Identifying the association between serum urate levels and gout flares in patients taking urate-lowering therapy: a post hoc cohort analysis of the CARES trial with consideration of dropout.

OBJECTIVE: To investigate gout flare rates based on repeated serum urate (SU) measurements in a randomised controlled trial of urate-lowering therapy (ULT), accounting for dropout and death. METHODS: We performed a secondary analysis using data from Cardiovascular Safety of Febuxostat or Allopurinol in Patients with Gout, which randomised participants to febuxostat or allopurinol, titrated to target SU <6 mg/dL with flare prophylaxis for 6 months. SU was categorised as ≤3.9, 4.0-5.9, 6.0-7.9, 8.0-9.9 or ≥ 10 mg/dL at each 3-6 month follow-up. The primary outcome was gout flare. Poisson regression models, adjusted for covariates and factors related to participant retention versus dropout, estimated gout flare incidence rate ratios by time-varying SU category. RESULTS: Among 6183 participants, the median age was 65 years and 84% were male. Peak gout flare rates for all SU categories were observed in months 0-6, coinciding with the initiation of ULT and months 6-12 after stopping prophylaxis. Flare rates were similar across SU groups in the initial year of ULT. During months 36-72, a dose-response relationship was observed between the SU category and flare rate. Lower flare rates were observed when SU ≤3.9 mg/dL and greater rates when SU ≥10 mg/dL, compared with SU 4.0-5.9 mg/dL (p for trend <0.01). CONCLUSION: Gout flare rates were persistently higher when SU ≥6 mg/dL after the first year of ULT after accounting for censoring. The spike in flares in all categories after stopping prophylaxis suggests a longer duration of prophylaxis may be warranted.

Changes in alcohol intake and serum urate changes: longitudinal analyses of annual medical examination database.

INTRODUCTION: Despite the established cross-sectional association between alcohol intake and serum urate (SU), its longitudinal association remains unknown. This study aimed to determine whether changes in alcohol intake have a clinically relevant association with SU change. METHOD: We conducted retrospective analyses using systematically collected annual medical examination data from October 2012 to October 2022 in a Japanese preventive medicine centre. The exposure was changes in alcohol intake between two consecutive visits. The association of SU changes with alcohol intake changes was estimated by mixed-effect linear regression with adjustment for relevant covariates. RESULTS: We analysed 63 486 participants (median age, 47.0 years; 55% women; 58.6% regular alcohol drinkers with a median of 1.4 drinks/day) with 370 572 visits. The median SU level was 5.3 mg/dL, and 506 (0.8%) participants had diagnoses of gout or hyperuricemia without medication use during the study period. Decreasing one daily alcohol intake had a clinically small association with SU changes (-0.019 (95% CI: -0.021 to -0.017) mg/dL). Beer had the largest association with SU (-0.036 (95% CI: -0.039 to -0.032) mg/dL for one beer decrease). Complete discontinuation of any alcohol from a mean of 0.8 drinks/day was associated with -0.056 mg/dL (95% CI: -0.068 to -0.043) decrease in SU; the association became larger in hyperuricemic participants (-0.110 mg/dL (95% CI: -0.154 to -0.066) for alcohol discontinuation from a mean of 1.0 drinks/day). CONCLUSIONS: This study revealed changes in alcohol intake had small associations with SU change at the general Japanese population level. Complete discontinuation of alcohol in hyperuricemic participants had only modest improvement in SU.

Systemic inflammatory cytokine profiles in patients with gout during flare, intercritical and treat-to-target phases: TNFSF14 as new biomarker.

