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. 2025 Aug 11;25:576. doi: 10.1186/s12876-025-04165-7

Long-term risk of irritable bowel syndrome associated with MASLD, MASLD type and different cardiometabolic risk factors: a large-scale prospective cohort study

Yesheng Zhou 1, Zhirong Yang 2,3, Si Liu 1, Sian Xie 1, Qian Zhang 1, Shutian Zhang 1, Shengtao Zhu 1,✉,#, Shanshan Wu 1,✉,#
PMCID: PMC12337495  PMID: 40790559

Abstract

Background

Despite the increased irritable bowel syndrome (IBS) risk associated with hepatic steatosis demonstrated in prior evidence, it is still unclear whether the newly coined metabolic dysfunction-associated steatotic liver disease (MASLD), could in reverse impact IBS development. We prospectively assessed the association of MASLD, MASLD type and different cardiometabolic risk factors (CMRFs) with incident IBS in a nationwide population-based cohort.

Methods

Participants free of IBS at baseline in UK Biobank were included (N = 380,619). MASLD, MASLD type [pure MASLD, MASLD with increased alcohol intake (MetALD)] and CMRFs were defined based on the new criteria in America and Europe. Cox proportional hazard model was used to assess the associated risk of incident IBS.

Results

Overall, 143,857 (37.8%) had MASLD at baseline. During a median 13.2-year follow-up, 7329 incident IBS cases were identified. Compared with normal individuals, MASLD patients had an 11% elevated risk of IBS (HR = 1.11, 95%CI: 1.04–1.20). The increased risk was present in both pure MASLD (HR = 1.12, 1.03–1.21) and MetALD (HR = 1.26, 1.09–1.45) patients. Moreover, a substantially greater risk of IBS was observed as the number of CMRFs increased in MASLD patients (Ptrend < 0.001), with 16% and 30% higher risk in MASLD with 3 CMRFs (HR = 1.16, 1.06–1.27) and ≥ 4 CMRFs (HR = 1.30, 1.17–1.43) patients. Additionally, risk of IBS was significantly higher among MASLD patients with a certain CMRF [overweight/obesity (HR = 1.14, 1.05–1.23), dysglycemia (HR = 1.15, 1.05–1.27) and dyslipidemia (HR = 1.18, 1.09–1.28)] versus normal individuals, respectively. Further sensitivity analysis and subgroup analysis indicated similar results.

Conclusions

MASLD, either pure MASLD or MetALD, was associated with an increased risk of incident IBS, with greater risk with more cardiometabolic risk factors, suggesting management of MASLD may help prevent IBS.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12876-025-04165-7.

Keywords: Metabolic dysfunction-associated steatotic liver disease, Irritable bowel syndrome, Cohort studies

Introduction

Irritable bowel syndrome (IBS) is one of the most common disorders of gut-brain interaction, characterized by recurrent abdominal pain or discomfort and altered bowel habits in the absence of identifiable organic cause [1]. It affects an estimated 10% of the world's population, severely impairs health-related quality of life and imposes a significant financial burden on both patients and society [2, 3]. Given the incomplete understanding of the pathophysiology of IBS and the absence of effective treatments [4], identifying potential contributing factors, especially modifiable lifestyle factors, is crucial for the future development of targeted prevention strategies.

Non-alcoholic fatty liver disease (NAFLD) is defined as hepatic steatosis in more than 5% of hepatocytes without other specific causes, affecting 25–30% of the global population with worsening epidemic [5–7]. Recently, the term metabolic dysfunction-associated steatotic liver disease (MASLD) was proposed to replace NAFLD by American Association for the Study of Liver Disease (AASLD), European Association for the Study of the Liver (EASL) and Asociación Latinoamericana para el Estudio del Hígado (ALEH) in June 2023 [8]. MASLD is defined as the presence of hepatic steatosis in conjunction with any cardiometabolic risk factors (CMRFs), including overweight/obesity, dysglycemia, hypertension, and dyslipidemia [8]. It is further classified into two types based on weekly alcohol consumption: pure MASLD and MASLD with increased alcohol intake (MetALD) [8]. Although previous population-based studies have shown a higher IBS risk in NAFLD patients [9, 10], they did not account for coexisting moderate alcohol consumption and CMRFs on risk of IBS occurrence [11, 12]. Experimental evidence suggests that NAFLD and CMRFs often share similar pathways in contributing to IBS, involving mechanisms like gut-brain axis, gut dysbiosis, altered gut motility, and heightened intestinal inflammation, permeability, and visceral sensitivity [13–15]. Furthermore, NAFLD may exacerbate IBS symptoms by disrupting the liver-brain-gut axis [16, 17]. However, it is still unclear whether hepatic steatosis in combination with CMRFs, the newly coined MASLD, as well as MASLD type, could further impact IBS development. In addition, the association between different number of CMRFs in MASLD and IBS development remains to be explored.

Herein, this study prospectively examined the association between the newly coined MASLD, MASLD type, MASLD with different number of or different CMRFs, and the risk of incident IBS in a large population-based, long-term follow-up UK cohort.

