Health Qual Life Outcomes. 2012; 10: 117.

Published online 2012 Sep 22. doi: 10.1186/1477-7525-10-117

Tophi and frequent gout flares are associated

Background

Gout affects 3.9% of the adult population in the US (8.3 million) [1] and over 1% of the adult
population in Germany and the UK [2]. Gout has increased in prevalence worldwide [3,4] and it
is the most common inflammatory arthritis in men [5]. Acute gout manifests when monosodium
urate (MSU) crystallizes and deposits in joints, bursae or tendon sheaths, and provokes an
inflammatory response that causes a typical gout flare. This flare is characterized by an acute
onset (maximum within 24 hours) of a heavily inflamed and extremely painful mono- or
oligoarthritis which often results in short-term sick leave [6]. Serum urate (sUA) concentration
above the limit of solubility (>6.8 mg/dL/400 μmol/L) leads to crystal deposition which is a
necessary precursor for this disease, though many with hyperuricemia will never develop gout
[7,8]. Gout patients whose sUA is maintained below 6 mg/dL (360 μmol/L) over time can expect
to remain flare free and this is a most important aspect of long-term management of gout [9-12].
Higher sUA levels predict more flares and development of tophi [2,13-16], and maintaining
lower sUA prevents the formation of new tophi and reduces the size of established ones [17,18].

Gout patients frequently have a number of comorbidities, including obesity, hypertension, high
serum lipid and cholesterol levels, kidney disease, diabetes, and cardiovascular disease [2,7,19-
21]. Due to the rising incidence and prevalence of gout, greater scrutiny has been directed
towards the impact of gout on health-related quality of life (HRQOL) [22,23], healthcare
resource utilization, and work productivity, a task complicated by the presence of the other
ailments.

Although there is an expanding literature on the humanistic and economic burden of gout, gaps
remain. Much of the current literature linking gout to HRQOL either relies on data from US
veterans [24], patients at a small number of medical facilities [20], or studies with small sample
sizes [22], while data on work productivity and activity impairment in gout is very sparse.
The objective of the present study was to better understand the burden associated with signs and
symptoms of gout (namely, tophi and flares) in larger, more diverse, and more representative
samples of patients across the US and EU than has been reported previously. The secondary
objective was to distinguish the burden of symptoms directly attributable to gout from those
associated with comorbid diseases. By highlighting the heterogeneity of disease burden within
the gout-diagnosed population, examining the impact of both tophi and flares across samples of
US and EU patients, this study adds breadth and specificity to current analyses of gout burden in
the literature and can thus contribute to more effective disease management and improved patient
outcomes.

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Methods
Data source

Self-reported data were obtained from patients identified through the US and EU versions of the
2010 National Health and Wellness Survey (NHWS; Kantar Health, New York, NY, USA) and
the Lightspeed Research (LSR; New York, NY, USA) ailment panel (used to supplement
respondents not available through NHWS). The NHWS is a demographically representative,
annual cross-sectional survey of adult respondents (18 years of age and over), providing self-
reported information on treatment, healthcare attitudes and behaviors, patient disease and
demographic characteristics, and health-related outcomes. NHWS and LSR ailment panel
members are both sourced from a more general LSR panel, whose members are recruited
through opt-in emails, co-registration with panel partners, e-newsletter campaigns, online banner
placements, and both internal and external affiliate networks. All panelists explicitly agreed to
become panel members, registered through unique email addresses, and completed in-depth
demographic registration profiles.

A stratified random sample procedure is implemented for NHWS so that the final sample mimics
the demographic composition of the country in which it is administered, in order to achieve
better representativeness. The US sample is stratified by age, gender, and ethnicity, and the EU
sample is stratified by age and gender. Comparisons between the NHWS sample, the US census,
and other national surveys have been made elsewhere [25,26].

All respondents, from both NHWS and the current study, gave informed consent, and the study
was approved by the Essex Institutional Review Board (Lebanon, NJ, USA).

Study population

Panel members reporting a physician diagnosis of gout were invited to participate via email in an
online, self-administered survey. Out of 1936 patients reporting a physician diagnosis of gout
invited to participate, 747 responded (a 39% response rate), and 620 patients completed the
survey (563 via NHWS), including 338 (54.5%) from the US, 181 (29.2%) from the UK, 85
(13.7%) from Germany, and 16 from France (2.6%).
Measures

Participants completed a web-based questionnaire that included questions regarding the patient’s
gout and several validated scales.