INTRODUCTION: Untreated gout is characterised by monosodium urate (MSU) crystal accumulation responsible for recurrent flares that are commonly separated by asymptomatic phases. Both phases are inflammatory conditions of variable intensity. Gout flares are self-limited inflammatory reactions involving multiple mediators. This study aimed to characterise the inflammatory profiles of gout at different phases. METHODS: Using the Olink targeted proteomics, levels of 92 inflammation-related proteins were measured in plasma samples of a prospective gout population (GOUTROS), collected at gout flare (T1), the intercritical phase (T2) and after reaching the target serum urate level under urate-lowering therapy (T3). Results were validated in an independent cohort (OLT1177-05) with plasmas collected at T1 and T2. Ex vivo and in vitro experiments were performed to assess the inflammatory properties of new biomarkers. RESULTS: In total, 21 inflammatory new biomarkers were differentially expressed during the three time-points of gout disease. The levels of four of these proteins (interleukin 6 (IL-6), colony-stimulating factor 1, vascular endothelial growth factor A and tumour necrosis factor superfamily 14 (TNFSF14)) were increased during gout flare in an independent cohort. IL-6 and TNFSF14 had the highest fold change in expression during T1 versus T2 or T3. TNFSF14 was produced at the inflamed joint and enhanced the inflammatory response induced by lipopolysaccharide and MSU crystal stimulation. Conversely, TNFSF14 blockade reduced the inflammatory response. Additionally, single nucleotide polymorphisms of TNFSF14 affected the ability of myeloid cells to produce inflammatory cytokines. CONCLUSION: Gout flare involves multiple inflammatory mediators that may be used as potential therapeutic targets.

Gout risk in adults with pre-diabetes initiating metformin.

OBJECTIVE: Despite the strong association between gout and pre-diabetes, the role of metformin in gout among individuals with pre-diabetes remains uncertain. We compared the incidence rates of gout in adults with pre-diabetes starting metformin with those not using antidiabetic treatments. METHODS: We conducted a new-user, propensity score-matched cohort study using electronic health records from an academic health system (2007-2022). Pre-diabetes was defined based on haemoglobin A1c levels. Metformin users were identified and followed from the first metformin prescription date. Non-users of antidiabetic medications were matched to metformin users based on propensity score and the start of follow-up. The primary outcome was incident gout. Cox proportional hazards models estimated the HR for metformin. Linear regression analyses assessed the association between metformin use and changes in serum urate (SU) or C-reactive protein (CRP). RESULTS: We identified 25 064 individuals with pre-diabetes and propensity score-matched 1154 metformin initiators to 13 877 non-users. Baseline characteristics were well balanced (all standardised mean differences <0.1). The median follow-up was 3.9 years. The incidence rate of gout per 1000 person-years was lower in metformin users 7.1 (95% CI 5.1 to 10) compared with non-users 9.5 (95% CI 8.8 to 10.2). Metformin initiation was associated with a reduced relative risk of gout (HR 0.68, 95% CI 0.48 to 0.96). No relationship was found between metformin and changes in SU or CRP. CONCLUSIONS: Metformin use was associated with a reduced risk of gout among adults with pre-diabetes, suggesting that metformin may be important in lowering gout risk in individuals with pre-diabetes.

A machine learning-based prediction model for gout in hyperuricemics: a nationwide cohort study.

OBJECTIVE: To develop a machine learning-based prediction model for identifying hyperuricemic participants at risk of developing gout. METHODS: A retrospective nationwide Israeli cohort study used the Clalit Health Insurance database of 473 124 individuals to identify adults 18 years or older with at least two serum urate measurements exceeding 6.8 mg/dl between January 2007 and December 2022. Patients with a prior gout diagnosis or on gout medications were excluded. Patients' demographic characteristics, community and hospital diagnoses, routine medication prescriptions and laboratory results were used to train a risk prediction model. A machine learning model, XGBoost, was developed to predict the risk of gout. Feature selection methods were used to identify relevant variables. The model's performance was evaluated using the receiver operating characteristic area under the curve (ROC AUC) and precision-recall AUC. The primary outcome was the diagnosis of gout among hyperuricemic patients. RESULTS: Among the 301 385 participants with hyperuricemia included in the analysis, 15 055 (5%) were diagnosed with gout. The XGBoost model had a ROC-AUC of 0.781 (95% CI 0.78-0.784) and precision-recall AUC of 0.208 (95% CI 0.195-0.22). The most significant variables associated with gout diagnosis were serum uric acid levels, age, hyperlipidemia, non-steroidal anti-inflammatory drugs and diuretic purchases. A compact model using only these five variables yielded a ROC-AUC of 0.714 (95% CI 0.706-0.723) and a negative predictive value (NPV) of 95%. CONCLUSIONS: The findings of this cohort study suggest that a machine learning-based prediction model had relatively good performance and high NPV for identifying hyperuricemic participants at risk of developing gout.