Methods

Study population

This study was based on the UK Biobank (UKB), a large population-based prospective cohort, with over half a million participants being recruited across the UK from 2006 to 2010. All participants provided information on socio-demographic, lifestyle, medications and other health-related aspects through questionnaires and physical assessment by trained research staff. More details about the UKB are described elsewhere [18]. The UKB study received ethical approval from the North West Multicenter Research Ethical Committee (21/NW/0157). All participants provided written informed consent.

Participants who were free of IBS with available fatty liver index (FLI) at recruitment were included in this study. Those who already had a diagnosis of cancer (N = 42,846), inflammatory bowel disease (N = 4,776), coeliac disease (N = 2,681), alcoholic liver disease (N = 563), hepatitis B/C virus seropositivity (N = 225) at enrollment were excluded. All diagnoses were identified through International Classification of Disease-10 (ICD-10) codes (Table S1). Besides, 14,305 participants who had heavy alcohol intake (defined as weekly intake ≥ 350 g in female and ≥ 420 g in male) and 52 participants who withdrew were also excluded. Finally, a total of 380,619 participants were included in the analysis. The flowchart of participant selection was listed in Figure S1.

Assessment of baseline MASLD status, MASLD type and different CMRFs

According to the latest diagnostic criteria proposed by AASLD, EASL and ALEH, MASLD was defined as the presence of hepatic steatosis in conjunction with ≥ 1 CMRF [8]. Since there was no available liver imaging, ultrasound or histology data in the UKB, hepatic steatosis was identified using FLI, with a threshold of ≥ 60 [19]. This index, derived from body mass index (BMI), waist circumference (WC), triglycerides (TG) and gamma-glutamyltransferase (GGT), is a well-established and validated surrogate measure widely accepted in population-based studies [19, 20]. It demonstrated strong discriminatory power, with an area under the receiver operator curve of 0.85 compared to both liver ultrasonography and transient elastography-determined NAFLD [19, 21]. The FLI has been externally validated, showing a weighted percent agreement of 75.1% with transient elastography in a nationally representative sample of the Western general population [21]. In terms of CMRFs, it consisted any of the following 5 items [8]: (i) BMI ≥ 25 kg/m2 or WC > 94/80 cm for male/female; (ii) fasting serum glucose ≥ 5.6 mmol/L or HbA1c ≥ 39 mmol/L or type 2 diabetes or treatment for type 2 diabetes; (iii) blood pressure ≥ 130/85 mmHg or specific antihypertensive drug treatment; (iv) plasma TG ≥ 1.70 mmol/L or lipid-lowering treatment; (v) plasma high density lipoprotein cholesterol (HDL-C) ≤ 1.0/1.3 mmol/L for male/female or lipid-lowering treatment. Herein, individuals at study enrollment (baseline) were categorized into four groups: normal (neither hepatic steatosis nor CMRFs), CMRFs only (any of CMRFs without hepatic steatosis), cryptogenic SLD (hepatic steatosis without any CMRF) and MASLD, with normal as reference group and the other three as exposure groups.

With impaired liver function hindering alcohol metabolism, alcohol may further damage the liver and alter metabolic pathways [22]. Therefore, MASLD participants were classified into two types based on their weekly alcohol intake according to the AASLD, EASL and ALEH criteria [8]: pure MASLD and MetALD. Pure MASLD was defined as MASLD with no or less alcohol intake (weekly intake < 140 g in female and < 210 g in male), whereas MetALD referred to those with MASLD who consumed greater amounts of alcohol (weekly intake 140 to 350 g in female and 210 to 420 g in male) [8].

Additionally, in order to assess the impact of different number of CMRFs in MASLD on IBS development, we further divided MASLD participants into four exposure groups based on the number of CMRFs: 1 CMRF, 2 CMRFs, 3 CMRFs, and ≥ 4 CMRFs, with normal as reference group. Meanwhile, we also evaluate the effect of a certain CMRF in MASLD [i.e., MASLD with overweight/obesity (item 1 of CMRFs), MASLD with dysglycemia (item 2 of CMRFs), MASLD with hypertension (item 3 of in CMRFs), and MASLD with dyslipidemia (item 4–5 of CMRFs)] on IBS development compared with the normal group, based on the presence or absence of a certain CMRF, regardless of the co-existence of other CMRFs.

Outcome ascertainment

The primary endpoint was incident IBS, which was defined using ICD-10 codes (K58) with a censoring date of May 31, 2022. IBS diagnosis was ascertained based on linkage to primary care and/or hospital admission data obtained from Hospital Episode Statistics for England, Scottish Morbidity Record data for Scotland and Patient Episode Database for Wales.

Covariates

Based on previous epidemiological evidence and data availability [1, 10, 18, 23–28], we selected the following covariates for model estimation: age (continuous), sex (male, female), socioeconomic status (Townsend deprivation index (TDI), quartiles) [29], education level (university, non-university), ethnicity (white, nonwhite), smoking status (never, previous, current), weekly alcohol intake (continuous, grams), physical activity, depression (Yes, No) and anxiety (Yes, No). Depression and anxiety are prevalent among IBS patients and have been shown to influence both the onset and severity of IBS symptoms [24]. Additionally, lifestyle factors such as smoking, alcohol consumption, and physical activity can affect both liver and gut function, thus requiring adjustment for confounding [25–28]. Weekly alcohol intake in grams was calculated by multiplying the average number of alcoholic drinks consumed each week by the average grams of alcohol contained in each type of drink (Table S2), determined using the UK Food Standard Agency’s guidelines [30]. Physical activity was divided into three levels (low, moderate, and high) according to the International Physical Activity Questionnaire (IPAQ).

