| 
  • If you are citizen of an European Union member nation, you may not use this service unless you are at least 16 years old.

  • You already know Dokkio is an AI-powered assistant to organize & manage your digital files & messages. Very soon, Dokkio will support Outlook as well as One Drive. Check it out today!

View
 

Assessing Insulin Sensitivity and Resistance in Humans

Page history last edited by muniyapr@mail.nih.gov 9 years, 4 months ago
The Web diabetesmanager

 

Assessing Insulin Sensitivity and Resistance in Humans  

 

Ranganath Muniyappa, M.D, PhD 

Ritu Madan, M.D

Michael J. Quon, M.D., Ph.D.

 

Diabetes, Endocrinology, and Obesity Branch,  National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)

National Institutes of Health (NIH), Bethesda, MD 20892

 

 

Chapter Submitted: 2009 

 

Editors’ Note:

A variety of tests have been developed to assess insulin sensitivity. In the recent literature, these tests have been used in an attempt to understand pathophysiology as well as compare the treatment modalities. Just as it is important for the clinician to understand statistics and epidemiology, it is imperative that we can evaluate the appropriateness of the measurements used for insulin sensitivity. To that end, this chapter will clear up the differences and describe the strengths and shortcomings of the current tests for insulin sensitively.

 


 

 

INTRODUCTION 

 

Insulin resistance plays a major patho-physiological role in type 2 diabetes and is tightly associated with major public health problems including obesity, hypertension, coronary artery disease, dyslipidemias, and a cluster of metabolic and cardiovascular abnormalities that define the metabolic syndrome [1][2][3]. A global epidemic of obesity is driving the increased incidence and prevalence of type 2 diabetes and its cardiovascular complications [4]. Insulin resistance is commonly associated with visceral adiposity, glucose intolerance, hypertension, dyslipidemia, hypercoaguable state, endothelial dysfunction, and/or elevated markers of inflammation. Therefore, the presence of these clinical abnormalities is usually characteristic of an insulin resistant state. In addition to clinical manifestations of the “Insulin Resistance Syndrome,” insulin resistance predisposes to accelerated cardiovascular disease (CVD). Therefore, it is of great importance to develop tools for quantifying insulin sensitivity/resistance in humans that may be used to appropriately investigate the epidemiology, pathophysiological mechanisms, outcomes of therapeutic interventions, and clinical course of patients with insulin resistance [5]. In this chapter, we will discuss some currently used methods for assessing insulin sensitivity, their applications, merits, and limitations.

 

Insulin Sensitivity and Resistance

Metabolic actions of insulin help to maintain glucose homeostasis and promote glucose utilization in the body [6]. Insulin increases glucose utilization in peripheral organs (e.g., skeletal muscle and adipose tissue) and suppresses hepatic glucose production (HGP). In addition to these classical metabolic insulin target tissues, there are many other important physiological targets of insulin including the brain, pancreatic beta cells, heart, and vascular endothelium that help to coordinate and couple metabolic and cardiovascular homeostasis under healthy conditions [7][8][9][10]. Insulin has concentration-dependent saturable actions to increase whole-body glucose disposal. The maximal effect of insulin defines “insulin responsiveness” while the insulin concentration required for a half-maximal response defines “insulin sensitivity” (Fig. 1). Although, other actions of insulin on fat and amino-acid metabolism, cardiovascular, kidney, and brain function also exhibit a concentration-dependent response, the term “insulin sensitivity” typically refers to insulin’s metabolic actions to promote glucose disposal.

 

Figure 1.   Schematic representation of concentration-response relationships between plasma insulin concentrations and insulin-mediated whole body glucose disposal. Curve a: normal insulin sensitivity and responsiveness. Curve b: rightward shift in insulin concentration-response curve.  This represents decreased insulin sensitivity (increased EC50) with normal insulin responsiveness.  Curve c: Decreased insulin sensitivity (increased EC50) and reduced insulin responsiveness. Curve d: Leftward shift in the insulin concentration-response response curve.  This represents increased insulin sensitivity (decreased EC50) with normal insulin responsiveness.  

 

 

 

 

 

The concept of insulin resistance was proposed as early as 1936 to describe diabetic patients requiring high doses of insulin [11]. Insulin resistance is typically defined as decreased sensitivity and/or responsiveness to insulin-mediated glucose disposal and/or inhibition of HGP. Rigorous evaluation of altered sensitivity and responsiveness therefore requires a comparison of insulin dose-response curves.

