Abstract
Objective:
Gout is a sign of a disturbed metabolism and associated with atrial fibrillation (AF) and other cardio-vascular diseases. Our aim was to study associations between gout and cardiovascular co-morbidities in patients with AF.
Methods:
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 co-morbidities. 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).
Results:
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, socio-economic 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.
Conclusions:
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
1. 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 one per cent of all musculo-skeletal diseases.4
However, gout is also associated with or caused by different cardio-metabolic 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 is also in itself is 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 to development of dementia.19
We have earlier studied AF patients in primary care regarding the effect of co-morbid 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 haemorrhagic 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 socio-economic status on mortality in AF patients.29
The aims herein were therefore to study the association between gout and cardiovascular co-morbidities 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.
2. Methods
2.1. 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).
2.2. 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 (6,646 men and 5,637 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.
2.3. 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).
2.4. Demographic and socio-economic 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).
2.5. Co-morbidities
We identified the following cardiovascular co-morbidities 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.
2.7. Statistical analyses
Analyses were performed stratified by sex, with results presented as means with standard deviation, or rates in per cent.
Multivariate logistic regression was performed to explore the association of gout with the background factors, i.e. socio-economic 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 with an earlier registered diagnosis of CHF, ischemic stroke and dementia, respectively, before first diagnosis of AF, for CHF (n=2,859), 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 socio-economic status), co-morbidities (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).
3. Results
Characteristics of the entire study population consisting of patients with AF (n=12,283), stratified by sex (6,646 men and 5,637 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 1st 2001 and December 31st 2007, divided by sex
| All | Men (N=6,646) | Women (N=5,637) | |||
|---|---|---|---|---|---|
| No gout | Gout | No gout | Gout | ||
| N=12,283 | N=5,959 | N=687 | N=5,275 | N=362 | |
| Numbers (%) | Numbers (%) | Numbers (%) | Numbers (%) | Numbers (%) | |
| Number of deaths | 3,954 (32.1) | 1,739 (29.2) | 244 (35.5) | 1,817 (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 | 1,743 (14.2) | 1,110 (18.6) | 112 (16.3) | 499 (9.5) | 22 (6.1) |
| 65–74 | 3,308 (26.9) | 1,852 (31.1) | 190 (27.7) | 1,191 (22.6) | 75 (20.7) |
| 75–79 | 2,427 (19.8) | 1,092 (18.3) | 165 (24.0) | 1,090 (20.7) | 80 (22.1) |
| 80–84 | 2,447 (19.9) | 962 (16.1) | 121 (17.6) | 1,261 (23.9) | 103 (28.5) |
| ≥85 | 1,883 (15.3) | 595 (10.0) | 77 (11.2) | 1,130 (21.4) | 81 (22.4) |
| Educational level | |||||
| Compulsory schooling | 5,085 (45.2) | 2,219 (39.4) | 267 (41.1) | 2,427 (52.1) | 172 (58.1) |