Statistical analysis

The 13-year cumulative incidence of IBS was calculated via the Kaplan–Meier method. The Cox proportional hazard model was conducted to investigate the association between MASLD (i.e., MASLD status, MASLD type, MASLD with different number of CMRFs or each certain CMRF) and incident IBS. The proportional hazard assumptions were tested and satisfied using Schoenfeld residuals (all P > 0.05). The follow-up period started from the date of enrollment until the date of the first IBS diagnosis, death, lost to follow-up, or the end of the study (May 31, 2022) for participants who did not develop IBS. Given a very small percentage of missing values for most variables, missing indicators were used.

For MASLD status, MASLD type, MASLD with different number of CMRFs or each certain CMRF, three adjustment models in addition to univariable analysis were performed: (i) model 1: adjusted for age and sex; (ii) model 2: additionally adjusted for TDI, education level, ethnicity, smoking status, weekly alcohol intake and IPAQ; (iii) model 3: additionally adjusted for depression and anxiety. Furthermore, similar three adjustment models were conducted when examining the association between pure MASLD or MetALD with different number of CMRFs and incident IBS.

Subgroup analysis was performed to examine whether the association between MASLD status, MASLD type as well as MASLD with different number of CMRFs and IBS development varied by age (< 60 years, ≥ 60 years), sex (male, female), smoking status (never, previous/current) and TDI (< -2.16, ≥ -2.16). Effect modification was evaluated by adding interaction terms of each stratified variable (i.e., age, sex, smoking status, TDI) and each MASLD exposure.

The following sensitivity analyses were performed based on model 3 for MASLD status, MASLD type and MASLD with different number of CMRFs, including: (i) excluding participants who had IBS diagnosis within 1 or 2 years after recruitment, in order to avoid reverse causation bias; (ii) additionally adjusting for healthy diet to further addressing potential confounding bias (based on adherence to at least four of the seven commonly consumed food groups: fruits, vegetables, fish, processed meats, unprocessed red meats, whole grains, and refined grains) [31]; (iii) using competing risk model by considering death and lost-to-follow-up as competing events, as these participants might develop IBS thereafter; (iv) to examine the impact of hepatic steatosis measurement on our findings, other well-established indexes, hepatic steatosis index (HSI) and lipid accumulation product (LAP), were also used to define hepatic steatosis [32, 33]; (v) to further rule out the possible misclassification bias for IBS diagnosis, we excluded those who fulfilled Rome III criteria via digestive health questionnaire (DHQ) without incident ICD-10 diagnosis (i.e., considering these IBS cases as prevalent cases at baseline) [34].

All statistical analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC) and figures were plotted using GraphPad Prism version 9.0 (GraphPad Software, San Diego, CA), with a two-tailed P value < 0.05 being considered as statistically significant.

Results

Baseline characteristics

Among 380,619 participants, 46.7% were male. The mean (SD) age was 56.24 (8.13) years at enrollment. The mean FLI was 47.73. Overall, 143,959 (37.8%) participants were diagnosed with hepatic steatosis (FLI ≥ 60). Of whom, 143,857 (99.9%) participants were accompanied by at least one CMRF and accordingly had a diagnosis of MASLD before or at enrollment. Participants with MASLD were more likely to be male, less educated, and with lower socioeconomic deprivation. In addition, the MASLD group had a higher BMI, WC, blood pressure and TG, a lower HDL-C, as well as a higher proportion of diabetes, depression and anxiety (Table 1). Median follow-up period was 13.2 years (interquartile range: 12.5–14.0 years).

Table 1.

Baseline characteristics according to baseline MASLD status in the UK Biobank cohort

