All relevant data are within the paper and its Supporting Information files.

Introduction {#sec006}
============

Gout is the most prevalent inflammatory arthritis, affecting up to 4% of the population in developed countries \[[@pone.0146172.ref001]\]. Gout attacks can be effectively prevented by use of urate lowering therapy (ULT) \[[@pone.0146172.ref002]\], such as allopurinol. The aim of ULT is to prevent future attacks of gouty arthritis primarily by decreasing plasma urate level to below 0.36 mmol/l (6.0 mg/dl), which is below the precipitation point of urate at physiological conditions \[[@pone.0146172.ref003]\].

Allopurinol is the most widely used ULT. In addition to its urate lowering properties, allopurinol has been shown to reduce blood pressure among patients with hypertension \[[@pone.0146172.ref004]\], increase work capacity in chronic stable angina patients \[[@pone.0146172.ref005]\], diminish vascular oxidative stress and endothelial dysfunction \[[@pone.0146172.ref006],[@pone.0146172.ref007]\]. In addition, allopurinol 300 mg daily or higher has recently been associated with improved cardiovascular outcome \[[@pone.0146172.ref008]\].

However, the above-mentioned effects on hypertension and work capacity has only been shown with very high doses (\> 400 mg/day and \> 600 mg/day respectively) \[[@pone.0146172.ref004],[@pone.0146172.ref005]\].

The treat-to-target approach (urate level below 0.36 mmol/l) with ULT is developed for gout related symptoms, but whether this approach is also sufficient to achieve a reduction in cardiovascular risk remains to be established. We sought to investigate the effect of achieving target plasma urate with allopurinol on cardiovascular outcomes in a case-control study nested within long-term users of allopurinol.

Methods {#sec007}
=======

This case-control study nested among long-term users of allopurinol was carried out in Funen County, Denmark, using health care registries furnished by the Danish health authorities. Cases were defined by an Antiplatelet Trialist's collaboration (APTC) cardiovascular endpoint and controls were without the APTC endpoint. We ascertained whether individuals had achieved target urate level and analyzed the association between in-target urate level and risk of the APTC endpoint.

Data sources {#sec008}
------------

We linked data from the following four health care registries:

1.  The laboratory database of Odense University Hospital is a clinical laboratorial system, which contains information on all blood samples analyzed at every hospital laboratories in the Funen County since November 1992. The coverage includes primary and secondary health care providers for both in- and outpatients. All urate measurements measured at inhabitants in the Funen County during the study period were covered.

2.  The Odense University Pharmaco-epidemiological Database (OPED) is a prescription database holding information on redeemed, reimbursed prescriptions for the citizens of Funen County since 1990 \[[@pone.0146172.ref009]\]. Data included are a full account of the dispensed product and the date of dispensing. The product is described in terms of the defined daily dose (DDD) and the anatomical-therapeutic-chemical (ATC) code \[[@pone.0146172.ref010]\]. OPED includes a demographic module with information on residency, migration, births and death, which allowed us to account for censoring.

3.  The Funen County Patient Administrative System provides hospital discharge diagnosis for the population of Funen County since 1977 for inpatients and since 1989 for outpatients. The diagnoses are encoded according to the International Classification of Diseases 8^th^ revision (ICD8) until January 1994 and ICD10 thereafter.

4.  The Danish Register of Causes of Death holds information on all causes of death among Danish citizens, encoded according to the ICD classification system. The National Board of Health established the current register in 1875 \[[@pone.0146172.ref011]\].

The Record linkage was made possible by a unique personal identifier, assigned to all Danish citizens since 1968 \[[@pone.0146172.ref012]\]. In Denmark public health authorities provide virtually all health services, which allows true population based epidemiological studies \[[@pone.0146172.ref013]\].

Cases and controls {#sec009}
------------------

### Source population {#sec010}

The population used in this setting was derived from the total population from Funen, Denmark (≈ 485,000), during the period of December 1992 through December 2010. Among these we identified first-ever users of allopurinol (ATC M04AA01) with an elevated (≥ 0.36 mmol/l) urate measurement within 2 years prior to their first allopurinol prescription (9,118 individuals) ([Fig 1](#pone.0146172.g001){ref-type="fig"}). From this basic cohort basic cohort of hyperuricemic allopurinol users both cases and control were selected.

