Plasma proteomic profiles identify biomarkers predicting Crohn’s disease up to 16 years before onset

Domain: neuroscience
Published: 2025-12-12
Editorial outcome: major_revision

Abstract
Reliable tools for early identification of Crohn’s disease (CD) remain lacking. We analyzed 2736 plasma proteins in 39,634 UK Biobank (UKB) participants and identified 44 associated with incident CD. CD274, CHI3L1, REG1B, ITGAV, PRSS8, ITGA11, GDF15, DEFA1_DEFA1B, and IL6 ranked highest in protein importance ordering. A machine learning model based on these 9 proteins achieved high prediction for CD in a geographically distinct UKB testing cohort (n = 13,262, AUC 0.76), outperforming clinical risk models. It was externally validated in EPIC-Norfolk (n = 2944, AUC 0.73) and exhibited high discriminatory capacity for CD in the cross-sectional Southern China cohort (n = 74, AUC 0.79). In the UKB testing cohort, combining proteins with clinical data improved predictive performance (AUC 0.78) up to 16 years pre-diagnosis. In the same cohort, individuals at high risk stratified by the protein model were 4.23 times more likely to develop CD. Our findings highlight proteomics-based models as a promising approach to predict CD up to 16 years before diagnosis, offering opportunities for early screening and intervention.

Introduction
Crohn’s disease (CD), one of the major types of inflammatory bowel disease (IBD), is a chronic, refractory bowel disease of unknown etiology1–3. With its rising global incidence (up to 29.3 per 100,000), CD has emerged as a significant public health challenge, predominantly affecting young adults and severely impairing their career aspirations and quality of life4–6. Due to the absence of consistent signs and symptoms in the preclinical phase, diagnosis is often delayed for months or even years. Furthermore, no definitive treatment for CD is currently available7. Therefore, establishing approaches to identify whether a person will develop CD in the future has become a public health imperative, which is critical for early diagnosis and timely intervention in at-risk populations. However, such reliable tools for early identification are still lacking.

The onset of CD is preceded by a key preclinical phase characterized by changes in the intestinal immune system, composition of the intestinal microbiome, intestinal permeability, and clinical parameters3,8. Understanding this phase is crucial for predicting the disease. However, although several studies have identified a set of plasma antibodies, gut microbiome compositions, or hematological and biochemical parameters using preclinical samples associated with future CD onset, predictive models based on these markers have shown low predictive performance9–11. Recently, the American PREDICTS study, using a nested case-control study design, showed a panel of 51 protein biomarkers that can predict CD within 5 years. Despite the high accuracy, the ability to identify proteins associated with CD more than 5 years in advance is limited by the lack of long-term data. It is also restricted by a small sample size and a predominantly active male military population12. Taken together, the association and long-term predictive value of proteins with the risk of future development of CD remain largely unknown.

Here, we employed a proteomic approach in a large prospective cohort with up to 16 years of follow-up to establish and validate proteomics-based models for the noninvasive prediction of CD in the future. Using 2736 Olink plasma protein measurements in 52,896 individuals in the UK Biobank (UKB), we first comprehensively assessed the correlation between plasma proteins and CD to discover a panel of candidate proteins. Next, we developed machine learning (ML) models with these proteins, whether in combination with clinical predictor data or not, and evaluated their predictive value of the onset of CD in general population. Lastly, we analyzed how these proteins stratify the risk of CD onset.

Results
Study population
After excluding participants diagnosed with CD at baseline and those with missing proteomic data, a total of 52,896 individuals from the UKB were finally included (Fig. 1). The cohort comprised 46% females, with a mean age of 56.8 years. Of these, 39,634 participants were assigned to the UKB training cohort, while 13,262 participants from geographically distinct recruitment centers were assigned to the UKB testing cohort for validation. Table 1 presents the baseline characteristics of the training (n = 39,634) and testing (n = 13,262) cohorts. We observed significant differences in key population characteristics, including age, ethnicity, Townsend deprivation index (TDI), BMI, alcohol consumption, and dietary habits (P < 0.05), highlighting the demographic and lifestyle heterogeneity between training and testing cohorts. Over a median follow-up of 13.6 years (interquartile range [IQR]: 12.9–14.4; maximum: 16.6 years), 139 (0.26%) incident CD cases were identified (Supplementary Table 1). The follow-up duration from baseline to CD diagnosis ranged from 0.12 to 14.88 years (median: 7.84 years; IQR: 4.37–10.74; mean: 7.47 years). When stratified by cohort, follow-up duration among CD cases ranged from 0.12 to 13.78 years in the training set (median: 8.69 years; IQR: 4.75–11.08; mean: 7.87 years), and from 0.38 to 14.88 years in the testing set (median: 6.42 years; IQR: 2.70–8.60; mean: 6.36 years).

In the European Prospective Investigation into Cancer (EPIC)-Norfolk study (n = 2944), 16 incident CD cases were identified within 16 years of follow-up. The cohort included 45% males, with a mean age of 61.0 years, and no significant differences in baseline characteristics were observed between cases and controls. In the cross-sectional Southern China cohort, 37 of 74 participants were diagnosed with CD and 64% were male, and the overall average age was 44 years old. Compared to participants without CD, those with CD were more frequently younger (P < 0.001). Baseline characteristics of the two cohorts are provided in Supplementary Tables 2 and 3.

Proteins associated with incident CD
Among the 2736 proteomic biomarkers examined, 44 proteins were significantly associated with incident CD after adjusting for age, sex, and ethnicity in model 1 (Fig. 2a and Supplementary Data 1). Then, we performed a sensitivity analysis using model 2, which additionally adjusted for TDI, BMI, alcohol intake frequency, smoking status, dietary habits, physical activity, comorbidities (depression and anxiety), and history use of antibiotics and nonsteroidal anti-inflammatory drugs (NSAIDs), and confirmed that 35 of the significant associations were consistent with those found in model 1 (Fig. 2a). Thirty-two proteins (GDF15, IL6, CHI3L1, TNFSF13B, CXCL11, CD274, CSF1, ASGR1, TNF, CXCL9, TXNDC15, WFDC2, REG1B, TNFSF13, ICAM1, IL15, PGLYRP1, TIMP1, PRSS8, VWA1, IL18BP, DSC2, IL10RB, LGALS9, TNFRSF10A, PLAUR, TNFRSF1A, VSIG2, DEFA1_DEFA1B, TNFRSF1B, TNFRSF14, REG3A) were positively associated with the risk of CD onset, while three proteins (GSN, ITGA11, ITGAV) were negatively associated. Notably, GDF15 (HR 2.16, P = 8.86 × 10–9) and IL6 (HR 1.51, P = 4.43 × 10−7) had the most significant associations with CD after Bonferroni corrections.

Biologic pathway analyses
Enrichment analyses of important CD-related proteins identified through two Cox proportional models revealed several biological pathways, including immune and inflammatory response, extracellular space, cytokine-cytokine receptor interaction, and TNF signaling pathway (Fig. 2b and Supplementary Data 2).

Protein importance ranking
For the proteins associated with CD in both models 1 and 2, we further ranked them according to their importance in predicting CD. As illustrated in the bar chart (Fig. 3a), CD274, CHI3L1, and REG1B were ranked as the top three in protein importance ordering. After using the sequential forward selection scheme, we ultimately selected the top nine proteins (CD274, CHI3L1, REG1B, ITGAV, PRSS8, ITGA11, GDF15, DEFA1_DEFA1B, and IL6) for CD prediction in subsequent analyses (Supplementary Fig. 1).

Shapley Additive Explanations (SHAP) summary plot was used to illustrate the influence of these selected proteins on CD risk prediction (Fig. 3b). The effects of each protein were represented by their value magnitude, and tendency directions were indicated by the horizontal axis. For example, participants with elevated levels (colored in red) of CD274 exhibited a higher likelihood of developing CD (right side), whereas those with lower levels (blue) were more likely to remain healthy (left). In contrast, for ITGA11 and ITGAV, lower values contributed to higher predictions, whereas higher values decreased them. The remaining proteins can be explained in a similar way.

Predictive accuracy of proteomics-based models
We evaluated the predictive performance of the 9-protein panel for future CD onset using four machine learning algorithms: Light Gradient Boosting Machine (LGBM), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Extra Trees (Fig. 4, Supplementary Tables 4, 5; Supplementary Figs. 2, 3 and Supplementary Data 3, 4). The 9-protein model demonstrated considerable prediction performance across all four algorithms for all-time incident CD. In the UKB testing set (geographically distinct), the area under the curves (AUCs) ranged from 0.71 to 0.73 for a 75/25 training/testing split and 0.71 to 0.77 for an 80/20 split. The model was further externally validated in the EPIC-Norfolk study, with AUCs ranging from 0.70 to 0.76. In the cross-sectional Southern China cohort, AUCs ranged from 0.76 to 0.79, demonstrating its ability to distinguish CD patients from controls. Notably, the XGBoost model obtained the highest AUC in the Southern China cohort (0.79, 95% CI 0.77–0.81), good performance in EPIC-Norfolk (0.73, 95% CI 0.71–0.75), and the second-best AUC in the UKB testing set (0.72 for a 75/25 split, 95% CI 0.71–0.73; 0.76 for an 80/20 split, 95% CI 0.74–0.77), indicating its robust generalizability across independent cohorts. Therefore, XGBoost was finally chosen as the optimal model. In the UKB testing set, the 9-protein model demonstrated superior predictive performance across four algorithms compared to clinical risk models based on demographics, serum markers, and polygenic risk score (PRS) for CD (highest AUC among clinical risk models for XGBoost: 0.67 for the 75/25 split and 0.60 for the 80/20 split, Mann–Whitney U test: P < 0.001). For XGBoost, combining the PRS of CD with the protein panel significantly improved the AUC to 0.74 (95% CI 0.73–0.75), and adding all clinical risk factors further increased the AUC to 0.78 (95% CI 0.77–0.79) for a 75/25 split.

We then deployed the six optimal proteins identified in the PREDICTS study12 into the UKB and compared the performance with our models. The results showed that the predictive performance of the PREDICTS model (AUC range: 0.64–0.69, XGBoost AUC: 0.68) was significantly inferior to our protein model (AUC range: 0.71–0.73, XGBoost AUC: 0.72) in predicting all-time incident CD whether or not it was combined with clinical risk factors (P < 0.05) (Supplementary Tables 6, 7 and Supplementary Fig. 4).

Results of replication validation analyses
Our findings remained consistent in the sensitivity analyses for XGBoost. At different time points, the protein model still showed higher predictive value (AUC 0.69–0.72) than clinical risk models (AUC 0.53–0.67) (Supplementary Data 5 and Supplementary Figs. 5, 6). When randomly subsampling a control group matched 1:1 with CD patients by age, sex, and race, the 9-protein model achieved an AUC of 0.71 (95% CI 0.68–0.73) in predicting all-time incident CD, increasing to 0.76 (95% CI 0.74–0.78) with the addition of all clinical risk factors (Supplementary Table 8; Supplementary Fig. 7 and Supplementary Data 6). The results remained consistent after excluding individuals who developed CD within the first 2 years of follow-up (Supplementary Table 9; Supplementary Fig. 8 and Supplementary Data 7). In addition, the associations between the nine proteins and incident CD remained significant after further adjustment for major chronic inflammatory comorbidities (Supplementary Table 10).

Risk stratification for CD onset
To further assess how the 9-protein model stratifies the risk of CD onset, participants were classified into high- and low-risk subgroups using the optimal probability cutoff (0.484 for the XGBoost model), determined by maximizing the Youden index in the training cohort. Kaplan–Meier survival curves illustrated distinct cumulative risk patterns between the stratified subgroups (Fig. 5a). Participants in the high-risk subgroup exhibited a significantly higher risk of developing CD than those in low-risk subgroup, both in the training set (HR 11.6, P = 1.44 × 10–22) and in the geographically distinct UKB testing set (HR 4.23, P = 3.26 × 10–5), after adjusting for age, sex, ethnicity in model 1 (Supplementary Table 11). Similarly, Cox regression analyses for individual plasma proteins also demonstrated significant associations with CD onset (Supplementary Table 12 and Supplementary Fig. 9).