Gout characteristics

Information was collected concerning the patient’s gout including their most recent serum urate
level (sUA: <6 mg/dL/360 μmol/L, 6 - 8 mg/dL/360-480 μmol/L, or >8 mg/dL/480 μmol/L), the
number of flares in the past 12 months (don’t recall, 0, 1–2, 3, 4–5, or 6+), the presence of tophi
(not sure, 0, 1, or 2+), and the number of years since they were diagnosed with gout.

Health-related quality of life

HRQOL was assessed using the Medical Outcomes Short Form 12 (SF-12v2) questionnaire [27].
This instrument allows for the calculation of physical (PCS) and mental (MCS) component
summary scores. Scores for the PCS and MCS are normed to the US population (Mean = 50,
SD = 10), with higher scores indicating greater HRQOL. SF-6D health utilities were also
calculated from responses to the SF-12v2 [28]. Scores for the SF-6D range from 0.29 (extremely
poor health) to 1 (perfect health). Differences in PCS and MCS exceeding 3 points are
considered minimally important differences (MIDs) [29], and 0.03 is the MID for the SF-6D
[30].

Resource use

The number of visits to a traditional healthcare provider in the past six months was assessed.

Work productivity and activity impairment

Work productivity impairments and impairment in daily activities were assessed using the
validated Work Productivity and Activity Impairment (WPAI) questionnaire [31]. Four subscales
(absenteeism, presenteeism, overall work impairment, and activity impairment) were generated
in the form of percentages, with higher values indicating greater impairment. Absenteeism
represents the percentage of work time missed due to health in the past seven days. Presenteeism
represents the percentage of impairment while at work due to health in the past seven days.
Overall work impairment (OWI) is the total percentage of work time missed in the last 7 days
due to either absenteeism or presenteeism. Activity impairment represents the percentage of
impairment during daily activities. Only employed respondents provided data on absenteeism,
presenteeism, and overall work impairment, but all respondents provided data on activity
impairment.

Statistical analyses

Descriptive statistics and frequency distributions were undertaken for all variables. Bivariate
comparisons on continuous variables and scales were conducted using t-tests for comparisons
between two groups and ANOVA for comparisons with three groups or more, followed by
Tukey HSD post hoc tests. Chi-square tests were conducted in the case of categorical variables.
Because symptom levels were collected as categories rather than exact numbers, correlations
between levels of flares, tophi, and sUA were conducted using Spearman’s rho.

For the purposes of analysis, patients who did not know whether they had experienced flares
were combined into the zero flares group, the assumption being that any flare that they might
have experienced was not serious enough to have been identified by a physician. Moreover,
analysis of the prevalence of flares revealed that there were only eight patients (1%) who did not
recall whether they had flares. Nearly one-third (n = 195, 31%) of respondents were unsure of
whether they had tophi, possibly indicating that they suspected a tophus but were not certain
given the description in the questionnaire (“Tophi are deposits of crystallized uric acid that can
appear as moveable lumps or whitish nodules”). Given the substantial number of such patients, it
was deemed prudent to examine them as a separate group.

Multivariable models predicting healthcare resource use, work productivity, and HRQOL
included frequency of flares in the last 12 months (1–2, 3, 4–5, or 6+, relative to 0/don’t recall
flares), the presence of tophi (1+, or not sure, relative to 0 tophi), age, gender, and length of gout
diagnosis (in years) as predictors. HRQOL analyses (MCS, PCS, and SF-6D) were conducted
using maximum likelihood multiple regression to adjust the MCS and PCS scores and SF-6D
health utilities for the effects of age, gender, and length of illness. Distributions of scores were
examined and were normally distributed, so no transformation was applied. Because work
productivity, impairment, and resource utilization are often highly skewed, we used a
generalized linear model (GLM) approach specifying a negative binomial distribution. Adjusted
logarithmically transformed counts were modeled in the GLM. This approach tested whether the
adjusted transformed counts differed among the groups while accounting for covariates. In order
to make the results more interpretable, we calculated the antilog of regression estimates, which
yields rate ratio (RR) values. The rate ratio indicates the number of times greater impairment was
for the given group compared with the reference group (e.g. a rate ratio value of 1.5 would
indicate that the mean level of absenteeism for the ≥ 3 flares/year group is 1.5 times that of no
flares).

