Vol.3, No.1, 22-27 (2013) Open Journal of Preventive Medicine
Temporal variation in cardiovascular disease risk
predicted by albuminuria: An opportunity for clinical
Katina D’Onise1, Robyn McDermott1, Adrian Esterman1, Bradley McCulloch2
1Sansom Institute, University of South Australia, Adelaide, Australia; Katina.d’onise@unisa.edu.au
2Tropical Regional Services, Queensland Health and School of Public Health, James Cook University, Townsville, Australia
Received 27 September 2012; revised 31 October 2012; accepted 8 November 2012
Albuminuria predict s cardiov ascular disease (CVD)
events but it is likely to vary over time in a non-
linear fashion. The aim of this study was to es-
timate the potentially differing predictive effect
of albuminuria on the risk of CVD or related
death over time. Data were from a cohort study
of 3505 predominately indigenous adults from
remote communities in Queensland, Australia,
1999-2006. Cox Proportional Hazards model
analysis of the predictive effects of urinary al-
bumin creatinine ratio on the risk of CVD or
CVD-related death was undertaken for incident
and prevalent CVD. Analyses sequentially re-
moved those w ho had a cardiovascular event or
related death for the first year through to six
years. The baseline prevalence of microalbu-
minuria was 21.2% and for macroalbuminuria
6.7%. The incidence of CVD was 92 in 13,812
person-years. Microalbuminuria predicted inci-
dent CVD with a Hazard Ratio (HR) of 3.0 (95 % CI
1.83 - 4.96) and for macroalbuminuria HR 10.8
(95% CI 6.58 - 17.68) and for those with pre-ex-
isting CVD, HR 2.6 (95% CI 1.65 - 3.97) and HR
9.7 (95% CI 6.38 - 14.82) respectively. People
with macroalbuminuria who survived the first
three years had a crude HR of an incident car-
diovascular event or death of 13.0 (95% CI 6.45 -
26.39) to a peak of 32.3 (95% CI 8.55 - 121.77) for
those who survived the first five years. The
hazard appeared t o drop in the 6th y ear although
this is based on small numbers. The first three
years after finding macroalbuminuria provide a
potential window opportuni ty to activ ely manage
the risk of incident C VD before the risk elevates .
Keywords: Cardiovascular Diseases; Albuminuria;
Mortality; Risk Factors; Epidemiology
Albuminuria is a well established independent risk
factor for cardiovascular disease events and associated
mortality. This has been reported in both high risk and
general population cohorts [1-3]. The mechanism for this
association is not clear, however a number of hypotheses
have been proposed. The association may be accounted
for by albuminuria as a marker of reduced renal function,
which is associated with increased cardiovascular disease
(CVD) risk [4]. Additionally, it may be that widespread
endothelial damage leads both to albuminuria and CVD
through enhanced atherogenesis [4]. It is also possible
that a common factor, such as hypertension, leads to both
albuminuria and CVD [5].
The range of studies reporting this association assume
constant risk (or hazard) over time. This is unlikely to be
true biologically as a high level of macroalbuminuria is
more likely in an advanced stage of disease such that the
risk of a cardiovascular event is greater in the short term
than those with lesser levels of macroalbuminuria, all
other factors equal. As such, using a model such as the
Cox model which assumes constant hazard over time
may not adequately estimate the risk of albuminuria for
cardiovascular disease. In this scenario, the hazard over
the length of the study period may overestimate the risk
in the short term and underestimate it in the long term.
This has implications on the importance clinicians place
on management of albuminuria in the prevention of both
advanced renal and cardiovascular disease in the short
term to reduce greater risk in the long term.
To examine this further, we used data from the Well
Person’s Health Check (WPHC), a cohort study in North
Queensland conducted in 26 remote Aboriginal and Tor-
res Strait Islander communities [6]. This general popula-
tion cohort has previously documented high levels of
albuminuria and CVD [7] and so provides an opportunity
to examine the effect of changing risk of albuminuria on
cardiovascular disease over time.