Clinical association between protein levels and CD-related phenotypes
To further explore whether the association between CD and proteins is influenced by modifiable risk factors for CD, we examined the relationships between the nine proteins in the protein model and CD-associated phenotypes. Obesity, physical inactivity, smoking, poor diet, and depression were significantly associated with all nine proteins after false discovery rate (FDR) correction (Fig. 5b and Supplementary Data 8). Specifically, CD274, CHI3L1, REG1B, PRSS8, GDF15, DEFA1_DEFA1B, and IL6 exhibited positive associations with these modifiable risk factors for CD, whereas ITGAV and ITGA11 showed negative associations. Notably, these associations further support the potential of these proteins as early biomarkers for CD, influenced by modifiable risk factors, which may offer opportunities for preventive strategies.

Methods
Studying population
The UKB is a significant biomedical resource that enrolled approximately 500,000 individuals aged 40–70 years between 2006 and 2010, collecting various types of data, including blood and urine biomarkers, lifestyle factors, and medical health records. Our access to the UKB data was granted in compliance with their ethical guidelines and access protocols, under project code 83339. For this study, participants with CD at baseline and those lacking proteomic data were excluded, resulting in a final analysis cohort of 52,896 individuals without CD. The 52,896 participants were randomly divided into training and testing cohorts according to the UKB recruitment centers in a ratio of 75%:25% roughly. The UKB training cohort included participants from Cardiff, Glasgow, Stoke, Reading, Bury, Newcastle, Leeds, Nottingham, Sheffield, Liverpool, Middlesbrough, Hounslow, and Croydon. The UKB testing cohort included participants from Stockport (pilot), Manchester, Oxford, Edinburgh, Bristol, Barts, Birmingham, Swansea, and Wrexham (Supplementary Table 13).

Model performance was evaluated in two additional cohorts. The EPIC-Norfolk study43 is a population-based prospective cohort that recruited participants aged 40–79 years from Norfolk, UK, between 1993 and 1997. At baseline, participants completed general health questionnaires and underwent a panel of measurements. Following the same inclusion criteria as in the UKB cohort, this study included 2944 individuals, serving as the external validation cohort.

The Southern China cohort comprised 37 patients with CD and 37 non-IBD controls recruited from Guangdong Provincial People’s Hospital between 2024 and 2025. Eligibility criteria for CD cases included individuals aged ≥16 years with a prior or new diagnosis of CD, confirmed by two experienced clinicians based on standard clinical, radiological, endoscopic, and histopathologic criteria44. Controls were individuals without IBD and with no clinical or endoscopic evidence of intestinal inflammation. Blood samples were collected at enrollment and centrifuged. Ultimately, 74 participants were enrolled in the study. This cross-sectional cohort was used to assess the protein panel’s ability to distinguish CD patients from controls.

Plasma proteomics
The UK Biobank Pharma Proteomics Project (UKB-PPP) consortium has provided extensive proteomic data derived from blood samples. Most of these samples were collected during participants’ initial visits to UK assessment centers between 2007 and 2010, with additional samples gathered from consortium members and participants involved in the COVID-19 repeat-imaging study. Blood specimens were obtained using EDTA tubes, followed by centrifugation at 4°C for 10 min to obtain plasma, which was then promptly stored at −80°C for preservation45. They were conveyed on dry ice to the Olink Analysis Service in Sweden for proteomic profiling using the Olink™ Explore 3072 Proximity Extension Assay (PEA)46. After rigorous quality control measures (https://biobank.ndph.ox.ac.uk/showcase/ukb/docs/PPP_Phase_1_QC_dataset_companion_doc.pdf), the study quantified 2736 unique proteins from 54,219 participants, distributed across eight protein panels: cardiometabolic, cardiometabolic II, inflammation, inflammation II, neurology, neurology II, oncology and oncology II47. Protein levels were translated into Normalized Protein expression (NPX) values on a log2 scale.

For the EPIC-Norfolk study, serum samples were collected at baseline between 1993 and 1997 and immediately stored in liquid nitrogen to preserve protein integrity. Proteomic profiling was performed using the Olink Explore 1536 and Olink Explore Expansion panels, targeting 2923 unique proteins via 2941 assays. Participants with failed proteomic quality control or missing data on age, sex, BMI, or smoking status were excluded.

In the Southern China cohort, enzyme-linked immunosorbent assay (ELISA) analyses were conducted to evaluate the reproducibility of the protein model developed in the training set. Blood samples were collected using EDTA tubes, and centrifuged at 1000×g for 15 min, and the plasma supernatants were aliquoted and stored at −80°C. Plasma samples of the case and control groups were used for ELISA analyses within 3 months after collection. ELISA assays were performed per the manufacturer’s protocol. Briefly, plasma samples (100 µL) and standards were added to pre-coated 96-well plates and incubated at 37°C for 80 min. After washing three times with 350 µL washing buffer, 100 µL of biotin-labeled antibody working solution was added and incubated at 37°C for 50 min, followed by horseradish peroxidase (HRP)-conjugated reagent incubation under the same conditions. Plates were washed again before adding 90 µL of 3,3′,5,5′-tetramethylbenzidine (TMB) substrate for color development at 37°C for 20 min in the dark. The reaction was terminated by adding 50 µL stop solution, and absorbance was measured at 450 nm using a CMax Plus microplate reader (Molecular Devices, USA). The resulting protein data were log2-transformed and standardized. A list of the ELISA kits used is provided in Supplementary Table 14.

Clinical predictors
In the UKB, to compare the predictive efficacy of proteins with other phenotypic indicators and to assess the combined predictive power, we included a range of clinical predictor data: sociodemographic characteristics (age, sex, and ethnicity), socioeconomic status (TDI and BMI), lifestyle factors (alcohol intake frequency, smoking status, dietary habits and physical activity), comorbidities (anxiety and depression), medication history (antibiotics and NSAIDs), serums (27 serum measures) and PRS for CD. Sex was determined based on self-report during recruitment. The serum biomarkers were derived from baseline hematological assessments conducted during the initial recruitment of UKB individuals. Detailed information on the serum predictors utilized in this analysis is provided in Supplementary Data 9. PRS was generated by UKB using a Bayesian method based on summary statistics from external Genome-wide association study (GWAS) meta-analyses and did not overlap with the UKB population, as described online (https://biobank.ndph.ox.ac.uk/showcase/refer.cgi?id=5202).

Definition of CD
In the UKB, individuals with a CD diagnosis were identified through the International Classification of Diseases-10 (ICD-10) under code K50. The diagnosis was based on hospital admission data. The follow-up period commenced from the initial visit to the UKB assessment centers, coinciding with the collection of blood samples and other clinical data. Individuals were followed up until the earliest recorded date of either a CD diagnosis, death, or the censoring date (31 October 2022), whichever came first. Those diagnosed with CD prior to baseline were excluded from the analysis.

In the Epic-Norfolk study43, mortality and hospitalization data were obtained linkage to the National Health Service digital database using participants’ NHS numbers. These records were coded by trained nosologists according to ICD-10. To ensure comparability with the 16-year observation window in UKB, cases occurring after 16 years of follow-up were treated as censored when evaluating predictive performance. For the Southern China cohort, all patients with CD were confirmed by two experienced physicians.

Statistical analysis
We conducted Cox proportional hazards model to figure out the association between each protein and incident CD in the training set, using hazard ratios (HRs) with 95% confidence intervals (CIs). Model 1 was adjusted for age, sex and ethnicity. For model 2, we additionally adjusted for BMI, TDI, alcohol intake frequency, smoking status, dietary habits, physical activity, anxiety, depression, and medication history of antibiotics and NSAIDs. Given the limited number of incident cases within each sex, stratified analyses were not performed, but sex was accounted for as a covariate in all models. All of these covariates were obtained from baseline data, and missing values observed were less than 20%. In this study, the missing covariate data were imputed using the multiple imputation by chained equations approach. Bonferroni corrections were employed to evaluate the significance of the associations accounting for the total number of proteins analyzed (n = 2736).

Enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), were performed to gain a deeper insight into the biological function and pathway behind the significant proteins identified by the two Cox proportional models. The statistical difference was determined using the hypergeometric test and the FDR multiple test adjustment method.

The important proteins were then identified using machine learning algorithms. Proteins that remained significant after Bonferroni corrections in both model 1 and model 2 were input into an FLAML AutoML framework, wherein LGBM was applied using 5-fold cross-validation with 10 repetitions on the training set. The significant proteins were subsequently ranked according to their mean SHAP values—a measure of feature importance assessing their contribution to model performance. Based on the SHAP ranking, a sequential forward selection approach was employed, where the top 10 proteins were sequentially added, and the protein panel yielding the highest AUC in the training set was ultimately selected, which was then visualized using SHAP plots.

Based on the optimally selected protein panel, we developed a protein predictive model using four machine-learning algorithms (LGBM, XGBoost, RF, and Extra Trees). To compare the predictive efficacy of proteins with other clinical indicators and to assess the combined predictive power, we further selected the clinical predictor data and delivered three sets of variables, demographic information (age), serological indicators (11 measures), and PRS of CD. These variables were selected from the training set using t-tests or chi-squared tests for demographic factors and Cox proportional hazard models for serological indicators. More details are shown in Supplementary Table 15 and Supplementary Data 9. Finally, we established 11 machine-learning models, including protein panel, demographics, serological indicators, and PRS of CD, both alone or in combinations.

All models were constructed using the same AutoML framework described above. We split the participants into training and testing sets according to the recruitment centers. To further reduce overfitting, internal 5-fold cross-validation (10 repeats) was performed on the training set, and the maximum number of iterations was capped at 100 with early stopping. Hyperparameter tuning was carried out using the built-in AutoML feature to identify the optimal hyperparameters for maximizing performance. Class imbalance was addressed using automated balanced class weighting in scikit-learn’s ‘balanced’ mode, with the computed weights incorporated into AutoML training (via the sample_weight parameter). Receiver operating characteristic (ROC) area under the curve (AUC) analyses were calculated to assess model performance for predicting CD in the UKB testing set (geographically distinct) and the EPIC-Norfolk study, and for distinguishing CD patients from controls in the cross-sectional Southern China cohort. The training and testing performance for each model was the average of 10 crossovers using the bootstrap method (1000 iterations per run).

Mann–Whitney U tests were utilized to determine if there were statistically significant differences in the AUC values between models. Furthermore, we compared our predictive model to that developed by the PREDICTS study.

To further evaluate the generalizability of above models and the predictive robustness of their predictive accuracy, we adopted five sensitivity analyses: (1) randomly splitting the studied population into 80% training (42,264 samples) and 20% (10,632 samples) testing set according to the recruitment centers (Supplementary Table 16); (2) conducting the analyses after matching the incident CD and control populations by age, sex and ethnicity in a 1:1 ratio; (3) repeating the analyses across different incidence time groups, ranging from 10 to 16 years; (4) repeating the analyses after excluding individuals who developed CD in the first 2 years of follow-up; (5) repeating the association analyses between the nine selected proteins and CD using Cox models with additional adjustment for key chronic inflammatory comorbidities48,49, including rheumatoid arthritis, diabetes mellitus, asthma, psoriasis, ankylosing spondylitis, multiple sclerosis, and ischemic heart disease, to assess the potential confounding by comorbid inflammatory conditions.

We also assessed model calibration using calibration slope and calibration curves50,51 (Supplementary Table 17 and Supplementary Fig. 10). In the 1:1 matched setting, calibration plots showed good agreement between predicted and observed outcomes, with slopes close to 1. In the unmatched population, models trained with class weighting tended to overestimate absolute risk. This is likely because class weighting increases the penalty for false negatives during training, encouraging the model to assign higher predicted probabilities to rare positive cases and potentially leading to calibration bias. Models trained without class weighting showed improved calibration but assigned lower predicted risks to true cases, which may limit sensitivity. Notably, we prioritized class weighting over perfect calibration to enhance sensitivity, consistent with our goal of building a screening-oriented model rather than a diagnostic tool. Subsequently, Kaplan–Meier survival curves were generated to visualize how the protein model stratifies the risk of CD onset. The model probability cutoff value for the protein model was determined by maximizing the Youden index using the training dataset. The same cutoff was applied to the testing dataset. Cox proportional hazard models were then applied to assess the associations between the protein model and CD risk in both the training and testing sets. Additionally, participants were categorized into low or high-protein level groups based on the median score of individual proteins within the model to further evaluate their role in the clinical progression of CD over time.

Lastly, we performed linear regression analyses to assess the cross-sectional associations between the levels of the nine proteins in the protein model and CD-related phenotypes. Protein levels were treated as the outcome variables, while phenotypes such as obesity, physical activity, smoking status, alcohol intake frequency, dietary habits, depression, and anxiety served as explanatory variables. All models were adjusted for age, sex, and ethnicity. The FDR method was applied to correct for multiple testing across all exposures and outcomes.