![Study flow diagram.](pone.0146172.g001){#pone.0146172.g001}

### Cases {#sec011}

Cases were identified as individuals developing their first APTC event \[[@pone.0146172.ref014]\] during follow-up, using the day of this event as the index date. In brief, the APTC endpoint is a non-fatal myocardial infarction (MI) (ICD8 410; ICD10 I21-I22), a non-fatal stroke (ICD8 430--434; ICD10 I60-I64), or cardiovascular death (ICD8 390--458; ICD10 I00-I99) including unknown causes of death (ICD8 780--796; ICD10 R96-R99).

In addition, we applied the following inclusion criteria prior to the index date:

1.  At least 18 years of age.

2.  Living in Funen at the index date and for the past five years (individuals who migrated inside this period were excluded, see [Fig 1](#pone.0146172.g001){ref-type="fig"}.).

3.  Cancer free for 5 year, defined by the absence of any of the following diagnosis of malignancies (ICD8 140--207; ICD10 C00-C96), not including non-melanoma skin cancer (ICD8 173; ICD10 C44).

4.  No treatment with other ULT than allopurinol.

5.  Continuous use of allopurinol in the year preceding the index date. Defined as follows; an individual was considered an allopurinol user if she or he had redeemed prescriptions for allopurinol that added together would cover the index date and the entire year preceding the index date. For each prescription, we assigned a period of usage starting on the date of the prescription and lasting a number of days corresponding to the number of tablets, thus assuming an average daily intake of one tablet. In order to allow for non-compliance and irregular prescription renewals we added a grace period (a period of time in which non-redeeming of allopurinol prescriptions was allowed) of 180 days to the usage period assigned to each prescription of allopurinol. The usage clock was reset at each new prescription.

For numbers of cases excluded in each step and final number of cases see [Fig 1](#pone.0146172.g001){ref-type="fig"}.

### Controls {#sec012}

Controls were selected randomly based on their availability in the cohort of hyperuricemic allopurinol users and matched by age and sex using risk set sampling with a maximum case:control ratio of 1:4. The controls were subjected to the same inclusion criteria as the cases (except the APTC endpoint). As we had full coverage of death and migrations out of the county, we could effectively account for all individuals' presence in the cohort. The controls were assigned an index date identical to the index date of the corresponding case in the risk set.

Controls were not allowed to have had an APTC endpoint before their selection as controls, but might have one later and thus be re-selected as cases. By this approach, the odds ratio is an unbiased estimate of the incidence rate ratio \[[@pone.0146172.ref015]\].

Not all cases had 4 possible matches, therefore the final case:control ratio differs from 1:4. The final number of controls and number excluded at each step of inclusion are given in [Fig 1](#pone.0146172.g001){ref-type="fig"}.

Urate level {#sec013}
-----------

Both cases and controls were classified as either hyperuricemic or treated-to-target. This classification was based on an average of all urate samples from the time of allopurinol prescription until the index date (weighted by the number of days spent at each different plasma urate level). The plasma urate level at any given day was set to the same value as the closest measurement prior to that day. Individuals would be classified as treated-to-target if the weighted urate level prior to index date were below 0.36 mmol/l.

Analysis {#sec014}
--------

The adjusted odds ratios (ORs) with 95% confidence intervals (CI) were derived using conditional logistic regression with adjustment for potential confounders. Variables known or suspected to influence cardiovascular diseases were included as potential confounders in the regression model. Current use of other drugs than allopurinol was defined by the redeeming of a prescription for this particular drug less than 120 days before the index date. Previous diagnoses were defined by having an in- or outpatient contact coded with the corresponding ICD8 or ICD10 code. See [S1 Table](#pone.0146172.s002){ref-type="supplementary-material"} for further details on included variables.

For missing continuous covariates we used median value imputation and missing dichotomous variables were set to absent. In subgroup analyses defined by covariates with imputed data, individuals with missing data were excluded.

