and non-

response groups based on their reaction to IFX. The response group consisted

of 57 patients (57.0%), while the non-response group had 43 patients (43.0%).

Clinical data, including gender, age, BMI, disease type (Crohn’s disease/ulcer-

ative colitis), disease activity indices (CDAI/UCAI), history of IFX treatment,

and infusion reactions, were collected and compared between the two groups.

Additionally, biomarker levels, such as TNF-α, CRP, calprotectin, anti-infliximab

antibody (ATI), IL-6, and IL-8, were measured during the midcourse of IFX

treatment. Single-factor analysis identified variables that differed, and logistic

regression showed that calprotectin level (OR=1.099, 95%CI=1.039-1.163),

ATI (OR=3.756, 95%CI=1.222-11.546), IL-6 (OR=1.261, 95%CI=1.069-

1.488), and IL-8 (OR=1.014, 95%CI=1.004-1.024) were key factors influenc-

ing treatment response (p < 0.05). A nomogram was created using these fac-

tors to predict treatment response in IBD patients. ROC analysis showed AUC

values of 0.809, 0.762, 0.850, and 0.775 for calprotectin, ATI, IL-6, and IL-

8, respectively, with corresponding 95% confidence intervals. The calibration

curve indicated good model fit. These findings underscore the important roles

of these cytokines in IBD pathogenesis and the action of IFX, as well as the high

predictive power of the nomogram model.

58 Ding et al.

Investigación Clínica 67(1): 2026

Estudio sobre el modelo predictivo de la respuesta

de los pacientes con enfermedad inflamatoria intestinal

al tratamiento con infliximab.

Invest Clin 2026; 67 (1): 57 – 72

Palabras clave: Enfermedades Inflamatorias del Intestino; Infliximab; Tratamiento

Farmacológico; Respuesta; Modelos de predicción de enfermedades.

Resumen. Este estudio tuvo como objetivo desarrollar un modelo predicti-

vo de la respuesta de los pacientes con enfermedad inflamatoria intestinal (EII)

al tratamiento con infliximab (IFX). Se incluyeron un total de 100 pacientes

adultos con EII ingresados en el Hospital Shulan (Hangzhou) desde agosto de

2023 hasta noviembre de 2024, y se dividieron en grupos de respuesta y no

respuesta según su reacción al IFX. El grupo de respuesta tenía 57 pacientes

(57,0%), mientras que el de no respuesta, 43 (43,0%). Se recolectaron y com-

pararon datos clínicos, como género, edad, IMC, tipo de enfermedad (Crohn/

colitis ulcerosa), índices de actividad de la enfermedad (CDAI/UCAI), histo-

rial de tratamiento con IFX y reacciones a la infusión, entre ambos grupos.

Además, se midieron los niveles de biomarcadores, incluidos TNF-α, PCR, cal-

protectina, ATI, IL-6 e IL-8, durante el período intermedio del tratamiento

con IFX. El análisis de variables individuales identificó diferencias significati-

vas, y el análisis de regresión logística reveló que los niveles de calprotectina

(OR=1,099, IC95%=1,039-1,163), ATI (OR=3,756, IC95%=1,222-11,546),

IL-6 (OR=1,261, IC95%=1,069-1,488) e IL-8 (OR=1,014, IC95%=1,004-

1,024) eran factores clave que influyen en la respuesta al tratamiento (p<0,05).

Se construyó un nomograma basado en estos factores para predecir la respues-

ta al tratamiento en pacientes con EII. El análisis de la curva ROC mostró valo-

res de AUC de 0,809, 0,762, 0,850 y 0,775 para calprotectina, ATI, IL-6 e IL-8,

respectivamente, con los rangos del IC del 95% correspondientes. La curva de

calibración indicó un buen ajuste del modelo. Estos hallazgos destacan el papel

importante de estas citocinas en la patogénesis de la EII y en el mecanismo

terapéutico del IFX, así como el alto valor predictivo del nomograma.

Received: 29-06-2025 Accepted: 23-11-2025

INTRODUCTION

Inflammatory Bowel Disease (IBD) is a

complex, chronic gastrointestinal inflamma-

tory condition that mainly includes Crohn’s

disease and ulcerative colitis. Its pathogene-

sis has not been fully understood yet 1. Glob-

ally, the incidence and prevalence of IBD are

continuously rising, especially in Western de-

veloped countries. However, in recent years,

its incidence has also sharply increased in

newly industrialized regions like Asia 2. IBD

causes long-term pain and suffering for pa-

tients, seriously affecting their quality of

life, and also places a significant economic

burden on the healthcare system 3.

Response of patients with inflammatory bowel disease to infliximab 59

Vol. 67(1): 57 - 72, 2026

The approach to treating IBD has shift-

ed greatly from traditional medications to

biological therapies. During the era of con-

ventional treatments, 5-aminosalicylic acid

drugs, corticosteroids, and immunosuppres-

sants were the primary options for managing

IBD. However, these medications often faced

challenges such as limited effectiveness and

notable side effects, making it difficult to

fully meet clinical needs 4, 5. With a deeper

understanding of IBD’s causes, especially the

development of targeted therapies aimed at

inflammatory mediators like tumor necrosis

factor (TNF), there has been groundbreak-

ing progress in IBD treatment. Infliximab

(IFX), the first TNF-α inhibitor approved for

treating IBD, effectively reduces intestinal

inflammation and significantly improves pa-

tients’ symptoms and quality of life by spe-

cifically binding to and neutralizing TNF-α 6,

7. The successful use of infliximab not only

offers new treatment options for IBD pa-

tients but also encourages widespread use

and ongoing research of biological agents in

managing IBD.

Although IFX has shown a remarkable

curative effect in the treatment of IBD,

there are significant individual differences

in patients’ treatment response. Some pa-

tients responded well to IFX, achieving rapid

symptom relief and a significant reduction

in disease activity. However, some patients

exhibit poor responses and even primary or

secondary treatment failure. The heteroge-

neity of this therapeutic response is a key

problem that urgently requires resolution in

the treatment of IBD 8.

Heterogeneity in treatment response

not only affects patients’ clinical prognosis

but also increases medical costs and psy-

chological burden. For patients with poor

response, it may be necessary to try other

biological agents or immunosuppressants,

which not only increase treatment costs but

may also bring additional drug-related side

effects and risks 9,10. In addition, heteroge-

neity in treatment response poses challeng-

es for physicians in developing treatment

plans. Doctors need to provide patients with

personalized treatment programs, given lim-

ited medical resources, to achieve optimal

treatment outcomes.

