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Insulin Resistance, Defective Insulin-Mediated Fatty Acid
Suppression, and Coronary Artery Calcification in
Subjects With and Without Type 1 Diabetes
The CACTI Study
Irene E. Schauer,
1
Janet K. Snell-Bergeon,
2
Bryan C. Bergman,
1
David M. Maahs,
2
Adam Kretowski,
1
Robert H. Eckel,
1
and Marian Rewers
2
OBJECTIVE—To assess insulin action on peripheral glucose
utilization and nonesterified fatty acid (NEFA) suppression as a
predictor of coronary artery calcification (CAC) in patients with
type 1 diabetes and nondiabetic controls.
RESEARCH DESIGN AND METHODS—Insulin action was
measured by a three-stage hyperinsulinemic-euglycemic clamp
(4, 8, and 40 mU/m
2
/min) in 87 subjects from the Coronary Artery
Calcification in Type 1 Diabetes cohort (40 diabetic, 47 nondia-
betic; mean age 45 ⫾8 years; 55% female).
RESULTS—Peripheral glucose utilization was lower in subjects
with type 1 diabetes compared with nondiabetic controls: glu-
cose infusion rate (mg/kg FFM/min) ⫽6.19 ⫾0.72 vs. 12.71 ⫾
0.66, mean ⫾SE, P⬍0.0001, after adjustment for age, sex, BMI,
fasting glucose, and final clamp glucose and insulin. Insulin-
induced NEFA suppression was also lower in type 1 diabetic
compared with nondiabetic subjects: NEFA levels (M) during 8
mU/m
2
/min insulin infusion ⫽370 ⫾27 vs. 185 ⫾25, P⬍0.0001,
after adjustment for age, sex, BMI, fasting glucose, and time
point insulin. Lower glucose utilization and higher NEFA levels,
correlated with CAC volume (r⫽⫺0.42, P⬍0.0001 and r⫽0.41,
P⬍0.0001, respectively) and predicted the presence of CAC
(odds ratio [OR] ⫽0.45, 95% CI ⫽0.22– 0.93, P⫽0.03; OR ⫽2.4,
95% CI ⫽1.08 –5.32, P⫽0.032, respectively). Insulin resistance
did not correlate with GHb or continuous glucose monitoring
parameters.
CONCLUSIONS—Type 1 diabetic patients are insulin resistant
compared with nondiabetic subjects, and the degree of resis-
tance is not related to current glycemic control. Insulin resis-
tance predicts the extent of coronary artery calcification and may
contribute to the increased risk of cardiovascular disease in
patients with type 1 diabetes as well as subjects without diabetes.
Diabetes 60:306–314, 2011
Cardiovascular disease (CVD) remains the lead-
ing cause of death in individuals with type 1
diabetes (1– 4). Although hyperglycemia ap-
pears to be the primary mediator of microvas-
cular disease (5,6), its role in macrovascular disease is less
clear (4). Tight glycemic control improves, but does not
normalize CVD risk, and correlation of GHb to CVD risk
remains controversial (7–15). In addition, standard predic-
tion rules for CVD risk do not accurately predict CVD in
type 1 diabetic populations (16). Thus, the mechanism of
accelerated atherosclerosis in type 1 diabetes is unclear
and identification of those patients at highest risk and
most in need of aggressive risk factor modification is
inaccurate.
In the general population, insulin resistance has been
implicated as an important contributor to accelerated
atherosclerosis (17–25). Although type 1 diabetes is pri-
marily a disease of insulin deficiency, previous studies
have demonstrated insulin resistance and suggested that
CVD may also be linked to insulin resistance in type 1
diabetes (10,26 –32). As early as 1968, Martin et al. (30)
demonstrated an “impaired glucose assimilation index”
and an inverse association between this index and preva-
lent macrovascular disease in type 1 diabetic subjects.
More recently, the Pittsburgh Epidemiology of Diabetes
Complications Study (10) found no correlation between
GHb and coronary artery disease outcomes. However, in
addition to other known CVD risk factors, estimated
glucose disposal rate correlated inversely with these out-
comes. Similar correlations of estimated insulin resistance
or a surrogate of insulin resistance (waist-to-hip ratio)
to coronary artery disease were also found in the Diabe-
tes Control and Complications Trial (DCCT) and the
EURODIAB study (33). These data suggest that an estimate
of insulin resistance may add to CVD risk prediction in type 1
diabetes. In addition, elevated nonesterified fatty acid
(NEFA) levels have been proposed to mediate the increased
atherosclerotic risk associated with insulin resistance in the
general population (18,34 –37). It is not known whether the
defects in insulin action in type 1 diabetes extend beyond
glucose utilization to NEFA suppression.
The Coronary Artery Calcification in Type 1 Diabetes
(CACTI) study has followed a cohort of type 1 diabetic
subjects and similar nondiabetic controls with electron
beam computed tomography for measurement of coronary
artery calcification (CAC) and CVD outcomes for 6 years
(15,38). We hypothesized that type 1 diabetic subjects
From the
1
Division of Endocrinology, Metabolism and Diabetes, Department
of Medicine, University of Colorado Denver, Aurora, Colorado; and the
2
Barbara Davis Center for Childhood Diabetes, School of Medicine, Univer-
sity of Colorado Denver, Aurora, Colorado.
