OBJECTIVE—To evaluate the role of adiposity in the relationship between specific and surrogate estimates of insulin-mediated glucose uptake (IMGU) in a large nondiabetic population.

RESEARCH DESIGN AND METHODS—Healthy volunteers were classified by BMI into normal weight (<25.0 kg/m2, n = 208), overweight (25.0–29.9 kg/m2, n = 168), and obese (≥30.0 kg/m2, n = 109) groups. We then assessed how differences in BMI affect the correlation between steady-state plasma glucose (SSPG) concentration at the end of a 180-min infusion of octreotide, glucose, and insulin (a specific measure of IMGU) and five surrogate estimates: fasting plasma glucose, fasting plasma insulin, homeostasis model assessment of insulin resistance (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), and area under the curve for insulin in response to oral glucose (I-AUC).

RESULTS—Correlation coefficients (r values) between SSPG and surrogate measures of IMGU were all significant (P < 0.05), but the magnitude varied between BMI groups: normal weight: fasting plasma glucose 0.20, fasting plasma insulin 0.33, HOMA-IR 0.36, QUICKI −0.33, and I-AUC 0.69; overweight: fasting plasma glucose 0.19, fasting plasma insulin 0.55, HOMA-IR 0.55, QUICKI −0.54, and I-AUC 0.72; and obese: fasting plasma glucose 0.40, fasting plasma insulin 0.56, HOMA-IR 0.60, QUICKI −0.61, and I-AUC 0.69.

CONCLUSIONS—The relationship between direct and surrogate estimates of IMGU varies with BMI, with the weakest correlations seen in the normal-weight group and the strongest in the obese group. In general, I-AUC is the most useful surrogate estimate of IMGU in all weight groups. Fasting plasma insulin, HOMA-IR, and QUICKI provide comparable information about IMGU. Surrogate estimates of IMGU based on fasting insulin and glucose account for no more than 13% of the variability in insulin action in the normal-weight group, 30% in the overweight group, and 37% in the obese group.

Individuals with insulin resistance are at increased risk to develop a series of adverse clinical outcomes, including diabetes, cardiovascular disease, essential hypertension, polycystic ovary syndrome, and nonalcoholic fatty liver disease (15).

Because of the number and severity of the clinical syndromes associated with insulin resistance, there is considerable interest in ways to identify the presence of this defect in insulin action in apparently healthy individuals. Although established techniques to quantify insulin-mediated glucose uptake (IMGU) are available (6,7), they are often viewed as being too time- and labor-intensive for routine use in studies of insulin resistance. In an attempt to overcome this perceived problem and enhance investigation of the role of insulin resistance in human disease, a variety of surrogate estimates of IMGU have been proposed (810). These approaches all have the advantage of being derived from measurements of fasting plasma glucose and insulin concentrations and thus provide the simple way to measure IMGU that is desired. On the other hand, they are likely to account for no more than 40% of the variability in IMGU in the nondiabetic population at large (11,12). In addition, the reliability of these surrogate estimates may vary with degree of obesity because adiposity is known to modify the relationship between insulin resistance, plasma glucose, and plasma insulin concentrations (1315). This may explain some of the conflicting reports in the literature about the performance of these tests, such that a skewed BMI distribution in the study population may overstate (10,1618) or seem to understate (19) the relationship between direct and indirect tests of IMGU. Few studies have addressed the role of adiposity on the efficacy of surrogate measures of IMGU; even then, these studies have been limited by small sample sizes (10,1618,20), loose BMI-group categories (18,20,21), and inclusion of both nondiabetic and diabetic individuals in the analysis (18,21). Information concerning this issue would be particularly useful in understanding the utility of these surrogate measures, particularly in the midst of the current obesity epidemic (2224). In order to address this issue we have evaluated the relationship between IMGU as quantified by the insulin suppression test (IST) and five surrogate estimates in 485 nondiabetic individuals, subdivided into three groups on the basis of their BMI: normal weight (n = 208), overweight (n = 168), and obese (n = 109).

The study population consisted of 485 nondiabetic (25) volunteers who had participated in our research studies from 1990 to 1998. The mean age (±SD) was 48 ± 13 years (range 19–79). Most participants were of European ancestry (77%), with the remainder being of Hispanic (12%), Asian (10%), and African (1%) background. Individuals were classified by BMI as normal weight (<25 kg/m2, n = 208), overweight (25–29.9 kg/m2, n = 168), or obese (≥30 kg/m2, n = 109).