To optimize treatment strategies for

IBD patients and improve IFX efficacy, re-

searchers have begun exploring factors that

affect treatment response. These factors in-

clude, but are not limited to, the patient’s

age, sex, disease type, disease activity, pre-

vious treatment history, complications, se-

rological marker levels, and genetic back-

ground11,12. However, given the complexity of

IBD pathogenesis and interpatient variabil-

ity, it is often difficult for a single factor to

fully account for the heterogeneity of treat-

ment responses. Therefore, it has become

a challenging and forward-looking research

direction to develop a predictive model that

comprehensively considers multiple factors

and individually predicts the treatment re-

sponse of IBD patients to IFX 13,14.

A prediction model is a mathematical

system based on large datasets that gener-

ates predictions for specific events or out-

comes by analyzing and processing input

data. In medicine, predictive models are

widely used in areas including disease di-

agnosis, prognosis assessment, treatment

strategy selection, and others 15,16. In the

treatment of IBD, the potential of prediction

models also remains broad 17, 18.

First, the prediction model can help

physicians more accurately evaluate IBD

patients’ responses to infliximab, enabling

more personalized treatment plans. Using

the model, doctors can identify patients un-

likely to respond to infliximab in advance

and adjust treatment strategies accordingly

to avoid unnecessary drug use and waste of

medical resources 19. Additionally, the model

can suggest other potentially effective treat-

ments or strategies tailored to individual pa-

tient conditions, thereby improving overall

treatment outcomes. Second, the prediction

model can also offer more comprehensive

health management services for patients

with IBD. By regularly monitoring relevant

60 Ding et al.

Investigación Clínica 67(1): 2026

indicators and dynamically evaluating them

with the model, healthcare providers can de-

tect changes in patients’ conditions prompt-

ly and implement appropriate interventions,

effectively preventing disease recurrence and

complications. This approach not only en-

hances patients’ quality of life but also helps

reduce medical costs and social burdens 20,

21. Finally, research on the prediction mod-

el can also advance understanding of IBD’s

pathogenesis and treatment strategies. By

analyzing the key factors identified by the

model, researchers can further uncover the

molecular mechanisms and immune regula-

tory networks involved in IBD, providing a

theoretical basis and experimental evidence

for the development of new drugs and thera-

pies 22. This will promote continuous innova-

tion and progress in IBD treatment.

Given the variability in IBD patients’ re-

sponses to infliximab and the wide-ranging

applications and challenges of predictive

models in IBD treatment, this study aims

to develop a predictive model for infliximab

response in IBD patients through retrospec-

tive analysis. It will utilize existing medical

resources, gather comprehensive patient

data, and apply scientific methods to clean

and standardize the data, build an accurate

and reliable prediction model, and rigorous-

ly validate and evaluate it. The importance

of this study lies in: providing more person-

alized treatment plans for IBD patients, en-

hancing treatment efficiency and success

rates, reducing medical costs and patient

burden, and equipping doctors with more

precise tools for disease management and

prediction. This enables timely detection of

changes in patients’ conditions and the im-

plementation of appropriate interventions

to improve overall treatment outcomes and

quality of life. Additionally, it promotes an

in-depth understanding of IBD pathogenesis

and treatment strategies, offers a theoreti-

cal basis and experimental evidence for de-

veloping new therapies, and supports con-

tinuous innovation and advancement in IBD

treatment.

PATIENTS AND METHODS

One hundred adult patients with IBD

admitted to Shulan (Hangzhou) Hospital,

Shulan International Medical College, Zheji-

ang Shuren University, from August 2023 to

November 2024 were included.

Inclusion criteria

• Inflammatory bowel disease (IBD)

was diagnosed by professional doctors

through endoscopy and imaging fin-

dings.

• No recent (within six months)

treatment with other biological agents

or immunosuppressants.

• 20~65 years old.

• Patients treated with infliximab for a

certain period (more than 14 weeks).

• The patient and his family agreed

and signed the informed consent form.

Exclusion criteria

• Clinical data were incomplete.

• Have a clear history of infection in

the respiratory system or urinary sys-

tem recently.

• Taking aspirin and other anticoagu-

lants recently.

• Complicated with serious diseases

such as heart, liver, kidney, biliary sys-

tem, and hematopoietic system.

• Combined with autoimmune diseases.

• Have a history of trauma and opera-

tion during pregnancy and within three

months.

• Previous history of biological

treatment.

METHOD

Baseline data collection

Baseline data of all IBD patients were

collected before the initiation of infliximab

Response of patients with inflammatory bowel disease to infliximab 61

Vol. 67(1): 57 - 72, 2026

(IFX) treatment, including gender, age, BMI

index, disease type [Crohn’s disease (CD)

or ulcerative colitis (UC)], baseline dis-

ease activity [Crohn’s disease activity index

(CDAI) 23 and ulcerative colitis activity index

(UCAI) 24], prior IFX treatment history, and

the occurrence of infusion-related reactions

(e.g., chest tightness or chest pain) during

previous treatments. In addition, baseline

biomarkers—such as serum levels of tumor

necrosis factor-α (TNF-α), C-reactive protein

(CRP), anti-Infliximab antibody (ATI), in-

terleukin-6 (IL-6), interleukin-8 (IL-8), and

fecal calprotectin—were measured prior to

treatment initiation to assess their predic-

tive value for treatment response.

Assessment instrument

The CDAI score consists of many fac-

tors, including symptoms (such as abdomi-

nal pain, diarrhea, weight loss, etc.), signs

(such as abdominal mass, perforation, in-

testinal obstruction, etc.), laboratory indi-

cators (such as hemoglobin level and white

blood cell count) and complications (such

as peripheral arthritis, skin lesions, etc.).

The score ranges from 0 to 600, and higher

scores indicate more severe disease. Spe-

cific scoring criteria: remission period: <

150 points; mild activity period: 150~220

points; moderate activity period: 221~450

points; severe activity period: > 450 points.

The UCAI score is primarily based on

patients’ clinical manifestations, including

defecation frequency, presence of blood in

stool, endoscopic findings, and physicians’

overall assessment. For example, the im-

proved Mayo scoring system is a common

form of UCAI, and its scoring comprises four

components: daily defecation frequency,

presence of blood in stool, degree of muco-

sal injury under endoscopic examination,

and overall physician assessment. The scor-

ing range is usually 0~12 points. Specific

grading: remission period: UCAI score < 2;

mild activity period: 2~3 points; moderate

activity period: 4~6 points; severe activity

period: > 6 points.

