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Original Studies |
Division of Endocrinology, Diabetes, and Hypertension (K.C.C., C.Y.), Department of Medicine, University of California–Los Angeles, School of Medicine, Los Angeles, California 90095-7097; and Department of Internal Medicine and Graduate Institute of Clinical Medicine (L.-M.C.), National Taiwan University Hospital, 10016 Taipei, Taiwan
Address all correspondence and requests for reprints to: Ken C. Chiu, M.D., F.A.C.E., 675 Charles E. Young Drive South, 4629 MacDonald Research Laboratories, Los Angeles, California 90095-7097. E-mail: kchiu{at}mednet.ucla.edu
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Abstract |
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Among the ethnic groups, differences were noted in the measured insulin sensitivity (P = 0.0006) and ß cell function (P = 0.006 for the first phase insulin response, P = 0.0002 for the second phase insulin response). Although the estimated indices correlated with the measured indices (r2 = 0.5184–0.3014), the estimated indices barely detected the differences among the ethnic groups. Multivariate analysis confirmed that ethnicity had an independent impact for the measured indices, but had only a modest impact on the estimated insulin sensitivity indices and had no impact on the estimated indices of ß cell function.
We conclude that although the estimated indices of insulin sensitivity and ß cell function from the oral glucose tolerance test correlated with the measured ones in a wide spectrum of healthy, glucose-tolerant, and normotensive subjects, they were much less likely to detect the differences than measured ones among the ethnic groups.
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Introduction |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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The oral glucose tolerance test (OGTT) was originally developed as a research tool and was subsequently adapted and standardized as a diagnostic tool for diabetes (12). It would be ideal and time efficient if one could classify the state of glucose tolerance and estimate insulin sensitivity and ß cell function from this single test. In addition to numerous attempts in the past, two groups recently presented their equations based on relatively large-scale studies (13, 14). Matsuda and DeFronzo (13) developed an estimated insulin sensitivity index (ISIM) obtained from the OGTT through comparison with the euglycemic clamp. Stumvoll et al. (14) used the results of the OGTT to calculate an estimated insulin sensitivity index (ISIS) by comparing with the euglycemic clamp. They also estimated the first-phase insulin release (1stPHS) and second-phase insulin release (2ndPHS) by comparison with the hyperglycemic clamp (14). Both groups showed excellent correlation with the measured indices (13, 14). These surrogate measures are easier, less invasive, and cheaper to employ and, therefore, can be applied more readily to a large number of subjects. However, the correlation between these surrogates and the direct measurements of insulin action and ß cell capacity are less than perfect (13, 14). It remains to be seen whether these indices with the loss of information as the results from their less-than-perfect correlation with direct measures of these indices can have adequate statistical power for the studies desired.
In the present study, we recruited 105 glucose-tolerant subjects for the assessment of insulin sensitivity and ß cell function using a hyperglycemic clamp. To examine the performance of the estimated indices, we first examined the impact of ethnicity on the measured indices of insulin sensitivity and ß cell function and compared these results against the estimated indices.
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Subjects and Methods |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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To minimize confounding factors, only healthy subjects who
received no regular medical treatments were invited to undergo a
screening test as outpatients after an overnight fast. It included a
standard OGTT with 75 g glucose and a brief physical examination
as previously described (15). To exclude the secondary
influence of insulin sensitivity and ß cell function from abnormal
glucose tolerance (16), only those who were noted to be
glucose tolerant (fasting plasma glucose (FPG) less than 6.1
mM, interval plasma glucose less than 11.1 mM,
and 2-h plasma glucose less than 7.7 mM) were invited back
for the assessment of ß cell function and insulin sensitivity using a
hyperglycemic clamp technique. Because hypertension has been shown to
be associated with insulin resistance (17), only
normotensive (less than 140/90 mm Hg) subjects were enrolled in the
study. For safety reasons, anemic (hemoglobin less than 11.0 g/dL)
subjects were also excluded from the hyperglycemic clamp of the study.
To minimize the effect of smoking, they were asked to refrain from
smoking for at least 12 h before the study. Briefly, after fasting
overnight and resting in the General Clinical Research Center of the
University of California, Los Angeles (Los Angeles, CA),
participants received a bolus of 50% dextrose solution based on their
body surface area (11.4 g/m2) at 0 min.