After signing an informed consent, participants were admitted to Stanford’s General Clinical Research Center for metabolic testing. We quantified IMGU by a modification of the IST (26) as originally introduced and validated by our research group (6,27). After an overnight fast, an intravenous catheter was placed in each of the subjects’ arms. One arm was used for the administration of a 180-min infusion of octreotide (0.27 μg · m−2 · min−1), insulin (32 mU · m−2 · min−1), and glucose (267 mg · m−2 · min−1); the other arm was used for collecting blood samples. Blood was drawn every 30 min initially and then at 10-min intervals from 150 to 180 min of the infusion in order to determine the steady-state plasma glucose (SSPG) and steady-state plasma insulin concentrations. Because steady-state plasma insulin concentrations are similar in all subjects, the SSPG concentration provides a direct measure of the ability of insulin to mediate disposal of an infused glucose load; therefore, the higher the SSPG concentration, the more insulin resistant the individual.

On a separate admission, fasting plasma glucose and insulin concentrations were measured before and 30, 60, 120, and 180 min after oral ingestion of 75 g of glucose (11,12). From these measurements, we evaluated five surrogate measures of insulin-mediated glucose disposal: 1) fasting plasma glucose, 2) fasting plasma insulin, 3) homeostasis model assessment of insulin resistance (HOMA-IR), 4) quantitative insulin sensitivity check index (QUICKI), and 5) total integrated area under the curve for insulin in response to oral glucose (I-AUC). I-AUC was quantified by calculating the insulin area under the curve by use of the trapezoidal method. We used the following formulas (9,10) for HOMA-IR (fasting insulin [in microunits per milliliter] × fasting glucose [in millimoles per liter]/22.5) and QUICKI (1/log fasting insulin [in microunits per milliliter] + log fasting glucose [in milligrams per deciliter]).

Data are expressed as means ± SD. All analyses were performed using Systat version 10 for Windows (Systat Software, Point Richmond, CA). Statistical differences in baseline characteristics between normal-weight, overweight, and obese groups were assessed by one-way ANOVA followed by the Bonferroni post hoc pairwise comparison for continuous variables and χ2 tests for dichotomous variables. Pearson correlation coefficients were calculated between SSPG and fasting glucose, fasting insulin, HOMA-IR, QUICKI, and I-AUC. To account for skewness and kurtosis, fasting insulin, HOMA-IR, and I-AUC were log transformed. The strongest correlation coefficient between SSPG and a surrogate test of IMGU was compared with the other correlations to assess significant differences between the associations by a two-tailed t test (28).

The clinical characteristics of the three BMI groups are provided in Table 1 as well as the various measures of insulin action. The groups are comparable in terms of age and sex distribution, with the only difference being that the overweight individuals are slightly older than their normal-weight counterparts. The results also indicate that the degree of insulin resistance, as estimated by either SSPG concentration or the five surrogate estimates of IMGU, increases progressively as the magnitude of adiposity increases.

Looking at Table 1 more closely, it appears that the degree of variability (±SD) for SSPG and the five surrogate measures appear quite different. In fact, in the whole study population, fasting plasma glucose concentrations vary by as little as 1.8-fold (3.66–6.66 mmol/l) and by as much as 24-fold for fasting insulin concentrations (6.95–167 pmol/l) when outliers are excluded (defined as >1.5 times the interquartile range). The distribution of these surrogate measures also varies by degree of obesity and insulin resistance. For example, Fig. 1 illustrates the increase in the range of fasting insulin concentrations in obese insulin-resistant individuals.

In the whole study population, the relationship (Pearson correlation coefficient) between SSPG and each indirect measure of IMGU with 95% CIs are as follows: fasting plasma glucose 0.38 (0.3–0.45, R2 = 0.14), fasting plasma insulin 0.61 (0.55–0.66, R2 = 0.37), HOMA-IRlog 0.64 (0.58–0.69, R2 = 0.41), QUICKI −0.60 (−0.65 to −0.54, R2 = 0.36), and I-AUClog 0.77 (0.73–0.80, R2 = 0.59). All of the correlation coefficients are statistically significant (P < 0.001), but the magnitude of the relationship clearly differs. I-AUC is the most closely related with SSPG, and this correlation is significantly different from the other r values (t >6 for all comparisons, degrees of freedom 482, P < 0.001). Therefore, I-AUC is the best surrogate estimate of IMGU, accounting for 59% of the variability in SSPG concentration. In contrast, fasting glucose is the least useful and accounts for only 14% of the variability. The correlation coefficients between SSPG and fasting insulin, HOMA-IR, and QUICKI are quite similar, with differences in each of these values explaining <41% of the variability in SSPG concentration.