Biochemical index detection method

Four mL of fasting venous blood was

routinely collected and placed in a dispos-

able vacuum blood collection tube with-

out anticoagulant for later use. The ELISA

method (the kits were purchased from Bei-

jing Baiao Innovation Technology Co., Ltd.,

Beijing Suolaibao Technology Co., Ltd., Ai-

meijie Technology Co., Ltd., Jianglai Biology

and Wuhan Feien Biotechnology Co., Ltd.

in turn; the serial numbers/goods numbers

are (E-EL-H0109c, SEKH-0138, KA4933,

JL14113, QT-EH0205) was used to detect

serum tumor necrosis factor -α (TNF-α), C-

reactive protein (CRP), anti-infliximab anti-

body (ATI), and interleukins (IL).

An ELISA kit (article number HR0593;

purchased from Suzhou Herui Pharmaceuti-

cal Technology Co., Ltd.) was used to mea-

sure calprotectin levels in the supernatant

of routinely collected fecal samples from pa-

tients.

The above indicators are included in

the baseline data.

Response vs. Non-response evaluation

standard

Based on post-IFX treatment respons-

es, patients were classified into a response

group and a non-response group.

Response group: After treatment, clini-

cal symptoms such as diarrhea, abdominal

pain, bloody stool, and intestinal absorption

disorder resolved or improved significantly,

and no new complications, including oral

ulcer, gallstone, arthritis, and gastrointesti-

nal bleeding, occurred. Colonoscopy showed

that intestinal mucosal inflammation, in-

cluding congestion, edema, erosion, and

ulceration, was significantly reduced or ab-

sent. Intestinal mucosal healing is good, as

evidenced by reduced ulcer size, increased

mucosal smoothness, and reduced submu-

cosal edema. The intestinal stenosis or dila-

tation has improved, and intestinal patency

has increased.

Unresponsive group: there was no sig-

nificant change or aggravation of clinical

symptoms after treatment; symptoms such

62 Ding et al.

Investigación Clínica 67(1): 2026

as diarrhea, abdominal pain, and others per-

sisted or worsened. New complications may

have occurred, or the original complications

may have been aggravated. Colonoscopy

showed that there was no significant change

or aggravation of intestinal mucosal inflam-

mation, such as the expansion of the lesion

scope and the increase in ulcer depth. Intes-

tinal stenosis or dilatation has not been im-

proved or aggravated.

Ethical considerations

This study strictly adheres to the prin-

ciples of the Declaration of Helsinki, and all

research procedures comply with interna-

tional ethical standards. Strictly adheres to

ethical principles to ensure the rationality

of the research design, the compliance with

data use, and the full protection of partici-

pants’ privacy. The research should aim to

improve treatment effectiveness, respect the

rights and interests of all participants, avoid

bias, and ensure the fairness and transpar-

ency of the research results.

Statistical methods

SPSS 22.0 was used for statistical anal-

ysis. Measurement data that conformed to

the normal distribution were presented as

(S), t-test; count data were presented as

n (%), χ2-test; and Logistic regression was

used for correlation factor analysis. The no-

mogram model was constructed in R, and

the Bootstrap method was used for internal

validation. A calibration curve and receiver

operating characteristic (ROC) curve were

drawn to evaluate the nomogram model. In-

spection standard α=0.05.

RESULTS

Immune response

A total of 57 cases, accounting for

57.0% of all IBD patients, were included in

the response group. The remaining 43 pa-

tients were unresponsive (43.0%) and were

included in the non-response group.

Baseline data analysis

In the baseline data of the two groups,

the levels of TNF-α, CRP, calprotectin, ATI,

IL-6, IL-8, and other cytokines were lower

than those of the non-response group. There

was no significant difference in other data

(Table 1).

Logistic regression analysis

The treatment response of patients with

IBD was used as the dependent variable (re-

sponse = 0, non-response = 1). The data with

a statistically significant difference in Table 1

were included in the independent variable, and

Logistic regression analysis was conducted.

The results showed that the levels of calpro-

tectin (OR=1.099, 95%CI=1.039~1.163),

ATI (OR=3.756, 95%CI=1.222~11.546),

IL-6 (OR=1.261, 95%CI=1.069~1.488),

and IL-8 (OR=1.014, 95%CI=1.004~1.024)

are the key factors affecting the response of

patients with IBD after treatment (p<0.05)

(Table 2).

Construction and verification

of the nomogram model

Based on the results of the logistic re-

gression analysis, a nomogram was devel-

oped to predict treatment response in IBD

patients (Fig. 1). ROC curve analysis showed

that the AUC values for calprotectin level, ATI

level, IL-6 level, and IL-8 level in this model

were 0.809, 0.762, 0.850, and 0.775, with

95% confidence intervals of 0.719–0.881,

0.666–0.841, 0.765–0.913, and 0.681–0.853

(Fig. 2). The calibration curve indicates a

good fit for the nomogram model (Fig. 3).

DISCUSSION

Analysis of baseline data difference

between response and non-response

groups

In this retrospective study, we exam-

ined how patients with inflammatory bowel

disease (IBD) responded to infliximab (IFX)

and developed a predictive model.

Response of patients with inflammatory bowel disease to infliximab 63

Vol. 67(1): 57 - 72, 2026

The results showed that 57 patients

(57.0%) responded in this study, and these

patients were included in the response

group. The remaining 43 patients were non-

responders (43.0%) and were included in the

non-response group. This proportion distri-

bution suggests that although IFX therapy

has a certain effect on IBD patients, there

are still many patients who can’t get the ide-

al therapeutic effect.

Table 1. Comparison of two groups of baseline data.

Index

Group

χ2/t p

Response group

(n=57)

Non-response

group (n=43)

Gender [n (%)] Male 33 (57.89) 28 (65.12) 0.537 0.464

Female 24 (42.11) 15 (34.88)

BMI (kg/m2) 22.07±0.90 21.86±0.63 1.275 0.205

Type of disease [n (%)] CD 20 (35.09) 17 (39.53) 0.208 0.648

UC 37 (64.91) 26 (60.47)

Age (years) CD 33.24±3.09 34.34±4.86 0.834 0.410

UC 37.52±5.48 38.48±4.32 0.743 0.460

Disease activity (points) CDAI 219.25±16.83 222.37±21.65 0.492 0.626

UCAI 4.89±0.53 5.04±0.66 1.020 0.312

History of IFX treatment

[n (%)]

Existent

3 (5.26)

7 (16.28) 3.305 0.069

Non-existent 54 (94.74) 36 (83.72)

Infusion reaction

[n (%)]

Chest Tightness/

Chest pain

1 (1.75)

1 (2.33)

1.283 0.733

Dyspnea 0 1 (2.33)

Flushing/Urticaria 0 1 (2.33)