Characteristics Total (N = 380,619) Normal (N = 60,441) CMRFs only (N = 176,219) Cryptogenic SLD (N = 102) MASLD (N = 143,857) P value
Age (years)a 56.24 ± 8.13 53.46 ± 8.03 56.56 ± 8.16 55.69 ± 8.73 57.01 ± 7.88  < 0.001
Sex  < 0.001
Male 177,746 (46.7) 19,611 (32.4) 65,189 (37.0) 98 (96.1) 92,848 (64.5)
Female 202,873 (53.3) 40,830 (67.6) 111,030 (63.0) 4 (3.9) 51,009 (35.5)
Ethnicity  < 0.001
Non-White 22,397 (5.9) 2570 (4.3) 11,193 (6.4) 6 (5.9) 8628 (6.0)
White 356,799 (93.7) 57,670 (95.4) 164,391 (93.3) 96 (94.1) 134,642 (93.6)
Unknown 1423 (0.4) 201 (0.3) 635 (0.4) 0 (0.0) 587 (0.4)
Education level  < 0.001
Non-university 250,099 (65.7) 33,108 (54.8) 113,925 (64.6) 57 (55.9) 103,009 (71.6)
University 125,939 (33.1) 26,904 (44.5) 60,224 (34.2) 43 (42.2) 38,768 (26.9)
Unknown 4581 (1.2) 429 (0.7) 2070 (1.2) 2 (2.0) 2080 (1.4)
Townsend deprivation index  < 0.001
Mean (SD)  − 1.32 ± 3.08  − 1.58 ± 2.94  − 1.46 ± 3.01  − 1.44 ± 3.34  − 1.06 ± 3.2  < 0.001
Q1 (≤ − 3.65) 95,081 (25.0) 16,509 (27.3) 45,715 (25.9) 29 (28.4) 32,828 (22.8)
Q2 (− 3.65 to − 2.16) 95,022 (25.0) 15,539 (25.7) 45,170 (25.6) 28 (27.5) 34,285 (23.8)
Q3 (− 2.16 to 0.50) 95,015 (25.0) 14,875 (24.6) 44,043 (25.0) 21 (20.6) 36,076 (25.1)
Q4 (> 0.50) 95,033 (25.0) 13,450 (22.3) 41,070 (23.3) 24 (23.5) 40,489 (28.1)
Unknown 468 (0.1) 68 (0.1) 221 (0.1) 0 (0.0) 179 (0.1)
Smoking status  < 0.001
Never 213,020 (56.0) 38,366 (63.5) 103,228 (58.6) 58 (56.9) 71,368 (49.6)
Previous 127,803 (33.6) 16,706 (27.6) 55,210 (31.3) 31 (30.4) 55,856 (38.8)
Current 37,858 (9.9) 5181 (8.6) 16,956 (9.6) 12 (11.8) 15,709 (10.9)
Unknown 1938 (0.5) 188 (0.3) 825 (0.5) 1 (1.0) 924 (0.6)
Alcohol drinking  < 0.001
Never 17,174 (4.5) 2000 (3.3) 8420 (4.8) 1 (1.0) 6753 (4.7)
Previous 13,316 (3.5) 1656 (2.7) 5723 (3.2) 4 (3.9) 5933 (4.1)
Current 349,163 (91.7) 56,684 (93.8) 161,656 (91.7) 96 (94.1) 130,727 (90.9)
Unknown 966 (0.3) 101 (0.2) 420 (0.2) 1 (1.0) 444 (0.3)
IPAQ  < 0.001
Low 56,936 (15.0) 6654 (11.0) 22,655 (12.9) 11 (10.8) 27,616 (19.2)
Moderate 125,906 (33.1) 20,102 (33.3) 58,997 (33.5) 37 (36.3) 46,770 (32.5)
High 125,470 (33.0) 23,908 (39.6) 61,379 (34.8) 37 (36.3) 40,146 (27.9)
Unknown 72,307 (19.0) 9777 (16.2) 33,188 (18.8) 17 (16.7) 29,325 (20.4)
BMI (kg/m2)  < 0.001
Mean (SD) 27.42 ± 4.77 22.44 ± 1.69 25.79 ± 2.64 23.99 ± 0.91 31.51 ± 4.48  < 0.001
 < 18.5 1812 (0.5) 1170 (1.9) 641 (0.4) 0 (0.0) 1 (0.0)
18.5–24.9 123,903 (32.6) 59,271 (98.1) 61,572 (34.9) 102 (100.0) 2958 (2.1)
25.0–29.9 162,603 (42.7) 0 (0.0) 104,063 (59.1) 0 (0.0) 58,540 (40.7)
 ≥ 30.0 92,301 (24.3) 0 (0.0) 9943 (5.6) 0 (0.0) 82,358 (57.2)
WC (cm)a 90.25 ± 13.41 75.82 ± 7.41 85.12 ± 8.13 89.33 ± 4.22 102.60 ± 9.98  < 0.001
FPG (mmol/L)a 5.12 ± 1.24 4.73 ± 0.46 5.05 ± 1.01 4.76 ± 0.49 5.35 ± 1.61  < 0.001
HbA1c (mmol/mol)a 36.09 ± 6.79 33.43 ± 3.00 35.45 ± 5.57 33.60 ± 3.06 38.00 ± 8.55  < 0.001
SBP (mmHg)a 140.32 ± 19.62 117.10 ± 8.44 140.03 ± 18.89 – 145.37 ± 18.83  < 0.001
DBP (mmHg)a 82.84 ± 10.86 71.85 ± 6.50 81.93 ± 10.24 – 86.34 ± 10.67  < 0.001
TG (mmol/L)a 1.74 ± 1.02 1.02 ± 0.31 1.45 ± 0.66 1.36 ± 0.26 2.39 ± 1.19  < 0.001
HDL-C (mmol/L)a 1.44 ± 0.38 1.73 ± 0.36 1.51 ± 0.36 1.67 ± 0.56 1.24 ± 0.29  < 0.001
AST (U/L)b 24.30 (20.90, 28.70) 23.00 (20.00, 26.70) 23.40 (20.40, 27.20) 32.00 (26.80, 43.30) 26.20 (22.40, 31.30)  < 0.001
ALT (U/L)b 20.10 (15.39, 27.30) 16.07 (13.00, 20.28) 18.15 (14.47, 23.20) 42.65 (26.76, 59.81) 26.10 (19.87, 35.16)  < 0.001
GGT (U/L)b 25.90 (18.40, 39.90) 19.00 (14.90, 26.30) 21.70 (16.70, 29.70) 182.75 (131.30, 281.20) 38.7 (27.60, 58.40)  < 0.001
PLT count (109 cells/L)a 252.75 ± 59.18 249.37 ± 56.48 254.60 ± 59.16 248.95 ± 72.25 251.92 ± 60.21  < 0.001
Diabetes (n, %) 9672 (2.5) 0 (0.0) 2406 (1.4) 0 (0.0) 7266 (5.1)  < 0.001
Medication for cholesterol, blood pressure, or diabetes  < 0.001
Cholesterol-lowering medication 64,805 (17.0) 0 (0.0) 27,124 (15.4) 0 (0.0) 37,681 (26.2)
Blood pressure medication 37,224 (9.8) 0 (0.0) 17,508 (9.9) 0 (0.0) 19,716 (13.7)
Insulin 605 (0.2) 0 (0.0) 397 (0.2) 0 (0.0) 208 (0.1)
FLIa 47.73 ± 29.98 12.55 ± 9.31 32.64 ± 15.77 65.96 ± 5.70 80.99 ± 11.51  < 0.001
FLI ≥ 60 (n, %) 143,959 (37.8) 0 (0.0) 0 (0.0) 102 (100.0) 143,857 (100.0)  < 0.001
HSIa 35.59 ± 5.88 29.56 ± 2.32 32.50 ± 2.44 37.78 ± 2.38 41.16 ± 4.58  < 0.001
HSI > 36 (n, %) 156,129 (41.1) 0 (0.0) 0 (0.0) 497 (100.0) 155,632 (100.0)  < 0.001
LAPa 3.68 ± 0.83 2.64 ± 0.58 3.49 ± 0.53 – 4.6 ± 0.39  < 0.001
LAP > 4/4.4 (male/female) (n, %) 112,495 (29.5) 0 (0.0) 0 (0.0) 0 (0.0) 112,495 (100.0)  < 0.001
Depression (n, %) 28,106 (7.4) 3676 (6.1) 12,251 (7.0) 7 (6.9) 12,172 (8.5)  < 0.001
Anxiety (n, %) 12,587 (3.3) 1786 (3.0) 5802 (3.3) 3 (2.9) 4996 (3.5)  < 0.001
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Categorical variables are presented as frequencies and percentages. a Data are presented as the mean ± standard deviation; b Data are presented as median (interquartile range); “-”: unavailable

