Introduction

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder primarily affecting the digestive system, with Crohn’s disease (CD) and ulcerative colitis (UC) being its predominant forms.1 The transition from agricultural to industrial economies in developing and newly industrialized countries has led to significant lifestyle, dietary, and environmental changes, contributing to a marked increase in the incidence and prevalence of IBD.2,3 Frailty is a progressive decline in the function of multiple physiological systems, resulting in increased susceptibility to adverse health outcomes.4 Among elderly individuals, its prevalence exceeds 65%.5,6 Common symptoms include weight loss, reduced grip strength, decreased physical endurance, fatigue, and slower gait speed.

The pathogenesis of IBD is complex, with prolonged inflammatory responses, chronic immune system activation, and inflammatory factor-induced metabolic dysregulation leading to muscle loss and decreased stress resilience, thereby increasing frailty risk.7 Additionally, persistent diarrhea, abdominal pain, and appetite loss in IBD patients contribute to impaired nutrient absorption, malnutrition, and increased energy expenditure, further accelerating frailty progression.8 The use of corticosteroids or biological agents may also compromise the body’s stress response, exacerbating frailty development.9 A recent national cohort study reported a frailty prevalence of 61% among individuals diagnosed with IBD.10

Frailty progression is closely associated with aging, with its prevalence rising in older populations. Frailty can affect the prognosis and complications of the primary disease. Early frailty screening can predict the risk and prognosis of patients. Early screening for frailty allows for risk assessment and prognosis prediction, while timely intervention can enhance disease outcomes, facilitate effective frailty management, and reduce in-hospital adverse events. However, there is no assessment of precise tools for measuring frailty in patients with IBD. This study aims to develop a frailty risk nomogram model for IBD patients, providing a framework for early and accurate identification of frailty by healthcare professionals and enabling targeted preventive and therapeutic interventions.

MethodsResearch Participants

Clinical and pathological data were retrospectively collected from October 1, 2022, to October 1, 2024, for all patients with a confirmed diagnosis of inflammatory bowel disease (IBD) receiving care at Affiliated Hospital of Jiangnan University. A total of 368 patients were included in the study. According to the inclusion and exclusion criteria, after excluding those with missing data, a total of 344 cases were finally included, among whom 61 were identified as frail based on the Frailty Scale. Patients were randomly assigned to training and validation sets in a 7:3 ratio.

Inclusion criteria: (1) Patients met the diagnostic criteria for IBD (including ulcerative colitis and Crohn’s disease) as defined by the International Classification of Diseases (ICD-10); (2) aged 18 years or older; (3) had never undergone colon resection treatment; (4) had no severe cognitive or psychiatric disorders and were able to communicate normally; (5) patients and their families provided informed consent and actively cooperated.

Exclusion criteria: (1) Presence of severe edema, ascites, or other complications; (2) pregnancy or lactation; (3) severe cardiac, hepatic, or renal insufficiency, or presence of malignant tumors; (4) severe cognitive or psychiatric impairment.

General Information Questionnaire

General and clinicopathologic data were collected from all patients. General information included age, gender, body mass index (BMI), marital status, presence of children, place of residence, disease duration, history of smoking, alcohol consumption, and allergy. Clinical indicators included disease type, staging of disease activity, and comorbidities such as hypertension, diabetes mellitus, and coronary heart disease. The activity of ulcerative colitis was assessed using the modified Mayo scoring system,11 where a score of ≤2 with no individual score >1 indicated remission, while all other conditions were classified as active disease. For Crohn’s disease, the simplified Crohn’s Disease Activity Index (sCDAI)12 was applied, with remission defined as a score of ≤4 and active disease as a score >4.

The FRAIL Scale

The Frailty Scale (FS),13 proposed by the International Association of Nutrition and Aging (IANA), was utilized to assess frailty status. The scale comprises five dimensions: fatigue, increased resistance/reduced endurance, decreased mobility, disease conditions, and weight loss. Patients who did not meet any of the five criteria were classified as normal, those meeting one or two criteria were categorized as pre-frail, and those meeting three or more criteria were considered frail.

Serological Indicator

The systemic immune-inflammation index (SII) is a novel biomarker of inflammation. Three hematological parameters—neutrophils, lymphocytes, and platelets—were collected from patients at admission. SII was calculated using the following formula: SII = platelet count (109/L) × neutrophil count (109/L)/ lymphocyte count (109/L). In addition, Alb and FIB were collected from patients on admission.

Pittsburgh Sleep Quality Index Score

Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI).14 The PSQI consists of seven components: subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of hypnotic drugs, and daytime dysfunction. Each component is scored on a scale from 0 to 3, with higher total scores indicating poorer sleep quality.

