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Division of Internal Medicine (G.P., M.C., L.L.), Biometrical Unit (A.C.), Unit of Clinical Spectroscopy (G.P., L.L.), Università Vita e Salute San Raffaele, Istituto Scientifico H San Raffaele, and International Center for the Assessment of Nutritional Status (G.T., L.L.), Università degli Studi di Milano, Milan, Italy 20132
Address all correspondence and requests for reprints to: Gianluca Perseghin, M.D., Division of Internal Medicine, Laboratory of Amino Acids and Stable Isotopes/Unit of Clinical Spectroscopy, via Olgettina 60, 20132, Milan, Italy. E-mail: perseghin.gianluca{at}hsr.it
Abstract
Insulin resistance plays a major role in the pathophysiology of diabetes and is associated with obesity and cardiovascular disease. Excellent methods exist for the assessment of insulin sensitivity in the laboratory setting, such as the glucose clamp. However, these methods are not suitable for large population studies, and, thus, surrogate estimates of insulin sensitivity based on measurements in a single blood sample have been developed. Recently an index based on the logarithm and the reciprocal of the insulin-glucose product (QUICKI) has been proposed. QUICKI correlated with insulin sensitivity across the entire spectrum of glucose tolerance, but its performance was less satisfactory in normal subjects. Aim of this study was to ascertain whether the inclusion of fasting plasma free fatty acids concentration into QUICKI improves its association with insulin sensitivity in nonobese subjects. To test this hypothesis, we performed a euglycemic hyperinsulinemic clamp [40 mU/(m2·min)] in 57 young, healthy, nonobese individuals with (n = 17) or without (n = 40) first-degree relatives affected by type 2 diabetes (the former group being an in vivo model of mild insulin resistance). We then compared the clamp-based index of insulin sensitivity with both QUICKI and a revised QUICKI, the latter index including the contribution of fasting free fatty acid concentration as well. The revised QUICKI considerably improved the relationship with the clamp-based index of insulin sensitivity (r = 0.51, P < 0.0001) with respect to QUICKI (r = 0.27, P < 0.05). In addition, the revised QUICKI revealed a reduction of insulin sensitivity in the offspring of type 2 diabetes (10%; P < 0.006) that QUICKI was unable to detect (3%; P = 0.28). In conclusion, this study suggests that the incorporation of fasting free fatty acid level into QUICKI is useful to improve its correlation with the clamp-based index of insulin sensitivity and its discriminatory power in case of mild insulin resistance. Further investigation is needed to ascertain its applicability to patients with obesity and type 2 diabetes.
INSULIN RESISTANCE IS a key factor involved in the pathogenesis of type 2 diabetes (1, 2). Longitudinal studies showed that insulin resistance is the stronger predictive factor of the future development of the disease (2, 3, 4). It was found to be associated with obesity, hypertension, dyslipidemias (5), and prevalent atherosclerosis (5, 6, 7), and the clustering of these metabolic disorders is now recognized as the insulin resistance syndrome (8, 9). The recognition of insulin resistance therefore has investigational and clinical relevance in the identification of subjects at high risk of developing this syndrome.
The insulin clamp technique (10) is considered the gold standard (11) for in vivo quantification of insulin sensitivity. A well-accepted alternative is the minimal model analysis of a frequently sampled iv glucose tolerance test (12), which is less laborious but is not as simple as required in large-scale studies.
The homeostasis model assessment index (HOMA), which is based on the product of the fasting plasma insulin and blood glucose concentrations measured in a single blood sample, has been proposed as a simple and inexpensive tool to measure insulin sensitivity (13). HOMA has been shown to be a reliable measure of in vivo insulin sensitivity in humans when compared with the euglycemic-hyperinsulinemic clamp technique across the entire spectrum of glucose tolerance (14).
