Download Different Lipid Variables Predict Incident Coronary

2374
Diabetes Care Volume 37, August 2014
Different Lipid Variables Predict
Incident Coronary Artery Disease
in Patients With Type 1 Diabetes
With or Without Diabetic
Nephropathy: The FinnDiane
Study
Nina Tolonen,1,2,3 Carol Forsblom,1,2,3
Ville-Petteri Mäkinen,1,2,4,5
Valma Harjutsalo,1,2,3,6 Daniel Gordin,1,2,3
Maija Feodoroff,1,2,3 Niina Sandholm,1,2,3,7
Lena M. Thorn,1,2,3 Johan Wadén,1,2,3
Marja-Riitta Taskinen,8 and
Per-Henrik Groop,1,2,3,9 on behalf of the
FinnDiane Study Group
Diabetes Care 2014;37:2374–2382 | DOI: 10.2337/dc13-2873
OBJECTIVE
To study the ability of lipid variables to predict incident coronary artery disease
(CAD) events in patients with type 1 diabetes at different stages of nephropathy.
CARDIOVASCULAR AND METABOLIC RISK
RESEARCH DESIGN AND METHODS
Patients (n = 3,520) with type 1 diabetes and available lipid profiles participating in
the Finnish Diabetic Nephropathy Study (FinnDiane) were included in the study.
During a follow-up period of 10.2 years (8.6–12.0), 310 patients suffered an incident CAD event.
RESULTS
Apolipoprotein B (ApoB)/ApoA-I ratio was the strongest predictor of CAD in normoalbuminuric patients (hazard ratio 1.43 [95% CI 1.17–1.76] per one SD
increase), and ApoB was the strongest in macroalbuminuric patients (1.47
[1.19–1.81]). Similar results were seen when patients were stratified by sex or
glycemic control. LDL cholesterol was a poor predictor of CAD in women, normoalbuminuric patients, and patients with HbA1c below the median (8.3%, 67
mmol/L). The current recommended triglyceride cutoff of 1.7 mmol/L failed to
predict CAD in normoalbuminuric patients, whereas the cohort median 0.94
mmol/L predicted incident CAD events.
CONCLUSIONS
In patients with type 1 diabetes, the predictive ability of the lipid variables differed substantially depending on the patient’s sex, renal status, and glycemic
control. In normoalbuminuric patients, the ratios of atherogenic and antiatherogenic lipoproteins and lipids were the strongest predictors of an incident CAD
event, whereas in macroalbuminuric patients, no added benefit was gained from
the ratios. Current treatment recommendations may need to be revised to capture residual CAD risk in patients with type 1 diabetes.
1
Folkhälsan Institute of Genetics, Folkhälsan Research Center, University of Helsinki, Helsinki,
Finland
2
Division of Nephrology, Department of Medicine, Helsinki University Central Hospital,
Helsinki, Finland
3
Research Programs Unit, Diabetes and Obesity,
University of Helsinki, Helsinki, Finland
4
Department of Integrative Biology and Physiology, University of California, Los Angeles, Los
Angeles, CA
5
South Australian Health and Medical Research
Institute, Adelaide, Australia
6
Diabetes Prevention Unit, Institute for Health
and Welfare, Helsinki, Finland
7
Aalto University, Espoo, Finland
8
Division of Cardiology, Department of Medicine,
Helsinki University Central Hospital, Helsinki,
Finland
9
Baker IDI Heart and Diabetes Institute, Melbourne, Victoria, Australia
Corresponding author: Per-Henrik Groop,
per-henrik.groop@helsinki.fi.
Received 9 December 2013 and accepted 18 April
2014.
This article contains Supplementary Data online
at http://care.diabetesjournals.org/lookup/
suppl/doi:10.2337/dc13-2873/-/DC1.
© 2014 by the American Diabetes Association.
Readers may use this article as long as the work
is properly cited, the use is educational and not
for profit, and the work is not altered.
care.diabetesjournals.org
Coronary artery disease (CAD) remains
the leading cause of death in the general
population (1). Patients with diabetes
are particularly susceptible to atherosclerosis and premature CAD deaths
(2,3). In type 1 diabetes, the additional
CAD risk appears to be intrinsically connected to diabetic nephropathy, as patients with normal albumin excretion
rate (AER) show similar mortality to
the background population, whereas
patients with macroalbuminuria or
end-stage renal disease (ESRD) are 10
times more likely to die prematurely
(4,5). Diabetic nephropathy has a substantial association with CAD incidence,
but it may also simultaneously obscure
other important risk factors (6,7). Therefore, it is important to examine the specific circumstances of type 1 diabetes
and different stages of nephropathy in
relation to established CAD risk factors
in the general population.
Hypercholesterolemia is the classical
causal risk factor for the development of
atherosclerosis, (8) and lowering LDL
cholesterol (LDL-C) continues to be the
cornerstone of pharmacological interventions to reduce CAD mortality (9).
On the other hand, uncomplicated
type 1 diabetes is associated with a favorable lipid profile (10), whereas diabetic nephropathy is associated with a
dyslipidemic pattern similar to that seen
in patients with type 2 diabetes (6,11). It
is thus uncertain if and how nephropathy modulates the lipid risk profile for
CAD in type 1 diabetes and whether
the lipid targets for CAD prevention in
type 1 diabetes differ from those of the
general population.
Therefore, we decided to assess the
capability of the lipid variables, the apolipoproteins, and their ratios to predict incident CAD events in a large
well-characterized cohort of type 1 diabetes. We will also explore how albuminuria, glycemic control, and sex
modulate the risk profile.
RESEARCH DESIGN AND METHODS
The Finnish Diabetic Nephropathy Study
(FinnDiane) has been previously prescribed (11,12). Estimated glomerular
filtration rate (eGFR) was calculated on
the basis of a serum creatinine measurement using the Chronic Kidney Disease
Epidemiology Collaboration (CKD-EPI)
formula (13). Nephropathy status was
determined based on the measurement
Tolonen and Associates
of AER in at least two out of three consecutive 24-h or overnight urine collections. AER ,20 mg/min or ,30 mg/24 h
was defined as normal AER, 20#
AER ,200 mg/min or 30# AER ,300
mg/24 h as microalbuminuria, and AER
$200 mg/min or AER $300 mg/24 h as
macroalbuminuria. ESRD was defined as
requirement of dialysis or renal transplantation. In addition to the urine collections used for the classification of the
renal status, 24-h AER was also measured centrally from a single sample
with an immunoturbidimetric method.
The result of this measurement was
used in the multivariate analyses. Estimated glucose disposal rate (eGDR)
was calculated with an equation developed by Williams et al. (14) modified for
use with HbA1c instead of HbA1 (12). Serum lipid and lipoprotein concentrations
were all measured centrally from serum
samples using earlier described methods
(11). LDL-C was calculated with the
Friedewald formula if triglycerides were
,4.0 mmol/L (15).
At baseline, patients underwent a
thorough clinical investigation at a regular visit to their attending physician.
The baseline data were collected between 1994 and 2008. Complete baseline data including centrally measured
lipid profiles were available for 3,872
patients. Follow-up data were obtained
from the Finnish Hospital Discharge
Register (HDR) based on hospital discharge records and the Finnish Cause
of Death Registry (CDR) through to 31
December 2010 and available for all patients. An incident CAD event was defined as myocardial infarction given as
ICD-10 code I21 (ICD-9, 410), coronary
artery bypass graft surgery or coronary
angioplasty. Researcher N.T. from the
FinnDiane study group verified the Finnish HDR data by double-checking the
hospital records of 28% of patients. In
this sample, no classification errors
were found, and only four borderline
cases of acute myocardial infarction
were identified. Otherwise all cases
were in accordance with the universal
definition of myocardial infarction (16)
or underwent either coronary artery
bypass graft surgery or coronary angioplasty. Fatal CAD events were identified
from a search of the Finnish CDR and
established when the immediate or underlying cause of death was from CAD,
i.e., given as ICD-10 codes I20–25 (ICD-9,
410–414). Death certificates were also
obtained in order to verify the register
data. Patients with acute myocardial infarction, coronary artery bypass graft
surgery, or coronary angioplasty already
at baseline were excluded from the
study (n = 306). Furthermore, patients
with ICD-10 diagnosis codes I20 and
I22–25 (ICD-9, 411–414) in the Finnish
HDR, with reported coronary heart disease, or taking long-acting nitroglycerin
medication at baseline were excluded
from the control group (n = 46).
Hence, a total of 3,520 patients were
included in the study.
Statistical Analyses
Data for normally distributed and continuous variables are presented as
mean 6 SD and data for nonnormally
distributed variables as median with
IQR. Differences between groups were
analyzed with Student t test, ANOVA, or
Mann-Whitney U test as appropriate.
Categorical variables were analyzed using Pearson x2 test. Nonnormally distributed values were logarithmically
transformed before inclusion in the
models. Univariate and multivariate
Cox regression models were used to analyze the associations between CAD risk
factors and incident CAD events. For the
multivariate analyses, model selection
from variables univariately associated
with CAD was accomplished by minimization of the Akaike information criteria
(AIC). Every variable reduced the AIC except for sex, but since it has previously
been well established as a CAD risk factor, it was included in the models. The
multivariate model used in the analyses
included diabetes duration, eGFR, systolic blood pressure (SBP), retinal laser
treatment, AER, sex, HbA1c, waist-to-hip
ratio (WHR), history of smoking, and one
of the lipid variables. Because of collinearity, only one lipid variable at a time
was entered into the models. Results
are presented as hazard ratios (HRs)
per one SD increase of the study cohort
with 95% CIs. The standardized score for
WHR was calculated separately for men
and women.
To compare the Cox models, we calculated the area under the curve (AUC)
of the receiver operating characteristics.
Likelihood ratio (LR) x2 statistics from
the Cox models were also calculated; a
higher value indicates a better global fit.
Net reclassification improvement (NRI)
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Lipid Profiles, Renal Disease, and CAD in T1DM
is the percentage reclassified after the
inclusion of the lipid variable in the
above-mentioned multivariate model,
distinguishing movement in the correct
direction, i.e., the proportion of subjects
being reclassified to a higher risk category among CAD cases or a lower risk
category among control subjects (17).
In CAD prevention, the 5, 10, and 20%
cutoff points have been proposed as relevant for clinical decision making
(18,19) and were therefore chosen as
NRI cutoff points. Fine and Gray (20) regression analysis, which extends the Cox
proportional hazard model to competing risk data by consideration of the subdistribution hazard, was also performed
to take into account the competing
event of non-CAD death. After these
analyses, figures of the cumulative incidence for CAD in normoalbuminuric or
macroalbuminuric patients divided by
the median of lipid variables were drawn.
A more stringent P value of ,0.01 was
considered statistically significant in order to correct for multiple testing. Statistical analyses were performed using
PASW Statistics 18 for Windows (SPSS
Inc., Chicago, IL), STATA Data Analysis
and Statistical Software (StataCorp LP,
College Station, TX), or MedCalc (MedCalc
Software BVBA, Ostend, Belgium).
RESULTS
During a follow-up period of 10.2 years
(8.6–12.0), 310 (male/female, 173/137)
out of 3,520 (9%) patients suffered an
incident CAD event. The clinical characteristics of the patients stratified by an
incident CAD event and non-CAD death
can be seen in Table 1. In general, patients who had an incident CAD event
were older and had longer diabetes duration, higher SBP, higher AER, lower
eGFR, lower eGDR, and a higher frequency of antihypertensive, lipid-lowering,
and retinal laser treatment than patients without a CAD event. The lipid profile in patients with an incident CAD
event was characterized by higher total
cholesterol, non–HDL cholesterol (non–
HDL-C), LDL-C, triglycerides, and apolipoprotein B (ApoB) as well as lower HDL-C
and HDL2 -C than in patients without
CAD. Likewise, the lipid and apolipoprotein ratios were worse in patients with
CAD than in patients without. The lipid
profiles of patients stratified by their
renal status can be seen in Supplementary Table 1.
Diabetes Care Volume 37, August 2014
In Cox regression analysis including
the entire cohort, diabetes duration,
eGFR, ApoB, SBP, and retinal laser treatment were independent predictors of
an incident CAD event (Supplementary
Table 1). The model also included sex,
HbA1c, WHR, AER, and history of smoking. Among the lipid variables, the highest HR per one SD increase of the study
cohort was found for ApoB. When ApoB
was replaced with all of the other lipid
variables, one at a time, triglycerides,
non–HDL-C, ApoB/ApoA-I, and triglyceride/HDL-C ratio were also good predictors of an incident CAD event (Table 2).
In order to further compare the performance of different lipid variables in predicting an incident CAD event, we
calculated the AUC of the different Cox
regression models. The highest AUCs
were found for ApoB/ApoA-I and triglyceride/HDL-C ratio followed by ApoB and
triglycerides. In line, P values for NRI
were ,0.05 for ApoB, ApoB/ApoA-I ratio, and triglycerides, indicating that
these parameters would be useful for
risk stratification. Diabetes duration and
age were highly correlated (r = 0.73) and
were therefore not entered into the same
model. In the univariate models, diabetes
duration was more strongly associated
with CAD, and it also decreased AIC
more than age and was therefore chosen
to be included in the multivariate models.
If, however, age was added to the original
Cox regression model, age was also a
significant independent predictor (P =
0.0002) of CAD together with diabetes
duration, eGFR, ApoB, and retinal laser
treatment. When use of lipid-lowering
agents was added to the original model,
it was not a significant predictor of CAD
(P = 0.10) and did not change the results.
When patients with lipid-lowering medication were excluded from the analysis,
diabetes duration, eGFR, and ApoB were
still independent predictors of CAD (data
not shown). When the competing event
of non-CAD death was taken into account
by performing Fine and Gray regression
analyses, similar results were found as
in the Cox regression analyses (Supplementary Table 1).
When men and women were analyzed
separately, ApoB was an independent
predictor of CAD in men (Supplementary
Table 1). In women, triglyceride/HDL-C,
ApoB/ApoA-I ratio, and triglycerides
had the highest HR for an incident
CAD event.
In patients with normal AER at baseline, ApoB/ApoA-I ratio, triglyceride/
HDL-C ratio, triglycerides, and ApoA-I
were all independent predictors of an
incident CAD event (Table 3). In microalbuminuric patients, the number
of events was only 41, and none of
the lipid variables were significant
predictors of CAD. In macroalbuminuric patients, ApoB had the highest
HR of CAD followed by non–HDL-C,
LDL-C, and total cholesterol. In patients with ESRD, non–HDL-C, total
cholesterol, and ApoB/ApoA-I ratio
reached borderline significance (P ,
0.05).
In Cox regression models stratified
by the median HbA 1c of the cohort
(8.3%, 67 mmol/mol), ApoB had the
highest HR for an incident CAD event
in patients with an HbA1c level above
the median followed by total cholesterol, non–HDL-C, and LDL-C (Supplementary Table 1). However, these
variables were poor predictors of CAD
in patients with an HbA1c level below
the median. Instead ApoB/ApoA-I ratio,
triglyceride/HDL-C ratio, and triglycerides were independent predictors of
CAD in patients with HbA1c below the
median.
To look at the effect of clustering of
risk factors, we divided patients into five
groups according to the amount of risk
factors present. In a Cox regression analysis, the HR was 3.27 in patients with
three out of the five risk factors, 5.96
in patients with four risk factors, and
7.02 in those with all five risk factors
compared with patients without any or
with only one of the five risk factors
(Supplementary Table 1).
Figure 1 and Supplementary Fig. 1
show the cumulative incidence of CAD
in normoalbuminuric and macroalbuminuric patients divided by their median lipid
levels. The median LDL-C and total cholesterol levels performed poorly in normoalbuminuric patients (Fig. 1A and
Supplementary Fig. 1A), but in macroalbuminuric patients, they were better
predictors together with non–HDL-C.
When normoalbuminuric patients were
divided by their median triglyceride
level (0.94 mmol/L), triglycerides performed well (Fig. 1C), but when they
were divided by the current guideline
for triglycerides (,1.7 mmol/L) (9),
they could not predict a future CAD
event (Fig. 1E). In macroalbuminuric
care.diabetesjournals.org
Tolonen and Associates
Table 1—Clinical characteristics at baseline of patients with type 1 diabetes stratified by an incident CAD event and non-CAD
death
No CAD event
Incident CAD event
Non-CAD death
P value*
n
3,037
310
173
Men (%)
50.3
55.8
60.1
0.09
35.4 6 10.8
47.6 6 9.6
42.5 6 11.2
,0.001
Age (years)
Age at onset (years)
15.5 6 8.5
14.4 6 8.0
14.9 6 8.9
0.02
Duration of diabetes (years)
19.9 6 11.3
33.2 6 9.1
27.6 6 9.7
,0.001
SBP (mmHg)
DBP (mmHg)
131 6 16
79 6 9
148 6 20
81 6 11
143 6 23
82 6 12
,0.001
0.007
BMI (kg/m2)
25.0 6 3.4
25.1 6 3.7
24.6 6 4.8
0.55
WHR
Men
Women
0.90 6 0.07
0.81 6 0.06
0.95 6 0.07
0.84 6 0.07
0.93 6 0.07
0.85 6 0.08
,0.001
,0.001
HbA1c (%)
HbA1c (mmol/mol)
8.4 6 1.5
68 6 16.4
8.6 6 1.6
70 6 17.5
8.9 6 1.6
74 6 17.5
0.008
AER (mg/24 h)
10 (5–29)
51 (8–495)
77 (11–601)
,0.001
eGFR (mL/min/1.73 m2)
100 6 23
68 6 33
66 6 38
,0.001
,0.001
eGDR (mg/kg/min)
6.90 (4.75–8.74)
4.49 (3.33–5.51)
4.54 (3.18–6.10)
Current smoking (%)
24.1
25.8
33.1
0.52
History of smoking (%)
44.2
53.2
59.6
0.007
Antihypertensive treatment (%)
29.2
75.1
65.1
,0.001
Lipid-lowering agents (%)
Normal AER (%)
7.3
69.5
25.1
31.4
17.4
22.5
,0.001
,0.001
Microalbuminuria (%)
12.7
13.9
13.3
0.57
Macroalbuminuria (%)
10.4
28.5
31.2
,0.001
ESRD (%)
2.5
23.0
28.3
,0.001
Retinal laser treatment (%)
25.5
73.5
69.4
,0.001
Total cholesterol (mmol/L)
4.86 6 0.92
5.42 6 1.20
5.28 6 1.09
,0.001
Non–HDL-C (mmol/L)
3.51 6 0.96
4.20 6 1.23
3.97 6 1.15
,0.001
LDL-C (mmol/L)
2.97 6 0.83
3.45 6 1.00
3.25 6 1.00
,0.001
0.98 (0.74–1.39)
1.30 (0.97–1.89)
1.35 (0.97–2.03)
,0.001
1.24 6 0.34
1.46 6 0.39
1.14 6 0.34
1.33 6 0.41
1.24 6 0.44
1.40 6 0.52
,0.001
,0.001
0.47 6 0.24
0.64 6 0.28
0.44 6 0.23
0.57 6 0.28
0.50 6 0.30
0.64 6 0.35
,0.001
,0.001
0.78 6 0.19
0.83 6 0.22
0.70 6 0.18
0.76 6 0.22
0.74 6 0.22
0.78 6 0.26
,0.001
0.002
1.32 6 0.20
1.46 6 0.23
1.32 6 0.20
1.41 6 0.23
1.37 6 0.24
1.49 6 0.31
†
0.002
ApoB (g/L)
0.86 6 0.22
1.00 6 0.25
0.95 6 0.25
,0.001
ApoB/A-I
0.63 6 0.20
0.75 6 0.23
0.70 6 0.23
,0.001
0.76 (0.51–1.17)
3.58 (2.91–4.46)
1.06 (0.73–1.90)
4.30 (3.55–5.60)
1.11 (0.70–1.80)
4.20 (3.19–5.30)
,0.001
,0.001
Triglycerides (mmol/L)
HDL-C (mmol/L)
Men
Women
HDL2-C (mmol/L)
Men
Women
HDL3-C (mmol/L)
Men
Women
ApoA-I (g/L)
Men
Women
Triglyceride/HDL-C
Total cholesterol/HDL-C
Data are means 6 SD, median (IQR), or %. DBP, diastolic blood pressure. *P values indicate differences between incident CAD event and no CAD
event groups. P values for lipid variables are adjusted for age, BMI, and sex (if not already stratified by sex). †The BMI- and age-corrected ApoA-I
levels in men were 1.27 and 1.33 g/L in no CAD and CAD event groups, respectively, and the difference between these was significant.
patients, the guideline cutoff level
performed better (Fig. 1F). In normoalbuminuric patients, the median of
triglyceride/HDL-C and ApoB/ApoA-I ratio were the best predictors for an incident CAD event (Supplementary Fig.
1I and K).
CONCLUSIONS
This study is the first report on a complete panel of both antiatherogenic and
atherogenic lipid parameters as predictors of incident CAD outcomes in patients with type 1 diabetes. In this
study we have shown that in patients
with type 1 diabetes, several lipid and
apolipoprotein variables are associated
with an incident CAD event depending
on the patient’s sex, glycemic control,
or renal status. In normoalbuminuric
patients, the ratios of concentrations
of atherogenic and antiatherogenic
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Lipid Profiles, Renal Disease, and CAD in T1DM
Diabetes Care Volume 37, August 2014
Table 2—Cox regression analyses with risk factors for a CAD event
Variable
HR (95% CI)
P value
LR x2
AUC
AIC
NRI % (5, 10, 20)
Total C (0.98 mmol/L)
1.22 (1.07–1.40)
0.003
350
0.854
2,684
2.2 (P = 0.30)
Non–HDL-C (1.02 mmol/L)
1.27 (1.12–1.46)
,0.001
353
0.857
2,680
4.7 (P = 0.06)
LDL-C (0.94 mmol/L)
1.21 (1.05–1.39)
0.009
340
0.854
2,625
3.2 (P = 0.22)
Ln triglycerides
1.34 (1.14–1.58)
,0.001
354
0.859
2,680
6.2 (P = 0.03)
HDL-C (0.39 mmol/L)
0.86 (0.73–1.00)
0.05
345
0.857
2,688
2.1 (P = 0.45)
HDL2-C (0.28 mmol/L)
0.85 (0.72–1.01)
0.06
338
0.855
2,630
3.9 (P = 0.14)
HDL3-C (0.21 mmol/L)
0.88 (0.76–1.03)
0.12
336
0.854
2,631
20.2 (P = 0.92)
ApoA-I (0.22 g/L)
ApoB (0.23 g/L)
0.85 (0.73–0.98)
1.40 (1.21–1.62)
0.03
,0.001
346
361
0.857
0.859
2,688
2,673
4.6 (P = 0.05)
7.7 (P = 0.01)
ApoB/A-I (0.21)
1.24 (1.13–1.37)
,0.001
356
0.860
2,678
7.1 (P = 0.02)
Ln triglyceride/HDL-C
1.28 (1.11–1.49)
0.001
352
0.860
2,682
5.2 (P = 0.10)
Ln total C/HDL-C
1.22 (1.06–1.39)
0.004
349
0.858
2,684
6.1 (P = 0.04)
The model also included the following: duration of diabetes, eGFR, SBP, retinal laser treatment, sex, HbA1c, WHR, ln AER, and history of smoking.
N = 2,521 (events = 198). Results are presented as HRs per one SD increase with 95% CI. AUC of the receiver operating characteristics indicates
discrimination. LR x2 statistics from the Cox model, higher values indicate better global fit. Lower values of AIC indicate better tradeoff between the
likelihood of the model against its complexity. NRI indicates the sum of correctly reclassified individuals with and without CAD events after the
inclusion of the lipid variable to the model when patients were divided into the risk groups ,5, 5–10, 10–20, and $20%. Total C, total cholesterol.
lipoproteins and lipids were the strongest predictors of an incident CAD
event, whereas in macroalbuminuric patients, no added benefit was gained
from the ratios. The same phenomenon
was seen when the patients were divided by sex or glycemic control.
Previous data are limited, but non–
HDL-C, HDL-C, and triglycerides have
been reported to be independent predictors of CAD in patients with type 1
diabetes (21,22). In this study, ApoB
was the best independent predictor of
an incident CAD event in the entire cohort followed by triglycerides, non–
HDL-C, ApoB/ApoA-I ratio, and lipid
ratios. Unexpectedly, LDL-C performed
less well than the other lipid variables.
The predictive performance of different
variables for CAD outcomes was also
evaluated by calculating NRI percentages
where ApoB and ApoB/ApoA-I ratio performed best followed by triglycerides
and total cholesterol/HDL-C ratio, outperforming LDL-C. ApoB is the main protein of the atherogenic particles VLDL,
IDL, and LDL (23), and .90% of the circulating ApoB is located in LDL particles (24). Thus, ApoB is a measure of
all ApoB-containing particles, including
triglyceride-rich lipoproteins, their remnants, as well as LDL particles. It should
be recognized that non–HDL-C is likewise capturing the risk associated with
triglyceride-rich lipoproteins, their remnants, and LDL-C. Thus, the poorer performance of LDL-C when replaced by
these variables is understandable. The
antiatherogenic HDL-C and related parameters were also significant predictors
of CAD outcomes but performed less
well than atherogenic parameters, indicating an imbalance between antiatherogenic and atherogenic variables.
Diabetic nephropathy is strongly associated with dyslipidemia in patients
with type 1 diabetes (6,11). Previous
studies have found even more robust
Table 3—Cox regression analyses with risk factors for a CAD event in patients stratified by renal status
Normal AER
Microalbuminuria
Macroalbuminuria
ESRD
n = 2,059 (events = 95)
n = 429 (events = 41)
n = 417 (events = 77)
n = 155 (events = 62)
HR (95% CI)
P value
HR (95% CI)
P value
HR (95% CI)
P value
HR (95% CI)
P value
Total C (0.98 mmol/L)
1.04 (0.84–1.30)
0.72
1.12 (0.80–1.59)
0.50
1.32 (1.11–1.58)
0.002
1.28 (1.01–1.63)
0.04
Non–HDL-C (1.02 mmol/L)
1.16 (0.94–1.44)
0.17
1.18 (0.85–1.66)
0.32
1.34 (1.12–1.60)
0.001
1.34 (1.03–1.73)
0.03
LDL-C (0.94 mmol/L)
1.10 (0.89–1.35)
0.38
1.19 (0.86–1.64)
0.30
1.33 (1.09–1.63)
0.005
1.23 (0.98–1.54)
0.07
Ln triglycerides
1.40 (1.11–1.77)
0.005
1.13 (0.81–1.58)
0.48
1.22 (0.96–1.56)
0.10
1.25 (0.89–1.74)
0.19
HDL-C (0.39 mmol/L)
0.74 (0.59–0.94)
0.01
0.85 (0.60–1.20)
0.36
0.87 (0.68–1.11)
0.26
0.93 (0.70–1.23)
0.61
HDL2-C (0.28 mmol/L)
HDL3-C (0.21 mmol/L)
0.75 (0.59–0.96)
0.82 (0.65–1.02)
0.02
0.07
0.90 (0.63–1.29)
0.88 (0.63–1.24)
0.58
0.47
0.87 (0.66–1.14)
0.80 (0.61–1.05)
0.32
0.10
0.81 (0.60–1.08)
1.12 (0.87–1.45)
0.15
0.39
ApoA-I (0.22 g/L)
0.72 (0.57–0.91)
0.006
0.77 (0.53–1.12)
0.17
0.98 (0.79–1.23)
0.88
0.90 (0.67–1.21)
0.48
ApoB (0.23 g/L)
1.35 (1.08–1.70)
0.01
1.31 (0.93–1.83)
0.12
1.47 (1.19–1.81)
,0.001
1.24 (0.92–1.67)
0.15
ApoB/A-I (0.21)
1.43 (1.17–1.76)
,0.001
1.32 (0.97–1.78)
0.08
1.21 (1.05–1.39)
0.007
1.33 (1.00–1.77)
0.048
Ln triglyceride/HDL-C
1.44 (1.15–1.81)
0.002
1.15 (0.83–1.61)
0.41
1.20 (0.96–1.49)
0.10
1.18 (0.87–1.61)
0.29
Ln total C/HDL-C
1.29 (1.04–1.61)
0.02
1.18 (0.86–1.63)
0.30
1.23 (1.03–1.48)
0.02
1.17 (0.92–1.50)
0.21
Variable
The model also included the following: duration of diabetes, eGFR, SBP, retinal laser treatment, sex, HbA1c, WHR, and history of smoking. Results are
presented as HRs per one SD increase of the study cohort with 95% CI. Total C, total cholesterol.
care.diabetesjournals.org
Tolonen and Associates
Figure 1—The cumulative incidence of CAD in normoalbuminuric patients stratified by the group median of LDL-C subdistribution HR (subHR) 1.07
(95% CI 0.70–1.64), P = 0.74 (A); macroalbuminuric patients stratified by the group median of LDL-C subHR 1.63 (0.99–2.67), P = 0.06 (B);
normoalbuminuric patients stratified by the group median of triglycerides (TG) subHR 1.74 (1.13–2.69), P = 0.01 (C); macroalbuminuric patients
stratified by the group median of TG subHR 1.26 (0.76–2.08), P = 0.38 (D); normoalbuminuric patients stratified by the recommended cutoff 1.7
mmol/L for TG subHR 0.88 (0.42–1.85), P = 0.74 (E); and macroalbuminuric patients stratified by the recommended cutoff 1.7 mmol/L for TG subHR
1.69 (1.00–2.85), P = 0.05 (F). The competing risk regression model also included the following: duration of diabetes, eGFR, SBP, retinal laser
treatment, sex, HbA1c, WHR, and history of smoking.
lipid disturbances associated with nephropathy than with cardiovascular disease (CVD) (25). To the best of our
knowledge, other prospective studies
have not taken into account the renal
status when evaluating the prediction
of lipid variables for an incident CAD
event in patients with type 1 diabetes.
When the patients were divided by their
renal status, the ApoB/ApoA-I and triglyceride/HDL-C ratios had the highest
HR of CAD in patients with normal AER
at baseline. Total cholesterol and LDL-C
were not independent predictors of CAD
in normoalbuminuric patients. Figure 1A
and Supplementary Fig. 1A illustrate
how poorly they predicted an incident
CAD event in multivariate models in normoalbuminuric patients. As expected, in
macroalbuminuric patients, total cholesterol and LDL-C were elevated and
2379
2380
Lipid Profiles, Renal Disease, and CAD in T1DM
they performed well as CAD predictors
together with ApoB and non–HDL-C. Notably, HDL-C or related parameters were
not significant predictors of CAD events
in macroalbuminuric patients. In a casecontrol study of the EURODIAB cohort,
CVD was not associated with any traditional lipid variables in normoalbuminuric
patients. However, in macroalbuminuric
patients, CVD was associated with increased triglyceride and LDL-C levels
(25). In the EURODIAB study, the ApoB/
ApoA-I or triglyceride/HDL-C ratios were
not calculated and the CAD events were
not analyzed separately.
HDL-C values are generally similar or
even higher in patients with type 1
diabetes compared with the general
population (10). The higher HDL-C concentrations can partly be explained by
the subcutaneous insulin administration, which enhances lipoprotein lipase
and is associated with an improved lipid
profile (26,27). However, whether or
not the functionality and protective effect of HDL-C are similar in patients with
type 1 diabetes compared with that of
the general population has been questioned (28). For example, the glycation
of ApoA-I affects the reverse cholesterol transport ability of HDL particles
(29), and the HDL of patients with type 1
diabetes protects erythrocyte membranes from oxidative damage less efficiently (30). Moreover, HDL particles
failed to counteract the inhibitory effect
of oxidized LDL on vasodilatation in patients with type 1 diabetes (31). In type 2
diabetes, increased concentrations of
advanced glycation end products are associated with impairment in the antioxidant ability of HDL particles in patients
with nephropathy (32), and the potency
of HDL to inhibit cytokine release by
macrophages is attenuated (33). In this
study, ApoA-I and HDL-C were poor predictors of incident CAD events in the entire cohort, in patients with diabetic
nephropathy and poor glycemic control
and in men. This suggests that the protective effect of HDL and ApoA-I could be
impaired, especially in patients with diabetic nephropathy or poor glycemic
control.
LDL-C is the established target of
therapy for the prevention of CAD
(9,34) as lack of data from randomized
clinical trials does not allow the definition of targets either for triglyceridelowering or HDL-C–raising interventions.
Diabetes Care Volume 37, August 2014
However, consensus exists that non–
HDL-C, ApoB, and triglyceride values
serve in the risk estimation in general
populations as well as in people with diabetes. In patients with type 1 diabetes
without diabetic nephropathy and HbA1c
,8.3% (67 mmol/mol) and women,
LDL-C was a surprisingly poor predictor
of CAD. Recent data suggest that in
statin-treated patients, the strength of
ApoB and non–HDL-C as risk predictors
may be better than that of LDL-C (35). In
this study, ApoB was a better predictor
than non–HDL-C and LDL-C. However,
the use of lipid-lowering agents (only a
few patients were on fibrate therapy) at
baseline was only 25.1 and 7.3% in patients with and without CAD, respectively. Furthermore, LDL-C was a good
predictor of CAD in macroalbuminuric patients, in whom the use of lipidlowering agents was much higher than
in normoalbuminuric patients. Therefore, the use of lipid-lowering medication is an unlikely explanation for the
poorer performance of LDL-C.
Patients with type 1 diabetes without
microvascular complications have traditionally been considered to have a low
risk of CAD; however, individuals with
insulin resistance and other features of
the metabolic syndrome should be recognized also from this patient group.
The prevalence of double diabetes
(i.e., when patients with type 1 diabetes
show features of insulin resistance and
type 2 diabetes) is increasing due to the
societal trend of increased adiposity as
well as weight gain caused by intensive
glycemic control (27). Furthermore, the
subcutaneous insulin administration
leads to relative peripheral hyperinsulinemia and hepatic hypoinsulinemia,
and a chronic adaption to this could reduce peripheral insulin uptake and increase hepatic glucose production,
inducing insulin resistance (27). We have
previously shown that in the FinnDiane
population of type 1 diabetes, the overall
prevalence of metabolic syndrome was
38% in men and 40% in women (36). In
this study, ApoB and the ApoB/ApoA-I
ratio were good predictors of CAD, but
unfortunately their measurement generates additional costs and they are usually
not available in the clinical setting. However, the triglyceride/HDL-C ratio, which
can be calculated without any additional
cost and strongly correlates with insulin
resistance (37), was a good predictor of
CAD in the patient groups in which LDL-C
failed to predict a future CAD event.
In this study, the triglycerides predicted an incident CAD event in women,
whereas in men, triglycerides were not a
significant independent predictor. In the
EURODIAB study, triglycerides also predicted CAD in women (22), and in the
general population, triglycerides have
been found to predict CVD more
strongly in women (38). The current
guidelines and the median values of
the lipid variables in normoalbuminuric
patients were close to each other, except regarding triglycerides, where the
currently recommended level for triglycerides is ,1.7 mmol/L. The median level
for normo- and macroalbuminuric patients was 0.94 and 1.38 mmol/L, respectively, indicating increased lipolysis of
triglyceride-rich lipoproteins due to activation of lipoprotein lipase by insulin
therapy. Figure 1E illustrates how poorly
the currently recommended cutoff level
for triglycerides predicted a future CAD
event in normoalbuminuric patients
whereas the median level of 0.94
mmol/L performed much better. However, in macroalbuminuric patients, the
current guideline level performed well.
This observation may be due to the
fact that atherogenic changes in LDL particle size and composition are seen
even when triglyceride concentrations
are ,1.7 mmol/L (39). Serum triglyceride levels are strongly associated with
glycemic control (11,40), but the results
of all the figures were corrected for
HbA1c since it was included in the multivariate model. Furthermore, when patients were divided by the median
HbA1c level of the entire cohort, surprisingly triglycerides were a stronger predictor of CAD in patients with HbA1c
below the median than in patients
with HbA1c above. This suggests that
there is an independent additive effect
of triglycerides on CAD risk beyond that
of glycemic control. Clustering of risk
factors was also observed, and in a Cox
regression analysis, an additive effect
with the increasing number of risk factors
was seen in patients with three or more
risk factors.
Strengths of this study include the
large cohort and that the lipid variables
were all centrally measured. Patients
were also geographically evenly distributed, resembling the distribution of
the general population in Finland.
care.diabetesjournals.org
Therefore, a selection bias is less likely
than in single hospital-based studies.
Furthermore, since the follow-up information was obtained from the HDR as
well as the CDR, no patients were lost at
follow-up. A limitation of the study is
the possible survival bias; however,
this was accounted for by performing
competing risk regression analyses.
Randomized clinical trials that take into
account concomitant microvascular complications and with sufficiently long
follow-up periods are needed to confirm
our findings.
In conclusion, we have shown that in
patients with type 1 diabetes, renal disease, glycemic control, and sex strongly
modulated the relationship between
lipid variables and CAD. Total and LDL-C
were poor predictors of an incident CAD
event in patients with normal AER and
HbA1c below the median of the cohort
and women, in which the ratios and triglycerides performed better. However,
the currently recommended cutoff level
for triglycerides of 1.7 mmol/L may be
too high to be able to predict an incident
CAD event in these patients. Current
treatment recommendations may need
to be revised to capture residual CAD
risk in patients with type 1 diabetes.
Acknowledgments. The authors acknowledge
all the patients who participated in the study as
well as all the physicians and nurses at the
participating study centers (Supplementary
Table 7). The authors are also thankful to
H. Hild én, V. Naatti, and H. Perttunen-Mio
(Division of Cardiology, Department of Medicine, Helsinki University Central Hospital) and
M. Parkkonen, A.-R. Salonen, A. Sandelin,
J. Tuomikangas, and T. Soppela (Folkhälsan Institute of Genetics, Folkhälsan Research Center,
University of Helsinki) for their skilled technical
assistance.
Funding. The study was supported by the
Folkhälsan Research Foundation; the Wilhelm
and Else Stockmann Foundation; the Finnish
Medical Society (Finska Läkaresällskapet); the
Finnish Kidney Foundation; the Biomedicum
Helsinki Foundation; the Dorothea Olivia, Karl
Walter, and Jarl Walter Perklen Foundation;
the Novo Nordisk Foundation; the Liv och Hälsa
Foundation; the Sigrid Jusélius Foundation; the
Waldemar von Frenckell Foundation; and the
American Heart Association (13POST17240095).
The funding sources were not involved in the
design or conduct of the study in any way.
Duality of Interest. M.-R.T. has been a consultant for MSD, Kowa, Roche, Sanofi, and
Novo Nordisk and has received travel and
accommodation support during scientific
meetings by MSD, Sanofi, and Novo Nordisk
and lecture fees from AstraZeneca, MSD,
Tolonen and Associates
Kowa, Sanofi, and Novo Nordisk. P.-H.G. has
received research grants from Eli Lilly and
Company and Roche, is an advisory board
member for Boehringer Ingelheim, Abbott and
AbbVie, Cebix, and Novartis, and has received
lecture fees from Boehringer Ingelheim, Eli Lilly
and Company, Genzyme, MSD, Novartis, Novo
Nordisk, and Sanofi. No other potential conflicts of interest relevant to this article were
reported.
The above-mentioned companies were not involved in the design or conduct of the study in
any way.
Author Contributions. N.T. collected data,
performed the statistical analyses, and wrote
the manuscript. C.F. and V.-P.M. collected data,
contributed to the discussion, and edited the
manuscript. V.H., D.G., M.F., N.S., L.M.T., and
J.W. collected data and edited the manuscript.
M.-R.T. contributed to the discussion and edited
the manuscript. P.-H.G. collected data, contributed to the discussion, and edited the manuscript. N.T. and P.-H.G. are the guarantors of this
work and, as such, had full access to all the data
in the study and take responsibility for the
integrity of the data and the accuracy of the
data analysis.
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