Accelerated biological aging, inflammatory bowel disease, genetic susceptibility and life expectancy: Evidence from UK Biobank and All of Us Cohorts

Accelerated biological aging, inflammatory bowel disease, genetic susceptibility and life expectancy: Evidence from UK Biobank and All of Us Cohorts | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Accelerated biological aging, inflammatory bowel disease, genetic susceptibility and life expectancy: Evidence from UK Biobank and All of Us Cohorts Hao Chen, Lingyi Li, Han Zhang, Lijun Zhang, Yu Long, Jing Feng, and 9 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5705746/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Inflammatory bowel disease (IBD) is a chronic condition affecting individuals across all age groups. However, the association between IBD and biological aging remains unclear. Methods: We utilized data from the UK Biobank and the diverse cohort of the All of Us (AoU) Research Programme to investigate the role of biological aging in the development of IBD and its subtypes. Biological age was assessed using the Klemera-Doubal method (KDMAge) and phenotypic biological age (PhenoAge), with KDMAgeAccel and PhenoAgeAccel defined as the residuals of chronological age minus KDMAge and PhenoAge, respectively. We assessed the impact of accelerated biological aging on life expectancy in patients with IBD through survival analysis. Additionally, we examined genetic susceptibility and its potential mediating effects on the association between biological aging and IBD. Findings: In the UK Biobank, accelerated biological aging was associated with an increased risk of IBD (KDMAgeAccel: HR 1.22, 95% CI 1.13-1.32; PhenoAgeAccel: HR 1.57, 95% CI 1.46-1.69). This association was further validated in the AoU cohort, where PhenoAgeAccel was also linked to an elevated risk of IBD (HR 1.57, 95% CI 1.18-2.09). An additive interaction was observed between accelerated biological aging and genetic risk for IBD. Individuals with both high genetic risk and accelerated aging exhibited the highest risk of developing IBD (KDMAgeAccel: HR 1.36, 95% CI 1.20-1.53; PhenoAgeAccel: HR 1.59, 95% CI 1.41-1.79). Life expectancy analysis indicated that IBD patients with accelerated biological aging experienced a significant reduction in life expectancy, with an average decrease of 1.36 years (KDMAgeAccel) and 1.95 years (PhenoAgeAccel). Mediation analyses suggested that accelerated biological aging partially mediated the protective effects of dried fruit and cooked vegetables on the risk of developing IBD. Results from multistate modelling showed that PhenoAgeAccel was also significantly associated with an increased risk of IBD occurrence to mortality (HR 1.44 [95% CI 1.17-1.77]). Interpretation: Biological aging is significantly associated with the risk of IBD and its subtypes, especially in individuals with high genetic susceptibility, and it reduces life expectancy in these patients. Identifying individuals with accelerated biological aging can serve as a marker for the effective prevention and management of IBD. Health sciences/Biomarkers/Predictive markers Health sciences/Biomarkers/Prognostic markers Accelerated biological aging Inflammatory bowel disease Genetic susceptibility Life expectancy Multistate modelling Figures Figure 1 Figure 2 1. Introduction Inflammatory bowel disease (IBD), which primarily includes Crohn's disease (CD) and ulcerative colitis (UC), is a complex, multifactorial disease. It involves a variety of factors, with genetic susceptibility and environmental influences being the most significant[ 1 ].IBD is a chronic condition with a rising incidence and prevalence worldwide, imposing a significant economic burden due to increased healthcare costs and limited treatment options.These factors underscore the need for further research to better understand and manage this complex disease[ 2 ]. Aging has been identified as a contributing factor in the pathogenesis of IBD[ 3 ]. While aging is a universal process, the rate at which individuals age varies. Thus, chronological age alone cannot account for the considerable variation in age-related phenotypes observed within same-age cohorts. Therefore, evaluating an individual's biological aging status is crucial for the prevention and management of IBD. Accelerated biological aging is defined as the residual difference between chronological age and biological age (whether a person behaves biologically older [positive value] or younger [negative value] than at chronological age)[ 4 ]. KDMAgeAccel and PhenoAgeAccel were defined as the residual difference between full age and KDMAge and PhenoAge.While actual age is commonly used to indicate aging, it only reflects the passage of time. Biological age, however, considers genetic and environmental factors, providing a more accurate assessment of an individual's aging status. Identifying individuals whose biological age exceeds their actual age can facilitate timely interventions to prevent disease onset[ 5 ]. Biological aging has been increasingly recognized for its role in the pathogenesis of various diseases, including cardiovascular diseases, depression, anxiety, rheumatoid arthritis, and cardiometabolic multimorbidity, where it contributes to an increased risk of these conditions. However, the potential link between biological aging and inflammatory bowel disease (IBD) remains largely unexplored. Previous genome-wide association studies (GWAS) have shown that PhenoAgeAccel is associated with APOE genes, inflammation, immune system function, and metabolic status, processes that are also involved in IBD pathogenesis[ 6 – 11 ]. In addition, methods of gene expression measurement, linear mixed effects model analysis, and pathway enrichment analysis demonstrated that gene expression of NOD-like receptors (NLRs) signaling pathways and ubiquitin-mediated protein degradation pathways were significantly associated with KDMAgeAccel[ 12 ], which likewise plays a key role in the pathogenesis of IBD[ 13 , 14 ]. Therefore, we aimed to determine whether PhenoAgeAccel and KDMAgeAccel could serve as two potential biomarkers to help identify individuals at increased risk for IBD. IBD is more likely to occur in individuals with genetic susceptibility, though it can also develop in those without a strong genetic predisposition. Previous GWAS have identified multiple risk alleles associated with IBD, but they confer only a modest increase in disease risk, suggesting that environmental factors play a crucial role in triggering disease phenotypes[ 15 ]. Therefore, in this study, we calculated polygenic risk scores (PRS) to assess the relationship between genetic risk, accelerated biological aging, and the development of IBD, aiming to identify individuals at potential risk. The aim of this study was to investigate the association between accelerated biological aging and the risk of IBD. Therefore, we first conducted large-scale prospective cohort studies at the UK Biobank and All of Us Research Program (AoU) cohort in order to determine whether KDMAgeAccel and PhenoAgeAccel are a potential risks factor for the development of IBD and its impact. Secondly, we explored the combined roles and interactions of accelerated biological aging and PRS in the development of IBD. In addition, the impact on life expectancy was calculated. Finally, we explored whether biological aging mediates the pathogenic process of common risk factors for IBD. 2. Method 2.1 Research design and population The UK Biobank is a population-based cohort of approximately 502,368 participants aged 37–73 years recruited between 2006 and 2010, with 3 rounds of follow-up visits. During the baseline assessment, participants completed a questionnaire, took physical and functional measurements, and provided biological information[ 16 ]. To collect, analyze, and link their data, written consents were obtained from all participants. Ethical approval was obtained for this study as part of the UK Biobank project 83339 (NHS National Research Ethics Service 11/NW/0382, 16/NW/0274, and 21/NW/0157). To make sure the study was valid, people who had IBD at the start (n = 3,570), did not provide follow-up data or quit in the middle of the study (n = 1,298), or did not have enough information on their biological age were left out (n = 158,754 KDMAge; n = 91,248 PhenoAge). Ultimately, 338,746 participants were included in the population analyzed for KDMAge and 406,252 in the population analyzed for PhenoAge (Fig. 1 ). For the life expectancy analyses in the UK Biobank, those who died within two years of baseline (n = 2,504) were excluded to mitigate the potential risk of reverse causation. Therefore, there were 340,449 participants in the analyzed population for KDMAge and 408,258 participants in the analyzed population for PhenoAge. AoU is an ongoing longitudinal cohort study designed to enroll at least 1 million participants[ 17 ]. Although AoU launched nationally in 2018, it already contains information about decades of data on participants (n = 409,420). AoU contains information on body measurements and vital signs collected at enrollment, surveys, EHR, and Fitbit data. Participants who had a Fitbit and agreed to share their Fitbit and EHR data were included in our analysis. We similarly retained 3,982 AoU participants for initial analysis after excluding individuals with IBD prior to biological age measurement and missing data of biological age ( Supplementary Fig. 2 ). 2.2 Biological aging To approximate biological age (BA), we combined two methods that have been published and applied to the prediction of disease and mortality: the Klemera-Doubal Method Biological Age[ 18 ] (KDM-BA) and the PhenoAge algorithm[ 19 ]. Their measurements are based on clinical biomarkers. KDMAge is calculated based on pulmonary function indicators and blood chemistry parameters, including forced expiratory volume at 1 second, systolic blood pressure, albumin concentration, alkaline phosphatase, blood urea nitrogen, creatinine, C-reactive protein, glycated hemoglobin and total cholesterol. KDMAge is calculated based on the regression modeling of biomarkers of age and represents an individual's predicted physiological age. PhenoAge was calculated based on five blood chemistry parameters, including albumin concentration, alkaline phosphatase, creatinine, glucose, and C-reactive protein, lymphocyte percentage (i.e., lymphocytes as a proportion of leukocytes), mean cell volume, erythrocyte cellular distribution width, and leukocyte count. PhenoAge was trained based on multivariate analyses of mortality risk[ 20 ]. We regressed the calculated BA on actual age and calculated residuals to quantify differences in BA between participants[ 21 ]. These residuals are referred to as "accelerated biological aging" and were used to measure biological aging in our subsequent analyses (as a 3 df natural spline) [ 22 ]. To examine the rate of biological aging more comprehensively, we performed the study with a continuous variable, biological aging status (defined as biological aging when biological age acceleration is greater than 0), and a quartile variable (classified according to quartiles of biological age acceleration) [ 22 ].Any observations with missing values were excluded from this analysis (Fig. 1 ). KDMAge and PhenoAge were calculated using the R package "BioAge" ( https://github.com/dayoonkwon/BioAge )[ 23 ]. 2.3 Outcome Health outcomes for patients in the UK Biobank are primarily available through links to eHealth and are updated regularly. In both the UK Biobank and AoU cohorts, the primary outcome was inflammatory bowel disease (IBD), with data from inpatient and death registry records. Secondary outcomes were subgroups of IBD, including Crohn's disease (K50) and ulcerative colitis (K51), through the 10th edition of the International Classification of Diseases (ICD-10)[ 24 ]. 2.4 Covariates Potential confounders were selected based on an a priori developed acyclic graph ( Supplementary Fig. 1 ). Including age, sex (male, female), ethnicity (white or non-white), educational attainment (based on self-reported highest level of qualification attained, categorized as university or non-university), body mass index (BMI) (continuous), socio-economic status (assessed according to the Townsend Deprivation Index, calculated on the basis of the national census output area prior to the subject's enrolment in the UK Biobank), smoking status (never, current, previous), alcohol intake (never, current, previous), fruit and vegetable intake, living environment score (calculated from the quality of the surrounding environment inside and outside the individual's home), exercise, medication consumption (including antibiotic, salicylic acid, immunoregulation, and corticosteroid), disease and surgical history (including depression, anxiety, heart disease, vitamin D deficiency, cancer, and appendicitis surgery)[ 25 – 28 ]. The above data were obtained from self-reported information and medical records at baseline. Moreover, in order to proxy the genetic propensity to IBD, polygenic risk scores (PRS) were used to approximate the genetic predisposition to IBD. PRS were computed by summing the score of reported risk allele for each SNP based on an additive genetic model linked to IBD[ 29 ]. PRS data for IBD were obtained from the UK Biobank or calculated internally for IBD ( Supplementary methods ). Covariates were multiply interpolated by chained equations (MICE package in R)[ 30 ] and predictive mean matching methods for missing values of covariates. 2.5 Multi-state model analysis To assess the association between biological aging and the transition from baseline to IBD development and then to death, multi-state models were employed. These models are valuable for estimating complex longitudinal data in which individuals are allowed to transition between several states, such as health, illness, and death. In the analysis, covariates were adjusted for age, sex, ethnicity, the Townsend Deprivation Index (TDI), education, smoking, alcohol use, BMI, vegetable and fruit intake, environmental factors, exercise, history of antibiotics, salicylic acid, corticosteroids, immunoregulation, cardiopathy, cancer, depression, anxiety, vitamin D deficiency, appendectomy, and genetic risk score for inflammatory bowel disease. Multi-state models allow the estimation of the risk of progressing from one state to another and are performed using the R package 'mstate'[ 31 ]. 2.6 Life expectancy analysis Life expectancy was calculated requiring the remaining life expectancy to be first estimated as the area under the survival curve up to age 100 years, conditional on survival between age 45 and 100 years (1-year interval), predicting the survival curve for each individual and averaging across individuals[ 22 ]. We used proportional risk survival analyses to assess the effect of biological aging on the life expectancy of individuals with or without IBD and its subtypes. 