Continuous infusion of 30% dextrose solution was commenced at 15 min
at variable rates, which were adjusted every 5 min based on the
prevailing plasma glucose levels, to maintain a plasma glucose level
around 10 mM toward 180 min using the negative feedback
principle (18). The insulin sensitivity index (ISI) was
calculated by dividing the average glucose infusion rate during the
last 60 min of the clamp by the average plasma insulin level. Glucose
clearance (GCl) was calculated as ISI divided by
the plasma glucose concentration (7). The coefficient of
variation for steady-state plasma glucose levels was 5.6 ± 0.2%.
The first-phase insulin response (1stIR) was the sum of plasma insulin
levels during the first 10 min (2.5, 5, 7.5, and 10 min) and the
second-phase insulin response (2ndIR) was the average of plasma insulin
levels at 130, 140, 150, 160, 170, and 180 min. FPG and fasting plasma
insulin (FPI) concentrations were the average of 3 samples before the
OGTT. Plasma glucose, insulin, and lipid were assayed as previously
described (19). The demographic features of the studied
subjects are shown in Table 1
. There were
28 Asian-, 11 African-, 46 Caucasian-, and 20 Mexican-Americans.
Because birth control pills have been shown to affect insulin
sensitivity (20), gender was categorically classified as
male, female taking birth control pills, and female without birth
control pills. Ethnicity was defined as the reported ethnicity by each
participant. The study was approved by the Human Subject Protection
Committee of University of California, Los Angeles. Written
informed consent was obtained from all of the participants before
entering the study. We confirm that the study has complied with the
recommendations of the Declaration of Helsinki.
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The estimated ISI proposed by Matsuda and DeFronzo (13) was calculated based on the following formula: ISIM = 10,000 x (FPG x FPI x mean OGTT glucose concentration x mean OGTT insulin concentration)0.5. Plasma glucose concentration was in milligrams per dl and plasma insulin concentration was in microunits per ml for this estimate by Matsuda and DeFronzo (13).
The estimated ISI proposed by Stumvoll et al. (14) was calculated based on the following formula: ISIS = 0.226 - (0.0032 x body mass index (BMI)) - (0.0000645 x plasma insulin concentration at 120 min) - (0.0037 x plasma glucose concentration at 90 min). The estimates of ß cell function proposed by Stumvoll et al. were calculated using the following two formulae: 1stPHS = 1283 + (1.829 x plasma insulin concentration at 30 min) - (138.7 x plasma glucose concentrations at 30 min) + (3.772 x FPI) for the 1stIR; and 2ndPHS = 287 + (0.4164 x plasma insulin concentration at 30 min) - (26.07 x plasma glucose concentration at 30 min) + (0.9226 x FPI) for the 2ndIR. These estimations were based on plasma glucose concentrations in milimoles per L and plasma insulin concentrations in picomoles per L (14).
Statistical analysis
The continuous variables that failed the Normality test were
logarithmically transformed before analysis. The variables transformed
were age, BMI, waist to hip ratio, plasma insulin levels, 1stIR, 2ndIR,
ISI, GCl, ISIM,
ISIS, 1stPHS, and
2ndPHS. Differences in continuous variables
between groups of subjects were tested with either one-way ANOVA or
Student’s t test when appropriate. Differences in
proportions were evaluated by a 
2 test. The
relationships between variables were determined by using a simple
regression analysis. To examine the influence of confounding variables,
a stepwise regression analysis was used. Backward stepwise with
-to-enter of 0.10 and -to-remove of 0.10 was employed to exclude
variables that had little or no influence on the parameter under
analysis. SYSTAT 8.0 for Windows package from SPSS, Inc.
(Chicago, IL) was used for the statistical analysis. Data were
presented as arithmetic means with 95% confidence intervals, unless
otherwise specified. A P value of less than 0.05
(two-tailed) was considered significant.
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Results |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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This study included 105 healthy subjects with normal glucose
tolerance and normal blood pressure as shown in Table 1
. During the
hyperglycemic clamp, a steady-state plasma glucose concentration was
achieved with a mean of 10.03 mM (range, 9.33–11.03
mM). There were wide variations in ISI (geometric mean,
5.2456; range, 1.3632–17.9944
µM/m2/min/pM), 1stIR
(geometric mean, 1,637; range, 465–7,415 pM), and 2ndIR
(geometric mean, 443; range, 104–1,567 pM).