Baseline characteristics were presented for cases and controls as well as exposed and unexposed controls (as a proxy for the source population) \[[@pone.0146172.ref016]\].

Additional analyses and pre-specified sensitivity analyses {#sec015}
----------------------------------------------------------

We carried out a range of supplementary analyses. First we used different exposure definitions, by using different urate cut-off values to define the treat-to-target group (urate \< 0.20 mmol/l; urate \< 0.3 mmol/l; urate \< 0.36 mmol/l; urate \< 0.42 mmol/l; urate \< 0.48 mmol/l), and by grouping individuals according to their decrease in achieved weighted plasma urate relative to the baseline value.

Subsequently we conducted a number of subgroup analyses. We restricted the analysis by strata of age, sex and renal function (defined as chronic kidney disease stages by the National kidney foundation \[[@pone.0146172.ref017]\]). We also restricted the analysis by only considering individuals with and without diabetes, hypertension and previous APTC events, in order to estimate the effect in groups with different risk of the outcome. Further we analyzed subgroups defined by cumulative doses of allopurinol; (\<200 g, 200--400 g, \> 400 g) and as average daily intake (\< 150 mg/d, 150--250 mg/d and \> 250 mg/d).

One critical issue in this study is the definition of when an individual is treated-to-target. First, we performed an analysis were the plasma urate level at any given day was set to the mean value as the closest measurement prior to and after that day, rather than the first value alone. Second, we only included urate measurement from the last 1 and 3 years before the index date to calculate the weighted urate levels. Third, we only considered the latest urate measurement. Fourth, we omitted urate measurements inside 3 month of the index date; this analysis was done to check for any biased results caused by urate levels measured in conjunction with an admission for a case event. Lastly, we changed the grace period for allopurinol prescriptions to 0, 1, 3 and 12 months.

Post hoc we conducted an analysis applying all covariates one by one and in groups (i.e. all comorbidities, all laboratory values or all prescriptions), because the estimates changed markedly after adjustment.

Ethics {#sec016}
------

The project was approved by the Danish Data Protection Agency. Registry based studies do not require ethical approval in Denmark, nor do it require personal written consent \[[@pone.0146172.ref018]\]. All individuals were anonymized prior to retrieval of data and all analyses were performed at anonymized data.

Results {#sec017}
=======

We identified 538 APTC cases and 1,791 controls with at least one year of continuous allopurinol use. Altogether 1,638 (70.3%) were men and the median age was 78 (IQR; 70--84) years. The median urate levels decreased markedly after allopurinol treatment start among both cases and controls---on average a 27.4% decrease was observed. Other characteristics of the APTC cases and their controls along with exposed and unexposed controls are presented in [Table 1](#pone.0146172.t001){ref-type="table"}.

10.1371/journal.pone.0146172.t001

###### Characteristics of APTC cases and their corresponding controls, and for unexposed and exposed controls (proxy for distribution in the source population).