Generate heat 1 (1.75) 2 (4.65)

Gastrointestinal reaction

[n (%)]

Nausea/Vomiting

3 (5.26)

4 (9.30)

0.774 0.679Abdominalgia 0 1 (2.33)

Diarrhea/

Constipation 1 (1.75) 1 (2.33)

TNF-α level (ng/L) 24.65±3.26 26.57±3.81 2.700 0.008

CRP level (mg/L) 7.58±1.21 9.23±1.87 5.329 <0.001

Calprotectin level (μg/g) 105.09±14.68 123.34±14.74 6.146 <0.001

ATI level (ng/mL) 3.17±0.64 3.82±0.74 4.665 <0.001

IL-6 level (pg/mL) 30.04±5.66 37.70±5.32 6.879 <0.001

IL-8 level (pg/mL) 567.48±78.63 662.70±89.59 5.646 <0.001

BMI: Body Mass Index; CD: Crohn’s Disease; UC: Ulcerative Colitis; CDAI: Crohn’s Disease Activity Index; UCAI:

Ulcerative Colitis Activity Index; TNF-α: Tumor Necrosis Factor-α; CRP: C-reactive Protein; ATI: Anti-Infliximab An-

tibody; IL-6: Interleukin-6; IL-8: Interleukin-8.

Data is expressed as n (%) or mean +- standard deviation. t: independent-samples t-test, χ²: Chi-square test.

64 Ding et al.

Investigación Clínica 67(1): 2026

Upon further comparison of baseline

characteristics between responders and non-

responders, this study revealed significant

variations in TNF-α, CRP, calprotectin, ATI,

IL-6, and IL-8 levels. Specifically, the levels

of these cytokines in the response group

were lower than those in the non-response

group. This discovery provides an important

clue and a basis for developing a predictive

model in the future.

As IFX’s direct target, TNF-α is crucial

in IBD development 25. This study found that,

although TNF-α concentrations in both the

response and non-response groups exceeded

normal levels, they were notably lower in the

response group than in the non-response

group. This result suggests that TNF-α levels

may reflect the intensity of intestinal inflam-

mation and, in turn, influence the therapeu-

tic outcome of IFX. In patients receiving ef-

fective IFX treatment, the decrease in TNF-α

levels may indicate that IFX neutralizes

TNF-α more effectively, thereby reducing in-

testinal inflammation 26.

Table 2. Logistic regression analysis of related influencing factors

Correlative factor βSE Wald χ2p-value OR 95%CI

TNF-α level 0.068 0.107 0.400 0.530 1.070 0.867~1.319

CRP level 0.323 0.272 1.408 0.235 1.381 0.810~2.354

Calprotectin level 0.094 0.029 10.596 0.001 1.099 1.039~1.163

ATI level 1.323 0.573 5.334 0.021 3.756 1.222~11.546

IL-6 level 0.232 0.084 7.622 0.006 1.261 1.069~1.488

IL-8 level 0.014 0.005 7.732 0.008 1.014 1.004~1.024

Note: TNF-α: Tumor Necrosis Factor-α; CRP: C-reactive Protein; ATI: Anti-Infliximab Antibody; IL-6: Interleukin-6;

IL-8: Interleukin-8.

Fig 1. Risk prediction nomogram model.

ATI: Anti-Infliximab Antibody; IL-6: Interleukin-6; IL-8: Interleukin-8.

Risk prediction nomogram model is a visual, point-based tool that translates the final logistic-regression mo-

del into a clinician-friendly graphic. It is built on the four independent predictors that remained significant

after multivariable adjustment: Calprotectin level, ATI level, IL-6 level, IL-8 level. Locate each biomarker

value on its axis, sum the corresponding points, drop the total to the probability scale to read the predicted

chance of non-response to infliximab.

Response of patients with inflammatory bowel disease to infliximab 65

Vol. 67(1): 57 - 72, 2026

CRP serves as a marker of acute inflam-

mation, indicating the body’s inflammatory

activity level 27. This study revealed lower

CRP levels among responders than among

non-responders, consistent with trends in

TNF-α levels. A decrease in CRP levels may

Fig. 2. ROC curve.

ATI: Anti-Infliximab Antibody; IL-6: Interleukin-6;

IL-8: Interleukin-8.

Fig 3. Calibration curve.

indicate relief of intestinal inflammation and

thus serve as an auxiliary index for predict-

ing the IFX response. However, it is worth

noting that CRP levels may be influenced

by multiple factors, such as infection and

trauma, and therefore should be considered

comprehensively in clinical practice.

In addition to the above cytokines, we

compared differences in age, sex, disease

type, and history between the response and

non-response groups. However, this study

found no significant difference between the

two groups in the baseline data. This result

suggests that the treatment response to IFX

may be more influenced by the intestinal lo-

cal inflammatory environment.

Correlation analysis between key

cytokines and IFX treatment response

Calprotectin: a sensitive marker of in-

testinal inflammation and a potential pre-

dictor of IFX response.

This study found a notable disparity be-

tween response and non-response groups,

with Logistic regression analysis confirm-

ing calprotectin level as a crucial predictor

of IBD patients’ response to IFX treatment

(OR=1.099, 95%CI=1.039~1.163). This

discovery implies that calprotectin is crucial

for monitoring IBD inflammation and may

serve as a predictive indicator of IFX treat-

ment response.

Calprotectin is a calcium-binding pro-

tein primarily secreted by neutrophils, and

its elevated expression in the intestine is of-

ten closely linked to the severity of the intes-

tinal inflammation 28. In patients with IBD,

an increase in calprotectin levels usually in-

dicates active intestinal inflammation. How-

ever, the research revealed that, although

calprotectin levels in responders were above

normal, they were markedly lower than those

of non-responders, potentially because IFX

treatment alleviated intestinal inflamma-

tion. As an anti-TNF-α monoclonal antibody,

IFX can reduce intestinal inflammation by

specifically neutralizing TNF-α. Therefore,

we speculate that the decrease in calpro-

Sensitivity

100-Specificity

Actual Probability

Predicted Pr{Response=1}

66 Ding et al.

Investigación Clínica 67(1): 2026

tectin levels in patients receiving effective

IFX may reflect the alleviation of intestinal

inflammation and could serve as a sensitive

index for predicting the IFX response.

Deeper insights into calprotectin’s role

in the IFX response suggest its potential in-

volvement in modulating the NF-κB pathway.