However, ISI correlated with 1stIR and 2ndIR very well
(r2 = 0.3289 and r2 =
0.5548, respectively). These results indicate that a reciprocal change
occurred between insulin sensitivity and ß cell function to maintain
glucose homeostasis in these subjects.
Simple regression analysis of the measured and estimated insulin sensitivity and ß cell function
In the univariate analyses, the measured ISI, 1stIR, and 2ndIR
were mutually interrelated as shown in Table 2. Similarly, there were very strong
correlations between the measured and estimated insulin sensitivity
indices (ISI, ISIM, and
ISIS). The relatively large portion of variation
in the measured ISI can be explained by ISIM
(52%) and ISIS (30%). There was a very close
relationship between 1stIR and 1stPHS and one
explained 45% of the variation of the other. A very close relationship
was also noted between 2ndIR and 2ndPHS,
explaining 32% of the variation in each other.
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This study included four ethnic groups of healthy subjects as
shown in Table 3. Although minor
differences were noted in the clinical features, only differences in
age and BMI reached the statistical level (P = 0.023
for both). During the hyperglycemic clamp, all four ethnic groups
achieved similar steady-state plasma glucose concentrations
(P = 0.30), as shown in Table 3. The coefficient of
variation for steady-state plasma glucose levels was 5.2 ± 0.5%
for Asian-Americans, 5.9 ± 1.0% for African-Americans, 5.8
± 0.3% for Caucasian-Americans, and 5.1 ± 0.5% for
Mexican-Americans. Univariate analysis revealed that there were
significant differences in ISI, 1stIR, and 2ndIR among the four ethnic
groups (P = 0.0006, P = 0.006, and
P = 0.0002, respectively). Asian-Americans had the
lowest ISI with the highest 2ndIR and the second highest 1stIR, whereas
Caucasian-Americans were most sensitive to insulin with the lowest
1stIR and 2ndIR.
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Multivariate analysis of the measured and estimated indices
A multivariate analysis was used to exclude the influence of confounding factors on insulin sensitivity and ß cell function. Ethnicity had an independent impact on ISI (P = 0.0004) and along with diastolic blood pressure, gender, age, waist-hip ratio, and BMI, accounted for 44% of the variation in ISI. Although ethnicity had no impact on ISIM during the univariate analysis, multivariate analysis revealed that ethnicity was a weak but independent determinant for ISIM (P = 0.0319). Systolic blood pressure was the only analyzed factor that had no impact on both ISI and ISIM. Despite that, up to 66% of the variation in ISIS could be explained by BMI, gender, diastolic blood pressure, and ethnicity. Ethnicity only had a very marginal impact on it (P = 0.0652). Because ISIS included BMI as one of its parameters in the estimation, BMI by itself accounted for 60% of the variation in ISIS (P < 0.000001). In contrast, waist-hip ratio, age, and systolic blood pressure had no influence on ISIS.
Multivariate analysis revealed that ethnicity (P =
0.0079) was an independent determinant for 1stIR; ethnicity with BMI
accounted for 17% of the variation in 1stIR. Because 2ndIR was a
denominator of ISI, the same confounding factors, including ethnicity
(P = 0.0011) determined 36% of the variation of 2ndIR.
In contrast, ethnicity had no influence on both
1stPHS and 2ndPHS. Because
both of them were derived from FPI and plasma glucose and insulin
concentrations at 30 min, they had the same set of covariates with
highly similar P values. Both ethnicity and systolic blood
pressure were excluded from the multivariate analysis and had very
similar insignificant P values (see Table 5).
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Discussion |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionReferences |
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Although excellent correlation of insulin sensitivity measured by
euglycemic and hyperglycemic clamps have been reported before (7, 18, 21), the hyperglycemic clamp is more dependent on
insulin-independent uptake than the euglycemic clamp for maintaining
plasma glucose concentrations at a higher level. To correct the mass
effect of plasma glucose on glucose uptake, we also calculated
GCl. Because the steady-state plasma glucose
concentrations were about the same for all four ethnic
groups, the differences among the four ethnic groups were about the
same for ISI and GCl (P < 0.001;
Table 3). Multivariate analyses revealed the same set of covariates for
both ISI and GCl at very similar P
values for each covariate (Table 4).
Ethnicity is an independent determinant for ISI (P =
0.0004) and GCl (P = 0.0008).
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, although differences
in clinical features were noted in age and waist-hip ratio among the
ethnic groups, minor differences were also noted in diastolic blood
pressure (P = 0.10) and BMI (P = 0.14).
