Serum Uric Acid Levels and Initial Presentation of Cardiovascular Diseases: a CALIBER Study
Conditions: Stable Angina, Unstable Angina, Myocardial Infarction, Unheralded Coronary Heart Disease Death, Heart Failure, Cardiac Arrest, Transient Ischaemic Attack, Ischemic Stroke, Subarachnoid Hemorrhage, Intracerebral Hemorrhage, Peripheral Arterial Disease, Abdominal Aortic Aneurysm, Atrial Fibrillation
Sponsor: University College, London

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Official Title of the study:  
Serum Uric Acid Levels and Initial Presentation of Cardiovascular Diseases: a 
CALIBER Study 
 
NCT number:  
Not yet assigned 
 
Date of the document:  
23 January 2018 
 
 
 

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Serum Uric Acid and the Incidence of 13 Cardiovascular Diseases 
Qianrui Li, 23 January 2018 
 
INTRODUCTION 
Serum uric acid (SUA) is the final metabolic product of purine, which is generated in 
the liver and intestine by xanthine oxidase and is excreted from the urine [1]. SUA 
level has been observed to be associated with the risk of developing cardiovascular 
diseases (CVDs) in some studies [2-4], while other studies showed controversial 
results [5-7]. Although currently remains unclear, several pathologic mechanisms 
have been promoted to explain the effect of SUA on CVDs, including deleterious 
effects on endothelial function, oxidative metabolism, platelet adhesiveness, 
hemorheology, and aggregation [8-14]. 
 
An umbrella review published in June 2017 on BMJ [7] summarized evidence from 
clinical studies investigating the relationship between SUA levels and CVDs. None of 
them estimated the lifetime incidence associated with SUA attributable to the initial 
presentation of specific CVDs. Although one previous study investigated the 
association between SUA and the initial presentations of coronary heart disease, the 
outcome was composite [5]. Besides, previous studies have not estimated the 
association between SUA and eight endpoints, namely stable angina, unstable 
angina, subarachnoid haemorrhage, intracerebral haemorrhage, peripheral arterial 
disease, abdominal aortic aneurysm, and cardiac arrest/sudden cardiac death, and 
total cardiovascular disease.  
 
We will address these gaps in one large prospective British cohort. Our objectives 
are to estimate the association between SUA and future risk of 13 of the most 
common initial cardiovascular presentations. 
 
METHODS 
Study overview 
We will first conduct an observational study in the CALIBER (Clinical disease 
research using LInked Bespoke studies and Electronic health Records) programme 
cohort [15] to estimate the association between SUA and the incidences of the initial 
presentation of 13 CVDs.  
 
Study population 
We will select anonymised patients from the CALIBER cohort [15], which links four 
sources of electronic health data in England: the Clinical Practice Research Datalink 
(CPRD) with primary healthcare records, the Hospital Episode Statistics (HES) with 
hospital discharge information, and the Office for National Statistics (ONS) mortality 
and social deprivation data. 
 
A patient will be included if he or she: a) was registered between January 1, 1998, 
and September 2015; b) aged 30 years or older at study entry; c) had no record of 

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previous diagnosis of cardiovascular disease; d) had been followed up for at least 
one year before the study entry; e) had at least one uric acid measurement. 
 
Exposure 
The main exposure will be the baseline serum uric acid (SUA) level measured within 
one year of the study entry as recorded in primary care. If a patient had more than 
one measurement, the mean of the values will be taken. We will perform sensitivity 
analyses to use the SUA level recorded closest to the study entry.   
 
Since gout is an important complication of hyperuricaemia and is associated with 
CVDs, we will also classify the patient state at baseline as with gout or with no gout, 
regardless of SUA levels, based on the diagnosis within one year of the study entry. 
Participants who developed new-onset gout during follow-up will be analysed 
according to their baseline status of no gout. 
 
Covariates 
The following baseline covariates will be considered:  
a) Sociodemographic: sex, age and index of multiple deprivation; 
b) Cardiovascular risk factors: body mass index, systolic blood pressure, high-
density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) 
cholesterol, triglyceride, and estimated glomerular filtration rate, smoking 
status, alcohol consumption;  
c) Medication use: any prescription in the year before index date of blood 
pressure lowering drugs (including diuretics, beta blockers, ACEIs, ARBs), 
statins, non-steroidal anti-inflammatory drugs (NSAIDs), aspirin, 
immunosuppressant drug, and in women only, oestrogen oral contraceptives 
and hormone replacement therapy; 
d) Comorbidities (defined by Read codes, a coded thesaurus of clinical terms 
used in the UK, in primary care data and by ICD-10 codes in hospital care 
data): Cancer, diabetes, asthma or atopy, COPD, connective tissue disease, 
inflammatory bowel diseases. 
 
For continuous variables (BMI, systolic blood pressure, and cholesterol), we will use 
the most recent value in the year before the study entry. For alcohol consumption, 
we will use the most recent record in the five years before entry to classify 
participants’ drinking behaviour into five categories: non-drinkers (Read codes as 
“teetotaller” or “non-drinker”), former drinkers (Read codes as “stopped drinking 
alcohol” or “ex-drinker”), occasional drinkers (Read codes as “drinks rarely” or 
“drinks occasionally”), current moderate drinkers (Read codes as “alcohol intake 
within recommended sensible limits” or “light drinker”), and heavy drinkers (Read 
codes as “alcohol intake above recommended sensible drinking limits” or “hazardous 
alcohol use”). 
 

