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Original Studies |
Hypertension-Endocrine Branch and Division of Epidemiology and Clinical Applications, National Heart, Lung, and Blood Institute, National Institutes of Health (D.A.F.), Bethesda, Maryland 20892; and Division of Endocrinology and Metabolism, Indiana University School of Medicine (K.M., A.D.B.), Indianapolis, Indiana 46202
Address all correspondence and requests for reprints to: Michael J. Quon, M.D., Ph.D., Hypertension-Endocrine Branch, National Heart, Lung, and Blood Institute, National Institutes of Health, Building 10, Room 8C-218, 10 Center Drive, MSC 1755, Bethesda, Maryland 20892-1755. E-mail: quonm{at}nih.gov
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Abstract |
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 Top Abstract IntroductionSubjects and MethodsResultsDiscussionReferences |
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Introduction |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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The hyperinsulinemic euglycemic glucose clamp technique is the "gold
standard" for quantifying insulin sensitivity in vivo
because it directly measures the effects of insulin to promote glucose
utilization under steady state conditions (7, 8). However, the glucose
clamp is not easily applied in large scale investigations because iv
infusion of insulin, frequent blood samples over a 3-h period, and
continuous adjustment of a glucose infusion are required for each
subject studied. A well accepted alternative for estimating insulin
sensitivity involves minimal model analysis of a frequently sampled iv
glucose tolerance test (FSIVGTT) (9, 10, 11). Although this approach is
less labor intensive than the glucose clamp, the FSIVGTT is still not
ideal for large studies because it requires obtaining approximately 30
blood samples over 3 h. Furthermore, although the minimal model
index of insulin sensitivity (SIMM) generally
correlates with glucose clamp measurements (Table 1
), identification of
SIMM in subjects with impaired insulin secretion
(e.g. patients with diabetes) is often problematic (12).
Moreover, recent studies have demonstrated systematic errors in minimal
model estimates of glucose effectiveness and insulin sensitivity that
may be due to oversimplified model representations of physiology
(13, 14, 15).
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Subjects and Methods |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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Subjects
Our study included 28 nonobese, 13 obese, and 15 diabetic
subjects whose clinical characteristics are listed in Table 2. Among these subjects were 38
Caucasians, 11 African-Americans, 5 Asians, and 2 Hispanics. Nonobese
subjects were defined as having a body mass index (BMI) less than 30
kg/m2, whereas subjects with a BMI of 30 or more
were considered obese. Diabetic subjects met the American Diabetes
Association criteria for type 2 diabetes (16). Subjects with liver or
pulmonary disease as well as end-organ damage, such as renal
insufficiency, coronary artery disease, heart failure, peripheral
vascular disease, proliferative retinopathy, or diabetic neuropathy,
were excluded from our study. In addition, diabetic patients whose
fasting blood glucose exceeded 300 mg/dL while not taking medication
were excluded from our study. We also obtained an independent dataset
from the Division of Endocrinology and Metabolism at Indiana University
School of Medicine. This comprised glucose clamp data obtained from 21
obese subjects (BMI, 37.3 ± 1.1; age, 35 ± 2 yr) and 14
nonobese subjects (BMI, 24.6 ± 0.9; age, 36 ± 2 yr) using
an insulin infusion rate of 120 mU/m2·min.
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At approximately 0800 h, after an overnight fast of at
least 10 h, subjects were admitted as out-patients to the Clinical
Center at NIH and placed in a recumbent position in an adjustable bed.