Copyright © 2013 SciRes. OPEN ACCE SS
K. D’Onise et al. / Open Journal of Preventive Medicine 3 (2013) 22-27 23
2.1. Study Population
Baseline data were collected from 3505 people in 26
rural Indigenous communities in Far North Queensland,
who participated in the “Well Person’s Health Check”
between 1999 and 2000 (methods for this cross-sectional
study have been reported in detail elsewhere [6]). Based
on the local census data, the study achieved a participa-
tion rate of 44.5% with greater participation noted in
smaller communities.
2.2. Albumin Creatinine Ratio
All participants were asked to provide urine from the
first morning void or to delay providing the sample for at
least two hours from the most recent void. First catch
urine samples were self-collected in a sterile 50 ml con-
tainer. All urine specimens were refrigerated at four to
eight degrees centigrade immediately following collec-
tion. A five to ten millilitre sample from the collection
jar was transferred into a ten-millilitre tube. Dipstick
urinalysis (Combur-test, Roche) was performed on the
remaining sample and the results for protein recorded.
For the first 15 months of the recruitment period (of a
total recruitment period of 34 months) albumin creatinine
ratio (ACR) testing was performed when the participants’
urine contained protein (detected on urinalysis) or they
were known to have diabetes, hypertension or had a body
mass index over 30 kg/m2. For the subsequent 19 months
of recruitment ACR testing was performed routinely on
all urine specimens [6]. Microalbuminuria was defined
as a urinary ACR of 3.4 mg/mmol to 33.9 mg/mmol and
macroalbuminuria of 34 mg/mmol or greater.
2.3. CVD Event or Related Death
A cardiovascular event or related death was deter-
mined from unit-level record linkage of the cohort mem-
bers to hospitalisation records and deaths up to a census
date of 1 January 2006. Hospitalisation and death records
for consenting WPHC participants were identified by a
manual search (conducted by a registered nurse with
experience working in the region) of the Queensland
Health hospital records systems. As there is no unique
patient identifier in Queensland, a mapping table, which
linked WPHC reference number, hospital facility code
and local unit record number, was developed. This table
was subsequently applied to the Queensland Hospital
Admitted Patient Data Collection, and hospitalisation
relevant to the match unit record, facility code tuples
were extracted. Matching of death records was per-
formed manually at the Queensland Registry of Births,
Deaths and Marriages.
Hospitalisations were considered to be CVD related if
they contained an International Classification of Diseases,
ninth revision, clinical modification (ICD-9-CM) code
commencing with 410, 411, 413 or 414, or an ICD-9-CM
procedure code between 3600 and 3699, inclusive. For
hospitalisations coded to the International Classification
of Diseases, 10th revision, the diagnosis code range I20 -
I25, and procedure code blocks 669 - 679, inclusive,
were used.
Both incident CVD and a separate analysis on the
whole sample that also included those with pre-existing
CVD were undertaken. Prior cardiovascular disease was
determined from participant report of a history of car-
diovascular disease or from a case note review at study
entry or hospitalisation (using the same International
Classification of Diseases codes as above) recorded prior
to study entry.
2.4. Baseline Data
Current smoking was defined as regular smoking of at
least one cigarette a day. Blood pressure was the average
of three measurements taken while seated over a ten
minute period. Height and weight were measured, with
weight recorded to the nearest 0.1 kg and height recorded
to the nearest centimetre. Body mass index was calcu-
lated as weight (kg) divided by the height squared (m2)
and categorised by using WHO definition of overweight
and obesity [8].
Blood for total cholesterol was collected by trained
health staff. Blood was collected in an eight point millili-
tre clotted (SST) vacuum tube which was spun for 10
minutes in a portable centrifuge within one hour of col-
lection and measured by photometric enzyme end point
assay, using the Cobas Integra 700/400 (Roche Diagnos-
tics, New York, USA).
Prior history of diabetes at baseline was determined
from a report at interview of diabetes or case note review
or a fasting blood glucose level of 7 mmol/L, 2-hour
glucose tolerance test result of 11.1 mmol/L at study
entry, or documented hospitalisation for diabetes prior to
study entry (through the linkage process described above
for ICD-09 codes E11-E14).
