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Rheumatology (Oxford). 2009 May; 48(5): 582–586.
Published online 2009 March 23. doi:  10.1093/rheumatology/kep047
PMCID: PMC2722803

Perceptions of disease and health-related quality of life among patients with gout


Objective. To assess the impact of gout on health-related quality of life (HRQoL) among patients in three large US cities.

Methods. Gout patients completed the Short Form-36 (SF-36) and a series of questions regarding their gout, comorbidities and demographics. Their physicians confirmed the gout diagnosis and evaluated the severity of patient's gout. The differences in mean norm-based SF-36 scores between the US norms and gout patients and between subgroups of gout patients were calculated. The relative weight and significance of gout-related characteristics associated with patients’ HRQoL were also calculated.

Results. The majority of the patients were males with a mean age of 62.2 years and median disease duration of 13.8 years. Most were overweight/obese with several comorbidities. Half of the patients experienced three or more gout attacks per year with a typical gout attack involving five joints and lasting for at least 4 days. The Physical Component Summary (PCS) and Mental Component Summary (MCS) was significantly lower for gout patients (P < 0.002 and P < 0.001, respectively). Among gout patients, the mean PCS and MCS were lower for those with more frequent gout attacks and greater number of affected joints (P < 0.005 and P < 0.001, respectively). After adjusting for age, gender and comorbidities, the number of joints involved during a typical and the worst gout attack had the greatest impact on patient's PCS and MCS.

Conclusion. Gout patients had clinically significant lower HRQoL than their age-matched US norm. Comorbidities and several additional gout-related factors significantly impacted the overall HRQoL.

Keywords: Gout, Health-related quality of life, SF-36, Outcomes


Gout has a high prevalence of ~1–2% in Western countries [1,2]. Moreover, the prevalence of gout in the USA has approximately doubled over the past two decades with the greatest increase among those over the age of 65 years. Central factors thought to drive the rising prevalence of gout include increases in longevity, greater use of diuretics and low-dose acetylsalicylic acid, obesity, end-stage renal disease, hypertension and metabolic syndrome [3,4]. To address refractory gout, novel therapeutic agents are being developed for the treatment of hyperuricaemia (e.g. pegylated uricases and selective xanthine oxidase inhibitors).

Despite the growing prevalence of gout, only a few studies have examined the overall impact of gout on patient's health-related quality of life (HRQoL). HRQoL refers to the physical, psychosocial and social domains of health and is shaped by a person's perception and expectations of their health. Several studies have noted that gout interferes with work, recreational activities and employment especially during an acute attack [5,6]. Three studies have documented reduced HRQoL using the Short Form-36 (SF-36) in severe gout patients as compared with the general population [7–9]. Two of these studies were limited to patients with severe gout as defined by chronic synovitis, more than 3 flares per year, erosions on X-rays and/or tophi. A Dutch population-based cohort study evaluating the HRQoL among 3 664 people with one or more self-reported musculoskeletal conditions, including 138 patients with gout, noted a lower SF-36, particularly in physical function compared with those without any musculoskeletal conditions. In a subgroup analysis by specific musculoskeletal conditions, patients with gout also had a lower physical function compared with non-gout patients [10]. Similarly, a recent case–control study from the UK found that gout patients had a lower overall HRQoL, as measured by World Health Organization's WHOQOL-BREF instrument, than control patients. Although the association between overall HRQoL and gout lessened when adjusted for comorbidities, the association between gout and the physical domain of HRQoL remained significant even after adjusting for several medical comorbidities (hypertension, diabetes, hyperlipidaemia, angina, myocardial infarction, renal failure/dialysis, transient ischaemic attack and stroke) [11]. Unfortunately, both of these studies had low response rates (47 and 23%, respectively), which could have biased the findings. The most recent study assessed the impact of gout on functional status, HRQoL, mortality and health care utilization among all US veterans in upper Midwest region. Of the 58% respondents (40 508), 2.7% had an International Code Diagnosis-9 (ICD-9) of gout. Patients with gout had lower physical HRQoL than those without gout but this was largely related to comorbidities and socio-demographic characteristics [9]. Unlike our study, this study did not assess the impact of gout-specific characteristics (e.g. attack frequency and severity) on patient's HRQoL.

The objectives of this study were to assess disease characteristics, HRQoL and the relationship between the two in a large, US community-based sample of gout patients using the SF-36. To increase the generalizability of the results and to minimize selection bias, we included all gout patients with a physician confirmation of the diagnosis regardless of their disease severity. We hypothesized that gout patients would have lower HRQoL than their age-adjusted US norms.


The study was conducted in three US cities: San Diego, Cincinnati and Minneapolis. To capture a wide spectrum of disease severity, patients were recruited from both arthritis and primary care clinics at University of California, San Diego (UCSD), San Diego Veterans Affairs (VA), Cincinnati VA and Minneapolis VA healthcare systems, as well as from the surrounding communities, using advertisements placed in the clinic waiting rooms. Inclusion criteria were age between 18 and 85 years, self-reported history of gout or physician diagnosis of gout and ability to provide informed consent. All patients included in the study signed the consent forms.

All patients were given a unique study identifier and completed version 2 of the SF-36 and a series of questions regarding their gout (e.g. number of attacks and treatment), comorbidities and demographics. The SF-36 is a widely used, self-administered general health status instrument consisting of 36 items, which can be scored as two summary scores: Physical Component Summary (PCS) and Mental Component Summary (MCS) score. The summary scores are normalized to the US population, where the mean score is 50 with an s.d. of 10. A score below the mean score of 50 implies lower health status as measured by PCS and MCS. Results of a given group can then be compared with published norms for the general population [12]. Factors associated with an increased risk of gout were also evaluated in the Gout Background Questionnaire including: age, BMI, alcohol consumption, family history, medications (diuretics and low-dose aspirin), presence of certain comorbidities (hypertension, heart disease, hyperlipidaemia, diabetes and kidney disease). Although a diet high in purine (e.g. red meat) has been associated with an increased risk of gout, we did not obtain this information due to our inability to ascertain the quantities consumed on self-report questionnaires. The SF-36 and the Gout Background Questionnaires were completed either during a regularly scheduled clinic visit or at the patient's home and returned via mail. Physicians of participating patients also completed a short questionnaire to confirm and characterize the severity of patient's gout: date and method of gout diagnosis, presence or absence of tophi, most recent and the highest serum urate levels in the preceding 1 year, and a physician's global assessment of patient's overall gout severity using a 10-cm visual analogue scale (VAS).

All study was approved by the Human Research Protection Programs of UCSD, University of Cincinnati, San Diego VA, Cincinnati VA and the Minneapolis VA Healthcare systems and met Health Insurance Portability and Accountability Act (HIPAA) of 1996 requirements.

Data analyses

Descriptive statistics were calculated for all variables. Frequency distributions were used to describe categorical variables. For continuous variables and scales, descriptive statistics included means and s.d.s. ANOVAs were used to investigate differences in mean norm-based SF-36 scores between the US norms and gout patients and between various subgroups of gout patients. A five-point difference (one-half the s.d.) in mean norm-based SF-36 scores was considered clinically significant [13]. Multiple step-wise regression analysis was used to determine the relative weight and significance of gout-related characteristics associated with patients’ HRQoL as calculated by unadjusted β-coefficient and R2. Partial correlation coefficients were used to examine the association between gout characteristics and SF-36 scores, controlling for age, comorbidities and gender. Statistical significance was set at P < 0.05 and correlations <0.29 were considered to be small, between 0.30 and 0.49 moderate and >0.5 large [14]. Analyses were performed using SPSS version 15.0 (SPSS, Cary, NC, USA).


Patient characteristics

A total of 371 patients were enrolled in the study. Sixty-three patients were lost to follow-up and/or did not complete the study. Of the remaining 308 patients, 298 (96.8%) completed the SF-36 and the gout characteristics questionnaire. Of these 298 patients with self-reported diagnosis of gout, physicians completed and returned the gout diagnosis confirmation sheet in 68% of the patients. Of these, physicians confirmed patient's self-diagnosis of gout in 89.9%, suggesting high reliability of self-reported gout diagnosis. Physicians used one or more of the following methods to confirm the gout diagnosis: urate crystals in joint aspirate (10%), X-ray findings consistent with gout (2%), elevated serum uric acid (sUA) level along with clinical exam consistent with gout (49%) or a combination of these three (38%). Patient characteristics are described in Tables 1 and and2.2. The majority of the patients was male (90.2%), with a mean age of 62.2 ± 11.7 years, and Caucasian (75.9%). Most (80.7%) were either overweight or obese with a mean BMI of 30.2 ± 6.2 kg/m2 and had other significant comorbidities including hypertension (73.9%) and hyperlipidaemia (58.9%). Approximately 15% of the patients drank heavily (more than 7 drinks/week, 12%) and had been exposed to lead in the past (18%) (Table 1).

Table 1.
Patient demographics (total number of subjects enrolled = 371)
Table 2.
Gout characteristics (total number of subjects enrolled = 371)

The median duration of gout diagnosis was 13.8 ± 12.3 years. Approximately two-thirds (n = 195) of the patients were taking anti-gout medications: 52.3% on allopurinol, 18.5% on colchicine and 29.2% on both. The mean sUA was 7.07 ± 1.90 mg/dl (421 ± 113 µmol/l) and tophi were present in 26% of the patients. Half of the patients had experienced three or more gout attacks in the past year. A typical gout attack involved 5.2 ± 6.9 joints and lasted for at least 4 days in 58.8% of patients (Table 2). Even in between acute gout attacks, 57.9% of the patients continued to have some pain related to gout from ‘a little’ to ‘all of the time’ (data not shown).

The overall severity of gout was assessed by both patients and physicians. In general, physicians tended to rate their patient's gout as less severe than the patient, with low correlation between the two assessments (r = 0.225). Most patients (62.4%) rated their gout severity as moderate to very severe while only 43.9% of their treating physicians rated their overall gout severity in this range (Table 2).


Mean PCS and MCS scores for the entire sample of gout patients were 37.9 ± 11.2 and 48.5 ± 12.8, respectively. In the general US population, the overall PCS declines with age while MCS remains steady throughout life (Table 3). However, the PCS was significantly lower for patients with gout compared with their age-matched US norm (P = 0.007). Similar differences in MCS was noted for gout patients between the ages of 45 and 64 years compared with their age-matched US norm (P < 0.001). Despite having the lowest PCS, gout patients over the age of 75 years had the highest MCS among all people (gout patients and the general population) regardless of their age.

Table 3.
PCS and MCS by age

Among gout patients, the mean PCS and MCS adjusted for age were both lower in patients with more frequent gout attacks (P = 0.001 and P = 0.033, respectively), especially if the last attack was in the 3 months preceding the study enrollment (P < 0.001 and P = 0.028, respectively). Patients with more severe pain during their typical attacks and greater amount of pain between attacks also had a significantly lower PCS (P = 0.012). Those patients who continued to have joint pain related to gout all or most of the time in between acute attacks had the worst PCS and MCS scores compared with those who remained relatively pain free in between acute flares. Significant differences in PCS and MCS scores (except for MCS related to pain severity of typical gout attack) were detected among groups of patients categorized by gout characteristics, with a significant linear trend in the expected direction for mean PCS and MCS scores to increase with greater attack frequency and severity (Table 4). These differences in the mean PCS and MCS were clinically meaningful ranging from 5 to 15 points.

Table 4.
PCS and MCS by gout characteristics (adjusted for age)

Factors associated with gout patient's HRQoL

Several disease characteristics (disease duration, number of joints involved during a typical gout attack and during the worst attack, frequency of gout attacks and patient's overall assessment of gout severity), age, gender, comorbidities and BMI were evaluated for their impact on patient's PCS and MCS scores (Table 5). Among the comorbidities, the presence of coronary artery disease (CAD), diabetes and kidney diseases were negatively associated with patient's PCS scores. Although statistically significant, these associations were small to moderate in magnitude (r = −0.18 to −0.43). Also, patients with more frequent gout attacks and greater number of joints involved during a typical and the worst gout attacks were more likely to have lower PCS. Similarly, the number of joints involved during a typical and the worst gout attack was moderately associated with lower MCS (r = −0.34 to −0.38).

Table 5.
Gout risk factors and disease characteristics associated with PCS and MCS (univariate-adjusted)

In accordance with our hypothesis, our analysis focused mainly on the impact of gout-related characteristics on patient's HRQoL. After adjusting for age, gender and comorbidities (the presence or absence of those listed in Table 5), the number of joints involved during a typical and the worst gout attack had the greatest impact on patient's PCS and MCS (standardized β-coefficient = −0.214 to −0.294, Table 6).

Table 6.
Factors associated with PCS and MCS (adjusted for age, gender and comorbidities)


Though there are several recently completed as well as ongoing clinical trials assessing the efficacy and safety of various medications for the treatment of gout, our study is one of the first studies to assess the HRQoL in a wide spectrum of gout patients in several US communities. Unlike prior studies, our study had a high response rate (83%) with a high reliability of self-reported gout diagnosis (89.9%). In this study, we evaluated the overall HRQoL among gout patients and potential contribution of the comorbidities, demographics (e.g. age and gender) and gout-specific factors (e.g. tophi and number of gout attacks) on patient's overall PCS and MCS.

Similar to other studies, our results revealed a mean PCS score of more than 10 points below the national norm and similar but less dramatic reduction in the MCS. In addition, our results indicated that patients with gout have a lower HRQoL, in both the physical and mental domains, than their aged-matched US norm. Among gout patients, those with diabetes, renal disease, cardiovascular diseases (CVD), prior history of lead exposure and more severe polyarticular gout (with greater number of joint involvement during an attack) had worse HRQoL. Not surprisingly, those patients with greater number of gout attacks/year especially in the past 3 months, more painful typical attacks and less pain-free intervals had lower PCS and MCS scores. These decreases in patient's HRQoL were both clinically (more than one-half the s.d. below the mean) and statistically significant compared with their age-matched norm. In many age groups, the PCS scores were lower by almost 10 points, twice what is considered clinically meaningful. Notably, the MCS scores were lower than the score that has been used as a cut-off screener (MCS of 42) for major depression or dysthymia [15].

Similar to other study results, the presence of comorbidities significantly affected patient's HRQoL in our study, with slightly greater impact on the physical summary scale (PCS) [11]. However, our study identified an additional gout-related factor, the number of joints affected during the worst gout attack, which also significantly impacted overall HRQoL (both PCS and MCS).

The current treatment of gout appears to be suboptimal as seen by nearly half of the patients experiencing three or more gout flares (approximately five joint involvements per flare) per year despite >80% of the patients receiving a chronic uric acid-lowering therapy with allopurinol. Similar suboptimal treatment was noted in a recent survey of patients in two UK primary care clinics assessing the concordance to the European League Against Rheumatism (EULAR) treatment recommendations for chronic gout. Only 30% of the chronic gout patients were on allopurinol with almost one third of the patients continuing to experience at least one gout attack in the preceding year despite the use of allopurinol [16]. Exacerbating this suboptimal therapy may be a perception discrepancy among physicians and patients about the severity of gout. In our study, the perception of gout severity differed between treating physicians and their patients with patients viewing their gout as more severe than their physicians.

Despite a high response rate from patients across a broad spectrum of gout severity, our study has several limitations. As patients were recruited from either arthritis or primary care clinics, those patients with mild disease or with minimal comorbidities who do not see their physicians frequently may have been missed, skewing our results to reflect findings among more moderate-to-severe gout patients. However, our results show that nearly 20% of the patients did not have any gout attacks in the past year consistent with mild disease. Secondly, as this was a questionnaire-based study, our results were based on patients’ responses and are thus subject to recall bias.

Several other chronic medical conditions such as congestive heart failure (CHF) and CAD are also associated with lower HRQoL. A longitudinal disease management program of approximately 6000 patients with heart failure and CAD noted a significantly lower baseline PCS and MCS scores (by over half of an s.d. each) in patients with CHF compared with their demographically matched US population norms [17]. Another study assessing the association between HRQoL with health outcomes also noted a low mean PCS score (36 ± 10) among patients with CHF. Furthermore, lower baseline PCS and MCS scores were associated with higher mortality and CHF-related hospitalization rate [18]. Our study indicates that gout is also a serious medical disease that affects patient's HRQoL to a similar magnitude as other chronic medical conditions such as CHF. Further studies are underway to evaluate the predictive value of several clinical variables (e.g. physician's assessment, sUA and tophi) on patient's overall HRQoL.

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Funding: This study was sponsored in part by TAP Pharmaceutical Products, Inc. and the UCSD General Clinical Research Center Program, M01 RR00827, National Center for Research Resources, National Institutes of Health. D.K. was also supported by a National Institutes of Health Award (NIAMS K23 AR053858-01A1) during this period.

Disclosure statement: D.K. has received consultant fees and research grants from Takeda Pharmaceuticals. J.A.S. has received research grants from TAP pharmaceuticals for this and other research projects. R.T. is a consultant for Takeda, Savient, EnzymeRx, Altus, BioCryst, Regeneron, Novartis, Pfizer, URL Pharma and Proctor & Gamble and has received grants from the Research Service of the Department of Veterans Affairs and Takeda. J.D.H. has received a research grant from TAP Pharmaceuticals for this project. All other authors have declared no conflicts of interest.


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