An iv catheter was placed in an antecubital vein for infusion of
insulin, glucose, and potassium phosphate. Another catheter was placed
in the contralateral hand for blood sampling. The hand used for
sampling was warmed with a heating pad to arterialize the blood. An
insulin solution (regular Humulin, Eli Lilly & Co.,
Indianapolis, IN) was prepared with normal saline at a concentration
ranging from 0.8–1.2 U/mL. The insulin solution was allowed to dwell
in the iv lines for at least 15 min, and the lines were then flushed
before the beginning of the insulin infusion. Insulin was infused at
120 mU/m2·min for 4 h using a calibrated
syringe pump (model A-99, Razel Industries, Stamford, CT). A solution
of potassium phosphate was infused at the same time (0.23 mEq/kg·h)
to prevent hypokalemia. Blood glucose concentrations were measured at
the bedside every 5–10 min using a glucose analyzer (YSI 2700 Select,
YSI, Inc., Yellow Springs, OH), and an infusion of 20%
dextrose was adjusted to maintain the blood glucose concentration at
the fasting level. Blood samples were also collected every 20–30 min
for determination of plasma insulin concentrations (IMX assay,
Abbott Laboratories, North Chicago, IL). The steady state
period of the clamp was defined as a 60-min or longer period (at least
1 h after the beginning of the insulin infusion) during which the
coefficient of variations for blood glucose, plasma insulin, and
glucose infusion rate were less than 5%. The glucose clamp-derived
index of insulin sensitivity (SIClamp) was
defined as M/(G x 
I) corrected for body weight (where M is the
steady state glucose infusion rate (milligrams per min), G is the
steady state blood glucose concentrations (milligrams per dL), and I
is the difference between basal and steady state plasma insulin
concentrations (microunits per mL)).
FSIVGTT and minimal model analysis
At approximately 0800 h, after an overnight fast of at least 10 h, subjects were admitted as out-patients to the Clinical Center at NIH and placed in a recumbent position in an adjustable bed. Intravenous catheters were placed in the antecubital vein of each arm. An insulin- modified FSIVGTT was performed as described previously (17). Briefly, a bolus of glucose (0.3 g/kg) was infused iv over 2 min. Twenty minutes after initiation of the glucose bolus, an iv infusion of insulin (4 mU/kg·min regular Humulin) was given for 5 min. Blood samples were collected for blood glucose and plasma insulin determinations as previously described (17). Data were subjected to minimal model analysis using the computer program MINMOD (gift from R. N. Bergman) to generate predictions of glucose disappearance and insulin sensitivity (SIMM) (10).
QUICKI
We performed a sensitivity analysis of glucose and insulin data from the glucose clamp and the first 20 min of the FSIVGTT of an initial subset of 14 normal, 5 obese, and 3 diabetic subjects to determine the time points that contained the most critical information related to insulin sensitivity as defined by SIClamp. We found that changes in fasting insulin and glucose levels were the most related to changes in SIClamp. We subjected the fasting data to various transformations and ultimately defined QUICKI = 1/[log(I0) + log(G0)], where I0 is the fasting insulin, and G0 is the fasting glucose. After QUICKI was derived from the initial subset of data, comparisons between QUICKI and the other indexes of insulin sensitivity were performed on the entire set of 28 nonobese, 13 obese, and 15 diabetic subjects. We also calculated QUICKI for the 21 obese and 14 nonobese subjects from Indiana University School of Medicine.
Statistical analysis
Student’s t tests were used to compare differences between various parameters when appropriate. Correlations (r) between pairs of indexes of insulin sensitivity were calculated. To evaluate the significance of differences in r values for various pairs of indexes, a percentile method bootstrap technique was used to calculate P values (18). The bootstrap was necessary because the r values were based on the same subjects, and thus, pairs of r values are not statistically independent. P < 0.05 was considered to indicate statistical significance.
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Results |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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Mean BMI, fasting glucose, and fasting insulin values were
calculated for each group of subjects (Table 2
). Both obese and
diabetic subjects had significantly greater BMIs and fasting insulin
levels than the nonobese subjects (P < 6 x
10-6), consistent with the
presence of obesity and insulin resistance. As expected, the fasting
glucose levels for both nonobese and obese groups were normal, whereas
the diabetic group had elevated levels.