 

 

DIRECT MEASURES OF INSULIN SENSITIVITY

 

Hyperinsulinemic Euglycemic Glucose Clamp

Procedure

The glucose clamp technique, originally developed by Andres and DeFronzo is widely accepted as the reference standard for directly determining metabolic insulin sensitivity in humans [12]. After an overnight fast, insulin is infused intravenously at a constant rate that may range from 5 - 120 mU/m2/min (dose per body surface area per minute). This constant insulin infusion results in a new steady-state insulin level that is above the fasting level (hyperinsulinemic). As a consequence, glucose disposal in skeletal muscle and adipose tissue is increased while HGP is suppressed. Under these conditions, a bedside glucose analyzer is used to frequently monitor blood glucose levels at 5 – 10 min intervals while 20% dextrose is given intravenously at a variable rate in order to “clamp” blood glucose concentrations in the normal range (euglycemic). An infusion of potassium phosphate is also given to prevent hypokalemia resulting from hyperinsulinemia and increased glucose disposal. After several hours of constant insulin infusion, steady-state conditions are typically achieved for plasma insulin, blood glucose, and the glucose infusion rate (GIR). Assuming that the hyperinsulinemic state is sufficient to completely suppress hepatic glucose production, and since there is no net change in blood glucose concentrations under steady-state clamp conditions, the GIR must be equal to the glucose disposal rate (M) (Fig. 2). Thus, whole body glucose disposal at a given level of hyperinsulinemia can be directly determined. M is typically normalized to body weight or fat-free mass to generate an estimate of insulin sensitivity. Alternatively, an insulin sensitivity index derived from clamp data can be defined as SIClamp= M/(G x ΔI), where M is normalized for G (steady-state blood glucose concentration) and ΔI (difference between fasting and steady-stateplasma insulin concentrations) [13].

 

Figure 2.   Schematic representation of the “steady state” dynamics of glucose and insulin during an euglycemic hyperinsulinemic glucose clamp.

 

 

The validity of glucose clamp measurements of insulin sensitivity depends on achieving steady-state conditions. “Steady-state” is often defined as a period greater than 30-min (at least 1 h after initiation of insulin infusion) during which the coefficient of variation for blood glucose,plasma insulin, and GIR are less than 5% [14][15]. It is possible to use radio-labeled glucose tracer under clamp conditions to estimate HGP so that appropriate corrections can be made to M in the event HGP is not completely supressed [16][17][18][19]. An alternative approach is to use an insulin infusion rate sufficiently high to completely suppress HGP according to the insulin sensitivity/resistance of the population to be studied. M is routinely obtained at only a single insulin infusion rate and therefore comparisons between M or SIClampamong different subjects is valid only if the same insulin infusion rate is used for all subjects.

 

Advantages and Limitations

The principal advantage of the glucose clamp in humans is that it directly measures whole body glucose disposal at a given level of insulinemia under steady-state conditions. Conceptually, the approach is straightforward and there are a limited number of assumptions which are clearly defined. In research settings where assessing insulin sensitivity/resistance is of primary interest and feasibility is not an issue (e.g., study population < 100) it is appropriate to use the reference standard glucose clamp technique. The main limitations of the clamp approach are that it is time-consuming, labor intensive, expensive, and requires an experienced operator to manage technical difficulties. Thus, for epidemiological studies, large clinical investigations, or routine clinical applications (e.g., following changes in insulin resistance after therapeutic intervention in individual patients) application of the glucose clamp is not feasible.

 

 

Insulin-suppression Test (IST)

 

Procedure

The insulin-suppression test, another method that directly measures metabolic insulin sensitivity/resistance, was introduced by Shen et. al. in 1970 and subsequently modified by Harano et. al. [20][21]. After an overnight fast, somatostatin (250 μg/h) or the somatostatin analogue octreotide (25 µg bolus, followed by 0.5 µg/min) [22] is intravenously infused to suppress endogenous secretion of insulin and glucagon. Simultaneously, insulin (25 mU/m2/min) and glucose (240 mg/m2/min) are infused into the same antecubital vein over 3 h. From the contralateral arm, blood samples for glucose and insulin determinations are taken every 30 min for 2.5 h and then at 10 min intervals from 150 - 180 min of the IST. The constant infusions of insulin and glucose determine steady-state plasma insulin (SSPI) and glucose (SSPG) concentrations. The steady-state period is assumed to be from 150 - 180 min after initiation of the IST. SSPI concentrations are generally (but not always) similar among subjects. Therefore, the SSPG concentration will be higher in insulin resistant subjects and lower in insulin sensitive subjects. That is, SSPG values are inversely related to insulin sensitivity. The IST provides a direct measure (SSPG) of the ability of exogenous insulin to mediate disposal of an intravenous glucose load under steady-state conditions where endogenous insulin secretion is suppressed.