Recently Katz et al. (15) showed that by taking both the logarithm and the reciprocal of the insulin-glucose product, one derives an index, denoted as Quantitative Insulin sensitivity Check Index (QUICKI), that provides a better prediction of insulin sensitivity. These authors examined nonobese, obese, and type 2 diabetic patients and found a very good agreement between QUICKI and the clamp-based index of insulin sensitivity (overall r = 0.78). In making subgroup comparisons, however, they found the lowest correlation coefficient (r = 0.49) and the greatest variability in the nonobese subgroup. This finding suggests that in such groups of individuals, QUICKI still has margins of improvement and that additional metabolic markers of insulin action—besides glucose and insulin—could be profitably taken into account to improve its association with insulin sensitivity.
The aim of this study is to determine whether incorporation of the fasting FFA concentration into QUICKI can improve the association of this index with insulin sensitivity in nonobese subjetcs. To test our hypothesis, we performed euglycemic hyperinsulinemic clamp studies in 57 young, healthy, nonobese individuals—with or without a first-degree relative affected by type 2 diabetes mellitus—and compared the clamp-based estimate of insulin sensitivity with both QUICKI and a revised QUICKI, the latter index including the contribution of fasting FFA concentration as well.
Materials and Methods
Subjects
Fifty-seven healthy, young, nonobese subjects were recruited at
the Istituto Scientifico H San Raffaele. The main criteria for their
inclusion in the study were the following: 1) age (19–45 yr); 2) white
race; 3) body mass index less than 27 kg/m2; 4)
sedentary lifestyle; and 5) no history of hypertension,
endocrine/metabolic disease, or cigarette smoking. Habitual physical
activity was assessed using a questionnaire (16). Body
weight was stable for at least 6–12 months. The anthropometric
characteristics of the subjects are summarized in Table 1
. Seventeen individuals resulted to have
at least one parent with type 2 diabetes; meanwhile, the remaining 40
individuals had no family history of diabetes. These subjects were
studied to test whether incorporation of fasting FFA concentration into
QUICKI can improve its association with insulin sensitivity. An
additional subgroup of 14 healthy, nonobese subjects was recruited to
undergo a dose-response study at low and high insulin infusion rate
aimed to test whether the antilipolytic insulin action was associated
with insulin sensitivity to glucose metabolism at different circulating
insulin levels. All subjects were in good health as assessed by medical
history, physical examination, hematological assay, and urinalysis.
Informed consent was obtained from all subjects after explanation of
purposes, nature, and potential risks of the study. The protocol was
approved by the Ethical Committee of the Istituto Scientifico H San
Raffaele.
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Subjects were instructed to consume an isocaloric diet and to abstain from exercise activity for 3 wk before the studies. Women were studied between days 3 and 8 of the menstrual cycle. Subjects were studied by means of the euglycemic-hyperinsulinemic clamp to assess insulin sensitivity after a 10-h overnight fast period. Within 2–3 d, they were also studied by means of dual-energy x-ray absorptiometry in the Department of Science, Nutrition, and Microbiology, Nutrition Section, Università degli Studi di Milano, to assess body composition.
Euglycemic-hyperinsulinemic clamp. Subjects were admitted to the Metabolic Unit of the Division of Internal Medicine I of the Istituto Scientifico H San Raffaele at 0700 h after a 10-h overnight fast. A Teflon catheter was inserted into an antecubital vein for infusions and an additional one was inserted retrogradely into a wrist vein for blood sampling. The hand was kept in a heated box (50 C) throughout the experiment to allow sampling of arterialized venous blood. Blood samples for postabsorptive plasma glucose, insulin and FFAs were performed in triplicate. Thereafter a euglycemic/hyperinsulinemic clamp was performed as previously described (17). Insulin was infused at 40 mU/[m2·min] to reach a plasma insulin concentration of about 350 pmol/liter, and plasma glucose concentration was kept at 5 mmol/liter for 150 min by means of a variable infusion of 20% dextrose infusion. Blood samples for plasma insulin, glucose, and FFAs were drawn every 15 min throughout the study.
Body composition. Body composition, with regional three-compartment analysis (arms, trunk, and legs), was performed with a Lunar Corp.-DPX-IQ scanner (Lunar Corp., Madison, WI) as previously described (17). Fat content is expressed as kilograms of fat mass and as percent of tissues.