A two-sided significance level of P < 0.05 was used. Data analyses were implemented with FLAML (v.2.1.1), LightGBM (v.4.3.0), XGBoost (v.1.6.2), scikit-learn (v.1.1.3), SHAP (v.0.45.0), and scipy (v1.10.1) under Python (v.3.10.12), and with survival (v.3.7-0), dplyr (v.1.1.4), and tableone (v0.13.2) under R (v.4.2.1).

Ethics approval
Ethical approval for this study was obtained from the North West Multi-Center Research Ethics Committee for the UK Biobank (Approval numbers: 11/NW/0382, 16/NW/0274, and 21/NW/0157; Project number: 83339), the Norfolk Research Ethics Committee for the EPIC-Norfolk study (Approval numbers: 05/Q0101/191), and the Ethics Review Committee of Guangdong Provincial People’s Hospital (Approval number: KY-Z-2021-021-04). Written informed consent was obtained from all participants. No participant compensation was included.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Feng et al. conducted an analysis using proteomics data from the UK Biobank, investigating the association between proteomics and incident Crohn's Disease (CD). Their study is notable, with data from 52,896 adults using the Olink Explore panel, among whom 139 developed CD over a median follow-up of 13.6 years. This is an interesting body of work that might provide new insights to the preclinical phase of CD. While the study is interesting, several concerns arise regarding the analyses and interpretation of the results:\n-The average age at baseline for this cohort was 56.8 years (SD 8.2). However, it is unclear what the duration of follow-up is for the 139 individuals who developed CD. Summary statistics (minimum, maximum, IQR, median, mean) for the follow-up duration (from baseline to diagnosis) among the 139 cases should be provided. Additionally, the age at diagnosis for the 139 subjects should also be shown.\n-Presumably, the age at diagnosis is close to or above 60 years, indicating that the CD cases in this cohort are predominantly elderly onset. This represents a relatively small subgroup of incident CD cases in the general population, which may limit the generalizability of the findings to the broader incident CD population. This limitation should be clearly acknowledged.\n-The diagnosis date was determined by the earliest recorded appearance of ICD-10 codes. However, a likely discrepancy exists between the actual diagnosis date and the date of ICD-10 code appearance. Some cases diagnosed near the time of code appearance may have had earlier diagnoses that were not captured by the ICD-10 codes. Were any efforts made to reduce this potential bias? This is distinct from the issue of diagnosis delay. Did the authors set a minimum follow-up duration prior to a CD diagnosis? At a minimum, a sensitivity analysis excluding cases that developed CD within 1, 2, 3, 4, and 5 years before diagnosis should be conducted.\n-In the baseline characteristics table, the authors compared those who developed CD over time to those who remained healthy as of October 2022, which should be explicitly stated.\n-The authors employed a machine learning algorithm to combine the 10 significant proteins from either model 1 or model 2. It appears that they developed a model to classify a binary outcome and subsequently applied this model to cumulative cases over time, up to 16 years. Have the authors considered using a machine learning algorithm with a time-to-event extension? Additionally, can they provide calibration data for the model in the testing set?\n-There is significant class imbalance in the dataset. The authors mention the use of balanced class weighting in the figure legend, but further details on how this was implemented should be provided.\n-The authors seem to have used an optimal cut-off based on the Youden index and then reapplied the same cut-off to the same cohort from which it was derived. This raises concerns about potential overfitting. It is unclear whether the cut-off was derived from a training set and then applied to a testing set.\nMinor Points:\n-The authors used the optimal proteins from the PREDICTS study and applied them to the UK Biobank cohort. However, it should be emphasized that the SOMAscan assay and the Olink Explore panel are two different assays with limited overlapping proteins.\n-The authors stated that the time before diagnosis in the GEM cohort was relatively short compared to the UK Biobank cohort. It should be noted that these two cohorts differ in how the CD diagnosis date was defined (physician-confirmed in GEM vs. ICD code appearance in UK Biobank) and represent different populations (at-risk first-degree relatives in GEM vs. an elderly general population in the UK Biobank).\n-Please use the TRIPOD+AI reporting guideline for the machine learning model that was used in this manuscript.", "reviewer_2": "The core objective of this study is to develop a panel of serum protein biomarkers to predict Crohn's disease prior to disease onset. The study analyzes data from the UK Biobank (n > 52K), where baseline plasma proteomics measurements were taken, and patients were followed for up to 16 years. Additional variables, including clinical data, serum assays, polygenic risk scores, and demographic information, were also incorporated to compare the model based on the protein signature alone and in combination with these variables.\nThe methodology is standard, starting with feature selection based on univariate statistical analyses (Cox proportional hazards model) and proceeding to feature importance ranking with SHAP analysis (resulting in a selection of 10 proteins). These selected proteins were then used as inputs in the LightGBM algorithm (for protein-only, combined indicators, and other combinations). Validation was conducted using a random data split or 5-fold cross-validation (internal validation).\nOverall, while the study may have some clinical merit, more robust validation is needed to clarify its potential utility. Here are some specific comments:\n1) It is unclear whether feature selection was performed on the training data alone or on the entire dataset. Based on the description, it appears that feature selection may have been conducted on the entire dataset, with data splitting only performed for model training and validation. If true, this would introduce information leakage, compromising the model’s generalizability.\n2)The initial feature selection approach is univariate (Cox model), followed by SHAP analysis to filter out less contributive features based on correlation analysis. While this is a reasonable approach, it is fairly basic. There have been several advances in machine learning feature selection methods that could be leveraged to enhance model performance.\n3) Only one machine learning model, LightGBM, was employed. Although LightGBM is a valid choice, the approach could be broadened by exploring multiple models or ensemble methods, such as meta-learning or superlearners, to integrate predictions from various algorithms and potentially improve performance.\n4) Multi-modal integration: The study uses only a simple concatenation of features (early fusion) for multi-modal integration. This approach is valid but basic.\n5) Internal validation: The study uses a single run of 5-fold cross-validation. To increase rigour and reduce selection bias, multiple runs of 5-fold cross-validation could be implemented, accounting for potential bias introduced by initial random ordering.\n6) Lack of external validation: The absence of external validation significantly impacts the generalisability and clinical relevance of the findings. The authors could consider testing their signature panel in an external dataset, such as the PREDICT study (Torres, 2020). Notably, the PREDICT study reported an AUROC of 0.76, comparable to the findings here. However, upon validation within the UK Biobank samples, the AUROC in this study was observed to decrease, highlighting the importance of independent validation to confirm model robustness and applicability.", "reviewer_3": "The authors did thorough work with suitable statistical modelling.\nThe noteworthy results are the identification of the serum proteins most associated with the onset of CD with an identification cohort and a replication cohort. This might give significant insight into the biological processes underlying the onset of CD.\nAs a predictor for CD in high risk populations this is not clinically useful: there are no protective measures against the development of CD. Especially when the signal appears 16 yrs ahead of the actual development of CD, it's not much of a clinical predictor, but more an opportunity to gain insight in pathomechanisms.\nThe AUC of these 11 proteins plus known disease predictors is bigger than that from similar studies.\nFor the most extent the work supports the conclusions. The only issue I have is with their conclusion that their predictive model is reliable 16 years before the onset of CD. With only 159 CD patients in the UK Biobank with a large variation in the time to onset I think that this is a bit of a stretch.\nWith the caveat mentioned above, I think that the methodology is otherwise sound. I again think one should not interpret along the outer borders (outliers) of a small case group, but otherwise the combined phenotype+PRS+protein model seem to have an exceptionally good AUC.\nThe methodology is sound: machine learning with capped iterations would be the way to go for this work. The data could be reproduced since UK Biokbank is available on request and authors are transparent in their methods.\nMy main issue is with the fact that a prediction model such a s proposed here is of extremely limited clinical use, since there are no therapies to prevent CD. This becomes an ever bigger issue if this protein signature arises >10 yrs before disease onset; if it were closer to disease onset one could start monitoring calprotectin on a 6-monthly basis to detect active disease early and start treatment before complications arise.\nThis protein prediction model is however a gold mine for the biological mechanisms that role into action even before the true onset of CD. I completely understand that the research group is not specialised in mucosal immunology or other wet lab work, but with the UK Biobank data and the other phenotypes present in this dataset and phenotypes associated with CD risk (depression, obesity etc) it would be worthwhile to check to which extend these proteins also predict depression or whether they are associated to smoking or obesity. To have a protein profile that predicts CD, and that is specifically associated with a \"modifiable\" phenotype could point into an actual preventative measure for CD with biological underpinnings."}, "author_response": "We thank for the reviewer’s constructive suggestions. We have now supplemented the description in the main text by providing the follow-up duration from baseline to CD diagnosis for the 139 individuals who developed CD, including the minimum, maximum, median, IQR, and mean. Additionally, we have included the age at CD diagnosis in baseline characteristics table. These modifications have been made in the corresponding parts of the manuscript, specifically in 2.1 Study Population and Supplementary Table 1.