Associations of phenotypic age acceleration, genetic risk, and lifestyle with chronic digestive diseases: A large-scale longitudinal cohort study

Highlights

• (1)Higher PhenoAgeAccel was independently associated with higher risks of several chronic digestive diseases, including IBD, NAFLD, and peptic ulcer.
• (2)Evidence of significant additive interaction between PhenoAgeAccel and genetic susceptibility was observed for diverticulosis, IBD, liver cirrhosis, and chronic pancreatitis.
• (3)A healthy lifestyle was associated with lower risks of most chronic digestive diseases among biologically older participants, except IBD.

Abstract

Background

Phenotypic age acceleration (PhenoAgeAccel) is a promising biological aging metric, but its associations with chronic digestive disease risk are unclear. This study evaluated these associations and assessed modification by genetic risk and lifestyle.

Methods

We analyzed 292,639 UK Biobank participants. PhenoAge and PhenoAgeAccel were calculated using a validated algorithm based on clinical biomarkers. Cox proportional hazards models estimated associations of PhenoAgeAccel, genetic risk, and lifestyle with incident chronic digestive diseases, including interaction and stratified analyses. Variance decomposition quantified contributions of aging, genetics, and lifestyle.

Results

Over a median 13.67-year follow-up, PhenoAgeAccel > 0 (accelerated aging) was independently associated with higher risk of most chronic digestive diseases, notably Crohn’s disease (HR per 5-year increase, 1.36; 95%CI, 1.30–1.42) and liver cirrhosis (HR, 1.35; 95%CI, 1.30–1.40). Significant additive interactions occurred between PhenoAgeAccel and genetic risk for diverticulosis, Crohn’s disease, ulcerative colitis, liver cirrhosis, and chronic pancreatitis; among biologically older individuals at high genetic risk, interaction-attributable excess risk reached 42.8% of total risk. A healthy lifestyle attenuated aging-related risk for all outcomes except Crohn's disease and ulcerative colitis. Variance decomposition revealed disease-specific risk contribution profiles: biological aging contributed most to Crohn’s disease and chronic pancreatitis, genetic risk to diverticulosis and ulcerative colitis, and lifestyle to gastroesophageal reflux disease and nonalcoholic fatty liver.

Conclusions

Higher PhenoAgeAccel was associated with higher risks of chronic digestive diseases, with associations modified by genetic risk and lifestyle. PhenoAgeAccel may be a useful risk marker and warrants further investigation of aging-targeted strategies.

1. Introduction

Chronic digestive diseases include disorders of the gastrointestinal tract, hepatobiliary system, and pancreas. They impose a substantial global health burden, significantly contributing to morbidity, mortality, and healthcare costs. Recent data show that the prevalence of these conditions is rising worldwide, potentially related to population aging, urbanization, and lifestyle changes [1]. Aging is a major risk factor for the onset and progression of digestive diseases, with most conditions showing higher prevalence and mortality in older adults [2,3]. Although chronological age is commonly used as a proxy for aging, it fails to capture heterogeneity among individuals of the same age [4]. However, accumulating evidence suggests that biological aging, which integrates molecular, cellular, and physiological attributes, may better predict age-related diseases [[5], [6], [7]].

A range of biomarkers capturing biological aging have been proposed and validated, including epigenetic, proteomic, metabolomic, telomeric, and routine clinical measures [8,9]. Epigenetic clocks such as the Horvath clock, DNAm PhenoAge, and GrimAge are derived from DNA methylation patterns and capture epigenetic aging, whereas phenotypic clocks such as PhenoAge are based on routinely measured clinical biomarkers and reflect phenotypic aging at the organ and system level. Among these phenotypic measures, phenotypic age acceleration (PhenoAgeAccel)—derived from chronological age and nine clinical biomarkers—reflects multiple hallmarks of aging [10] and is more accessible and computationally straightforward than omics-based biological age metrics, making it a clinically practical tool for assessing individual aging status and intervention effects. Prior studies have shown strong links between PhenoAgeAccel and diseases such as cardiovascular disease, chronic kidney disease, and all-cause mortality in diverse populations [7,11,12]. However, its role in chronic digestive diseases remains unclear. Multi-omics research indicates that genetic risk significantly contributes to many digestive conditions [[13], [14], [15], [16], [17]]. Yet, the combined effects of PhenoAgeAccel and genetic susceptibility on these diseases are not well understood. Furthermore, emerging evidence suggests that PhenoAgeAccel can be influenced by modifiable lifestyle factors [18]. Therefore, it is crucial to determine whether adherence to a healthy lifestyle can mitigate the negative associations of PhenoAgeAccel with chronic digestive diseases to better guide prevention efforts.

In this UK Biobank study, we comprehensively evaluated the associations between PhenoAgeAccel and chronic digestive diseases and assessed whether these associations vary by genetic risk and lifestyle.