Corresponding author: Irene E. Schauer, irene.schauer@ucdenver.edu.
Received 5 March 2010 and accepted 14 October 2010. Published ahead of
print at http://diabetes.diabetesjournals.org on 26 October 2010. DOI:
10.2337/db10-0328.
© 2011 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. See http://creativecommons.org/licenses/by
-nc-nd/3.0/ for details.
The costs of publication of this article were defrayed in part by the payment of page
charges. This article must therefore be hereby marked “advertisement” in accordance
with 18 U.S.C. Section 1734 solely to indicate this fact.
ORIGINAL ARTICLE
306 DIABETES, VOL. 60, JANUARY 2011 diabetes.diabetesjournals.org
would be more insulin resistant than nondiabetic controls
in terms of both glucose utilization and NEFA suppres-
sion, and that both measures of insulin resistance would
correlate with CAC, a marker of the extent of coronary
atherosclerosis.
RESEARCH DESIGN AND METHODS
Study population and clamp substudy design. Subjects were recruited
from the CACTI study cohort described previously (38) for a hyperinsuline-
mic-euglycemic clamp substudy. Inclusion criteria for initial enrollment of
type 1 diabetic subjects in the CACTI study were age 19 to 56, no history of
CVD, insulin therapy within a year of diagnosis and current insulin therapy,
diagnosis before age 30 and/or positive antibodies, and diabetes duration ⱖ10
years. The nondiabetic controls were of similar age and free of CVD. Within
this cohort, inclusion criteria for the clamp substudy presented here included:
GHb ⱕ9.5%, albumin excretion rate ⬍200 g/min, triglycerides ⬍400 mg/dl,
blood pressure ⬍160/100, and a CAC measurement at the 6-year CACTI
follow-up. Recruitment of the subgroup was performed by a mailing to the full
CACTI cohort with the 6-year follow-up reminder. The initial recruitment goal
was 96 subjects, 24 each of type 1 diabetic men and women, and nondiabetic
men and women with frequency matching for age (by 5-year intervals), and
BMI (normal, overweight, and obese). Interested and eligible members of the
cohort were accepted into the substudy as long as groups remained open and
frequency-matching criteria were satisfied. Recruitment was stopped at 91
participants for funding reasons. Clamps were performed 10 to 967 (median
207) days after the 6-year follow-up visit. All participants provided informed
consent, and the study was approved by the Colorado Combined Institutional
Review Board.
Body composition. Dual X-ray absorptiometry scans were performed for
body composition and fat-free mass (FFM) just before the clamp study.
Abdominal computed tomography scans for calculation of visceral and
subcutaneous fat areas and liver to spleen density ratios were performed at
the 6-year CACTI follow-up and within 1 year of the clamp study. Anthropo-
metric measures included waist and hip circumference, height, weight, and
sagittal diameter.
Continuous glucose monitoring. Type 1 diabetic subjects (n⫽44) under-
went masked continuous glucose monitoring (CGM) (Medtronic MiniMed
Gold System) for 3 days before the hyperinsulinemic-euglycemic clamp.
Reported CGM measures include 1) the overall mean glucose (mean
T
), the
average of all glucose values; 2) hypoglycemia, percentage of glucose values
⬍70 mg/dl (3.9 mmol/l); 3) hyperglycemia, percentage of glucose values ⬎180
mg/dl (10 mmol/l); and 4) glycemic variability within days, overall SD (SD
T
),
the SD of all glucose values.
Hyperinsulinemic-euglycemic clamp visit. Subjects were asked to refrain
from vigorous physical activity and provided a standardized diet (50% carbo-
hydrate, 20% protein, 30% fat) for 3 days before their study day. Caloric needs
were based on FFM measured by DEXA scan and a standard activity factor.
Premenopausal women were scheduled for their study day within days 2 to 10
of their menstrual cycle. Subjects were admitted to the inpatient clinical
research unit before dinner the evening before their study. Type 1 diabetic
subjects were instructed to take their last long-acting insulin injections at least
12 h before admission. Dinner was provided on the unit and subjects then
fasted overnight and through the clamp. Type 1 diabetic subjects were given
bolus insulin for dinner per their usual regimen. Those on an insulin pump had
the pump removed after dinner, and all type 1 diabetic subjects were
maintained overnight on intravenous insulin with adjustments to achieve
euglycemia by morning. Blood samples for determination of baseline hormone
and substrate (insulin, glucose, C-peptide, NEFA, glycerol, and lactate)
concentrations were drawn before initiation of the clamp protocol. NEFA
were assayed using an Olympus AU400e Chemistry Analyzer two step
spectrophotometric assay. A three stage hyperinsulinemic- euglycemic clamp
was initiated and continued for the next 4.5 h using the method of DeFronzo
et al. (39). Briefly, a primed continuous infusion of insulin was administered
at 4, 8, and then 40 mU/m
2
/min for 1.5 h each. A variable infusion of 20%
dextrose was infused to maintain blood glucose ⬃90 mg/dl. Arterialized blood
was sampled every 5 min for bedside determination of glucose concentration
(Analox, Lunenberg, MA) and the dextrose infusion adjusted as necessary.