2.5. Study Sample
Those who did not have an ACR measured (n = 643)
and missing data on variables blood pressure or smoking
(a further n = 413), date of the first screen (n = 1) or for
cause of death (n = 3) lead to a sample for analysis of
2445. Exclusion of those with pre-existing CVD brought
the sample to 2349. As noted above, the majority of the
ACR data were missing due to the selection criteria of
the study and not at random.
2.6. Analysis
A descriptive analysis was undertaken comparing
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K. D’Onise et al. / Open Journal of Preventive Medicine 3 (2013) 22-27
Copyright © 2013 SciRes. OPEN ACCE SS
those participants who did and did not have an ACR
taken or had missing data was undertaken, using chi
square test or a student’s t-test.
All CVD related deaths or events (CVD) were pooled
for subsequent analyses. Time in the study was calcu-
lated from date of first screen to CVD or death from any
cause or the census date (whichever occurred first). Car-
diovascular event or death rates per 1000 person-years
were analysed using the Kaplan-Meier method by ACR
The effect of ACR level at baseline on CVD was ana-
lysed using a Cox Proportional Hazards model, estimat-
ing a Hazard Ratio (HR). This analysis was conducted on
the entire study sample with a total of up to seven years
of follow up (n = 2445), assuming proportional hazards
over time. In order to assess the potential effect of dif-
fering hazard over time, the analysis was repeated by
excluding all those events or deaths that occurred in the
first year subsequent to study entry and then again ex-
cluding the events or deaths that occurred in the first two
years subsequent to study entry, and so on until six years
from study entry. In this way these models assess the
subsequent risk of a CVD event or related death for peo-
ple who survived from one to six years following study
Crude Hazard Ratios were calculated using Cox mod-
els. The independent contribution of albuminuria as a
marker of cardiovascular disease risk was estimated us-
ing multivariate analysis. For each of the time periods,
the analysis was conducted first adjusted for sex and age
only (model 2, there was minimal difference in estimates
stratified by gender and so to maximize precision the
analysis controlled for gender rather than stratifying by
gender). The second model further adjusted for a history
of prior CVD and the third model also adjusted for
smoking status, systolic blood pressure (adding diastolic
blood pressure to the model did not appreciably alter
results and so was omitted, data not shown), diabetes,
body mass index and total cholesterol level, factors that
have been previously found to have an association both
with CVD and ACR [4,9,10]. All analyses were con-
ducted using Stata statistical software v 10.0 [11]. The
study was approved by the Cairns and Hinterland Health
Service District Ethics Committee, with support from the
relevant peak Indigenous health councils. All participants
gave informed consent.
The study cohort had a 21.2% baseline prevalence of
microalbuminuria and 6.7% of macroalbuminuria, with
no difference between men and women (p = 0.3). Table
1 shows the baseline characteristics of the study popula-
tion, comparing the study sample with those who par-
ticipated in the baseline survey but either did not have an
ACR taken (as they were deemed to be ‘low risk’) or had
missing data. The study sample, was more likely to be
female, had higher blood pressure, higher BMI, lower
total cholesterol, higher prevalence of diabetes and was
older, but there was no difference in the prevalence of
The rate of incident CVD events or death per 1000
person-years by ACR category and length of time since
study entry is presented in Table 2. The overall inci-
dence of cardiovascular event or death was 92 in 13,812
person-years. The rate increased across ACR category
from normal (3.3, 95% CI 2.3 - 4.6) to microalbuminuria
(9.8, 95% CI 6.8 - 14.1) and then macroalbuminuria
(35.5, 95% CI 24.8 - 50.8).
Kaplan-Meier survival estimates are presented in the
Figure 1. There was decreasing survival with increases
in level of albuminuria.
The overall risk of an incident cardiovascular event or
death was 1.3 (95% CI 0.78 - 2.31) for microalbuminuria
Table 1. Baseline characteristics of the study sample (analysis cohort) and those who had missing data and were excluded from the
analysis, Well Person’s Health Check, 1999-2006, n = 3377.