Statistical Analysis

Statistical analyses were conducted using SPSS 26 (IBM SPSS, USA). Categorical variables in the baseline data were expressed as frequency and percentage (%), and patients were classified into two groups based on the presence or absence of frailty. The Kolmogorov–Smirnov test was applied to assess the normality of continuous variables. Comparisons of categorical data were performed using the χ2 test or Fisher’s exact test, while two-group comparisons were conducted using either the t-test or the Mann–Whitney U-test, depending on data distribution.

Data analysis was also performed using R language (version 4.1.0). Univariate and multivariable binary logistic regression analyses were conducted for all included variables, and conduct a collinearity diagnosis. Variables with two-side P < 0.05 were selected and combined with clinical considerations to develop a multivariable binary logistic regression model. A nomogram prediction model was constructed based on these variables. The concordance index (C-index) was calculated, and calibration plots were generated to assess model calibration. The Hosmer-Lemeshow goodness-of-fit test and external validation were performed to evaluate the overall model performance.

ResultsStudy Cohort

Based on the statistical results, 61 patients were classified as frail, while 283 patients were categorized as non-frail in both the training and validation sets. The overall prevalence of frailty in IBD patients was 17.7%. Patients in the frailty group exhibited significantly higher age, lower BMI, longer disease duration, and increased disease activity. A higher prevalence of smoking history, coronary heart disease, and diabetes was observed in the frailty group compared to the non-frailty group (P < 0.05).

Additionally, significant differences were found in the Pittsburgh Sleep Quality Index (PSQI) score (10.75 ±3.22 vs 9.00 ±3.36), systemic immune-inflammation index (SII) (699.37 ±162.03 vs 575.55 ±177.18), and fibrinogen (FIB) levels (4.42 ±0.53 vs 4.00 ±0.55), all of which were significantly higher in the frailty group. Conversely, albumin (Alb) levels were significantly lower in frail patients compared to non-frail patients (37.15 ±5.44 vs 40.96 ±4.35, P < 0.05), as shown in Table 1.

Table 1 Baseline Data of Frailty and Non-Frailty Group

In the training set, 43 patients (17.8%) were classified as frail, whereas 18 patients (17.6%) in the validation set were identified as frail. No statistically significant differences were observed between the training and validation sets in terms of age, BMI, disease duration, SII, Alb, or other clinical indicators (P ≥ 0.05), as shown in Table 2.

Table 2 Baseline Data of Training and Verification Set

Analysis of Risk Factors for Frailty in IBD Patients

Variables with significant differences in baseline characteristics were included in a multivariable binary logistic regression analysis. The results identified several independent risk factors for frailty in IBD patients:

Older age (OR = 1.078, 95% CI: 1.026–1.132), longer disease duration (OR = 1.082, 95% CI: 1.021–1.146), presence of diabetes (OR = 3.232, 95% CI: 1.224–8.537), active IBD (OR = 2.791, 95% CI: 1.079–7.222), higher PSQI score (OR = 1.225, 95% CI: 1.048–1.431), elevated SII (OR = 1.005, 95% CI: 1.002–1.008), increased FIB levels (OR = 4.451, 95% CI: 1.933–10.251), and lower Alb levels (OR = 0.857, 95% CI: 0.771–0.954). The collinearity diagnostic value of all factors was less than 1.2. These findings indicate that older age, prolonged disease duration, diabetes, active IBD, poor sleep quality, increased inflammation (SII and FIB), and reduced albumin levels are significant risk factors for frailty in IBD patients, as shown in Table 3.

Table 3 Risk Factors of Frailty Analyzed by Multivariable Binary Logistic Regression Models

ROC Curve Analysis for Serological and Demographic Assessment of Frailty in Patients with IBD

ROC analysis was performed to evaluate the predictive ability of serological and demographic indicators for frailty in IBD patients. The discrimination metrics for predicting frailty in the training set were as follows: SII: (Sensitivity: 0.791; Specificity: 0.515; Youden index: 0.306; areas under the curve (AUCs):0.680 (95% CI: 0.600–0.759)), Alb: (Sensitivity: 0.581; Specificity: 0.803; Youden index: 0.384; AUCs: 0.679 (95% CI: 0.577–0.780)), FIB: (Sensitivity: 0.581; Specificity: 0.768; Youden index: 0.349; AUCs: 0.701 (95% CI: 0.612–0.790)), combined serological indicators: (Sensitivity: 0.930; Specificity: 0.636; Youden index: 0.566; AUCs: 0.838 (95% CI: 0.775–0.900)), demographic indicators: (Sensitivity: 0.744; Specificity: 0.778; Youden index: 0.522; AUCs: 0.826 (95% CI: 0.758–0.894), and combined serological and demographic indicators: (Sensitivity: 0.860; Specificity: 0.813; Youden index: 673; AUCs: 0.906 (95% CI: 0.859–0.952), as shown in Figure 1A and B.