2. Methods

2.1. Study population

UK Biobank is a large, prospective, population-based cohort. It enrolled more than 500,000 volunteers in the United Kingdom between 2006 and 2010. Most participants were aged 40–69 years, with an approximately balanced sex distribution. The resource integrates rich, multidimensional data, including genetics, health measurements, lifestyle factors, and environmental exposures, to elucidate the causes and risk mechanisms of chronic diseases. Baseline assessments include detailed questionnaires, physical examinations, and biospecimen collection (e.g., blood and urine). Longitudinal follow-up is conducted through linkage to national health records, such as hospital admissions, cancer registrations, and death records. The study also incorporates imaging and wearable-device monitoring to support comprehensive epidemiologic analyses. All participants provided written informed consent, and UK Biobank has received ethical approval from the North West Multi-centre Research Ethics Committee as a Research Tissue Bank.

2.2. Assessment of PhenoAge and PhenoAgeAccel

We calculated PhenoAge using a validated algorithm described previously [10]. Briefly, PhenoAge is a biomarker-based estimate of biological age derived from nine routinely measured clinical markers (albumin, creatinine, glucose, log-transformed C-reactive protein, lymphocyte percentage, mean corpuscular volume, red cell distribution width, alkaline phosphatase, and white blood cell count) together with chronological age. The algorithm, trained on NHANES III data via a Cox proportional hazards model, derives a 10-year mortality probability from a linear predictor of the biomarkers and then maps it to an age-equivalent value using a Gompertz parameterization. Relative to chronological age, PhenoAge demonstrates superior prognostic performance for mortality and morbidity. PhenoAgeAccel was defined as the residual obtained by regressing PhenoAge on chronological age, representing age-adjusted phenotypic age. PhenoAgeAccel ≥ 0 indicates that biological age exceeds chronological age (biologically older), whereas PhenoAgeAccel < 0 indicates the reverse (biologically younger). The biomarkers used to compute PhenoAge, along with their corresponding UK Biobank Field IDs, are listed in Table S1. Because this algorithm is based on clinical chemistry markers and chronological age, the PhenoAgeAccel measure used in this study should be interpreted as a phenotypic, biomarker-based aging metric and does not directly reflect DNA methylation–based epigenetic aging.

2.3. PRS calculation

Detailed methodologies for genome-wide genotyping, imputation, and quality control in the UK Biobank have been described previously [19,20]. In this study, we constructed polygenic risk scores (PRS) using a weighted approach restricted to participants of European ancestry. We systematically queried the GWAS Catalog and prioritized the largest genome-wide association studies (GWAS) in order to identify single-nucleotide polymorphisms (SNPs) significantly associated with each chronic digestive disease. For each disease, we applied linkage disequilibrium (LD) clumping to select independent variants (r² ≤ 0.01 within a 1000-kb window). Table S2 summarizes the SNPs included in each disease-specific PRS (effect allele, odds ratio, and P value), and provides corresponding source citations from the GWAS Catalog and PubMed-indexed publications. The weighted PRS was calculated as follows. It was then categorized into tertiles: low (low genetic risk), medium (medium genetic risk), and high (high genetic risk).

$$PRS=\sum _{i=1}^N{w}_i\bullet g_i$$

Where N represents the number of genetic variants, ${\text{w}}_{\text{i}}$ denotes the effect size (weight) of the i-th genetic variant, and ${\text{g}}_{\text{i}}$ indicates the genotype of the i-th genetic variant, typically represented using binary encoding (0: non-risk allele; 1: one risk allele; 2: two risk alleles). A higher PRS indicates a higher genetic predisposition to pancreatic cancer.

2.4. Ascertainment of outcomes

The study outcomes comprised various chronic digestive diseases. These included gastroesophageal reflux disease (GERD), dyspepsia, irritable bowel syndrome (IBS), constipation, diverticulosis, gastric and duodenal ulcers, ulcerative colitis (UC), Crohn’s disease (CD), cholecystitis, cholelithiasis, nonalcoholic fatty liver disease (NAFLD), liver cirrhosis, chronic pancreatitis, and pancreatic cyst. Outcomes were identified from inpatient hospital records when listed as a primary or secondary diagnosis and coded according to the International Classification of Diseases, 10th Revision (ICD-10) (Table S3). Dates of first diagnosis were obtained through linkage to cancer and death registries from the Health and Social Care Information Centre (England and Wales) and the National Health Service Central Register (Scotland). For each participant, we followed up from baseline assessment until the earliest of outcome occurrence, administrative censoring (October 31, 2022), loss to follow-up, or death.

2.5. Lifestyle scores calculation

Lifestyle scores were constructed based on the World Cancer Research Fund/American Institute for Cancer Research (WCRF/AICR) lifestyle score. Additionally, we referred to the American Cancer Society (ACS) Guidelines on Nutrition and Physical Activity for Cancer Prevention [[21], [22], [23]]. These frameworks emphasize modifiable determinants, such as diet quality, physical activity, weight management, and alcohol intake. These factors are closely linked to cancer prevention and share mechanistic pathways with broader digestive health. Therefore, they are appropriate for studies of chronic digestive diseases. Following the WCRF/AICR recommendations and considering data availability in UK Biobank, we selected nine components for the lifestyle score: body mass index (BMI), waist circumference (WC), physical activity, sedentary time (including driving, watching television, and computer use), fruit and vegeTable intake, whole-grain intake, red and processed meat intake, alcohol consumption frequency, and smoking (Table S3). Consistent with the guidelines, each component was scored either from 0 to 0.5 points or from 0 to 1 point, with higher scores indicating healthier behaviors. We summed the component scores to calculate an overall lifestyle score ranging from 0 to 6. This score was categorized into tertiles: unhealthy (<3.25), medium (3.25–4), and healthy (>4) [24]. Detailed scoring criteria are provided in Table S4.