\nNoteworthy, our manuscript now includes a Southern China external cohort for model validation, and we performed testing by splitting UK Biobank participants based on recruitment centers, following the Type 2b design from the TRIPOD statement1. Compared to random splitting (Type 2a), this approach leverages meaningful geographic divisions, providing a more realistic assessment of model performance and enhancing robustness by accounting for site-specific variations. Therefore, Table 1 has been revised to reflect the baseline characteristics table divided by UKB training and testing sets. We have also attached the relevant modifications below for reference (2.1 Study Population, Table 1, and Table 2).\n“Result 2.1 Study population …The follow-up duration from baseline to CD diagnosis ranged from 0.12 to 14.88 years (median: 7.84 years; IQR: 4.37-10.74; mean: 7.47 years) (Supplementary Table 1).…”\nSupplementary Table 1: Baseline characteristics of UK Biobank participants by Crohn's disease case status.\nCD Control P value\nn=139 n=52,757\nAge at recruitment, mean (SD), years 58.76 (7.71) 56.81 (8.21) 0.005\nAge at diagnosis, mean (SD), years 66.27 (8.82) -\nSex 0.678\nFemale, n(%) 72 (51.8) 28,445 (53.9)\nMale, n(%) 67 (48.2) 24,312 (46.1)\nEthnicity 0.134\nOther, n(%) 4 (2.9) 3,344 (6.3)\nTD index, mean (SD) -0.88 (3.13) -1.18 (3.19) 0.264\nBMI, mean (SD), kg/m2 27.90 (4.81) 27.47 (4.80) 0.292\nPhysical activity 0.661\nPhysically inactive, n(%) 29 (20.9) 10,046 (19.0)\nPhysically active, n(%) 110 (79.1) 42,711 (81.0)\nSmoking status 0.044\nNever smoker, n(%) 61 (43.9) 28,670 (54.3)\nPrevious smoker, n(%) 61 (43.9) 18,470 (35.0)\nCurrent smoker, n(%) 17 (12.2) 5,617 (10.6)\nAlcohol consumption 0.759\nDaily or almost daily, n(%) 28 (20.1) 10,668 (20.2)\nThree or four times a week, n(%) 32 (23.0) 11,869 (22.5)\nOnce or twice a week, n(%) 29 (20.9) 13,691 (26.0)\nOne to three times a month, n(%) 16 (11.5) 5,760 (10.9)\nSpecial occasions only, n(%) 19 (13.7) 6,214 (11.8)\nNever, n(%) 15 (10.8) 4,555 (8.6)\nDietary habit 0.088\nPoor diet 97 (69.8) 32,928 (62.4)\nHealthy diet 42 (30.2) 19,829 (37.6)\nDepression (%) 10 (7.2) 3,043 (5.8) 0.591\nAnxiety (%) 4 (2.9) 730 (1.4) 0.254\nHistory use of antibiotics (%) 3 (2.2) 732 (1.4) 0.68\nHistory use of NSAIDs (%) 1 (0.7) 844 (1.6) 0.244\nNote: Data are presented as mean (SD) or n (%). Differences between participants who developed Crohn’s disease over time and those who remained healthy as of October 2022 were assessed using Student’s t-test for continuous variables and Pearson’s chi-squared test for discrete variables.\nAbbreviations: BMI, body mass index; TD index, Townsend deprivation index; CD, Crohn’s disease; NSAIDs, nonsteroidal anti-inflammatory drugs.\n“Results 2.1 Study population After excluding participants diagnosed with CD at baseline and those with missing proteomic data, a total of 52,896 individuals from the UKB were finally included (Fig. 1). Of these, 39,634 participants were assigned to the UKB training cohort, while 13,262 participants from geographically distinct recruitment centers were assigned to the UKB testing cohort for validation. Table 1presents the baseline characteristics of the training (n = 39,634) and testing (n = 13,262) cohorts. We observed significant differences in key population characteristics, including age, ethnicity, Townsend deprivation index (TDI), BMI, alcohol consumption, and dietary habits (P<0.05), highlighting the demographic and lifestyle heterogeneity between training and testing cohorts. Over a median follow-up of 13.6 years (interquartile range [IQR]: 12.9- 14.4; maximum: 16.6 years), 139 (0.26%) incident CD cases were identified. The follow-up duration from baseline to CD diagnosis ranged from 0.12 to 14.88 years (median: 7.84 years; IQR: 4.37-10.74; mean: 7.47 years) (Supplementary Table 1). Details of the validation cohort in China are shown in Table 2. In this validation cohort, 37 of 74 participants were diagnosed with CD and 46% were male, and the overall average 2 age was 44 years old. Compared to participants without CD, those with CD were more frequently younger (P < 0.001).”\nTable 1: Baseline characteristics of UK Biobank participants in the training and testing cohorts.\nTraining cohort Testing cohort P value\nn=39,634 n=13,262\nAge, mean (SD), years 57.06 (8.13) 56.06 (8.41) <0.001\nSex 0.173\nFemale, n(%) 21299 (53.7) 7218 (54.4)\nMale, n(%) 18335 (46.3) 6044 (45.6)\nEthnicity <0.001\nOther, n(%) 2253 (5.7) 1095 (8.3)\nWhite, n(%) 37381 (94.3) 12167 (91.7)\nTD index, mean (SD) -1.36 (3.03) -0.65 (3.57) <0.001\nBMI, mean (SD), kg/m2 27.54 (4.79) 27.28 (4.84) <0.001\nPhysical activity 0.289\nPhysically inactive, n(%) 7591 (19.2) 2484 (18.7)\nPhysically active, n(%) 32043 (80.8) 10778 (81.3)\nSmoking status 0.014\nNever smoker, n(%) 21565 (54.4) 7166 (54.0)\nPrevious smoker, n(%) 13937 (35.2) 4594 (34.6)\nCurrent smoker, n(%) 4132 (10.4) 1502 (11.3)\nAlcohol consumption <0.001\nDaily or almost daily, n(%) 7798 (19.7) 2898 (21.9)\nThree or four times a week, n(%) 8931 (22.5) 2970 (22.4)\nOnce or twice a week, n(%) 10456 (26.4) 3264 (24.6)\nOne to three times a month, n(%) 4360 (11.0) 1416 (10.7)\nSpecial occasions only, n(%) 4680 (11.8) 1553 (11.7)\nNever, n(%) 3409 (8.6) 1161 (8.8)\nDietary habit 0.022\nPoor diet 24856 (62.7) 8169 (61.6)\nHealthy diet 14778 (37.3) 5093 (38.4)\nDepression (%) 2315 (5.8) 738 (5.6) 0.246\nAnxiety (%) 548 (1.4) 186 (1.4) 0.899\nHistory use of antibiotics (%) 553 (1.4) 182 (1.4) 0.879\nHistory use of NSAIDs (%) 669 (1.7) 176 (1.3) 0.005\nAbbreviations: BMI, body mass index; TD index, Townsend deprivation index; NSAIDs, nonsteroidal anti-inflammatory drugs.\nTable 2. Baseline characteristics of Southern China participants included in the study.\nOverall CD Control P value\nn=74 n=37 n=37\nAge, mean (SD), years 43.56 (18.31) 34.81 (14.88) 53.49 (16.94) <0.001\nSex 0.093\nFemale, n(%) 26 (36.1) 10 (27.0) 18 (48.6)\nMale, n(%) 46 (63.9) 27 (73.0) 19 (51.4)\nBMI, mean (SD), kg/m2 22.28 (3.37) 21.68 (3.61) 22.90 (2.93) 0.115\nSmoking status 0.588\nNever smoker, n(%) 62 (86.1) 33 (89.2) 31 (83.8)\nPrevious smoker, n(%) 4 (5.6) 1 (2.7) 3 (8.1)\nCurrent smoker, n(%) 6 (8.3) 3 (8.1) 3 (8.1)\nAlcohol consumption 8 (11.1) 2 (5.4) 6 (16.2) 0.261\nNote: Data are presented as mean (SD) or n (%). Differences between CD and healthy control groups were assessed using Student’s t-test for continuous variables and Pearson’s chi-squared test for discrete variables.\nAbbreviations: BMI, body mass index; CD, Crohn’s disease.\nReference\n1. Collins, G. S., Reitsma, J. B., Altman, D. G. & Moons, K. G. M. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 350, g7594 (2015).\n2. Presumably, the age at diagnosis is close to or above 60 years, indicating that the CD cases in this cohort are predominantly elderly onset. This represents a relatively small subgroup of incident CD cases in the general population, which may limit the generalizability of the findings to the broader incident CD population. This limitation should be clearly acknowledged.\nAuthors’ response:\nWe thank for the reviewer’s valuable suggestions. According to your comments, we have acknowledged the limitation in the 3. Discussion.\n4 “3. Discussion …Third, our model was based on an older CD population, with a mean age at diagnosis of 66 years. This predominantly elderly onset may limit the generalizability of our findings to a broader age range of incident CD cases.…”\n3. The diagnosis date was determined by the earliest recorded appearance of ICD-10 codes. However, a likely discrepancy exists between the actual diagnosis date and the date of ICD-10 code appearance. Some cases diagnosed near the time of code appearance may have had earlier diagnoses that were not captured by the ICD-10 codes. Were any efforts made to reduce this potential bias? This is distinct from the issue of diagnosis delay. Did the authors set a minimum follow-up duration prior to a CD diagnosis? At a minimum, a sensitivity analysis excluding cases that developed CD within 1, 2, 3, 4, and 5 years before diagnosis should be conducted.\nAuthors’ response:\nWe thank the reviewer for the valuable suggestions. To address the potential discrepancy between the actual diagnosis date and the first recorded appearance of ICD-10 codes, we conducted a comprehensive sensitivity analysis excluding cases that developed CD within 1, 2, 3, 4, and 5 years before diagnosis. The results remained consistent with our main analysis, demonstrating the robustness of our findings. The results of excluding cases that developed CD within 2 years before diagnosis are shown in our revised manuscript: Results of replication validation analyses, Supplementary Tables 13–14 and Supplementary Fig. 8. Additionally, we provide below the complete results for all exclusion periods (1–5 years) for reference.\n“2.6 Results of replication validation analyses…The results remained consistent after excluding individuals who developed CD within the first two years of follow-up (Supplementary Tables 13–14 and Supplementary Fig. 8) …”\nSupplementary Table 13: Results of ROC analyses after excluding individuals who developed Crohn’s disease in the first two years of follow-up.\nAUC (95%CI)\nLGBM XGBoost RF Extra trees\nTesting set\n9-protein panel 0.68 (0.67-0.70) 0.70 (0.69-0.71) 0.66 (0.64-0.67) 0.70 (0.69-0.71)\nDemographic 0.56 (0.55-0.57) 0.56 (0.55-0.57) 0.55 (0.55-0.55) 0.60 (0.60-0.60)\nSerum 0.59 (0.57-0.61) 0.62 (0.60-0.65) 0.62 (0.60-0.63) 0.67 (0.65-0.68)\nPRS 0.64 (0.62-0.65) 0.61 (0.60-0.62) 0.66 (0.65-0.66) 0.66 (0.66-0.66)\nProtein+Demographic 0.70 (0.69-0.71) 0.70 (0.69-0.71) 0.71 (0.70-0.72) 0.72 (0.70-0.73)\nProtein+Serum 0.69 (0.68-0.71) 0.72 (0.71-0.73) 0.71 (0.69-0.73) 0.74 (0.73-0.75)\nProtein+PRS 0.67 (0.65-0.69) 0.69 (0.67-0.71) 0.71 (0.70-0.73) 0.71 (0.70-0.73)\nProtein+Demographic+Serum 0.71 (0.70-0.72) 0.71 (0.69-0.73) 0.73 (0.71-0.74) 0.72 (0.70-0.74)\nProtein+Demographic+PRS 0.70 (0.69-0.72) 0.71 (0.70-0.73) 0.73 (0.72-0.74) 0.76 (0.75-0.77)\nProtein+Serum+PRS 0.73 (0.71-0.75) 0.71 (0.69-0.74) 0.75 (0.74-0.76) 0.73 (0.72-0.73)\nCombined 0.73 (0.72-0.74) 0.73 (0.71-0.74) 0.70 (0.67-0.73) 0.74 (0.71-0.77)\nTraining set\n5 9-protein panel 0.92 (0.89-0.95) 0.92 (0.88-0.96) 0.78 (0.75-0.81) 0.77 (0.75-0.79)\nDemographic 0.63 (0.63-0.63) 0.63 (0.63-0.63) 0.63 (0.63-0.63) 0.62 (0.62-0.62)\nSerum 0.86 (0.80-0.93) 0.88 (0.80-0.96) 0.81 (0.74-0.89) 0.70 (0.69-0.70)\nPRS 0.62 (0.59-0.64) 0.63 (0.61-0.65) 0.67 (0.65-0.68) 0.60 (0.60-0.60)\nProtein+Demographic 0.90 (0.87-0.92) 0.85 (0.83-0.87) 0.85 (0.80-0.89) 0.82 (0.76-0.87)\nProtein+Serum 0.98 (0.96-1.00) 0.96 (0.94-0.99) 0.95 (0.92-0.98) 0.87 (0.82-0.91)\nProtein+PRS 0.97 (0.94-1.00) 0.95 (0.91-0.99) 0.95 (0.91-0.99) 0.82 (0.78-0.87)\nProtein+Demographic+Serum 0.98 (0.97-0.99) 0.94 (0.91-0.98) 0.84 (0.79-0.88) 0.81 (0.76-0.86)\nProtein+Demographic+PRS 0.96 (0.92-0.99) 0.94 (0.90-0.97) 0.90 (0.86-0.94) 0.81 (0.79-0.84)\nProtein+Serum+PRS 0.98 (0.96-1.00) 0.98 (0.96-0.99) 0.94 (0.91-0.98) 0.79 (0.78-0.79)\nCombined 0.99 (0.98-1.00) 0.97 (0.95-0.98) 0.87 (0.83-0.90) 0.80 (0.78-0.82)\nAfter excluding individuals who developed Crohn’s disease in the first two years of follow-up, we randomly split the participants into a training and a testing set according to the recruitment centers in a ratio of 3:1 roughly. To further reduce overfitting, internal 5-fold cross-validation (10 repeats) was conducted on a training set and the maximum number of iterations was capped at 100 with early stopping. Balanced class weighting was applied to adjust for class imbalances. We reported the performance metrics using mean AUCs and 95% CIs, which were obtained from 10 repeated runs, each incorporating a bootstrap strategy with 1,000 iterations.