IL-33 enhances macrophage release of IL-1β and promotes pain and inflammation in gouty arthritis

OBJECTIVE

To investigate the role of IL-33 in gouty arthritis.

MATERIAL

174 Balb/c (wild-type) and 54 ST2^-/- mice were used in this study. In vitro experiments were conducted in bone marrow-derived macrophages (BMDMs). Synovial fluid samples from gouty arthritis (n = 7) and osteoarthritis (n = 8) hospital patients were used to measure IL-33 and sST2 levels.

METHODS

Gout was induced by injection of monosodium urate (MSU) crystals in the knee joint of mice. Pain was determined using the electronic von Frey and static weight bearing. Neutrophil recruitment was determined by H&E staining, Rosenfeld staining slides, and MPO activity. ELISA was used for cytokine and sST2 measurement. The priming effect of IL-33 was determined in BMDM.

RESULTS

Synovial fluid of gout patients showed higher IL-33 levels and neutrophil counts than osteoarthritis patients. In mice, the absence of ST2 prevented mechanical pain, knee joint edema, neutrophil recruitment to the knee joint, and lowered IL-1β and superoxide anion levels. In macrophages, IL-33 enhanced the release of IL-1β and TNF-α, and BMDMs from ST2^-/- showed reduced levels of these cytokines after stimulus with MSU crystals.

CONCLUSION

IL-33 mediates gout pain and inflammation by boosting macrophages production of cytokines upon MSU crystals stimulus.

Physical activity measured using wearable activity tracking devices associated with gout flares

Objective

To determine the feasibility and validity of using wearable activity trackers to test associations between gout flares with physical activity and sleep.

Methods

Participants with physician-diagnosed gout, hyperuricemia (≥ 6.8 mg/dl), current smartphone use, and ≥ 2 self-reported flares in the previous 6 months were enrolled. Physical activity, heart rate, and sleep data were obtained from wearable activity trackers (Fitbit Charge HR2). Daily compliance was defined by the availability of sufficiently complete activity data at least 80% of the day. Associations of weekly gout flares with sleep and activity were measured by comparing flare-related values to average sleep and steps per day. We used mixed linear models to account for repeated observations.

Results

Forty-four participants enrolled; 33 met the criteria for minimal wear time and flare reporting, with activity tracker data available for 60.5% of all total study days. Mean ± SD age was 48.8 ± 14.9 years; 85% were men; 15% were black; 88% were on allopurinol or febuxostat, and 30% reported ≥ 6 flares in the prior 6 months. Activity trackers captured 204 (38%) person-weeks with flares and 340 (62%) person-weeks without flares. Mean ± SD daily step count was significantly lower (p < 0.0001) during weeks with gout flares (5900 ± 4071) than during non-flare periods (6972 ± 5214); sleep however did not differ.

Conclusion

The pattern of wear in this study illustrates reasonable feasibility of using such devices in future arthritis research. The use of these devices to passively measure changes in physical activity patterns may provide an estimate of gout flare occurrence and duration.

Trial registration

NCT, NCT02855437. Registered 4 August 2016

Comparison of an interactive voice response system and smartphone application in the identification of gout flares

OBJECTIVE

To examine the feasibility, preference, and satisfaction of an interactive voice response (IVR) system versus a customized smartphone application (StudyBuddy) to capture gout flares METHODS: In this 24-week prospective, randomized, crossover, open-label pilot study, 44 gout patients were randomized to IVR vs. StudyBuddy and were crossed over to the other technology after 12 weeks. Flares were reported via weekly (and later daily) scheduled StudyBuddy or IVR queries. Feasibility was ascertained via response rate to scheduled queries. At 12 and 24 weeks, participants completed preference/satisfaction surveys. Preference and satisfaction were assessed using dichotomous or ordinal questions. Sensitivity was assessed by the frequency of flare reporting with each approach.

RESULTS

Thirty-eight of 44 participants completed the study. Among completers, feasibility was similar for IVR (81%) and StudyBuddy (80%). Conversely, most (74%) preferred StudyBuddy. Measures of satisfaction (ease of use, preference over in-person clinic visits, and willingness for future use) were similar between the IVR and StudyBuddy; however, more participants deemed the StudyBuddy as convenient (95% vs. 73%, P = 0.01) and less disruptive (97% vs. 82%, P = 0.03). Although the per patient number of weeks in flare was not significantly different (mean 3.4 vs. 2.6 weeks/patient, P = 0.15), the StudyBuddy captured more of the total flare weeks (35%) than IVR (27%, P = 0.02).

CONCLUSION

A smartphone application and IVR demonstrated similar feasibility but overall sensitivity to capture gout flares and participant preference were greater for the smartphone application. Participant preference for the smartphone application appeared to relate to perceptions of greater convenience and lower disruption.

TRIAL REGISTRATION

NCT, NCT02855437 . Registered 4 August 2016.

Gout, flares, and allopurinol use: a population-based study

BACKGROUND

There is a paucity of community-based data regarding the prevalence and impact of gout flares as these may often be self-managed. The aim of this study was to determine the prevalence of self-reported gout and gout flares, the use of urate-lowering therapy (ULT), and the association of gout flares with health-related quality of life (HRQoL) in a large community sample. Covariate associations with flare frequency and allopurinol use were also examined.