2.7 Mediation analysis To evaluate the role of biological aging in the pathological process of IBD under dietary and pharmacological exposures, we performed mediation analyses using KDMAgeAccel and PhenoAgeAccel as mediators. The mediation was analyzed in three steps that should be satisfied: step 1, the association between dietary/pharmacological exposures and the onset of IBD; step 2, the association between IBD and KDMAgeAccel and PhenoAgeAccel; and step 3, the association between dietary/pharmacological exposures and KDMAgeAccel/PhenoAgeAccel. If all three associations are confirmed, causal mediation analyses can be performed using the "mediation" package in R (using 1000 bootstrap iterations). Indirect effects, direct effects, total effects, and mediation proportions were calculated for each mediation model by combining the mediation and outcome models. Based on previous studies, we chose vegetables and fruits as part of dietary exposure, while for drug exposure we chose antibiotics, salicylic acid, corticosteroids and immunomodulatory drugs. 2.8 Statistical analyses The baseline characteristics summarize the baseline demographic and clinical characteristics, utilizing the number (percentages) of categorical variables and the means (standard deviation [SD]) of continuous variables[ 32 ]. Cox proportional risk models were used to prospectively analyze the association between KDMAgeAccel and PhenoAgeAccel and events of IBD in the UK Biobank. Further, three models based on confounders: model 1 (including age, sex, ethnicity, education level, BMI, Townsend deprivation index, smoking status, alcohol intake, fruit and vegetable intake, living environment, and physical activity), model 2 (model 1 + history of medication), and model 3 (model 2 + history of disease and surgery). Dose-response associations related to the risk of incident IBD were evaluated by restricted cubic spline regression[ 33 ]. To further validate our results, we conducted a validation using the AoU large-scale cohort study. Since the AoU cohort covered multiple races and several different populations, it makes our results more broadly generalizable. We similarly analyzed the association between PhenoAgeAccel and IBD and its subgroups using Cox proportional risk models, adjusting appropriately for age, sex, education, race, smoking, and alcohol consumption. Secondly, subgroup analyses were conducted on KDMAgeAccel and PhenoAgeAccel according to baseline age ( 30 kg/m 2 ), ethnicity (white or non-white), smoking status (never, current, previous), alcohol intake (never, current, previous), and medication use stratified by subgroup. Furthermore, interaction terms were included between each categorical factor and accelerated aging status to estimate the likelihood of effect modification[ 34 ].To explore the effect of KDMAgeAccel and PhenoAgeAccel on the risk of IBD in populations with different genetic risks, we performed subgroup analyses based on the corresponding PRS. The genetic risk categories were defined according to a weighted PRS as low (lowest tertile), medium (intermediate tertile), and high (highest tertile)[ 35 ]. We performed sensitivity analyses. Participants with incident IBD within 2 years of baseline were excluded and then subjected to lagged analyses. General data analyses and manipulations were performed using R (version 4.2.3), and survival analyses of life expectancy were performed using Stata (version 16.0). 3. Results 3.1 Baseline Characteristics of the Study Population In the population analyzed for KDMAge, there were 2,871 individuals with IBD with a mean age of 57.1 years, including 1,442 females. Among individuals with accelerated biological aging (n = 159,655), 1,536 were patients with IBD, and 158,119 were non-IBD individuals. In the population analyzed for PhenoAge, there were 3,406 individuals with IBD with a mean age of 57.2 years, including 1,736 females. Among individuals with accelerated biological aging (n = 97,769), 1,214 were patients with IBD, and 96,555 were individuals without IBD (Table 1 ). Individuals with biological aging were more frequently female, had a higher BMI, a higher Townsend Deprivation Index, and a higher likelihood of smoking and drinking alcohol than participants without biological aging. Additionally, a greater proportion of persons with IBD had taken salicylates, corticosteroids, and immunomodulators. Table 1 Baseline characteristics of participants of KDMAge and PhenoAge in the UK Biobank. KDMAge PhenoAge Non-IBD IBD Non-IBD IBD No. of participants, n 335,875 2,871 402,846 3,406 Age (years), mean (SD) 56.4(8.1) 57.1(8.1) 56.5(8.1) 57.2(8.0) Sex, n (%) Female 179,987(53.6) 1,442(50.2) 217,440(54.0) 1,736(51.0) Male 155,888(46.4) 1,429(49.8) 185,406(46.0) 1,670(49.0) Ethnicity, n (%) Non-White 16,956(5.0) 128(4.5) 20,893(5.2) 153(4.5) White 318,919(95.0) 2,743(95.5) 381,953(94.8) 3,253(95.5) TDI, (mean (SD)) -1.4(3.0) -1.2(3.1) -1.3(3.1) -1.2(3.2) BMI, n (%) 1.9(0.8) 1.9(0.8) 27.4(4.7) 27.6(4.8) < 18.5kg/m 2 1,670(0.5) 15(0.5) 2,027(0.5) 18(0.5) 18.5–24.9kg/m 2 110,909(33.0) 893(31.1) 131,464(32.6) 1,040(30.5) 25.0–29.9kg/m 2 144,162(42.9) 1,247(43.4) 172,402(42.8) 1,497(44.0) ≥ 30kg/m 2 79,134(23.6) 716(24.9) 96,953(24.1) 851(25.0) Education (%) Non-university 297,253(88.5) 2,572(89.6) 356,321(88.5) 3,052(89.6) University 38,622(11.5) 299(10.4) 46,525(11.5) 354(10.4) Smoking status, n (%) Never 185,814(55.3) 1,309(45.6) 221,294(54.9) 1,511(44.4) Previous 115,790(34.5) 1,203(41.9) 139,235(34.6) 1,450(42.6) Current 34,271(10.2) 359(12.5) 42,317(10.5) 445(13.1) Alcohol intake frequency, n (%) Never 25,608(7.6) 266(9.3) 31,587(7.8) 328(9.6) Previous 37,422(11.1) 359(12.5) 45,723(11.3) 436(12.8) Current 272,845(81.2) 2,246(78.2) 325,536(80.8) 2,642(77.6) Living environment score (mean (SD)) 18.6(15.2) 18.65(15.3) 18.71(15.3) 18.65(15.4) Total physical activity, MET-h per week 2,663.7(2712.1) 2,658.7(2731.0) 2657.9(2715.3) 2668.3(2707.7) Drugs Antibiotic, n (%) 3,929(1.2) 57(2.0) 5159(1.3) 74(2.2) Salicylic acid, n (%) 101,893(30.3) 1,008(35.1) 126,147(31.3) 1,232(36.2) Corticosteroid, n (%) 3,232(1.0) 104(3.6) 4,070(1.0) 111(3.3) Immunoregulation, n (%) 2,522(0.8) 115(4.0) 2,878(0.7) 114(3.3) Disease Cardiopathy, n (%) 24,411(7.3) 220(7.7) 30,062(7.5) 287(8.4) Cancer, n (%) 18,321(5.5) 171(6.0) 22,467(5.6) 218(6.4) Depression, n (%) 4,397(1.3) 36(1.3) 5,373(1.3) 45(1.3) Anxiety, n (%) 67,131(20.0) 576(20.1) 84,089(20.9) 712(20.9) Appendix, n (%) 4,531(1.3) 52(1.8) 5,573(1.4) 65(1.9) Biological age acceleration, (mean (SD)) -0.05(1.5) 0.1(1.4) -2.7(4.4) -1.2(4.8) Biological age, non-accelerated aging, n (%) 177,756(52.9) 1,335(46.5) 306,291(76.0) 2,192(64.4) Biological age, accelerated aging, n (%) 158,119(47.1) 1,536(53.5) 96,555(24.0) 1,214(35.6) *IBD: Inflammatory bowel disease Supplementary Table 1 shows the baseline characteristics of AoU participants. 3.2 Risks of Accelerated biological aging and IBD The results of the study showed a positive correlation between biological aging acceleration (either KDMAgeAccel or PhenoAgeAccel) and the risk of IBD. Individuals with accelerated aging had an increased risk of IBD compared with those without accelerated aging (KDMAgeAccel HR 1.22 [95% CI 1.13–1.32]; P < 0.001; PhenoAgeAccel HR 1.57 [95% CI 1.46–1.69]; P < 0.001) ( Table 2 ) . In addition, the risk of IBD increased by 5% and 6% for each 1-year increase in KDMAgeAccel and PhenoAgeAccel, respectively. In the observed accelerated aging groups, participants in the highest quartile had a significantly increased risk of developing IBD compared to participants in the lowest quartile. Specifically, in the population analyzed for KDMAge, the HR for the risk of developing IBD in the highest quartile of participants was 1.21 (95% CI 1.09–1.36, P < 0.001). While, in the population analyzed for PhenoAge, the HR was 1.96 (95% CI 1.76–2.17, P < 0.001) ( Table 2 ) . Table 2 Association between accelerated biological aging and risk of IBD. Model 1 Model 2 Model 3 HR (95% CI) P -value HR (95% CI) P -value HR (95% CI) P -value KDMAgeAccel Continuous 1.07(1.04–1.10) < 0.001 1.05(1.03–1.08) < 0.001 1.05(1.03–1.08) < 0.001 Quartile 1 1 (ref) .. 1 (ref) .. 1 (ref) .. Quartile 2 0.91(0.80–1.02) 0.110 0.91(0.81–1.03) 0.129 0.91(0.81–1.03) 0.129 Quartile 3 1.13(1.00-1.27) 0.049 1.07(0.85–1.35) 0.067 1.27(1.05–1.53) 0.067 Quartile 4 1.27(1.14–1.41) < 0.001 1.21(1.09–1.35) < 0.001 1.21(1.09–1.36) < 0.001 Non-accelerated aging 1 (ref) .. 1 (ref) .. 1 (ref) .. Accelerated aging 1.25(1.16–1.35) < 0.001 1.22(1.13–1.32) < 0.001 1.22(1.13–1.32) < 0.001 PhenoAgeAccel Continuous 1.07(1.06–1.07) < 0.001 1.06(1.05–1.07) < 0.001 1.06(1.05–1.07) < 0.001 Quartile 1 1 (ref) .. 1 (ref) .. 1 (ref) .. Quartile 2 1.26(1.13–1.41) < 0.001 1.25(1.12–1.40) < 0.001 1.25(1.12–1.40) < 0.001 Quartile 3 1.45(1.30–1.61) < 0.001 1.42(1.28–1.58) < 0.001 1.42(1.28–1.58) < 0.001 Quartile 4 2.11(1.91–2.34) < 0.001 1.96(1.77–2.18) < 0.001 1.96(1.76–2.17) < 0.001 Non-accelerated aging 1 (ref) .. 1 (ref) .. 1 (ref) .. Accelerated aging 1.68(1.56–1.81) < 0.001 1.57(1.46–1.69) < 0.001 1.57(1.46–1.69) < 0.001 1: Model 1 was adjusted for age, sex, ethnicity, the Townsend Deprivation Index (TDI), education, smoking, alcohol, BMI, vegetables, fruit, the environment, and exercise. 2: Model 2 was adjusted for age, sex, ethnicity, the Townsend Deprivation Index (TDI), education, smoking, alcohol, BMI, vegetables, fruit, the environment, exercise, history of antibiotic, history of salicylic acid, history of corticosteroid, history of immunoregulation. 3: Model 3 was adjusted for age, sex, ethnicity, the Townsend Deprivation Index (TDI), education, smoking, alcohol, BMI, vegetables, fruit, the environment, exercise, history of antibiotics, history of salicylic acid, history of corticosteroids, history of immunoregulation, history of cardiopathy, history of cancer, history of depression, history of anxiety, history of vitamin D deficiency, history of appendectomy, genetic risk score for inflammatory bowel disease. *BMI: body mass index We found similar trends in patients with UC and CD: patients with accelerated aging were at increased risk compared with patients without accelerated aging, both in the population analyzed for KDMAgeAccel and PhenoAgeAccel ( Supplementary Tables 2–3 ). Compared to individuals without accelerated aging characteristics, those with accelerated aging exhibit a significantly increased risk of CD and UC. Specifically, the analysis based on KDMAgeAccel indicates that individuals with accelerated aging have a 1.30-fold increased risk of CD and a 1.19-fold increased risk of UC. In contrast, the PhenoAgeAccel analysis reveals a 1.93-fold increase in CD risk and a 1.50-fold increase in UC risk. These findings suggest a strong association between accelerated aging and the incidence of CD and UC, indicating that accelerated aging may serve as a potential risk factor for these diseases. 3.3 All of Us Research Program (AoU) A significant association between PhenoAgeAccel and the occurrence of IBD and its subtypes was found based on the results validated against the AoU database. After completely adjusting for covariates, individuals with accelerated aging had an increased risk of developing IBD (HR 1.57 [95% CI 1.18–2.09]; P = 0.002) compared with those without accelerated aging ( Supplementary Table 4 ). In addition, participants in the highest quartile had a significantly increased risk of IBD (PhenoAge HR 2.90 [95% CI 1.93–4.34]; P < 0.001) compared with those in the lowest quartile. Similarly, the association of PhenoAgeAccel with CD was equally significant, the HR was 1.82 (95% CI 1.26–2.63, P < 0.001). For UC, the HR for risk of IBD in the highest quartile of participants was 2.22 (95% CI 1.37–3.59, P < 0.001). The results of UC and CD can be found in Supplementary Tables 5–6 . 3.4 Results of multi-state model analysis The results of multi-state modeling showed that accelerated biological aging was associated with an increased risk of transition from baseline status to IBD for both populations analyzed for KDMAgeAccel and PhenoAgeAccel. In particular, the risk ratio was 1.24 (95% CI 1.15–1.34; P < 0.001) for KDMAgeAccel and 1.61 (95% CI 1.49–1.73; P < 0.001) for PhenoAgeAccel. Moreover, PhenoAgeAccel was also significantly associated with an increased risk from IBD onset to death (HR 1.44 [95% CI 1.17–1.77]; P < 0.001) ( Supplementary Table 7–8 ). 3.5 Results of life expectancy analysis The results of our life expectancy analyses revealed that people with IBD aged 45 years or older had a shorter life expectancy than those without IBD. At age 45 years, among participants in the UK Biobank, life expectancy was 36.4 years (95% CI 36.1–36.6) for those with IBD and 37.4 years (95% CI 37.3–37.4) for those without IBD ( Fig. 2 ) . Among patients with IBD, life expectancy in patients without accelerated biological aging was 37.1 years (95% CI 36.5–37.8; KDMAgeAccel) or 37.4 years (95% CI 36.8–37.9; PhenoAgeAccel). In contrast, life expectancy for patients with accelerated aging was 35.4 years (95% CI 35.1–35.8; KDMAgeAccel) or 35.5 years (95% CI 35.4–35.6; PhenoAgeAccel). Remarkably, at age 45 years, individuals with IBD who were in the top quartile for accelerated aging had a loss of life expectancy ranging from 2.37 years (95% CI 1.41–3.33; KDMAgeAccel) to 3.02 years (95% CI 2.06–3.99; PhenoAgeAccel), compared with those in the lowest quartile. Results on the patients with UC and CD life expectancy analyses are shown in Supplementary Fig. 2 . 3.6 Results of Mediation analysis In the UK Biobank, we found that cooked vegetables and dried fruits were significantly associated with a significantly decreased risk of IBD ( Supplementary Table 9 ). In addition, we observed that vegetables and dried fruits would significantly slow down biological aging ( Supplementary Table 10 ). The combined analysis showed that the association between dried fruits and IBD could be mediated by biological acceleration of aging, with 8.1% (95% CI 5.3–24.2%) of the effect mediated by KDMAgeAccel (β = -0.05) and 22.1% (95% CI 18.0-54.2%) mediated by PhenoAgeAccel (β = -0.14). ( Supplementary Tables 11–12 ). For cooked vegetables, the mediating proportion of KDMAgeAccel was 2.2% (95% CI 1.3–4.1%) with β = -0.01, and PhenoAgeAccel was 13.0% (95% CI 10.7–23.6%) with β = -0.08. These results suggest that the association between dried fruits, cooked vegetables, and IBD may be partially mediated by accelerated biological aging, with KDMAgeAccel and PhenoAgeAccel offering potential biomarkers and intervention targets for early warning and treatment of IBD. Besides, we observed four drugs exhibiting risk factors mediated by accelerated biological aging. Among them, antibiotics (KDMAgeAccel of proportion mediated of 1.3% with β = 0.11; PhenoAgeAccel of 13.8% with β = 1.00), salicylic acid drugs (KDMAgeAccel of 6.4% with β = 0.19; PhenoAgeAccel of 25.8% with β = -0.73), cortisol drugs (KDMAgeAccel of 2.4% with β = 0.44; PhenoAgeAccel of 20.0% with β = 3.39), and immunomodulators (KDMAgeAccel of 1.9% with β = 0.41; PhenoAgeAccel of 20.7% with β = 4.96). These findings suggest that accelerated biological aging may mediate the adverse effects of certain drugs, highlighting the potential importance of considering aging processes in the management of IBD treatment. 