 

Advantages and Limitations

The SSPG is a highly reproducible direct measure of metabolic actions of insulin that is less labor-intensive and less technically demanding than the glucose clamp. Indeed, since there are no variable infusions with the IST, steady-state conditions are more easily achieved with the IST than with the glucose clamp. Estimates of insulin sensitivity determined by SSPG correlate well with reference standard glucose clamp estimates in normal subjects (r = 0.93) and in patients with type 2 diabetes mellitus (r = 0.91).[23][24]. Indeed, SSPG has positive predictive power for cardiovascular disease events and onset of type 2 diabetes [25][26]. In research settings where assessing insulin sensitivity/resistance is of primary interest and feasibility is not an issue, it is appropriate to use the IST. Moreover, the IST can be used for larger populations that may pose difficulties for application of the glucose clamp [27]. Many of the limitations of the IST are similar to those described above for the glucose clamp (with the exception that the IST is less technically demanding). Thus, it is impractical to apply the IST in large epidemiological studies or in the clinical care setting. SSPG under ideal conditions determines primarily skeletal muscle insulin sensitivity and is not designed to reflect hepatic insulin sensitivity.

 

 

INDIRECT MEASURES OF INSULIN SENSITIVITY

 

Minimal Model Analysis of Frequently Sampled Intravenous Glucose Tolerance Test (FSIVGTT)

Procedure

The minimal model, developed by Bergman, Cobelli and colleagues in 1979, provides an indirect measure of metabolic insulin sensitivity/resistance based on glucose and insulin data obtained during an FSIVGTT [28]. After an overnight fast, an intravenous bolus of glucose (0.3 g/kg body weight) is infused over 2 min starting at time 0. Currently, a modified FSIVGTT is used where exogenous insulin (4 mU/kg/min) is also infused over 5 min beginning 20 min after the intravenous glucose bolus [29][30][31]. Some studies use tolbutamide instead of insulin in the modified FSIVGTT to stimulate endogenous insulin secretion [32][33][34][35]. Blood samples are taken for plasma glucose and insulin measurements at -10, -1, 1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 14, 16, 20, 22, 23, 24, 25, 27, 30, 40, 50, 60, 70, 80, 90, 100, 120, 160, and 180 min. These data are then subjected to minimal model analysis using the computer program MINMOD to generate an index of insulin sensitivity (SI).

 

The minimal model is defined by two coupled differential equations with four model parameters (Fig. 3). The first equation describes plasma glucose dynamics in a single compartment. The second equation describes insulin dynamics in a “remote compartment”. The structure of the minimal model allows MINMOD to uniquely identify model parameters that determine a best fit to glucose disappearance during the modified FSIVGTT. SIis calculated from two of these model parameters and is defined as fractional glucose disappearance per insulin concentration unit. In addition to SI, other minimal model parameters may be used to estimate a “glucose effectiveness” index (SG). SGis defined as the ability of glucose per seto promote its own disposal and inhibit HGP in the absence of an incremental insulin effect (i.e., when insulin is at basal levels).

 

 

Figure 3.  Schematic, equations, and parameters for the minimal model of glucose metabolism. Differential equations describing glucose dynamics (G(t)) in a monocompartmental “glucose space” and insulin dynamics in a “remote compartment” (X(t)) are shown at the top.  Glucose leaves or enters its space at a rate proportional to the difference between plasma glucose level, G(t) and the basal fasting level, Gb.  In addition, glucose also disappears from its compartment at a rate proportional to insulin levels in the “remote” compartment (X(t)).  In this model, t = time; G(t) = plasma glucose at time t; I(t) = plasma insulin concentration at time t; X(t) = insulin concentration in “remote” compartment at time t; Gb = basal plasma concentration; Ib = basal plasma insulin concentration; G(0) = G0 (assuming instantaneous mixing of the IV glucose load); p1, p2, p3, and G0 = unknown parameters in the model that are uniquely identifiable from FSIVGTT; glucose effectiveness, SG = p1; and insulin sensitivity, SI = p3/p2..