Glucose clamp studies
To determine the insulin sensitivity of each subject using the
gold standard method, hyperinsulinemic isoglycemic glucose clamps were
performed using an insulin infusion rate of 120
mU/m2·min (Fig. 1). Steady state conditions were
generally achieved about 2 h after the initiation of each study
and were maintained for at least 60 min. During the steady state
period, the mean blood glucose levels were 85 ± 2 mg/dL for
nonobese subjects, 86 ± 3 for obese subjects, and 158 ± 15
for diabetic subjects. The steady state plasma insulin levels were
272 ± 24, 334 ± 22, and 286 ± 19 µU/mL for
nonobese, obese, and diabetic subjects, respectively, while the glucose
infusion rates were 870 ± 50 (nonobese subjects), 802 ± 64
(obese subjects), and 900 ± 94 mg/min (diabetic subjects). The
mean values for SIClamp calculated from these
data were 6.19 ± 0.43 (nonobese subjects), 2.94 ± 0.42
(obese subjects), and 2.39 ± 0.26 (diabetic subjects). Thus, as
expected, the obese and diabetic subjects were significantly more
insulin resistant than the nonobese subjects (P <
2 x 10-5). For nine
nonobese subjects, the glucose clamp studies were repeated using a
lower insulin infusion rate (40 mU/m2·min) that
gave a mean steady state blood glucose level of 82 ± 2 mg/dL, a
mean plasma insulin level of 72 ± 3 µU/mL, and a mean glucose
infusion rate of 621 ± 81 mg/min. The correlation between glucose
clamps with high and low insulin infusion rates for these nine subjects
was very good (r = 0.69; P < 0.04).
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To calculate an alternative insulin sensitivity index for each
subject based on minimal model analysis, insulin- modified FSIVGTTs
were performed (Fig. 2). Both nonobese
and obese subjects had normal basal glucose levels (83 ± 2 and
88 ± 2 mg/dL, respectively). The basal glucose levels in the
diabetic group were significantly elevated compared with those in the
other groups (166 ± 15 mg/dL; P < 4 x
10-5). The basal insulin
levels were 7 ± 1, 17 ± 3, and 15 ± 2 µU/mL for the
nonobese, obese, and diabetic groups, respectively. The basal insulin
levels in both the obese and diabetic groups were significantly higher
than those in the normal group (P < 0.02). In
addition, endogenous insulin secretion (0–20 min) in response to the
iv glucose bolus in obese subjects was greater than that in nonobese
subjects (mean insulin peak, 157 ± 34 vs. 100 ±
14 µU/mL; P < 0.04), whereas the insulin response
was markedly diminished in the diabetic subjects. When glucose and
insulin data from the FSIVGTT were analyzed using the MINMOD program,
minimal model predictions of glucose disappearance fit well with the
actual glucose disappearance data (Fig. 2). The minimal model index of
insulin sensitivity (SIMM) was 5.3 ± 0.6
for nonobese subjects, 3.5 ± 1.2 for obese subjects, and 4.8
± 1.0 for diabetic subjects. Note that for 7 of the 15 diabetic
subjects, minimal model analysis generated large negative values for
SIMM (implying that rises in insulin somehow
cause glucose levels to increase in these subjects). This is a well
documented artifact of the minimal model that occurs when data from
subjects with poor insulin secretion are analyzed (12). Therefore, the
minimal model results for these 7 diabetic subjects were excluded from
our analyses.
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To derive a novel index of insulin sensitivity, we analyzed data obtained from an initial subset of studies in 14 nonobese, 5 obese, and 3 diabetic subjects. We used a sensitivity analysis to determine which data points from the first 20 min of the FSIVGTT contained the most information about insulin sensitivity as determined by SIClamp. For nonobese and obese subjects, we discovered that fasting insulin levels correlated well with SIClamp. Moreover, because fasting insulin levels had a skewed distribution, log transformation of these data was even more highly correlated with SIClamp. This result is consistent with the reasoning that fasted nondiabetic subjects are in a steady state in which normal glucose levels are maintained by appropriately adjusting insulin levels to match the degree of insulin sensitivity. However, this relationship between fasting insulin and SIClamp is not maintained for diabetic subjects who have fasting hyperglycemia and are unable to appropriately secrete insulin to fully compensate for their insulin resistance. Interestingly, we found that the product of fasting insulin and glucose yielded an index of insulin sensitivity that was applicable to both diabetic and nondiabetic subjects. To obtain a positive correlation with SIClamp and transform the data further, we took the reciprocal of this product. Thus, we defined the QUICKI as: QUICKI = 1/[(log(I0) + log(G0)], where I0 is the fasting plasma insulin level (microunits per mL), and G0 is the fasting blood glucose level (milligrams per dL). Subsequent to our initial sensitivity analysis of the first subset of subjects, as described above, QUICKI was calculated for all study subjects (mean, 0.382 ± 0.007, 0.331 ± 0.010, and 0.304 ± 0.007 for nonobese, obese, and diabetic subjects, respectively).