Multivariate analysis confirmed that ethnicity was an independent
determinant for ISI (P = 0.0004; Table 4), 1stIR, and
2ndIR (P = 0.0079 and P = 0.0011,
respectively; Table 5). The fundamental issue was whether ethnicity had an independent influence on both insulin sensitivity and ß cell function or whether ethnicity had a primary effect on one and a secondary effect on the others. In glucose-tolerant subjects, it is essential to maintain plasma glucose levels in a relatively narrow physiological range and ß cells have to respond to prevailing insulin resistance (sensitivity). If ß cells fail to compensate for insulin resistance, abnormal glucose tolerance (either impaired glucose tolerance or diabetes) will develop. In contrast, if ß cells respond too strongly (over-response) to insulin resistance, hypoglycemia will develop. Therefore, in glucose-tolerant subjects, there is a dynamic balance between insulin sensitivity and ß cell function. Any interruption of the balance will lead to a change in glucose homeostasis and a pathological state will develop. The subject will no longer be glucose tolerant. Therefore, it is impossible, at least in the present time, to disentangle the independent effects of ethnicity on insulin sensitivity and ß cell function in glucose-tolerant subjects. We attempted to resolve this issue by using multivariate analysis. First, we assumed that the primary influence of ethnicity was on ISI and we considered ISI as a covariate for 1stIR in the multivariate analysis. We found that ISI was the only determinant for 1stIR (r2 = 0.3289, P < 0.000001), accounting for 0.5903 of the variation of 1stIR, whereas ethnicity had no impact on 1stIR (P = 0.5213). Conversely, we also considered 1stIR as a covariate for ISI. We found that ethnicity (P = 0.0200) was an independent determinant for ISI and ethnicity, along with 1stIR (P < 0.000001), diastolic blood pressure (P = 0.0002), gender (P = 0.0003), age (P = 0.0008), and waist-hip ratio (P = 0.0010) accounted for 54% of the variation of ISI. Because 2ndIR was a denominator for ISI, they were very tightly related to each other statistically. Ethnicity (P = 0.5342) was removed from the multivariate analysis when ISI was considered as a covariate for 2ndIR. Similarly, when 2ndIR was considered as a covariate for ISI, ethnicity (P = 0.2867) was excluded from the analysis as expected. Therefore, it was impossible to disentangle the relationship between ISI and 2ndIR using a multivariate analytical approach. Nonetheless, the multivariate analyses of ISI and 1stIR suggested that the impact of ethnicity was primarily on insulin sensitivity, and we observed a compensatory change in ß cell function. This is consistent with the notion that insulin levels are increased in insulin-resistant subjects (22), which results from both increased secretion and reduced clearance of insulin (23). This compensatory hypersecretion of insulin not only reflects the expansion of ß cell mass (24, 25), but also altered expression of key enzymes of glucose metabolism in ß cells as observed in Zucker fatty rats (26).
Among the four ethnic groups, Asian-Americans were the most insulin resistant with the lowest adjusted ISI (3.7334 µM/m2/min/pM), followed by Mexican-Americans (4.7025 µM/ m2/min/pM) and African-Americans (4.9027 µM/m2/min/pM). Caucasian-Americans were the most insulin sensitive (6.7810 µM/m2/min/pM). A reciprocal change was noted in their adjusted ß cell function (2,035 pM in Asian-Americans, 1,877 pM in Mexican-Americans, 1,707 pM in African-Americans, and 1,337 pM in Caucasian-Americans for the adjusted 1stIR; 582 pM in Asian-Americans, 487 pM in both Mexican- and African-Americans, and 353 pM in Caucasian-Americans for the adjusted 2ndIR). However, ethnicity had very little impact on the estimated indices. It remains to be seen whether the enhanced statistical power obtained by using the estimated indices that can be more readily applied to relatively large samples outweighs the loss of information resulting from their less-than-perfect correlations with more definitive measures of insulin sensitivity and ß cell function. Because the estimated indices reflected the variation of the measured indices poorly, it is essential to measure insulin sensitivity and ß cell function in a thorough manner if one plans to compare the impact of various factors, such as ethnicity, on these indices.
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Acknowledgments |
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Footnotes |
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Received August 18, 2000.
Revised October 23, 2000.
Revised December 6, 2000.
Accepted January 4, 2001.
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References |
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