Dose-response study of the antilipolytic insulin action. To assess whether insulin sensitivity of lipolysis and glucose metabolism were associated, a subgroup of 14 subjects (age = 35 ± 3 yr, body mass index = 24.5 ± 1.1 kg/m2) performed a dose-response clamp study using insulin infusion rates of 10 (low dose) and 40 (high dose) mU/(m2·min). The low-dose insulin clamp was designed to reach appropriate (150 pmol/liter) plasma insulin levels to investigate the antilipolytic insulin action.
Analytical procedures
Plasma glucose was measured with a glucose analyzer (Beckman Coulter, Inc., Fullerton, CA) (17) and coefficient of variation (CV) was 1.7 ± 0.1% and 3.0 ± 0.4%, respectively, in the fasting and clamp conditions. Plasma insulin was measured with a microparticle enzyme immunoassay technology (18) with no cross-reactions with proinsulin, c-peptide, and glucagon (IMx insulin assay, Abbott Laboratories, Rome, Italy). CV was 7.2 ± 1.7% and 5.7 ± 0.7%, respectively, in the fasting and clamp conditions. Blood samples for FFA assessment were collected in prechilled tubes containing 0.1% EDTA. Tubes were immediately placed in ice and plasma was immediately processed by centrifugation at 4 C. Plasma was then frozen and stored at -70 C, and FFA determinations were performed as previously described (17) within 0–5 working days. CV was 5.9 ± 1.8% and 9.3 ± 3.8%, respectively, in the fasting and clamp conditions.
Calculations
Clamp-based index of insulin sensitivity. The steady-state
period of the insulin clamp was defined as the final 30-min period
(i.e. 120–150 min) during which the CV for blood glucose,
plasma insulin, and glucose infusion rate (GIR) was less than 7% and
the correlation of each variable with time was not significant. M value
was defined as the GIR corrected for the glucose added or removed from
the glucose space (space correction) as previously described
(10). The glucose clamp-derived index of insulin
sensitivity (SI(clamp)) was calculated as
follows:
 | (1) |
Iss is the difference between steady-state and
basal insulin concentration (µU/ml) (19).
QUICKI. QUICKI hinges, like HOMA-IR, on the measurement of
fasting insulin and glucose concentrations. Whereas HOMA-IR is
proportional to the product of fasting insulin and glucose
concentrations (13, 14), QUICKI takes both the logarithm
and the reciprocal of the glucose-insulin product
(15):
 | (2) |
 | (3) |
Statistical analysis
All data are presented as a mean ± SEM. The relationships SI(clamp) vs. QUICKI and SI(clamp) vs. revised QUICKI were investigated by means of simple regression analysis. Comparisons between the normal and the offspring group of subjects were performed using the two-tailed unpaired t test.
To ascertain how important the relative contribution of fasting
glucose, insulin, and FFA concentration to predict insulin sensitivity
was, we used the following approach: First, we expressed in a more
general way how QUICKI describes the nonlinear relationship between
insulin sensitivity and predictor variables; then, by inverting such a
relationship, we obtained an expression for insulin resistance as a
linear combination of the predictor variables, which allowed us to
resort to standard regression techniques to determine their relative
importance. The expression that generalizes the way QUICKI describes
the relationship between insulin sensitivity (IS) and predictor
variables is given by:
 | (4) |
with weights c0 = 0 and
ci = 1 for i 
1. If one refers to the inverse
of IS [i.e. insulin resistance (IR)], it becomes simpler
to ascertain which variables are more informative. In fact, by taking
the inverse of the two members of Eq. 4 one has:
 | (5) |
states that the relationship between IR and the predictor
variables is linear and thus is well suited for being analyzed by
standard regression analysis techniques. We used simple regression
analysis to determine the association between IR (calculated as the
inverse of SI(clamp)) and
log(Ib), log(Gb), and
log(FFAb), separately. Then we used forward and
backward stepwise regression (using F ratio-to-remove of 4 and F
ratio-to-enter of 3.996) to assess which of these variables were more
relevant as predictors. Results
Anthropometric and laboratory characteristics of the study subjects
The anthropometric data of the study subjects are summarized in
Table 1 together with the plasma glucose, insulin, and FFA
concentrations measured in the fasting and insulin-stimulated
conditions.