![](pone.0146172.t001){#pone.0146172.t001g}

                                                    Cases (n = 538)     Controls (n = 1,791)   Unexposed Controls (n = 1,063)   Exposed Controls (n = 728)
  ------------------------------------------------- ------------------- ---------------------- -------------------------------- ----------------------------
  Men                                               365 (67.8%)         1,273 (71.1%)          815 (76.7%)                      458 (62.9%)
  Women                                             173 (32.2%)         518 (28.9%)            248 (23.3%)                      270 (37.1%)
  Age, median (IQR)                                 79 (71--86)         77 (70--84)            77 (70--83)                      78 (71--85)
  History of:                                                                                                                   
   Ischemic heart disease                           284 (52.8%)         589 (32.9%)            390 (36.7%)                      199 (27.3%)
   Heart failure                                    241 (44.8%)         393 (21.9%)            271 (25.5%)                      122 (16.8%)
   Atrial fibrillation                              176 (32.7%)         363 (20.3%)            264 (24.8%)                      99 (13.6%)
   Stroke                                           101 (18.8%)         249 (13.9%)            158 (14.9%)                      91 (12.5%)
   Diabetes mellitus                                128 (23.8%)         274 (15.3%)            176 (16.6%)                      98 (13.5%)
   Hypertension                                     221 (41.1%)         532 (29.7%)            339 (31.9%)                      193 (26.5%)
   COPD                                             92 (17.1%)          180 (10.1%)            121 (11.4%)                      59 (8.1%)
  Charlson comorbidity index:                                                                                                   
   0                                                70 (13.0%)          706 (39.4%)            392 (36.9%)                      314 (43.1%)
   1                                                140 (26.0%)         440 (24.6%)            248 (23.3%)                      192 (26.4%)
   2                                                105 (19.5%)         266 (14.9%)            166 (15.6%)                      100 (13.7%)
   ≥3                                               223 (41.4%)         379 (21.2%)            257 (24.2%)                      122 (16.8%)
  Current drug use (baseline):                                                                                                  
   Diabetes-drugs (ever use)                        87 (16.2%)          259 (14.5%)            168 (15.8%)                      91 (12.5%)
   Vitamin K antagonists                            71 (13.2%)          206 (11.5%)            166 (15.6%)                      40 (5.5%)
   ADP-receptor inhibitors                          9 (1.7%)            20 (1.1%)              14 (1.3%)                        6 (0.8%)
   Low-dose ASA                                     177 (32.9%)         457 (25.5%)            269 (25.3%)                      188 (25.8%)
   Dipyridamole                                     37 (6.9%)           86 (4.8%)              45 (4.2%)                        41 (5.6%)
   Heart glycosides                                 155 (28.8%)         288 (16.1%)            203 (19.1%)                      85 (11.7%)
   Nitrate vasodilators                             128 (23.8%)         207 (11.6%)            112 (10.5%)                      95 (13.0%)
   Thiazide diuretics                               80 (14.9%)          223 (12.5%)            142 (13.4%)                      81 (11.1%)
   Loop diuretics                                   330 (61.3%)         748 (41.8%)            463 (43.6%)                      285 (39.1%)
   Aldosterone antagonists                          99 (18.4%)          172 (9.6%)             124 (11.7%)                      48 (6.6%)
   Betablockers                                     159 (29.6%)         506 (28.3%)            332 (31.2%)                      174 (23.9%)
   Calcium antagonists                              128 (23.8%)         426 (23.8%)            243 (22.9%)                      183 (25.1%)
   RAAS blockers                                    248 (46.1%)         746 (41.7%)            454 (42.7%)                      292 (40.1%)
   Statins                                          114 (21.2%)         451 (25.2%)            292 (27.5%)                      159 (21.8%)
   COPD related drug use                            76 (14.1%)          229 (12.8%)            148 (13.9%)                      81 (11.1%)
   Systemic corticosteroids                         70 (13.0%)          116 (6.5%)             71 (6.7%)                        45 (6.2%)
   NSAIDs                                           111 (20.6%)         387 (21.6%)            228 (21.4%)                      159 (21.8%)
  Blood measurements (baseline):                                                                                                
   Urate level prior to allopurinol, median (IQR)   0.55 (0.49--0.64)   0.52 (0.46--0.60)      0.54 (0.49--0.62)                0.49 (0.43--0.55)
   Weighted urate level, median (IQR)               0.39 (0.33--0.47)   0.38 (0.32--0.43)      0.42 (0.39--0.47)                0.31 (0.27--0.34)
   Urate \< 0.36 mmol/l (treated to target)         197 (36.6%)         728 (40.6%)            0 (0.0%)                         728 (100.0%)
   eGFR, median (IQR)                               43 (31--58)         52 (40--67)            50 (38--65)                      55 (43--69)
   High HbA1c (\> 6.5%)                             82 (15.2%)          212 (11.8%)            140 (13.2%)                      72 (9.9%)
   High cholesterol (\> 5 mmol/l)                   115 (21.4%)         329 (18.4%)            215 (20.2%)                      114 (15.7%)
   Proteinuria                                      40 (7.4%)           61 (3.4%)              49 (4.6%)                        12 (1.6%)

