PubMed · 243167
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L Boykin. 1977. Controlling gout through diet.. https://pubmed.ncbi.nlm.nih.gov/243167/
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Gout affects over 55 million people worldwide, with prevalence projected to rise by 70% by 2050. Although observational studies have implicated several dietary, lifestyle, and metabolic risk factors, causal relationships remain uncertain because of confounding and reverse causation. Univariable and multivariable two-sample Mendelian randomization (MR) analyses were performed using genome-wide association data from 2 European cohorts: UK Biobank (6543 self-reported gout cases and 456,390 controls) and FinnGen (3576 cases and 147,221 controls). Genetic instruments for 12 exposures, including dried fruit, salad, and cheese intake; smoking initiation; physical activity; body mass index (BMI); and lipid traits, were derived from established genome-wide association study datasets. Inverse-variance weighted regression with multiplicative random effects was the primary analysis, complemented by weighted median, weighted mode, MR-Egger, MR-Pleiotropy RESidual Sum and Outlier, and 3 predefined multivariable models. Benjamini-Hochberg correction was applied across all 24 tests. BMI showed the strongest and most consistent causal effect on gout (FinnGen: odds ratio [OR] = 1.97, 95% confidence interval [CI] = 1.51-2.57; UK Biobank: OR = 1.006, 95% CI = 1.003-1.009; both P < .001) and remained significant in 5 of 6 multivariable models. Triglycerides increased risk in both cohorts (FinnGen: OR = 1.34, 95% CI = 1.08-1.66; UK Biobank: OR = 1.008, 95% CI = 1.004-1.012). These associations survived Benjamini-Hochberg correction, as did high-density lipoprotein cholesterol in UK Biobank (OR = 0.997, 95% CI = 0.994-0.999). Dried fruit intake was inversely associated with gout in FinnGen (OR = 0.34, 95% CI = 0.13-0.92, P = .034) but not in UK Biobank, and did not survive correction for multiple testing. No other exposure reached significance in either cohort. This study provides genetic evidence that BMI is the dominant modifiable causal determinant of gout, with triglycerides contributing independently. Dietary associations were weaker and did not withstand correction for multiple testing, and should be regarded as hypothesis-generating. These findings support prioritizing weight management and metabolic health in gout prevention.
AIMS: The Outcome Measures in Rheumatology (OMERACT) initiative has endorsed core domain sets for gout trials. The aims of this study were to evaluate the time points and frequencies at which the gout core domains are measured in existing gout urate-lowering therapy and gout flare trials, and whether all collected measurements were reported. METHODS: Urate-lowering therapy (n = 29) and gout flare randomised clinical trials (n = 14) from 2005 were identified from a prior systematic review of core domain reporting. Data were extracted for the time points and frequencies at which each core domain was measured, as well as whether all collected measurements were reported. RESULTS: In urate-lowering therapy trials, the core domains were measured at seven different frequencies. Serum urate and gout flares were most commonly measured monthly, and tophus burden was most commonly measured three monthly. Reporting of all collected measurements varied, from 24/29 (83%) trials for serum urate to 0/2 (0%) trials for activity limitation. In gout flare trials, core domains were measured at nine different frequencies. Pain, joint tenderness and joint swelling were most commonly measured monthly. Reporting of all collected measurements varied, from 13/14 (93%) trials for pain to 3/8 (37.5%) trials for joint tenderness. CONCLUSION: In both urate-lowering therapy and gout flare trials, there is substantial variability in when the core domains are measured, and reporting of collected measurements is inconsistent. This work provides the foundation for a consensus process to establish standardised time points and frequencies for measuring the OMERACT-endorsed gout core domains.
INTRODUCTION: Gout, a highly serious inflammatory disease that is caused by monosodium urate crystals, is becoming an increasingly significant health concern. Artificial Intelligence and multi-omics-based research have made significant gains for the early detection and prevention of gout based on diverse approaches. This review intends to summarize current advances in forecasting gout susceptibility and gout-related symptoms, evaluate the predictive efficacy of different features, and ascertain which clinical and omics characteristics are most effective in these prediction models. METHODS: We explored the PubMed database after 2010 using keywords such as "gout", "predictive model", "risk prediction", and "machine learning", and confined our search to Englishlanguage articles. The original peer-reviewed research articles that developed gout models were selected. Research that was not original or lacked internal validation was excluded. RESULTS: Clinical features, genomics, microbiomics, radiomics, and metabolomics have been utilized to construct models related to gout and have demonstrated excellent predictive performance. Multisource data prediction models usually exhibit better effectiveness. DISCUSSION: Gout-oriented models performed excellently in predictive performance but present limitations in certain clinical and omics domains. However, if they are to affect actual patient care, they must overcome some external confirmation roadblocks and the fiscal and practical implications they will face ahead of time. CONCLUSION: This review indicates that clinical and multi-omics models of gout are significant instruments for clinical decision-making. The models constructed in these studies may be crucial for the treatment of gout and its practical benefits.