Indices of insulin sensitivity
Clamp-based indices of insulin sensitivity. Clamp-based index
of IS are reported in Table 1. As expected,
SI(clamp), M value, and GIR were very well
correlated with one another (r = 0.83, P <
0.0001). In addition, both SI(clamp) (r =
-0.40, P < 0.01) and GIR (r = -0.58,
P < 0.0001) were inversely associated with fasting
FFA.
Relationship between the clamp-derived indices of insulin sensitivity and HOMA. HOMA-IR was weakly associated with SI(clamp) (r = 0.23, P = 0.09). The relationship was significantly improved when fasting FFA was incorporated (r = 0.47, P = 0.0002).
Relationship between the clamp-derived indices of insulin
sensitivity and QUICKI. QUICKI was associated with
SI(clamp) (Fig. 1, left panel: r = 0.27, P < 0.05) but
poorly with GIR (r = 0.23, P = 0.08). The use of
the revised QUICKI markedly improved the relationships with both
SI(clamp) (Fig. 1, right panel: r
= 0.51, P < 0.0001) and GIR (r = 0.62,
P < 0.0001). The revised QUICKI was associated with
SI(clamp) also when data were separately plotted
in men (r = 0.65, P < 0.01), women (r =
0.41, P = 0.01), offspring of type 2 diabetic parents
(r = 0.56, P < 0.02), and individuals without
family history of diabetes (r = 0.45, P <
0.004).
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To compare the ability of the revised QUICKI with respect to
the QUICKI to detect differences in insulin sensitivity between normal
subjects and the offspring of type 2 diabetic parents, a randomized
selection of 17 control subjects was performed to obtain tight matching
of anthropometric characteristics (Table 2). Fasting plasma glucose and FFA
concentrations were significantly increased in the offspring of type 2
diabetic parents with respect to the normal subjects, whereas insulin
concentrations were comparable both in the fasting and clamp condition.
Impaired insulin sensitivity in the offspring of type 2 diabetic
parents was reflected by 21% and 20% reduction in GIR and
SI(clamp), respectively, with respect to normal
subjects. Whereas HOMA-IR and QUICKI did not detect any significant
difference in insulin sensitivity between the two groups, the revised
QUICKI found a significant reduction in insulin sensitivity in the
offspring group (10%).
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During the low-dose euglycemic hyperinsulinemic clamp, circulating insulin levels increased from 43 ± 4 to 163 ± 14 pmol/liter; meanwhile, during the high-dose clamp, plasma insulin increased similarly to the previous protocol. GIRs were 2.62 ± 0.20 and 6.13 ± 0.53 mg/(kg·min), respectively, during the low- and high-dose clamps. Fasting FFA levels (0.577 ± 0.077 mmol/liter) decreased by 64 ± 5% (0.205 ± 0.035 mmol/liter) during the low-dose and by 84 ± 5% (0.094 ± 0.014 mmol/liter) during the high-dose study. FFA levels during the low-dose insulin clamp (which is more appropriate to study the antilipolytic insulin action) were inversely associated with the SI(clamp) calculated during both the low- (r = - 0.57, P = 0.033) and high-dose insulin clamp (r = - 0.58, P = 0.028).
Linear regression analysis
Table 3 shows the results of the
simple and multiple stepwise regression analysis between IR (IR =
1/SI(clamp)) and the metabolic predictor
variables (e.g. log[Ib],
log[FFAb], and
log[Gb]). Simple regression analysis showed
that, when taken separately, log(Ib) and
log(FFAb) were significantly associated with IR,
whereas log(Gb) was not. The multiple stepwise
regression analysis selected log(Ib) and
log(FFAb) as the best set of predictors of
IR.