\nDemographic variables included age, while serum markers consisted of 11 indicators detailed in Supplementary Table 20. The combined model included protein, demographics, serum, and PRS of Crohn's disease.\nLGBM\nProtein+ Protein+ Protein+\nProtein+ Protein+ Protein+\n9-protein panel Demographic Serum PRS Demographic+ Demographic+ Serum+ Combined\nDemographic Serum PRS\nSerum PRS PRS\n9-protein panel 1.77E-04 1.83E-04 7.00E-04 3.12E-02 3.85E-01 3.45E-01 2.57E-02 3.12E-02 1.31E-03 1.01E-03\nDemographic 1.77E-04 2.08E-02 1.57E-04 1.77E-04 1.77E-04 1.77E-04 1.77E-04 1.77E-04 1.77E-04 1.77E-04\nSerum 1.83E-04 2.08E-02 2.62E-03 1.83E-04 1.83E-04 4.40E-04 1.83E-04 1.83E-04 1.83E-04 1.83E-04\nPRS 7.00E-04 1.57E-04 2.62E-03 2.21E-04 5.29E-04 4.35E-02 2.21E-04 1.63E-04 1.63E-04 1.63E-04\nProtein+\n3.12E-02 1.77E-04 1.83E-04 2.21E-04 4.73E-01 1.04E-01 4.27E-01 9.10E-01 9.11E-03 4.59E-03\nDemographic\nProtein+\n3.85E-01 1.77E-04 1.83E-04 5.29E-04 4.73E-01 1.21E-01 3.07E-01 5.21E-01 1.73E-02 4.59E-03\nSerum\nProtein+\n3.45E-01 1.77E-04 4.40E-04 4.35E-02 1.04E-01 1.21E-01 4.52E-02 1.04E-01 3.61E-03 1.31E-03\nPRS\nProtein+\nDemographic+ 2.57E-02 1.77E-04 1.83E-04 2.21E-04 4.27E-01 3.07E-01 4.52E-02 4.27E-01 3.76E-02 2.11E-02\nSerum\nProtein+\nDemographic+ 3.12E-02 1.77E-04 1.83E-04 1.63E-04 9.10E-01 5.21E-01 1.04E-01 4.27E-01 3.76E-02 2.11E-02\nPRS\nSerum+ 1.31E-03 1.77E-04 1.83E-04 1.63E-04 9.11E-03 1.73E-02 3.61E-03 3.76E-02 3.76E-02 9.70E-01\nPRS\nCombined 1.01E-03 1.77E-04 1.83E-04 1.63E-04 4.59E-03 4.59E-03 1.31E-03 2.11E-02 2.11E-02 9.70E-01\nXGBoost\nProtein+ Protein+ Protein+\nProtein+ Protein+ Protein+\n9-protein panel Demographic Serum PRS Demographic+ Demographic+ Serum+ Combined\nDemographic Serum PRS\nSerum PRS PRS\n9-protein panel 1.73E-04 4.40E-04 1.32E-04 5.21E-01 1.73E-02 7.34E-01 1.04E-01 1.21E-01 2.41E-01 7.28E-03\nDemographic 1.73E-04 3.13E-04 1.69E-04 1.73E-04 1.73E-04 1.73E-04 1.73E-04 1.73E-04 1.73E-04 1.73E-04\nSerum 4.40E-04 3.13E-04 1.13E-01 1.83E-04 1.83E-04 1.01E-03 3.30E-04 3.30E-04 1.31E-03 1.83E-04\nPRS 1.32E-04 1.69E-04 1.13E-01 1.32E-04 1.32E-04 1.32E-04 1.32E-04 1.32E-04 1.32E-04 1.32E-04\nProtein+\n5.21E-01 1.73E-04 1.83E-04 1.32E-04 5.80E-03 6.23E-01 6.40E-02 8.90E-02 2.41E-01 3.61E-03\nDemographic\nProtein+\n1.73E-02 1.73E-04 1.83E-04 1.32E-04 5.80E-03 1.62E-01 4.27E-01 8.50E-01 7.91E-01 3.85E-01\nSerum\nProtein+\n7.34E-01 1.73E-04 1.01E-03 1.32E-04 6.23E-01 1.62E-01 4.27E-01 3.45E-01 1.40E-01 4.52E-02\nPRS\nProtein+\nDemographic+ 1.04E-01 1.73E-04 3.30E-04 1.32E-04 6.40E-02 4.27E-01 4.27E-01 7.91E-01 6.78E-01 1.86E-01\nSerum\nDemographic+ 1.21E-01 1.73E-04 3.30E-04 1.32E-04 8.90E-02 8.50E-01 3.45E-01 7.91E-01 6.78E-01 3.45E-01\nPRS\nProtein+\nSerum+ 2.41E-01 1.73E-04 1.31E-03 1.32E-04 2.41E-01 7.91E-01 1.40E-01 6.78E-01 6.78E-01 6.78E-01\nPRS\nCombined 7.28E-03 1.73E-04 1.83E-04 1.32E-04 3.61E-03 3.85E-01 4.52E-02 1.86E-01 3.45E-01 6.78E-01\nRF\nProtein+ Protein+ Protein+\nProtein+ Protein+ Protein+\n9-protein panel Demographic Serum PRS Demographic+ Demographic+ Serum+ Combined\nDemographic Serum PRS\nSerum PRS PRS\n9-protein panel 4.17E-05 1.32E-04 1.97E-01 4.39E-04 5.88E-04 4.41E-04 3.25E-04 2.42E-04 1.80E-04 3.75E-03\nDemographic 4.17E-05 6.39E-05 5.11E-05 6.34E-05 6.39E-05 6.39E-05 6.29E-05 6.34E-05 6.39E-05 6.29E-05\nSerum 1.32E-04 6.39E-05 2.09E-04 1.82E-04 1.83E-04 1.83E-04 1.81E-04 1.82E-04 1.83E-04 1.81E-04\nPRS 1.97E-01 5.11E-05 2.09E-04 1.53E-04 1.54E-04 5.03E-04 1.52E-04 1.53E-04 1.54E-04 1.36E-01\nProtein+\n4.39E-04 6.34E-05 1.82E-04 1.53E-04 5.71E-01 1.62E-01 2.72E-01 7.24E-03 1.70E-03 1.00E+00\nDemographic\nProtein+\n5.88E-04 6.39E-05 1.83E-04 1.54E-04 5.71E-01 9.10E-01 2.73E-01 3.76E-02 2.20E-03 8.50E-01\nSerum\nProtein+\n4.41E-04 6.39E-05 1.83E-04 5.03E-04 1.62E-01 9.10E-01 6.23E-01 3.76E-02 2.20E-03 1.00E+00\nPRS\nDemographic+ 3.25E-04 6.29E-05 1.81E-04 1.52E-04 2.72E-01 2.73E-01 6.23E-01 6.23E-01 8.87E-02 2.72E-01\nSerum\nProtein+\nDemographic+ 2.42E-04 6.34E-05 1.82E-04 1.53E-04 7.24E-03 3.76E-02 3.76E-02 6.23E-01 5.38E-02 3.84E-01\nPRS\nProtein+\nSerum+ 1.80E-04 6.39E-05 1.83E-04 1.54E-04 1.70E-03 2.20E-03 2.20E-03 8.87E-02 5.38E-02 1.72E-02\nPRS\nCombined 3.75E-03 6.29E-05 1.81E-04 1.36E-01 1.00E+00 8.50E-01 1.00E+00 2.72E-01 3.84E-01 1.72E-02\nExtra trees\nProtein+ Protein+ Protein+\nProtein+ Protein+ Protein+\n9-protein panel Demographic Serum PRS Demographic+ Demographic+ Serum+ Combined\nDemographic Serum PRS\nSerum PRS PRS\n9-protein panel 1.76E-04 5.76E-03 6.34E-05 2.73E-01 3.28E-04 1.62E-01 8.87E-02 1.82E-04 4.05E-03 5.76E-03\nDemographic 1.76E-04 1.76E-04 6.11E-05 1.77E-04 1.77E-04 1.76E-04 1.76E-04 1.77E-04 1.44E-04 1.76E-04\nSerum 5.76E-03 1.76E-04 1.15E-01 1.82E-04 1.82E-04 1.69E-03 2.81E-03 1.82E-04 1.49E-04 2.19E-03\nPRS 6.34E-05 6.11E-05 1.15E-01 6.39E-05 6.39E-05 1.41E-03 6.34E-05 6.39E-05 4.92E-05 1.41E-03\nProtein+\n2.73E-01 1.77E-04 1.82E-04 6.39E-05 1.13E-02 5.71E-01 4.27E-01 1.71E-03 2.90E-02 2.57E-02\nDemographic\nProtein+\n3.28E-04 1.77E-04 1.82E-04 6.39E-05 1.13E-02 3.60E-03 1.40E-01 2.20E-03 2.38E-02 6.23E-01\nSerum\n1.62E-01 1.76E-04 1.69E-03 1.41E-03 5.71E-01 3.60E-03 2.73E-01 2.45E-04 5.07E-02 2.56E-02\nPRS\nProtein+\nDemographic+ 8.87E-02 1.76E-04 2.81E-03 6.34E-05 4.27E-01 1.40E-01 2.73E-01 7.65E-04 9.08E-01 8.87E-02\nSerum\nProtein+\nDemographic+ 1.82E-04 1.77E-04 1.82E-04 6.39E-05 1.71E-03 2.20E-03 2.45E-04 7.65E-04 4.91E-04 2.12E-01\nPRS\nProtein+\nSerum+ 4.05E-03 1.44E-04 1.49E-04 4.92E-05 2.90E-02 2.38E-02 5.07E-02 9.08E-01 4.91E-04 3.01E-01\nPRS\nCombined 5.76E-03 1.76E-04 2.19E-03 1.41E-03 2.57E-02 6.23E-01 2.56E-02 8.87E-02 2.12E-01 3.01E-01\nMann–Whitney U tests were conducted to compare the AUCs between each pair of models in the testing set and the resulting p-values are presented in the table. P-values were calculated under two-sided tests. Demographic variables included age, while serum markers consisted of 11 indicators detailed in Supplementary Table 20. PRS represents the polygenic risk score for Crohn’s disease. The combined model included protein, demographics, serum, and PRS of Crohn's disease.\nDemographic variables included age, while serum markers consisted of 11 indicators detailed in Supplementary Table 20. PRS represents the polygenic risk score for Crohn’s disease. The combined model included protein, demographics, serum, and PRS of Crohn’s disease.\nTable. The results of excluding cases that developed CD within 1, 2, 3, 4, and 5 years before diagnosis after excluding individuals who developed Crohn’s disease in the first one year of follow-up\nAUC (95%CI)\nLGBM XGBoost RF Extra trees\nTesting set\n9-protein panel 0.70 (0.69-0.71) 0.69 (0.68-0.70) 0.73 (0.73-0.74) 0.72 (0.72-0.72)\nDemographic 0.56 (0.55-0.56) 0.56 (0.55-0.57) 0.56 (0.55-0.56) 0.59 (0.59-0.60)\nSerum 0.60 (0.58-0.63) 0.60 (0.57-0.63) 0.66 (0.65-0.67) 0.63 (0.60-0.66)\nPRS 0.63 (0.62-0.65) 0.66 (0.65-0.66) 0.64 (0.64-0.65) 0.66 (0.66-0.66)\nProtein+Demographic 0.71 (0.69-0.72) 0.71 (0.71-0.72) 0.70 (0.69-0.71) 0.71 (0.69-0.73)\nProtein+Serum 0.72 (0.71-0.73) 0.72 (0.71-0.73) 0.72 (0.70-0.73) 0.73 (0.71-0.75)\nProtein+PRS 0.73 (0.72-0.74) 0.72 (0.71-0.73) 0.74 (0.73-0.74) 0.75 (0.73-0.76)\nProtein+Demographic+Serum 0.72 (0.72-0.73) 0.73 (0.72-0.74) 0.74 (0.73-0.74) 0.69 (0.66-0.72)\nProtein+Demographic+PRS 0.73 (0.71-0.74) 0.71 (0.70-0.73) 0.74 (0.73-0.75) 0.75 (0.74-0.76)\nProtein+Serum+PRS 0.75 (0.74-0.76) 0.76 (0.75-0.77) 0.76 (0.75-0.76) 0.73 (0.71-0.76)\nCombined 0.76 (0.75-0.77) 0.75 (0.73-0.76) 0.76 (0.76-0.77) 0.76 (0.75-0.77)\n12\nafter excluding individuals who developed Crohn’s disease in the first two years of follow-up\nAUC (95%CI)\nLGBM XGBoost RF Extra trees\nTesting set\n9-protein panel 0.68 (0.67-0.70) 0.70 (0.69-0.71) 0.66 (0.64-0.67) 0.70 (0.69-0.71)\nDemographic 0.56 (0.55-0.57) 0.56 (0.55-0.57) 0.55 (0.55-0.55) 0.60 (0.60-0.60)\nSerum 0.59 (0.57-0.61) 0.62 (0.60-0.65) 0.62 (0.60-0.63) 0.67 (0.65-0.68)\nPRS 0.64 (0.62-0.65) 0.61 (0.60-0.62) 0.66 (0.65-0.66) 0.66 (0.66-0.66)\nProtein+Demographic 0.70 (0.69-0.71) 0.70 (0.69-0.71) 0.71 (0.70-0.72) 0.72 (0.70-0.73)\nProtein+Serum 0.69 (0.68-0.71) 0.72 (0.71-0.73) 0.71 (0.69-0.73) 0.74 (0.73-0.75)\nProtein+PRS 0.67 (0.65-0.69) 0.69 (0.67-0.71) 0.71 (0.70-0.73) 0.71 (0.70-0.73)\nProtein+Demographic+Serum 0.71 (0.70-0.72) 0.71 (0.69-0.73) 0.73 (0.71-0.74) 0.72 (0.70-0.74)\nProtein+Demographic+PRS 0.70 (0.69-0.72) 0.71 (0.70-0.73) 0.73 (0.72-0.74) 0.76 (0.75-0.77)\nProtein+Serum+PRS 0.73 (0.71-0.75) 0.71 (0.69-0.74) 0.75 (0.74-0.76) 0.73 (0.72-0.73)\nCombined 0.73 (0.72-0.74) 0.73 (0.71-0.74) 0.70 (0.67-0.73) 0.74 (0.71-0.77)\nafter excluding individuals who developed Crohn’s disease in the first three years of follow-up\nAUC (95%CI)\nLGBM XGBoost RF Extra trees\nTesting set\n9-protein panel 0.66 (0.64-0.68) 0.67 (0.65-0.69) 0.69 (0.68-0.70) 0.69 (0.68-0.70)\nDemographic 0.60 (0.59-0.60) 0.60 (0.59-0.60) 0.60 (0.59-0.61) 0.63 (0.63-0.63)\nSerum 0.62 (0.60-0.64) 0.58 (0.55-0.62) 0.68 (0.67-0.70) 0.68 (0.67-0.68)\nPRS 0.59 (0.57-0.61) 0.59 (0.59-0.59) 0.57 (0.55-0.59) 0.64 (0.64-0.64)\nProtein+Demographic 0.67 (0.66-0.69) 0.67 (0.65-0.69) 0.69 (0.68-0.71) 0.70 (0.69-0.71)\nProtein+Serum 0.69 (0.67-0.71) 0.67 (0.65-0.69) 0.71 (0.69-0.72) 0.70 (0.68-0.71)\nProtein+PRS 0.68 (0.67-0.70) 0.69 (0.67-0.70) 0.70 (0.68-0.71) 0.74 (0.73-0.75)\nProtein+Demographic+Serum 0.68 (0.66-0.70) 0.70 (0.68-0.72) 0.69 (0.66-0.71) 0.68 (0.67-0.69)\nProtein+Demographic+PRS 0.71 (0.71-0.72) 0.71 (0.70-0.72) 0.75 (0.74-0.76) 0.72 (0.71-0.73)\nProtein+Serum+PRS 0.73 (0.71-0.74) 0.73 (0.71-0.75) 0.71 (0.69-0.72) 0.73 (0.71-0.74)\nCombined 0.72 (0.71-0.72) 0.73 (0.71-0.74) 0.70 (0.67-0.73) 0.71 (0.67-0.74)\nafter excluding individuals who developed Crohn’s disease in the first four years of follow-up\nAUC (95%CI)\nLGBM XGBoost RF Extra trees\nTesting set\n9-protein panel 0.69 (0.67-0.70) 0.69 (0.67-0.71) 0.69 (0.67-0.71) 0.69 (0.67-0.71)\nDemographic 0.60 (0.59-0.61) 0.59 (0.58-0.60) 0.62 (0.61-0.62) 0.64 (0.64-0.64)\nSerum 0.62 (0.59-0.66) 0.66 (0.60-0.71) 0.71 (0.70-0.73) 0.64 (0.60-0.68)\nPRS 0.64 (0.63-0.66) 0.62 (0.61-0.64) 0.63 (0.62-0.65) 0.64 (0.64-0.64)\nProtein+Demographic 0.68 (0.67-0.70) 0.66 (0.64-0.68) 0.63 (0.63-0.64) 0.69 (0.66-0.71)\nProtein+Serum 0.69 (0.68-0.70) 0.70 (0.68-0.72) 0.71 (0.70-0.72) 0.73 (0.71-0.75)\nProtein+PRS 0.71 (0.69-0.72) 0.71 (0.69-0.72) 0.68 (0.66-0.70) 0.69 (0.68-0.71)\nProtein+Demographic+PRS 0.71 (0.70-0.72) 0.71 (0.70-0.73) 0.71 (0.70-0.73) 0.74 (0.72-0.76)\nProtein+Serum+PRS 0.70 (0.68-0.73) 0.73 (0.70-0.76) 0.72 (0.70-0.74) 0.72 (0.70-0.74)\nCombined 0.72 (0.70-0.74) 0.73 (0.70-0.75) 0.76 (0.75-0.77) 0.78 (0.76-0.80)\nafter excluding individuals who developed Crohn’s disease in the first five years of follow-up\nAUC (95%CI)\nLGBM XGBoost RF Extra trees\nTesting set\n9-protein panel 0.67 (0.65-0.68) 0.66 (0.64-0.68) 0.64 (0.60-0.67) 0.66 (0.65-0.67)\nDemographic 0.62 (0.61-0.63) 0.62 (0.61-0.63) 0.65 (0.64-0.65) 0.64 (0.64-0.64)\nSerum 0.61 (0.58-0.64) 0.63 (0.59-0.67) 0.65 (0.63-0.67) 0.68 (0.65-0.70)\nPRS 0.57 (0.54-0.61) 0.60 (0.57-0.64) 0.63 (0.61-0.65) 0.65 (0.65-0.66)\nProtein+Demographic 0.66 (0.64-0.67) 0.68 (0.67-0.69) 0.67 (0.64-0.69) 0.64 (0.61-0.66)\nProtein+Serum 0.68 (0.67-0.70) 0.69 (0.66-0.71) 0.64 (0.60-0.68) 0.67 (0.65-0.69)\nProtein+PRS 0.69 (0.69-0.69) 0.68 (0.66-0.70) 0.70 (0.68-0.73) 0.64 (0.61-0.66)\nProtein+Demographic+Serum 0.69 (0.67-0.71) 0.67 (0.64-0.71) 0.69 (0.67-0.71) 0.70 (0.67-0.72)\nProtein+Demographic+PRS 0.67 (0.63-0.71) 0.68 (0.67-0.70) 0.70 (0.68-0.71) 0.72 (0.69-0.75)\nProtein+Serum+PRS 0.74 (0.73-0.75) 0.73 (0.71-0.74) 0.70 (0.67-0.72) 0.72 (0.70-0.74)\nCombined 0.72 (0.72-0.73) 0.73 (0.71-0.75) 0.71 (0.69-0.73) 0.74 (0.73-0.75)\n4. In the baseline characteristics table, the authors compared those who developed CD over time to those who remained healthy as of October 2022, which should be explicitly stated.