IQR = interquartile range; IHD = ischemic heart disease; COPD = chronic obstructive pulmonary disease; DM = diabetes mellitus; ASA = acetylsalicylic acid; ADP = adenosinediphosphat; VKA = vitamin-K-antagonists; RAAS = renin-angiotensin-aldosterone-receptor; NSAID = non-steroidal anti-inflammatory drugs; eGFR = estimated glomerular filtration rate; HbA1c = hemoglobin A1c

Treat to target approach {#sec018}
------------------------

Among cases and controls, 197 (36.6%) and 728 (40.6%) were treated-to-target plasma urate level below 0.36 mmol/l respectively. The crude and adjusted odds ratios (OR) of APTC events among individuals treated-to-target urate below 0.36 mmol/l were 0.77 (95% CI 0.62--0.95) and 1.01 (95% CI 0.79--1.28) ([Table 2](#pone.0146172.t002){ref-type="table"}).

10.1371/journal.pone.0146172.t002

###### Odds ratios for APTC event of treated-to-target for different subgroups.

![](pone.0146172.t002){#pone.0146172.t002g}

  Exposure pattern                      Cases In target / Not in target   Controls In target / Not in target   Crude OR (95% CI)   Adjusted[\*](#t002fn001){ref-type="table-fn"} OR (95% CI)
  ------------------------------------- --------------------------------- ------------------------------------ ------------------- -----------------------------------------------------------
  All users                             197 / 341                         728 / 1063                           0.77 (0.62--0.95)   1.01 (0.79--1.28)
  Sex                                                                                                                              
  Men                                   119 / 246                         458 / 815                            0.81 (0.62--1.04)   1.03 (0.77--1.38)
  Women                                 78 / 95                           270 / 248                            0.68 (0.47--1.00)   0.97 (0.62--1.53)
  Age groups                                                                                                                       
  Age \< 60 years                       8 / 32                            49 / 99                              0.41 (0.16--1.08)   0.45 (0.09--2.36)
  Age 60--79 years                      86 / 155                          356 / 535                            0.80 (0.59--1.08)   0.98 (0.69--1.41)
  Age ≥ 80 years                        103 / 154                         323 / 429                            0.79 (0.57--1.08)   1.10 (0.76--1.59)
  Comorbidities                                                                                                                    
  Diabetes                              44 / 103                          141 / 228                            0.88 (0.48--1.60)   1.64 (0.47--5.77)
  No diabetes                           153 / 238                         587 / 835                            0.88 (0.68--1.14)   1.12 (0.84--1.50)
  Hypertension                          70 / 151                          193 / 339                            0.72 (0.45--1.14)   0.99 (0.56--1.72)
  No hypertension                       127 / 190                         535 / 724                            0.82 (0.61--1.10)   1.08 (0.75--1.55)
  Previous APTC                         52 / 106                          129 / 232                            1.00 (0.53--1.89)   1.11 (0.39--3.15)
  No previous APTC                      145 / 235                         599 / 831                            0.75 (0.58--0.96)   0.95 (0.70--1.29)
  Renal function                                                                                                                   
  eGFR \> 60 ml / min                   48 / 71                           290 / 366                            0.94 (0.54--1.63)   1.33 (0.60--2.96)
  eGFR 30--60 ml / min                  107 / 196                         369 / 567                            0.76 (0.54--1.05)   1.00 (0.68--1.48)
  eGFR \< 30 ml / min                   42 / 74                           69 / 130                             1.15 (0.47--2.82)   N / A
  Cumulated doses of allopurinol (g)                                                                                               
  \< 200 g                              66 / 195                          253 / 540                            0.68 (0.44--1.04)   0.97 (0.57--1.65)
  200--400 g                            72 / 97                           238 / 307                            0.77 (0.42--1.42)   0.62 (0.16--2.31)
  ≥ 400 g                               59 / 49                           237 / 216                            0.95 (0.52--1.75)   1.25 (0.23--6.83)
  Average mg allopurinol per day (mg)                                                                                              
  \< 150 mg                             51 / 229                          235 / 714                            0.63 (0.42--0.95)   0.83 (0.51--1.37)
  150--250 mg                           62 / 79                           201 / 231                            0.81 (0.43--1.50)   1.30 (0.36--4.67)
  ≥ 250 mg                              84 / 33                           290 / 117                            0.84 (0.40--1.79)   4.41 (0.83--23.41)
  Duration of allopurinol therapy                                                                                                  
  1--2 years                            66 / 137                          224 / 360                            0.73 (0.46--1.18)   0.88 (0.47--1.66)
  ≥ 3 years                             86 / 122                          336 / 461                            0.88 (0.60--1.29)   1.16 (0.71--1.91)