The gout questionnaire did not assess respondents’ comorbid status. However, given the likely
contribution of higher comorbidities to poorer health outcomes among respondents, comorbid
status may be confounded with gout flares and tophi in the analyses. Therefore, a supplementary
set of analyses was conducted on the subsample of respondents re-contacted via the NHWS
(n = 563), using the same predictors and outcomes as described above, but adjusting additionally
for relevant variables available from the NHWS, in which respondents had participated
previously. In particular, the supplementary models controlled for self-reported diagnosis with
diabetes mellitus, hypertension, or chronic kidney disease, as well as BMI category (overweight,
obese, or missing BMI, vs. normal/underweight as reference). Given the loss of statistical power
associated with the reduced sample, additional covariates, and noise introduced by non-
contemporaneous measures assessed at different lags over the course of the previous year, the
supplementary results are reported only as a footnote to the main analyses, for the purpose of
helping rule out obvious confounds.
Statistical significance was set at p < 0.05 for all hypotheses tested. Analyses were performed
using SPSS version 19.0.

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Results
Demographics & patient characteristics

The characteristics of the sample are presented in Table Table11 and Table Table2.2. The
average age was 60.9 years, and patients had been diagnosed an average of 12 years. Most were
male (81%), and 38% were employed. Approximately half were on allopurinol (51%). Thirteen
percent had at least one tophus, and an additional 31% were unsure of whether they had any
tophi. One quarter (25%) of the sample had been flare-free for the past 12 months, while 38%
experienced at least 3 flares in the same time period.

Table 1

Patient characteristics

Table 2

Patient characteristics by tophi and frequency of gout flares

The subsample of 563 respondents sourced from the NHWS was similar to the overall sample.
Mean age was 61.2 years, 82% were male, and mean duration of gout was 12 years. The
distribution of flares in the past 12 months was the same as in the full sample, and the proportion
of patients reporting tophi was similar (12% reporting at least 1 tophus, 33% unsure, and 55%
reporting none). Information about BMI and comorbidities were also available for this subgroup.
Most were obese (53.8%) or overweight (33.9%), with 9.9% underweight or of normal weight,
and 2.3% declining to answer questions about weight or height. In terms of comorbidities, 5%
reported chronic kidney disease, 26% reported type 2 diabetes, and 63% reported hypertension.

sUA levels

Nearly three quarters of the patients (72%) did not know their most recent sUA level; of those
who knew their levels (n = 172), 62 (36%) reported sUA below 6 mg/dL/360/μmol/L. sUA level
was positively correlated with flares experienced in the last 12 months, rs = 0.36, as well as the
number of tophi currently present, rs = 0.37, both p < 0.001. Tophi were also associated with a
higher number of flares in the last 12 months, rs = 0.29, p < 0.001. Being unsure of tophi was
also associated with higher sUA, rs = 0.19, p < 0.05, as well as experiencing more frequent flares,
rs = 0.20, p < 0.001.

The burden of tophi

Tophi were associated with considerable impairment of HRQOL. Patients with tophi had lower
MCS, PCS, and SF-6D scores than patients without tophi, while the scores of those patients
unsure of tophi fell in-between the two other groups. Post hoc tests revealed that both the tophi
group (≥ 1 tophus) and the unsure group had significantly impaired HRQOL relative to the no
tophi group for all three measures (all p < 0.01). The magnitude of these decrements for all three
measures exceeded the commonly accepted MIDs [29,30,32]. Mean values and omnibus tests of
statistical significance are presented in Table Table33.

Table 3

Health-related quality of life, work productivity, and resource use by the presence of tophi (unadjusted)

Patients with tophi also had significantly greater impairment of work productivity and activity.
Tophaceous gout was associated with greater absenteeism and overall work impairment than
both the group without tophi and the unsure group (both p < 0.05). The presence of tophi was
also associated with significantly greater impairment in daily activities as compared with those
free of tophi and those unsure of their presence (both p < 0.01).

The burden of flares

Unadjusted comparisons revealed that HRQOL varied across the different flare groups, with
more flares associated with lower HRQOL in a dose-dependent manner. SF-12 MCS, PCS, and
SF-6D health utilities were all statistically significantly lower in patients with more frequent
flares (all p < 0.001). Although flares were associated with an apparent numerical increase in
work impairment, this difference did not reach statistical significance within the employed
subsample. However, more frequent flares were associated with statistically significantly greater
impairment of activity in the total sample, with those having the most frequent flares suffering
nearly double the activity impairment of those without. Means and tests of significance are
presented in Table Table44.