2.6. Assessment of covariates

At the UK Biobank baseline, covariates were collected using touchscreen questionnaires, interviews conducted by interviewers, and physical measurements. Based on prior knowledge, we selected potential confounders related to phenotypic age, lifestyle, and chronic digestive diseases. These include chronological age, gender, ethnicity, Townsend deprivation index (TDI), BMI, education level, smoking status, alcohol consumption, regular physical activity, acid-inhibitor use, history of hypertension, history of diabetes, and comorbidities. Comorbidities included heart failure; myocardial infarction; stroke; asthma; renal failure; chronic obstructive pulmonary disease; thyroid disease; anxiety; dementia; and depression. Missing categorical values were coded as “Unknown” while missing continuous values were imputed using sex-specific means.

2.7. Statistical analyses

Baseline characteristics were summarized as mean (SD) for continuous variables and as n (%) for categorical variables. Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of PhenoAgeAccel with incident chronic digestive diseases. PhenoAgeAccel was analyzed per 5-year increment and by category, along with genetic risk and lifestyle factors. Two hierarchically adjusted models were fitted: Model 1 adjusted for chronological age, gender, ethnicity, TDI, BMI, and education level, while Model 2 further adjusted for smoking status, alcohol consumption, regular physical activity, acid-inhibitor use, history of hypertension, history of diabetes, and comorbidities. Analyses of genetic risk also adjusted for the genotyping array and the top ten genetic ancestry principal components. Tests based on Schoenfeld residuals indicated no violation of the proportional hazards assumption. Nonlinearity in the association between PhenoAgeAccel and chronic digestive diseases was evaluated using restricted cubic splines (RCS) with knots at the 10th, 50th, and 90th percentiles, adjusting for all covariates [25].

To assess whether genetic risk or lifestyle modified the association between PhenoAgeAccel and the outcomes, we evaluated interaction on both additive and multiplicative scales [26,27]. On the additive scale, interaction was quantified using the relative excess risk due to interaction (RERI) and the attributable proportion (AP). 95% CIs for RERI and AP were obtained by nonparametric bootstrapping with 1000 resamples; CIs not including 0 indicated significant additive interaction. On the multiplicative scale, we included product terms for genetic risk and PhenoAgeAccel, as well as lifestyle and PhenoAgeAccel, in Cox proportional hazards models. We then assessed significance using likelihood-ratio tests comparing models with and without these interaction terms. 95% CIs for multiplicative interaction effects were derived from the fitted models; CIs not including 1 indicated significant multiplicative interaction.

Furthermore, we conducted stratified analyses to identify subgroups more susceptible to the effects of phenotypic age acceleration. The stratification variables included age (<60 vs ≥60 years), gender (male vs female), obesity (yes vs no), smoking status (current vs former vs never), alcohol consumption (heavy vs moderate vs never), and regular physical activity (yes vs no). To assess robustness, we performed three sensitivity analyses: (1) excluding participants with missing covariate data to compare results with those after imputation; (2) excluding participants who developed the outcome within the first year after baseline to reduce reverse causation; and (3) additionally adjusting Model 2 for baseline use of other potentially relevant medications, including non-steroidal anti-inflammatory drugs (NSAIDs), antibiotics, and anti-diabetic drugs.

For each digestive disease, we fitted multivariable Cox models that included PhenoAgeAccel, PRS, and the lifestyle score. Within each model, we quantified the relative importance of each variable using analysis of variance (ANOVA) based on the Wald χ² statistic, expressing it as the proportion of the variable-specific χ² to the total model χ², following established approaches for assessing the importance of covariates in regression models [28].

Two-sided P values <0.05 were considered statistically significant. All analyses were conducted in R (version 4.2.1).

3. Results

3.1. Participants

This longitudinal cohort study excluded participants with cancer or prevalent digestive disease at baseline. We also excluded those with missing data on phenotypic age, lifestyle, or genetic risk, as well as those who had withdrawn. Finally, a total of 292,639 participants were included in the primary analysis (Fig. S1).

Over a median follow-up of 13.67 years (IQR, 12.90–14.39), 7142 incident cases were ascertained on average per disease. Baseline characteristics are shown in Table S5. Participants were predominantly White (95.2%), with 51.1% males and a mean age of 55.86 years. Participants who were biologically older had a slightly lower chronological age compared to biologically younger participants (55.81 ± 8.28 vs. 55.90 ± 7.98 years), but their mean PhenoAgeAccel values differed by about 7.89 years (4.35 ± 4.79 vs.−3.54 ± 2.49). Biologically younger participants were more likely to be female, have higher educational attainment, experience lower socioeconomic deprivation, have lower BMI, adhere to healthier lifestyles, and have fewer baseline comorbidities.