\*) Adjusted by variables presented in [S1 Table](#pone.0146172.s002){ref-type="supplementary-material"}.

Allopurinol exposure patterns {#sec019}
-----------------------------

We found no association of increasing cumulative amounts of allopurinol with respect to APTC outcomes ([Table 2](#pone.0146172.t002){ref-type="table"}). Similarly, we did not find any association with duration of continuous allopurinol use or with increasing amounts of daily use ([Table 2](#pone.0146172.t002){ref-type="table"}). All tests for trend were statistically non-significant.

Subgroups {#sec020}
---------

No differences in analysis of different subgroups defined by age, sex or a history of diabetes, hypertension or cardiovascular disease were found ([Table 2](#pone.0146172.t002){ref-type="table"}).

Urate exposure patterns and cardiovascular events {#sec021}
-------------------------------------------------

We found no association between the degree of change in urate level and cardiovascular events. Changing the urate target to different levels showed an increased risk of APTC events for very low levels of urate (\< 0.20 mmol/l). Results are presented in [Table 3](#pone.0146172.t003){ref-type="table"}.

10.1371/journal.pone.0146172.t003

###### Odds ratios for different exposure definitions of treated-to-target urate levels.

![](pone.0146172.t003){#pone.0146172.t003g}

                                             Cases In target / Not in target   Controls In target / Not in target   Crude OR (95% CI)   Adjusted[\*](#t003fn001){ref-type="table-fn"} OR (95% CI)
  ------------------------------------------ --------------------------------- ------------------------------------ ------------------- -----------------------------------------------------------
  Urate threshold for treat-to-target                                                                                                   
  \< 0.20 mmol/l                             16 / 522                          22 / 1769                            2.17 (1.07--4.43)   2.29 (1.05--4.97)
  \< 0.30 mmol/l                             94 / 444                          317 / 1474                           0.88 (0.66--1.16)   1.11 (0.81--1.52)
  \< 0.36 mmol/l                             197 / 341                         728 / 1063                           0.77 (0.62--0.95)   1.01 (0.79--1.28)
  \< 0.42 mmol/l                             326 / 212                         1231 / 560                           0.63 (0.51--0.78)   0.94 (0.73--1.20)
  \< 0.48 mmol/l                             437 / 101                         1559 / 232                           0.60 (0.46--0.79)   0.88 (0.65--1.20)
  Percent change from baseline urate level                                                                                              
  0--10                                      508 / 30                          1665 / 126                           1.27 (0.83--1.94)   1.24 (0.78--1.98)
  \> 10                                      467 / 71                          1501 / 290                           1.18 (0.88--1.58)   1.22 (0.89--1.68)
  \> 20                                      387 / 151                         1212 / 579                           1.15 (0.91--1.44)   1.07 (0.83--1.38)
  \> 30                                      268 / 270                         794 / 997                            1.10 (0.89--1.35)   1.07 (0.85--1.36)
  \> 40                                      160 / 378                         398 / 1393                           1.32 (1.05--1.66)   1.15 (0.88--1.50)
  \> 50                                      71 / 467                          153 / 1638                           1.57 (1.15--2.16)   1.43 (1.00--2.04)
  \> 60                                      30 / 508                          47 / 1744                            2.17 (1.31--3.61)   1.97 (1.13--3.46)

\*) Adjusted by variables presented in [S1 Table](#pone.0146172.s002){ref-type="supplementary-material"}

Sensitivity analyses {#sec022}
--------------------

Changing the definition of treated-to-target to include only the last urate measurement or the definition of weighted urate to only include the last 1 and 3 years of urate measurements did not change the overall results (data not shown). Neither did using the alternative approach to calculate the weighted urate levels. Changing the grace periods did not alter the main results, even though there was a non-statistically significant association with lower ORs for the primary endpoint as the grace periods decreased ([S2 Table](#pone.0146172.s003){ref-type="supplementary-material"}).