Variable (mean unless otherwise indicated) Study sample n = 2445
(95% CI)
Missing ACR and other covariates n = 932
(95% CI)
Age* 36.9 (36.2 - 37.5) 34.2 (33.3 - 35.1)
Female* (%) 53.2 (51.2 - 55.1) 47.8 (44.7 - 50.7)
Smoker (%) 51.8 (49.8 - 53.8) 52.2 (49.2 - 55.3)
Systolic blood pressure* 129.5 (128.7 - 130.2) 124.6 (123.5 - 125.6)
Body Mass Index* 27.7 (27.4 - 28.0) 26.1 (25.7 - 26.5)
Total cholesterol* 4.8 (4.78 - 4.87) 5.0 (4.9 - 5.1)
Diabetes*(%) 14.3 (12.9 - 15.7) 8.9 (7.2 - 10.6)
*p value 0.05; CI confidence interval.
K. D’Onise et al. / Open Journal of Preventive Medicine 3 (2013) 22-27 25
Table 2. Incident cardiovascular events or death rates per 1000 person years, by category of albuminuria and years since study entry,
Well Person’s Health Check, 1999-2006, n = 2349.
Normal Microalbuminuria Macroalbuminuria
(Years) Person-years EventsRate (95% CI) Person-yearsEventsRate (95% CI)Person-years Events Rate (95% CI)
0 - 1 1687.4 6 3.6 (1.6 - 7.9) 493.9 5 10.1 (4.2 - 24.3)150.8 10 66.3 (35.7 - 123.2)
1 - 2 1675.1 4 2.4 (0.9 - 6.4) 487.9 7 14.3 (6.8 - 30.1)142.6 2 14.0 (3.5 - 56.1)
2 - 3 1664.6 8 4.8 (2.4 - 9.6) 476.3 5 10.5 (4.4 - 25.2)138.2 2 14.5 (3.6 - 57.9)
3 - 4 41654.6 8 4.8 (2.4 - 9.7) 468.5 4 8.5 (3.2 - 22.7)136.6 4 29.3 (11.0 - 78.0)
4 - 5 1644.4 4 2.4 (0.9 - 6.5) 462.0 5 10.8 (4.5 - 26.0)131.5 4 30.4 (11.4 - 81.0)
5 - 6 1123.8 2 1.8 (0.4 - 7.1) 355.4 2 5.6 (1.4 - 22.5)90.8 7 77.1 (36.8 - 161.7)
6 - 7 488.0 1 2.0 (0.3 - 14.5) 186.0 0 0 46.4 1 21.5 (3.0 - 153.0)
7+ 73.2 0 - 27.7 1 36.1 (5.1 - 256.1)7.0 0 0
Total 10011.1 33 3.3 (2.3 - 4.6) 2957.8 29 9.8 (6.8 - 14.1)843.9 30 35.5 (24.8 - 50.8)
CI: confidence interval.
0.00 0.25 0.50 0.75 1.00
Survival estimate
0 2 4 6 8
Tim e (years)
Normal ……………..
Macroalbuminuria -------------
Figure 1. Kaplan-Meier survival estimate by albuminuria
category for incident cardiovascular disease, Well Person’s
Health Check, 1999-2006, n = 2349.
and 4.4 (95% CI 2.52 - 7.83) for macroalbuminuria
compared with normal levels of albuminuria, when ad-
justing for age, gender, smoking, systolic blood pressure,
total cholesterol and diabetes (Table 3, model 3). The
equivalent risk estimates for the sample that included
those with pre-existing CVD was similar, with a HR of
1.1 (95% CI 0.70 - 1.80) for microalbuminuria and 3.8
(95% CI 2.37 - 6.24) for macroalbuminuria. The risk was
lowest for people with macroalbuminuria in the first
three years. For example, those people with macroalbu-
minuria that survived the first three years had a crude HR
of an incident cardiovascular event or death of 13.0 (95%
CI 6.45 - 26.39) to a peak of 32.3 (95% CI 8.55 - 121.77)
for those who survived the first five years (multivariate
adjustment not undertaken due to small event numbers).
The hazard dropped in the 6th year although this estimate
is based on small numbers.