 

 

Advantages and Limitations

Minimal model analysis of the modified FSIVGTT is easier than the glucose clamp method because it is slightly less labor intensive, steady-state conditions are not required, and there are no intravenous infusions that require constant adjustment. Unlike the glucose clamp or IST, information about insulin sensitivity, glucose effectiveness, and beta cell function can be derived from a single dynamic test. The minimal model generates excellent predictions of glucose disappearance during the FSIVGTT. SIis a strong predictor of the development of diabetes in a prospective study of children of diabetic parents [36]. Moreover, the insulin-modified FSIVGT may be used in relatively large-scale population studies [37]. Therefore, in research settings where assessing insulin sensitivity along with glucose effectiveness and beta cell function is of interest, minimal model analysis of the insulin-modified FSIVGTT may be appropriate. The minimal model approach is simpler than direct methods for determining insulin sensitivity. Nevertheless, it still involves intravenous infusions with multiple blood sampling over a 3 h period that is nearly as labor intensive as the glucose clamp or IST. In addition, many limitations of minimal model analysis stem from the fact that the model oversimplifies the physiology of glucose homeostasis and is discussed in detail elsewhere [38].

 

 

Oral Glucose Tolerance Test (OGTT)

The oral glucose tolerance test (OGTT) is a simple test widely used in clinical practice to diagnose glucose intolerance and type 2 diabetes [39]. After overnight fast, blood samples for determinations of glucose and insulin concentrations are taken at 0, 30, 60, and 120 min following a standard oral glucose load (75 g). Oral glucose tolerance reflects the efficiency of the body to dispose of glucose after an oral glucose load or meal. The OGTT mimics the glucose and insulin dynamics of physiological conditions more closely than conditions of the glucose clamp, IST, or FSIVGTT. However, it is important to recognize that glucose tolerance and insulin sensitivity are not equivalent concepts. In addition to metabolic actions of insulin, insulin secretion, incretin effects, and other factors contribute importantly to glucose tolerance. Thus, the OGTT and meal tolerance tests provide useful information about glucose tolerance but not insulin sensitivity/resistance per se.

 

 

SIMPLE SURROGATE INDEXES FOR INSULIN SENSITIVITY/RESISTANCE

 

Surrogates Derived from Fasting Steady-state Conditions

Procedure

After an overnight fast, a single blood sample is taken for determination of blood glucose and plasma insulin. In healthy humans, the fasting condition represents a basal steady-state where glucose is homeostatically maintained in the normal range such that insulin levels are not significantly changing and HGP is constant. That is, basal insulin secretion by pancreatic β cells determines a relatively constant level of insulinemia that will be lower or higher in accordance with insulin sensitivity/resistance such that HGP matches whole body glucose disposal under fasting conditions. Surrogate indexes based on fasting glucose and insulin concentrations reflect primarily hepatic insulin sensitivity/resistance. However, under most conditions, hepatic and skeletal muscle insulin sensitivity/resistance are proportional to each other. In the diabetic state with fasting hyperglycemia, fasting insulin levels are inappropriately low and insufficient to maintain euglycemia. Therefore, definitions of the more useful surrogate indexes take these considerations into account. Due to lack of a standardized insulin assay, it is not possible to use surrogate indexes to define universal cutoff points for insulin resistance.

 

Advantages and Limitations

Simple surrogate indexes of insulin sensitivity/resistance are inexpensive quantitative tools that can be easily applied in almost every setting including epidemiologal studies, large clinical trials, clinical research investigations, and clinical practice. If a direct measure of insulin sensitivity is not required, not feasible to obtain, or if insulin sensitivity is of secondary interest, it may be appropriate to use a surrogate index. The relative merits and limitations of individual surrogate indexes are discussed below.