The results in Table 2 display the relationship between SSPG and the five surrogate estimates of IMGU when the population is subdivided on the basis of adiposity into normal-weight, overweight, and obese groups. These data are similar to values obtained in the whole study population in that the strongest relationship is between SSPG and I-AUC, the weakest between SSPG and fasting glucose, and intermediate between SSPG and fasting insulin, HOMA-IR, and QUICKI. However, the results in Table 2 provide two additional important points. First, I-AUC is the only surrogate estimate that yields a consistent relationship with SSPG across weight groups. In contrast, the relationship between SSPG and fasting glucose, fasting insulin, HOMA-IR, and QUICKI change substantially as a function of BMI. Thus, in the obese group none of these surrogate estimates based on fasting glucose and insulin can account for >37% of the variability in IMGU as measured by SSPG and only ∼13% of the variability of IMGU in normal-weight individuals. Secondly, it is once more apparent from Table 2 that essentially identical estimates of IMGU are obtained with use of fasting insulin concentration, HOMA-IR, or QUICKI. This is not surprising given the near-perfect correlation between fasting insulin, HOMA-IR, and QUICKI (correlation between fasting insulinlog and HOMA-IRlog, 0.98; fasting insulinlog and QUICKIlog, −0.98; and HOMA-IRlog and QUICKIlog, −0.99; all P < 0.001). It should also be noted that the data in Table 2 do not meaningfully change when these relationships are adjusted for age (data not shown).

Before discussing the significance of the study results, we should first address whether the IST provides a specific measure of IMGU. The IST, initially described and validated in 1970 (6), is conceptually very similar to the glucose clamp (7) method for measuring IMGU. Both techniques are based on measuring glucose disposal rates during a steady-state period of physiological hyperinsulinemia, but they differ in the variable that is used to quantify IMGU. The IST is based on infusing a fixed glucose load and allowing the plasma glucose concentration to seek its own level. In contrast, the glucose clamp method relies on maintaining the plasma glucose concentration constant by varying the amount of glucose infused. Thus, differences in IMGU with the IST are a direct function of the height of the plasma glucose concentration achieved during the infusion (SSPG concentration), whereas the difference in the amount of glucose infused provides the measure of IMGU with the glucose clamp. Not only are the two methods conceptually quite similar, they are highly correlated (r >0.9) when both techniques are used to quantify IMGU in individuals over a wide range of insulin sensitivity (27). Based on the above considerations, we believe that the IST provides a specific measurement of IMGU that can be used as a standard to evaluate the adequacy of surrogate estimates of this variable.

Focusing on the relative utility of the five surrogate estimates of IMGU, three issues seem worthy of discussion. To begin, the results presented provide an unequivocal answer to the question posed in the introduction: the five surrogate estimates of IMGU vary dramatically with the degree of adiposity. The lowest correlations between SSPG and these surrogate tests are seen in the normal-weight group and the highest in the obese group. This may only reflect the higher prevalence of insulin resistance in the obese group. However, if this were the only factor, the association between SSPG and I-AUC would also differ as a result of BMI, but it does not. What is more likely is that the degree of obesity modifies the relationship among insulin resistance, insulin secretion, and insulin catabolism, such that plasma glucose and insulin concentrations are better able to delineate differences in IMGU in more obese individuals. The mechanism by which this occurs is debated with evidence for increased insulin secretion (13,2931) and decreased insulin clearance (14,32) in obesity. Regardless, hyperinsulinemia in obesity has long been appreciated, with higher insulin concentrations seen during fasting and postglucose challenge conditions in obese individuals when compared with normal-weight individuals, even when they are matched for glucose tolerance (33) or insulin sensitivity (13,15,34). The hyperinsulinemia is even more pronounced in obese insulin-resistant individuals (13,15,34). This phenomenon may better help delineate differences in insulin sensitivities in obese individuals, especially when utilizing fasting values. To illustrate this point, Fig. 1 shows box plots of fasting insulin concentrations in the different weight classes subdivided by insulin-resistant (highest tertile of SSPG) and insulin-sensitive (lowest tertile of SSPG) groups. Obese insulin-resistant individuals have higher insulin concentrations, a greater range in insulin values, and a clearer delineation from their insulin-sensitive counterparts when compared with normal-weight individuals who are insulin resistant. Therefore, how insulin is regulated may make fasting insulin a better surrogate measure of IMGU in obese individuals but not in normal-weight or overweight individuals.