NF-κB serves as a crucial regulatory protein

essential for inflammatory responses. When

the intestine is injured or infected, NF-κB

is activated, inducing the expression of a

series of inflammatory cytokines, including

TNF-α, IL-6, and IL-8. The release of cal-

protectin may affect NF-κB activity through

an unknown mechanism, thereby indirectly

influencing the expression levels of inflam-

matory factors 29,30. In patients receiving ef-

fective IFX treatment, a decrease in calpro-

tectin levels may indicate inhibition of the

NF-κB signaling pathway, thereby reducing

the production of inflammatory mediators

and promoting the regression of intestinal

inflammation.

ATI: Correlation between Drug Anti-

body Reaction and Response State of IFX

In this study, the ATI (anti-IFX antibody)

level has also been established as a crucial pre-

dictor of IBD patients’ response to IFX treat-

ment (OR=3.756, 95%CI=1.222~11.546).

This discovery underscores the importance

of ATI in the treatment response to IFX and

suggests that ATI levels may be a key factor

influencing the efficacy of IFX.

In patients, the therapeutic effect of IFX

may be influenced by the immune system.

When IFX is administered, some patients

may develop antibodies (ATI) against IFX,

which may bind to IFX, thereby reducing its

biological activity and further affecting its

therapeutic effect 31. Therefore, the increase

in ATI level often indicates a decrease in the

IFX treatment response.

Further analysis of the molecular mech-

anism of ATI in the IFX response indicates

that ATI may influence the pharmacokinet-

ics and pharmacodynamics of IFX. On the

one hand, the combination of ATI and IFX

may accelerate the clearance of IFX, thereby

reducing its concentration in the body and

potentially affecting its therapeutic effect.

On the other hand, the combination of ATI

and IFX may also affect the binding of IFX

to TNF-α, thereby reducing the neutralizing

activity of IFX 32, 33. Therefore, in patients re-

ceiving effective IFX treatment, a decrease

in ATI levels may indicate that IFX maintains

high biological activity in vivo, thereby neu-

tralizing TNF-α more effectively and reduc-

ing intestinal inflammation.

IL-6 and IL-8: Dual Roles of Inflammatory

Factors and Predictive Value of IFX

Response

Research revealed IL-6 and IL-8 con-

centrations as crucial indicators of IBD

patients’ response to IFX treatment (IL-

6: OR=1.261, 95%CI=1.0691.488; IL-8:

OR=1.014, 95%CI=1.0041.024). This dis-

covery reveals the important roles of IL-6

and IL-8 in the pathogenesis of IBD and in

the therapeutic response to IFX.

IL-6 and IL-8 are two key inflammato-

ry cytokines that play a central role in the

pathogenesis of IBD. On the one hand, IL-6

and IL-8 can induce inflammatory respons-

es in intestinal mucosal cells and promote

the progression of intestinal inflammation.

On the other hand, they can also affect the

function of the intestinal immune system

and further exacerbate the progression of in-

testinal inflammation 34, 35. Therefore, the in-

crease of IL-6 and IL-8 levels often indicates

the aggravation of IBD patients.

However, this study found that although

IL-6 and IL-8 levels in the response group

were higher than normal, they were mark-

edly lower than in the non-response group.

This result suggests that the decrease in

IL-6 and IL-8 levels may reflect the relief

of intestinal inflammation in patients with

effective IFX treatment. Further analysis

of the molecular mechanisms of IL-6 and

IL-8 in the IFX response indicates that they

may regulate multiple signaling pathways,

including the JAK-STAT, NF-κB, and MAPK

pathways 36,37. Abnormal activation of these

Response of patients with inflammatory bowel disease to infliximab 67

Vol. 67(1): 57 - 72, 2026

signaling pathways is often closely linked to

the pathogenesis of IBD. IFX therapy may in-

hibit the activity of these signaling pathways

by neutralizing TNF-α, thereby reducing IL-6

and IL-8 expression and promoting the re-

gression of intestinal inflammation 38, 39.

Construction and verification

of the nomogram model

Using logistic regression results, this

study developed a predictive model to forecast

IBD patients’ responses to IFX treatment. The

model contains the key influencing factors

such as calprotectin level, ATI level, IL-6 level,

and IL-8 level, and can accurately predict the

response of IBD patients to IFX treatment.

To evaluate the model’s predictive

performance, we use ROC and calibration

curves. The ROC curve analysis results show

that the AUC values for calprotectin, ATI, IL-

6, and IL-8 levels in this model for predict-

ing the response of IBD patients after treat-

ment are 0.809, 0.762, 0.850, and 0.775,

respectively, indicating high predictive accu-

racy. The results of calibration curve analysis

also indicate that the nomogram model has

a good fit and can accurately predict the re-

sponse of IBD patients to IFX treatment.

Discussion on the mechanism of response

and loss of response: signal pathway and

molecular network

Neutralization of TNF-α Signal Pathway

and IFX

As an important inflammatory factor,

TNF-α plays a key role in the pathogenesis of

IBD. TNF-α activates downstream signaling

pathways, such as the NF-κB and MAPK path-

ways, by binding to its cell-surface receptor,

thereby inducing a series of inflammatory

responses 40. As an anti-TNF-α monoclonal

antibody, IFX can specifically neutralize

TNF-α, thereby blocking activation of its

downstream signaling pathways. In patients

receiving effective IFX treatment, inhibition

of the TNF-α signaling pathway may alleviate

intestinal inflammation and promote muco-

sal healing 41.

Interaction between calprotectin

and the NF-κB signaling pathway

As mentioned above, calprotectin may

be involved in the regulation of the NF-κB sig-

naling pathway. In patients with IBD, calpro-

tectin release may modulate NF-κB activity

via unknown mechanisms, thereby indirectly

influencing the expression levels of inflam-

matory factors. In patients with effective IFX

treatment, the decrease of calprotectin level

may mean the inhibition of NF-κB signal-

ing pathway, thus reducing the production

of inflammatory factors 42, 43. This interac-

tion may constitute an important molecular

mechanism of IFX therapeutic response.

Interaction between ATI and IFX

pharmacokinetics

The production of ATI can affect the

pharmacokinetics of IFX in vivo. On one

side, combining ATI with IFX might speed up

the clearance of IFX, lowering its concentra-

tion and half-life in the body. On the other

side, ATI production can also impair IFX’s

ability to bind to TNF-α, thereby reducing its

capacity to neutralize TNF-α 44, 45. Therefore,

in patients effectively treated with IFX, de-

creasing ATI levels could allow IFX to sustain

high biological activity and concentration in

vivo, resulting in more effective neutraliza-

tion of TNF-α and relief from intestinal in-

flammation.

Limitations and future prospects

of research

Despite certain advancements, the

study harbors limitations. Notably, being

retrospective, it may be subject to issues

such as selection and information biases.