3.2. Biological aging and risk of incident chronic digestive diseases over follow-up

Participants suffering from chronic digestive diseases showed higher PhenoAgeAccel than those without (Fig. S2). In Model 1, which controlled for core covariates, PhenoAgeAccel showed significant associations with GERD, IBS, constipation, diverticulosis, gastric ulcer, duodenal ulcer, UC, CD, cholecystitis, cholelithiasis, NAFLD, liver cirrhosis, chronic pancreatitis, and pancreatic cysts. After additional adjustment for potential confounders (Model 2), associations with IBS and pancreatic cysts were no longer significant. However, associations with the other diseases remained significant (all P < 0.001; Table 1). Biological aging had the strongest associations with CD and liver cirrhosis. For every 5-year increase in PhenoAgeAccel, the risks of CD and liver cirrhosis rose by 36% and 35%, respectively; biologically older individuals also faced 2.05-fold and 2.22-fold higher risks compared to younger counterparts. RCS analyses showed significant nonlinearity for all outcomes except GERD, gastric ulcer, and duodenal ulcer (P for nonlinearity <0.05). Importantly, risks of constipation, UC, CD, and liver cirrhosis increased sharply in individuals with marked aging acceleration (Fig. 1). Cumulative incidence curves showed similar patterns (Fig. S3).

Table 1.

Association of PhenoAgeAccel (per 5-year increase and category) with the risk of chronic digestive diseases in different models.

	PhenoAgeAccel	Cases/Total	Model 1 HR (95% CI)	P value	Model 2 HR (95% CI)	P value
GERD	Per 5-year increase	25,126/292,639	1.08(1.07,1.10)	<0.0001	1.03(1.02,1.05)	<0.0001
	Biologically older	12,210/128,002	1.15(1.12,1.18)	<0.0001	1.06(1.04,1.09)	<0.0001
Gastrointestinal dysfunction
Dyspepsia	Per 5-year increase	5408/292,639	1.01(0.99,1.04)	0.40	0.98(0.96,1.01)	0.20
	Biologically older	2345/128,002	1.05(0.99,1.11)	0.11	1.00(0.94,1.06)	1.00
IBS	Per 5-year increase	5121/292,639	1.05(1.02,1.07)	<0.001	1.00(0.97,1.03)	0.99
	Biologically older	2195/128,002	1.05(0.99,1.11)	0.09	0.98(0.92,1.04)	0.52
Constipation	Per 5-year increase	14,290/292,639	1.15(1.13,1.17)	<0.0001	1.09(1.08,1.11)	<0.0001
	Biologically older	6995/128,002	1.20(1.16,1.24)	<0.0001	1.11(1.07,1.15)	<0.0001
Diverticulosis	Per 5-year increase	32,352/292,639	1.08(1.07,1.09)	<0.0001	1.06(1.04,1.07)	<0.0001
	Biologically older	15,755/128,002	1.16(1.13,1.19)	<0.0001	1.11(1.08,1.14)	<0.0001
Peptic Ulcer Diseases
Gastric ulcer	Per 5-year increase	3368/292,639	1.18(1.14,1.21)	<0.0001	1.12(1.09,1.15)	<0.0001
	Biologically older	1781/128,002	1.29(1.20,1.38)	<0.0001	1.18(1.10,1.27)	<0.0001
Duodenal ulcer	Per 5-year increase	1872/292,639	1.22(1.17,1.26)	<0.0001	1.15(1.10,1.19)	<0.0001
	Biologically older	1052/128,002	1.39(1.26,1.53)	<0.0001	1.24(1.12,1.37)	<0.0001
Inflammatory bowel diseases
UC	Per 5-year increase	1611/292,639	1.27(1.23,1.31)	<0.0001	1.26(1.21,1.31)	<0.0001
	Biologically older	929/128,002	1.73(1.56,1.91)	<0.0001	1.66(1.49,1.85)	<0.0001
CD	Per 5-year increase	819/292,639	1.37(1.31,1.42)	<0.0001	1.36(1.30,1.42)	<0.0001
	Biologically older	507/128,002	2.15(1.86,2.49)	<0.0001	2.05(1.76,2.39)	<0.001
Gallbladder diseases
Cholecystitis	Per 5-year increase	1821/292,639	1.14(1.10,1.18)	<0.0001	1.10(1.06,1.15)	<0.0001
	Biologically older	1014/128,002	1.32(1.20,1.45)	<0.0001	1.25(1.13,1.38)	<0.0001
Cholelithiasis	Per 5-year increase	9750/292,639	1.13(1.11,1.15)	<0.0001	1.09(1.07,1.11)	<0.0001
	Biologically older	5221/128,002	1.30(1.25,1.36)	<0.0001	1.23(1.18,1.29)	<0.0001
Liver diseases
NAFLD	Per 5-year increase	3688/292,639	1.20(1.18,1.23)	<0.0001	1.10(1.07,1.13)	<0.0001
	Biologically older	2246/128,002	1.42(1.32,1.52)	<0.0001	1.25(1.16,1.34)	<0.0001
Liver cirrhosis	Per 5-year increase	1056/292,639	1.47(1.42,1.51)	<0.0001	1.35(1.30,1.40)	<0.0001
	Biologically older	780/128,002	2.64(2.29,3.05)	<0.0001	2.22(1.91,2.58)	<0.0001
Pancreatic diseases
Chronic pancreatitis	Per 5-year increase	342/292,639	1.38(1.30,1.46)	<0.0001	1.23(1.15,1.33)	<0.0001
	Biologically older	226/128,002	2.25(1.78,2.84)	<0.0001	1.90(1.48,2.43)	<0.0001
Pancreatic cyst	Per 5-year increase	518/292,639	1.12(1.04,1.21)	0.005	1.04(0.96,1.13)	0.36
	Biologically older	243/128,002	1.15(0.96,1.38)	0.12	1.07(0.89,1.29)	0.46

Model 1, adjusted for age, gender, ethnicity, Townsend deprivation index, body mass index and education level.