Although not the primary goal of our study, the results provide additional evidence that fasting plasma glucose and insulin measurements do not provide precise estimates of IMGU. The information in Table 1 shows that the range in fasting plasma insulin concentrations in this population is greater than that for fasting plasma glucose concentrations and can vary by 24-fold versus only 1.8-fold for fasting glucose. This finding is consistent with the physiological fact that it is the compensatory hyperinsulinemia in insulin-resistant individuals that prevents the decompensation of glucose homeostasis. Thus, the observation that fasting plasma glucose accounts for ∼14% of the variability in IMGU, compared with ∼37% for fasting insulin, is predictable. Similarly, surrogate estimates of IMGU based on using both fasting glucose and insulin concentrations cannot be that different from those obtained with insulin levels alone; the impact of a variable that varies by 24-fold (fasting insulin) will outweigh the contribution of one that changes by only 1.8-fold (fasting glucose) and be the major determinant of the combined effect.

It must be noted that other studies have reported higher correlations between a specific measure of IMGU and HOMA-IR or QUICKI than we have found. The highest r values are reported in the original works for HOMA-IR (9) (r = 0.83), which included only 12 nondiabetic subjecs (90–142% of ideal body weight), and for QUICKI (10) (r = 0.89), which included 13 obese (BMI ≥30 kg/m2) subjects. In fact, most previous studies validating the use of HOMA-IR and QUICKI usually have <30 nondiabetic subjects by any specific weight class (1618,20,35). In addition, weight classifications have not been as rigorously delineated as they have in this study, with some groups defining obesity as BMI greater than or equal to 25 (21) or 27 (18,20) kg/m2. Therefore, it is not surprising that subgroup analyses by weight either have not been done or have given conflicting reports, with some noting correlation coefficients between specific and surrogate estimates of IMGU as being similar between nonobese and obese individuals (18,21), as higher in nonobese individuals (20), and as lower in nonobese individuals (10,17,35). To the best of our knowledge, this is the only work that has specifically analyzed the impact of degree of obesity in a large nondiabetic population by three different weight categories.

In conclusion, useful information may be gained by using measurements of fasting glucose and insulin concentrations to estimate insulin resistance in large population-based studies, but these values will vary with degree of obesity and explain as little as ∼13% of the variability in IMGU in normal-weight individuals and no more than ∼37% in obese people. In addition, more sophisticated calculations incorporating fasting glucose and insulin add little to the information gained from fasting insulin alone. Thus, use of these surrogate estimates of insulin resistance in physiological studies involving relatively few subjects has considerable potential for providing confounded experimental data.

Figure 1—

Box plots illustrating the median and range of fasting plasma insulin concentrations in insulin-resistant (highest tertile of SSPG) and insulin-sensitive (lowest tertile of SSPG) individuals according to degree of obesity. Boundaries of the box signify the lower and upper quartiles. ○, outliers with values between 1.5 and 3 box lengths from the boundaries of the box. *Extreme values representing >3 box lengths.

Figure 1—

Box plots illustrating the median and range of fasting plasma insulin concentrations in insulin-resistant (highest tertile of SSPG) and insulin-sensitive (lowest tertile of SSPG) individuals according to degree of obesity. Boundaries of the box signify the lower and upper quartiles. ○, outliers with values between 1.5 and 3 box lengths from the boundaries of the box. *Extreme values representing >3 box lengths.