Table 4

Health-related quality of life, work productivity, and resource use by annual gout flares (unadjusted)

Multivariable models

Multivariable models, adjusting for covariates and simultaneous presence of flares and tophi,
showed that tophi (vs. no tophi) were associated with statistically significantly lower MCS
(p < 0.01) and PCS (p < 0.01) scores, as was being unsure of the presence of tophi (both
p < 0.01). More frequent flares (≥ 4) were also associated with lower MCS (p < 0.05); those with
3 or more flares also had lower PCS scores (p < 0.05). The adjusted means are presented in Table
Table5.5. A similar pattern emerged in the analyses of health utilities, presented in Figure
Figure1.1. As in the analysis of MCS and PCS scores, both confirmed tophi and being unsure of
tophi were associated with significant impairment of HRQOL (both p < 0.01). More frequent
flares (≥ 3) were associated with additional decrements in SF-6D utilities, after adjustment for
age, gender, and length of diagnosis (p = 0.001).

Table 5

Mean MCS and PCS values by flare frequency and the presence of tophi, adjusted for age, gender, and
length of illness
Figure 1

Mean SF-6D health utilities by flare frequency and the presence of tophi, adjusted for age, gender,
and length of illness. Lower values indicate worse health; error bars represent standard error of the
mean. Tophi (1+ or not sure) and flares (4+) are ...

Patients with 1–2 flares in the past year showed significant activity impairment compared with
those without, even after adjustment for covariates (p < 0.05), and impairment increased with
frequency of flares. Those unsure of tophi were significantly more impaired in non-work
activities than those without (p < 0.05), though lack of power prevented this effect from reaching
conventional statistical significance among those with tophi (p = 0.07). The adjusted means are
displayed in Figure Figure22.

Figure 2

Mean overall activity impairment by flare frequency and presence of tophi, after adjusting for age,
gender, and length of illness. Higher values indicate greater impairment; error bars represent standard
error of the mean. Those with any flares are significantly ...

No associations between flares or tophi and measures of work productivity were observed after
adjusting for covariates, but numbers were small as these analyses were of necessity limited to
the employed 38% of the sample.

We also examined the data from the smaller sample of respondents re-contacted from NHWS,
and adjusting for additional covariates (i.e., BMI, diagnosis with chronic kidney disease, type 2
diabetes, and hypertension), the presence of tophi, uncertainty about tophi, and frequent flares (≥
4) were all significantly associated with decreased MCS, PCS, and health utilities, as well as
increased activity impairment. Specifically, having more than 4 flares in the past year (vs.
0/unknown) was associated with lower MCS (p < 0.05), PCS (p < 0.05), SF-6D (p < 0.05), and
activity impairment (p < 0.05) in the supplementary regressions. Likewise, those who were not
sure of whether they had tophi exhibited lower MCS (p < 0.01), PCS (p < 0.001), SF-6D
(p < 0.001), and activity impairment (p < 0.01) than those with no tophi. Those reporting at least
one tophus (vs. no tophi) also demonstrated lower MCS (p < 0.05), PCS (p < 0.01), SF-6D
(p < 0.01), and activity impairment (p < 0.05). Despite accounting for potential confounders and
with reduced power, almost all results were replicated in terms of magnitude, direction, and
significance. The only discrepancies in results were that 3 flares became non-significantly
associated with a health utilities decrement (p = 0.31), and the associations between 1–3 flares
and greater activity impairment (relative to no flares) fell below conventional levels of
significance (1–2 flares: p = 0.051; 3 flares: p = 0.075).

Comparisons across the spectrum of disease phenotypes

Patients were categorized into three subgroups based on frequency of flares and the presence of
tophi, without regard to treatment; 1) asymptomatic gout (no flares in the past year, no tophi), 2)
severe tophaceous gout (defined as confirmed tophi and ≥ 3 flares in the past year) and 3) very
severe tophaceous gout (defined as confirmed tophi and ≥ 6 flares in the past year). The
groupings were similar to those employed in a previous study [33]. Both the severe group
(n = 44) and very severe subgroup (n = 11) had significantly lower SF-6D health utilities than did
the asymptomatic group (both p < 0.01).