Compared with prospective cohort studies,

this study is retrospective and inherently has

a higher risk of selection bias and informa-

tion bias. The reliance on 100 patients from

a single center (Shulan Hospital) severely

limited the universality of the nomogram. To

overcome these limitations, we need to fur-

ther increase the sample size and conduct

multicenter prospective research to validate

68 Ding et al.

Investigación Clínica 67(1): 2026

the study’s conclusions. Furthermore, the

model relies solely on internal validation

(Bootstrap). A robust predictive model must

be validated using an independent cohort of

patients (external validation) to confirm its

clinical utility in different settings.

Secondly, the study examined only the

effects of cytokines such as calprotectin, ATI,

IL-6, and IL-8 on IFX outcomes in IBD pa-

tients, without considering other potential

biomarkers or genetic factors. To fully un-

derstand the pathogenesis of IBD and the

therapeutic mechanism of IFX, we need to

conduct further exploration. For example, we

can use gene chips, protein genomics, and

other technologies to screen for additional

biomarkers and apply machine learning algo-

rithms to build multivariate predictive mod-

els 46, 47. The application of these new tech-

nologies and methods will help us understand

the pathogenesis of IBD and the therapeutic

mechanism of IFX, and provide stronger sup-

port for individualized treatment.

In addition, we need to consider the

long-term effects and safety of IFX in the

treatment of IBD. Although IFX can signifi-

cantly improve the clinical symptoms of pa-

tients in the short term, long-term use may

increase the risk of infection and malignant

tumors 48. Therefore, we need to conduct

long-term follow-up of patients to identify

and address potential adverse reactions in a

timely manner. At the same time, we need

to explore additional effective treatment

modalities and strategies to further improve

treatment outcomes and quality of life in pa-

tients with IBD.

In sum, this study developed a predictive

tool for IBD patients’ IFX outcomes based

on a retrospective analysis and validated its

predictive performance. The results indicated

that calprotectin, ATI, IL-6, and IL-8 concen-

trations were pivotal in determining the IFX

treatment response in IBD patients. These

cytokines play important roles in the patho-

genesis of IBD and in the therapeutic mecha-

nism of IFX. In the future, we need to further

expand the sample size, more deeply explore

the pathogenesis of IBD and the treatment

mechanism of IFX, and leverage new technol-

ogies and methods to develop a more precise

and stable predictive model, thereby provid-

ing a more comprehensive foundation for per-

sonalized treatment. Meanwhile, attention

should be given to the long-term efficacy and

safety of IFX in IBD treatment to ensure opti-

mal patient outcomes.

Through this study, we not only identi-

fied the key determinants of IBD patients’

responses to IFX treatment but also devel-

oped a predictive model with clinical appli-

cability. This achievement provides strong

support for individualized treatment of IBD

and also provides an important reference

for follow-up research. We believe that in

the near future, as our comprehension of

IBD’s disease processes and IFX’s therapeu-

tic mechanisms deepens, as well as the con-

tinuous emergence of new technologies and

methods, we can provide more accurate and

effective treatment strategies for patients

with IBD and help them get rid of the dis-

ease and regain their health and happiness.

Acknowledgment

None.

Funding

None.

Consent to publish

The manuscript has neither been previ-

ously published nor is under consideration

by any other journal. The authors have all ap-

proved the content of the paper.

Consent to participate

We secured a signed informed consent

form from every participant.

Ethic approval

This study was approved by the Ethics

Committee of the Shulan (Hangzhou) Hos-

pital, Shulan International Medical College,

Zhejiang Shuren University (KY2025018)

Response of patients with inflammatory bowel disease to infliximab 69

Vol. 67(1): 57 - 72, 2026

Data availability statement

The data that support the findings of

this study are available from the correspond-

ing author upon reasonable request.

ORCID numbers of authors

• Ru Ding (RD):

0009-0009-9395-5853

• Mengdi Fan (MF):

0009-0000-2327-2146

• Juanjuan Gu (JG):

0009-0000-9408-912X

• Zhangning Zhou (ZZ):

0009-0002-3277-4142

Participation of each author

RD: Edited and refined the manuscript

with a focus on critical intellectual contri-

butions. MF, JG: Participated in collecting,

assessing, and interpreting the data. Made

significant contributions to date interpreta-

tion and manuscript preparation. ZZ: Provid-

ed substantial intellectual input during the

drafting and revision of the manuscript.

Conflicts of interest

The authors declare that they have no

financial conflicts of interest.

REFERENCES

1. Bruner LP, White AM, Proksell S. In-

flammatory Bowel Disease. Prim Care.

2023;50(3):411-427. https://doi.org/10.

1016/j.pop.2023.03.009

2. Singh N, Bernstein CN. Environmental

risk factors for inflammatory bowel di-

sease. United European Gastroenterol

J. 2022;10(10):1047-1053. https://doi.

org/10.1002/ueg2.12319.

3. Bisgaard TH, Allin KH, Keefer L,

Ananthakrishnan AN, Jess T. Depres-

sion and anxiety in inflammatory bowel

disease: epidemiology, mechanisms and

treatment. Nat Rev Gastroenterol Hepa-

tol. 2022;19(11):717-726. https://doi.

org/10.1038/s41575-022-00634-6.

4. Liu D, Saikam V, Skrada KA, Merlin D, Iyer

SS. Inflammatory bowel disease biomar-

kers. Med Res Rev. 2022;42(5):1856-1887.

https://doi.org/10.1002/med.21893.

5. Wang CR, Tsai HW. Seronegative spond-

yloarthropathy-associated inflammatory

bowel disease. World J Gastroenterol.

2023;29(3):450-468. https://doi.org/10.3

748/wjg.v29.i3.450.

6. Ashton JJ, Beattie RM. Inflammatory

bowel disease: recent developments. Arch

Dis Child. 2024;109(5):370-376. https://

doi.org/10.1136/archdischild-2023-325

668.

7. Kumar M, Murugesan S, Ibrahim N,

Elawad M, Al Khodor S. Predictive bio-

markers for anti-TNF alpha therapy in IBD

patients. J Transl Med. 2024;22(1):284.

https://doi.org/10.1186/s12967-024-

05058-1

8. Buisson A, Nachury M, Reymond M,

Yzet C, Wils P, Payen L, et al. Effective-

ness of Switching From Intravenous to

Subcutaneous Infliximab in Patients With

Inflammatory Bowel Diseases: the REM-

SWITCH Study. Clin Gastroenterol Hepa-

tol. 2023;21(9):2338-2346.e3. https://doi.

org/10.1016/j.cgh.2022.08.011

9. Peyrin-Biroulet L, Arkkila P, Armuzzi A,

Danese S, Guardiola J, Jahnsen J, et al.