Correlations between indexes of insulin sensitivity
We first compared our glucose clamp-derived estimates of insulin
sensitivity with those obtained from minimal model analysis (Fig. 3). The overall correlation coefficient
(r) calculated from a linear least squares regression was 0.57
(P < 2 x
10-5). When each group was
analyzed separately, we found that r = 0.48 for nonobese subjects
(P < 0.01), r = 0.82 for obese subjects
(P < 6 x
10-4), and r = 0.51
for diabetic subjects (P < 0.2). In addition, linear
regression analysis for the subgroups showed regression lines that were
parallel to the overall regression line but shifted up for the nonobese
subgroup and shifted down for the obese and diabetic subgroups.
However, the parallel relationship between regression lines may not be
significant because of the large variability observed in the nonobese
group. As expected, correlations between our glucose clamp and FSIVGTT
studies gave results comparable to those of previously published
studies (c.f. Table 1).
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When QUICKI was calculated using the average data from two fasting
blood samples obtained at -10 and 0 min of the glucose clamp study
(i.e. before the administration of insulin) we found that
QUICKI and SIClamp were highly correlated for all
subjects (r = 0.78; P < 2 x
10-12). Similar results
were obtained when QUICKI was derived from a single blood sample
(r = 0.71; P < 6 x
10-10). Note that in
contrast to the minimal model-derived SIMM, we
were able to calculate meaningful values of QUICKI for all diabetic
subjects studied. More importantly, the overall correlation between
QUICKI and SIClamp was significantly better than
that between SIMM and
SIClamp (P < 0.028). However, we
could only calculate the P value for the differences between
these two correlations for the 49 subjects for whom we had complete
information on all three methods. In this case, r = 0.74 for the
comparison between QUICKI and SIClamp. As r
= 0.78 when all 56 subjects are included for this comparison, the
significance of the difference between correlations of QUICKI with
SIClamp and of SIMM with
SIClamp is even greater than the value we
calculated. We also assessed the interassay reproducibility of QUICKI
by comparing SIClamp to QUICKI derived from
baseline blood samples obtained during the FSIVGTT (performed at least
1 week apart from the glucose clamp). Reassuringly, in these
independent studies, we found that the overall correlation between
QUICKI and SIClamp was similar to that in our
original analysis (r = 0.77; P < 2 x
10-11). When correlations
between QUICKI and SIClamp were calculated for
each group separately, we found that r = 0.49 for nonobese
subjects (P < 0.01), r = 0.89 for obese subjects
(P < 2 x
10-5), and r = 0.7
for diabetic subjects (P < 4 x
10-3). The linear
regression lines for the subgroup analysis followed the overall
regression more closely than when SIClamp was
compared with SIMM. When QUICKI and
SIClamp were compared for the nine nonobese
subjects who were studied at the lower insulin infusion rate, r =
0.30. To validate QUICKI with a completely independent dataset, we also
correlated QUICKI with SIClamp using data from
Indiana University School of Medicine. This analysis yielded r =
0.74 for obese subjects (n = 21) and r = 0.91 for nonobese
subjects (n = 14), with an overall correlation coefficient of
r = 0.90 (n = 35). Interestingly, among these nonobese
subjects, one individual had an extremely low BMI of 19.3
kg/m2 with a fasting insulin level of 1.05
µU/mL. If the data from this individual were excluded from the
analysis, the overall correlation between QUICKI and
SIClamp would be r = 0.77 (n =
34).
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). When
correlations between QUICKI and SIMM were
calculated for each group separately, we found that r = 0.36 for
nonobese subjects (P < 0.07), r = 0.75 for obese
subjects (P < 4
x10-3), and r = 0.67
for diabetic subjects (P < 0.07). However, linear
regression analysis for the subgroups revealed that the regression line
for the diabetic and obese subgroups had a very different slope than
that for the nonobese group. Because QUICKI bears some similarities to
a previously reported insulin sensitivity index derived from the
so-called homeostasis model assessment (HOMA) (19, 20) we also compared
SIClamp, SIMM, and QUICKI
with HOMA (calculated as (fasting insulin x fasting
glucose)/22.5) (Table 3). Interestingly,
although HOMA was highly correlated with QUICKI, the overall
correlation between the gold standard SIClamp and
QUICKI was still significantly better than the overall correlation
between SIClamp and HOMA (P <
0.004). As HOMA becomes larger with decreased insulin sensitivity, we
compared SIClamp with HOMA to obtain a positive
correlation (Fig. 6). Strikingly, linear
regression analysis of the subgroups showed that the nonobese group had
a regression line with a very steep slope, whereas the obese group had
a more moderate slope, and the diabetic group had a very shallow slope.
