Data source and study population
The UK Biobank is a large prospective, observational, population-based cohort of half a million adult residents of the United Kingdom, aged 37–73 years, from 22 assessment centers across England, Wales, and Scotland between 2006 and 2010. Participants were asked to complete a touch screen questionnaire, a face-to-face interview and a series of physical measurements, as well as provide biological samples for laboratory analysis. The details of the study design have been described previously [10, 11]. The UK Biobank was approved by the North West Research Ethics Committee (06/MRE08/65) and all participants signed an informed consent.
In this study, we restricted our analysis to participants who had complete information on the use of glucosamine and were free of gout (n = 484,720). We also excluded participants with unavailable genetic data and data on the important covariates (n = 48,126). Therefore, a total of 436,594 participants were enrolled in the present analysis (Additional file 1: Fig. S1).
Ascertainment of exposure and covariates
At baseline, habitual glucosamine information was collected through a touch-screen questionnaire. Participants were asked, “Do you regularly take any of the following?” and could select their answer from a list of supplementations, including vitamin, mineral, fish oil and glucosamine. From this information, regular use of glucosamine was defined as “1 = yes” and “0 = no”.
Detailed information on covariates was available through standardized questionnaires, including age, sex, race, Townsend Deprivation Index (TDI), smoking status, alcohol consumption, comorbidities (hypertension, diabetes, high cholesterol, osteoarthritis, rheumatoid arthritis, and joint pain), and drug use (cholesterol lowering medication, anti-hypertensive drug, insulin, aspirin, ibuprofen, paracetamol, and diuretics). Body mass index (BMI) (kg/m2) was calculated based on measured weight and height. Prevalent diabetes at baseline was identified through multiple procedures considering type of diabetes and sources of the diagnosis [12]. A healthy diet score was evaluated using a more recent dietary recommendation for cardiovascular health, which considered adequate consumption of fruit, vegetables, whole grains, fish, shellfish, dairy products, and vegetable oils and reduced consumption of refined grains, processed meats, unprocessed meats, and sugar sweetened beverages, and a healthy diet was defined as meeting at least five items of the recommendations [13]. In addition, biochemistry measures were performed at a dedicated central laboratory, including creatinine, urate and C-reactive protein (CRP). Estimated glomerular filtration rate (eGFR) was calculated by Chronic Kidney Disease–Epidemiology Collaboration equation (CKD–EPI) using serum creatinine [14].
Definition of genetic risk score
Detailed information about genotyping and quality control in the UK Biobank study has been described previously [15]. We selected 13 single nucleotide polymorphisms (SNPs) which showed independently significant genome-wide association with gout in recent published genome-wide associations studies (Additional file 1: Table S1) [16]. Genetic risk score (GRS) was calculated using a weighted method [17] and a higher score indicates a higher genetic predisposition to gout, and participants were divided into low, intermediate, or high genetic risk for gout according to the tertiles of GRS.
Ascertainment of outcomes
The primary outcome of the study was the incidence of gout, and gout diagnosis was extracted from “first occurrence of health outcomes defined by a 3-character International Statistical Classification of Diseases and Related Health Problems 10th Revision code (M10)” based on self-report or linkage to death register and/or primary care and/or hospital admission data. The follow-up person-time for each participant was calculated from the date of first assessment until the date of death, first date of outcome diagnosis, date of lose to follow-up, or end of follow-up, whichever came first.
Statistical analysis
Population characteristics are presented as mean ± standard deviation (SD) for continuous variables and proportions for categorical variables. Comparisons of characteristics according to glucosamine use (yes or no) by sex were performed by chi-square tests for categorical variables and t tests for continuous variables.
Cox proportional hazards models were used to estimate hazard ratio (HR) and 95% confidence interval (CI) of gout for habitual glucosamine use (yes vs. no). The proportional hazard assumption was evaluated by the interaction between exposures and follow-up time and no violation of this assumption was detected. In multivariable models, potential confounders that were known to be traditional or suspected risk factors for gout were adjusted for, including age, sex, race, TDI, BMI, smoking status, alcohol consumption, healthy diet score, vitamin or mineral supplementation, fish oil supplementation, comorbidities (hypertension, diabetes, high cholesterol, osteoarthritis, rheumatoid arthritis, and joint pain), drug uses (cholesterol lowering medication, anti-hypertensive drug, insulin, aspirin, ibuprofen, paracetamol, and diuretics), eGFR, and urate. To control the potential influence of genetic predisposition to gout, we further adjusted for gout GRS, as well as estimated the joint association of glucosamine use and gout GRS with the risk of incident gout using glucosamine non-users with low genetic risk as reference.
Stratified analysis was conducted to assess potential modification effects of glucosamine use according to age (< 60 or ≥ 60 years), BMI (< 30 or ≥ 30 kg/m2), smoking status (never or ever), alcohol consumption (< 1 or ≥ 1 times/week), health diet (yes or no), supplementation use (yes or no), diabetes (yes or no), hypertension (yes or no), diuretics use (yes or no), aspirin use (yes or no), paracetamol or ibuprofen use (yes or no), and CRP (tertiles). Potential modifying effects were assessed by modelling the cross product term of the stratifying variable with glucosamine use.
A two-tailed P < 0.05 was considered to be statistically significant in all analyses. Analyses were performed using R 4.1.1 software (http://www.R-project.org/).