3.7 Subgroup Analysis In subgroup analyses, the association between KDMAgeAccel and IBD risk was stronger in those aged 60 years and older (HR 1.28 [95% CI 1.15–1.43]; P < 0.001; Pi = 0.023) than in younger participants ( Supplementary Fig. 3–4 ). Participants with a history of alcohol intake had a higher risk ratio (HR 1.24 [95% CI 1.14–1.35]; P < 0.001; Pi = 0.030). The results of the analyses in the population analyzed for PhenoAgeAccel also validated these findings. Additionally, subgroup analyses based on the medication history showed significant differences, especially in the population analyzed for PhenoAgeAccel. PhenoAgeAccel may be more likely to increase the risk of IBD in individuals using antibiotics (HR 1.90 [95% CI 1.16–3.10], P = 0.011, Pi < 0.001) and corticosteroid drugs (HR 1.83 [95% CI 1.22–2.73], P = 0.003, Pi < 0.001). The significance of these interactions suggests that medication use may influence the association between PhenoAgeAccel and increased IBD risk. In addition, individuals in the high genetic risk group and with accelerated biological aging had a significantly increased risk of IBD compared with participants without biological aging in the low genetic risk group (KDMAgeAccel HR 1.36 [95% CI 1.20–1.53]; PhenoAgeAccel HR 1.59 [95% CI 1.41–1.79]; P < 0.001 for both). This suggests that KDMAgeAccel and PhenoAgeAccel are independent risk factors for IBD regardless of genetic susceptibility ( Table 3 ) . For subgroup analyses on patients with UC and CD, refer to Supplementary Figs. 5–8 . The results of the restriction triple spline analysis are shown in Supplementary Fig. 9. Table 3 Association between biological aging and risk of IBD, stratified by polygenic risk status. Low genetic risk Medium genetic risk High genetic risk HR (95% CI) P -value HR (95% CI) P -value HR (95% CI) P -value KDMAgeAccel Continuous 1.01(0.94–1.08) 0.841 1.05(1.00-1.10) 0.068 1.09(1.04–1.14) < 0.001 Quartile 1 1 (ref) .. 1 (ref) .. 1 (ref) .. Quartile 2 0.84(0.63–1.12) 0.238 1.05(0.79–1.39) 0.737 1.10(0.84–1.44) 0.475 Quartile 3 1.05(0.83–1.32) 0.677 1.07(0.85–1.35) 0.574 1.26(1.02–1.55) 0.033 Quartile 4 0.94(0.77–1.14) 0.510 1.27(1.05–1.53) 0.012 1.32(1.11–1.58) 0.002 Non-accelerated aging 1 (ref) .. 1 (ref) .. 1 (ref) .. Accelerated aging 1.19(0.99–1.43) 0.061 1.11(0.96–1.29) 0.152 1.36(1.20–1.54) < 0.001 PhenoAgeAccel Continuous 1.05(1.04–1.07) < 0.001 1.05(1.03–1.06) < 0.001 1.06(1.05–1.07) < 0.001 Quartile 1 1 (ref) .. 1 (ref) .. 1 (ref) .. Quartile 2 1.18(0.91–1.53) 0.218 1.26(0.97–1.64) 0.078 1.86(1.45–2.38) < 0.001 Quartile 3 1.21(0.99–1.49) 0.066 1.30(1.06–1.60) 0.014 1.55(1.31–1.84) < 0.001 Quartile 4 1.86(1.45–2.38) < 0.001 1.77(1.45–2.17) < 0.001 2.11(1.77–2.50) < 0.001 Non-accelerated aging 1 (ref) .. 1 (ref) .. 1 (ref) .. Accelerated aging 1.62(1.36–1.93) < 0.001 1.47(1.27–1.70) < 0.001 1.59(1.41–1.79) < 0.001 *Both models were adjusted for age, sex, ethnicity, the Townsend Deprivation Index (TDI), education, smoking, alcohol, BMI, vegetables, fruit, the environment, exercise, history of antibiotics, history of salicylic acid, history of corticosteroids, history of immunoregulation, history of cardiopathy, history of cancer, history of depression, history of anxiety, history of vitamin D deficiency, history of appendectomy. *BMI: body mass index Additionally, in lagged analyses, the increased risk of IBD remained statistically significant with a 2-year lag (KDMAgeAccel HR 1.19 [95% CI 1.09–1.29]; PhenoAgeAccel HR 1.48 [95% CI 1.36–1.64]; P < 0.001 for both) to avoid reversal of causality ( Supplementary Table 13 ). 4. Discussion We found that biological aging acceleration was significantly associated with the risk of both IBD and its subtypes in these two prospective cohorts from the UK Biobank and AoU, and that this risk increased progressively with accelerated aging. Notably, genetic susceptibility to IBD synergized with accelerated biological aging to increase the risk of IBD. Life expectancy was significantly lower in IBD patients with accelerated biological aging. Furthermore, KDMAgeAccel and PhenoAgeAccel could mediate the protective effect of cooked vegetables and dried fruits against IBD occurrence. This emphasizes the importance of considering biological aging acceleration in prevention and management strategies for IBD. Our study presents several novel findings. First, we identify for the first time a link between accelerated biological aging and IBD. Previous studies have shown an association between aging and the development of IBD. Noh JY et al. showed that changes in pro-inflammatory gut flora associated with aging that interact with GHS-R (Growth hormone secretagogue receptor) signaling may increase the risk of IBD in older adults or exacerbate the condition of patients with IBD[ 36 ]. Similar results have been observed in other studies, especially in biomarker studies of biological aging, such as telomere shortening and activation of DNA damage response pathways[ 37 ]. However, these studies have largely focused on a single biomarker or physiological age[ 38 ], which does not fully capture the complexity of aging’s role in IBD, Epidemiological investigations have been limited in examining biological aging comprehensively, thus failing to reflect the multifaceted impact of aging on IBD. In contrast, our study overcomes these limitations by using KDMAge and PhenoAge as comprehensive biological age indicators. These biomarkers consider multiple dimensions, including inflammation, immunity, metabolism, and organ homeostasis, offering a more holistic assessment of biological aging. The significance of our finding lies in its ability to integrate these dimensions and better elucidate the association between biological aging and IBD. Secondly, our study is the first to explore the interaction between biological aging and genetic risk for IBD, aiming to explain the complex association between genetic susceptibility and environmental factors. The results suggest a significant correlation between accelerated biological aging and the development of IBD in populations at high genetic risk. KDMAgeAccel and PhenoAgeAccel can be used as potential predictive biomarkers of IBD and, combined with genetic risk, can identify high-risk individuals who are most likely to be adversely affected in the absence of intervention. Individuals with high PRS and accelerated aging could be targeted for focused interventions that may reduce their risk of IBD through lifestyle modifications and early treatment. Our study found for the first time that accelerated biological aging significantly increased mortality and shortened survival time in patients with IBD. Accelerated aging may affect the course of IBD and lead to exacerbation of the disease through mechanisms such as chronic inflammation, immune dysfunction, and decreased tissue repair capacity. Aging-associated immune dysregulation and chronic low-grade inflammation may exacerbate the inflammatory response in IBD, and imbalances in the gut microbiota may also be involved. Thus, biological senescence, as a new risk factor, enables the detection of senescence markers and the implementation of targeted interventions that may slow down the aging process and thus improve patient survival. Our study is the first to demonstrate that accelerated biological aging plays a mediating role in the relationship between dietary habits, particularly vegetable and dried fruit intake, and the risk of IBD. The human gut, with its complex micro-ecosystem and immune functions, is influenced by various dietary factors and medications, which can induce inflammatory responses and modulate immune activity[ 39 – 41 ]. Previous research using the National Health and Nutrition Examination Survey (NHANES) data has shown that flavonoid intake is associated with a lower biological age difference (∆age), suggesting that flavonoids may help slow biological aging[ 42 ]. Fruits and vegetables are the main sources of flavonoids for humans[ 43 ]. In our analysis of UK Biobank, we found that accelerated biological aging, as measured by KDMAgeAccel and PhenoAgeAccel, mediates approximately 10%-20% of the association between vegetable and dried fruit intake and IBD risk. Specifically, both KDMAgeAccel and PhenoAgeAccel were found to partially explain how higher intake of vegetables and dried fruits may reduce the risk of IBD by slowing biological aging. These findings highlight a potential mechanism through which dietary factors, particularly flavonoid-rich foods, may exert protective effects against IBD by modulating the biological aging process.. Strength and limitation There are several key strengths in our study. Firstly, utilizing the large sample size of the UK Biobank, we have revealed for the first time how accelerated biological aging affects IBD risk. Secondly, we validated our results using the AoU prospective cohort. The multi-ethnic, multi-age coverage of the AoU cohort makes our results more generalizable. Third, our study used two biological age calculation methods, KDMAge and PhenoAge, which improved the reliability and accuracy of biological aging measurements through mutual validation. Fourth, for the first time, genetic risk factors were considered to explore the interaction between accelerated biological aging and IBD. This provides new insights into understanding the genetic susceptibility to IBD and the complex mechanisms by which it is interlinked with the aging process. Fifth, our use of biological aging assessment is more accessible and more applicable to basic clinical assessment. However, there are some limitations to this study. First, although we adjusted for several confounders, we were unable to completely rule out the potential effects of unmeasured confounders. Second, biological aging is a modifiable process, yet the limited follow-up data in both the UK Biobank and AoU databases constrained our ability to assess changes over time. However, it is important to note that the results were consistent across both cohorts, strengthening the robustness of our findings. Third, while we could not validate KDMAge in the AoU database due to the small sample size of valid data for the FEV1 metric (fewer than 100 participants after complete population exclusion), the validation results for PhenoAgeAccel were highly consistent with those obtained from the UK Biobank, further supporting the reliability of our biological aging measures. Despite these limitations, our study provides compelling evidence linking accelerated biological aging to IBD risk, with findings that are consistent across different populations and analytic methods. 5. Conclusion This study analyzes two large prospective cohorts from the UK Biobank and AoU, confirming that accelerated biological aging is associated with increased risk of IBD and its subtypes, and reduced life expectancy in IBD patients. Additionally, PhenoAgeAccel significantly increases the risk of progression from onset to death in IBD patients. Combined with genetic risk scores, KDMAge and PhenoAge can be used for IBD risk stratification and provide effective means for early identification, monitoring, and intervention in high-risk groups Declarations Conflict of interest statement The authors declared no conflict of interest. Acknowledgement The authors thank the UK Biobank for the access of data, and this research has been performed under approval. The All of Us (AoU) Research Programme are available on application to All of Us. References Kong, C., et al., Ketogenic diet alleviates colitis by reduction of colonic group 3 innate lymphoid cells through altering gut microbiome. Signal Transduct Target Ther, 2021. 6(1): p. 154. Harbord, M., et al., The First European Evidence-based Consensus on Extra-intestinal Manifestations in Inflammatory Bowel Disease. J Crohns Colitis, 2016. 10(3): p. 239-54. Arbeev, K.G., et al., "Physiological Dysregulation" as a Promising Measure of Robustness and Resilience in Studies of Aging and a New Indicator of Preclinical Disease. J Gerontol A Biol Sci Med Sci, 2019. 74(4): p. 462-468. Liu, Z., et al., A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study. PLoS Med, 2018. 15(12): p. e1002718. Huang, Z., et al., Dynamics of leukocyte telomere length in adults aged 50 and older: a longitudinal population-based cohort study. Geroscience, 2021. 43(2): p. 645-654. Kuo, C.L., et al., Genetic associations for two biological age measures point to distinct aging phenotypes. Aging Cell, 2021. 20(6): p. e13376. Li, K., et al., Polymorphisms of the macrophage inflammatory protein 1 alpha and ApoE genes are associated with ulcerative colitis. Int J Colorectal Dis, 2009. 24(1): p. 13-7. Cai, H., et al., Discovery of a dual-acting inhibitor of interleukin-1beta and STATs for the treatment of inflammatory bowel disease. RSC Med Chem, 2024. 15(1): p. 193-206. Wang, L., et al., Targeting JAK/STAT signaling pathways in treatment of inflammatory bowel disease. Inflamm Res, 2021. 70(7): p. 753-764. Zhang, K., et al., Macrophage polarization in inflammatory bowel disease. Cell Commun Signal, 2023. 21(1): p. 367. Zaiatz, B.V., et al., Targeting Immune Cell Metabolism in the Treatment of Inflammatory Bowel Disease. Inflamm Bowel Dis, 2021. 27(10): p. 1684-1693. Lin, H., et al., Transcriptome-wide association study of inflammatory biologic age. Aging (Albany NY), 2017. 9(11): p. 2288-2301. Zhou, Y., S. Yu and W. Zhang, NOD-like Receptor Signaling Pathway in Gastrointestinal Inflammatory Diseases and Cancers. Int J Mol Sci, 2023. 24(19). Martinez-Torres, R.J. and M. Chamaillard, The Ubiquitin Code of NODs Signaling Pathways in Health and Disease. Front Immunol, 2019. 10: p. 2648. El, H.J., et al., The Genetics of Inflammatory Bowel Disease. Mol Diagn Ther, 2024. 28(1): p. 27-35. Sudlow, C., et al., UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med, 2015. 12(3): p. e1001779. Ramirez, A.H., et al., The All of Us Research Program: Data quality, utility, and diversity. Patterns (N Y), 2022. 3(8): p. 100570. Parker, D.C., et al., Association of Blood Chemistry Quantifications of Biological Aging With Disability and Mortality in Older Adults. J Gerontol A Biol Sci Med Sci, 2020. 75(9): p. 1671-1679. Klopack, E.T., et al., Lifetime exposure to smoking, epigenetic aging, and morbidity and mortality in older adults. Clin Epigenetics, 2022. 14(1): p. 72. Kresovich, J.K., et al., Healthy eating patterns and epigenetic measures of biological age. Am J Clin Nutr, 2022. 115(1): p. 171-179. Ramasubramanian, R., et al., Evaluation of T-cell aging-related immune phenotypes in the context of biological aging and multimorbidity in the Health and Retirement Study. Immun Ageing, 2022. 