These results suggest that HOMA does not vary linearly across wide
ranges of insulin sensitivity and patient groups. Indeed, the
correlation between log [HOMA] and QUICKI was very high (r =
0.98).
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Discussion |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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We performed both hyperinsulinemic isoglycemic glucose clamps and insulin-modified FSIVGTTs in nonobese, obese, and diabetic subjects with a wide range of insulin sensitivities. As expected, when subjects were evaluated with the gold standard glucose clamp method, obese subjects were more insulin resistant, on the average, than nonobese subjects, and diabetics were the most insulin-resistant group. In contrast, when the same subjects underwent FSIVGTT and minimal model analysis, the obese group seemed to have the greatest level of insulin resistance. This is most likely due to the fact that 7 of 15 diabetic subjects had to be excluded from analysis because the minimal model was unable to identify meaningful estimates of insulin sensitivity in these cases. Indeed, these 7 excluded subjects had higher levels of insulin resistance than the other diabetic subjects (as assessed by glucose clamp). The inability of the minimal model to identify meaningful values for SIMM in a large fraction of our diabetic subjects is consistent with the experience of others and is most likely related to well described difficulties in estimating SIMM under conditions of inadequate insulin secretion (12). The overall correlation we obtained between SIClamp and SIMM was comparable to previous reports whose study subjects included diabetics, suggesting that our studies were technically adequate. Nevertheless, the level of correlation obtained between direct measures of insulin sensitivity (i.e. glucose clamp) and indirect measures, such as minimal model analysis, in both the present study and previous studies suggests that investigators should be cautious in applying minimal model analysis of insulin sensitivity to population studies. This is further highlighted by recent studies demonstrating particular inadequacies of the minimal model approach that result in overestimation of glucose effectiveness and underestimation of insulin sensitivity (14, 17).
Although the glucose clamp is considered to be the gold standard method for directly measuring insulin sensitivity in vivo, it can be implemented in a number of different ways. We chose to use a single, relatively high, insulin infusion rate (120 mU/m2·min) because we anticipated that our diabetic and obese subjects would have significant insulin resistance. That is, achieving a high steady state insulin level in these subjects may be required to measure a significant effect of insulin on net glucose disposal. To help ensure that these conditions were also appropriate for nonobese subjects, we repeated studies in nine nonobese subjects using a lower insulin infusion rate (40 mU/m2·min). The good correlation we obtained between studies performed at low and high insulin infusion rates suggests that the higher insulin infusion rate was also appropriate for the nonobese subjects. We decided to clamp glucose levels at the fasting value (isoglycemic clamp) rather than at normal levels (euglycemic clamp) because acute changes in insulin sensitivity related to large changes in glycemia may complicate the interpretation of glucose clamp results. In the case of nonobese and obese subjects who had normal fasting glucose levels, the isoglycemic clamp is equivalent to a euglycemic clamp. Diabetic subjects were taken off of antidiabetic medication for 1 week before each study, and the glucose clamp was performed under isoglycemic conditions to avoid difficulties in interpretation of glucose clamp data that are acquired at levels of glycemia acutely different from fasting levels.