Close modal
Table 1—

Clinical characteristics of the study population

Normal weight (BMI <25 kg/m2)Overweight (BMI 25–29.9 kg/m2)Obese (BMI ≥30 kg/m2)P*
n 208 168 109 — 
Age (years) 46.0 ± 13.6 51 ± 12.6 49.2 ± 12.1 0.001 
Sex (M/F) 89/119 88/80 48/61 NS 
BMI (kg/m222.4 ± 1.8 27.1 ± 1.4 32.8 ± 2.5 <0.001 
SSPG (mmol/l) 5.9 ± 2.9 9.0 ± 4.2 11.8 ± 4.1 <0.001 
Fasting glucose (mmol/l) 4.8 ± 0.6 5.1 ± 0.6 5.4 ± 0.6 <0.001 
Fasting insulin (pmol/l) 58.3 ± 26.6 80.2 ± 37.4 125.5 ± 66.6 <0.001 
HOMA-IR 1.82 ± 0.90 2.66 ± 1.39 4.42 ± 2.57 <0.001 
QUICKI 0.36 ± 0.03 0.34 ± 0.03 0.32 ± 0.02 <0.001 
I-AUC (pmol · l−1 · 3 h−1864.6 ± 464.4 1,254.3 ± 838.9 1,853.9 ± 1,278.2 <0.001 
Normal weight (BMI <25 kg/m2)Overweight (BMI 25–29.9 kg/m2)Obese (BMI ≥30 kg/m2)P*
n 208 168 109 — 
Age (years) 46.0 ± 13.6 51 ± 12.6 49.2 ± 12.1 0.001 
Sex (M/F) 89/119 88/80 48/61 NS 
BMI (kg/m222.4 ± 1.8 27.1 ± 1.4 32.8 ± 2.5 <0.001 
SSPG (mmol/l) 5.9 ± 2.9 9.0 ± 4.2 11.8 ± 4.1 <0.001 
Fasting glucose (mmol/l) 4.8 ± 0.6 5.1 ± 0.6 5.4 ± 0.6 <0.001 
Fasting insulin (pmol/l) 58.3 ± 26.6 80.2 ± 37.4 125.5 ± 66.6 <0.001 
HOMA-IR 1.82 ± 0.90 2.66 ± 1.39 4.42 ± 2.57 <0.001 
QUICKI 0.36 ± 0.03 0.34 ± 0.03 0.32 ± 0.02 <0.001 
I-AUC (pmol · l−1 · 3 h−1864.6 ± 464.4 1,254.3 ± 838.9 1,853.9 ± 1,278.2 <0.001 

Data are means ± SD.

*

P values reflect differences between the three groups and are assessed by one-way ANOVA

P value = 0.001 for comparison between normal-weight versus overweight groups

P value <0.001 for comparison between normal weight versus overweight, normal weight versus obese, and overweight versus obese.

Table 2—

Pearson correlation coefficients between SSPG and surrogate measures of insulin resistance by degree of obesity

Normal weight
Overweight
Obese
r (95% CI)R2r (95% CI)R2r (95% CI)R2
Fasting glucose 0.20 (0.07–0.33)* 0.04 0.19 (0.04–0.33) 0.04 0.40 (0.23–0.55) 0.16 
Fasting insulinlog 0.33 (0.20–0.45) 0.11 0.55 (0.43–0.65) 0.30 0.56 (0.42–0.68) 0.31 
HOMA-IRlog 0.36 (0.24–0.47) 0.13 0.55 (0.43–0.65) 0.30 0.60 (0.46–0.71) 0.36 
QUICKI −0.33 (−0.45 to −0.20) 0.11 −0.54 (−0.64 to −0.42) 0.29 −0.61 (−0.72 to −0.48) 0.37 
I-AUClog 0.69 (0.61–0.76) 0.48 0.72 (0.64–0.79) 0.52 0.69 (0.58–0.78) 0.48 
Normal weight
Overweight
Obese
r (95% CI)R2r (95% CI)R2r (95% CI)R2
Fasting glucose 0.20 (0.07–0.33)* 0.04 0.19 (0.04–0.33) 0.04 0.40 (0.23–0.55) 0.16 
Fasting insulinlog 0.33 (0.20–0.45) 0.11 0.55 (0.43–0.65) 0.30 0.56 (0.42–0.68) 0.31 
HOMA-IRlog 0.36 (0.24–0.47) 0.13 0.55 (0.43–0.65) 0.30 0.60 (0.46–0.71) 0.36 
QUICKI −0.33 (−0.45 to −0.20) 0.11 −0.54 (−0.64 to −0.42) 0.29 −0.61 (−0.72 to −0.48) 0.37 
I-AUClog 0.69 (0.61–0.76) 0.48 0.72 (0.64–0.79) 0.52 0.69 (0.58–0.78) 0.48 
*

P < 0.01;

P < 0.05; all other P values are <0.001.