19(1): p. 33. Chen, L., et al., Associations between biological ageing and the risk of, genetic susceptibility to, and life expectancy associated with rheumatoid arthritis: a secondary analysis of two observational studies. Lancet Healthy Longev, 2024. 5(1): p. e45-e55. Kwon, D. and D.W. Belsky, A toolkit for quantification of biological age from blood chemistry and organ function test data: BioAge. Geroscience, 2021. 43(6): p. 2795-2808. Borra, V., et al., Is dependent cannabis use in adult hospitalizations with inflammatory bowel disease associated with major adverse cardiovascular and cerebrovascular events? Insights from National Inpatient Sample Analysis. Curr Med Res Opin, 2024. 40(4): p. 605-611. Teofani, A., et al., Intestinal Taxa Abundance and Diversity in Inflammatory Bowel Disease Patients: An Analysis including Covariates and Confounders. Nutrients, 2022. 14(2). Jones, P.D., et al., Exercise decreases risk of future active disease in patients with inflammatory bowel disease in remission. Inflamm Bowel Dis, 2015. 21(5): p. 1063-71. Bollen, L., et al., The Occurrence of Thrombosis in Inflammatory Bowel Disease Is Reflected in the Clot Lysis Profile. Inflamm Bowel Dis, 2015. 21(11): p. 2540-8. Vigano, C.A., et al., Alexithymia and Psychopathology in Patients Suffering From Inflammatory Bowel Disease: Arising Differences and Correlations to Tailoring Therapeutic Strategies. Front Psychiatry, 2018. 9: p. 324. Arnau-Soler, A., et al., Genome-wide by environment interaction studies of depressive symptoms and psychosocial stress in the UK Biobank and Generation Scotland. Transl Psychiatry, 2019. 9(1): p. 14. Cooke, R., F. Eigenbrod and A.E. Bates, Projected losses of global mammal and bird ecological strategies. Nat Commun, 2019. 10(1): p. 2279. Pang, Y., et al., The role of lifestyle factors on comorbidity of chronic liver disease and cardiometabolic disease in Chinese population: A prospective cohort study. Lancet Reg Health West Pac, 2022. 28: p. 100564. Tan, B., et al., Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study. EClinicalMedicine, 2023. 55: p. 101724. Bellettiere, J., et al., Sedentary behavior and cardiovascular disease in older women: The Objective Physical Activity and Cardiovascular Health (OPACH) Study. Circulation, 2019. 139(8): p. 1036-1046. Chudy-Onwugaje, K., et al., Age Modifies the Association Between Depressive Symptoms and Adherence to Self-Testing With Telemedicine in Patients With Inflammatory Bowel Disease. Inflamm Bowel Dis, 2018. 24(12): p. 2648-2654. Yang, R., et al., Modification effect of ideal cardiovascular health metrics on genetic association with incident heart failure in the China Kadoorie Biobank and the UK Biobank. BMC Med, 2021. 19(1): p. 259. Noh, J.Y., et al., Novel Role of Ghrelin Receptor in Gut Dysbiosis and Experimental Colitis in Aging. Int J Mol Sci, 2022. 23(4). Faye, A.S. and J.F. Colombel, Aging and IBD: A New Challenge for Clinicians and Researchers. Inflamm Bowel Dis, 2022. 28(1): p. 126-132. Cinegaglia, N., et al., Shortening telomere is associated with subclinical atherosclerosis biomarker in omnivorous but not in vegetarian healthy men. Aging (Albany NY), 2019. 11(14): p. 5070-5080. Liu, Y., J. Wang and C. Wu, Modulation of Gut Microbiota and Immune System by Probiotics, Pre-biotics, and Post-biotics. Front Nutr, 2021. 8: p. 634897. Zhang, M., et al., Interactions between Intestinal Microbiota and Host Immune Response in Inflammatory Bowel Disease. Front Immunol, 2017. 8: p. 942. Liu, Y., J. Wang and C. Wu, Modulation of Gut Microbiota and Immune System by Probiotics, Pre-biotics, and Post-biotics. Front Nutr, 2021. 8: p. 634897. Xing, W., et al., Dietary flavonoids intake contributes to delay biological aging process: analysis from NHANES dataset. J Transl Med, 2023. 21(1): p. 492. Khan, J., et al., Dietary Flavonoids: Cardioprotective Potential with Antioxidant Effects and Their Pharmacokinetic, Toxicological and Therapeutic Concerns. Molecules, 2021. 26(13). Additional Declarations There is NO Competing Interest. Supplementary Files SupplementarymethodsBA.docx Supplemental Material of Accelerated biological aging, inflammatory bowel disease, genetic susceptibility and life expectancy: Evidence from UK Biobank and All of Us Cohorts Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5705746","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":404482253,"identity":"6c18ee51-e210-47a4-9527-0b05b5d901f8","order_by":0,"name":"Hao 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Duan","email":"","orcid":"https://orcid.org/0000-0001-7973-0353","institution":"Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chongyang","middleName":"","lastName":"Duan","suffix":""},{"id":404482267,"identity":"458cae2b-8b0e-4e55-9608-7e45acb279b0","order_by":14,"name":"Weihong Sha","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weihong","middleName":"","lastName":"Sha","suffix":""}],"badges":[],"createdAt":"2024-12-24 11:05:36","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5705746/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5705746/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":74448836,"identity":"8163b742-8647-44f0-b639-fcf539378da2","added_by":"auto","created_at":"2025-01-22 11:37:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":200200,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComponents of biological age, classification of participants and exclusion criteria.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5705746/v1/df72f744199308f7d8f900aa.png"},{"id":74449964,"identity":"9a91d93e-c182-4ac7-bbf2-3242b537a721","added_by":"auto","created_at":"2025-01-22 11:45:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":122914,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between life expectancy lost and biological aging.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5705746/v1/5480a144bb02f2f6799fa8d9.png"},{"id":80236943,"identity":"ff26897d-f0a8-4620-88eb-b85d15ca684d","added_by":"auto","created_at":"2025-04-09 14:04:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1831041,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5705746/v1/827b62ec-51d1-4c32-9888-eed51d336fc4.pdf"},{"id":74448837,"identity":"df5f847b-8672-4f62-92f8-95b1b9921f70","added_by":"auto","created_at":"2025-01-22 11:37:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1546164,"visible":true,"origin":"","legend":"Supplemental Material of Accelerated biological aging, inflammatory bowel disease, genetic susceptibility and life expectancy: Evidence from UK Biobank and All of Us Cohorts","description":"","filename":"SupplementarymethodsBA.docx","url":"https://assets-eu.researchsquare.com/files/rs-5705746/v1/57bae7559c3d1635c6f5889c.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Accelerated biological aging, inflammatory bowel disease, genetic susceptibility and life expectancy: Evidence from UK Biobank and All of Us Cohorts","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eInflammatory bowel disease (IBD), which primarily includes Crohn's disease (CD) and ulcerative colitis (UC), is a complex, multifactorial disease. It involves a variety of factors, with genetic susceptibility and environmental influences being the most significant[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].IBD is a chronic condition with a rising incidence and prevalence worldwide, imposing a significant economic burden due to increased healthcare costs and limited treatment options.These factors underscore the need for further research to better understand and manage this complex disease[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAging has been identified as a contributing factor in the pathogenesis of IBD[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While aging is a universal process, the rate at which individuals age varies. Thus, chronological age alone cannot account for the considerable variation in age-related phenotypes observed within same-age cohorts. Therefore, evaluating an individual's biological aging status is crucial for the prevention and management of IBD. Accelerated biological aging is defined as the residual difference between chronological age and biological age (whether a person behaves biologically older [positive value] or younger [negative value] than at chronological age)[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. KDMAgeAccel and PhenoAgeAccel were defined as the residual difference between full age and KDMAge and PhenoAge.While actual age is commonly used to indicate aging, it only reflects the passage of time. Biological age, however, considers genetic and environmental factors, providing a more accurate assessment of an individual's aging status. Identifying individuals whose biological age exceeds their actual age can facilitate timely interventions to prevent disease onset[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBiological aging has been increasingly recognized for its role in the pathogenesis of various diseases, including cardiovascular diseases, depression, anxiety, rheumatoid arthritis, and cardiometabolic multimorbidity, where it contributes to an increased risk of these conditions. However, the potential link between biological aging and inflammatory bowel disease (IBD) remains largely unexplored. Previous genome-wide association studies (GWAS) have shown that PhenoAgeAccel is associated with APOE genes, inflammation, immune system function, and metabolic status, processes that are also involved in IBD pathogenesis[\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In addition, methods of gene expression measurement, linear mixed effects model analysis, and pathway enrichment analysis demonstrated that gene expression of NOD-like receptors (NLRs) signaling pathways and ubiquitin-mediated protein degradation pathways were significantly associated with KDMAgeAccel[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], which likewise plays a key role in the pathogenesis of IBD[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, we aimed to determine whether PhenoAgeAccel and KDMAgeAccel could serve as two potential biomarkers to help identify individuals at increased risk for IBD.\u003c/p\u003e \u003cp\u003eIBD is more likely to occur in individuals with genetic susceptibility, though it can also develop in those without a strong genetic predisposition. Previous GWAS have identified multiple risk alleles associated with IBD, but they confer only a modest increase in disease risk, suggesting that environmental factors play a crucial role in triggering disease phenotypes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Therefore, in this study, we calculated polygenic risk scores (PRS) to assess the relationship between genetic risk, accelerated biological aging, and the development of IBD, aiming to identify individuals at potential risk.\u003c/p\u003e \u003cp\u003eThe aim of this study was to investigate the association between accelerated biological aging and the risk of IBD. Therefore, we first conducted large-scale prospective cohort studies at the UK Biobank and All of Us Research Program (AoU) cohort in order to determine whether KDMAgeAccel and PhenoAgeAccel are a potential risks factor for the development of IBD and its impact. Secondly, we explored the combined roles and interactions of accelerated biological aging and PRS in the development of IBD. In addition, the impact on life expectancy was calculated. Finally, we explored whether biological aging mediates the pathogenic process of common risk factors for IBD.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Research design and population\u003c/h2\u003e\n \u003cp\u003eThe UK Biobank is a population-based cohort of approximately 502,368 participants aged 37\u0026ndash;73 years recruited between 2006 and 2010, with 3 rounds of follow-up visits. During the baseline assessment, participants completed a questionnaire, took physical and functional measurements, and provided biological information[\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. To collect, analyze, and link their data, written consents were obtained from all participants. Ethical approval was obtained for this study as part of the UK Biobank project 83339 (NHS National Research Ethics Service 11/NW/0382, 16/NW/0274, and 21/NW/0157). To make sure the study was valid, people who had IBD at the start (n\u0026thinsp;=\u0026thinsp;3,570), did not provide follow-up data or quit in the middle of the study (n\u0026thinsp;=\u0026thinsp;1,298), or did not have enough information on their biological age were left out (n\u0026thinsp;=\u0026thinsp;158,754 KDMAge; n\u0026thinsp;=\u0026thinsp;91,248 PhenoAge). Ultimately, 338,746 participants were included in the population analyzed for KDMAge and 406,252 in the population analyzed for PhenoAge (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). For the life expectancy analyses in the UK Biobank, those who died within two years of baseline (n\u0026thinsp;=\u0026thinsp;2,504) were excluded to mitigate the potential risk of reverse causation. Therefore, there were 340,449 participants in the analyzed population for KDMAge and 408,258 participants in the analyzed population for PhenoAge.\u003c/p\u003e\n \u003cp\u003eAoU is an ongoing longitudinal cohort study designed to enroll at least 1\u0026nbsp;million participants[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although AoU launched nationally in 2018, it already contains information about decades of data on participants (n\u0026thinsp;=\u0026thinsp;409,420). AoU contains information on body measurements and vital signs collected at enrollment, surveys, EHR, and Fitbit data. Participants who had a Fitbit and agreed to share their Fitbit and EHR data were included in our analysis. We similarly retained 3,982 AoU participants for initial analysis after excluding individuals with IBD prior to biological age measurement and missing data of biological age (\u003cstrong\u003eSupplementary Fig.