The average SF-6D scores of the severe and very severe gout patients were also compared with
the average SF-6D scores of US patients with other chronic rheumatic diseases. Average SF-6D
scores for gout patients who did not report rheumatoid arthritis (RA), osteoarthritis (OA), or
systemic lupus erythematosus (SLE), and average utilities of patients with RA, OA, and SLE
patients without comorbid gout were calculated from the 2010 US NHWS. The means are
presented in Figure Figure3.3. Independent-sample t-tests revealed that asymptomatic gout
patients had higher health utilities than all other patient groups (all p < 0.05). In contrast to the
asymptomatic patients, those in our sample with severe gout had health utilities similar to the
average SLE or RA patient (both p > 0.05), and significantly worse than the average gout or
osteoarthritis patient in the NHWS (p < 0.05). Patients with very severe gout had significantly
lower health utilities than any comparison group (p < 0.01).

Figure 3

Average health utilities of patients with gout and patients with other rheumatic diseases. Average
health utilities are unadjusted values taken from 2010 US NHWS (red) and the current study (orange).
Error bars represent standard error of the mean. Mean ...

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Discussion

This study demonstrates that gout patients from the US and three EU countries suffer from
significantly decreased HRQOL, with the average patient suffering decreased mental as well as
physical well-being, relative to population norms. These decrements in HRQOL vary across the
spectrum of clinical phenotypes according to the presence and frequency of gout symptoms and
signs. Patients with confirmed tophi and more frequent acute gout attacks had lower HRQOL,
and the association between HRQOL, tophi and flares remained significant even after accounting
for covariates. Impairments were not limited to HRQOL, and unadjusted comparisons revealed
that patients reporting tophi had greater work impairment than those without, while more
frequent flares were associated with increased healthcare resource use. Both flares and tophi
were also associated with significant impairments in non-work activities in multivariable models.

Impairment of the physical components of quality of life have been found consistently in studies
of gout patients [22,24,34,35], and was notably related to the burden of symptoms and signs in
this sample. Those with more severe gout had low PCS compared with both population norms
and those gout patients who were free of tophi and flares. These differences were statistically
significant, and greater than those which are generally accepted as being clinically meaningful.
These studies confirm a relationship between symptom load and HRQOL previously observed in
smaller samples of gout patients in the US [20,35]. The PCS levels in the most severely affected
patients in these studies were comparable to those reported in previous studies of patients with
severe symptomatic gout and patients with gout who had not responded to urate-lowering
therapy [20,35].

Gout patients are known to use more healthcare resources. An analysis of claims data showed
that gout sufferers incurred more costs for medical claims, prescription claims, sick leave, short-
term disability, and worker’s compensation than did other employees [36]. Another database
study of gout in the elderly found higher healthcare resource utilization in gout patients than in
matched controls, which was attributable in part to having more comorbidities [37]. While
comorbid conditions may account for some of the elevated resource use among gout patients,
gout-related healthcare utilization increases with severity of gout [38]. A study using the
MarketScan database showed that patients having 3 or more flares per year had more
comorbidities, and incurred $10,222 more per year in healthcare costs than age and gender
matched controls without gout [33]. Administrative claims data also show that higher sUA is
associated not only with a greater number of flares, but also with higher costs per flare [16,39].

There is evidence that gout affects worker productivity. A diary study of patients with chronic
gout refractory to conventional urate-lowering therapy found an average annual workday loss of
25 days [36]. Another study showed that employees with gout missed 4.56 more days of work
per year [40]. Gout symptoms and signs were also associated with impairment in daily activities,
although the limited number of patients reporting tophi restricted the possibility of detecting
activity impairment associated with tophi over and above the strong effect of flares. It is
important to bear in mind that these impairments are compared with those of gout patients who
are free of acute symptoms, rather than those in the population at large. It seems likely that
comparisons between these patients and the general population would demonstrate larger
differences in HRQOL and impairment of activities. The failure to demonstrate significant
reduction in work productivity in the gout patients participating in these studies should not be
interpreted as indicating that gout has no effect on work performance or productivity. The
number of patients in employment in the study was relatively small and as the average age was
61 years, many are likely to have been in retirement. A recent diary study documented
productivity impairments due to gout flares [41], a finding that needs to be confirmed in a larger
sample of employed patients. It is certainly possible, however, that patients with severe gout lose
their jobs or decide to retire as a result of their disease; such loss of productivity would not be
ascertained in a diary study or with the WPAI questionnaire used in this study.
In the present study, patients who reported symptoms of inadequately controlled chronic gout
(similar to refractory chronic gout), defined here as at least three flares in the past year and the
presence of at least one tophus, had severely impaired HRQOL, with SF-6D health utilities 0.13
below those in patients free of tophi and free of flares for the past 12 months. The magnitude of
these decrements can be placed into context by comparing the health utilities of patients with
gout across the spectrum of clinical phenotypes with those reported by patients with other
rheumatic diseases. As indicated in Figure Figure3,3, patients with gout had similar health
utilities to patients suffering from RA or SLE. Those with most severe gout, characterized by
tophi and six or more flares in the past year, had health utilities that were significantly lower than
the average for either RA or SLE. The findings emphasize the importance of seeking to provide
effective treatment for all patients with gout and particularly those with tophi and frequent flares.