Arterialized blood samples were taken twice during the last 10 min of each
stage of the clamp for hormone and substrate measurements as above. A
hyperinsulinemic- euglycemic steady state was achieved during the last 30 min
of the high insulin infusion stage and mean glucose infusion rate ([GIR], mg/kg
FFM/min) during this time was used as the measure of insulin sensitivity.
Imaging of coronary artery calcium. CAC was measured using an Imatron
C-150 Ultrafast computed tomography scanner. All participants underwent
two electron beam computed tomography scans without contrast within 5 min
at baseline and two scans at follow-up visits at years 3 and 6 using the
standard acquisition protocol. Calcium volume scores (CVS) were calculated
using the volumetric method, which is based on isotropic interpolation, as
previously described (40,41).
Statistical analyses. Variables were examined for normality, and non-
normally distributed variables were log transformed for analysis. Differences
in clinical and clamp parameters and unadjusted GIR between type 1 diabetic
and non-DM subjects were examined using unpaired Student ttests. Multiply
adjusted least squares means of GIR and NEFA and glycerol levels were
calculated using generalized linear models. Correlation of insulin resistance
measures to CAC was examined using Spearman rank test and partial
adjustment was made for age, diabetes status, and sex. Comparison of insulin
resistance by GHb quartile was done by ANOVA.
RESULTS
We present data from 87 subjects (40 with type 1 diabetes
and 47 age- and sex-matched nondiabetic controls) re-
cruited for the clamp substudy between 2005 and 2008.
Initial recruitment included 91 subjects (44 with diabetes
and 47 nondiabetic controls). Four diabetic subject clamps
were excluded from the final analysis due to errors in
clamp and/or overnight insulin preparation detected by
inappropriate insulin responses in the subjects and post-
clamp measurement of infusate insulin concentration.
Subjects in the clamp study were representative of the
full CACTI cohort seen at the 6-year follow-up with no
differences found for parameters known to impact insulin
sensitivity, including age (years: 44.8 ⫾7.9 vs. 45.0 ⫾9.1,
P⫽0.87), BMI (kg/m
2
: 26.2 ⫾4.2 vs. 26.8 ⫾4.9, P⫽0.19),
visceral fat (log visceral fat area: 10.67 ⫾0.52 vs. 10.74 ⫾
0.59, P⫽0.32), and habitual daily physical activity (log
Kcal: 7.28 ⫾1.00 vs. 7.15 ⫾1.45, P⫽0.24). Similarity
between the clamp cohort and the full 6-year visit cohort
was also found within sex and diabetes strata (not shown).
In addition, type 1 diabetic subjects in the clamp study
were similar to the full CACTI cohort in diabetes duration
(28.6 ⫾8.0 vs. 29.4 ⫾8.8 years, P⫽0.57). Clinical
characteristics of the study population by diabetes status
are shown in Table 1. Type 1 diabetic and nondiabetic
groups were well matched for age and BMI. In addition,
the two groups were similar for other measures of body
composition, including total percentage of body fat, waist
circumference, waist to hip ratio, and habitual physical
activity. As expected, type 1 diabetic and nondiabetic
groups differed significantly in diabetes-related parame-
ters of GHb and fasting glucose. Total and LDL cholesterol
and triglycerides also differed significantly by diabetes
status, with the nondiabetic subjects exhibiting the higher
cardiovascular risk phenotype of higher LDL and triglyc-
erides. Statin use was significantly more frequent in type 1
diabetic subjects, but the difference in lipid profile re-
mained after adjustment for statin use (not shown). Type
1 diabetic subjects were more likely to be taking antihy-
pertensive medication, but there was no difference in
blood pressure between type 1 diabetic and nondiabetic
subjects who were not taking medications (not shown).
Adiponectin was significantly higher in type 1 diabetic
than nondiabetic subjects as has been reported previously
(42– 45).
Type 1 diabetic subjects in the clamp cohort exhibited
stable glycemic control over the full duration of the study
from baseline through the clamp visit (mean GHb ⫽7.71 ⫾
1.0, 7.57 ⫾1.1, 7.9 ⫾1.0, and 7.51 ⫾0.87 at baseline,
3-year, 6-year, and clamp visits, respectively).
Impaired peripheral glucose utilization. Three-stage
hyperinsulinemic-euglycemic clamps were performed in
87 subjects (46% with type 1 diabetes, 55% women). Final
I.E. SCHAUER AND ASSOCIATES
diabetes.diabetesjournals.org DIABETES, VOL. 60, JANUARY 2011 307
clamp glucose and insulin levels were not different be-
tween the groups (Table 2). Whole-body insulin sensitivity
represented by GIR during the last 30 min of the clamp
was significantly lower in type 1 diabetic than nondiabetic
subjects (5.8 ⫾3.5 vs. 13.2 ⫾5.7 mg/kg FFM/min, P⬍
0.0001). This difference was only modestly attenuated after
multivariate adjustment for age, fasting glucose, final
clamp glucose and insulin, and BMI or visceral fat area
(Table 2).
Correlates of insulin resistance in type 1 diabetes.