QUICKI
Sensitivity analyses of an initial subset of data (40%) from glucose clamp studies and the first 20 min of the FSIVGTT revealed that fasting steady state values of insulin and glucose contain sufficient information to accurately assess insulin sensitivity. We only explored the first 20 min of the FSIVGTT data because the insulin infusion initiated at 20 min would necessitate the development of a test complicated by iv infusion of insulin. After the QUICKI formula was derived from the initial subset of subjects, we then analyzed our entire study population. Similar to our results from glucose clamp studies (and in contrast to minimal model results), insulin sensitivity as assessed by QUICKI was highest in the nonobese group, intermediate in the obese group, and lowest in the diabetic group. More importantly, the overall correlation between QUICKI and SIClamp was significantly better than that obtained between SIMM and SIClamp. In addition, the linear regression analysis of the subgroups corresponded more closely to the overall regression line when comparing QUICKI and SIClamp. Taken together with the fact that the overall correlation between QUICKI and SIClamp was also better than the correlation between QUICKI and SIMM, our results suggest that QUICKI contains additional independent information about insulin sensitivity that is not captured by the minimal model approach. Furthermore, QUICKI provides a reproducible and robust estimate of insulin sensitivity, because equally strong correlations with SIClamp were obtained when fasting data from either the glucose clamp or FSIVGTT studies were used to calculate QUICKI. In addition, QUICKI derived from the average results of two fasting blood samples (over 10 min) was similar to QUICKI calculated from a single sample. Finally, the good correlation between QUICKI and SIClamp obtained from a completely independent dataset acquired at a different institution provides further validation of the reliability of QUICKI.
Interestingly, when we performed subgroup comparisons between QUICKI and SIClamp, the correlations for the obese and diabetic subjects (r = 0.89 and 0.7, respectively) were similar to the overall correlation. However, the correlation coefficient for the nonobese subgroup was 0.49, and the greatest variability in the correlation between QUICKI and SIClamp was observed among the most insulin-sensitive subjects. There are several potential explanations for the lower correlation we observed within the nonobese subgroup. The most likely explanation for this finding is that variability in insulin determinations due to limitations in assay sensitivity causes larger percentage of errors in QUICKI when insulin levels are lowest (typical of the most insulin-sensitive subjects). Alternatively, periodic oscillations in insulin secretion (both ultradian and 10- to 15-min periods) have been reported in healthy subjects and may also contribute to the weaker correlation in this subgroup (21, 22). Interestingly, these oscillations diminish with impaired glucose tolerance and diabetes (23, 24). Therefore, in our nonobese subjects there may be a sampling error that results in aliasing of the data. However, this effect is unlikely to be occurring in our studies, because fasting samples were obtained at the same time in the morning for each subject, and calculating QUICKI from the average of several blood samples (instead of a single sample) did not significantly affect our correlations. Another possible explanation for the lower correlation between QUICKI and SIClamp in the nonobese subgroup is that the insulin infusion rate used in our glucose clamp studies was inappropriately high for individuals who are very insulin sensitive. Nevertheless, as discussed above, the good correlation between SIClamp derived from high and low insulin infusion rates suggests that our choice of high insulin infusion rate did not introduce significant error into SIClamp estimates in nonobese subjects. However, it is possible that comparison of QUICKI with clamp data obtained with low insulin infusion rates has additional variability, because hepatic glucose production may not be completely suppressed under these conditions.
Previous studies have suggested that fasting insulin per se
may provide a reasonable index of insulin sensitivity that has positive
predictive power with respect to the development of diseases associated
with insulin resistance, such as obesity, hypertension, and diabetes
(25, 26, 27, 28, 29, 30). However, in diabetes, where fasting hyperglycemia is
accompanied by inadequate insulin secretion, this relationship may not
be maintained. To account for this, the so-called HOMA approach uses a
mathematical model to obtain an insulin sensitivity index that is
defined as the product of the fasting plasma insulin and blood glucose
values divided by a constant (19). Several recent studies have
demonstrated that the HOMA approach to estimating insulin sensitivity
is useful in large epidemiological studies (28, 31). Interestingly, our
novel index, QUICKI, is similar to HOMA, except that QUICKI also
transforms the data by taking both the logarithm and the reciprocal of
the glucose-insulin product. One rational for these transformations is
the fact that the distribution of fasting insulin values is skewed.
Thus, transformation of these data might be predicted to generate a
better fit to glucose clamp measurements of insulin sensitivity. As
expected, given the similarities between QUICKI and HOMA, the two
methods correlate well. Nevertheless, the correlation between QUICKI
and SIClamp is significantly better than the
correlation between HOMA and SIClamp.
Furthermore, it is clear that HOMA is not linear over wide ranges of
insulin sensitivity, because the slopes of the linear regression lines
for each subgroup change and generally correlate with the insulin
sensitivity of each subgroup. From inspection of Fig. 6, one might
predict that the relationship between HOMA and QUICKI would be
described by log transformation. Indeed, log [HOMA] correlates very
highly with QUICKI. This suggests that transformation of the data is
beneficial for estimating insulin sensitivity and that QUICKI may be a
more accurate index of insulin sensitivity than HOMA across a broad
range of insulin sensitivities.
