Nonalcoholic fatty liver disease is associated with an increased risk of irritable bowel syndrome: a prospective cohort study | BMC Medicine

Study population

This study population consisted of over 500,000 participants from an ongoing large-scale prospective cohort, UK Biobank (UKB). Briefly, participants aged 37 to 73 from 22 assessment centers in England, Wales and Scotland were recruited between 2006 and 2010. All participants completed baseline questionnaires with anthropometric assessments and reported medical conditions. Details of the UKB’s design have been described elsewhere [18]. The study was approved by the North West Multicenter Research Ethics Board and all participants signed informed written content.

Participants who did not have IBS with an available non-alcoholic fatty liver disease index at the time of enrollment were included in this study. Those who already had cancer, inflammatory bowel disease (IBD), alcoholic liver disease (ALD), or a diagnosis of celiac disease at the time of enrollment were excluded. All diagnoses were identified using International Classification of Diseases-10 (ICD-10) codes (Supplementary File 1: Table S1). In addition, the withdrawal of a participant from the UKB has been ruled out. Therefore, a total of 396,838 participants were included in the final analysis. The participant selection flowchart has been listed in Fig. 1.

Fig. 1

Organizational chart of the population studied. UKB: UK Biobank; IBS: irritable bowel syndrome; IBD: inflammatory bowel disease; ALD: alcoholic liver disease; FLI: fatty liver index

Evaluation of the initial degree of non-alcoholic fatty liver disease and NAFLD

As no imaging, ultrasound, or histological data regarding fatty liver disease were available in the large-scale UKB cohort, we used a well-established index, the fatty liver index (FLI), to measure the degree of non-fatty liver disease. alcoholic. [19]. Briefly, FLI was calculated using four variables including BMI, waist circumference (WC), triglycerides (TG) and gamma-glutamyltransferase (GGT) using a model previously published and validated regression model. [19]. It has proven to be a reliable index with good performance in discriminating NAFLD determined by liver ultrasound. [area under the receiver operator curve, AUROC = 0.85 (95%CI: 0.81–0.88)] and NAFLD determined by transient elastography (AUROC=0.85), which has been externally validated and widely accepted in a population-based study [19,20,21]. Meanwhile, the weighted percent agreement between FLI and transient elastography was as high as 75.11% (95% CI: 75.10%-75.12%) when validated in a sample representative of the general western population rather than in a clinical population. [21]. We categorized the FLI according to the quartile distribution with the lowest quartile group as the reference group and the other three quartile groups as the exposure groups. In addition, we also used the diagnosis NAFLD or not according to a predefined threshold, with FLI ≥ 60 as an indicator of NAFLD [19]. Participants who had a baseline FLI 2 and ≥ 25 kg/m2 would be defined as lean and non-lean NAFLD, respectively. Accordingly, NAFLD patients with a BMI ≥ 30 kg/m2 and 2 would be considered obese and non-obese NAFLD, separately [22, 23]. Additionally, to examine the impact of fatty liver measurement on our results, another well-established index, the fatty liver index (HSI), was used to define NAFLD in sensitivity analyses. The HSI could be calculated as 8* (serum alanine aminotransferase (ALT)/aspartate aminotransferase (AST) ratio) + BMI (+2, if female; +2, if type 2 diabetes) [24]. An HSI > 36 has been defined as an indicator of NAFLD [24].

Recognition of the result

The primary endpoint was incident IBS, which was determined via ICD-10 codes (K58, Supplementary File 1: Table S1). The diagnosis of IBS was based on self-report or link to primary care and/or hospital admission data with a censored date of June 2021.

Covariates

Based on epidemiological evidence, certain sociodemographic characteristics, health behaviors and comorbidities at baseline were adjusted as covariates [1, 4, 16, 17]. Potential confounders included age (continuous variable), gender (male or female), ethnicity (white or non-white), socioeconomic status, education level, smoking status ( never, current or previous), alcohol consumption (never, current or previous), type 2 diabetes (yes or no) and physical activity. Socio-economic status was based on the Townsend Deprivation Index, which was calculated immediately before participants joined the UKB using exit areas from the previous national census [25]. The Townsend Deprivation Index for socio-economic deprivation was divided into four quartiles. Education was based on self-declaration of the highest qualification obtained and classified as university or non-university. Physical activity was self-reported and divided into three levels (high, moderate and low) based on the International Physical Activity Questionnaire (IPAQ).

statistical analyzes

The incidence rate with 95% confidence interval (CI) of IBS was calculated as the number of events per 1000 person-years by Poisson regression. The cumulative incidence of IBS over 12 years was calculated by the Kaplan-Meier method. The Cox proportional hazard model was performed to examine the association between fatty liver disease and incident IBS. The follow-up period started from baseline to date of first diagnosis of IBS or censored at end of study (June 2021), date of death or lost to follow-up for participants who did not develop IBS. Given a very low percentage of missing values ​​(0.1 to 1.2% for all covariates were missing), missing indicators were used.

For the quartiles of FLI, by change in standard deviation (SD) of FLI and diagnosis of NAFLD or not according to a predefined threshold, three multivariate models in addition to the univariate analysis were carried out: model 1, adjusted for age and gender; model 2, additionally adjusted for the Townsend deprivation index, level of education, ethnic origin, smoking status and alcohol consumption; model 3, additionally adjusted for physical activity and type 2 diabetes. In addition, restricted cubic spline analysis was performed to examine the potential nonlinear association between baseline FLI and incident IBS, with nodes placed at the 10th, 50th and 90th percentiles and the median value of the base FLI (46.55) as a reference point. In addition, a subgroup analysis was performed to determine whether the association between degree of non-alcoholic fatty liver disease as well as NAFLD and IBS varied by age (

In order to assess the robustness of the results, several sensitivity analyzes were carried out. First, we excluded participants who had been diagnosed with IBS within 1 or 2 years of enrollment respectively, to avoid detection bias. Second, to exclude the influence of alcohol consumption on non-alcoholic fatty liver disease throughout the follow-up period, incident cases of ALD after baseline were further excluded. Third, the competing risk model by considering loss to follow-up and death as competing events was conducted, since these participants might subsequently develop IBS. Fourth, participants who were seropositive for hepatitis B/C virus were excluded. Fifth, we also adjusted for psychological disorders including depression and anxiety as confounders. Finally, an age-matched cohort (matching 1:1, ±2 years) was generated as a new dataset to further investigate the association between NAFLD and IBS.

Additionally, sensitivity analyzes were performed using HSI [diagnosis of NAFLD or not according to predefined cutoff (HSI > 36), per SD change] via adjusted model 3, with additional similar analyzes excluding incident cases of IBS within 1 or 2 years of baseline, excluding incident cases of ALD, excluding participants seropositive for hepatitis B virus /C or by running a concurrent risk model.

One to two tails P a value

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