Insulin resistance in type 1 diabetes correlated with the
expected parameters known to predict insulin resistance
in nondiabetic and type 2 diabetic subjects, but relation-
ships were left-shifted in type 1 diabetes. For instance,
triglyceride levels and BMI correlated with insulin resis-
tance similarly in both groups (Fig. 1). In an analysis of the
whole group, there was no interaction by diabetes for the
relationship of GIR to triglycerides or BMI (P⫽0.966 and
0.734, respectively), but the yintercepts were significantly
different for triglycerides and trended toward significance
for BMI (P⫽0.002 and 0.156, respectively). Similar
relationships were found for waist circumference, visceral
fat area, and total body fat (not shown).
Impaired insulin-mediated NEFA suppression. Insu-
lin-mediated serum NEFA suppression was also impaired
in type 1 diabetic subjects (Fig. 2, top panel). The increase
in serum NEFA levels during the first clamp stage in
diabetic subjects reflected an initial insulin infusion rate
(4mU/m
2
/min) that was lower than their basal require-
ment. The second stage insulin infusion rate (8mU/m
2
/
min) was sufficient to lower glucose and NEFA levels in all
type 1 diabetic subjects. Despite this, NEFA levels re-
mained significantly higher in type 1 diabetic than nondi-
abetic subjects at the end of the second clamp stage (least
squares mean ⫾SE: 370 ⫾27 vs. 185 ⫾25 mol/l, P⬍
0.0001) after adjustment for age, sex, BMI, fasting glucose,
and time point insulin. The highest insulin infusion rate
(40mU/m
2
/min), was sufficient to similarly suppress NEFA
levels in type 1 and nondiabetic subjects. NEFA levels at
all clamp stages were strongly inversely correlated with
peripheral glucose uptake (r⫽⫺0.56, P⬍0.0001, r⫽
TABLE 1
Baseline characteristics for clamp study cohort
Type 1 diabetes
(n⫽40)
Controls
(n⫽47) P
Age (years) 45.2 ⫾9.2 45.9 ⫾7.2 0.7
Male/female 19/21 20/27 —
Duration of diabetes (years) 22.6 ⫾7.8 — —
GHb (%) 7.5 ⫾0.9 5.4 ⫾0.3 ⬍0.0001
Fasting glucose (mg/dl) 116 ⫾39 96 ⫾8 0.0024
Body fat (%) 28.6 ⫾7.5 29.6 ⫾7.1 0.53
BMI (kg/m
2
)27.0 ⫾4.4 26.0 ⫾4.1 0.31
Visceral fat area (cm
2
)72.7 ⫾50.5 80.5 ⫾50.5 0.49
Subcutaneous fat area (cm
2
)271 ⫾126 273 ⫾112 0.97
Sagittal diameter (mm) 214 ⫾36 214 ⫾31 0.96
Waist circumference (cm) 88.5 ⫾12.3 86.3 ⫾12.4 0.42
Waist:hip ratio 0.84 ⫾0.08 0.83 ⫾0.09 0.44
Liver:spleen density ratio 1.26 ⫾0.08 1.24 ⫾0.24 0.58
Total cholesterol (mg/dl) 139.9 ⫾32.4 171.1 ⫾28.8 ⬍0.0001
HDL cholesterol (mg/dl) 58.0 ⫾22.4 53.4 ⫾15.0 0.27
Triglycerides (mg/dl) 69.7 ⫾33.8 108.0 ⫾57.2 0.0002
LDL cholesterol (mg/dl) 67.6 ⫾24.8 96.1 ⫾26.0 ⬍0.0001
Systolic blood pressure (mmHg) 114 ⫾11 113 ⫾10 0.61
Diastolic blood pressure (mmHg) 73 ⫾776⫾8 0.09
On hypertension medications (%) 45 13 0.0005
Habitual physical activity (log kcal) 7.2 ⫾1.2 7.4 ⫾7.1 0.42
Adiponection (geometric mean ⫾SD) 10.9 ⫾1.7 8.2 ⫾1.8 0.02
Cortisol (g/dl) 11.8 ⫾4.3 11.5 ⫾4.4 0.8
Values are unadjusted mean ⫾SD except where otherwise indicated.
TABLE 2
Insulin sensitivity by glucose infusion rate from hyperinsulinemic-euglycemic clamp
Type 1
diabetes
Nondiabetic
controls P
Stage 3 clamp parameters
Final glucose (mg/dl) 89.1 ⫾3.2 89.9 ⫾3.4 0.28
Final insulin (U/ml) 106 ⫾35 99 ⫾30 0.36
Glucose infusion rate (mg/kg FFM/min)
Unadjusted (mean ⫾SD) 5.8 ⫾3.5 13.2 ⫾5.7 ⬍0.0001
LS mean ⫾SE, adjusted for age, sex, BMI, fasting glucose,
final glucose and insulin 6.19 ⫾0.72 12.71 ⫾0.66 ⬍0.0001
LS mean ⫾SE, adjusted for age, sex, visceral fat, fasting
glucose, final glucose and insulin 6.24 ⫾0.68 12.75 ⫾0.62 ⬍0.0001
INSULIN RESISTANCE IN TYPE 1 DIABETES
308 DIABETES, VOL. 60, JANUARY 2011 diabetes.diabetesjournals.org
⫺0.63, P⬍0.0001, r⫽⫺0.40, P⫽0.0002 for stage 1, 2, and
3 NEFA levels, respectively).
Insulin-mediated suppression of glycerol followed a
similar pattern to NEFA levels (Fig. 1, bottom panel). The
defect in glycerol suppression in type 1 diabetic subjects
remained significant in the unadjusted data during the
third clamp stage, though this was attenuated at the final
time point by adjustment for age, sex, BMI, fasting insulin,
and time point insulin level.