What Is Allopurinol Failure and What Should We Do About It?

Allopurinol is the most widely used urate-lowering medication worldwide. However, allopurinol failure is frequently observed in clinical practice. In this review, we provide a framework for assessing allopurinol failure, which includes failure of allopurinol to control serum urate concentrations, failure of allopurinol to control clinical symptoms, and failure of allopurinol due to an adverse drug reaction. Understanding the causes of allopurinol failure underpins the approach required to turn failure into success in gout management.

Effects of ertugliflozin on uric acid and gout-related outcomes in persons with type 2 diabetes and cardiovascular disease: Post hoc analyses from VERTIS CV.

AIM: To conduct post hoc analyses of the VERTIS CV (NCT01986881) trial to explore the effects of ertugliflozin on serum uric acid (UA) and gout-related outcomes. MATERIALS AND METHODS: Participants with type 2 diabetes and atherosclerotic cardiovascular disease were randomised (1:1:1) to placebo, ertugliflozin 5 mg or ertugliflozin 15 mg. Mean UA over time (260 weeks) was evaluated for pooled ertugliflozin versus placebo overall, and by baseline quintile of UA (≤4.3 mg/dL [≤255.8 µmol/L], >4.3-5.1 mg/dL [>255.8-303.4 µmol/L], >5.1-5.8 mg/dL [>303.4-345.0 µmol/L], >5.8-6.9 mg/dL [>345.0-410.4 µmol/L] and >6.9 mg/dL [>410.4 µmol/L]), glycated haemoglobin level, albuminuria status, estimated glomerular filtration rate and KDIGO (Kidney Disease: Improving Global Outcomes in Chronic Kidney Disease) risk category. The effect of ertugliflozin on a composite of gout onset or initiation of anti-gout medication was assessed. RESULTS: The mean UA levels at baseline were 5.67 and 5.62 mg/dL in the placebo and ertugliflozin groups, respectively. Ertugliflozin reduced UA over Weeks 6-260 compared with placebo, with least squares mean (LSM) changes (95% confidence interval [CI]) from baseline at Week 260 of 0.07 mg/dL (-0.02, 0.15) and -0.19 mg/dL (-0.25, -0.13) in the placebo and pooled ertugliflozin groups, respectively. At Week 260, placebo-adjusted LSM change (95% CI) from baseline in UA was -0.26 mg/dL (-0.36, -0.16) with ertugliflozin. Ertugliflozin was associated with reductions in UA across baseline UA quintiles compared with placebo. The incidence of the composite of gout-related outcomes was 84/2539 (3.3%) for placebo and 133/5091 (2.6%) for ertugliflozin (hazard ratio for the composite 0.76 [95% CI 0.580, 1.002]). CONCLUSIONS: Ertugliflozin was generally associated with lowering UA overall and across subgroups compared with placebo, and numerically reduced rates of gout-related outcome events.

The development and evaluation of dose-prediction tools for allopurinol therapy (Easy-Allo tools).