 

The Homeostasis Model Assessment (HOMA)

HOMA, developed in 1985, is a model of interactions between glucose and insulin dynamics that is then used to predict fasting steady-state glucose and insulin concentrations for a wide range of possible combinations of insulin resistance and beta cell function [40]. The model assumes a feedbackloop between the liver and β-cell [41][42][43]; glucose concentrations are regulated by insulin-dependent HGP while insulin levels depend on the pancreatic β-cell response to glucose concentrations. Thus, deficient β-cell function reflects a diminished response to glucose-stimulated insulin secretion. Likewise, insulin resistance is reflected by diminished suppressive effect of insulin on HGP. HOMA model describes this glucose-insulin homeostasis by a set ofempirically derived non-linear equations. The model predicts fasting steady-state levels of plasma glucose and insulin for any given combination of pancreatic β-cell function and insulin sensitivity. Computer simulations, have been used to generate a grid from which, mathematical transformations of fasting glucose and insulin data from individual subjects determine unique combinations of insulin sensitivity (HOMA %S) and beta cell function (HOMA %B) from steady-state conditions. An important caveat for HOMA is that it imputes a dynamic beta cell function (i.e., glucose-stimulated insulin secretion) from fasting steady-state data. In the absence of dynamic data, it is difficult, if not impossible, to determine the true dynamic function of beta cell insulin secretion.

 

In practical terms, most studies using HOMA employ an approximation described by a simple equation to determine a surrogate index of insulin resistance. This is defined by the product of the fasting glucose and fasting insulin divided by a constant. Thus, HOMA-IR = [(Fasting Insulin (µU/mL)) X (Fasting Glucose (mmol/L))]/22.5. The constant is a normalizing factor, the product of fasting plasma insulin of 5 µU/mL and plasma glucose of 4.5 mmol/L obtained from an “ideal” and “normal” individual. Therefore, for an individual with normal insulin sensitivity, HOMA-IR = 1. It is important to note that over wide ranges of insulin sensitivity/resistance Log (HOMA-IR),which normalizes the skewed distribution of fasting insulin values determines a much stronger linear correlation with glucose clamp estimates of insulin sensitivity [44]. HOMA or Log (HOMA) is extensively used in large epidemiological studies, prospective clinical trials, and clinical research studies [45][46][47]. In research settings where assessing insulin sensitivity/resistance is of secondary interest or feasibility issues preclude the use of direct measures by glucose clamp, it may be appropriate to use Log (HOMA-IR). However, as discussed below, other surrogate indexes have certain advantages over HOMA or Log (HOMA) in some circumstances.

 

Quantitative Insulin Sensitivity Check Index (QUICKI)

QUICKI is an empirically-derived mathematical transformation of fasting blood glucose and plasma insulin concentrations that provides a reliable, reproducible, and accurate index of insulin sensitivity with excellent positive predictive power [48][49][50][51][52]. Since fasting insulin levels have a non-normal skewed distribution, log transformation improves its linear correlation with SIclamp. However, as with 1/(fasting insulin) and the G/I ratio, this correlation is not maintained in diabetic subjects with fasting hyperglycemia and impaired beta cell function that is insufficient to maintain euglycemia. To accommodate these clinically important circumstances where fasting glucose is inappropriately high and insulin is inappropriately low, addition of log (fasting glucose) to log (fasting insulin) provides a reasonable correction such that the linear correlation with SIClampis maintained in both diabetic and non-diabetic subjects. The reciprocal of this sum results in further transformation of the data generating an insulin sensitivity index that has a positive correlation with SIclamp. Thus, QUICKI = 1/[Log (Fasting Insulin, µU/ml) + Log (Fasting Glucose, mg/dl)]. Over a wide range of insulin sensitivity/resistance, QUICKI has a substantially better linear correlation with SIclamp(r ≈ 0.8 – 0.9) than SI derived from the minimal model or HOMA-IR [53][54][55]. Log (HOMA) is roughly comparable to QUICKI in this regard. Multiple independent studies find excellent linear correlations between QUICKI and glucose clamp estimates (either GIR or SIClamp) in healthy subjects, obesity, diabetes, hypertension, and many other insulin-resistant states [56][57][58][59][60][61][62]. QUICKI is among the most thoroughly evaluated and validated surrogate index for insulin sensitivity. As a simple, useful, inexpensive, and minimally invasive surrogate for glucose clamp-derived measures of insulin sensitivity, QUICKI is appropriate and effective for use in large epidemiological or clinical research studies, to follow changes after therapeutic interventions, and for use in studies where evaluation of insulin sensitivity is not of primary interest.