Impaired peripheral glucose utilization and insulin-
mediated NEFA suppression correlate with coronary
artery calcification. A relationship between insulin re-
sistance and CAC is suggested by a plot of the raw data of
peripheral glucose utilization (GIR) versus CAC volume at
the 6-year visit (Fig. 3). In fact, after adjustment for age,
GIR correlated inversely with CAC volume at the 6-year
CACTI follow-up visit and with an increase in CAC volume
from baseline to the 6-year visit and from 3-year to 6-year
follow-up visits (,P⫽⫺0.42, ⬍0.0001; ⫺0.41, ⬍0.0001;
⫺0.24, ⬍0.028, respectively) (Table 3). In a logistic regres-
sion analysis adjusted for age, sex, BMI, and diabetes
status, lower GIR was independently associated with the
presence of CAC: odds ratio (OR) 0.45, 95% CI (0.22– 0.93)
for a one standard deviation (SD) increase in GIR. Thus,
for every 6.1 mg/kg FFM/min increase in GIR (signifying
greater insulin sensitivity), the odds of having CAC de-
creased by 55%.
The NEFA levels during stage 2 of the clamp were
similarly but positively correlated with CAC volume at the
6-year visit, and CAC increase from baseline to 6-year and
0 100 200 300 400
Triglycerides (mg/dl)
Glucose Infusion Rate
(mg/kgFFM/min)
30
20
10
18 20 22 24 26 28 30 32 34 36 38 40
0
Glucose Infusion Rate
(mg/kgFFM/min)
30
20
10
0
Body Mass Index (kg/m2)
Type 1 diabetes
Non-diabetes
y = -0.037x + 17.14
y = -0.038x + 8.48
y = -0.33x + 14.73
y = -0.41x + 23.87
FIG. 1. Correlation of insulin sensitivity to triglycerides and BMI is retained, but left-shifted, in type 1 diabetes. For the relationship of GIR to
triglycerides (top panel) the regression equation in type 1 diabetes is GIR ⴝ8.478 – 0.038 (triglycerides), Pⴝ0.022; for nondiabetic subjects
GIR ⴝ17.14 – 0.037 (triglycerides), Pⴝ0.011. In combined analysis, Pⴝ0.966 for an interaction by diabetes and Pⴝ0.002 for the difference in
yintercept. For BMI (bottom panel) in type 1 diabetes, GIR ⴝ14.732– 0.33 (BMI), Pⴝ0.008; for nondiabetic subjects, GIR ⴝ23.87–0.41(BMI),
Pⴝ0.05. In combined analysis, Pⴝ0.734 for an interaction by diabetes, Pⴝ0.156 for the difference in the yintercept.
I.E. SCHAUER AND ASSOCIATES
diabetes.diabetesjournals.org DIABETES, VOL. 60, JANUARY 2011 309
from 3-year to 6-year follow-up visit (Table 3). Partial
adjustment for sex and diabetes status attenuated all
correlations somewhat (Table 3), but there was no signif-
icant interaction with diabetes status. In a logistic regres-
sion analysis adjusted for age, sex, BMI, and diabetes
status, higher stage 2 NEFA levels (insulin resistance),
were independently associated with the presence of CAC,
OR 2.40, 95% CI (1.08 –5.32) for one SD difference in NEFA
level. Thus, for every 186 mol/l increase in stage 2 NEFA
(signifying insulin resistance), the odds of having CAC
increased by 140%. These one SD differences in measures
of insulin action are comparable to the differences seen
between type 1 diabetic and nondiabetic subjects in this
study. In other logistic regression models LDL and HDL
levels did not have a significant effect on the odds ratio for
0100 200 300 400
0
10
20
30
40
50
60
70
80
90
100
110
*
**
†
*
clamp time (minutes)
Glycerol (
µ
M)
0
100
200
300
400
500
600
700
800
900
**
**
NEFA (
µ
M)
Preclamp 4mU/m2/min 40mU/m
22
/min
/min8mU/m
FIG. 2. Insulin-mediated NEFA and glycerol suppression are impaired
in type 1 diabetic subjects. NEFA and glycerol values are M. Data are
least squares mean ⴞSE (adjusted for age, sex, BMI, fasting glucose,
and time point insulin). f, type 1 diabetic subjects; Œ, nondiabetic
controls; *P<0.0001, †P<0.05.
CAC Volume,
square root transformed
30
30
20
20
10
10
0
0
Glucose Infusion Rate (mg/kgFFM/min)
Type 1 diabetes
Non-diabetes
FIG. 3. Plot of raw data for CAC volume at 6-year follow-up visit as a function of GIR (nⴝ87). Open (white) squares ⴝnondiabetic subjects,
black triangles ⴝtype 1 diabetic subjects.