AIMS: Dose escalation at the initiation of allopurinol therapy can be protracted and resource intensive. Tools to predict the allopurinol doses required to achieve target serum urate concentrations would facilitate the implementation of more efficient dose-escalation strategies. The aim of this research was to develop and externally evaluate allopurinol dosing tools, one for use when the pre-urate-lowering therapy serum urate is known (Easy-Allo1) and one for when it is not known (Easy-Allo2). METHODS: A revised population pharmacokinetic-pharmacodynamic model was developed using data from 653 people with gout. Maintenance doses to achieve the serum urate target of <0.36 mmol L-1 in >80% of individuals were simulated and evaluated against external data. The predicted and observed allopurinol doses were compared using the mean prediction error (MPE) and root mean square error (RMSE). The proportion of Easy-Allo predicted doses within 100 mg of the observed was quantified. RESULTS: Allopurinol doses were predicted by total body weight, baseline urate, ethnicity and creatinine clearance. Easy-Allo1 produced unbiased and suitably precise dose predictions (MPE 2 mg day-1 95% confidence interval [CI] -13-17, RMSE 91%, 90% within 100 mg of the observed dose). Easy-Allo2 was positively biased by about 70 mg day-1 and slightly less precise (MPE 70 mg day-1 95% CI 52-88, RMSE 131%, 71% within 100 mg of the observed dose). CONCLUSIONS: The Easy-Allo tools provide a guide to the allopurinol maintenance dose requirement to achieve the serum urate target of <0.36 mmol L-1 and will aid in the development of novel dose-escalation strategies for allopurinol therapy.

Estimation of adherence to urate-lowering therapy in people living with gout using Australia's Pharmaceutical Benefits Scheme and patient-reported dosing.

AIMS: The aim of this study was to estimate adherence to urate-lowering therapy (ULT), predominately allopurinol, from Australia's Pharmaceutical Benefits Scheme (PBS) claims database in association with (1) patient-reported doses and (2) World Health Organization's (WHO) defined daily doses (DDD), namely, allopurinol (400 mg/day) or febuxostat (80 mg/day). METHODS: Proportion of days covered (PDC) was calculated in 108 Gout App (Gout APP) trial participants with at least two recorded ULT dispensings in an approximately 12-month period before provision of intervention or control apps. Adherence was defined as PDC ≥80%. We measured the correlation between the two methods of calculating PDC using a Wilcoxon signed rank test. Agreement between ULT-taking status (self-reports) and ULT-dispensed status (PBS records) was tested with Cohen's kappa (κ), and positive and negative percent agreement. RESULTS: Allopurinol was prescribed in 93.5% of participants taking ULT. Their self-reported mean daily dose (SD) was 291 (167) mg/day. Mean PDC (SD) for allopurinol was 83% (21%) calculated using self-reported dose, and 63% (24%) using WHO's DDD. Sixty-three percent of allopurinol users were identified as adherent (PDC ≥80%) using self-reported dose. There was good agreement between self-reported ULT use and PBS dispensing claims (κ = 0.708, P < .001; positive percent agreement = 90%, negative percent agreement = 82%). CONCLUSIONS: Participant-reported allopurinol daily doses, in addition to PBS dispensing claims, may enhance confidence in estimating PDC and adherence compared to using DDD. This approach improves adherence estimations from pharmaceutical claims datasets for medications where daily doses vary between individuals or where there is a wide therapeutic dose range.

The Clinical Significance of Serum Interleukin-36α Levels in Patients with Gout.

BACKGROUND: Gout is a chronic inflammatory diseases caused by monosodium urate crystal deposition. However, the role of interleukin (IL)-36 in gout has not dbeen elucidated. METHODS: We enrolled 75 subjects, including 20 healthy controls (HC), 30 patients with acute gout attack and 25 patients in remission. Baseline data were obtained through clinical interrogation and laboratory data were obtained through tests of blood samples. Serum levels of IL-36α were detected using enzyme-linked immunosorbent assay. Spearman correlation analysis was used to investigate the correlation of IL-36α with other parameters. The diagnostic value of IL-36α was demonstrated using a receiver operating characteristic curve. RESULTS: The serum IL-36α level of gout patients in acute attack and remission stage was significantly higher than that of HC. Serum IL-36α was positively correlated with alanine transaminase (ALT) and aspartate transaminase (AST). Serum amyloid A (SAA) levels positively correlated with C-reactive protein levels and erythrocyte sedimentation rates. Glutamyl transpeptidase levels positively correlated with AST and ALT levels. CONCLUSION: In conclusion, serum IL-36α levels were elevated in patients with gout and correlated with the clinical markers of inflammation. Our findings suggest that IL-36α may be a novel inflammatory indicator for gout.