Comparative efficacy and safety of inflixi-

mab and vedolizumab therapy in patients

with inflammatory bowel disease: a syste-

matic review and meta-analysis. BMC Gas-

troenterol. 2022;22(1):291. https://doi.

org/10.1186/s12876-022-02347-1

10. Herrlinger KR, Stange EF. Prioritiza-

tion in inflammatory bowel disease the-

rapy. Expert Rev Gastroenterol Hepatol.

2023;17(8):753-767. https://doi.org/10.1

080/17474124.2023.2240699

11. Kapizioni C, Desoki R, Lam D, Balendran

K, Al-Sulais E, Subramanian S, et al.

Biologic Therapy for Inflammatory Bowel

Disease: Real-World Comparative Effective-

ness and Impact of Drug Sequencing in 13

222 Patients within the UK IBD BioResou-

70 Ding et al.

Investigación Clínica 67(1): 2026

rce. J Crohns Colitis. 2024;18(6):790-800.

https://doi.org/10.1093/ecco-jcc/jjad203

12. Sharma KK, Gupta S, Bisen PS. Enhan-

cing Gastrointestinal (GI) Cancer Thera-

pies with Ganoderma Lucidum: A Review

of Mechanisms and Efficacy. J Can Bio-

mol Therap 2025;2(1):15-44. https://doi.

org/10.62382/jcbt.v2i1.28

13. Qiu Y, Hu S, Chao K, Huang L, Huang

Z, Mao R, et al. Developing a Machine-

Learning Prediction Model for Infliximab

Response in Crohn’s Disease: Integrating

Clinical Characteristics and Longitudi-

nal Laboratory Trends. Inflamm Bowel

Dis. 2025;31(5):1334-1343. https://doi.

org/10.1093/ibd/izae176

14. Meng X. Therapeutic Efficacy of Platinum

Based Medicines Combined with Various

Nanoparticles in the Treatment of Colo-

rectal Cancer: A Comprehensive Review.

J Can Biomol Therap 2025;2(1):57-72.

https://doi.org/10.62382/jcbt.v2i1.32

15. Lee C, Jo B, Woo H, Im Y, Park RW, Park

C. Chronic Disease Prediction Using the

Common Data Model: Development Study.

JMIR AI. 2022;1(1):e41030. https://doi.

org/10.2196/41030

16. Rahman MS, Islam KR, Prithula J, Ku-

mar J, Mahmud M, Alam MF, et al. Machi-

ne learning-based prognostic model for 30-

day mortality prediction in Sepsis-3. BMC

Med Inform Decis Mak. 2024;24(1):249.

https://doi.org/10.1186/s12911-024-026

55-4

17. Huang L, Li H, Wang S, Ren Y. Predic-

tion of early mucosal healing of Crohn’s

disease after treatment with biologics- a

novel nomogram based on radiomics and

clinical risk factors. Front Pharmacol.

2025;16:1586300. https://doi.org/10.338

9/fphar.2025.1586300

18. Wang Y, Song J, Zheng Z, Peng X, Li X,

Wu W. Development and Validation of a

Machine Learning Model to Predict Anti-

Drug Antibody Formation During Inflixi-

mab Induction in Crohn’s Disease. Bio-

medicines. 2025;13(10):2464. https://doi.

org/10.3390/biomedicines13102464.

19. Caenepeel C, Falony G, Machiels K, Vers-

tockt B, Goncalves PJ, Ferrante M, et al.

Dysbiosis and Associated Stool Features Im-

prove Prediction of Response to Biological

Therapy in Inflammatory Bowel Disease.

Gastroenterology. 2024;166(3):483-495.

https://doi.org/10.1053/j.gastro.2023.

11.304

20. Pinton P. Impact of artificial intelligen-

ce on prognosis, shared decision-making,

and precision medicine for patients with

inflammatory bowel disease: a pers-

pective and expert opinion. Ann Med.

2023;55(2):2300670. https://doi.org/10.1

080/07853890.2023.2300670

21. Li M, Tao Y, Sun Y, Wu J, Zhang F, Wen

Y, et al. Constructing a prediction mo-

del of inflammatory bowel disease re-

currence based on factors affecting the

quality of life. Front Med (Lausanne).

2023;10:1041505. https://doi.org/10.338

9/fmed.2023.1041505

22. Xiong Z, Fang Y, Lu S, Sun Q, Huang J.

Identification and Validation of Signature

Genes and Potential Therapy Targets of

Inflammatory Bowel Disease and Periodon-

titis. J Inflamm Res. 2023; 16:4317-4330.

https://doi.org/10.2147/jir.S426004

23. Lahiff C, Safaie P, Awais A, Akbari M,

Gashin L, Sheth S, et al. The Crohn’s di-

sease activity index (CDAI) is similarly ele-

vated in patients with Crohn’s disease and

in patients with irritable bowel syndrome.

Aliment Pharmacol Ther. 2013;37(8):786-

794. https://doi.org/10.1111/apt.12262

24. Gürel S, Kiyici M. Ulcerative colitis acti-

vity index: a useful prognostic factor for

predicting ulcerative colitis outcome. J Int

Med Res. 2005;33(1):103-110. https://doi.

org/10.1177/147323000503300111

25. Little RD, McKenzie J, Srinivasan A, Hi-

lley P, Gilmore RB, Chee D, et al. Swit-

ching from Dose-Intensified intravenous to

SubCutaneoUS infliximab in Inflammatory

Bowel Disease (DISCUS-IBD): protocol for

a multicentre randomised controlled trial.

BMJ Open. 2024;14(7):e081787. https://

doi.org/10.1136/bmjopen-2023-081787

26. Zheng M, Zhai Y, Yu Y, Shen J, Chu S,

Focaccia E, et al. TNF compromises intes-

tinal bile-acid tolerance dictating colitis

progression and limited infliximab respon-

Response of patients with inflammatory bowel disease to infliximab 71

Vol. 67(1): 57 - 72, 2026

se. Cell Metab. 2024;36(9):2086-2103.

e9. https://doi.org/10.1016/j.cmet.2024.

06.008

27. Levinson T, Wasserman A. C-Reactive

Protein Velocity (CRPv) as a New Bio-

marker for the Early Detection of Acute

Infection/Inflammation. Int J Mol Sci.