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We will impute missing covariate values using multiple imputation, as implemented in 
the “mice” algorithm in the statistical package R. We will extract the following values 
of the continuous variables to use as auxiliary variables for multiple imputation: the 
first measurement after the time window and the most recent measurement before 
the time window, along with the timing of these measurements relative to the study 
entry. We will check whether the imputations are plausible by comparing plots of the 
distribution of recorded and imputed values of all variables. 
 
Follow up 
The observation period for all participants will begin on the date when all the study 
inclusion criteria are fulfilled. Participants will be followed up until the occurrence of 
an initial presentation of cardiovascular disease, death, or de-registration from the 
practice, whichever occurred first. 
 
Endpoints 
Endpoints will be the initial presentation of cardiovascular disease as any of the 
following 13 CVDs diagnosed in primary care (defined by Read codes), secondary 
care (defined by ICD-10 codes), or at death.(defined by ICD-10 codes): Stable 
angina, unstable angina, myocardial infarction, unheralded coronary heart disease 
death, heart failure, cardiac arrest/sudden cardiac death, transient ischaemic attack, 
ischaemic stroke, subarachnoid haemorrhage, intracerebral haemorrhage, peripheral 
arterial disease, abdominal aortic aneurysm, atrial fibrillation, and a composite total 
cardiovascular disease (all 13 cardiovascular diseases combined). An example is 
given by the definition of atrial fibrillation [16] 
 
Statistical analysis 
We will analyse the SUA level as a categorical variable in order to avoid presuming a 
particular shape for the association with CVDs. Since the normal range for SUA and 
the diagnosis cut-off for hyperuricemia diagnosis varies [17-19], we will use the 
following cut-offs:  
1) 3 mg/dL (180 umol/L, the lower limit of target SUA level for long-term 
management recommended by the 2016 updated EULAR guideline [20]),  
2) 4 mg/dL (240 umol/L),  
3) 5 mg/dL (300 umol/L, the target SUA for severe cases or patients with tophi 
recommended by various guidelines [20-23]),  
4) 6 mg/dL (360 umol/L, the general target SUA for long-term management 
recommended by various guidelines [20-23]),  
5) 7 mg/dL (420 umol/L). 
 
If the shape of the association between SUA and CVDs is found to be linear, we will 
then perform analyses with SUA level as a continuous variable. 
 
Quantitative variables will be summarized in mean and standard deviation and 
qualitative variables in number and percentages. Between group differences of 

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parametric values will be compared using analysis of variance (ANOVA) and that of 
categorical variables using chi-square tests. 
 
We will plot crude cumulative incidence curves for each specific cardiovascular 
endpoint for people with SUA levels between 240-300 umol vs. 360-420 umol/L, 
using age as the timescale, under a competing risks framework (i.e., participants 
could experience only one initial presentation). 
 
We will use multivariable Cox regression to calculate cause-specific hazards for 
associations between categories of SUA levels and initial presentations of 
cardiovascular disease. We will plot Schoenfeld residuals to verify the proportional 
hazards assumption.  
 
In the primary analysis, we will adjust for age (linear and quadratic), sex, index of 
multiple deprivation, diabetes, smoking status, BMI, systolic blood pressure, HDL 
cholesterol, LDL cholesterol, and prescription of statins or antihypertensive 
medication in the year before study entry. The baseline hazard function of each 
model will be stratified by general practice and sex, and we will use multiple 
imputation to account for missing covariate data. 
 
Assuming independence in effects, heterogeneity in associations across CVD 
endpoints for the fourth vs. the first quintile will be assessed with tau-square statistic, 
which is the between-CVD endpoint variance, and I2 that can be interpreted as the 
proportion of the total variation in estimates that is due to heterogeneity. 
 
We will conduct a series of sensitivity analyses: 
a) Covariate adjustment: We will conduct analyses adjusted for age and sex 
only, and analyses adjusted for age, sex, and cardiovascular risk factors. We 
will assess interactions with age and sex. 
b) Gout: We will categories patients into with gout and with no gout, regardless 
of SUA. 
c) Imputed data: We will check whether the imputations are plausible by 
comparing plots of the distribution of recorded and imputed values of all 
variables. 
d) Data source: We will conduct analysis ignoring endpoints recorded only in 
primary care (CPRD). 
e) Endpoints: We will conduct analysis restricting endpoints to fatal endpoints. 
f) 
Time: We will conduct analysis restricting participants to individuals who 
entered the study after 2004 (when recording of covariates would be 
expected to be more complete after introduction of the Quality and Outcomes 
Framework to incentivise good performance in the care of specified 
diseases). 
 
We will conduct all analyses using R 3.4.1.  

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Role of funding 
The funders had no role in the study design, data collection, data analysis, data 
interpretation, or writing of the report. 
 
 

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