Macroalbuminuria increased the risk of an incident
cardiovascular event or death, with increasing magnitude
over time, ranging from a HR of 9.0 for those who sur-
vived the first year to HR 32.3 for those who survived
the first five years. These estimates were attenuated by
age, gender and the co-variates added to the model, but a
strong independent association with cardiovascular dis-
ease remained. The estimates that included those with
pre-existing CVD were similar in magnitude to those for
incident CVD, suggesting that albuminuria may be a
relevant clinical indicator in both primary health care and
specialist medical settings. Microalbuminuria also con-
sistently elevated the risk of a cardiovascular event or
death compared with normal (less so than macroalbu-
minuria), but did not appear to follow the same time
varying pattern as macroalbuminuria. These findings
suggest that the risks associated with macroalbuminuria
are less in the first three years than the overall hazard
would suggest and so suggest that a window of opportu-
nity exists to reduce incident cardiovascular disease and
a subsequent CVD event or related death for those with
pre-existing CVD before the risk increases up to three
fold over the next three years.
The hazard ratio for those who survived the first six
years was a third of that for the previous five year esti-
mate. This difference may reflect small numbers of
events in the seventh year and so be an unstable and
non-valid estimate. However, if this is a true finding it
suggests that macroalbuminuria is a strong predictor of
incident cardiovascular disease in the short term (within
the first six years) and less so in the long term. This
finding is consistent with a study conducted in 1993
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K. D’Onise et al. / Open Journal of Preventive Medicine 3 (2013) 22-27
Table 3. Level of albuminuria on the risk of cardiovascular event or death, Well Person’s Health Check, 1999-2006.
Incident cardiovascular disease, n = 2349 Including pre-existing cardiovascular disease, n = 2445
Model 1: Crude
Hazard Ratio,
(95% CI)
Model 2:
Hazard Ratio,
(95% CI)
Model 3: Hazard
Ratio, (95% CI)
Model 1: Crude
Hazard Ratio,
(95% CI)
Model 2: Hazard
Ratio, (95% CI)
Model 3:
Hazard Ratio,
(95% CI)
All Years Micro§ 3.0 (1.83 - 4.96) 1.6 (0.97 - 2.73)1.3 (0.78 - 2.31)2.6 (1.65 - 3.97)1.4 (0.86 - 2.13) 1.1 (0.70 - 1.80)
Macro§ 10.8 (6.58 - 17.68) 6.1 (3.66 - 10.15)4.4 (2.52 - 7.83)9.7 (6.38 - 14.82)5.3 (3.45 - 8.22) 3.8 (2.37 - 6.24)
1 Year Micro 3.0 (1.76 - 5.28) 1.6 (0.9102.87) 1.3 (0.71 - 2.33)2.7 (1.66 - 4.42)1.4 (0.85 - 2.32) 1.13 (0.67 - 1.92)
Macro 9.0 (5.03 - 15.98) 5.1 (2.79 - 9.16)3.5 (1.82 - 6.75)8.1 (4.90 - 13.43)4.5 (2.66 - 7.48) 3.1 (1.72 - 5.41)
2 Years Micro 2.7 (1.45 - 4.97) 1.4 (0.74 - 2.67)1.2 (0.60 - 2.29)2.5 (1.43 - 4.44)1.3 (0.72 - 2.30) 1.1 (0.59 - 1.98)
Macro 9.5 (5.14 - 17.66) 5.4 (2.84 - 10.09)4.1 (2.05 - 8.29)9.7 (5.64 - 16.78)5.3 (3.05 - 9.32) 4.0 (2.14 - 7.39)
3 Years Micro 2.7 (1.27 - 5.82) 1.6 (0.71 - 3.41)1.4 (0.63 - 3.20)2.5 (1.23 - 4.91)1.4 (0.68 - 2.82) 1.2 (0.58 -2.55)
Macro 13.0 (6.45 - 26.39) 7.9 (3.84 - 16.36)6.9 (3.07 - 15.43)10.2 (5.32 - 19.72)6.2 (3.17 - 12.08) 4.9 (2.33 - 10.51)
4 Years Micro 3.8 (1.38 - 10.52) 2.2 (0.77 - 6.30)2.2 (0.75 - 6.46)3.0 (1.20 - 7.27)1.3 (0.57 - 3.17) 1.6 (0.62 - 4.17)
Macro 21.1 (8.31 - 53.65) 13.1 (5.02 - 34.01)11.8 (4.14 - 33.59)14.9 (6.55 - 34.10)7.2 (3.38 - 15.23) 7.7 (3.01 - 19.86)
5 Years Micro 3.1 (0.63 - 15.56) 1.7 (0.34 - 8.95)- 1.9 (0.44 - 7.80)1.0 (0.23 - 4.32) -
Macro 32.3 (8.55 - 121.77) 19.2 (4.94 - 74.41)- 20.3 (6.81 - 60.76)11.8 (3.88 - 36.23) -
6 Years Micro 2.6 (0.16 - 42.00) - - 1.3 (0.12 - 14.40)- -
Macro 10.1 (0.63 - 161.1) - - 4.80 (0.43 - 52.91)- -
Model 2: adjusted for age and gender; Model 3: also adjusted for baseline smoking, systolic blood pressure, total cholesterol and diabetes. CI confidence
interval. §Micro—microalbuminuria, Macro—macroalbuminuria. Each year denotes the number of events excluded—e.g. 1 year analyses all data excluding
those who had a cardiovascular event or death in the previous 1 year, 2 year analyses all data excluding those who had a cardiovascular event or death in the
first 2 years, etc.
where after 5 years the predictive effect of albuminuria
on the risk of CVD had reduced to no effect [12]. This
suggests the possibility of different trajectories of risk for
different potential causal pathways between macroalbu-
minuria and cardiovascular disease, with one or more
trajectories leading to a short term high increased risk of
death and a possible other whereby macroalbuminuria is
less predictive of cardiovascular disease from the start.
Another possible explanation is a healthy survivor effect,
where those who survive or do not develop cardiovascu-
lar disease by five years being healthier (in ways for
which we were unable to adjust) and so there is an ap-
parent reduction in the associated risk with cardiovascu-
lar disease by the seventh year. This warrants further
exploration in other cohorts as it may help to understand
the pathological processes involved by examining defin-
ing characteristics of the different cohorts (survivors
versus those who developed cardiovascular disease).
The findings from this study may not be generalisable
to other populations given the high prevalence of albu-
minuria and other CVD risk conditions especially diabe-
tes in this cohort relative to the general Australian popu-
lation. The magnitude of the risk estimates was also
higher than has been commonly reported in the literature
[2,4], although the reduction in risk for in the first few
years relative to the overall hazard may be generalisable.
There was also no difference in cardiovascular disease
risk for women compared with men, a finding that has
been reported from this cohort previously. This is
thought to be a result of higher rates of metabolic disease
especially diabetes in women compared with men and so
eliminating the protective effect of being female [7].
The findings of this study are possibly limited by un-
der-enumeration of outcome events. This could have
occurred through the data linkage process as it was un-
dertaken manually, and it may also be that deaths were
under-recorded in the latter years of the study given the
time-lag involved in registering deaths and cause of
death. These possible misclassification errors are likely
to have been non-differential and so are likely to have
had an effect of biasing the findings towards the null. For
example, if an assumption is made that the sensitivity of
the classification method was 95% and the specificity
99% (an ideal test), then the biasing effect on the esti-
mate would be an increase in the relative risk from 9.7
(the equivalent of the HR of 10.8) to 19.0 [13]. The se-
lection method for the first half of the cohort lead to
fewer people being tested for ACR than the latter half of
the cohort and so the results reflect that for a relatively
high risk cohort. While this introduces a potential risk of
Copyright © 2013 SciRes. OPEN ACCE SS
K. D’Onise et al. / Open Journal of Preventive Medicine 3 (2013) 22-27 27
selection bias, there was no evidence for this on
re-analysis of the data restricted to only those communi-
ties where ACR testing was universal (16 of the 26
communities). There was minimal change in the effect
estimates, although they were measured with less preci-
sion (data not presented, available on request from the
In summary, the findings here suggest that the risk of
CVD with macroalbuminuria are not linear over time,
and that the first three years after finding macroalbumin-
uria provide a window of opportunity to manage the risk
of both incident and pre-existing cardiovascular disease
before the risk of a CVD related event or death elevates
substantially in subsequent years. These findings to-
gether suggest the need for increased use of ACR testing
in the course of clinical care for adults, including those
who are apparently at low risk of CVD.
