Causal association between subtypes of osteoarthritis and common comorbidities: A Mendelian randomisation study

Specific blood metabolite associations with Gout: a Mendelian randomization study

OBJECTIVE

Gout, common metabolic disorders, have poorly understood links with blood metabolites. Exploring these relationships could enhance clinical prevention and treatment strategies.

METHODS

We applied bidirectional two-sample Mendelian randomization (MR) analysis, using data from a genome-wide association (GWAS) study of 486 blood metabolites. Gout data was obtained from FinnGen R8 (7461 gout and 221,323 control cases). We implemented the inverse variance-weighted (IVW) method for main analytical approach. Extensive heterogeneity, pleiotropy tests, leave-one-out analysis, and reverse MR were conducted to validate the robustness of our findings. Both Bonferroni and False Discovery Rate (FDR) corrections were used to adjust for multiple comparisons, ensuring stringent validation of our results.

RESULTS

Initial MR identified 31 candidate metabolites with potential genetic associations to gout. Following rigorous sensitivity analysis, 23 metabolites as potential statistical significance after final confirmation. These included metabolites enhancing gout risk such as X-11529 (OR = 1.225, 95% CI 1.112-1.350, P < 0.001), as well as others like piperine and stachydrine, which appeared to confer protective effects. The analysis was strengthened by reverse MR analysis. Additionally, an enrichment analysis was conducted, suggesting that 1-methylxanthine may be involved in the metabolic process of gout through the caffeine metabolism pathway.

CONCLUSION

Identifying causal metabolites offers new insights into the mechanisms influencing gout, suggesting pathways for future research and potential therapeutic targets.

Patient Experience with Chronic Refractory Gout and Its Impact on Health-Related Quality of Life: Literature Review and Qualitative Analysis

INTRODUCTION

Patients with chronic refractory gout face a considerable burden of disease due to unexpected flares characterized by severe and debilitating pain, which can lead to chronic pain and joint damage. This study aimed to understand the symptoms and impacts of chronic refractory gout on health-related quality of life (HRQoL).

METHODS

A targeted literature review was conducted to identify and review key articles describing the symptoms and impacts of gout, and articles examining the psychometric performance of the Medical Outcomes Survey Short Form-36 (SF-36) and Health Assessment Questionnaire-Disability Index (HAQ-DI) in gout. Qualitative interviews were conducted with 20 participants with chronic refractory gout. The results were used to develop the conceptual model and determine the appropriateness of the SF-36 and HAQ-DI in evaluating HRQoL in this population.

RESULTS

Most frequently reported symptoms included bodily pain (n = 18, 90.0%), joint swelling (n = 18, 90.0%), joint tenderness (n = 18, 90.0%), and joint pain (n = 16, 80.0%). Most frequently reported impacts were difficulties climbing a flight (n = 20, 100.0%) or several flights of stairs (n = 20, 100.0%), climbing five steps (n = 19, 95.0%), completing chores (n = 19, 95.0%), and running errands and shopping (n = 19, 95.0%). All assessed items from SF-36 and HAQ-DI were reported by ≥ 25% (n = 5) of participants and mapped sufficiently to concepts elicited by participants.

CONCLUSIONS

Patients with chronic refractory gout report symptoms and impacts that are highly bothersome and burdensome to everyday life. Items included in the HAQ-DI and SF-36 mapped directly to these symptoms and impacts and are relevant to understand the burden of disease of chronic refractory gout.

Variability in the prevalence of depression among adults with chronic pain: UK Biobank analysis through clinical prediction models

BACKGROUND

The prevalence of depression among people with chronic pain remains unclear due to the heterogeneity of study samples and definitions of depression. We aimed to identify sources of variation in the prevalence of depression among people with chronic pain and generate clinical prediction models to estimate the probability of depression among individuals with chronic pain.

METHODS

Participants were from the UK Biobank. The primary outcome was a "lifetime" history of depression. The model's performance was evaluated using discrimination (optimism-corrected C statistic) and calibration (calibration plot).

RESULTS

Analyses included 24,405 patients with chronic pain (mean age 64.1 years). Among participants with chronic widespread pain, the prevalence of having a "lifetime" history of depression was 45.7% and varied (25.0-66.7%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.66; good calibration on the calibration plot) included age, BMI, smoking status, physical activity, socioeconomic status, gender, history of asthma, history of heart failure, and history of peripheral artery disease. Among participants with chronic regional pain, the prevalence of having a "lifetime" history of depression was 30.2% and varied (21.4-70.6%) depending on patient characteristics. The final clinical prediction model (optimism-corrected C statistic: 0.65; good calibration on the calibration plot) included age, gender, nature of pain, smoking status, regular opioid use, history of asthma, pain location that bothers you most, and BMI.

CONCLUSIONS

There was substantial variability in the prevalence of depression among patients with chronic pain. Clinically relevant factors were selected to develop prediction models. Clinicians can use these models to assess patients' treatment needs. These predictors are convenient to collect during daily practice, making it easy for busy clinicians to use them.