Model 2, additionally adjusted for smoking status, drinking status, regular physical activity, acid inhibitor use, history of hypertension, history of diabetes and comorbidities.

Biologically older groups were all compared against biologically younger as the control.

PhenoAgeAccel, phenotypic age acceleration; HR, hazard ratio; CI, confidence interval; GERD, gastroesophageal reflux disease; IBS, irriTable bowel syndrome; UC, ulcerative colitis; CD, Crohn’s disease; NAFLD, non-alcohol fatty liver disease.

Fig. 1.

Dose-response relationships of phenotypic age acceleration (PhenoAgeAccel) with the risk of chronic digestive diseases. A) gastroesophageal reflux disease (GERD); B) constipation; C) diverticulosis; D) gastric ulcer; E) duodenal ulcer; F) ulcerative colitis (UC); G) Crohn’s disease (CD); H) cholecystitis; I) cholelithiasis; J) non-alcohol fatty liver disease (NAFLD); K) liver cirrhosis; L) chronic pancreatitis. The blue solid lines represent hazard ratio curves, with shaded blue bands indicating 95% confidence intervals. Extreme outliers were addressed by trimming the top and bottom 2.5% of observations. Splines were adjusted for age, gender, ethnicity, Townsend deprivation index, body mass index, education level, smoking status, drinking status, regular physical activity, acid inhibitor use, history of hypertension, history of diabetes, comorbidities, genotyping batch and the first 10 genetic principal components. HR, hazard ratio; CI, confidence interval.

Stratified analyses indicated that the association between PhenoAgeAccel and GERD risk was stronger among participants aged ≥60 years and those without obesity. The association with constipation was stronger in men, current and previous smokers, and individuals with irregular physical activity. The association with diverticulosis was stronger in men, while the association with chronic pancreatitis was stronger among heavy drinkers. In addition, compared with participants with obesity, participants without obesity showed stronger associations of PhenoAgeAccel with gastric ulcer, UC, CD, and NAFLD. By contrast, the risks of duodenal ulcer, cholecystitis, cholelithiasis, and liver cirrhosis did not differ significantly across subgroups (Table S6).

The conclusions were robust in sensitivity analyses that excluded participants with missing covariates (Table S7) and those with incident chronic digestive diseases within the first year after recruitment (Table S8). Similar results were observed when we further adjusted Model 2 for baseline use of NSAIDs, antibiotics, and anti-diabetic drugs (Table S9).

3.3. Joint effect of genetic risk with PhenoAgeAccel

In fully adjusted models (Table S10), the PRS showed significant positive associations with the incidence of the corresponding diseases. These associations were observed when PRS was modeled either continuously or by tertiles. However, the effect sizes were comparatively modest for constipation, gastric ulcer, and chronic pancreatitis. Fig. 2 shows the combined effects of PhenoAgeAccel and PRS. Compared with participants having low PRS and younger biological age, those with high PRS and older biological age showed elevated risks across outcomes—GERD (HR 1.20, 95% CI 1.15–1.25), constipation (HR 1.09, 95% CI 1.00–1.19), diverticulosis (HR 1.81, 95% CI 1.74–1.89), gastric ulcer (HR 1.31, 95% CI 1.16–1.47), duodenal ulcer (HR 1.70, 95% CI 1.43–2.02), UC (HR 2.98, 95% CI 2.47–3.60), CD (HR 4.06, 95% CI 3.07–5.37), cholecystitis (HR 1.98, 95% CI 1.67–2.36), cholelithiasis (HR 1.97, 95% CI 1.83–2.13), NAFLD (HR 2.01, 95% CI 1.77–2.28), liver cirrhosis (HR 6.04, 95% CI 4.48–8.15), and chronic pancreatitis (HR 2.06, 95% CI 1.40–3.05). All outcomes, except constipation, demonstrated significant linear trends (Table S11; P-trend <0.05).

Fig. 2.

Joint effects of phenotypic age acceleration (PhenoAgeAccel) and polygenic risk score (PRS) with the risk of chronic digestive diseases. A) gastroesophageal reflux disease (GERD); B) constipation; C) diverticulosis; D) gastric ulcer; E) duodenal ulcer; F) ulcerative colitis (UC); G) Crohn’s disease (CD); H) cholecystitis; I) cholelithiasis; J) non-alcohol fatty liver disease (NAFLD); K) liver cirrhosis; L) chronic pancreatitis. PRS was categorized into low (lowest tertiles), medium (second tertiles) and high (highest tertiles) groups according to the distributions. PhenoAgeAccel was categorized into biologically younger (PhenoAgeAccel<0) and biologically older (PhenoAgeAccel≥0). Multivariable model was adjusted for age, gender, ethnicity, Townsend deprivation index, body mass index, education level, smoking status, drinking status, regular physical activity, acid inhibitor use, history of hypertension, history of diabetes, comorbidities, genotyping batch and the first 10 genetic principal components. HR, hazard ratio; CI, confidence interval.