0
1
2
3
4
5
6
7
8
<6.8 6.8-7.6 >7.6-8.1 >8.1
Hemoglobin A1c quartile/range (%)
Glucose Infusion Rate (mg/kgFFM/min)
P=0.89
FIG. 4. Insulin resistance does not correlate with poor glycemic
control. Insulin sensitivity is expressed as glucose infusion rate per
fat-free mass (GIR, mg/kg FFM/min) and shown by quartile of GHb
measured 3 days before the clamp study day. GHb range for each
quartile is shown and nⴝ10 for each quartile. Analysis by ANOVA
yields a Pvalue of 0.89 for differences between quartiles. Pairwise
comparisons are all nonsignificant with Pvalues ranging from 0.49
(2nd and 4th quartiles) to 0.96 (1st and 4th quartiles).
INSULIN RESISTANCE IN TYPE 1 DIABETES
310 DIABETES, VOL. 60, JANUARY 2011 diabetes.diabetesjournals.org
the presence of CAC in the whole cohort, nor did insulin
dose in logistic regression analysis of the diabetic group
(not shown).
The above analyses were also performed on the diabetic
and nondiabetic groups separately. Similar correlation
coefficients resulted from these individual analyses. How-
ever, statistical significance was lost for the 3-year to
6-year visit change and otherwise attenuated to near
statistical significance in the diabetic group only (Table 3,
models 3 and 4). Similarly, logistic regression analysis of
the odds ratio for the presence of CAC at the 6-year
follow-up visit associated with a change in GIR was
analyzed separately for diabetic and nondiabetic subjects.
Odds ratios for the two groups were very similar, but
statistical significance was lost for the diabetic group (e.g.,
for GIR: OR ⫽0.38 [95% CI: 0.11–1.37] and 0.40 [95% CI:
0.18 – 0.90], respectively).
Insulin resistance does not correlate with poor gly-
cemic control. Neither peripheral glucose uptake nor
insulin-mediated NEFA suppression correlated with mea-
sures of current or recent glycemic control in this study
(Table 4). Pearson coefficients revealed no significant
correlation between GIR or stage 2 NEFA levels and GHb
or CGM measures of mean glucose, percentage of values
⬎180 mg/dl, percentage of values ⬍70 mg/dl, and overall
SD (glycemic variability).
In addition, GIR and stage 2 NEFA did not differ by GHb
quartile (ANOVA, P⫽0.89; Fig. 4 and data not shown)
over the range of GHb values represented by this cohort
(⬍9.5%). BMI also did not differ by GHb quartiles (26.4,
28.1, 27.4, and 26.0 kg/m
2
, ANOVA, P⫽0.27), excluding the
possibility that glycemia-related changes in insulin sensi-
tivity were countered by glycemia-related changes in BMI.
DISCUSSION
Our study demonstrates significant insulin resistance in
subjects with type 1 diabetes relative to nondiabetic
controls of similar age, BMI, and habitual physical activity
levels. Importantly, type 1 diabetic patients were pro-
foundly insulin resistant despite similar overall adiposity,
body fat composition, and HDL cholesterol levels, and,
paradoxically, lower fasting triglycerides and higher adi-
ponectin levels. These findings, consistent with previous
smaller studies (28,29,31), suggest a novel component in
the etiology of insulin resistance in type 1 diabetes. Our
type 1 diabetic cohort exhibits the expected relationship
between insulin resistance and BMI, waist circumference,
and triglycerides. However, at any given value, the type 1
diabetic subjects are likely to be more insulin resistant
than nondiabetic subjects, thus effectively left-shifting the
relationship between insulin sensitivity and insulin resis-
tance-associated clinical parameters in type 1 diabetes.
Beyond hyperglycemia and the chronic exogenous periph-
eral hyperinsulinemia of type 1 diabetes, plausible expla-
nations for this “left shift” are lacking.