2022;23(15):8100. https://doi.org/10.339

0/ijms23158100

28. Tung KK, Chen ST, Lee CH, Tung CF, Wei

JC. Calprotectin in spondyloarthritis and

gut inflammation, is it clinically meanin-

gful? Int J Rheum Dis. 2023;26(4):609-612.

https://doi.org/10.1111/1756-185x. 14619

29. Kostrzewska P, Całkosiński A, Majews-

ki M, Malinowski K. [Fecal calprotectin

as a diagnostic marker of inflammatory

bowel disease]. Pol Merkur Lekarski.

2022;50(295):58-61. PMID: 35278302.

30. Costa MHM, Sassaki LY, Chebli JMF. Fe-

cal calprotectin and endoscopic scores:

The cornerstones in clinical practice for

evaluating mucosal healing in inflamma-

tory bowel disease. World J Gastroente-

rol. 2024;30(24):3022-3035. https://doi.

org/10.3748/wjg.v30.i24.3022

31. Levitte S. Point-of-Care Assays for In-

fliximab Therapeutic Drug Monitoring

in Patients with IBD: Is Quicker Better?

Dig Dis Sci. 2024;69(1):5-6. https://doi.

org/10.1007/s10620-023-08140-8

32. Smith PJ, Critchley L, Storey D, Gregg

B, Stenson J, Kneebone A, et al. Effica-

cy and Safety of Elective Switching from

Intravenous to Subcutaneous Infliximab

[CT-P13]: A Multicentre Cohort Study. J

Crohns Colitis. 2022;16(9):1436-1446.

https://doi.org/10.1093/ecco-jcc/jjac053

33. Zhu K, Ding X, Chen Z, Xi Q, Pang X,

Chen W, et al. Association between genetic

variants and development of antibodies to

infliximab: A cross-sectional study in Chi-

nese patients with Crohn’s disease. Front

Pharmacol. 2023;14:1096816. https://doi.

org/10.3389/fphar.2023.1096816

34. Tataru C, Livni M, Marean-Reardon C,

Franco MC, David M. Cytokine indu-

ced inflammatory bowel disease model

using organ-on-a-chip technology. PLoS

One. 2023;18(12):e0289314. https://doi.

org/10.1371/journal.pone.0289314

35. Kuenstner JT, Xu Q, Bull TJ, Foddai

ACG, Grant IR, Naser SA, et al. Cytokine

expression in subjects with Mycobacterium

avium ssp. paratuberculosis positive blood

cultures and a meta-analysis of cytokine ex-

pression in Crohn’s disease. Front Cell In-

fect Microbiol. 2024;14:1327969. https://

doi.org/10.3389/fcimb.2024.1327969

36. Mukherjee T, Kumar N, Chawla M, Phil-

pott DJ, Basak S. The NF-κB signaling

system in the immunopathogenesis of

inflammatory bowel disease. Sci Signal.

2024;17(818):eadh1641. https://doi.org/

10.1126/scisignal.adh1641

37. Ren J. Advances in Combination Thera-

py for Gastric Cancer: Integrating Targe-

ted Agents and Immunotherapy. Adv Clin

Pharmacol Ther. 2024;1(1):1-15. https://

doi.org/10.63623/9k14tf70

38. Salah N, Dubuquoy L, Carpentier R, Betbe-

der D. Starch nanoparticles improve curcu-

min-induced production of anti-inflamma-

tory cytokines in intestinal epithelial cells.

Int J Pharm X. 2022;4:100114. https://

doi.org/10.1016/j.ijpx.2022.100114

39. Podolski M, Požgaj L, Faraho I, Petrinić

Grba A, Belamarić D, Paravić Radičević

A, et al. Correlation of ex vivo cytoki-

ne secretion profiles with scoring indi-

ces in ulcerative colitis. Eur J Clin In-

vest. 2023;53(12):e14070. https://doi.

org/10.1111/eci.14070

40. Gros B, Kaplan GG. Ulcerative Co-

litis in Adults: A Review. JAMA.

2023;330(10):951-965. https://doi.org/

10.1001/jama.2023.15389

41. Griffin H, Ceron-Gutierrez L, Gharahda-

ghi N, Ebrahimi S, Davies S, Loo PS, et

al. Neutralizing Autoantibodies against In-

terleukin-10 in Inflammatory Bowel Disea-

se. N Engl J Med. 2024;391(5):434-441.

https://doi.org/10.1056/NEJMoa2312302

42. Hu T, Zhang Z, Song F, Zhang W, Yang J.

Evaluation of Mucosal Healing in Ulcera-

tive Colitis by Fecal Calprotectin vs. Fecal

Immunochemical Test: A Systematic Re-

view and Meta-analysis. Turk J Gastroen-

72 Ding et al.

Investigación Clínica 67(1): 2026

terol. 2023;34(9):892-901. https://doi.

org/10.5152/tjg.2023.22812

43. Newman KL, Higgins PDR. Fecal calpro-

tectin level is nonlinearly associated with

GI pathogen detection in patients with

and without inflammatory bowel disease.

J Clin Microbiol. 2023;61(12):e0094623.

https://doi.org/10.1128/jcm.00946-23

44. Zhu K, Ding X, Xue L, Liu L, Wang Y, Li

Y, et al. Optimising infliximab induction

dosing to achieve clinical remission in Chi-

nese patients with Crohn’s disease. Front

Pharmacol. 2024;15:1430120. https://doi.

org/10.3389/fphar.2024.1430120.

45. Kampa KC, Loures MR, Ivantes CAP,

Petterle RR, Pedroso MLA. The eva-

luation of infliximab trough level favors

maintenance therapy of patients with in-

flammatory bowel disease. Arq Gastroen-

terol. 2023;60(1):48-56. https://doi.

org/10.1590/s0004-2803.202301000-07.

46. Pandey H, Jain D, Tang DWT, Wong

SH, Lal D. Gut microbiota in patho-

physiology, diagnosis, and therapeu-

tics of inflammatory bowel disease. In-

test Res. 2024;22(1):15-43. https://doi.

org/10.5217/ir.2023.00080.

47. Liu X, Reigle J, Prasath VBS, Dhaliwal

J. Artificial intelligence image-based pre-

diction models in IBD exhibit high risk

of bias: A systematic review. Comput

Biol Med. 2024;171:108093. https://doi.

org/10.1016/j.compbiomed.2024.108093.

48. Wijnands AM, Penning de Vries BBL, Lut-

gens M, Bakhshi Z, Al Bakir I, Beauge-

rie L, et al. Dynamic Prediction of Advan-

ced Colorectal Neoplasia in Inflammatory

Bowel Disease. Clin Gastroenterol Hepa-

tol. 2024;22(8):1697-1708. https://doi.

org/10.1016/j.cgh.2024.02.014.