In interaction analyses, significant additive interactions were observed between PhenoAgeAccel and PRS for several diseases. These include diverticulosis, UC, CD, liver cirrhosis, and chronic pancreatitis (Table S12). Compared to participants with low PRS and younger biological age, those with high PRS and older biological age had RERI (95% CI) values as follows: diverticulosis, 0.071 (0.01–0.14); UC, 0.558 (0.11–0.99); CD, 0.779 (0.02–1.55); liver cirrhosis, 2.039 (1.13–2.94); and chronic pancreatitis, 0.776 (0.18–1.36). The corresponding AP (95% CI) was 0.039 (0.01–0.07), 0.184 (0.04–0.32), 0.190 (0.01–0.37), 0.343 (0.22–0.47), and 0.428 (0.04–0.81), indicating that 3.9%, 18.4%, 19.0%, 34.3%, and 42.8% of the total risk, respectively, was attributable to additive interaction. However, no consistent multiplicative interactions were observed (Table S13).

3.4. Joint effect of lifestyle with PhenoAgeAccel

We derived a lifestyle score and categorized it into tertiles (unhealthy, medium, healthy) to assess whether a healthy lifestyle reduces the excess risk of being biologically older. In multivariable-adjusted models (Table S14), maintaining a healthy lifestyle was linked to significantly reduced risks of chronic digestive diseases relative to an unhealthy lifestyle. Fig. 3 illustrates the joint effects of PhenoAgeAccel and lifestyle. Compared with the biologically older and unhealthy lifestyle participants, the biologically younger and healthy lifestyle participants had reduced risks across outcomes: GERD (0.72, 95% CI 0.69–0.75), constipation (0.76, 95% CI 0.72–0.80), diverticulosis (0.56, 95% CI 0.54–0.58), gastric ulcer (0.62, 95% CI 0.55–0.70), duodenal ulcer (0.59, 95% CI 0.50–0.70), UC (0.48, 95% CI 0.40–0.58), CD (0.32, 95% CI 0.24–0.42), cholecystitis (0.42, 95% CI 0.36–0.50), cholelithiasis (0.43, 95% CI 0.40–0.46), NAFLD (0.28, 95% CI 0.24–0.32), liver cirrhosis (0.20, 95% CI 0.15–0.26), and chronic pancreatitis (0.33, 95% CI 0.21–0.50). Linear trends were significant for all outcomes (Table S15; P-trend <0.05). However, among biologically older participants, a healthy lifestyle did not significantly reduce the risk of UC or CD.

Fig. 3.

Joint effects of phenotypic age acceleration (PhenoAgeAccel) and lifestyle scores with the risk of chronic digestive diseases. A) gastroesophageal reflux disease (GERD); B) constipation; C) diverticulosis; D) gastric ulcer; E) duodenal ulcer; F) ulcerative colitis (UC); G) Crohn’s disease (CD); H) cholecystitis; I) cholelithiasis; J) non-alcohol fatty liver disease (NAFLD); K) liver cirrhosis; L) chronic pancreatitis. Lifestyle scores were categorized into unhealthy (lowest tertiles), medium (second tertiles) and healthy (highest tertiles) groups according to the distributions. PhenoAgeAccel was categorized into biologically younger (PhenoAgeAccel<0) and biologically older (PhenoAgeAccel≥0). Multivariable model was adjusted for age, gender, ethnicity, Townsend deprivation index, education level, acid inhibitor use, history of hypertension, history of diabetes and comorbidities. HR, hazard ratio; CI, confidence interval.

In interaction analyses, significant additive-scale interactions between PhenoAgeAccel and lifestyle were detected for constipation, diverticulosis, cholecystitis, cholelithiasis, NAFLD, liver cirrhosis, and chronic pancreatitis (Table S16). The corresponding AP due to interaction was 12.2%, 5.2%, 27.6%, 11.9%, 14.1%, 36.5%, and 30.5%, respectively. We also identified significant multiplicative interactions for constipation and cholecystitis (Table S17).

3.5. Relative importance of PhenoAgeAccel, PRS and lifestyle scores to chronic digestive diseases

Fig. 4 shows the relative proportions of PhenoAgeAccel, PRS, and lifestyle scores contributing to the total χ² variance in chronic digestive diseases. Notably, PhenoAgeAccel was the dominant contributor for both CD and chronic pancreatitis, accounting for more than 50% of the total χ² in each disease. By contrast, PRS contributed most to diverticulosis and UC, which indicates strong genetic influences on these diseases. Lifestyle contributed most to GERD (>60%), followed by NAFLD, cholecystitis, and gastric ulcer, while its contribution to CD and UC was minimal (Table S18).

Fig. 4.