We have found that in these relatively well controlled
type 1 diabetic patients with mean GHb 7.5% ⫾0.9%, the
TABLE 3
Correlation of insulin resistance to CAC volume; OR of any CAC at 6-year visit associated with insulin resistance by GIR and by failure
of NEFA suppression in clamp stage 2
Model
Spearman correlation coefficient: glucose infusion rate vs. CAC volume
for total clamp cohort (n⫽87)
6-year visit CAC
volume
Baseline to 6-year
visit change
in CAC
3-year to 6-year
visit change
in CAC
1: Adjusted for age
GIR (mg/kg FFM/min) ⫺0.42 (P⬍0.0001) ⫺0.41 (P⬍0.0001) ⫺0.24 (P⫽0.028)
Stage 2 NEFA level (M) 0.41 (P⬍0.0001) 0.40 (P⫽0.0001) 0.27 (P⫽0.01)
2: Adjusted for age, sex, diabetes status
GIR (mg/kg FFM/min) ⫺0.31 (P⫽0.005) ⫺0.30 (P⫽0.006) ⫺0.19 (P⫽0.08)
Stage 2 NEFA level (M) 0.32 (P⫽0.003) 0.30 (P⫽0.005) 0.24 (P⫽0.03)
3: Adjusted for age: type 1 diabetic group alone
GIR (mg/kg FFM/min) ⫺0.28 (P⫽0.08) ⫺0.28 (P⫽0.09) NS
Stage 2 NEFA level (M) 0.31 (P⫽0.06) 0.28 (P⫽0.08) NS
4: Adjusted for age: nondiabetic group alone
GIR (mg/kg FFM/min) ⫺0.39 (P⫽0.006) ⫺0.41 (P⫽0.004) NS
Stage 2 NEFA level (M) 0.39 (P⫽0.007) 0.39 (P⫽0.008) NS
Logistic regression analysis: odds ratio for any CAC at 6-year visit with
increase in insulin resistance (n⫽87)
Mean ⫾SD OR per 1 SD change (95% CI) (P)
GIR (mg/kg FFM/min) 9.8 ⫾6.1 0.45 (0.22–0.93) (0.03)
Stage 2 NEFA (M) 268 ⫾186 2.40 (1.08–5.32) (0.032)
TABLE 4
Insulin resistance does not correlate with recent glycemic con-
trol. Pearson correlation coefficients are shown for correlation of
GIR and stage 2 NEFA levels to GHb and CGM measures of
glycemic control for all type 1 diabetic subjects
Mean ⫾SD
Correlation
to GIR
Correlation
to stage 2
NEFA
rPrP
GHb (%) 7.5 ⫾0.9 0.13 0.5 ⫺0.02 0.92
CGM parameters
Mean glucose (mg/dl) 141 ⫾26 0.2 0.26 ⫺0.16 0.37
%⬎180 mg/dl 26.2 ⫾12.9 0.25 0.14 ⫺0.15 0.40
%⬍70 mg/dl 16.2 ⫾11.9 ⫺0.02 0.91 ⫺0.04 0.84
Overall SD 65.9 ⫾20.1 0.21 0.22 ⫺0.16 0.35
I.E. SCHAUER AND ASSOCIATES
diabetes.diabetesjournals.org DIABETES, VOL. 60, JANUARY 2011 311
degree of insulin resistance was not explained by recent
glycemic control defined by GHb and CGM measures.
Since GHb was measured at four points during the study
and remained consistent, it also does not seem likely that
periods of loss of control are responsible for the insulin
resistance measured in this study. These data do challenge
the general belief that poor glycemic control is the entire
etiology of insulin resistance in type 1 diabetes. However,
our data do not explicitly contradict existing data. In the
Pittsburgh Epidemiology of Diabetes Study, where GHb
was found to be a significant predictor of insulin resis-
tance, GHb ⬍9.5–10 defined the low risk group, and a GHb
difference representing the full range of our diabetic
subjects would result in only a 1 mg/kg/min change in GIR
(27). Early studies demonstrating improved GIR with
glycemic control involved improvement from very poor
control. Our entire diabetic clamp cohort was controlled
to the level of the improved GHb in these previous reports.
Finally, in the important 1986 study of Yki-Jarvinen,
(46,47) the authors found improved insulin sensitivity with
initiation of treatment and improved glycemic control in
newly diagnosed type 1 diabetes. However, the scatter
plots in this report indicate that improved mean GIR was
largely driven by subjects who no longer needed insulin at
the 3-month and 1-year follow-ups. Including only the
insulin-dependent subjects at these visits appears to yield
a remarkably consistent 40% reduction in GIR across GHb
values from 8.8 to 13% and durations of diabetes from 2
weeks to 20 years. These results are more consistent with
some aspect of nonphysiologic insulin delivery rather than
glycemic control as the etiology of insulin resistance. Our
data do not, however, rule out the possibility that very
poor glycemic control may contribute to insulin
resistance.
We also make the novel observation that insulin resis-
tance in type 1 diabetic subjects also included impaired
insulin-mediated suppression of serum NEFA and glycerol
levels. The parallel lack of suppression of NEFA and
glycerol levels suggests that lipolysis was the source of
most of the NEFA measured. However, the patterns were
not identical, suggesting that regulation of re-esterification
may also be altered between groups.
Insulin resistance has been strongly implicated as a
cardiovascular risk factor in the general population (17–
25). Our demonstration of insulin resistance in type1
diabetic subjects in terms of both glucose and NEFA
metabolism suggests that insulin resistance may also
contribute to increased CVD risk in type 1 diabetes. The
Pittsburgh Epidemiology of Diabetes Study demonstrated
a correlation between estimated insulin resistance and
coronary artery disease (10). In support of this finding, we
report significant associations between directly measured
insulin resistance and both presence and progression of
CAC in our full cohort, as well as in our nondiabetic
subgroup. This is a novel finding, representing the only
existing demonstration of a correlation between a direct
measure of insulin sensitivity by modern validated meth-
odology and an accepted measure of CVD. The finding of
statistical significance with this small study group is
surprising and supports the hypothesis that insulin resis-
tance is an important factor in CAC development. Though
we failed to achieve statistical significance to the level of
P⬍0.05 in the diabetic group, the near significance (P⫽
0.06 – 0.09) for the correlation of GIR and NEFA suppres-
sion to CAC volume at 6 years and to CAC progression
from 0 to 6 years suggests that the relationship between
insulin resistance and CAC is also present in type 1
diabetes. Furthermore, the striking similarity in correla-
tion coefficients between the diabetic and nondiabetic
populations in the separate analyses suggests that the
slope of the relationship between insulin resistance and
CAC is similar in subjects with and without type 1 diabe-
tes. The failure to achieve statistical significance in the
diabetic group is a limitation of this study. Because of the
near significance and the similarity in correlation coeffi-
cients and odds ratios to the statistically significant rela-
tionship found in the nondiabetic group, we believe this is
most likely a power issue related to the smaller range in
insulin resistance values and the smaller subject number.