 

Surrogates Derived from Dynamic Tests

Procedure

Surrogate indexes of insulin sensitivity that use information derived from dynamic tests include OGTT, meal tolerance tests, and IVGTT. Procedures for these tests have been described in a previous section. Specific indexes including Matsuda index [63], Stumvoll index [64], Avignon index [65], oral glucose insulin sensitivity index (OGSI) [66], Gutt index [67], and Belfiore index [68] use particular sampling protocols during the OGTT or the meal. In addition, minimal model approaches have been used to model plasma glucose and insulin dynamics during an OGTT or a meal to determine insulin sensitivity/resistance [69]. Glucose disposal of an oral glucose load or a meal is mediated by a complex dynamic process that includes gut absorption, glucose effectiveness, neurohormonal actions, incretin actions, insulin secretion, and metabolic actions of insulin that primarily determine the balance between peripheral glucose utilization and HGP. Surrogate indexes that depend on dynamic testing take into account both fasting steady-state and dynamic post-glucose load plasma glucose and insulin levels.

 

The oral route of glucose delivery is more physiological than intravenous glucose infusion. However, poor reproducibility of the OGTT and meal tolerance test due to variable glucose absorbtion, splanchnic glucose uptake, and additional incretin effects need to be considered. Thus, distinguishing direct metabolic actions of insulin following oral ingestion of glucose or a mixed mealis more problematic than after FSIVGTT. In addition, as with many other measures of insulin sensitivity, surrogates derived from dynamic testing generally incorporate both peripheral and hepatic insulin sensitivity. Although OGTT involves considerably less work than FSIVGTT, dynamic testing in general requires more effort and cost than fasting blood sampling.

 

Advantages and Limitations

 Many surrogate measures derived from dynamic data correlate reasonably well with glucose clamp estimates of insulin sensitivity [70][71][72]. Estimates of insulin sensitivity derived from OGTT predict the development of type 2 diabetes in epidemiologic studies [73][74][75]. The advantage of surrogates based on dynamic testing is that information about insulin secretion can be obtained at the same time as information about insulin action. However, if one is only interested in estimating insulin sensitivity/resistance, fasting surrogates may be preferable to dynamic surrogates because they are simpler to obtain.

 

SUMMARY

In this chapter we have discussed a representative variety of methods currently available for estimating insulin sensitivity/resistance (but this by no means an exhaustive review) (Table 1). These range from complex, time consuming, labor-intensive, invasive procedures to simple tests involving a single fasting blood sample. It is important to understand the physiological concepts informing each method so that relative merits and limitations of particular approaches are appropriately matched with proposed applications and data is interpreted correctly. The glucose clamp method is the reference standard for direct measurement of insulin sensitivity. Regarding simple surrogates, QUICKI and Log (HOMA) are among the best and most extensively validated. Dynamic tests are useful if information about both insulin secretion and insulin action are needed.

 

 

 

 

 

 

 

ACKNOWLEDGEMENTS

This work was supported by the Intramural Research Program, NIDDK, NIH

 