METHODS

The South Australian Health Omnibus Survey is an annual, face-to-face population-based survey. Data collected in the 2017 survey included self-reported medically diagnosed gout, allopurinol use (first-line ULT in Australia), and gout attacks (flares) in the last 12 months, in addition to sociodemographic variables and health-related quality of life (HRQoL, SF-12). Data were weighted to the Australian Bureau of Statistics 2016 census data to reflect the South Australian population. Participants 25 years and over (n = 2778) were included in the analysis.

RESULTS

The prevalence of gout was 6.5% (95%CI 5.5, 7.5). Amongst participants with gout, 37.1% (95%CI 29.6, 45.3) reported currently using allopurinol, while 23.2% (95%CI 16.9, 21.0) reported prior use (38% discontinuation rate). Frequent flares (≥ 2 in the last year) were reported by 25% of participants with gout and were more likely with younger age, higher body mass index, and current allopurinol use (p < 0.05). The frequency of gout flares was associated with a lower physical HRQoL (p = 0.012). Current allopurinol use was reported by 51% of participants with frequent gout flares.

CONCLUSION

Flares were frequently reported by people with gout in the community. Gout flares were associated with reduced physical HRQoL. Almost one half of people with frequent gout flares were not receiving allopurinol, and current allopurinol use was associated with frequent gout flares, suggesting undertreated disease and suboptimal use of ULT. Determining covariate associations with flares and ineffective allopurinol use may identify means of improving treatment and reducing flares.

Serum urate as surrogate endpoint for flares in people with gout: A systematic review and meta-regression analysis

OBJECTIVES

The primary efficacy outcome in trials of urate lowering therapy (ULT) for gout is serum urate (SU). The aim of this study was to examine the strength of the relationship between SU and patient-important outcomes to determine whether SU is an adequate surrogate endpoint for clinical trials.

METHODS

Multiple databases through October 2017 were searched. Randomized controlled trials comparing any ULT in people with gout with any control or placebo, ≥three months duration were included. Open label extension (OLE) trial data were included in secondary analyses. Standardized data elements were extracted independently by two reviewers.

RESULTS

Ten RCTs and 3 OLE studies were identified. From the RCTs (maximum duration 24 months) meta-regression did not reveal an association between the relative risk of a gout flare and the difference in proportions of individuals with SU < 6mg/dL (P = 0.47; R2 = 8%). In a post hoc analysis, the ratio of the time in months at which the proportion of individuals having a flare was reported/time in months at which the proportion of individuals with SU < 6mg/dL was reported was calculated and studies where the ratio was <2 were excluded. Using the remaining 6 studies there was an association between proportion of individuals achieving SU < 6mg/dL and gout flares (over patient years). Duration of ULT was inversely associated with the proportion of patients experiencing a flare. Study duration and variability in reporting of outcomes limited the analysis. Observational studies supported the trend of fewer flares in those with lower SU.

CONCLUSIONS

Based on aggregate clinical trial-level data an association between SU and gout flare could not be confirmed. However, based on observational ecological study design data-including longer duration extension studies-SU < 6mg/dL was associated with reduced gout flares.

Patient and clinical characteristics associated with gout flares in an integrated healthcare system

Gout flares have been challenging to identify in retrospective databases due to gout flares not being well documented by diagnosis codes, making it difficult to conduct accurate database studies. Previous studies have used different algorithms, and in this study, we used a computer-based method to identify gout flares. The objectives of this study were to identify gout flares in gout patients newly initiated on urate-lowering therapy and evaluate factors associated with a patient experiencing gout flares after starting drug treatment. This was a retrospective cohort study identifying gout patients newly initiated on a urate-lowering therapy (ULT) during the study time period of January 1, 2007-December 31, 2010. The index date was the first dispensed ULT prescription during the study time period. Patients had to be ≥18 years of age on index date, have no history of prior ULT prescription during 12 months before index date, and were required to have 12 months of continuous membership with drug benefit during pre-/post-index. Electronic chart notes were reviewed to identify gout flares; these reviews helped create a validated computer-based method to further identify patients with gout flares and were categorized into 0 gout flares, 1-2 gout flares, and ≥3 gout flares during the 12 months post-index period. Multivariable logistic regression was used to examine patient and clinical factors associated with gout flares during the 12-month follow-up period. There were 8905 patients identified as the final cohort and 68 % of these patients had one or more gout flares during the 12-month follow-up: 2797 patients (31 %) had 0 gout flares, 4836 (54 %) had 1-2 gout flares, and 1272 patients (14 %) had ≥3 gout flares. Using a multivariate regression analyses, factors independently associated with 1-2 gout flares and ≥3 gout flares versus no gout flares were similar, however, with slight differences, such as younger patients were more likely to have 1-2 gout flares and patients ≥65 years of age had ≥3 gout flares. Factors such as male gender, not attaining sUA goal, having ≥3 comorbidities, diuretics use, no changes in initial ULT dose, and not adhering to ULT all were associated with gout flares versus no gout flares. Using a new method to identify gout flares, we had the opportunity to compare our findings with the previous studies. Our study findings echo other previous studies where older patients, male, diuretics, having a greater number of comorbidities, and non-adherence are more likely to have more gout flares during the first year of newly initiating ULT. There is an unmet need for patients with gout to be educated and managed more closely, especially during the first year.