\u0026nbsp;2\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Biological aging\u003c/h2\u003e\n \u003cp\u003eTo approximate biological age (BA), we combined two methods that have been published and applied to the prediction of disease and mortality: the Klemera-Doubal Method Biological Age[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] (KDM-BA) and the PhenoAge algorithm[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Their measurements are based on clinical biomarkers. KDMAge is calculated based on pulmonary function indicators and blood chemistry parameters, including forced expiratory volume at 1 second, systolic blood pressure, albumin concentration, alkaline phosphatase, blood urea nitrogen, creatinine, C-reactive protein, glycated hemoglobin and total cholesterol. KDMAge is calculated based on the regression modeling of biomarkers of age and represents an individual\u0026apos;s predicted physiological age. PhenoAge was calculated based on five blood chemistry parameters, including albumin concentration, alkaline phosphatase, creatinine, glucose, and C-reactive protein, lymphocyte percentage (i.e., lymphocytes as a proportion of leukocytes), mean cell volume, erythrocyte cellular distribution width, and leukocyte count. PhenoAge was trained based on multivariate analyses of mortality risk[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. We regressed the calculated BA on actual age and calculated residuals to quantify differences in BA between participants[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. These residuals are referred to as \u0026quot;accelerated biological aging\u0026quot; and were used to measure biological aging in our subsequent analyses (as a 3 df natural spline) [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. To examine the rate of biological aging more comprehensively, we performed the study with a continuous variable, biological aging status (defined as biological aging when biological age acceleration is greater than 0), and a quartile variable (classified according to quartiles of biological age acceleration) [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e].Any observations with missing values were excluded from this analysis (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eKDMAge and PhenoAge were calculated using the R package \u0026quot;BioAge\u0026quot; ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/dayoonkwon/BioAge\u003c/span\u003e\u003c/span\u003e )[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Outcome\u003c/h2\u003e\n \u003cp\u003eHealth outcomes for patients in the UK Biobank are primarily available through links to eHealth and are updated regularly. In both the UK Biobank and AoU cohorts, the primary outcome was inflammatory bowel disease (IBD), with data from inpatient and death registry records. Secondary outcomes were subgroups of IBD, including Crohn\u0026apos;s disease (K50) and ulcerative colitis (K51), through the 10th edition of the International Classification of Diseases (ICD-10)[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Covariates\u003c/h2\u003e\n \u003cp\u003ePotential confounders were selected based on an a priori developed acyclic graph (\u003cstrong\u003eSupplementary Fig.\u0026nbsp;1\u003c/strong\u003e). Including age, sex (male, female), ethnicity (white or non-white), educational attainment (based on self-reported highest level of qualification attained, categorized as university or non-university), body mass index (BMI) (continuous), socio-economic status (assessed according to the Townsend Deprivation Index, calculated on the basis of the national census output area prior to the subject\u0026apos;s enrolment in the UK Biobank), smoking status (never, current, previous), alcohol intake (never, current, previous), fruit and vegetable intake, living environment score (calculated from the quality of the surrounding environment inside and outside the individual\u0026apos;s home), exercise, medication consumption (including antibiotic, salicylic acid, immunoregulation, and corticosteroid), disease and surgical history (including depression, anxiety, heart disease, vitamin D deficiency, cancer, and appendicitis surgery)[\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. The above data were obtained from self-reported information and medical records at baseline. Moreover, in order to proxy the genetic propensity to IBD, polygenic risk scores (PRS) were used to approximate the genetic predisposition to IBD. PRS were computed by summing the score of reported risk allele for each SNP based on an additive genetic model linked to IBD[\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. PRS data for IBD were obtained from the UK Biobank or calculated internally for IBD (\u003cstrong\u003eSupplementary methods\u003c/strong\u003e).\u003c/p\u003e\n \u003cp\u003eCovariates were multiply interpolated by chained equations (MICE package in R)[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e] and predictive mean matching methods for missing values of covariates.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Multi-state model analysis\u003c/h2\u003e\n \u003cp\u003eTo assess the association between biological aging and the transition from baseline to IBD development and then to death, multi-state models were employed. These models are valuable for estimating complex longitudinal data in which individuals are allowed to transition between several states, such as health, illness, and death. In the analysis, covariates were adjusted for age, sex, ethnicity, the Townsend Deprivation Index (TDI), education, smoking, alcohol use, BMI, vegetable and fruit intake, environmental factors, exercise, history of antibiotics, salicylic acid, corticosteroids, immunoregulation, cardiopathy, cancer, depression, anxiety, vitamin D deficiency, appendectomy, and genetic risk score for inflammatory bowel disease. Multi-state models allow the estimation of the risk of progressing from one state to another and are performed using the R package \u0026apos;mstate\u0026apos;[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Life expectancy analysis\u003c/h2\u003e\n \u003cp\u003eLife expectancy was calculated requiring the remaining life expectancy to be first estimated as the area under the survival curve up to age 100 years, conditional on survival between age 45 and 100 years (1-year interval), predicting the survival curve for each individual and averaging across individuals[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. We used proportional risk survival analyses to assess the effect of biological aging on the life expectancy of individuals with or without IBD and its subtypes.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e2.7 Mediation analysis\u003c/h2\u003e\n \u003cp\u003eTo evaluate the role of biological aging in the pathological process of IBD under dietary and pharmacological exposures, we performed mediation analyses using KDMAgeAccel and PhenoAgeAccel as mediators. The mediation was analyzed in three steps that should be satisfied: step 1, the association between dietary/pharmacological exposures and the onset of IBD; step 2, the association between IBD and KDMAgeAccel and PhenoAgeAccel; and step 3, the association between dietary/pharmacological exposures and KDMAgeAccel/PhenoAgeAccel. If all three associations are confirmed, causal mediation analyses can be performed using the \u0026quot;mediation\u0026quot; package in R (using 1000 bootstrap iterations). Indirect effects, direct effects, total effects, and mediation proportions were calculated for each mediation model by combining the mediation and outcome models. Based on previous studies, we chose vegetables and fruits as part of dietary exposure, while for drug exposure we chose antibiotics, salicylic acid, corticosteroids and immunomodulatory drugs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e2.8 Statistical analyses\u003c/h2\u003e\n \u003cp\u003eThe baseline characteristics summarize the baseline demographic and clinical characteristics, utilizing the number (percentages) of categorical variables and the means (standard deviation [SD]) of continuous variables[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eCox proportional risk models were used to prospectively analyze the association between KDMAgeAccel and PhenoAgeAccel and events of IBD in the UK Biobank. Further, three models based on confounders: model 1 (including age, sex, ethnicity, education level, BMI, Townsend deprivation index, smoking status, alcohol intake, fruit and vegetable intake, living environment, and physical activity), model 2 (model 1\u0026thinsp;+\u0026thinsp;history of medication), and model 3 (model 2\u0026thinsp;+\u0026thinsp;history of disease and surgery). Dose-response associations related to the risk of incident IBD were evaluated by restricted cubic spline regression[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. To further validate our results, we conducted a validation using the AoU large-scale cohort study. Since the AoU cohort covered multiple races and several different populations, it makes our results more broadly generalizable. We similarly analyzed the association between PhenoAgeAccel and IBD and its subgroups using Cox proportional risk models, adjusting appropriately for age, sex, education, race, smoking, and alcohol consumption.\u003c/p\u003e\n \u003cp\u003eSecondly, subgroup analyses were conducted on KDMAgeAccel and PhenoAgeAccel according to baseline age (\u0026lt;\u0026thinsp;60 or \u0026ge;\u0026thinsp;60 years), sex, BMI (\u0026le;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e or \u0026gt;\u0026thinsp;30 kg/m\u003csup\u003e2\u003c/sup\u003e), ethnicity (white or non-white), smoking status (never, current, previous), alcohol intake (never, current, previous), and medication use stratified by subgroup. Furthermore, interaction terms were included between each categorical factor and accelerated aging status to estimate the likelihood of effect modification[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e].To explore the effect of KDMAgeAccel and PhenoAgeAccel on the risk of IBD in populations with different genetic risks, we performed subgroup analyses based on the corresponding PRS. The genetic risk categories were defined according to a weighted PRS as low (lowest tertile), medium (intermediate tertile), and high (highest tertile)[\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eWe performed sensitivity analyses. Participants with incident IBD within 2 years of baseline were excluded and then subjected to lagged analyses.\u003c/p\u003e\n \u003cp\u003eGeneral data analyses and manipulations were performed using R (version 4.2.3), and survival analyses of life expectancy were performed using Stata (version 16.0).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline Characteristics of the Study Population\u003c/h2\u003e \u003cp\u003eIn the population analyzed for KDMAge, there were 2,871 individuals with IBD with a mean age of 57.1 years, including 1,442 females. Among individuals with accelerated biological aging (n\u0026thinsp;=\u0026thinsp;159,655), 1,536 were patients with IBD, and 158,119 were non-IBD individuals. In the population analyzed for PhenoAge, there were 3,406 individuals with IBD with a mean age of 57.2 years, including 1,736 females. Among individuals with accelerated biological aging (n\u0026thinsp;=\u0026thinsp;97,769), 1,214 were patients with IBD, and 96,555 were individuals without IBD (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Individuals with biological aging were more frequently female, had a higher BMI, a higher Townsend Deprivation Index, and a higher likelihood of smoking and drinking alcohol than participants without biological aging. Additionally, a greater proportion of persons with IBD had taken salicylates, corticosteroids, and immunomodulators.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants of KDMAge and PhenoAge in the UK Biobank.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eKDMAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003ePhenoAge\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-IBD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIBD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNon-IBD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIBD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of participants, n\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e335,875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e402,846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,406\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years), mean (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.4(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57.1(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.5(8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.2(8.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e179,987(53.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,442(50.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e217,440(54.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,736(51.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155,888(46.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,429(49.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e185,406(46.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,670(49.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthnicity, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,956(5.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e128(4.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20,893(5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e153(4.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e318,919(95.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,743(95.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e381,953(94.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,253(95.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTDI, (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.4(3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.2(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.3(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.2(3.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.9(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.9(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.4(4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e27.6(4.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,670(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,027(0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(0.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;24.9kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110,909(33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e893(31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131,464(32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,040(30.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25.0\u0026ndash;29.9kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144,162(42.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,247(43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e172,402(42.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,497(44.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e79,134(23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e716(24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96,953(24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e851(25.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-university\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297,253(88.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,572(89.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e356,321(88.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3,052(89.