Figure 1 ROC curve analysis for serological and demographic assessment of frailty in patients with IBD (A and B) Training set; (C and D) Validating set.

In the validation set, the discrimination metrics for frailty were as follows: SII (Sensitivity: 0.556; Specificity: 0.824; Youden index: 0.379; AUCs: 0.718 (95% CI: 0.584–0.852)), Alb (Sensitivity: 0.556; Specificity: 0.894; Youden index: 0.450; AUCs: 0.755 (95% CI: 0.622–0.887)), FIB (Sensitivity: 0.722; Specificity: 0.659; Youden index: 0.381; AUCs: 0.710 (95% CI: 0.605–0.816)), combined serological indicators (Sensitivity: 0.611; Specificity: 0.941; Youden index: 0.552; AUCs: 0.839 (95% CI: 0.733–0.946)), demographic indicators (Sensitivity: 0.889; Specificity: 0.576; Youden index: 0.465; AUCs: 0.810 (95% CI: 0.707–0.913)) and combined serological and demographic indicators (Sensitivity: 0.833; Specificity: 0.824; Youden index: 0.657; AUCs: 0.904 (95% CI: 0.827–0.981)), as shown in Figure 1C and D.

Construction and Verification of Nomogram Model

The independent predictors of frailty, including age, disease duration, disease activity, diabetes, PSQI score, SII, Alb, and FIB, were integrated into a nomogram model (Figure 2). The concordance index (C-index) for the nomogram in the training set was 0.917 (95% CI: 0.863–0.958), demonstrating strong predictive accuracy.

Figure 2 A nomogram model to predict frailty in patients with IBD.

The calibration curves for both the training and validation sets closely aligned with the reference lines, indicating good calibration and predictive performance of the nomogram model (training set: intercept: 0.000; slope: 1.000; validation set: intercept: 0.000; slope: 1.000)(Figure 3). Finally, the Decision Curve Analysis indicates that this model can yield clinical net benefits and is helpful for clinical doctors in making decisions (Figure 4).

Figure 3 Calibration curve for predicting frailty in patients with IBD (A) Training set; (B) Validating set.

Figure 4 Decision curve analysis.

Discussion

IBD is a chronic immune-mediated disorder of unknown etiology that predisposes patients to frailty. Frailty is associated with adverse clinical outcomes, including increased mortality and hospitalization rates.5 Currently, clinicians primarily assess frailty based on patients’ clinical symptoms; however, these assessments are prone to bias due to subjective factors, and the clinical manifestations of frailty lack specificity. Therefore, greater attention should be given to frailty, and more objective and effective assessment indicators need to be identified.

The findings of this study indicate that age is a significant risk factor for frailty in IBD patients. With advancing age, organ dysfunction and physiological decline lead to reduced muscle strength and impaired function across multiple organ systems, increasing susceptibility to adverse health outcomes.15 A previous study16 confirmed that older age is an independent prognostic factor for frailty. Among patients with advanced breast cancer, a significant decline in resilience to external stressors has been observed, directly contributing to a higher risk of frailty.17 This observation aligns with a cohort study conducted in England and Wales by Jauhari et al, which reported a progressive increase in frailty prevalence among women aged 50–69 years (15%), 70–79 years (28%), and 80 years and older (47%).18 Additionally, older individuals frequently present with multiple comorbidities, further increasing frailty risk. The presence of multiple comorbidities necessitates polypharmacy, which heightens susceptibility to complications and adverse drug reactions, leading to a chronic state of depletion and functional decline, ultimately contributing to frailty.

This study also identified diabetes mellitus as a significant risk factor for frailty in IBD patients. This relationship may be a two-way and collaborative one. IBD and diabetes work together through multiple pathways such as amplifying inflammation, exacerbating malnutrition, disrupting hormone balance, and altering the microbiome, all of which accelerate the occurrence and development of frailty. For patients with IBD, routine screening for diabetes should be conducted. At the same time, frailty assessment and grading should be carried out to facilitate the subsequent determination of intervention plans.