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The results of the present study show that incorporation of
fasting plasma FFA concentration into QUICKI improves its association
with insulin sensitivity in healthy, nonobese individuals. There are
three observations supporting the beneficial effect of calculating a
revised QUICKI that includes the contribution of the fasting FFA
concentration. First, the revised QUICKI considerably improved the
relationship with SI(clamp) with respect to
QUICKI (Fig. 1). Second, the revised QUICKI was able to detect a
reduction in insulin sensitivity (P < 0.01)—that
QUICKI was unable to detect (P = 0.27)—in a subgroup
of individuals with a parent affected by type 2 diabetes in comparison
with a subgroup of matched individuals without family history of
diabetes (Table 2). Third, the results of the multiple regression
analysis indicate that the fasting FFA concentration gives a
significant contribution to improve the ability of QUICKI to predict
insulin sensitivity (Table 3).
QUICKI is an attractive approach to measure insulin sensitivity in population studies because it is based on a single blood sample and is well correlated with the clamp-based estimate of insulin sensitivity (15). However, QUICKI seems to be less efficient when used to measure insulin sensitivity in the normal range (15). Additional supporting evidence comes from the present study in which we found that QUICKI was unable to detect differences in insulin sensitivity between groups of nonobese individuals with or without family history for type 2 diabetes, the former group having a significant 20% reduction in insulin sensitivity, according to the euglycemic hyperinsulinemic clamp. The finding that QUICKI is less powerful when applied to nonobese subjects is probably related to the fact that in this group of individuals, fasting glucose and insulin are both within narrow ranges, which makes it difficult for QUICKI to span with accuracy the wide spectrum of insulin sensitivity characterizing normal individuals. These difficulties motivated our search for an additional postabsorptive metabolic marker of insulin action that could improve the ability of QUICKI to measure insulin sensitivity. We hypothesized that the fasting FFA level might be useful to this purpose because 1) in healthy normal subjects considered to be at high risk of developing diabetes—offspring of type 2 diabetic parents—the postabsorptive plasma FFA concentration was increased in the presence of normal plasma glucose and insulin levels (8, 17, 20, 21); 2) lipolysis is very sensitive to insulin, being that a half-maximal effective dose of insulin for suppression of lipolysis is about 50% of that for suppression of endogenous glucose production (22, 23); and 3) the dose-response study performed in an additional subgroup of 14 individuals showed that FFA levels during the low-dose insulin clamp were inversely associated with SI(clamp) both during the low- and high-dose insulin clamp.
The use of fasting FFA level was beneficial to QUICKI because it
strengthened its association with insulin sensitivity (Fig. 1) and also
increased its discriminatory power, enabling it to detect a difference
in insulin sensitivity between the individuals with or without family
history of diabetes (Table 2). The latter finding is of particular
importance if QUICKI is to be used in population studies aiming to
identify groups at risk of developing diabetes in which the impairment
of insulin sensitivity is at an early stage.
The importance of the contribution of FFA concentration to QUICKI was further corroborated by the results of the regression analysis. In our group of nonobese subjects, we found that insulin concentration is the most important correlate of insulin resistance; FFA concentration yields additional, independent information that significantly improves the association with insulin resistance; and glucose concentration has only a minor, nonsignificant role. We speculate that the relative unimportance of glucose as predictor of IR is due to the fact that in normal subjects the glucose-insulin homeostatic system is successful in maintaining glycemia within a very narrow range. It is plausible that the role of glucose as predictor of IR may be more important in obese individuals and in type 2 diabetic patients (i.e. in groups in which the homeostatic loop is disturbed).
This study in normal subjects represents a prerequisite, and further work is needed to ascertain whether including the fasting FFA concentration into QUICKI can be useful throughout the entire spectrum of glucose tolerance. What needs to be assessed is whether FFA concentration is still a source of meaningful, independent information on insulin sensitivity also in groups with a higher degree of insulin resistance. Anyway, it is encouraging to find evidence in the literature suggesting that fasting FFA concentration might be helpful in the prediction of insulin resistance also in obese and type 2 diabetic patients. In fact, it has been observed that obese patients with type 2 diabetes mellitus have impaired regulation of lypolisis (23, 24), and that in vivo experimental increments of plasma FFA concentrations in healthy humans can induce an IR similar to that observed in type 2 diabetes (25, 26).