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38,622(11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e299(10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46,525(11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e354(10.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e185,814(55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,309(45.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e221,294(54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,511(44.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115,790(34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,203(41.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e139,235(34.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,450(42.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34,271(10.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e359(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42,317(10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e445(13.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol intake frequency, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25,608(7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266(9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31,587(7.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e328(9.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37,422(11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e359(12.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45,723(11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e436(12.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e272,845(81.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,246(78.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e325,536(80.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,642(77.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving environment score\u003c/p\u003e \u003cp\u003e(mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.6(15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.65(15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.71(15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.65(15.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal physical activity,\u003c/p\u003e \u003cp\u003eMET-h per week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,663.7(2712.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2,658.7(2731.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2657.9(2715.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2668.3(2707.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrugs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibiotic, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,929(1.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57(2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5159(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74(2.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalicylic acid, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101,893(30.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,008(35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126,147(31.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,232(36.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCorticosteroid, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,232(1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e104(3.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4,070(1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e111(3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmunoregulation, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,522(0.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115(4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2,878(0.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114(3.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiopathy, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24,411(7.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e220(7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30,062(7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e287(8.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCancer, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18,321(5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e171(6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22,467(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e218(6.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,397(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,373(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45(1.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67,131(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e576(20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84,089(20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e712(20.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAppendix, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,531(1.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52(1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5,573(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65(1.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological age acceleration, (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.05(1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.1(1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.7(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.2(4.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological age,\u003c/p\u003e \u003cp\u003enon-accelerated aging, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e177,756(52.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,335(46.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e306,291(76.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2,192(64.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological age,\u003c/p\u003e \u003cp\u003eaccelerated aging, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158,119(47.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,536(53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e96,555(24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,214(35.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*IBD: Inflammatory bowel disease\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e shows the baseline characteristics of AoU participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Risks of Accelerated biological aging and IBD\u003c/h2\u003e \u003cp\u003eThe results of the study showed a positive correlation between biological aging acceleration (either KDMAgeAccel or PhenoAgeAccel) and the risk of IBD. Individuals with accelerated aging had an increased risk of IBD compared with those without accelerated aging (KDMAgeAccel HR 1.22 [95% CI 1.13\u0026ndash;1.32]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; PhenoAgeAccel HR 1.57 [95% CI 1.46\u0026ndash;1.69]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. In addition, the risk of IBD increased by 5% and 6% for each 1-year increase in KDMAgeAccel and PhenoAgeAccel, respectively. In the observed accelerated aging groups, participants in the highest quartile had a significantly increased risk of developing IBD compared to participants in the lowest quartile. Specifically, in the population analyzed for KDMAge, the HR for the risk of developing IBD in the highest quartile of participants was 1.21 (95% CI 1.09\u0026ndash;1.36, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). While, in the population analyzed for PhenoAge, the HR was 1.96 (95% CI 1.76\u0026ndash;2.17, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between accelerated biological aging and risk of IBD.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eKDMAgeAccel\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContinuous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07(1.04\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05(1.03\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.05(1.03\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.91(0.80\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.91(0.81\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.91(0.81\u0026ndash;1.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13(1.00-1.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07(0.85\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.27(1.05\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27(1.14\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.21(1.09\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.21(1.09\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-accelerated aging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAccelerated aging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25(1.16\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.22(1.13\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22(1.13\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhenoAgeAccel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContinuous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07(1.06\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06(1.05\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06(1.05\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.26(1.13\u0026ndash;1.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.25(1.12\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.25(1.12\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.45(1.30\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42(1.28\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.42(1.28\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.11(1.91\u0026ndash;2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96(1.77\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.96(1.76\u0026ndash;2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-accelerated aging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAccelerated aging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.68(1.56\u0026ndash;1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.57(1.46\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.57(1.46\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e1: Model 1 was adjusted for age, sex, ethnicity, the Townsend Deprivation Index (TDI), education, smoking, alcohol, BMI, vegetables, fruit, the environment, and exercise.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e2: Model 2 was adjusted for age, sex, ethnicity, the Townsend Deprivation Index (TDI), education, smoking, alcohol, BMI, vegetables, fruit, the environment, exercise, history of antibiotic, history of salicylic acid, history of corticosteroid, history of immunoregulation.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e3: Model 3 was adjusted for age, sex, ethnicity, the Townsend Deprivation Index (TDI), education, smoking, alcohol, BMI, vegetables, fruit, the environment, exercise, history of antibiotics, history of salicylic acid, history of corticosteroids, history of immunoregulation, history of cardiopathy, history of cancer, history of depression, history of anxiety, history of vitamin D deficiency, history of appendectomy, genetic risk score for inflammatory bowel disease.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*BMI: body mass index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe found similar trends in patients with UC and CD: patients with accelerated aging were at increased risk compared with patients without accelerated aging, both in the population analyzed for KDMAgeAccel and PhenoAgeAccel (\u003cb\u003eSupplementary Tables\u0026nbsp;2\u0026ndash;3\u003c/b\u003e). Compared to individuals without accelerated aging characteristics, those with accelerated aging exhibit a significantly increased risk of CD and UC. Specifically, the analysis based on KDMAgeAccel indicates that individuals with accelerated aging have a 1.30-fold increased risk of CD and a 1.19-fold increased risk of UC. In contrast, the PhenoAgeAccel analysis reveals a 1.93-fold increase in CD risk and a 1.50-fold increase in UC risk. These findings suggest a strong association between accelerated aging and the incidence of CD and UC, indicating that accelerated aging may serve as a potential risk factor for these diseases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 All of Us Research Program (AoU)\u003c/h2\u003e \u003cp\u003eA significant association between PhenoAgeAccel and the occurrence of IBD and its subtypes was found based on the results validated against the AoU database. After completely adjusting for covariates, individuals with accelerated aging had an increased risk of developing IBD (HR 1.57 [95% CI 1.18\u0026ndash;2.09]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) compared with those without accelerated aging (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). In addition, participants in the highest quartile had a significantly increased risk of IBD (PhenoAge HR 2.90 [95% CI 1.93\u0026ndash;4.34]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared with those in the lowest quartile.\u003c/p\u003e \u003cp\u003eSimilarly, the association of PhenoAgeAccel with CD was equally significant, the HR was 1.82 (95% CI 1.26\u0026ndash;2.63, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For UC, the HR for risk of IBD in the highest quartile of participants was 2.22 (95% CI 1.37\u0026ndash;3.59, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The results of UC and CD can be found in \u003cb\u003eSupplementary Tables\u0026nbsp;5\u0026ndash;6\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Results of multi-state model analysis\u003c/h2\u003e \u003cp\u003eThe results of multi-state modeling showed that accelerated biological aging was associated with an increased risk of transition from baseline status to IBD for both populations analyzed for KDMAgeAccel and PhenoAgeAccel. In particular, the risk ratio was 1.24 (95% CI 1.15\u0026ndash;1.34; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for KDMAgeAccel and 1.61 (95% CI 1.49\u0026ndash;1.73; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for PhenoAgeAccel. Moreover, PhenoAgeAccel was also significantly associated with an increased risk from IBD onset to death (HR 1.44 [95% CI 1.17\u0026ndash;1.77]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cb\u003eSupplementary Table\u0026nbsp;7\u0026ndash;8\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Results of life expectancy analysis\u003c/h2\u003e \u003cp\u003eThe results of our life expectancy analyses revealed that people with IBD aged 45 years or older had a shorter life expectancy than those without IBD. At age 45 years, among participants in the UK Biobank, life expectancy was 36.4 years (95% CI 36.1\u0026ndash;36.6) for those with IBD and 37.4 years (95% CI 37.3\u0026ndash;37.4) for those without IBD \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Among patients with IBD, life expectancy in patients without accelerated biological aging was 37.1 years (95% CI 36.5\u0026ndash;37.8; KDMAgeAccel) or 37.4 years (95% CI 36.8\u0026ndash;37.9; PhenoAgeAccel). In contrast, life expectancy for patients with accelerated aging was 35.4 years (95% CI 35.1\u0026ndash;35.8; KDMAgeAccel) or 35.5 years (95% CI 35.4\u0026ndash;35.6; PhenoAgeAccel). Remarkably, at age 45 years, individuals with IBD who were in the top quartile for accelerated aging had a loss of life expectancy ranging from 2.37 years (95% CI 1.41\u0026ndash;3.33; KDMAgeAccel) to 3.02 years (95% CI 2.06\u0026ndash;3.99; PhenoAgeAccel), compared with those in the lowest quartile. Results on the patients with UC and CD life expectancy analyses are shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Results of Mediation analysis\u003c/h2\u003e \u003cp\u003eIn the UK Biobank, we found that cooked vegetables and dried fruits were significantly associated with a significantly decreased risk of IBD (\u003cb\u003eSupplementary Table\u0026nbsp;9\u003c/b\u003e). In addition, we observed that vegetables and dried fruits would significantly slow down biological aging (\u003cb\u003eSupplementary Table\u0026nbsp;10\u003c/b\u003e). The combined analysis showed that the association between dried fruits and IBD could be mediated by biological acceleration of aging, with 8.1% (95% CI 5.3\u0026ndash;24.2%) of the effect mediated by KDMAgeAccel (β = -0.05) and 22.1% (95% CI 18.0-54.2%) mediated by PhenoAgeAccel (β = -0.14). (\u003cb\u003eSupplementary Tables\u0026nbsp;11\u0026ndash;12\u003c/b\u003e). For cooked vegetables, the mediating proportion of KDMAgeAccel was 2.2% (95% CI 1.3\u0026ndash;4.1%) with β = -0.01, and PhenoAgeAccel was 13.0% (95% CI 10.7\u0026ndash;23.6%) with β = -0.08. These results suggest that the association between dried fruits, cooked vegetables, and IBD may be partially mediated by accelerated biological aging, with KDMAgeAccel and PhenoAgeAccel offering potential biomarkers and intervention targets for early warning and treatment of IBD.