However, we cannot rule out the possibility that the
association between insulin resistance and CAC is weaker
in individuals with type 1 diabetes than those without
diabetes.
Because of the left-shift in the relationship between GIR
and metabolic syndrome criteria described above, type 1
diabetic subjects in our study had a phenotype less con-
sistent with “metabolic syndrome” than nondiabetic con-
trols, despite being more insulin resistant. For instance,
type 1 diabetic subjects had a healthier lipid profile than
nondiabetic controls, even after adjustment for statin use.
Similarly, hypertension treatment was more common
among type 1 diabetic subjects, but overall blood pressure
was not higher, even after adjustment for medication use.
Body composition did not differ between type 1 diabetic
and nondiabetic subjects. Thus, standard characteristics
that predict insulin resistance in the general population
were not present in our insulin-resistant type 1 diabetic
patients. This is consistent with a previous report that
standard prediction models of CVD risk (highly dependent
on hypertension and lipids) do not accurately predict CVD
risk in the type 1 diabetic population (16). We also find that
about half the CVD risk in our type 1 diabetic cohort is not
predicted by standard CVD risk prediction models (J.K.S.-
B., I.E.S., B.C.B., D.M.M., R.H.E., M.R., unpublished obser-
vations). We have reported differences in lipoprotein
cholesterol subfraction distribution in this clamp group
that may explain some of this excess risk (48). Unfortu-
nately, this suggests that traditional approaches to control
these risk factors may be inadequate to slow premature
formation of coronary atherosclerosis in type 1 diabetes.
The main limitations of this study are the single time
point measurement of insulin action, the late study time of
this measurement, and the lack of power to reach signifi-
cance in the type 1 diabetic cohort. We assumed that the
single point measure of insulin resistance reflected a
parameter that has been fairly constant over the time of
the CACTI follow-up. Supporting this assumption, CACTI
follow-up has demonstrated essentially stable weight, gly-
cemic control, and lifestyle over the 6-year study span, but
a prospective design with repeat clamp studies would be
optimal. We are developing an insulin sensitivity predic-
tion model based on this study, and will apply this model
to baseline and follow-up data to allow a prospective
analysis of the correlation of estimated insulin resistance
with CAC and to increase the power of the analysis in the
diabetic cohort.
Conclusions. In summary, we found that profound insulin
resistance in type 1 diabetes extends beyond glucose
control to regulation of fat metabolism, may be associated
with increased coronary atherosclerosis, and cannot be
easily identified using standard clinical predictors, includ-
ing poor glycemic control. These findings suggest that
INSULIN RESISTANCE IN TYPE 1 DIABETES
312 DIABETES, VOL. 60, JANUARY 2011 diabetes.diabetesjournals.org
insulin resistance, possibly through effects on overall
NEFA exposure and lipotoxicity, may play a role in the
residual risk of CVD in type 1 diabetes, as well as in the
absence of diabetes, and thus may represent an important
therapeutic target that is not currently considered in type
1 diabetes.
ACKNOWLEDGMENTS
This study was supported in part by National Institutes of
Health National Heart, Lung, and Blood Institute grants
R01 HL-61753 and R01 HL-079611, and Diabetes Endocri-
nology Research Center Clinical Investigation Core P30
DK-57516. I.E.S. was supported by the National Institutes
of Health Office of Research in Women’s Health, Building
Interdisciplinary Research Careers in Women’s Health,
BIRCWH K12 Program. J.K.S.-B. was supported by an
American Diabetes Association Takeda postdoctoral fel-
lowship 7-09-CVD-06.
This study was performed at the Adult General Clinical
Research Center (now the Clinical Translational Research
Center) at the University of Colorado Denver Anschutz
Medical Center supported by the National Institutes of
Health M01 RR-000051, at the Barbara Davis Center for
Childhood Diabetes in Denver, Colorado, and at the Col-
orado Heart Imaging Center in Denver, Colorado.
No potential conflicts of interest relevant to this article
were reported.
I.E.S. contributed to data collection and analysis and
wrote the manuscript. J.K.S.-B. contributed to data collec-
tion, performed statistical analyses, participated in discus-
sion, and reviewed and edited the manuscript. B.C.B.
contributed to study design and data collection and re-
viewed and edited the manuscript. D.M. contributed to
data collection, participated in discussion, and reviewed
and edited the manuscript. A.K. contributed to data col-
lection. R.H.E. and M.R. contributed to study design,
participated in discussion, and reviewed and edited the
manuscript.
Portions of these data have previously been reported in
abstract/poster form at the 68th and 69th Scientific Ses-
sions of the American Diabetes Association (June 7, 2008
and June 6, 2009) and the 2008 annual National Institutes
of Health—Building Interdisciplinary Careers in Women’s
Health meeting (November 16 –17, 2009).
The authors would like to thank Jane E.-B. Reusch, MD,
in the Division of Endocrinology, Diabetes, and Metabo-
lism at the University of Colorado Denver for assistance
with manuscript preparation.
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