\nAuthors’ response:\nWe thank the reviewer for this valuable suggestion. In response to your comment, we have explicitly noted this detail in the annotation of the baseline characteristics table, as detailed in Supplementary Table 1 and Supplementary Table 21.\nSupplementary Table 1: Baseline characteristics of UK Biobank participants by Crohn's disease case status.\nCD Control P value\nn=139 n=52,757\nAge at recruitment, mean (SD), years 58.76 (7.71) 56.81 (8.21) 0.005\nAge at diagnosis, mean (SD), years 66.27 (8.82) -\nSex 0.678\nFemale, n(%) 72 (51.8) 28,445 (53.9)\nMale, n(%) 67 (48.2) 24,312 (46.1)\nEthnicity 0.134\nOther, n(%) 4 (2.9) 3,344 (6.3)\nTD index, mean (SD) -0.88 (3.13) -1.18 (3.19) 0.264\nPhysical activity 0.661\nPhysically inactive, n(%) 29 (20.9) 10,046 (19.0)\nPhysically active, n(%) 110 (79.1) 42,711 (81.0)\nSmoking status 0.044\nNever smoker, n(%) 61 (43.9) 28,670 (54.3)\nPrevious smoker, n(%) 61 (43.9) 18,470 (35.0)\nCurrent smoker, n(%) 17 (12.2) 5,617 (10.6)\nAlcohol consumption 0.759\nDaily or almost daily, n(%) 28 (20.1) 10,668 (20.2)\nThree or four times a week, n(%) 32 (23.0) 11,869 (22.5)\nOnce or twice a week, n(%) 29 (20.9) 13,691 (26.0)\nOne to three times a month, n(%) 16 (11.5) 5,760 (10.9)\nSpecial occasions only, n(%) 19 (13.7) 6,214 (11.8)\nNever, n(%) 15 (10.8) 4,555 (8.6)\nDietary habit 0.088\nPoor diet 97 (69.8) 32,928 (62.4)\nHealthy diet 42 (30.2) 19,829 (37.6)\nDepression (%) 10 (7.2) 3,043 (5.8) 0.591\nAnxiety (%) 4 (2.9) 730 (1.4) 0.254\nHistory use of antibiotics (%) 3 (2.2) 732 (1.4) 0.68\nHistory use of NSAIDs (%) 1 (0.7) 844 (1.6) 0.244\nNote: Data are presented as mean (SD) or n (%). Differences between participants who developed Crohn’s disease over time and those who remained healthy as of October 2022 were assessed using Student’s t-test for continuous variables and Pearson’s chi-squared test for discrete variables.\nAbbreviations: BMI, body mass index; TD index, Townsend deprivation index; CD, Crohn’s disease; NSAIDs, nonsteroidal anti-inflammatory drugs.\nSupplementary Table 21: Baseline clinical demographics for training set.\nCD Control P value\nn=102 n=39,532\nAge, mean (SD), years 58.75 (8.21) 57.06 (8.13) 0.035\nSex 1\nFemale, n(%) 55 (53.9) 21,244 (53.7)\nMale, n(%) 47 (46.1) 18,288 (46.3)\nEthnicity 0.578\nOther, n(%) 4 (3.9) 2,249 (5.7)\nWhite, n(%) 98 (96.1) 37,283 (94.3)\nTD index, mean (SD) -1.12 (2.92) -1.36 (3.03) 0.426\nBMI, mean (SD), kg/m2 27.56 (4.36) 27.54 (4.79) 0.965\nPhysical activity 0.621\nPhysically inactive, n(%) 22 (21.6) 7,569 (19.1)\nSmoking status 0.549\nNever smoker, n(%) 50 (49.0) 21,515 (54.4)\nPrevious smoker, n(%) 40 (39.2) 13,897 (35.2)\nCurrent smoker, n(%) 12 (11.8) 4,120 (10.4)\nAlcohol consumption 0.558\nDaily or almost daily, n(%) 16 (15.7) 7,782 (19.7)\nThree or four times a week, n(%) 25 (24.5) 8,906 (22.5)\nOnce or twice a week, n(%) 23 (22.5) 10,433 (26.4)\nOne to three times a month, n(%) 11 (10.8) 4,349 (11.0)\nSpecial occasions only, n(%) 14 (13.7) 4,666 (11.8)\nNever, n(%) 13 (12.7) 3,396 (8.6)\nDietary habit 0.257\nPoor diet 70 (68.6) 24,786 (62.7)\nHealthy diet 32 (31.4) 14,746 (37.3)\nDepression (%) 8 (7.8) 2,307 (5.8) 0.514\nAnxiety (%) 2 (2.0) 546 (1.4) 0.939\nHistory use of antibiotics (%) 3 (2.9) 550 (1.4) 0.363\nHistory use of NSAIDs (%) 0 (0.0) 669 (1.7) 0.347\nNote: Data are presented as mean (SD) or n (%). Differences between participants who developed Crohn’s disease over time and those who remained healthy as of October 2022 were assessed using Student’s t-test for continuous variables and Pearson’s chi-squared test for discrete variables.\nVariables that showed significant differences between groups, including age, were finally included in the demographic model. Abbreviations: BMI, body mass index; TDI, Townsend deprivation index; CD, Crohn’s disease; NSAIDs, nonsteroidal anti-inflammatory drugs.\n5. The authors employed a machine learning algorithm to combine the 10 significant proteins from either model 1 or model 2. It appears that they developed a model to classify a binary outcome and subsequently applied this model to cumulative cases over time, up to 16 years. Have the authors considered using a machine learning algorithm with a time-to-event extension? Additionally, can they provide calibration data for the model in the testing set?\nAuthors’ response:\nWe sincerely appreciate the reviewer’s thorough review and insightful suggestions, which have been invaluable in refining our manuscript. Below, we provide detailed responses to your two queries:\n1. Use of Time-to-Event Models\nWe are truly grateful for your suggestion regarding the use of machine learning algorithms with time-to-event extensions. We completely agree that survival analysis models, which account for the time-to-event nature of the data, could offer additional insights. To explore this approach, we implemented a Random Survival Forest (RSF) model to evaluate the predictive performance of the protein panel for Crohn’s disease (CD) onset. The RSF model achieved an average AUC of 0.70 (0.67–0.73) across 10 repetitions in the UK Biobank testing set, aligning with the results from our primary analysis.\nGiven these considerations, we did not include the Random Survival Forest algorithm in our final analyses.\nWe truly appreciate the reviewer’s insightful suggestion and hope for your understanding of our approach. If the reviewer has any concerns or would like to discuss this further, we are more than willing to engage in additional communication.\n2. Calibration Data\nWe sincerely appreciate the reviewer’s inquiry regarding calibration performance. To ensure a robust evaluation, we have evaluated calibration performance (e.g. calibration curves, Brier score, calibration slope) on both the geographically distinct UKB testing set and an external testing set. These analyses were conducted separately for models trained on unmatched populations and matched populations.\nFor the matched population, the protein-related models demonstrated strong calibration performance, with calibration slopes close to 1 and Brier scores consistently below 0.25.\nFor the unmatched population, the calibration curves indicated that in the lower predicted probability range (0.0–0.6), the models tended to overestimate risk, as evidenced by the calibration curves falling below the diagonal. However, in the higher predicted probability range (>0.6), the models exhibited better calibration. We hypothesize that this pattern may be influenced by the use of class weighting during model training to address class imbalance. Given that Crohn’s disease cases are significantly rarer than non-cases, class weighting increases the importance of correctly identifying cases, which may lead to slightly inflated predicted probabilities in lower-risk individuals. However, in the higher probability range, class weighting helps the model focus on case identification, ultimately improving calibration performance. Notably, similar calibration patterns have been observed in prior machine learning studies for disease prediction, including Schalkamp et al., Nature Medicine (2023)1.\nGiven the long-term progression of Crohn’s disease, our model is primarily designed to identify high-risk individuals, thereby providing valuable insights for potential clinical management. While it may slightly overestimate risk in the lower probability ranges, its strong calibration in high-risk ranges aligns well with the practical needs of early risk assessment. Moreover, the protein-related models consistently achieved Brier scores below 0.25, further supporting their good performance.\nWe are deeply grateful for the reviewer’s thoughtful feedback, which has greatly enhanced the clarity and robustness of our work. The corresponding calibration data are provided below for your reference. Thank you again for your invaluable input and constructive suggestions. If the reviewer has any further concerns or wishes to explore this issue in more detail, we are open to further discussions.\nUKB testing set (unmatched controls)\nBrier Scores Across Different Testing Sets\nMean Brier\n95% CI\nScore\nUKB testing set (Matched controls)\n9-protein panel 0.2293 (0.2136 - 0.2609)\nDemographic 0.2515 (0.2500 - 0.2550)\nSerum 0.2486 (0.2443 - 0.2564)\nPRS 0.2286 (0.2173 - 0.2406)\nProtein+Demographic 0.239 (0.2143 - 0.2879)\nProtein+Serum 0.2415 (0.2270 - 0.2593)\nProtein+PRS 0.2011 (0.1711 - 0.2349)\nProtein+Demographic+Serum 0.2248 (0.2005 - 0.2488)\nProtein+Demographic+PRS 0.2071 (0.1846 - 0.2436)\nProtein+Serum+PRS 0.2203 (0.1928 - 0.2494)\nCombined 0.2107 (0.1826 - 0.2476)\nUKB testing set (Unmatched controls)\n9-protein panel 0.206 (0.1629 - 0.2465)\nDemographic 0.2285 (0.2175 - 0.2412)\nPRS 0.2402 (0.2249 - 0.2484)\nProtein+Demographic 0.1856 (0.1621 - 0.2237)\nProtein+Serum 0.1775 (0.1208 - 0.2180)\nProtein+PRS 0.154 (0.0869 - 0.2255)\nProtein+Demographic+Serum 0.1616 (0.1029 - 0.2332)\nProtein+Demographic+PRS 0.1682 (0.0726 - 0.2391)\nProtein+Serum+PRS 0.1608 (0.0800 - 0.2397)\nCombined 0.1583 (0.1087 - 0.2248)\nExternal testing set (Unmatched controls)\n9-protein panel 0.2266 (0.1982 - 0.2478)\nCalibration Slopes for Matched Controls in the UKB Testing Set\nMean Calibration Slope 95% CI\n9-protein panel 0.6497 (0.5454 - 0.7390)\nDemographic 0.2868 (0.2758 - 0.2988)\nSerum 0.3283 (0.2886 - 0.4051)\nPRS 0.4525 (0.3499 - 0.5787)\nProtein+Demographic 0.6061 (0.3228 - 0.7360)\nProtein+Serum 0.4794 (0.2943 - 0.7272)\nProtein+PRS 0.8466 (0.6289 - 1.0840)\nProtein+Demographic+Serum 0.523 (0.2951 - 0.7272)\nProtein+Demographic+PRS 0.7936 (0.6071 - 0.9272)\nProtein+Serum+PRS 0.6382 (0.2907 - 0.9710)\nCombined 0.6635 (0.3025 - 0.9343)\nReference\n1. Schalkamp, A.-K., Peall, K. J., Harrison, N. A. & Sandor, C. Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis. Nat Med 29, 2048–2056 (2023).\n6. There is significant class imbalance in the dataset. The authors mention the use of balanced class weighting in the figure legend, but further details on how this was implemented should be provided.\nAuthors’ response:\nWe thank for the reviewer’s valuable suggestions. According to your comments, we have supplemented the details on how the balanced class weighting was implemented in the section 4.5 Statistical analysis.\n“4.5 Statistical analysis …Class imbalance was addressed using automated balanced class weighting in scikit-learn's 'balanced' mode, with the computed weights incorporated into AutoML training (via the sample_weight parameter)….”\n7. The authors seem to have used an optimal cut-off based on the Youden index and then reapplied the same cut-off to the same cohort from which it was derived. This raises concerns about potential overfitting. It is unclear whether the cut-off was derived from a training set and then applied to a testing set.