This research was supported by the National Institutes of Health (General Clinical Research Center, RR-00070). S.H.K. is supported by a National Research Service Award (AA-014470-01).

1
Reaven GM: Banting Lecture 1988: role of insulin resistance in human disease.
Diabetes
37
:
1595
–1607,
1988
2
Lillioja S, Mott DM, Spraul M, Ferraro R, Foley JE, Ravussin E, Knowler WC, Bennett PH, Bogardus C: Insulin resistance and insulin secretory dysfunction as precursors of non-insulin-dependent diabetes mellitus: prospective studies of Pima Indians.
N Engl J Med
329
:
1988
–1992,
1993
3
Dunaif A, Segal KR, Futterweit W, Dobrjansky A: Profound peripheral insulin resistance, independent of obesity, in polycystic ovary syndrome.
Diabetes
38
:
1165
–1174,
1989
4
Ferrannini E, Buzzigoli G, Bonadonna R, Giorico MA, Oleggini M, Graziadei L, Pedrinelli R, Brandi L, Bevilacqua S: Insulin resistance in essential hypertension.
N Engl J Med
317
:
350
–357,
1987
5
Sanyal AJ, Campbell-Sargent C, Mirshahi F, Rizzo WB, Contos MJ, Sterling RK, Luketic VA, Shiffman ML, Clore JN: Nonalcoholic steatohepatitis: association of insulin resistance and mitochondrial abnormalities.
Gastroenterology
120
:
1183
–1192,
2001
6
Shen SW, Reaven GM, Farquhar JW: Comparison of impedance to insulin-mediated glucose uptake in normal subjects and in subjects with latent diabetes.
J Clin Invest
49
:
2151
–2160,
1970
7
DeFronzo RA, Tobin JD, Andres R: Glucose clamp technique: a method for quantifying insulin secretion and resistance.
Am J Physiol
237
:
E214
–E223,
1979
8
Laakso M: How good a marker is insulin level for insulin resistance?
Am J Epidemiol
137
:
959
–965,
1993
9
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC: Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.
Diabetologia
28
:
412
–419,
1985
10
Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, Quon MJ: Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans.
J Clin Endocrinol Metab
85
:
2402
–2410,
2000
11
Yeni-Komshian H, Carantoni M, Abbasi F, Reaven GM: Relationship between several surrogate estimates of insulin resistance and quantification of insulin-mediated glucose disposal in 490 healthy nondiabetic volunteers.
Diabetes Care
23
:
171
–175,
2000
12
Abbasi F, Reaven GM: Evaluation of the quantitative insulin sensitivity check index as an estimate of insulin sensitivity in humans.
Metabolism
51
:
235
–237,
2002
13
Ferrannini E, Natali A, Bell P, Cavallo-Perin P, Lalic N, Mingrone G: Insulin resistance and hypersecretion in obesity: European Group for the Study of Insulin Resistance (EGIR).
J Clin Invest
100
:
1166
–1173,
1997
14
Jones CN, Abbasi F, Carantoni M, Polonsky KS, Reaven GM: Roles of insulin resistance and obesity in regulation of plasma insulin concentrations.
Am J Physiol Endocrinol Metab
278
:
E501
–E508,
2000
15
Ferrannini E, Balkau B: Insulin: in search of a syndrome.
Diabet Med
19
:
724
–729,
2002
16
Bastard JP, Rabasa-Lhoret R, Maachi M, Ducluzeau PH, Andreelli F, Vidal H, Laville M: What kind of simple fasting index should be used to estimate insulin sensitivity in humans?
Diabetes Metab
29
:
285
–288,
2003
17
Rabasa-Lhoret R, Bastard JP, Jan V, Ducluzeau PH, Andreelli F, Guebre F, Bruzeau J, Louche-Pellissier C, MaItrepierre C, Peyrat J, Chagne J, Vidal H, Laville M: Modified quantitative insulin sensitivity check index is better correlated to hyperinsulinemic glucose clamp than other fasting-based index of insulin sensitivity in different insulin-resistant states.
J Clin Endocrinol Metab
88
:
4917
–4923,
2003
18