| Secondary schooling | 3,995 (35.5) | 2,110 (37.4) | 257 (39.5) | 1,535 (33.0) | 93 (31.4) |
| College and/or university studies | 2,161 (19.2) | 1,310 (23.2) | 127 (19.5) | 693 (14.9) | 31 (10.5) |
| Marital status | |||||
| Married | 5,613 (45.9) | 3,567 (60.1) | 383 (55.9) | 1,554 (29.6) | 109 (30.3) |
| Unmarried | 1,029 (8.4) | 566 (9.5) | 64 (9.3) | 384 (7.3) | 15 (4.2) |
| Divorced | 1,813 (14.8) | 908 (15.3) | 113 (16.5) | 744 (14.2) | 48 (13.3) |
| Widowed | 3,777 (30.9) | 895 (15.1) | 125 (18.3) | 2,659 (48.9) | 188 (52.2) |
| Neighborhood SES | |||||
| High | 4,604 (37.5) | 2,385 (40.0) | 271 (39.5) | 1,834 (34.8) | 114 (31.5) |
| Middle | 5,807 (47.3) | 2,712 (45.5) | 318 (46.3) | 2,600 (49.3) | 177 (48.9) |
| Low | 1,872 (15.2) | 862 (14.5) | 98 (14.3) | 841 (15.9) | 71 (19.6) |
| Diagnosis | |||||
| Hypertension | 5,449 (44.4) | 2,396 (40.2) | 330 (48.0) | 2,532 (48.0) | 191 (52.8) |
| Coronary heart disease | 3,234 (26.3) | 1,472 (24.7) | 250 (36.4) | 1,395 (26.5) | 117 (32.3) |
| Congestive heart failure | 5,684 (46.3) | 2,432 (40.8) | 424 (61.7) | 2,560 (48.5) | 268 (74.0) |
| Cerebrovascular diseases | 2,566 (20.9) | 1,139 (19.1) | 138 (20.1) | 1,208 (22.9) | 81 (22.4) |
| Obesity | 614 (4.6) | 297 (5.0) | 55 (8.0) | 222 (4.2) | 40 (11.1) |
| Diabetes mellitus | 2,366 (19.3) | 1,109 (18.6) | 185 (26.9) | 958 (18.2) | 114 (31.5) |
| COPD | 1,416 (11.5) | 618 (10.4) | 92 (13.4) | 648 (12.3) | 58 (16.0) |
| Depression | 1,039 (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 1st 2001 and December 31st 2007
| All | Men | Women | Interaction between men and women | |
|---|---|---|---|---|
| N=11,209 | N=6,273 | N=4,936 | ||
| 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 (1,042) and marital status (n=51) is missing for some individuals, giving lower numbers than in Table 1. Statistically significant values shown in bold.
Table 3 shows the follow-up analysis of mortality (n=12,283), incident CHF (n=9,424) 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 significantly lower risk of dementia in patients with gout in a model adjusted for CHA2DS2-VASc, age and sex. Improvement in Harrell’s C were 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 | |||||
|---|---|---|---|---|---|---|---|---|
| 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, socio-economic variables, co-morbidity, anticoagulant treatment) and models adjusted for CHA2DS2-VASc (with age and sex included) are shown. Bold values are statistically significant for HRs, or exceeding 0.01 in model improvement for Harrell’s
4. 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 favour 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,33–35 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 have 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 co-morbidities 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 have affected the results, and yielded 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.
Significance and relevance:
In this clinical setting we found gout to be associated with most cardiometabolic diseases
A diagnosis of gout was be associated with a decreased risk of ischemic stroke in patients with atrial fibrillation
Identifying a gout diagnosis in patients with atrial fibrillation improves to the predictive value to the risk for ischemic stroke compared to CHA2DS2-VASc without gout included
Acknowledgments
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.
Footnotes
Conflict of interest
The authors have no conflict of interest to disclose.