MASLD metabolic dysfunction-associated steatotic liver disease, CMRFs cardiometabolic risk factors, SLD steatotic liver disease, IPAQ International Physical Activity Questionnaire, BMI body mass index, WC waist circumstance, FPG fasting blood glucose, HbA1c glycated hemoglobin, SBP systolic blood pressure, DBP diastolic blood pressure, TG triglycerides, HDL-C high-density lipoprotein cholesterol, AST aspartate aminotransferase, ALT alanine aminotransferase, GGT gamma-glutamyltransferase, PLT platelet, FLI fatty liver index, HSI hepatic steatosis index, LAP lipid accumulation product

Baseline MASLD status and risk of incident IBS

During a total of 4,889,620 person-years’ follow-up, 7329 cases of incident IBS were identified. The 13-year cumulative incidence of IBS was 2.0% (95%CI: 1.9–2.1%) and 1.9% (1.9–2.0%) in CMRFs only and MASLD group versus 1.9% (1.8–2.1%) in the normal group. After multivariable adjustment, MASLD patients were associated with an 11% increased risk of IBS compared with normal group (HR = 1.11, 95%CI: 1.04–1.20, Ptrend < 0.001, Table 2).

Table 2.

The association between baseline MASLD status and incident IBS

Normal CMRFs only Cryptogenic SLD MASLD P for trend
No. of participants 60,441 176,219 102 143,857
No. of incident IBS 1167 3443 2 2717
Follow-up, person-years 784,572 2,269,944 1272 1,833,832
Follow-up, years Median (IQR) 13.2 (12.6, 13.9) 13.2 (12.5, 14.0) 13.3 (12.4, 14.1) 13.2 (12.4, 14.0)
Hazard ratio for incident IBS (95%CI, P value)
Adjusted model 1 Reference 1.06 (0.99–1.14, 0.080) 1.82 (0.46–7.29, 0.397) 1.28 (1.19–1.37, < 0.001)  < 0.001
Adjusted model 2 Reference 1.02 (0.95–1.09, 0.546) 1.82 (0.46–7.30, 0.396) 1.16 (1.08–1.25, < 0.001)  < 0.001
Adjusted model 3 Reference 1.01 (0.94–1.08, 0.860) 1.74 (0.43–6.95, 0.436) 1.11 (1.04–1.20, 0.004)  < 0.001
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Adjusted model 1: Age and sex were adjusted; Adjusted model 2: Townsend deprivation index, education level, ethnicity, smoking status, weekly alcohol intake, and IPAQ (International Physical Activity Questionnaire) were additionally adjusted; Adjusted model 3: Depression and anxiety were additionally adjusted. P for trend was calculated based on the categorical variable of MASLD status (0, 1, 2, 3)

MASLD metabolic dysfunction-associated steatotic liver disease, IBS irritable bowel syndrome, CMRFs cardiometabolic risk factors, SLD steatotic liver disease, IQR interquartile range, CI confidence interval

In subgroup analysis, the increased IBS risk associated with baseline MASLD was observed among age < 60 years, female, previous/current smoking, and TDI ≥ -2.16 subgroups (Fig. 1, Table S3-6). Moreover, we observed significant interactions across age/sex/TDI and MASLD status (Pinteraction 0.001 for age, 0.022 for sex, and 0.010 for TDI).