\u003c/p\u003e \u003cp\u003eBesides, we observed four drugs exhibiting risk factors mediated by accelerated biological aging. Among them, antibiotics (KDMAgeAccel of proportion mediated of 1.3% with β\u0026thinsp;=\u0026thinsp;0.11; PhenoAgeAccel of 13.8% with β\u0026thinsp;=\u0026thinsp;1.00), salicylic acid drugs (KDMAgeAccel of 6.4% with β\u0026thinsp;=\u0026thinsp;0.19; PhenoAgeAccel of 25.8% with β = -0.73), cortisol drugs (KDMAgeAccel of 2.4% with β\u0026thinsp;=\u0026thinsp;0.44; PhenoAgeAccel of 20.0% with β\u0026thinsp;=\u0026thinsp;3.39), and immunomodulators (KDMAgeAccel of 1.9% with β\u0026thinsp;=\u0026thinsp;0.41; PhenoAgeAccel of 20.7% with β\u0026thinsp;=\u0026thinsp;4.96). These findings suggest that accelerated biological aging may mediate the adverse effects of certain drugs, highlighting the potential importance of considering aging processes in the management of IBD treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Subgroup Analysis\u003c/h2\u003e \u003cp\u003eIn subgroup analyses, the association between KDMAgeAccel and IBD risk was stronger in those aged 60 years and older (HR 1.28 [95% CI 1.15\u0026ndash;1.43]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003ePi\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) than in younger participants (\u003cb\u003eSupplementary Fig.\u0026nbsp;3\u0026ndash;4\u003c/b\u003e). Participants with a history of alcohol intake had a higher risk ratio (HR 1.24 [95% CI 1.14\u0026ndash;1.35]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; \u003cem\u003ePi\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030). The results of the analyses in the population analyzed for PhenoAgeAccel also validated these findings. Additionally, subgroup analyses based on the medication history showed significant differences, especially in the population analyzed for PhenoAgeAccel. PhenoAgeAccel may be more likely to increase the risk of IBD in individuals using antibiotics (HR 1.90 [95% CI 1.16\u0026ndash;3.10], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011, \u003cem\u003ePi\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and corticosteroid drugs (HR 1.83 [95% CI 1.22\u0026ndash;2.73], \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003ePi\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The significance of these interactions suggests that medication use may influence the association between PhenoAgeAccel and increased IBD risk. In addition, individuals in the high genetic risk group and with accelerated biological aging had a significantly increased risk of IBD compared with participants without biological aging in the low genetic risk group (KDMAgeAccel HR 1.36 [95% CI 1.20\u0026ndash;1.53]; PhenoAgeAccel HR 1.59 [95% CI 1.41\u0026ndash;1.79]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both). This suggests that KDMAgeAccel and PhenoAgeAccel are independent risk factors for IBD regardless of genetic susceptibility \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. For subgroup analyses on patients with UC and CD, refer to \u003cb\u003eSupplementary Figs.\u0026nbsp;5\u0026ndash;8\u003c/b\u003e. The results of the restriction triple spline analysis are shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;9.\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between biological aging and risk of IBD, stratified by polygenic risk status.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLow genetic risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMedium genetic risk\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHigh genetic risk\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eKDMAgeAccel\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContinuous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01(0.94\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05(1.00-1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.09(1.04\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.84(0.63\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05(0.79\u0026ndash;1.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10(0.84\u0026ndash;1.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05(0.83\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.07(0.85\u0026ndash;1.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.26(1.02\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94(0.77\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.27(1.05\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.32(1.11\u0026ndash;1.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-accelerated aging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAccelerated aging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.19(0.99\u0026ndash;1.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11(0.96\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.36(1.20\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhenoAgeAccel\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContinuous\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.05(1.04\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.05(1.03\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.06(1.05\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.18(0.91\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.26(0.97\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.86(1.45\u0026ndash;2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21(0.99\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.30(1.06\u0026ndash;1.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.55(1.31\u0026ndash;1.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQuartile 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.86(1.45\u0026ndash;2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.77(1.45\u0026ndash;2.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.11(1.77\u0026ndash;2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-accelerated aging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1 (ref)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e..\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAccelerated aging\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.62(1.36\u0026ndash;1.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.47(1.27\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.59(1.41\u0026ndash;1.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*Both models were adjusted for age, sex, ethnicity, the Townsend Deprivation Index (TDI), education, smoking, alcohol, BMI, vegetables, fruit, the environment, exercise, history of antibiotics, history of salicylic acid, history of corticosteroids, history of immunoregulation, history of cardiopathy, history of cancer, history of depression, history of anxiety, history of vitamin D deficiency, history of appendectomy.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*BMI: body mass index\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdditionally, in lagged analyses, the increased risk of IBD remained statistically significant with a 2-year lag (KDMAgeAccel HR 1.19 [95% CI 1.09\u0026ndash;1.29]; PhenoAgeAccel HR 1.48 [95% CI 1.36\u0026ndash;1.64]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for both) to avoid reversal of causality (\u003cb\u003eSupplementary Table\u0026nbsp;13\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWe found that biological aging acceleration was significantly associated with the risk of both IBD and its subtypes in these two prospective cohorts from the UK Biobank and AoU, and that this risk increased progressively with accelerated aging. Notably, genetic susceptibility to IBD synergized with accelerated biological aging to increase the risk of IBD. Life expectancy was significantly lower in IBD patients with accelerated biological aging. Furthermore, KDMAgeAccel and PhenoAgeAccel could mediate the protective effect of cooked vegetables and dried fruits against IBD occurrence. This emphasizes the importance of considering biological aging acceleration in prevention and management strategies for IBD.\u003c/p\u003e \u003cp\u003eOur study presents several novel findings. First, we identify for the first time a link between accelerated biological aging and IBD. Previous studies have shown an association between aging and the development of IBD. Noh JY et al. showed that changes in pro-inflammatory gut flora associated with aging that interact with GHS-R (Growth hormone secretagogue receptor) signaling may increase the risk of IBD in older adults or exacerbate the condition of patients with IBD[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Similar results have been observed in other studies, especially in biomarker studies of biological aging, such as telomere shortening and activation of DNA damage response pathways[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. However, these studies have largely focused on a single biomarker or physiological age[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], which does not fully capture the complexity of aging\u0026rsquo;s role in IBD, Epidemiological investigations have been limited in examining biological aging comprehensively, thus failing to reflect the multifaceted impact of aging on IBD. In contrast, our study overcomes these limitations by using KDMAge and PhenoAge as comprehensive biological age indicators. These biomarkers consider multiple dimensions, including inflammation, immunity, metabolism, and organ homeostasis, offering a more holistic assessment of biological aging. The significance of our finding lies in its ability to integrate these dimensions and better elucidate the association between biological aging and IBD.\u003c/p\u003e \u003cp\u003eSecondly, our study is the first to explore the interaction between biological aging and genetic risk for IBD, aiming to explain the complex association between genetic susceptibility and environmental factors. The results suggest a significant correlation between accelerated biological aging and the development of IBD in populations at high genetic risk. KDMAgeAccel and PhenoAgeAccel can be used as potential predictive biomarkers of IBD and, combined with genetic risk, can identify high-risk individuals who are most likely to be adversely affected in the absence of intervention. Individuals with high PRS and accelerated aging could be targeted for focused interventions that may reduce their risk of IBD through lifestyle modifications and early treatment.\u003c/p\u003e \u003cp\u003eOur study found for the first time that accelerated biological aging significantly increased mortality and shortened survival time in patients with IBD. Accelerated aging may affect the course of IBD and lead to exacerbation of the disease through mechanisms such as chronic inflammation, immune dysfunction, and decreased tissue repair capacity. Aging-associated immune dysregulation and chronic low-grade inflammation may exacerbate the inflammatory response in IBD, and imbalances in the gut microbiota may also be involved. Thus, biological senescence, as a new risk factor, enables the detection of senescence markers and the implementation of targeted interventions that may slow down the aging process and thus improve patient survival.\u003c/p\u003e \u003cp\u003eOur study is the first to demonstrate that accelerated biological aging plays a mediating role in the relationship between dietary habits, particularly vegetable and dried fruit intake, and the risk of IBD. The human gut, with its complex micro-ecosystem and immune functions, is influenced by various dietary factors and medications, which can induce inflammatory responses and modulate immune activity[\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Previous research using the National Health and Nutrition Examination Survey (NHANES) data has shown that flavonoid intake is associated with a lower biological age difference (∆age), suggesting that flavonoids may help slow biological aging[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Fruits and vegetables are the main sources of flavonoids for humans[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. In our analysis of UK Biobank, we found that accelerated biological aging, as measured by KDMAgeAccel and PhenoAgeAccel, mediates approximately 10%-20% of the association between vegetable and dried fruit intake and IBD risk. Specifically, both KDMAgeAccel and PhenoAgeAccel were found to partially explain how higher intake of vegetables and dried fruits may reduce the risk of IBD by slowing biological aging. These findings highlight a potential mechanism through which dietary factors, particularly flavonoid-rich foods, may exert protective effects against IBD by modulating the biological aging process..