Relative importance of phenotypic age acceleration (PhenoAgeAccel), polygenic risk score (PRS) and lifestyle scores contributions to variance in chronic digestive diseases. Variable importance was quantified using Wald tests; each variable’s importance is expressed as its Wald χ² divided by the model’s total χ² (sum = 100%). GERD, gastroesophageal reflux disease; UC, ulcerative colitis; CD, Crohn’s disease; NAFLD, nonalcoholic fatty liver disease.

4. Discussion

This large prospective cohort study included 292,639 UK Biobank participants. We investigated the associations of PhenoAgeAccel, PRS, and lifestyle factors with the incidence of 15 chronic digestive diseases during a median follow-up of 13.67 years. Higher PhenoAgeAccel was independently associated with higher risk of most outcomes, with the strongest associations for CD (HR 1.36 per 5-year increase) and liver cirrhosis (HR 1.35 per 5-year increase). Higher genetic susceptibility, an unhealthy lifestyle, and higher PhenoAgeAccel were jointly associated with higher risks of several digestive diseases, with evidence of additive interaction for selected outcomes. In contrast, the PhenoAgeAccel–risk association was weaker among participants with a healthy lifestyle, particularly for NAFLD, liver cirrhosis, cholecystitis, and chronic pancreatitis. Variance decomposition based on model χ² statistics suggested that PhenoAgeAccel accounted for the largest share of explained variation for Crohn’s disease and chronic pancreatitis (>50%), PRS for diverticular disease and ulcerative colitis, and lifestyle for GERD. These findings highlight heterogeneity in the relative importance of biological aging, genetics, and lifestyle in digestive disease risk and support the potential utility of biological age metrics for risk stratification. To our knowledge, this is the first study to systematically evaluate associations between PhenoAgeAccel and incident chronic digestive diseases.

PhenoAge is calculated from nine routine clinical chemistry biomarkers and integrates information related to multisystem physiological status, including hepatic and renal function, immune and inflammatory profiles, and glucose metabolism. Compared with chronological age or single risk factors, PhenoAge may better capture heterogeneity in physiological aging and subclinical dysregulation. Accordingly, prior studies have reported that PhenoAge predicts incident disease and mortality across diverse conditions [7]. Consistent with this literature, large-scale UK Biobank analyses have also reported that accelerated biological age (including PhenoAge-based metrics) is associated with higher risks of multiple age-related diseases and all-cause mortality [10,29].

Biological aging is a complex process shaped by genetic and environmental factors. Accordingly, multiple aging biomarkers have been proposed, including DNA methylation [30], proteomic signatures [5], metabolomic profiles [31] and telomere length [32]. A large UK methylation study reported marked epigenetic age acceleration in patients with CD (GrimAge +2 years) compared with controls [33]. Liu et al. [34] used shorter leukocyte telomere length as an indicator of aging and demonstrated its association with increased risks of several digestive diseases. Collectively, these findings suggest that biological aging markers are associated with chronic digestive disease risk. However, many biomarkers require complex omics assays, highlighting the need for simple and clinically practical measures of physiological age. In the present study, we calculated PhenoAgeAccel as the residual from regressing PhenoAge on chronological age and found that higher PhenoAgeAccel was associated with higher risks of multiple chronic digestive diseases. This clinically accessible metric may facilitate population risk stratification and support future studies evaluating interventions targeting biological aging.

Several interrelated biological pathways may help explain the observed associations between PhenoAgeAccel and incident chronic digestive diseases. Chronic low-grade inflammation (“inflammaging”) is a hallmark of aging and has been linked to a pro-inflammatory secretome from senescent cells [[35], [36], [37]]. In the gut, this inflammatory milieu may compromise epithelial barrier integrity, promote translocation of microbial products, and contribute to mucosal immune dysregulation—processes implicated in inflammatory bowel disease and in the progression of NAFLD and liver cirrhosis [[38], [39], [40], [41], [42]]. Immune senescence may also play a role [43]. Thymic involution, reduced naïve T-cell output, and impaired regulatory T-cell function may weaken mucosal tolerance and antigen-specific responses, potentially predisposing to persistent inflammation and greater susceptibility to chronic intestinal diseases such as CD and UC [[44], [45], [46]].

At the molecular level, age-related changes in NAD+/sirtuin signaling, chronic oxidative stress, and impaired autophagy promote cellular senescence, hepatocyte apoptosis, and tissue fibrosis, increasing vulnerability to diseases such as cirrhosis and chronic pancreatitis [[47], [48], [49]]. Emerging data additionally point to the gut microbiome as a critical mediator. Aging is associated with dysbiosis—characterized by reduced microbial diversity and loss of beneficial taxa—which can exacerbate local and systemic inflammation, modify bile acid profiles, and impair mucosal barrier function [[50], [51], [52]]. Microbial metabolite alterations with age further weaken immune regulation [53]. The interplay of these biological mechanisms underlies the robust associations observed.