This might represent an opportunity to step up primary
health care-based clinical intervention, especially aggres-
sive management of albuminuria with a view to revers-
ing or reducing subsequent CVD and other events. Fur-
ther, clinicians identifying an elevated ACR should con-
sider a broader assessment of the patient and manage-
ment of potential cardiovascular risk. This change in prac-
tice could be monitored in cohorts over time utilising
new capabilities in unit level record linkage.
Thanks to the participants of the WPHC study, Rohan Pratt for data
extraction, and staff from the Torres Strait and Northern Peninsula Area
Health Service District for ongoing collaboration. This study was
funded by National Health and Medical Research Council project
[1] Basi, S., Fesler, P., Mimran, A. and Lewis, J.B. (2008)
Microalbuminuria in type 2 diabetes and hypertension.
Diabetes Care, 31 , S194-S201. doi:10.2337/dc08-s249
[2] Chronic Kidney Disease Prognosis Consortium (2010)
Association of estimated glomerular filtration rate and
albuminuria with all-cause and cardiovascular mortality
in general population cohorts: A collaborative meta-
analysis. Lancet, 375, 2073-2081.
[3] van der Velde, M., Matsushita, K., Coresh, J., Astor, B.C.,
Woodward, M., Levey, A. de Jong, P., Gansevoort, R.T.
and Chronic Kidney Disease Prognosis Consortium (2011)
Lower estimated glomerular filtration rate and higher al-
buminuria are associated with all-cause and cardiovascu-
lar mortality. A collaborative meta-analysis of high-risk
population cohorts. Kidney International, 79, 1341-1352.
[4] Pedrinelli, R., Dell’Omo, G., Penno, G. and Mariani, M.
(2001) Non-diabetic microalbuminuria, endothelial dys-
function and cardiovascular disease. Vascular Medicine, 6,
257-264. doi:10.1177/1358836X0100600410
[5] Ussai, K., Keith, S., Pequignot, E. and Falkner, B. (2011)
Risk factors associated with urinary albumin excretion in
African Americans. Journal of Human Hypertension, 25,
3-10. doi:10.1038/jhh.2010.79
[6] Miller, G., McDermott, R., McCulloch, B., Leonard, D.,
Arabena, K. and Muller, R. (2002) The well persons
health check: A population screening program in indige-
nous communities in North Queensland. Australian
Health Review, 25, 140-151. doi:10.1071/AH020136b
[7] McDermott, R., McCulloch, B. and Li, M. (2011) Gly-
caemia and albuminuria predict excess incident coronary
heart disease in Aboriginal and Torre Strait Islander
adults: A North Queensland cohort. Medical Journal of
Australia, 194, 514-518.
[8] Munoz, A. and Gange, S.J. (1998) Methodological issues
for biomarkers and intermediate outcomes in cohort stud-
ies. Epidemiology Reviews, 20, 29-42.
[9] Pinto-Sietsma, S.J., Mulder, J., Janssen, W.M.T., Hillege,
H.L., de Zeeuw, D. and de Jong, P.E. (2000) Smoking is
related to albuminuria and abnormal renal function in
nondiabetic persons. Annals of Internal Medicine, 133,
[10] Cirillo, M., Senigalliesi, M., Laurenzi, M., Alfieri, R.,
Stamler, J., Stamler, R., Panarelli, W. and De Santo, N.G.
(1998) Microalbuminuria in nondiabetic adults: Relation
of blood pressure, body mass index, plasma cholesterol
levels, and smoking: The Gubbio population study. Ar-
chives of Internal Medicine, 158, 1933-1939.
[11] StataCorp (2007) Stata statistical software: Release 10.
StataCorp LP, College Station.
[12] Damsgaard, E., Frøland, A., Jørgensen, O. and Mogensen,
C. (1993) Prognostic value of urinary albumin excretion
rate and other risk factors in elderly diabetic patients and
non-diabetic control subjects surviving the first 5 years
after assessment. Diabetologia, 36, 1030-1036.
[13] Lash, T., Fox, M. and Fink, A. (2009) Applying quantita-
tive bias analysis to epidemiologic data. Springer, New
York. doi:10.1007/978-0-387-87959-8
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