Fig. 1.

Fig. 1

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Subgroup analysis for the association between baseline MASLD, MASLD type and incident IBS. Note All adjusted HRs were compared with the normal group, and calculated by adjusting the following covariates: age, sex, Townsend deprivation index, education level, ethnicity, smoking status, weekly alcohol intake, IPAQ (International Physical Activity Questionnaire), depression and anxiety. P for trend was calculated based on the categorical variable of MASLD (0, 1, 2, 3) or MASLD type (0, 1, 2). Subgroup analyses of cryptogenic steatotic liver disease (SLD) were not performed because of the limited number of events. MASLD metabolic dysfunction-associated steatotic liver disease, IBS irritable bowel syndrome, CMRFs cardiometabolic risk factors, MetALD MASLD with increased alcohol intake, HR hazard ratio, CI confidence interval

Baseline MASLD type and risk of incident IBS

Over a median 13.2 years of follow-up, 2285 and 432 new IBS cases were confirmed in pure MASLD (N = 114,043) and MetALD (N = 29,814) group, respectively. Compared with normal individuals, both pure MASLD (HR = 1.12, 1.03–1.21) and MetALD patients (HR = 1.26, 1.09–1.45) had a significantly higher risk of incident IBS after multivariable adjustment (Ptrend < 0.001, Fig. 2).

Fig. 2.

Fig. 2

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The association between baseline different MASLD types and incident IBS. Note Adjusted model 1: Age and sex were adjusted; Adjusted model 2: Townsend deprivation index, education level, ethnicity, smoking status, weekly alcohol intake, and IPAQ (International Physical Activity Questionnaire) were additionally adjusted; Adjusted model 3: Depression and anxiety were additionally adjusted. P for trend was calculated based on the categorical variable of pure MASLD or MetALD (0, 1, 2). MASLD metabolic dysfunction-associated steatotic liver disease, IBS irritable bowel syndrome, MetALD MASLD with increased alcohol intake, HR hazard ratio, CI confidence interval

Regarding subgroup analysis, the increased IBS risk associated with both pure MASLD and MetALD was generally observed among age < 60 years, female, previous/current smoking, and TDI ≥ -2.16 subgroups (Fig. 1, Table S7−10). Meanwhile, a significant interaction was detected between age and MASLD type (Pinteraction = 0.010).

Baseline different number of CMRFs in MASLD and risk of incident IBS

Overall, 261, 790, 951 and 715 new IBS cases developed in 1 CMRF (N = 16,304), 2 CMRFs (N = 46,500), 3 CMRFs (N = 49,697) and ≥ 4 CMRFs (N = 31,356) groups among all MASLD individuals, respectively. The 13-year cumulative incidence of IBS was 1.6% (1.4–1.8%), 1.7% (1.6–1.9%), 2.0% (1.8–2.1%) and 2.4% (2.2–2.6%) in 1 CMRF, 2 CMRFs, 3 CMRFs and ≥ 4 CMRFs groups. Particularly, MASLD with 3 CMRFs (HR = 1.16, 1.06–1.27) and ≥ 4 CMRFs patients (HR = 1.30, 1.17–1.43) showed a 16% and 30% excess risk of developing IBS compared with normal individuals (Ptrend < 0.001, Fig. 3, Table S11). Similar findings were observed in both pure MASLD and MetALD individuals, with evidently greater risk of incident IBS accompanied by increased number of CMRFs (Ptrend < 0.001 for pure MASLD and 0.030 for MetALD, Fig. 3, Table S12,13). Moreover, subgroup analyses demonstrated consistent findings in age < 60 years, female, all smoking, and TDI ≥ -2.16 subgroups (all Ptrend < 0.05, Fig. 4, Table S14−17).

Fig. 3.

Fig. 3

Open in a new tab

The association between baseline different number of CMRFs in MASLD and incident IBS. Note All adjusted HRs were calculated by adjusting the following covariates: age, sex, Townsend deprivation index, education level, ethnicity, smoking status, weekly alcohol intake, IPAQ (International Physical Activity Questionnaire), depression and anxiety. P for trend was calculated based on the categorical variable of the number of CMRFs (0, 1, 2, 3, 4). MASLD metabolic dysfunction-associated steatotic liver disease, IBS irritable bowel syndrome, CMRFs cardiometabolic risk factors, MetALD MASLD with increased alcohol intake, HR hazard ratio, CI confidence interval

Fig. 4.

Fig. 4

Open in a new tab

Subgroup analysis for the association between baseline different number of CMRFs in MASLD and incident IBS. Note All adjusted HRs were calculated by adjusting the following covariates: age, sex, Townsend deprivation index, education level, ethnicity, smoking status, weekly alcohol intake, IPAQ (International Physical Activity Questionnaire), depression and anxiety. P for trend was calculated based on the categorical variable of the number of CMRFs (0, 1, 2, 3, 4). MASLD metabolic dysfunction-associated steatotic liver disease, IBS irritable bowel syndrome, CMRFs cardiometabolic risk factors, HR hazard ratio, CI confidence interval

As for examining the effect of a certain CMRF presence in MASLD, either MASLD with overweight/obesity, dysglycemia or dyslipidemia was associated with an increased risk of incident IBS versus normal group, with a HR of 1.14 (1.05–1.23), 1.15 (1.05–1.27) and 1.18 (1.09–1.28), respectively (Table S18).