In this study, we observed evidence of additive-scale interaction between PhenoAgeAccel and genetic susceptibility (PRS) for several outcomes, including diverticulosis, UC, CD, liver cirrhosis, and chronic pancreatitis, suggesting that the joint association exceeded the sum of the individual associations on the additive scale. These findings may help identify subgroups at higher risk and motivate future studies to evaluate targeted prevention strategies. We also found evidence of additive-scale interaction between PhenoAgeAccel and lifestyle for constipation, diverticulosis, cholecystitis, cholelithiasis, NAFLD, liver cirrhosis, and chronic pancreatitis, with the attributable proportion due to interaction up to 36.5%, suggesting stronger PhenoAgeAccel–outcome associations in the unhealthy lifestyle group. In joint analyses, participants with biologically younger profiles and healthier lifestyles had substantially lower risks of several outcomes (e.g., liver cirrhosis HR ≈ 0.20 vs. the biologically older/unhealthy group). Prior studies, including trials and prospective cohorts, have reported associations between healthier behaviors (diet, physical activity, avoidance of tobacco and excess alcohol) and slower biological aging as well as lower risk of age-related conditions [[54], [55], [56]]. Mechanistically, overweight and sedentary behavior may contribute to oxidative stress and systemic inflammation—pathways implicated in accelerated biological aging [57,58].

Variance analyses revealed substantial heterogeneity in the relative importance of phenotypic aging, genetic risk, and lifestyle across digestive diseases, which may help contextualize disease-specific risk profiles. PhenoAgeAccel accounted for the largest share of explained variation for Crohn’s disease and chronic pancreatitis—conditions characterized by chronic inflammation, immune dysregulation, and fibrotic processes that overlap with features of tissue senescence [59,60]. In contrast, genetic risk accounted for a greater proportion of explained variation in diverticulosis and ulcerative colitis, consistent with their high heritability reported in twin and genome-wide association studies [61,62]. Lifestyle factors accounted for the largest share for GERD and NAFLD, aligning with established associations of diet, obesity, and physical activity with these conditions [[63], [64], [65]]. These patterns are consistent with current clinical management guidelines that emphasize lifestyle modification in GERD and NAFLD care [66]. Evidence from intervention and observational studies has further shown that healthier dietary patterns and regular physical activity are associated with favorable changes in gut microbiota composition, inflammatory markers, and epigenetic aging measures [[67], [68], [69]], supporting the biological plausibility of lifestyle-related modification of aging-associated digestive disease risk.

This study has several notable strengths. First, this study has a large-scale prospective design with a long follow-up period. It includes nearly 300,000 participants from the UK Biobank, providing substantial statistical power. This allows a robust assessment of both main effects and interactions between biological aging, genetic, and lifestyle factors. Second, the use of validated PRS and comprehensive lifestyle indices enables a precise evaluation of both genetic risks and environmental risk factors. Third, we adjusted our analyses for a broad range of potential confounders, including detailed sociodemographic and clinical characteristics. We also conducted multiple sensitivity analyses, which confirmed the robustness of our findings.

Several limitations warrant consideration. First, the observational nature of this study precludes causal inference. Although we attempted to reduce reverse causation, it cannot be completely ruled out. Second, the predominantly White European cohort limits generalizability to other populations. Third, baseline-only assessments of lifestyle and covariates may not capture changes over follow-up, potentially leading to underestimation of associations. In addition, residual confounding from unmeasured factors, such as detailed dietary patterns or gut microbiome profiles, remains possible. Future studies should validate these findings in more diverse populations and further explore underlying biological pathways using multi-omics approaches. Randomized or interventional studies will be needed to determine whether modifying biological aging trajectories influences the risk of digestive diseases.

5. Conclusion

Higher PhenoAgeAccel was independently associated with higher risks of several chronic digestive diseases. These associations varied by genetic susceptibility and lifestyle, with weaker associations observed among individuals with healthier lifestyles. Variance analyses revealed heterogeneity in the relative importance of biological aging, genetic risk, and lifestyle across diseases. Incorporating measures of biological aging into risk assessment models may help improve stratification of digestive disease risk.

CRediT authorship contribution statement

S.X., Y.X.L. and Y.L.L. conceptualized and designed the study, S.X. performed the data analysis, and S.X. and Y.X.L. drafted the manuscript. S.X. and Y.L.L. contributed to the interpretation of data. Y.X.D. and X.C. contributed to the revision of the manuscript and approved the final draft. Y.X.D. and S.Z.X. obtained funding for the study. Y.X.D. and X.C. were involved in study supervision. All authors contributed to the intellectual content and critical revisions to the drafts of the paper and approved the final version.

Ethics approval and consent to participate

All participants provided written informed consent, and UK Biobank has received ethical approval from the North West Multi-centre Research Ethics Committee as a Research Tissue Bank.

Consent for publication

Not applicable.

Declaration of generative AI and AI-assisted technologies in the writing process

Not applicable.

Funding

This study was sponsored by Beijing Physician Scientist Training Project (BJPSTP-2024-11), Beijing Natural Science Foundation (7242117), National Natural Science Foundation (62573428) and Project of the Health Commission of Shanxi Province (2023079).

Data availability

The datasets supporting this study were sourced from UK Biobank, but access is restricted due to licensing agreements. While the data are not publicly accessible, they can be obtained from the authors upon justified request and with the approval of UK Biobank. For further details, please visit https://www.ukbiobank.ac.uk/.

Declaration of competing interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Appendix A. Supplementary data

The following is Supplementary data to this article: