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The Association Between Gout and Cardiovascular Disease in Patients with Atrial Fibrillation

  • Per WändellEmail author
  • Axel C. Carlsson
  • Jan Sundquist
  • Kristina Sundquist
Medicine
  • 39 Downloads
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  1. Topical Collection on Medicine

Abstract

Gout is a sign of a disturbed metabolism and associated with atrial fibrillation (AF) and other cardiovascular diseases. Our aim was to study associations between gout and cardiovascular comorbidities in patients with AF. The study population included all adults (n = 12,283) ≥ 45 years diagnosed with AF visiting 75 primary care centers in Sweden 2001–2007. Logistic regression was used to calculate odds ratios with 95% confidence intervals (CIs) for the associations between prevalent gout and cardiovascular comorbidities. In subsamples, we studied incident congestive heart failure (CHF) and ischemic stroke (IS), excluding patients with earlier registered specific diagnosis, using Cox regression (to estimate hazard ratios (HR) with 95% CIs). Gout was significantly and positively associated with CHF, obesity and diabetes among men and women, and among men also with hypertension and coronary heart disease. Prevalent gout was negatively associated with incident IS (HR and 95% CI: 0.64, 0.49–0.82; 0.50, 0.39–0.64) in both full model (adjusted for sex, age, socioeconomic factors, and comorbidities) and CHA2DS2-VASc model (adjusted for CHA2DS2-VASc, sex, and age). Adding gout to full model increased Harrell’s C by 1% in CHA2DS2-VASc model. In this clinical setting, we found gout to be associated with most cardiometabolic diseases except cerebrovascular diseases, and with decreased risk of IS, with gout adding significantly to the predictive value compared to CHA2DS2-VASc without gout included.

Keywords

Atrial fibrillation Gout Congestive heart failure Ischemic stroke Gender Hypertension 

Introduction

Gout is the most common inflammatory arthritis, with deposition of monosodium urate crystals in both joints and soft tissues. Gout leads not only to acute attacks of excruciating pain, but could also lead to debilitating complications, including chronic joint damage and renal insufficiency [1], without proper treatment. The prevalence of gout in Sweden is estimated at between 1.4 and 1.8% [2, 3]. Worldwide, the prevalent number of patients with gout is estimated at 34 million, compared to 21 million with rheumatoid arthritis [4]. However, in terms of years lived with disability, gout only contributes to less than 1% of all musculoskeletal diseases [4].

However, gout is also associated with or caused by different cardiometabolic conditions, including not only insulin resistance [5], the metabolic syndrome, and diabetes mellitus [6], but also different cardiovascular diseases, i.e., hypertension and antihypertensive treatment [7, 8], coronary heart disease [9], chronic heart failure [7], and chronic kidney disease [10], and may thus be used as a sign of a more aggressive disease development.

As one important cardiovascular disease, atrial fibrillation (AF) is also of interest in relation to gout. AF is the most common arrhythmia in the population, with a prevalence of 2% in Sweden [11]. Worldwide, 46 million individuals are living with prevalent AF or atrial flutter, i.e., around 10% of all with prevalent cardiovascular diseases and 11% of years lived with disability due to cardiovascular diseases [4]. High serum acid levels are found to be associated with an increased risk of incident AF [12]. AF itself is also related to other cardiovascular diseases [13], out of which ischemic stroke is the most important complication [14]. As regards gout in relation to ischemic stroke, hyperuricemia is shown to increase the risk of ischemic stroke [15], and add to predictive value to the most common clinical risk predictor of ischemic stroke in AF, i.e., the CHA2DS2-VASc [16]. However, there are also other risks with AF, with congestive heart failure (CHF) being more common among patients with AF compared to individuals without AF [17], and CHF is the most common cause of death among patients with AF [18]. AF is also associated with development of dementia [19].

We have earlier studied AF patients in primary care regarding the effect of comorbid conditions or cardiovascular medications in relation to incident heart failure [20], incident dementia [21, 22], and mortality [23, 24]. We have also studied anticoagulant treatment in AF in relation to ischemic stroke [25], hemorrhagic stroke [26], and myocardial infarction [27]. As patients with AF not only have different cardiovascular comorbidities but are also treated with many cardiovascular drugs which may increase the risk of gout [28], it is also important to examine gout in patients with AF. We have also studied the effect of socioeconomic status on mortality in AF patients [29].

The aims herein were therefore to study the association between gout and cardiovascular comorbidities among men and women with AF in Swedish primary care, and to study the association between prevalent gout and incident CHF, ischemic stroke and dementia among patients with AF. We believe that a patient population with AF is of particular interest as the cardiovascular comorbidities that are common in patients with gout are also common in patients with AF.

Methods

Design

The study used individual-level patient data from 75 primary health care centers (PHCCs), 48 of which were located in Stockholm County. Individuals attending any of the participating PHCCs between 2001 and 2008 were included in the study. We used Extractor software (http://www.slso.sll.se/SLPOtemplates/SLPOPage1____10400.aspx; accessed September 19, 2010) to extract individual electronic patient records (EPRs). National identification numbers were replaced with new unique serial numbers to ensure anonymity. The files were linked to a dataset including data from the Total Population Register, the National Patient Register (NPR), and the Swedish Cause of Death Register, which contains individual-level data on age, sex, education, cause of death, and hospital diagnosis for all residents registered in Sweden. Thus, a new research dataset containing clinical data and information on socioeconomic status on the individuals (n = 1,098,420) registered at the 75 PHCCs was created. Data from the Cause of Death Register were used for the follow-up.

The investigation conforms with the principles outlined in the Declaration of Helsinki. Ethical approvals were obtained from regional boards at Karolinska Institutet (Dnr 12/2000) and the University of Lund (Dnr 409/2008 with completion from 19/1 2010).

Study Population

The study included all patients with diagnosed AF, identified by the presence of the ICD-10 code (10th version of the World Health Organization’s International Classification of Diseases) for atrial fibrillation (I48) in patients’ medical records at the PHCCs. The following cardiovascular-related disorders were used as covariates: hypertension, CHD, cerebrovascular diseases (CVD), and diabetes mellitus (for specific codes, see below). Patients with CHF during the study period were identified in two ways, either through a diagnosis in the EPR in the PHCC or through a hospital diagnosis. In total, 12,283 individuals (6646 men and 5637 women), aged 45 years or older at the time of AF diagnosis and who visited any of the 75 participating PHCCs from January 1, 2001 until December 31, 2007, with data on neighborhood socioeconomic status available, were included in the study.

Outcome Variable

For logistic regression: prevalent gout. For Cox regression: time from first AF diagnosis to first diagnosis of CHF, IS, or dementia (until December 31, 2010).

Demographic and Socioeconomic Variables

Sex was stratified into men and women.

We classified individuals into the following age groups 45–54, 55–64, 65–74, 75–84, and > 85 years, and also excluded individuals younger than 45 years of age.

Educational level was categorized as ≤ 9 years (partial or complete compulsory schooling), 10–12 years (partial or complete secondary schooling), and > 12 years (college and/or university studies).

Marital status was classified as married, unmarried, divorced, or widowed.

Neighborhood socioeconomic status (SES) was categorized into three groups according to the neighborhood index: more than one standard deviation (SD) below the mean (high SES or low deprivation), more than one SD above the mean (low SES or high deprivation), and within one SD of the mean (middle SES or deprivation). The neighborhood index was based derived from the following four variables: low educational status (< 10 years of formal education), low income (< 50% of the median individual income from all sources), unemployment, and receipt of social welfare. The neighborhood deprivation index was categorized into three groups: more than one standard deviation (SD) below the mean (high SES or low deprivation level), more than one SD above the mean (low SES or high deprivation level), and within one SD of the mean (moderate SES or moderate deprivation level).

Comorbidities

We identified the following cardiovascular comorbidities from the EPRs among the individuals in the study population: hypertension (I10-15); CHD (I20-25), also including registered hospitalizations for myocardial infarction from the NPR; CHF (I50 or I110), also including hospitalizations for CHF from the NPR; CVD (I60-I69), also including registered hospitalizations for ischemic or hemorrhagic stroke from the NPR; obesity (E65-E68); diabetes mellitus (E10-E14); COPD (J40-J47); depression (F32-F34, F38-F39); anxiety disorders (F40-F41); and dementia (F00-F03, F10.7A, G30).

Results were also estimated by CHA2DS2-VASc scores, however after omitting the CHF item in the CHF analysis, with scores between 0 and 7 for men, and 1 and 8 for women, otherwise 0–8 for men and 1–9 for women.

Statistical Analyses

Analyses were performed stratified by sex, with results presented as means with standard deviation, or rates in percent.

Multivariate logistic regression was performed to explore the association of gout with the background factors, i.e., socioeconomic factors and comorbidities, categorized into sex, with results of interaction with sex for the different factors.

Cox regression analyses were performed, with hazard ratios (HRs) with 95% confidence intervals (CIs), to analyze the estimate of gout using time to diagnosis of mortality, incident CHF, ischemic stroke, and dementia. In these analyses, we excluded patients with an earlier registered diagnosis of CHF, ischemic stroke, and dementia, respectively, before first diagnosis of AF, for CHF (n = 2859), ischemic stroke (n = 766), and dementia (n = 187). We also used C-statistics (Harrell’s C) to study the predictive value of adding gout to the Cox regression analyses and considered a 1% increment in prediction as clinically relevant. Full Cox regression models were adjusted for the following variables in separate models: age, socio-demographic factors (educational level, marital status, and neighborhood socioeconomic status), comorbidities (hypertension, CHD, CHF, cerebrovascular diseases, obesity, diabetes, COPD, depression, anxiety, dementia) with the exclusion of some comorbidities in the specific models, but including anticoagulant treatment. Results for mortality or newly diagnosed CHF, ischemic stroke, and dementia were also calculated using Cox regression models adjusted for CHA2DS2-VASc scores, age, and sex.

A two-sided p value of < 0.05 was considered statistically significant for variables in the logistic regression and Cox regression models. All analyses were performed in STATA 15.2 (StataCorp, College Station, TX, USA).

Results

The characteristics of the entire study population consisting of patients with AF (n = 12,283), stratified by sex (6646 men and 5637 women), and into those with a diagnosis of gout (yes/no) are shown in Table 1.
Table 1

Baseline characteristics for patients aged ≥ 45 years with diagnoses of AF (N = 12,283), and with or without gout in primary care attending the 75 PHCCs between January 1 2001 and December 31 2007, divided by sex

 

All

Men (N = 6646)

Women (N = 5637)

N = 12,283

No gout

N = 5959

Gout

N = 687

No gout

N = 5275

Gout

N = 362

Numbers (%)

Numbers (%)

Numbers (%)

Numbers (%)

Numbers (%)

Number of deaths

3954 (32.1)

1739 (29.2)

244 (35.5)

1817 (34.5)

154 (42.5)

Age (years), mean (SD)

74.4 (10.1)

72.0 (10.2)

73.5 (9.3)

77.0 (9.3)

78.4 (8.1)

Age groups (years)

 45–54

475 (3.9)

348 (5.8)

22 (3.2)

104 (2.0)

1 (0.3)

 55–64

1743 (14.2)

1110 (18.6)

112 (16.3)

499 (9.5)

22 (6.1)

 65–74

3308 (26.9)

1852 (31.1)

190 (27.7)

1191 (22.6)

75 (20.7)

 75–79

2427 (19.8)

1092 (18.3)

165 (24.0)

1090 (20.7)

80 (22.1)

 80–84

2447 (19.9)

962 (16.1)

121 (17.6)

1261 (23.9)

103 (28.5)

 ≥ 85

1883 (15.3)

595 (10.0)

77 (11.2)

1130 (21.4)

81 (22.4)

Educational level

 Compulsory schooling

5085 (45.2)

2219 (39.4)

267 (41.1)

2427 (52.1)

172 (58.1)

 Secondary schooling

3995 (35.5)

2110 (37.4)

257 (39.5)

1535 (33.0)

93 (31.4)

 College and/or university studies

2161 (19.2)

1310 (23.2)

127 (19.5)

693 (14.9)

31 (10.5)

Marital status

 Married

5613 (45.9)

3567 (60.1)

383 (55.9)

1554 (29.6)

109 (30.3)

 Unmarried

1029 (8.4)

566 (9.5)

64 (9.3)

384 (7.3)

15 (4.2)

 Divorced

1813 (14.8)

908 (15.3)

113 (16.5)

744 (14.2)

48 (13.3)

 Widowed

3777 (30.9)

895 (15.1)

125 (18.3)

2659 (48.9)

188 (52.2)

Neighborhood SES

 High

4604 (37.5)

2385 (40.0)

271 (39.5)

1834 (34.8)

114 (31.5)

 Middle

5807 (47.3)

2712 (45.5)

318 (46.3)

2600 (49.3)

177 (48.9)

 Low

1872 (15.2)

862 (14.5)

98 (14.3)

841 (15.9)

71 (19.6)

Diagnosis

 Hypertension

5449 (44.4)

2396 (40.2)

330 (48.0)

2532 (48.0)

191 (52.8)

 Coronary heart disease

3234 (26.3)

1472 (24.7)

250 (36.4)

1395 (26.5)

117 (32.3)

 Congestive heart failure

5684 (46.3)

2432 (40.8)

424 (61.7)

2560 (48.5)

268 (74.0)

 Cerebrovascular diseases

2566 (20.9)

1139 (19.1)

138 (20.1)

1208 (22.9)

81 (22.4)

 Obesity

614 (4.6)

297 (5.0)

55 (8.0)

222 (4.2)

40 (11.1)

 Diabetes mellitus

2366 (19.3)

1109 (18.6)

185 (26.9)

958 (18.2)

114 (31.5)

 COPD

1416 (11.5)

618 (10.4)

92 (13.4)

648 (12.3)

58 (16.0)

 Depression

1039 (8.5)

372 (6.2)

40 (5.8)

586 (11.1)

41 (11.3)

 Anxiety

496 (4.0)

168 (2.8)

15 (2.2)

285 (5.4)

28 (7.7)

 Dementia

937 (7.6)

343 (5.8)

45 (6.6)

512 (9.7)

37 (10.2)

Information on educational level (men 356, women 686) and marital status (men 25, women 26) is missing for some individuals

Results from the logistic regression are shown in Table 2, also stratified by sex. Statistically significant factors more commonly prevalent in patients with gout were for both men and women age, CHF, obesity, and diabetes; and for men only, also prevalent hypertension and coronary heart disease. Gout was statistically less common in divorced and widowed women. Educational status and neighborhood socioeconomic status were not associated with prevalent gout.
Table 2

Multivariate logistic regression for the association between baseline characteristics and prevalent gout, with odds ratios (ORs) and 95% confidence interval (CI), for patients aged ≥ 45 years with diagnoses of AF (n = 11,209 patients) in primary care attending the 75 PHCCs between January 1 2001 and December 31 2007

 

All

N = 11,209

Men

N = 6273

Women

N = 4936

Interaction between men and women

OR (95% CI)

OR (95% CI)

OR (95% CI)

p value

Age (years), mean (SD)

1.01 (1.00–1.02)

1.01 (1.00–1.02)

1.02 (1.00–1.04)

0.86

Educational level

 Compulsory schooling

1 (ref)

1 (ref)

1 (ref)

 

 Secondary schooling

1.04 (0.89–1.22)

1.08 (0.89–1.31)

0.97 (0.74–1.28)

0.33

 College and/or university studies

0.89 (0.73–1.10)

0.92 (0.73–1.17)

0.85 (0.56–1.29)

0.34

Marital status

 Married

1 (ref)

1 (ref)

1 (ref)

 

 Unmarried

0.92 (0.71–1.19)

1.12 (0.84–1.51)

0.55 (0.32–0.98)

0.070

 Divorced

1.02 (0.84–1.25)

1.15 (0.91–1.46)

0.73 (0.50–1.07)

0.15

 Widowed

0.89 (0.74–1.07)

1.03 (0.81–1.31)

0.67 (0.50–0.90)

0.065

Neighborhood SES

 High

1.05 (0.89–1.23)

1.13 (0.93–1.38)

0.87 (0.65–1.16)

0.53

 Middle

1 (ref)

1 (ref)

1 (ref)

 Low

0.98 (0.78–1.23)

0.85 (0.64–1.13)

1.27 (0.88–1.84)

0.35

Diagnosis

 Hypertension

1.24 (1.07–1.42)

1.32 (1.11–1.56)

1.09 (0.85–1.40)

0.45

 Coronary heart disease

1.21 (1.04–1.40)

1.38 (1.15–1.65)

0.94 (0.72–1.23)

0.067

 Congestive heart failure

2.32 (2.00–2.69)

2.12 (1.77–2.53)

2.80 (2.12–3.70)

0.097

 Cerebrovascular diseases

0.99 (0.83–1.17)

0.98 (0.79–1.21)

1.03 (0.77–1.37)

0.84

 Obesity

1.94 (1.51–2.49)

1.57 (1.14–2.17)

2.73 (1.82–4.09)

0.020

 Diabetes mellitus

1.44 (1.23–1.69)

1.31 (1.08–1.59)

1.70 (1.30–2.23)

0.054

 COPD

1.07 (0.87–1.30)

1.06 (0.82–1.36)

1.12 (0.80–1.56)

0.59

 Depression

0.97 (0.75–1.25)

1.00 (0.71–1.42)

0.95 (0.65–1.40)

0.98

 Anxiety

0.94 (0.65–1.36)

0.75 (0.43–1.33)

1.13 (0.69–1.85)

0.28

 Dementia

0.97 (0.74–1.26)

0.92 (0.64–1.31)

1.02 (0.69–1.52)

0.63

Information on educational level (1042) and marital status (n = 51) is missing for some individuals, giving lower numbers than in Table 1. Statistically significant values shown in italic

Table 3 shows the follow-up analysis of mortality (n = 12,283), incident CHF (n = 9424) incident ischemic stroke (n = 11,527), or incident dementia (n = 12,096), with HRs for gout being significantly higher for incident CHF in the model adjusted for CHA2DS2-VASc and with borderline significance for full model (p = 0.053). A significantly lower risk was seen in both models for ischemic stroke, and also a significantly lower risk of dementia in patients with gout in a model adjusted for CHA2DS2-VASc, age, and sex. Improvement in Harrell’s C was below 1% for all models but the CHA2DS2-VASc model for ischemic stroke. We also tested the risk of incident MI, finding a HR of 1.00.
Table 3

Cox regression models with hazard ratios (HRs) and 95% confidence interval (95% CI) for the association between gout and mortality, as well as incident congestive heart failure (CHF), ischemic stroke (IS), and dementia

 

Mortality

Incident CHF

Incident IS

Incident dementia

Full model

CHA2DS2-VASc model

Full model

CHA2DS2-VASc model

Full model

CHA2DS2-VASc model

Full model

CHA2DS2-VASc model

Gout

0.92 (0.82–1.03)

0.98 (0.88–1.09)

1.21 (1.00–1.47)

1.25 (1.04–1.51)

0.64 (0.49–0.82)

0.50 (0.39–0.64)

0.79 (0.58–1.08)

0.64 (0.48–0.86)

Harrell’s C without gout

0.7488

0.7541

0.6926

0.6679

0.6650

0.6172

0.7556

0.7387

Harrell’s C with gout included

0.7490

0.7541

0.6940

0.6690

0.6679

0.6279

0.7563

0.7414

Model improvement with gout

0.0002

0.0000

0.0014

0.0011

0.0029

0.0107

0.0007

0.0027

Fully adjusted models (age, sex, socioeconomic variables, comorbidity, anticoagulant treatment) and models adjusted for CHA2DS2-VASc (with age and sex included) are shown. Italic values are statistically significant for HRs, or exceeding 0.01 in model improvement for Harrell’s

Discussion

The main finding of this study was that gout was associated with a decreased risk for ischemic stroke in patients with AF. Adding information about prevalent gout lead to an improved prediction of ischemic stroke than CHA2DS2-VASc alone.

We found gout to be statistically significantly associated with a lower risk of incident ischemic stroke. In contrast, an earlier study from Taiwan using the “National Health Insurance Research Database” in Taiwan found hyperuricemia, actually defined as at least one attack of gout and on long-term anti-gout treatment, to be a significant risk factor of ischemic stroke in patients with AF [15]. Earlier reviews have found a modestly increased risk of stroke in general by hyperuricemia [30, 31]. We have no good explanation to this discrepancy.

We also found some gender differences in comorbidities to gout, however, with only obesity showing a significant difference between men and women. In an earlier Swedish study, both diabetes and insulin resistance were associated with gout in both women and men [13]. However, we also found different patterns in men and women, although not statistically significant: with gout being associated with hypertension and CHD in men with AF, but surprisingly not among women with AF. The frequency of hypertension is remarkably low in the present cohort compared to another Swedish study using data from all caregivers with a hypertension rate of 65% [11], and possibly this diagnosis could have been dropped earlier in favor of other cardiovascular diagnoses such as CHD, CHF, or cerebrovascular diseases. Antihypertensive agents such as diuretics are associated with higher levels of uric acid and gout [32], why a higher rate of hypertension could be expected in patients with AF. One explanation could be that thiazides are prescribed in lower doses in clinical practice in Sweden now, compared to how these drugs were used previously. The association between gout and prevalent CHF, as well as an association with incident CHF showing a trend value, could partly be explained by the use of loop diuretics. In an earlier study, we found loop diuretics to be associated with incident CHF, and an explanation for this could be that loop diuretic treatment is used owing to signs of CHF without a formally registered diagnosis.

We found no improvement in the Cox regression models when adding gout to models with established risk factors to study mortality and myocardial infarction. Earlier systematic reviews have only found a marginally increased CHD risk [33, 34, 35], however with an increased risk among women [33], including both CHD and all-cause mortality [34, 35]. With this marginally increased risk, the present study has no statistical power to detect these differences.

There are several limitations of this study. As the study sample is a subgroup of the AF population, i.e., patients registered in primary health care, the results could not directly be extrapolated to all AF patients. It has previously been estimated that 36% of all patients registered with a diagnosis of AF in Stockholm County were not known with this diagnosis in primary health care [11]. The medical comorbidities were based on registered diagnoses, with risk of both over- and under-estimation. Severity of AF, gout, CHF, or CHD could not be classified, and the indications for the specific pharmacotherapies were not registered. Drugs to treat gout were not assessed. Thus, the results may partly have been biased due to these limitations. Furthermore, AF could not be classified as paroxysmal, persistent, or permanent, and heart rhythm could not be classified as sinus rhythm or fibrillation rhythm. Some diagnoses, such as obesity, are less often registered the electronic patient records, and an obesity diagnosis probably reflects a more severe condition. All these mentioned factors could affect the results and yield discrepant findings. A major strength of this study was that we were able to link clinical data from individual EPRs to data from national demographic and socioeconomic registers with less than 1% of information missing.

In conclusion, in this clinical setting with patients with AF treated in primary care, we found gout associated with a decreased risk of incident ischemic stroke.

Notes

Funding Information

This work was supported by ALF funding awarded to Jan Sundquist and Kristina Sundquist and by grants from the Swedish Research Council (awarded to Kristina Sundquist), the Swedish Council for Working Life and Social Research, Forte (Jan Sundquist), and the National Heart, Lung, And Blood Institute of the National Institutes of Health under Award Number R01HL116381 to Kristina Sundquist.

Compliance with Ethical Standards

The investigation conforms with the principles outlined in the Declaration of Helsinki. Ethical approvals were obtained from regional boards at Karolinska Institutet (Dnr 12/2000) and the University of Lund (Dnr 409/2008 with completion from 19/1 2010).

Conflict of Interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Division of Family Medicine and Primary Care, Department of Neurobiology, Care Science and SocietyKarolinska InstitutetHuddingeSweden
  2. 2.Center for Primary Health Care ResearchLund UniversityMalmöSweden
  3. 3.Department of Family Medicine and Community Health, Department of Population Health Science and PolicyIcahn School of Medicine at Mount SinaiNew YorkUSA
  4. 4.Center for Community-based Healthcare Research and Education (CoHRE), Department of Functional Pathology, School of MedicineShimane UniversityIzumoJapan

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