References
- 1.Roddy E, Choi HK. Epidemiology of gout. Rheum Dis Clin North Am 2014;40:155–75 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Dehlin M, Drivelegka P, Sigurdardottir V, Svard A, Jacobsson LT. Incidence and prevalence of gout in Western Sweden. Arthritis Res Ther 2016;18:164. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Kapetanovic MC, Hameed M, Turkiewicz A, Neogi T, Saxne T, Jacobsson L, et al. Prevalence and incidence of gout in southern Sweden from the socioeconomic perspective. RMD Open 2016;2:e000326. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Global, regional, and national incidence, prevalence, and years lived with disability for 328 diseases and injuries for 195 countries, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet 2017;390:1211–59 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Nakamura K, Sakurai M, Miura K, Morikawa Y, Nagasawa SY, Ishizaki M, et al. HOMA-IR and the risk of hyperuricemia: a prospective study in non-diabetic Japanese men. Diabetes Res Clin Pract 2014;106:154–60 [DOI] [PubMed] [Google Scholar]
- 6.Li C, Hsieh MC, Chang SJ. Metabolic syndrome, diabetes, and hyperuricemia. Curr Opin Rheumatol 2013;25:210–6 [DOI] [PubMed] [Google Scholar]
- 7.Zhu Y, Pandya BJ, Choi HK. Comorbidities of gout and hyperuricemia in the US general population: NHANES 2007–2008. Am J Med 2012;125:679–87 e1 [DOI] [PubMed] [Google Scholar]
- 8.Gibson TJ. Hypertension, its treatment, hyperuricaemia and gout. Curr Opin Rheumatol 2013;25:217–22 [DOI] [PubMed] [Google Scholar]
- 9.Kim SY, De Vera MA, Choi HK. Gout and mortality. Clin Exp Rheumatol 2008;26:S115–9 [PubMed] [Google Scholar]
- 10.Wijnands JM, Viechtbauer W, Thevissen K, Arts IC, Dagnelie PC, Stehouwer CD, et al. Determinants of the prevalence of gout in the general population: a systematic review and meta-regression. Eur J Epidemiol 2015;30:19–33 [DOI] [PubMed] [Google Scholar]
- 11.Forslund T, Wettermark B, Wandell P, von Euler M, Hasselstrom J, Hjemdahl P. Risk scoring and thromboprophylactic treatment of patients with atrial fibrillation with and without access to primary healthcare data: experience from the Stockholm health care system. Int J Cardiol 2013;170:208–14 [DOI] [PubMed] [Google Scholar]
- 12.Tamariz L, Hernandez F, Bush A, Palacio A, Hare JM. Association between serum uric acid and atrial fibrillation: a systematic review and meta-analysis. Heart Rhythm 2014;11:1102–8 [DOI] [PubMed] [Google Scholar]
- 13.Wandell P, Carlsson AC, Ljunggren G. Gout and its comorbidities in the total population of Stockholm. Prev Med 2015;81:387–91 [DOI] [PubMed] [Google Scholar]
- 14.Hobbs FR, Taylor CJ, Jan Geersing G, Rutten FH, Brouwer JR. European Primary Care Cardiovascular Society (EPCCS) consensus guidance on stroke prevention in atrial fibrillation (SPAF) in primary care. Eur J Prev Cardiol 2016;23:460–73 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Chao TF, Liu CJ, Chen SJ, Wang KL, Lin YJ, Chang SL, et al. Hyperuricemia and the risk of ischemic stroke in patients with atrial fibrillation--could it refine clinical risk stratification in AF? Int J Cardiol 2014;170:344–9 [DOI] [PubMed] [Google Scholar]
- 16.Lip GY, Frison L, Halperin JL, Lane DA. Identifying patients at high risk for stroke despite anticoagulation: a comparison of contemporary stroke risk stratification schemes in an anticoagulated atrial fibrillation cohort. Stroke 2010;41:2731–8 [DOI] [PubMed] [Google Scholar]
- 17.O’Neal WT, Salahuddin T, Broughton ST, Soliman EZ. Atrial Fibrillation and Cardiovascular Outcomes in the Elderly. Pacing Clin Electrophysiol 2016;39:907–13 [DOI] [PubMed] [Google Scholar]
- 18.Healey JS, Oldgren J, Ezekowitz M, Zhu J, Pais P, Wang J, et al. Occurrence of death and stroke in patients in 47 countries 1 year after presenting with atrial fibrillation: a cohort study. Lancet 2016;388:1161–9 [DOI] [PubMed] [Google Scholar]