Our study has a number of limitations. As in all cross-sectional analyses based on self-reported
patient information the data reported may be subject to recall bias and the ability of the patients
to accurately report information about their condition. A total of 8 patients did not recall whether
they had flares. While this small number was not expected to alter results significantly, to the
extent that there were patients with flares in that unknown group, this would render our results
more conservative. The survey did not attempt to ascertain the size or location of tophi, which
may be an important determinant of impairment of HRQOL in patients with tophaceous gout.
Although it is likely that comorbid status was confounded with tophi/flares in contributing to
poorer health outcomes, controlling for comorbidities (and BMI) in the current study did not
detract from the overall findings, in spite of the reduced statistical power. Having tophi and at
least four flares in the past year were still significantly associated with the poorer outcomes.
Patients’ diagnosis with gout was not verified. Previous studies have, however, shown that a high
proportion of self-reported cases of gout meet classification criteria when assessed by physician,
hospital discharge diagnosis, or use of gout-specific medication [20,42-44]. The generalizability
of the findings may be limited by self-selection of subjects into the survey panel and/or the
survey itself. The relationships between variables observed are correlations which cannot be
deemed to be causal. Unmeasured variables (such as the number or severity of comorbid
conditions) may explain a portion of the observed effects, although an association between
physical HRQOL and gout symptoms has been observed after controlling for comorbidities in a
previous study [34]. The comparisons across rheumatic diseases must be interpreted with
caution. No attempt was made to control for covariates or potential confounders, which may
explain most or all of the observed differences in health utilities.

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Conclusions

The impairment of HRQOL in patients with gout increases with the presence of tophi and the
frequency of flares, even after adjusting for covariates. Gout symptoms are also associated with
greater impairment in daily activities. Gout characterized by severe symptoms and signs (i.e., 3+
flares/year and tophi) imposes a substantial and clinically meaningful burden on the patient at
least comparable to the impairment of HRQOL associated with other rheumatic diseases. These
findings, across representative samples of patients in both the US and EU, highlight the different
potential impacts of gouty tophi and flares and underscore the importance of effective
management of this potentially curable condition.

Previous presentation: American College of Rheumatology, 2011 Annual Scientific Meeting,

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Competing interests

Savient Pharmaceuticals, Inc. provided funding for this study. AF was an employee of Savient
during its execution. AG and JV are employees of Kantar Health, who conducted the study and
contributed to manuscript preparation with funding from Savient. GN, TB, A-KT, and DK
received funding from Savient for consulting purposes. In addition to funding from Savient, A-
KT also received funding from Berlin-Chemie Menarini and Novartis for giving lectures and
serving on Advisory Boards on the topic of gout; PK has served on speakers bureaus for Takeda,
and has received research funding from Savient and ARDEA; DK serves as a consultant to
ARDEA and Takeda, and is co-Principal Investigator for the 2012 American College of
Rheumatology Guidelines for the management of gout. GN has served as a consultant for Ardea,
Ipsen, Menarini, Metabolex, Novartis, and convenes the group to revise treatment guidelines for
the British Society of Rheumatology. TB has received consultancy fees from the following
companies, in the field of gout: Savient, Ipsen, Menarini, Takeda, Teijin, Sobi, Ardea
Biosciences, Biocryst, Novartis, and Mayoli Spindler.

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Authors’ contributions

PK, GN, TB, A-KT, AF, and DK contributed to the design of the study and the writing of the
manuscript. AG contributed to the design and analysis of the study, and the writing of the
manuscript. JV contributed to the analysis and writing of the manuscript. All authors have read
and approved the manuscript.

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