In conclusion, this work represents the first attempt to include other metabolic markers, in addition to fasting glucose and insulin, to obtain a more accurate prediction of insulin sensitivity based on a fasting blood sample. Incorporation of the fasting FFA concentration into QUICKI not only improved its association with insulin sensitivity but also enhanced its discriminatory power. Thus, incorporation of the fasting FFA concentration into QUICKI may be helpful for earlier identification of metabolic abnormalities in at-risk subjects and, if validated also in disease states, may contribute to a more widespread use of QUICKI in large-scale or epidemiological studies.
Acknowledgments
We thank Antonella Scollo, R.N., of the Metabolic Unit of the Istituto Scientifico H San Raffaele for nursing assistance.
Footnotes
This work was supported by grants from the Italian Minister of Health (RF96.305, RF98.49, RF99.55) and Italian National Research Council (CNR 97.00485.CT04). The financial support of Telethon–Italy (1032C) is also gratefully acknowledged.
Abbreviations: CV, Coefficient of variation; GIR, glucose infusion rate; HOMA, homeostasis model assessment index; IR, insulin resistance; QUICKI, Quantitative Insulin sensitivity Check Index; SI(clamp), glucose clamp-derived index of insulin sensitivity.
Received April 11, 2001.
Accepted June 19, 2001.
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H. Yokoyama, M. Emoto, S. Fujiwara, K. Motoyama, T. Morioka, M. Komatsu, H. Tahara, T. Shoji, Y. Okuno, and Y. Nishizawa Quantitative Insulin Sensitivity Check Index and the Reciprocal Index of Homeostasis Model Assessment in Normal Range Weight and Moderately Obese Type 2 Diabetic Patients Diabetes Care, August 1, 2003; 26(8): 2426 - 2432. [Abstract] [Full Text] [PDF]
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G. Perseghin, A. Caumo, C. Arcelloni, S. Benedini, R. Lanzi, E. Pagliato, L. P. Sereni, G. Testolin, A. Battezzati, G. Comi, et al. Contribution of Abnormal Insulin Secretion and Insulin Resistance to the Pathogenesis of Type 2 Diabetes in Myotonic Dystrophy Diabetes Care, July 1, 2003; 26(7): 2112 - 2118. [Abstract] [Full Text] [PDF]
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G. Perseghin, A. Caumo, L. P. Sereni, A. Battezzati, and L. Luzi Fasting Blood Sample-Based Assessment of Insulin Sensitivity in Kidney-Pancreas-Transplanted Patients Diabetes Care, December 1, 2002; 25(12): 2207 - 2211. [Abstract] [Full Text] [PDF]
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R. C. Ho, K. P. Davy, M. S. Hickey, S. A. Summers, and C. L. Melby Behavioral, metabolic, and molecular correlates of lower insulin sensitivity in Mexican-Americans Am J Physiol Endocrinol Metab, October 1, 2002; 283(4): E799 - E808. [Abstract] [Full Text] [PDF]
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A. Katsuki, Y. Sumida, E. C. Gabazza, S. Murashima, H. Urakawa, K. Morioka, N. Kitagawa, T. Tanaka, R. Araki-Sasaki, Y. Hori, et al. QUICKI Is Useful for Following Improvements in Insulin Sensitivity after Therapy in Patients with Type 2 Diabetes Mellitus J. Clin. Endocrinol. Metab., June 1, 2002; 87(6): 2906 - 2908. [Abstract] [Full Text] [PDF]
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G. E. Duncan, A. D. Hutson, and P. W. Stacpoole QUICKI Is Not a Useful and Accurate Index of Insulin Sensitivity following Exercise Training J. Clin. Endocrinol. Metab., February 1, 2002; 87(2): 950 - 951. [Full Text]
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