- 19.Alonso A, Arenas de Larriva AP. Atrial Fibrillation, Cognitive Decline And Dementia. Eur Cardiol 2016;11:49–53 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Wandell P, Carlsson AC, Holzmann MJ, Arnlov J, Sundquist J, Sundquist K. The association between relevant co-morbidities and prevalent as well as incident heart failure in patients with atrial fibrillation. J Cardiol 2018;72:26–32 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Wandell P, Carlsson AC, Sundquist J, Sundquist K. The association between relevant comorbidities and dementia in patients with atrial fibrillation. Geroscience 2018;40:317–24 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Wandell P, Carlsson AC, Sundquist J, Sundquist K. Antihypertensive drugs and relevant cardiovascular pharmacotherapies and the risk of incident dementia in patients with atrial fibrillation. Int J Cardiol 2018;272:149–54 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Wandell P, Carlsson AC, Holzmann MJ, Arnlov J, Sundquist J, Sundquist K. Mortality in patients with atrial fibrillation and common co-morbidities - a cohort study in primary care. Ann Med 2018;50:156–63 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Wandell P, Carlsson AC, Sundquist K, Johansson SE, Sundquist J. Effect of cardiovascular drug classes on all-cause mortality among atrial fibrillation patients treated in primary care in Sweden: a cohort study. Eur J Clin Pharmacol 2013;69:279–87 [DOI] [PubMed] [Google Scholar]
- 25.Wandell P, Carlsson AC, Holzmann MJ, Arnlov J, Johansson SE, Sundquist J, et al. Warfarin treatment and risk of stroke among primary care patients with atrial fibrillation. Scand Cardiovasc J 2016:1–6 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Wandell P, Carlsson AC, Holzmann M, Arnlov J, Johansson SE, Sundquist J, et al. Association between antithrombotic treatment and hemorrhagic stroke in patients with atrial fibrillation-a cohort study in primary care. Eur J Clin Pharmacol 2017;73:215–21 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Wandell P, Carlsson AC, Holzmann MJ, Arnlov J, Johansson SE, Sundquist J, et al. Warfarin treatment and risk of myocardial infarction - A cohort study of patients with atrial fibrillation treated in primary health care. Int J Cardiol 2016;221:789–93 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Carlsson AC, Wandell P, Sundquist K, Johansson SE, Sundquist J. Differences and time trends in drug treatment of atrial fibrillation in men and women and doctors’ adherence to warfarin therapy recommendations: a Swedish study of prescribed drugs in primary care in 2002 and 2007. Eur J Clin Pharmacol 2013;69:245–53 [DOI] [PubMed] [Google Scholar]
- 29.Wandell P, Carlsson AC, Gasevic D, Holzmann MJ, Arnlov J, Sundquist J, et al. Socioeconomic factors and mortality in patients with atrial fibrillation-a cohort study in Swedish primary care. Eur J Public Health 2018; [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Kim SY, Guevara JP, Kim KM, Choi HK, Heitjan DF, Albert DA. Hyperuricemia and risk of stroke: a systematic review and meta-analysis. Arthritis Rheum 2009;61:885–92 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Li M, Hou W, Zhang X, Hu L, Tang Z. Hyperuricemia and risk of stroke: a systematic review and meta-analysis of prospective studies. Atherosclerosis 2014;232:265–70 [DOI] [PubMed] [Google Scholar]
- 32.Bruderer S, Bodmer M, Jick SS, Meier CR. Use of diuretics and risk of incident gout: a population-based case-control study. Arthritis Rheumatol 2014;66:185–96 [DOI] [PubMed] [Google Scholar]
- 33.Kim SY, Guevara JP, Kim KM, Choi HK, Heitjan DF, Albert DA. Hyperuricemia and coronary heart disease: a systematic review and meta-analysis. Arthritis Care Res (Hoboken) 2010;62:170–80 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Li M, Hu X, Fan Y, Li K, Zhang X, Hou W, et al. Hyperuricemia and the risk for coronary heart disease morbidity and mortality a systematic review and dose-response meta-analysis. Sci Rep 2016;6:19520. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Zuo T, Liu X, Jiang L, Mao S, Yin X, Guo L. Hyperuricemia and coronary heart disease mortality: a meta-analysis of prospective cohort studies. BMC Cardiovasc Disord 2016;16:207. [DOI] [PMC free article] [PubMed] [Google Scholar]