The analyses including potential confounders one at a time showed that no single dominating covariate or group of covariates was responsible for the heavy adjustment ([S3 Table](#pone.0146172.s004){ref-type="supplementary-material"}).

Discussion {#sec023}
==========

We found no association between treated-to-target urate level and cardiovascular events among long-term (\> one year) allopurinol treated individuals. Extensive supplementary analyses failed to identify subgroups or exposure patterns that were plausibly related to a reduced risk of the APTC outcome.

Given the well-established link between hyperuricemia and adverse cardiovascular outcomes, and the extensive literature on beneficial effect of allopurinol on proxies for cardiovascular risk \[[@pone.0146172.ref004],[@pone.0146172.ref005],[@pone.0146172.ref007],[@pone.0146172.ref019]\], the result may seem surprising. Several counter-hypotheses need to be considered.

One hypothesis is that the effect on cardiovascular risk is mediated through urate lowering but this association takes place at higher allopurinol doses than used for gout treatment. This is supported by evidence suggesting that high doses of allopurinol will result in non competitive inhibition of xanthine oxidase (the rate-limiting enzyme in purine catabolism) \[[@pone.0146172.ref020]\], and the fact that many gout patients are treated with too low doses of allopurinol \[[@pone.0146172.ref021]\], which also seems to be the case in this study population with an average allopurinol intake below 140 mg daily.

Another hypothesis would be that the cardiovascular effect of allopurinol is mediated through inhibition of xanthine oxidase rather than the lowering of plasma urate. Xanthine oxidase holds important oxidative and antioxidant properties \[[@pone.0146172.ref022]\]. In pathological conditions, such as high levels of xanthine, this enzyme produces reactive oxygen species that induces inflammation and cardiovascular diseases \[[@pone.0146172.ref023]\]. A very steep dose-response relationship between allopurinol and cardiovascular diseases without any direct relation to urate level has been suggested in a study comparing different doses of allopurinol (300 vs. 600 mg / day) showing an improvement on endothelial function with high doses of allopurinol but not low doses, placebo or probenecid \[[@pone.0146172.ref007]\]. The effect of another xanthine oxidase inhibitor (febuxostat) has in a small study been shown to reduce the arterial stiffness \[[@pone.0146172.ref024]\]. Unfortunately, other studies have indicated an increased cardiovascular risk among febuxostat treated compared to allopurinol although not statistically significant \[[@pone.0146172.ref025],[@pone.0146172.ref026]\].

Finally, our results could be confounded. The use of health care registries means, that we do not have access to information on some important covariates e.g. smoking. Heavy smoking has been associated with increased activity of xanthine oxidase \[[@pone.0146172.ref027]\], but is in part controlled for, by including surrogate markers of smoking in the regression model (e.g. COPD diagnoses and COPD related prescriptions) and to our knowledge the plasma urate level is not affected by smoking. Information on dietary factors \[[@pone.0146172.ref028]\] and alcohol consumptions \[[@pone.0146172.ref029]\], both known to alter urate levels were not available either. Some of these factors are also associated with the metabolic syndrome and increased cholesterol, and might have confounded the results. However, the effect of such confounding is likely limited, as it is widely appreciated that the diet alone will not change urate levels sufficiently in gout patients \[[@pone.0146172.ref030]\]. Another limitation is the missing information of some intermediate endpoints (e.g. arterial stiffness or coronary calcifications) along the path to develop an APTC outcome. We had prescription data dating back to 1990. Some of our patients may have used allopurinol before then, and their pre-treatment duration of hyperuricemia could therefore not be estimated precisely. As it potentially could affect their pre-treatment cardiovascular risk, this entails a risk of residual confounding. However, we do not have evidence to support that the duration of hyperuricemia affects the ability of allopurinol to reach target urate.

These limitations aside, our study has several strengths. It is based on the entire population in Funen County, with full access to prescription records, admissions, blood samples and migrations. Virtually all health care related contacts in Denmark is provided by the national public health authorities and hence included in this study. Thereby, there is little risk of selection bias in our material. We expect almost complete capture of outcome events. As it is standard practice to admit all individuals with chest pain or dyspnea along with individuals with impaired neurology or symptoms of transitory ischemic attacks, it is highly unlikely that we have missed a substantial number of cases. As for patients who died immediately from the MI or stroke, the causes of death were ascertained from the Danish register of causes of death.

Given the very high validity of cardiovascular discharge diagnoses in Danish registries \[[@pone.0146172.ref031],[@pone.0146172.ref032]\] there is little risk of misclassification of outcomes. We based our exposure measure on allopurinol prescriptions and urate measurements and we required the individuals to have been treated for at least a year. We thus find it unlikely that the patient would redeem multiple allopurinol prescriptions with patient co-payment without taking the drug. The urate levels were measured in an accredited laboratory. In all, there is little risk of substantial misclassification of exposure.

We have shown that the extensive adjustment is caused by a"little strokes fell great oak" phenomenon and not by one single covariate, which could have biased the results.

A treat-to-target approach is the mainstay in gout treatment \[[@pone.0146172.ref002],[@pone.0146172.ref030]\], but this approach does not seem to confer a better cardiovascular prognosis than an allopurinol treatment failing to reach target. We propose that the cardiovascular effect might not be mediated through urate lowering. At first glance, it may seem as an awkward suggestion, since it is well established that hyperuricemia is associated with high cardiovascular risk, that allopurinol lowers urate levels and that allopurinol improves the cardiovascular risk. However, there are equivalent examples from other parts of the pharmacotherapeutic literature. E.g., the effect of statins on cardiovascular risk is not entirely mediated by cholesterol lowering \[[@pone.0146172.ref033]--[@pone.0146172.ref035]\], and the effect of ACE-inhibitors on stroke risk is not entirely explained by lowering of blood pressure \[[@pone.0146172.ref036]\].

It is important to emphasize that our results do not contradict that allopurinol is effective in preventing cardiovascular event. Our result is fully compatible with a dose dependent effect of allopurinol, but for a given dose of allopurinol the effect on urate level is not important for achieving the cardiovascular effect. Future studies will establish the role of allopurinol in cardiovascular prophylaxis and in particular, the nature of its dose-response relationship.

Supporting Information {#sec025}
======================

###### This supporting information file includes the data used for the present study.

(XLS)

###### 

Click here for additional data file.

###### Covariates included in the conditional logistic regression analysis as potential confounders.

(DOCX)

###### 

Click here for additional data file.

###### Main analyses with different grace periods assigned to each allopurinol prescription.

(DOCX)

###### 

Click here for additional data file.

###### Confounder control, main analysis.

(DOCX)

###### 

Click here for additional data file.

We would like to thank Morten Olesen for validating the programing used in this study.

[^1]: **Competing Interests:**All authors have completed the ICMJE uniform disclosure form at [www.icmje.org/coi_disclosure.pdf](http://www.icmje.org/coi_disclosure.pdf) and declare no support from any organization for the submitted work. KSL and JH have participated in research projects funded by Menarini, with grants paid to institutions where they have been employed and received personal fees; JH has participated in research projects funded by Nycomed/Takeda and received personal fees for teaching; HL has received honoraria and/or participated in research projects funded by Berlin-Chemie/Menarini, Eli Lilly, MSD, Roche and Pfizer. There are no other relationships or activities that could appear to have influenced the submitted work. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

[^2]: Conceived and designed the experiments: KSL AP HL JH. Analyzed the data: KSL AP JH. Wrote the paper: KSL AP HL JH.