Sleep status was also found to be a risk factor for frailty. A cohort study of 309 older adults reported that individuals sleeping fewer than 5 h per day had a significantly higher risk of developing frailty.19 Similarly, a prospective study conducted in Spain demonstrated that an adequate sleep duration of 7–8 h per day was associated with a significantly lower frailty risk.20 The underlying biological mechanisms linking sleep disturbances and frailty are complex. Short sleep duration or insomnia has been associated with reduced testosterone levels, increased oxidative stress, and chronic inflammation, which may contribute to frailty. Furthermore, sleep disturbances may exacerbate hormonal imbalances, further influencing frailty development.21

SII is an indicator reflecting the balance between immune and inflammatory states, derived from a complete blood count. Under systemic inflammatory conditions, peripheral blood neutrophil and platelet counts increase, while lymphocyte counts decrease, leading to elevated SII levels, which indicate a strong inflammatory response. A case-control study conducted by Zhang et al22 demonstrated significantly higher SII levels in patients with UC and identified elevated SII as an independent risk factor for active UC.

Patients with active IBD often exhibit intestinal mucosal symptoms such as abdominal pain and mucopurulent bloody stools, along with extra-intestinal manifestations, including anemia, skin and mucosal lesions, musculoskeletal symptoms, and ocular involvement. These manifestations pose significant health challenges, increasing the likelihood of depletion and contributing to frailty. Additionally, a study by Yan et al23 highlighted the feasibility of SII as a potential biomarker for diagnosing UC disease activity and monitoring disease severity, particularly for assessing mucosal and histologic healing during long-term management. This further supports the predictive capability of SII for frailty in IBD patients.

Recent systematic evaluations have recognized nutrition as a key factor in delaying frailty onset and slowing its progression in older adults.24,25 Serum albumin (Alb) serves as a biomarker for nutritional status and the body’s response to acute inflammation. A decline in albumin levels following inflammatory stimuli suggests its potential predictive value in inflammatory diseases, including IBD.26

Moreover, patients with low albumin levels often exhibit poor nutritional status, making albumin a potentially significant predictor of frailty. Studies have indicated that IBD patients tend to be in a hypercoagulable state, where the balance between coagulation and fibrinolysis is disrupted. This imbalance leads to abnormal coagulation cascade reactions and irregular coagulation factor activity, increasing the risk of thrombus formation, particularly during the active phase of the disease.27 It has also been suggested that the inflammatory activity of the intestinal tract and the hypercoagulable state of the blood are mutually influencing processes.28 The hypercoagulable state observed in IBD patients is closely associated with fibrinolytic system dysregulation. Findings from this study indicated that FIB levels were significantly higher in frail patients compared to the non-frailty group, identifying FIB as an independent factor influencing frailty. The fibrinolytic system plays a crucial role in dissolving transient fibrin clots formed during physiological hemostasis. However, in IBD patients, its activity is reduced, characterized by lower fibrinogen activator levels, increased expression of its inhibitor, and reduced thrombus dissolution.29 FIB is the raw material and degradation product of fibrin generation, and elevated FIB increases blood viscosity, which in turn involves intestinal microcirculation and thus has a serious impact on the intestinal status of IBD patients.

In this study, a frailty prediction model for IBD patients was developed, providing a simple and practical tool for clinical application. This model facilitates the early identification of high-risk patients, enabling timely interventions aimed at improving health outcomes and quality of life, while also helping to reduce healthcare costs and alleviate the economic burden associated with frailty. However, certain limitations should be acknowledged. The prediction model was constructed using single-center data, lacking external validation, which limits the generalizability of the findings. Further multicenter studies are required to externally validate the model and ensure its applicability across diverse populations. In addition, this study is a retrospective observational study. Although attempts were made to adjust for known confounding factors through binary regression models, the findings of this study should be interpreted as revealing a certain correlation rather than a causal conclusion. Future prospective studies, especially randomized controlled trials, are needed to further confirm whether there is a causal effect. And in the future, this research will extend the duration of long-term follow-up observations on patients, so as to promptly adjust the subsequent treatment plans and further verify the specific extent to which improving sleep and nutrition can alleviate the patients’ risk of frailty. Of course, we will also further investigate whether this prediction model performs differently in ulcerative colitis and Crohn’s disease to find the most appropriate prediction tool for each disease.

Conclusion

Preoperative serologic index testing provides an effective and objective approach for predicting frailty in IBD patients during preoperative evaluation, demonstrating greater prognostic value compared to demographic data alone. The frailty risk prediction model developed in this study exhibited strong predictive accuracy, serving as a valuable reference for the clinical assessment and management of frailty risk in IBD patients.

Ethics Approval and Consent to Participate

The study was conducted in accordance with the Declaration of Helsinki (as was revised in 2013). The study was approved by Ethics Committee of the Affiliated Hospital of Jiangnan University.

Funding

Top Talent Support Program for young and middle -aged people of Wuxi Health Committee (HB2023043).

Disclosure

The authors affirm that no conflicts of interest exist regarding this research.

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