\nAuthors’ response:\nWe appreciate the reviewer’s valuable suggestion. In response, we have repeated the analyses using the protein model, deriving the cutoff from the training set and applying it to the testing set to reduce potential overfitting. After applying the training-derived cutoff, participants in the high-risk subgroup also exhibited a significantly higher risk of developing CD than those in the low-risk subgroup in the UKB testing set. The detailed revisions can be found in Section 2.7 Risk Stratification for CD Onset, 4.5 Statistical Analysis, Fig. 5, and Supplementary Table 15.\n“2.7 Risk stratification for CD onset To further assess how the 9-protein model stratifies the risk of CD onset, participants were classified into high- and low-risk subgroups using the optimal probability cutoff (0.484 for the XGBoost model), determined by maximizing the Youden index in the training cohort. Kaplan–Meier survival curves illustrated distinct cumulative risk patterns between the stratified subgroups (Fig. 5A). Participants in the high-risk subgroup exhibited a significantly higher risk of developing CD than those in low-risk subgroup, both in the training set (HR 11.6, P = 1.44 × 10-22) and in the geographically distinct UKB testing set (HR 4.23, P = 3.26 × 10-5), after adjusting for age, gender, ethnicity in model 1 (Supplementary Table 15). Similarly, Cox regression analyses for individual plasma proteins also demonstrated significant associations with CD onset (Supplementary Table 16 and Supplementary Fig. 9).”\n“4.5 Statistical analysis…Subsequently, Kaplan–Meier survival curves were generated to visualize how the protein model stratifies the risk of CD onset. The model probability cutoff value for the protein model was determined by maximizing the Youden index using the training dataset. The same cutoff was applied to the testing dataset. Cox proportional hazard models were then applied to assess the associations between the protein model and CD risk in both the training and testing sets..….”\nSupplementary Table 15: Results of protein model stratifying the risk of CD onset.\nHR LCI UCI P value\nModel 1\n9-protein panel 4.23 2.14 8.35 3.26E-05\nModel 2\n9-protein panel 3.64 1.78 7.45 4.19E-04\nUKB training set\nHR LCI UCI P value\nModel 1\n9-protein panel 11.60 7.10 18.97 1.44E-22\nModel 2\n9-protein panel 13.14 7.90 21.84 2.99E-23\nParticipants were classified into high‐ and low‐risk subgroups using the optimal probability cutoff (0.484 for the XGBoost model), determined by maximizing the Youden index in the training cohort, and applied to both the training and testing sets. Cox proportional-hazard protein model and Crohn's disease risk. Model 1 was adjusted for age, sex and ethnicity. Model 2 was additionally adjusted for BMI, Townsend deprivation index, smoking, alcohol intake frequency, physical activity, dietary habits, anxiety, depression, medication history of antibiotics and medication history of NSAIDs.\nMinor Points:\n-The authors used the optimal proteins from the PREDICTS study and applied them to the UK Biobank cohort. However, it should be emphasized that the SOMAscan assay and the Olink Explore panel are two different assays with limited overlapping proteins.\nAuthors’ response:\nWe thank the reviewer for highlighting this point. In response, we have added a sentence in the 3. Discussion section immediately following the comparison between the PREDICTS and our protein models. This addition emphasizes that the SOMAscan assay used in the PREDICTS study and the Olink Explore panel used in our UK Biobank cohort have limited overlapping proteins, which may contribute to the differences in predictive performance.\n“3. Discussion …It should be noted, though, that the PREDICTS study utilized the SOMAscan assay, whereas the UK Biobank data were generated using the Olink Explore panel and the limited overlap in the proteins assessed may partly account for the observed differences in model performance....…”\n-The authors stated that the time before diagnosis in the GEM cohort was relatively short compared to the UK Biobank cohort. It should be noted that these two cohorts differ in how the CD diagnosis date was defined (physician-confirmed in GEM vs. ICD code appearance in UK Biobank) and represent different populations (at-risk first-degree relatives in GEM vs. an elderly general population in the UK Biobank).\nAuthors’ response:\nWe thank the reviewer for this insightful comment. In the 3. Discussion, we have now briefly clarified that the shorter pre-diagnostic time window in the GEM cohort may partly result from differences in how the diagnosis date is defined and from the differing characteristics of the study populations. Additionally, we have corrected an inconsistency in our reported time window for our cohort, ensuring that it accurately reflects the case population rather than the overall cohort. We apologize for this oversight and appreciate the opportunity to correct it.\n“Discussion …Notably, the time window before diagnosis in the GEM cohort was relatively short (IQR 1.0–3.5 years) compared with our cohort (IQR 4.4–10.7 years), though this difference should also be considered in the context of variations in diagnostic definitions (physician-confirmed vs. ICD code-based) and differences in study populations (at-risk first-degree relatives vs. an elderly general population).….”\n-Please use the TRIPOD+AI reporting guideline for the machine learning model that was used in this manuscript.\nAuthors’ response:\nWe sincerely appreciate the reviewer’s suggestion regarding the TRIPOD+AI reporting guideline. We have followed the TRIPOD+AI framework in preparing our manuscript and have ensured that our study adheres to its recommendations. Below, we provide the completed TRIPOD+AI statement for reference.\nDevelopment\nSection/Topic Item Checklist item\n/ evaluation1 Reported\nTITLE\non page\nTitle Identify the study as developing or evaluating the performance of a multivariable prediction model, the\n1\nABSTRACT\nAbstract 2 D;E See TRIPOD+AI for Abstracts checklist\n1-2\nINTRODUCTION\nExplain the healthcare context (including whether diagnostic or prognostic) and rationale for\nBackground 3-4\n3a D;E developing or evaluating the prediction model, including references to existing models\nDescribe the target population and the intended purpose of the prediction model in the context of the 3-4\n3b D;E care pathway, including its intended users (e.g., healthcare professionals, patients, public)\n3c D;E Describe any known health inequalities between sociodemographic groups 3-4\nObjectives Specify the study objectives, including whether the study describes the development or validation of a\n4\nMETHODS\nDescribe the sources of data separately for the development and evaluation datasets (e.g.,\nData 5a D;E 14-15\nrandomised trial, cohort, routine care or registry data), the rationale for using these data, and\nrepresentativeness of the data\nSpecify the dates of the collected participant data, including start and end of participant accrual; and, if\n14-15\n5b D;E applicable, end of follow-up\nParticipants Specify key elements of the study setting (e.g., primary care, secondary care, general population)\n14-15\n6a D;E including the number and location of centres\n6b D;E Describe the eligibility criteria for study participants\n14-15\nGive details of any treatments received, and how they were handled during model development or\nNot Applicable\n6c D;E evaluation, if relevant\nData Describe any data pre-processing and quality checking, including whether this was similar across\n15-16\npreparation 7 D;E relevant sociodemographic groups\nClearly define the outcome that is being predicted and the time horizon, including how and when 16-17\n8a D;E\nassessed, the rationale for choosing this outcome, and whether the method of outcome assessment is\nconsistent across sociodemographic groups\nIf outcome assessment requires subjective interpretation, describe the qualifications and demographic\n15\n8b D;E characteristics of the outcome assessors\n8c D;E Report any actions to blind assessment of the outcome to be predicted\n16-18\n9a D any pre-selection of predictors before model building\nClearly define all predictors, including how and when they were measured (and any actions to blind\n16-18\n9b D;E assessment of predictors for the outcome and other predictors)\nIf predictor measurement requires subjective interpretation, describe the qualifications and\n16-18\n9c D;E demographic characteristics of the predictor assessors\nSample size Explain how the study size was arrived at (separately for development and evaluation), and justify that\n14-15\nthe study size was sufficient to answer the research question. Include details of any sample size\nMissing data 11 D;E Describe how missing data were handled. Provide reasons for omitting any data\n14, 17-20\nmethods 12a D analysis, including whether the data were partitioned, considering any sample size requirements\nDepending on the type of model, describe how predictors were handled in the analyses (functional\n15-16\n12b D form, rescaling, transformation, or any standardisation).\nSpecify the type of model, rationale2, all model-building steps, including any hyperparameter tuning,\n17-20\n12c D and method for internal validation\nDescribe if and how any heterogeneity in estimates of model parameter values and model 15-16, 17-20\n12d D;E\nperformance was handled and quantified across clusters (e.g., hospitals, countries). See TRIPOD-\nCluster for additional considerations3\nSpecify all measures and plots used (and their rationale) to evaluate model performance (e.g.,\n17-20\n12e D;E discrimination, calibration, clinical utility) and, if relevant, to compare multiple models\nDescribe any model updating (e.g., recalibration) arising from the model evaluation, either overall or\n17-20\n12f E for particular sociodemographic groups or settings\nFor model evaluation, describe how the model predictions were calculated (e.g., formula, code, object,\n17-20\n12g E application programming interface)\nClass If class imbalance methods were used, state why and how this was done, and any subsequent methods\n19\nimbalance 13 D;E to recalibrate the model or the model predictions\nFairness 14 D;E Describe any approaches that were used to address model fairness and their rationale\n17-20\nModel output Specify the output of the prediction model (e.g., probabilities, classification). Provide details and\n17-20\n25"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors thoroughly responded to the comments that were raised. However, some concerns remain.\nThe follow-up duration from baseline to Crohn’s disease (CD) diagnosis includes cases with a duration of 0.12 years (approximately 44 days) prior to diagnosis. Given the limitations of relying on the appearance of ICD codes—which may be biased toward a later diagnosis date—it is possible that some of these subjects had already been diagnosed. Therefore, caution is warranted when interpreting the results. The authors performed additional sensitivity analyses excluding individuals with shorter follow-up durations, which showed slightly lower predictive performance. The distribution of follow-up duration should be presented separately for both the training and testing cohorts.