Bonora E, Targher G, Alberiche M, Bonadonna RC, Saggiani F, Zenere MB, Monauni T, Muggeo M: Homeostasis model assessment closely mirrors the glucose clamp technique in the assessment of insulin sensitivity: studies in subjects with various degrees of glucose tolerance and insulin sensitivity.
Diabetes Care
23
:
57
–63,
2000
19
Gonzalez-Ortiz M, Martinez-Abundis E: Comparison of several formulas to assess insulin action in the fasting state with the hyperglycemic-hyperinsulinemic clamp technique in healthy individuals.
Rev Invest Clin
55
:
419
–422,
2003
20
Lansang MC, Williams GH, Carroll JS: Correlation between the glucose clamp technique and the homeostasis model assessment in hypertension.
Am J Hypertens
14
:
51
–53,
2001
21
Gutt M, Davis CL, Spitzer SB, Llabre MM, Kumar M, Czarnecki EM, Schneiderman N, Skyler JS, Marks JB: Validation of the insulin sensitivity index (ISI(0,120)): comparison with other measures.
Diabetes Res Clin Pract
47
:
177
–184,
2000
22
Mokdad AH, Bowman BA, Ford ES, Vinicor F, Marks JS, Koplan JP: The continuing epidemics of obesity and diabetes in the United States.
JAMA
286
:
1195
–1200,
2001
23
Freedman DS, Khan LK, Serdula MK, Galuska DA, Dietz WH: Trends and correlates of class 3 obesity in the United States from 1990 through 2000.
JAMA
288
:
1758
–1761,
2002
24
Haffner S, Taegtmeyer H: Epidemic obesity and the metabolic syndrome.
Circulation
108
:
1541
–1545,
2003
25
Expert Committee on the Diagnosis and Classification of Diabetes Mellitus: Report of the Expert Committee on the Diagnosis and Classification of Diabetes Mellitus.
Diabetes Care
20
:
1183
–1197,
1997
26
Pei D, Jones CN, Bhargava R, Chen YD, Reaven GM: Evaluation of octreotide to assess insulin-mediated glucose disposal by the insulin suppression test.
Diabetologia
37
:
843
–845,
1994
27
Greenfield MS, Doberne L, Kraemer F, Tobey T, Reaven G: Assessment of insulin resistance with the insulin suppression test and the euglycemic clamp.
Diabetes
30
:
387
–392,
1981
28
Walker H, Lev J:
Statistical Inference
. New York, Holt, Rinehart, and Winston,
1953
29
Elahi D, Nagulesparan M, Hershcopf RJ, Muller DC, Tobin JD, Blix PM, Rubenstein AH, Unger RH, Andres R: Feedback inhibition of insulin secretion by insulin: relation to the hyperinsulinemia of obesity.
N Engl J Med
306
:
1196
–1202,
1982
30
Bonora E, Zavaroni I, Bruschi F, Alpi O, Pezzarossa A, Guerra L, Dall’Aglio E, Coscelli C, Butturini U: Peripheral hyperinsulinemia of simple obesity: pancreatic hypersecretion or impaired insulin metabolism?
J Clin Endocrinol Metab
59
:
1121
–1127,
1984
31
Polonsky KS, Given BD, Hirsch L, Shapiro ET, Tillil H, Beebe C, Galloway JA, Frank BH, Karrison T, Van Cauter E: Quantitative study of insulin secretion and clearance in normal and obese subjects.
J Clin Invest
81
:
435
–441,
1988
32
Meistas MT, Margolis S, Kowarski AA: Hyperinsulinemia of obesity is due to decreased clearance of insulin.
Am J Physiol
245
:
E155
–E159,
1983
33
Reaven GM, Moore J, Greenfield M: Quantification of insulin secretion and in vivo insulin action in nonobese and moderately obese individuals with normal glucose tolerance.
Diabetes
32
:
600
–604,
1983
34
Abbasi F, Brown BW, Jr, Lamendola C, McLaughlin T, Reaven GM: Relationship between obesity, insulin resistance, and coronary heart disease risk.
J Am Coll Cardiol
40
:
937
–943,
2002
35
Mather KJ, Hunt AE, Steinberg HO, Paradisi G, Hook G, Katz A, Quon MJ, Baron AD: Repeatability characteristics of simple indices of insulin resistance: implications for research applications.
J Clin Endocrinol Metab
86
:
5457
–5464,
2001

A table elsewhere in this issue shows conventional and Système International (SI) units and conversion factors for many substances.