Sensitivity analysis

Results of sensitivity analysis by baseline MASLD status, MASLD type, and different number of CMRFs in MASLD were similar to the main analysis, when excluding incident IBS cases within 1 year or 2 years after baseline, additionally adjusting for healthy diet, performing competing risk model, diagnosing hepatic steatosis with HSI or LAP, or employing different definitions of incident IBS (Table S19−21).

Discussion

In this prospective cohort study with long-term follow-up of nearly 0.4 million adults, we demonstrated for the first time that MASLD individuals had an 11% higher risk of developing IBS. Meanwhile, individuals with pure MASLD and MetALD had a 1.12 and 1.26-fold increased risk of IBS, respectively. More specifically, there was a substantially greater risk of incident IBS as the number of CMRFs increased in MASLD individuals, either in pure MASLD or MetALD individuals.

Dysregulation of the liver-gut axis may partially explain the positive relationship [17]. Firstly, MASLD may contribute to the harmful microbial proliferation and gut dysbiosis (such as increased Clostridium and reduced Lactobacilli and Bifidobacteria), which may adversely affect intestinal permeability, motility, and visceral sensitivity [17]. Secondly, elevated inflammatory cytokines associated with MASLD (such as tumor necrosis factor-alpha and interleukin-6) may promote intestinal inflammation, which in turn disrupt the intestinal barrier and increase permeability [35, 36]. Additionally, abnormalities in liver-brain-gut neural arc may disrupt the autonomic nervous system, further impairing immune regulation and compromising intestinal barrier [16]. Further research exploring microbial therapies may further support these benefits by optimizing gut microbiota composition and maintaining intestinal homeostasis [37–39].

Although no obvious association was observed between CMRFs only and IBS in this study, the risk of IBS in MASLD patients rose with the number of CMRFs. This prospective association was directionally consistent with several cross-sectional and case–control studies [40–42]. Additionally, a Mendelian randomization study found that genetic predisposition to type 2 diabetes was associated with an increased risk of IBS [43]. The underlying mechanisms remain incompletely understood. CMRFs associated dietary patterns (i.e., high-fat, high-sugar, high-salt) were closely associated with alterations in the composition of gut microbiota and its metabolites [44–46], which may heighten the production of lipopolysaccharide, and the expression of pro-inflammatory cytokines [15]. Consequently, increased intestinal permeability, bacterial translocation, systemic inflammatory response and impaired gut motility might occur [15, 47, 48]. Moreover, visceral adipose tissue secreted various inflammatory cytokines and adipokines that induce a systemic chronic low-grade inflammatory state, which can promote leaky gut and visceral hypersensitivity [49]. The excess of visceral fat could also cause abnormal intestinal motility mechanically [11]. Additionally, the enteric nervous system disturbed by CMRFs may be a contributing factor to intestinal dysfunction and further inducing IBS occurrence [50].

Given the mutually causal relationship between CMRFs and hepatic steatosis, CMRFs may elevate the risk of incident IBS by promoting the progression of hepatic steatosis [51]. As for the greater IBS risk in MetALD compared to pure MASLD, it may be attributed to the direct effects of excessive alcohol consumption and further liver damage, thereby exacerbating IBS [52]. More research is required to further clarify the exact underlying mechanism.

Our findings indicated a higher IBS susceptibility among female MASLD patients, consistent with previous studies in general populations [2]. Sex-specific hormones like estrogen and progesterone may affect intestinal motility and sensory perception via neuroendocrine pathways, potentially increasing visceral sensitivity and psychological distress in females [53]. Additionally, sex differences in stress response and emotional processing—mediated by the brain-gut axis—may predispose women to IBS symptoms when gut-brain dysregulation occurs [53]. The predominance of IBS in younger populations is also in line with existing epidemiological data [2]. This could be due to increased stressors from work and society in younger individuals [54]. Considering these sex and age differences, early screening, diagnosis, and targeted treatment are crucial for effective management in this subgroup.

Given the growing incidence of hepatic steatosis and cardiometabolic diseases globally in recent decades, our results may partially explain the current rising trend in IBS [3, 6, 9, 15]. Patients with MASLD, especially those with at least 3 CMRFs, should be screened and evaluated for symptoms of IBS for timely detection and treatment, allowing for improved well-being and quality of life, as well as significant savings in health resources and medical costs. Furthermore, given the close interconnection among the liver, gut, and metabolic components, future strategies for managing MASLD, including lifestyle interventions and pharmacologic therapies, may reduce the risk of developing IBS [25–28]. For instance, a low-fat, calorie-restricted diet could reduce liver fat accumulation, promote weight loss, improve lipid profiles and blood sugar control, and lower inflammation levels [55–57]. Future well-designed randomized controlled trials are needed to confirm the effect of these interventions targeting MASLD on risk of IBS onset.