\nRegarding the external validation cohort, it is unclear whether the Southern China cohort represents a new-onset inception cohort. A description of how this cohort was identified should be included. Additionally, it is important to report the disease duration and medication use for patients with CD. Regardless, it appears that this cohort does not provide external validation that the biomarkers precede CD diagnosis. As such, the validity of this external validation cohort is questionable.\nAs demonstrated by the calibration curves, there appears to be substantial overestimation, likely due to the use of balanced class weighting to address class imbalance. Building the model without adjusting for class imbalance would more accurately reflect predictive performance in the actual target population. This is important contextual information for readers and should be included as supplemental material and acknowledged in the discussion as a limitation, warranting caution when interpreting the results.\n(Remarks on code availability)\nYes, they have provided the code.", "reviewer_2": "The authors have made a commendable effort in addressing the reviewers’ comments. While the response letter was somewhat dense, it is clear that substantial effort went into revising the manuscript and improving its overall quality. In particular, the addition of external validation significantly strengthens the study by supporting its generalisability. I have no further comments.\n(Remarks on code availability)\nI reviewed the code to assess the approach used for external validation, specifically to verify whether the models were retrained on the validation data—a common pitfall in such evaluations. I can confirm that the external validation is implemented appropriately, with previously trained models being loaded and tested without retraining, which supports the robustness of the reported results.\nThat said, the codebase is somewhat disorganized and not easily reusable. Making it more accessible—e.g., through packaging for installation via pip—would improve usability. However, I do not consider this a major limitation.", "reviewer_4": "Authors’ response to Reviewer 3.\nThe comments from reviewer 3 have been addressed sufficiently by the authors, especially with respect to the associated phenotypes, e.g., obesity and smoking. However, these results generate one important question as the nine proteins seem to be part of general immunological processes and homeostasis and not specific to Crohn’s disease.\n1. Patients with CD are more likely to develop other chronic inflammation-driven disease such as rheumatoid arthritis, diabetes, asthma, psoriasis and many more. These diseases can occur years before the CD diagnosis and are typically seen with increasing prevalence in cohorts of elderly patients, like this one. Do the authors have access to this kind of information on potential significant co-morbidities in the UK Biobank? If so, this should be account and adjusted for. If not, this should be mentioned and discussed accordingly, as other chronic inflammatory disease might impose a significant bias in the association between CD and the nine identified proteins."}, "author_response": "We thank the reviewer for this important comment. As suggested, we have now provided the distribution of follow-up durations (from baseline to CD diagnosis) separately for the training and testing cohorts. Specifically, the follow-up durations among CD cases ranged from 0.12 to 13.78 years (median: 8.69 years, IQR: 4.75–11.08, mean: 7.87 years) in the training cohort, and from 0.38 to 14.88 years (median: 6.42 years, IQR: 2.70–8.60, mean: 6.36 years) in the testing cohort. As shown in the Kaplan–Meier curves (Figure 5A), the cumulative incidence of CD increased steadily over time, rather than being predominantly concentrated in the initial years of follow-up.\nA small number of individuals had short follow-up durations before CD diagnosis. To address potential misclassification due to reliance on ICD-10 codes, we performed sensitivity analyses excluding cases diagnosed within 1 to 5 years of follow-up (with 2-year exclusion results shown in our manuscript). The predictive performance was slightly reduced, but results remained largely consistent with the primary analysis. This limitation has been acknowledged in the revised Discussion section.\nThese modifications have been made in the corresponding parts of the manuscript, specifically in Results section (2.1 Study Population) and Discussion.\n“Results 2.1 Study population …When stratified by cohort, follow-up duration among CD cases ranged from 0.12 to 13.78 years in the training set (median: 8.69 years; IQR: 4.75–11.08; mean: 7.87 years), and from 0.38 to 14.88 years in the testing set (median: 6.42 years; IQR: 2.70–8.60; mean: 6.36 years)…”\n“Discussion …Fourth, CD diagnosis was based on ICD-10 code, which may lag behind the actual clinical diagnosis, leading to potential misclassification—especially for cases with shorter follow-up durations. Nevertheless, sensitivity analyses excluding these cases remained largely consistent with the main findings, supporting the robustness of our model.…”\nFig. 5A Protein model stratifies the risk of CD onset.\n2. Regarding the external validation cohort, it is unclear whether the Southern China cohort represents a new-onset inception cohort. A description of how this cohort was identified should be included. Additionally, it is important to report the disease duration and medication use for patients with CD. Regardless, it appears that this cohort does not provide external validation that the biomarkers precede CD diagnosis. As such, the validity of this external validation cohort is questionable.\nAuthors’ response:\nWe sincerely thank the reviewer for raising this important point. To strengthen the external validation of our protein model, we incorporated the EPIC-Norfolk cohort (n = 2,944), which provides long-term follow-up with pre-diagnostic blood samples. The model demonstrated strong predictive performance in this prospective setting (AUC = 0.73 with XGBoost; highest AUC = 0.76 with Extra Trees), supporting its validity and generalizability for long-term risk prediction.\nWe acknowledge that the Southern China cohort was cross-sectional rather than a new-onset inception cohort, and most CD participants had been diagnosed and were receiving treatment at the time of blood sampling. Nevertheless, this cohort offered an opportunity to assess the model’s performance in an ethnically and geographically distinct population, where it achieved an AUC of 0.79, further underscoring its robust case–control discrimination and suggesting potential applicability in diverse clinical populations.\nIn response to the reviewer’s suggestions, we have now included a detailed description of how the Southern China cohort was established, including information on disease duration and medication use among CD patients, and have explicitly acknowledged the limitations of this cohort design. We have also incorporated the details and results of the EPIC-Norfolk cohort into the manuscript. These revisions are reflected throughout the manuscript, including the Abstract, Results section (2.1 Study population, 2.5 Predictive accuracy of proteomics-based models), Methods section (4.1 Studying population, 4.2 Plasma proteomics, 4.4 Definition of CD, 4.5 Statistical analysis), Discussion, Fig 1 and 4, and Supplementary Tables 2, 3, 6 and 8.\nWe hope this clarification and the corresponding manuscript revisions address the reviewer’s concerns.\n“Abstract …Independent validation was conducted in a geographically distinct UKB cohort (n = 13,262) and in two external cohorts: the EPIC-Norfolk study (n = 2,944) and Southern China cohort (n = 74). … In the geographically distinct UKB cohort, the protein model (area under the curve [AUC] 0.76) outperformed clinical risk models (highest AUC 0.67; P < 0.05) and was externally validated in EPIC-Norfolk (AUC 0.73) and the Southern China cohort (AUC 0.79)….”\n“Results 2.1 Study population …In the European Prospective Investigation into Cancer (EPIC)-Norfolk study (n = 2,944), 16 incident CD cases were identified within 16 years of follow-up. The cohort included 45% males, with a mean age of 61.0 years, and no significant differences in baseline characteristics were observed between cases and controls...\n2.5 Predictive accuracy of proteomics-based models …In the external validation cohorts, AUCs ranged from 0.70 to 0.76 in EPIC-Norfolk and from 0.76 to 0.79 in the Southern China cohort. Notably, the XGBoost model obtained the highest AUC in the Southern China cohort (0.79, 95% CI 0.77–0.81), good performance in EPIC-Norfolk (0.73, 95% CI 0.71–0.75), and the second-best AUC in the UKB testing set (0.72 for a 75/25 split, 95% CI 0.71–0.73; 0.76 for an 80/20 split, 95% CI 0.74–0.77), indicating its robust generalizability across independent cohorts. Therefore, XGBoost was finally chosen as the optimal model….”\n“Methods 4.1 Studying population…External validation was performed in two independent cohorts. The EPIC-Norfolk study43 is a population-based prospective cohort that recruited participants aged 40–79 years from Norfolk, UK, between 1993 and 1997. Ethical approval was granted by the Norfolk Research Ethics Committee (ref. 05/Q0101/191), and all participants provided written informed consent. At baseline, participants completed general health questionnaires and underwent a panel of measurements. Following the same inclusion criteria as described above, this study included 2,944 individuals.\nThe Southern China cohort comprised 37 patients with CD and 37 non-IBD controls recruited from ×× Hospital between 2024 and 2025. Eligibility criteria for CD cases included individuals aged ≥16 years with a prior or new diagnosis of CD, confirmed by two experienced clinicians based on standard clinical, radiological, endoscopic, and histopathologic criteria44. Controls were individuals without IBD and with no clinical or endoscopic evidence of intestinal inflammation. Blood samples were collected at enrollment and centrifuged. Ultimately, 74 participants were enrolled in the study…. \n4.2 Plasma proteomics …For the Southern China cohort, all patients with CD were clinically diagnosed by physicians who were blinded to the model predictions.”"}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors have made substantial efforts and responded sufficiently to the comments that were raised. I appreciate the effort they have put into addressing these points. The addition of the EPIC-Norfolk cohort strengthens the paper and enhances the generalizability of predicting the onset of CD. I also thank the authors for showing the calibration curves, presenting the model performance without adjustment for class imbalance, and adding the discussion point regarding the caution needed in interpreting the results.\nI have one remaining comment regarding the Southern China cohort. I do not think it is appropriate to state that biomarkers preceding CD were validated in a cross-sectional cohort with no prediagnostic samples. I suggest removing the label of “external validation” for the Southern China cohort, or considering removing this cohort altogether. The EPIC-Norfolk cohort already serves the purpose of external validation.\n(Remarks on code availability)\nThe authors provided the R and Python scripts that were used for developing and validating the model.", "reviewer_4": "Review comments have been sufficiently addressed, thank you!\n(Remarks on code availability)"}, "author_response": ""}]

Decision letter