February 17, 2025

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Impaired pulmonary function increases the risk of gout: evidence from a large cohort study in the UK Biobank | BMC Medicine

Impaired pulmonary function increases the risk of gout: evidence from a large cohort study in the UK Biobank | BMC Medicine

Research design and participants

Our study included participants from the UK Biobank ( a comprehensive longitudinal cohort study initiated between 2006 and 2010. It includes a wide demographic, enrolling over 500,000 individuals aged between 37 and 73 years from various locations in England. Initially, these participants provided detailed health and lifestyle data through structured questionnaires and participated in physical assessments and biological sampling at one of 22 designated centers [15]. The UK Biobank consistently updates morbidity and mortality data by integrating information from national health registries. We included 420,002 participants whose pulmonary function data were fully documented (Additional file 1: Table S1). The detailed selection process is illustrated in Fig. 1. Ethical approval for this study was granted by the UK Biobank Ethics Committee (approval number 106397), which adheres to the Research Tissue Bank standards.

Fig. 1
figure 1

Flow diagram of participants included in the study. PSM: Propensity score matching

Spirometry measurement techniques

Spirometry was conducted by professionally trained personnel using the Vitalograph Pneumotrac 6800 spirometer during the initial data collection phase, adhering to a rigorously standardized protocol [16]. Each participant was required to complete two to three forced exhalations of a minimum 6-s duration within 6 min, with the spirometer calibrated before each session. Only the highest recorded values were considered for further analysis if the first two attempts were within 5% variability; otherwise, the third attempt was conducted. The parameters of forced expiratory volume in 1 s (FEV1) and forced vital capacity (FVC) were calculated as a percentage of the predicted values based on the Global Lung Initiative 2012 norms [17, 18]. Definitions were applied to categorize participants into groups: COPD was defined as an FEV1 less than 80% predicted and an FEV1/FVC ratio less than 0.7; PRISm, a concept assessing pulmonary function impairment, was identified as an FEV1 less than 80% predicted but an FEV1/FVC ratio equal to or greater than 0.7 [19]; and participants with normal spirometry results had FEV1 and FEV1/FVC ratios equal to or exceeding 80% and 0.7, respectively.

Clinical outcomes

Gout status was defined using the International Classification of Diseases (ICD), Tenth Revision, code M10, which was extracted from the first occurrence variables in the UK Biobank. Briefly, gout diagnosis information was obtained predominantly from primary care records, hospital admissions, and self-reported health conditions. The follow-up time was calculated from the baseline interview date to the date of incident gout diagnosis, death, or the end of follow-up on August, 2023, whichever occurred first.

Covariates

We considered various factors, including sociodemographic characteristics, lifestyle factors, dietary habits, and comorbidities, as covariates to address potential confounding. The sociodemographic factors included continuous age, sex (male/female), ethnicity (white, Asian/Asian British, black/black British, mixed, and others), and socioeconomic status. The latter was derived using the Townsend index based on the postcode of residence. Body mass index (BMI) was calculated as weight divided by height squared (kg/m2) and categorized according to the World Health Organization criteria: underweight (< 18.5 kg/m2), normal weight (18.5–24.9 kg/m2), overweight (25–29.9 kg/m2), and obese (≥ 30 kg/m2). Smoking status was classified into never smoked, ever smoked, or currently smoking. Alcohol intake frequency was assessed using a questionnaire and categorized into three levels from never to daily. Physical activity was measured through total metabolic equivalent task (MET) minutes of all exercise during the previous week and categorized into four levels: none, < 600 MET minutes/week, 600–3000 MET minutes/week, and ≥ 3000 MET minutes/week. Comorbidities were confirmed through a combination of self-reported medical history, review of medication records, and inpatient diagnostic reports. Dietary information was obtained through the food frequency questionnaire (FFQ), which provides a comprehensive overview of participants’ usual dietary intake. Given that fish and meat are primary dietary triggers for gout due to their high purine contents, we focused on these food groups. The intake of meat and fish was scored based on their frequency: oily fish, non-oily fish, processed meat, and unprocessed meat (including poultry, beef, lamb, and pork).

MR analysis

We obtained publicly available genome-wide association study (GWAS) data for lung function indicators and gout from the IEU OpenGWAS database ( The specific IEU IDs for the lung function indicators are as follows: ebi-a-GCST90029027-finn-b for FVC, ukb-a-235-finn-b for FEV1, ukb-a-337-finn-b for the predicted percentage of FEV1, and finn-b-GOUT for gout. Instrumental variables for genetic variants of lung function indicators were selected based on a linkage disequilibrium R2 of 0.001, a clumping distance of 10,000 kb, and a P-value threshold of 5e − 08. The main MR analyses were conducted using the random-effects inverse-variance weighted (RE-IVW) approach, which is robust to the presence of heterogeneity in MR settings [20].