Our study had several strengths including its large and geographically diverse cohort and the 13.2-year length of follow-up. So far, this is the first report to shed light on the connection between the newly coined MASLD, MASLD type and IBS development based on the latest definition of fatty liver disease. More importantly, we first demonstrated the significant dose–response relationship between the number of CMRFs in MASLD and risk of IBS, which revealed the critical role of those cardiometabolic risk factors in the pathogenesis of IBS in addition to hepatic steatosis. Besides, a variety of sensitivity analyses against potential protopathic bias and misclassification bias confirmed the robustness of the results.

Nonetheless, our findings should be interpreted with caution considering these limitations. Firstly, hepatic steatosis was measured using estimated indices instead of liver imaging or histological assessment due to data unavailability, leading to potential measurement errors. However, FLI has been considered to possess sufficient accuracy and is widely used as a reasonable substitute for population estimates [21, 58]. Moreover, results of sensitivity analysis via HSI and LAP adjudicating hepatic steatosis were also consistent with principal findings [32, 33]. Secondly, incident IBS may be underdiagnosed since some IBS cases may not seek medical consultation, although we ascertained IBS cases according to ICD-10 codes from both primary care and hospital admission sources. However, we additionally incorporated data from the DHQ with available Rome III criteria, and the results were consistent. Thirdly, MASLD status was assessed only once at baseline. Thus, the association between changes in MASLD status and IBS risk could not be assessed. Fourthly, covariates like lifestyle factors were also not collected during follow-up, making it difficult to evaluate influence of variations in these factors on the association between MASLD and IBS over time. Fifthly, despite carefully adjusting for numerous potential confounders, residual confounders yet cannot be completely ruled out. Sixthly, owing to the observational design of this study, causality cannot be inferred. Finally, given the predominantly White ethnicity of our study population, generalizability of our findings to other populations or ethnicities is limited. Future prospective cohort studies in different ethnicities are necessary to confirm our findings.

Conclusions

In conclusion, in this large-scale population-based prospective cohort study of UK adults, the newly coined metabolic dysfunction-associated steatotic liver disease, either pure MASLD or MetALD type, was associated with a higher risk of incident IBS. Furthermore, the presence of more cardiometabolic risk factors with MASLD can substantially increase the risk of IBS occurrence, with a significant dose–response relationship. Given that CMRFs can be effectively managed, future strategies should consider IBS screening for MASLD patients, especially those with multiple CMRFs, in order to further reduce IBS risk. Nevertheless, these findings need to be replicated and validated in other well-designed prospective studies with diverse ethnic populations. More experimental studies are also warranted to elucidate the underlying mechanisms in the future.

Supplementary Information

Supplementary Material 1 (748.7KB, docx)

Acknowledgements

This research has been conducted using the UK Biobank Resource under application number [74444].

Abbreviations

AASLD

American association for the study of liver disease

ALD

Alcoholic liver disease

ALEH

Asociación Latinoamericana para el Estudio del Hígado

ALT

Alanine aminotransferase

AST

Aspartate aminotransferase

BMI

Body mass index

CI

Confidence interval

CMRFs

Cardiometabolic risk factors

DBP

Diastolic blood pressure

DHQ

Digestive health questionnaire

EASL

European association for the study of the liver

FLI

Fatty liver index

FPG

Fasting blood glucose

GGT

Gamma-glutamyltransferase

HbA1c

Glycated hemoglobin

HDL-C

High-density lipoprotein cholesterol

HR

Hazard ratio

HSI

Hepatic steatosis index

IBD

Inflammatory bowel disease

IBS

Irritable bowel syndrome

ICD

International classification disease

IPAQ

International physical activity questionnaire

IQR

Interquartile range

LAP

Lipid accumulation product

MASLD

Metabolic dysfunction-associated steatotic liver disease

MetALD

MASLD with increased alcohol intake

NAFLD

Non-alcoholic fatty liver disease

PLT

Platelet

SBP

Systolic blood pressure

SD

Standard deviation

SLD

Steatotic liver disease

TDI

Townsend deprivation index

TG

Triglycerides

UKB

UK biobank

WC

Waist circumstance

Author contributions

SSW and STZhu designed the study. YSZ and SSW drafted the manuscript. YSZ analyzed the data. SSW verified the data analysis. SSW, ZRY and QZ revised the manuscript. SSW, ZRY, SL, SAX, QZ, STZhu and STZhang interpreted the results, incorporated comments for the co-authors and finalized the manuscript. All authors approved the final version of the paper.

Funding

This work was supported by the National Natural Science Foundation of China (No. 82204119) and Beijing Nova Program (No. 20230484349).

Data availability

All data relevant to the study were using the UK Biobank Resource under application number [74444]. No additional data available.

Declarations

Ethics approval and consent to participate

The UKB study was approved by the North West Multicenter Research Ethical Committee (21/NW/0157), and all participants or their proxy respondents provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Shanshan Wu and Shengtao Zhu have equally contributed to the study.

Contributor Information

Shengtao Zhu, Email: zhushengtao@ccmu.edu.cn.

Shanshan Wu, Email: shanshanwu@ccmu.edu.cn.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (748.7KB, docx)

Data Availability Statement

All data relevant to the study were using the UK Biobank Resource under application number [74444]. No additional data available.

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