\u003c/p\u003e \u003cp\u003e \u003cb\u003eStrength and limitation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere are several key strengths in our study. Firstly, utilizing the large sample size of the UK Biobank, we have revealed for the first time how accelerated biological aging affects IBD risk. Secondly, we validated our results using the AoU prospective cohort. The multi-ethnic, multi-age coverage of the AoU cohort makes our results more generalizable. Third, our study used two biological age calculation methods, KDMAge and PhenoAge, which improved the reliability and accuracy of biological aging measurements through mutual validation. Fourth, for the first time, genetic risk factors were considered to explore the interaction between accelerated biological aging and IBD. This provides new insights into understanding the genetic susceptibility to IBD and the complex mechanisms by which it is interlinked with the aging process. Fifth, our use of biological aging assessment is more accessible and more applicable to basic clinical assessment.\u003c/p\u003e \u003cp\u003eHowever, there are some limitations to this study. First, although we adjusted for several confounders, we were unable to completely rule out the potential effects of unmeasured confounders. Second, biological aging is a modifiable process, yet the limited follow-up data in both the UK Biobank and AoU databases constrained our ability to assess changes over time. However, it is important to note that the results were consistent across both cohorts, strengthening the robustness of our findings. Third, while we could not validate KDMAge in the AoU database due to the small sample size of valid data for the FEV1 metric (fewer than 100 participants after complete population exclusion), the validation results for PhenoAgeAccel were highly consistent with those obtained from the UK Biobank, further supporting the reliability of our biological aging measures. Despite these limitations, our study provides compelling evidence linking accelerated biological aging to IBD risk, with findings that are consistent across different populations and analytic methods.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study analyzes two large prospective cohorts from the UK Biobank and AoU, confirming that accelerated biological aging is associated with increased risk of IBD and its subtypes, and reduced life expectancy in IBD patients. Additionally, PhenoAgeAccel significantly increases the risk of progression from onset to death in IBD patients. Combined with genetic risk scores, KDMAge and PhenoAge can be used for IBD risk stratification and provide effective means for early identification, monitoring, and intervention in high-risk groups\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the UK Biobank for the access of data, and this research has been performed under approval. The All of Us (AoU) Research Programme are available on application to All of Us.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKong, C., et al., Ketogenic diet alleviates colitis by reduction of colonic group 3 innate lymphoid cells through altering gut microbiome. Signal Transduct Target Ther, 2021. 6(1): p. 154.\u003c/li\u003e\n \u003cli\u003eHarbord, M., et al., The First European Evidence-based Consensus on Extra-intestinal Manifestations in Inflammatory Bowel Disease. J Crohns Colitis, 2016. 10(3): p. 239-54.\u003c/li\u003e\n \u003cli\u003eArbeev, K.G., et al., \u0026quot;Physiological Dysregulation\u0026quot; as a Promising Measure of Robustness and Resilience in Studies of Aging and a New Indicator of Preclinical Disease. J Gerontol A Biol Sci Med Sci, 2019. 74(4): p. 462-468.\u003c/li\u003e\n \u003cli\u003eLiu, Z., et al., A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study. PLoS Med, 2018. 15(12): p. e1002718.\u003c/li\u003e\n \u003cli\u003eHuang, Z., et al., Dynamics of leukocyte telomere length in adults aged 50 and older: a longitudinal population-based cohort study. Geroscience, 2021. 43(2): p. 645-654.\u003c/li\u003e\n \u003cli\u003eKuo, C.L., et al., Genetic associations for two biological age measures point to distinct aging phenotypes. Aging Cell, 2021. 20(6): p. e13376.\u003c/li\u003e\n \u003cli\u003eLi, K., et al., Polymorphisms of the macrophage inflammatory protein 1 alpha and ApoE genes are associated with ulcerative colitis. Int J Colorectal Dis, 2009. 24(1): p. 13-7.\u003c/li\u003e\n \u003cli\u003eCai, H., et al., Discovery of a dual-acting inhibitor of interleukin-1beta and STATs for the treatment of inflammatory bowel disease. RSC Med Chem, 2024. 15(1): p. 193-206.\u003c/li\u003e\n \u003cli\u003eWang, L., et al., Targeting JAK/STAT signaling pathways in treatment of inflammatory bowel disease. Inflamm Res, 2021. 70(7): p. 753-764.\u003c/li\u003e\n \u003cli\u003eZhang, K., et al., Macrophage polarization in inflammatory bowel disease. Cell Commun Signal, 2023. 21(1): p. 367.\u003c/li\u003e\n \u003cli\u003eZaiatz, B.V., et al., Targeting Immune Cell Metabolism in the Treatment of Inflammatory Bowel Disease. Inflamm Bowel Dis, 2021. 27(10): p. 1684-1693.\u003c/li\u003e\n \u003cli\u003eLin, H., et al., Transcriptome-wide association study of inflammatory biologic age. Aging (Albany NY), 2017. 9(11): p. 2288-2301.\u003c/li\u003e\n \u003cli\u003eZhou, Y., S. Yu and W. Zhang, NOD-like Receptor Signaling Pathway in Gastrointestinal Inflammatory Diseases and Cancers. Int J Mol Sci, 2023. 24(19).\u003c/li\u003e\n \u003cli\u003eMartinez-Torres, R.J. and M. Chamaillard, The Ubiquitin Code of NODs Signaling Pathways in Health and Disease. Front Immunol, 2019. 10: p. 2648.\u003c/li\u003e\n \u003cli\u003eEl, H.J., et al., The Genetics of Inflammatory Bowel Disease. Mol Diagn Ther, 2024. 28(1): p. 27-35.\u003c/li\u003e\n \u003cli\u003eSudlow, C., et al., UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med, 2015. 12(3): p. e1001779.\u003c/li\u003e\n \u003cli\u003eRamirez, A.H., et al., The All of Us Research Program: Data quality, utility, and diversity. Patterns (N Y), 2022. 3(8): p. 100570.\u003c/li\u003e\n \u003cli\u003eParker, D.C., et al., Association of Blood Chemistry Quantifications of Biological Aging With Disability and Mortality in Older Adults. J Gerontol A Biol Sci Med Sci, 2020. 75(9): p. 1671-1679.\u003c/li\u003e\n \u003cli\u003eKlopack, E.T., et al., Lifetime exposure to smoking, epigenetic aging, and morbidity and mortality in older adults. Clin Epigenetics, 2022. 14(1): p. 72.\u003c/li\u003e\n \u003cli\u003eKresovich, J.K., et al., Healthy eating patterns and epigenetic measures of biological age. Am J Clin Nutr, 2022. 115(1): p. 171-179.\u003c/li\u003e\n \u003cli\u003eRamasubramanian, R., et al., Evaluation of T-cell aging-related immune phenotypes in the context of biological aging and multimorbidity in the Health and Retirement Study. Immun Ageing, 2022. 19(1): p. 33.\u003c/li\u003e\n \u003cli\u003eChen, L., et al., Associations between biological ageing and the risk of, genetic susceptibility to, and life expectancy associated with rheumatoid arthritis: a secondary analysis of two observational studies. Lancet Healthy Longev, 2024. 5(1): p. e45-e55.\u003c/li\u003e\n \u003cli\u003eKwon, D. and D.W. Belsky, A toolkit for quantification of biological age from blood chemistry and organ function test data: BioAge. Geroscience, 2021. 43(6): p. 2795-2808.\u003c/li\u003e\n \u003cli\u003eBorra, V., et al., Is dependent cannabis use in adult hospitalizations with inflammatory bowel disease associated with major adverse cardiovascular and cerebrovascular events? Insights from National Inpatient Sample Analysis. Curr Med Res Opin, 2024. 40(4): p. 605-611.\u003c/li\u003e\n \u003cli\u003eTeofani, A., et al., Intestinal Taxa Abundance and Diversity in Inflammatory Bowel Disease Patients: An Analysis including Covariates and Confounders. Nutrients, 2022. 14(2).\u003c/li\u003e\n \u003cli\u003eJones, P.D., et al., Exercise decreases risk of future active disease in patients with inflammatory bowel disease in remission. Inflamm Bowel Dis, 2015. 21(5): p. 1063-71.\u003c/li\u003e\n \u003cli\u003eBollen, L., et al., The Occurrence of Thrombosis in Inflammatory Bowel Disease Is Reflected in the Clot Lysis Profile. Inflamm Bowel Dis, 2015. 21(11): p. 2540-8.\u003c/li\u003e\n \u003cli\u003eVigano, C.A., et al., Alexithymia and Psychopathology in Patients Suffering From Inflammatory Bowel Disease: Arising Differences and Correlations to Tailoring Therapeutic Strategies. Front Psychiatry, 2018. 9: p. 324.\u003c/li\u003e\n \u003cli\u003eArnau-Soler, A., et al., Genome-wide by environment interaction studies of depressive symptoms and psychosocial stress in the UK Biobank and Generation Scotland. Transl Psychiatry, 2019. 9(1): p. 14.\u003c/li\u003e\n \u003cli\u003eCooke, R., F. Eigenbrod and A.E. Bates, Projected losses of global mammal and bird ecological strategies. Nat Commun, 2019. 10(1): p. 2279.\u003c/li\u003e\n \u003cli\u003ePang, Y., et al., The role of lifestyle factors on comorbidity of chronic liver disease and cardiometabolic disease in Chinese population: A prospective cohort study. Lancet Reg Health West Pac, 2022. 28: p. 100564.\u003c/li\u003e\n \u003cli\u003eTan, B., et al., Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study. EClinicalMedicine, 2023. 55: p. 101724.\u003c/li\u003e\n \u003cli\u003eBellettiere, J., et al., Sedentary behavior and cardiovascular disease in older women: The Objective Physical Activity and Cardiovascular Health (OPACH) Study. Circulation, 2019. 139(8): p. 1036-1046.\u003c/li\u003e\n \u003cli\u003eChudy-Onwugaje, K., et al., Age Modifies the Association Between Depressive Symptoms and Adherence to Self-Testing With Telemedicine in Patients With Inflammatory Bowel Disease. Inflamm Bowel Dis, 2018. 24(12): p. 2648-2654.\u003c/li\u003e\n \u003cli\u003eYang, R., et al., Modification effect of ideal cardiovascular health metrics on genetic association with incident heart failure in the China Kadoorie Biobank and the UK Biobank. BMC Med, 2021. 19(1): p. 259.\u003c/li\u003e\n \u003cli\u003eNoh, J.Y., et al., Novel Role of Ghrelin Receptor in Gut Dysbiosis and Experimental Colitis in Aging. Int J Mol Sci, 2022. 23(4).\u003c/li\u003e\n \u003cli\u003eFaye, A.S. and J.F. Colombel, Aging and IBD: A New Challenge for Clinicians and Researchers. Inflamm Bowel Dis, 2022. 28(1): p. 126-132.\u003c/li\u003e\n \u003cli\u003eCinegaglia, N., et al., Shortening telomere is associated with subclinical atherosclerosis biomarker in omnivorous but not in vegetarian healthy men. Aging (Albany NY), 2019. 11(14): p. 5070-5080.\u003c/li\u003e\n \u003cli\u003eLiu, Y., J. Wang and C. Wu, Modulation of Gut Microbiota and Immune System by Probiotics, Pre-biotics, and Post-biotics. Front Nutr, 2021. 8: p. 634897.\u003c/li\u003e\n \u003cli\u003eZhang, M., et al., Interactions between Intestinal Microbiota and Host Immune Response in Inflammatory Bowel Disease. Front Immunol, 2017. 8: p. 942.\u003c/li\u003e\n \u003cli\u003eLiu, Y., J. Wang and C. Wu, Modulation of Gut Microbiota and Immune System by Probiotics, Pre-biotics, and Post-biotics. Front Nutr, 2021. 8: p. 634897.\u003c/li\u003e\n \u003cli\u003eXing, W., et al., Dietary flavonoids intake contributes to delay biological aging process: analysis from NHANES dataset. J Transl Med, 2023. 21(1): p. 492.\u003c/li\u003e\n \u003cli\u003eKhan, J., et al., Dietary Flavonoids: Cardioprotective Potential with Antioxidant Effects and Their Pharmacokinetic, Toxicological and Therapeutic Concerns. Molecules, 2021. 26(13).\u003cstrong\u003e\u003cem\u003e\u003c/em\u003e\u003c/strong\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Accelerated biological aging, Inflammatory bowel disease, Genetic susceptibility, Life expectancy, Multistate modelling","lastPublishedDoi":"10.21203/rs.3.rs-5705746/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5705746/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Inflammatory bowel disease (IBD) is a chronic condition affecting individuals across all age groups. However, the association between IBD and biological aging remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We utilized data from the UK Biobank and the diverse cohort of the All of Us (AoU) Research Programme to investigate the role of biological aging in the development of IBD and its subtypes. Biological age was assessed using the Klemera-Doubal method (KDMAge) and phenotypic biological age (PhenoAge), with KDMAgeAccel and PhenoAgeAccel defined as the residuals of chronological age minus KDMAge and PhenoAge, respectively. We assessed the impact of accelerated biological aging on life expectancy in patients with IBD through survival analysis. Additionally, we examined genetic susceptibility and its potential mediating effects on the association between biological aging and IBD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings: \u003c/strong\u003eIn the UK Biobank, accelerated biological aging was associated with an increased risk of IBD (KDMAgeAccel: HR 1.22, 95% CI 1.13-1.32; PhenoAgeAccel: HR 1.57, 95% CI 1.46-1.69). This association was further validated in the AoU cohort, where PhenoAgeAccel was also linked to an elevated risk of IBD (HR 1.57, 95% CI 1.18-2.09). An additive interaction was observed between accelerated biological aging and genetic risk for IBD. Individuals with both high genetic risk and accelerated aging exhibited the highest risk of developing IBD (KDMAgeAccel: HR 1.36, 95% CI 1.20-1.53; PhenoAgeAccel: HR 1.59, 95% CI 1.41-1.79). Life expectancy analysis indicated that IBD patients with accelerated biological aging experienced a significant reduction in life expectancy, with an average decrease of 1.36 years (KDMAgeAccel) and 1.95 years (PhenoAgeAccel). Mediation analyses suggested that accelerated biological aging partially mediated the protective effects of dried fruit and cooked vegetables on the risk of developing IBD. Results from multistate modelling showed that PhenoAgeAccel was also significantly associated with an increased risk of IBD occurrence to mortality (HR 1.44 [95% CI 1.17-1.77]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInterpretation:\u003c/strong\u003e Biological aging is significantly associated with the risk of IBD and its subtypes, especially in individuals with high genetic susceptibility, and it reduces life expectancy in these patients. Identifying individuals with accelerated biological aging can serve as a marker for the effective prevention and management of IBD.\u003c/p\u003e","manuscriptTitle":"Accelerated biological aging, inflammatory bowel disease, genetic susceptibility and life expectancy: Evidence from UK Biobank and All of Us Cohorts","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-22 11:36:56","doi":"10.21203/rs.3.rs-5705746/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2d7bd929-bbbc-4a39-8887-98dfdb6bd6a3","owner":[],"postedDate":"January 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":43118796,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":43118797,"name":"Health sciences/Biomarkers/Prognostic markers"}],"tags":[],"updatedAt":"2025-04-11T07:55:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-22 11:36:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5705746","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5705746","identity":"rs-5705746","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}