Assessment of potential mediators

The exploration of potential mediators was based on blood biomarkers and blood cells as candidates for intermediate variables [11, 21,22,23]. These biomarkers and blood cells involved in inflammation, metabolism, and liver and kidney function may mediate the relationship between pulmonary function and the risk of gout (Additional file 1: Table S2). Within the UK Biobank initiative, blood tests were conducted on participants who provided informed consent. Approximately 4 ml of blood was drawn from the participants, processed for separation, and then stored at − 80 °C before being analyzed within 24 h using a Beckman Coulter LH750 instrument. Blood biomarkers were subjected to rigorous quality checks and externally validated. Inflammation-related biomarkers included leukocyte, neutrophil, monocyte, and lymphocyte counts and C-reactive protein (CRP) levels. Markers associated with liver function included alanine aminotransferase (ALT), alkaline phosphatase (ALP), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), total bilirubin (TBIL), total protein, and albumin. Renal function was assessed using cystatin C, urate, and urea levels.

Statistical analysis

The FEV1% predicted was determined using the Global Lung Initiative 2012 reference values calculated using the RSpiro software package. Participant characteristics were statistically compared across the normal spirometry, PRISm, and COPD groups using the Kruskal–Wallis and chi-squared tests for continuous and categorical variables. Missing data were addressed by including a separate category for incomplete data. [24]. Logistic regression was used for cross-sectional analysis to determine the prevalence of gout across different pulmonary function levels, and odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to quantify the strength of this association. In this cohort study, we utilized propensity score matching (PSM) to ensure comparability among participants with varying levels of pulmonary function. This method matched participants based on various factors, including demographic features, lifestyle, and comorbidities. The matching was conducted at a 1:1 ratio with a stringent criterion, employing a caliper set to 0.1 standard deviations of the propensity score to ensure precision in the matching process [25, 26]. Three models with increasing adjustments were fitted for PSM: Model 1 was adjusted for age, sex, socioeconomic status, and BMI, and Model 2 was additionally adjusted for lifestyle factors, including activities, smoking, alcohol consumption, and dietary intake of meat and fish. Model 3 was further adjusted for comorbidities, such as hypertension, diabetes, cardiovascular disease (CVD), chronic kidney disease (CKD), and asthma. The association between baseline impaired pulmonary function status and the risk of developing gout was assessed using a stratified Cox model based on the match ID generated by PSM to address the paired samples [27], producing hazard ratios (HRs) and 95% CIs. To assess the impact of unmeasured confounders, we conducted a sensitivity analysis using the E-value method. The E-value estimates the minimum strength of association that an unmeasured confounder would need to have with both the exposure and the outcome to explain away the observed association [28]. Subgroup analyses based on various variables were conducted to explore potential effect modifications of the association between pulmonary function and gout across these factors. Furthermore, the dose–response relationship between the percentage of predicted values for FVE1 (% predicted) and FVC (% predicted) and the risk of gout was examined using restricted cubic spline (RCS) regression with four knots (5th, 35th, 65th, and 95th percentiles) of the pulmonary function variable distribution, allowing for the detection of potential linear and nonlinear associations.

Selected blood biomarkers and blood cells could serve as potential mediators based on the following analyses [11, 21,22,23]. First, to address potential biases caused by missing values of potential mediators, we employed multivariate imputations by chained equations using the Mice R package to impute these variables with a missing percentage of ≥ 1% [29]. Second, we normalized the raw data using a z-score and applied multiple linear regression models to assess the associations between pulmonary function and these biomarkers or blood cells. Next, we used Cox regression models to explore the relationships among pulmonary function, biomarkers, and incident gout. Significant biomarkers or blood cells in these steps were considered potential mediators for subsequent mediation analyses. We estimated the proportion mediated (PM) using the “mediation” package [30], and the non-parametric bootstrap method (with 1000 draws) was employed to calculate 95% CIs for the PM.

All the analyses were conducted using R software (version 4.2.2). A false discovery rate (FDR)-adjusted P-value < 0.05 for analyses involving biomarkers or blood cells was considered statistically significant. A two-tailed P-value < 0.05 was also deemed statistically significant in the prospective association analysis.

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