Relative merits of QUICKI
Of the three alternatives to the glucose clamp method for estimating insulin sensitivity in vivo that we examined in this study, QUICKI had the best overall linear correlation with the gold standard clamp measurement. In contrast to the multiple frequent blood samples and the lengthy time course required for both the glucose clamp and the minimal model approach, QUICKI can be obtained from a fasting blood sample. In addition, the ability to calculate QUICKI does not depend on a robust insulin secretory capacity, and we were able to use this method to estimate insulin sensitivity for all of our diabetic subjects (as opposed to the minimal model approach). Furthermore, in our study population, QUICKI was more accurate than either SIMM or HOMA and displayed excellent reproducibility. Potential limitations to QUICKI include difficulty in applying it to subjects with type 1 diabetes who lack endogenous insulin secretion. In addition, we were unable to determine whether QUICKI is applicable to subjects with severe diabetes who could not be safely taken off of their antidiabetic medications. Nevertheless, it is also problematic to determine insulin sensitivity in subjects with type 1 diabetes and uncontrolled type 2 diabetes using other methods. Furthermore, the determination of relative insulin sensitivity and resistance in these types of subjects may be of less interest in large epidemiological studies. As QUICKI was derived from an initial subset of subjects (40%) that we then applied to our entire study population, it is possible that the correlation between QUICKI and SIClamp obtained in future studies may by slightly less than that obtained here. However, in analyzing an independent dataset, we observed very similar correlations between QUICKI and SIClamp, strongly suggesting that QUICKI is a robust index of insulin sensitivity.
We conclude that fasting glucose and insulin levels contain sufficient information to accurately assess insulin sensitivity in vivo over a wide range in a diverse population. QUICKI is a novel, simple, accurate, and reproducible method for determining insulin sensitivity in humans that may be a useful tool in large epidemiological investigations that study the role of insulin resistance in the pathophysiology of important public health problems such as obesity, cardiovascular diseases, and diabetes.
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Acknowledgments |
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Received December 2, 1999.
Revised February 7, 2000.
Accepted April 4, 2000.
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References |
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P. U. Freda, W. Shen, S. B. Heymsfield, C. M. Reyes-Vidal, E. B. Geer, J. N. Bruce, and D. Gallagher Lower Visceral and Subcutaneous but Higher Intermuscular Adipose Tissue Depots in Patients with Growth Hormone and Insulin-Like Growth Factor I Excess Due to Acromegaly J. Clin. Endocrinol. Metab., June 1, 2008; 93(6): 2334 - 2343. [Abstract] [Full Text] [PDF]
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C. Beauregard, A. L. Utz, A. E. Schaub, L. Nachtigall, B. M. K. Biller, K. K. Miller, and A. Klibanski Growth Hormone Decreases Visceral Fat and Improves Cardiovascular Risk Markers in Women with Hypopituitarism: A Randomized, Placebo-Controlled Study J. Clin. Endocrinol. Metab., June 1, 2008; 93(6): 2063 - 2071. [Abstract] [Full Text] [PDF]
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T. Reinehr, B. Stoffel-Wagner, and C. L. Roth Retinol-Binding Protein 4 and Its Relation to Insulin Resistance in Obese Children before and after Weight Loss J. Clin. Endocrinol. Metab., June 1, 2008; 93(6): 2287 - 2293. [Abstract] [Full Text] [PDF]
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S Cupisti, N Kajaia, R Dittrich, H Duezenli, M W Beckmann, and A Mueller Body mass index and ovarian function are associated with endocrine and metabolic abnormalities in women with hyperandrogenic syndrome Eur. J. Endocrinol., May 1, 2008; 158(5): 711 - 719. [Abstract] [Full Text] [PDF]
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S. M. Rossner, M. Neovius, S. M. Montgomery, C. Marcus, and S. Norgren Alternative Methods of Insulin Sensitivity Assessment in Obese Children and Adolescents Diabetes Care, April 1, 2008; 31(4): 802 - 804. [Abstract] [Full Text] [PDF]
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), plasma insulin concentration (
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