Footnotes

  1. Reaven GM 2005 The insulin resistance syndrome: definition and dietary approaches to treatment. Annu Rev Nutr 25:391-406
  2. Petersen KF, Dufour S, Savage DB, Bilz S, Solomon G, Yonemitsu S, Cline GW, Befroy D, Zemany L, Kahn BB, Papademetris X, Rothman DL, Shulman GI 2007 Inaugural Article: The role of skeletal muscle insulin resistance in the pathogenesis of the metabolic syndrome. Proc Natl Acad Sci U S A 104:12587-12594
  3. DeFronzo RA, Ferrannini E 1991 Insulin resistance. A multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia, and atherosclerotic cardiovascular disease. Diabetes Care 14:173-194
  4. Poirier P, Giles TD, Bray GA, Hong Y, Stern JS, Pi-Sunyer FX, Eckel RH 2006 Obesity and cardiovascular disease: pathophysiology, evaluation, and effect of weight loss: an update of the 1997 American Heart Association Scientific Statement on Obesity and Heart Disease from the Obesity Committee of the Council on Nutrition, Physical Activity, and Metabolism. Circulation 113:898-918
  5. Muniyappa R, Lee S, Chen H, Quon MJ 2008 Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab 294:E15-26
  6. Accili D 2004 Lilly lecture 2003: the struggle for mastery in insulin action: from triumvirate to republic. Diabetes 53:1633-1642
  7. Accili D 2004 Lilly lecture 2003: the struggle for mastery in insulin action: from triumvirate to republic. Diabetes 53:1633-1642
  8. Prodi E, Obici S 2006 Minireview: the brain as a molecular target for diabetic therapy. Endocrinology 147:2664-2669
  9. Muniyappa R, Montagnani M, Koh KK, Quon MJ 2007 Cardiovascular Actions of Insulin. Endocr Rev
  10. Kahn SE, Hull RL, Utzschneider KM 2006 Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444:840-846
  11. Himsworth HP 1936 DIABETES MELLITUS : ITS DIFFERENTIATION INTO INSULIN-SENSITIVE AND INSULIN-INSENSITIVE TYPES. The Lancet 227:127-130
  12. DeFronzo RA, Tobin JD, Andres R 1979 Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol 237:E214-223
  13. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, Quon MJ 2000 Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 85:2402-2410
  14. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, Quon MJ 2000 Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 85:2402-2410
  15. Chen H, Sullivan G, Yue LQ, Katz A, Quon MJ 2003 QUICKI is a useful index of insulin sensitivity in subjects with hypertension. Am J Physiol Endocrinol Metab 284:E804-812
  16. Rizza RA, Mandarino LJ, Gerich JE 1981 Dose-response characteristics for effects of insulin on production and utilization of glucose in man. Am J Physiol 240:E630-639
  17. Finegood DT, Bergman RN, Vranic M 1987 Estimation of endogenous glucose production during hyperinsulinemic-euglycemic glucose clamps. Comparison of unlabeled and labeled exogenous glucose infusates. Diabetes 36:914-924
  18. McMahon MM, Schwenk WF, Haymond MW, Rizza RA 1989 Underestimation of glucose turnover measured with [6-3H]- and [6,6-2H]- but not [6-14C]glucose during hyperinsulinemia in humans. Diabetes 38:97-107
  19. Radziuk J, Pye S 2002 Quantitation of basal endogenous glucose production in Type II diabetes: importance of the volume of distribution. Diabetologia 45:1053-1084
  20. Shen SW, Reaven GM, Farquhar JW 1970 Comparison of impedance to insulin-mediated glucose uptake in normal subjects and in subjects with latent diabetes. J Clin Invest 49:2151-2160
  21. Harano Y, Hidaka H, Takatsuki K, Ohgaku S, Haneda M, Motoi S, Kawagoe K, Shigeta Y, Abe H 1978 Glucose, insulin, and somatostatin infusion for the determination of insulin sensitivity in vivo. Metabolism 27:1449-1452
  22. Pei D, Jones CN, Bhargava R, Chen YD, Reaven GM 1994 Evaluation of octreotide to assess insulin-mediated glucose disposal by the insulin suppression test. Diabetologia 37:843-845
  23. Greenfield MS, Doberne L, Kraemer F, Tobey T, Reaven G 1981 Assessment of insulin resistance with the insulin suppression test and the euglycemic clamp. Diabetes 30:387-392
  24. Mimura A, Kageyama S, Maruyama M, Ikeda Y, Isogai Y 1994 Insulin sensitivity test using a somatostatin analogue, octreotide (Sandostatin). Horm Metab Res 26:184-187
  25. Yip J, Facchini FS, Reaven GM 1998 Resistance to insulin-mediated glucose disposal as a predictor of cardiovascular disease. J Clin Endocrinol Metab 83:2773-2776
  26. Facchini FS, Hua N, Abbasi F, Reaven GM 2001 Insulin resistance as a predictor of age-related diseases. J Clin Endocrinol Metab 86:3574-3578
  27. Yeni-Komshian H, Carantoni M, Abbasi F, Reaven GM 2000 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
  28. Bergman RN, Ider YZ, Bowden CR, Cobelli C 1979 Quantitative estimation of insulin sensitivity. Am J Physiol 236:E667-677
  29. Finegood DT, Hramiak IM, Dupre J 1990 A modified protocol for estimation of insulin sensitivity with the minimal model of glucose kinetics in patients with insulin-dependent diabetes. J Clin Endocrinol Metab 70:1538-1549
  30. Saad MF, Steil GM, Kades WW, Ayad MF, Elsewafy WA, Boyadjian R, Jinagouda SD, Bergman RN 1997 Differences between the tolbutamide-boosted and the insulin-modified minimal model protocols. Diabetes 46:1167-1171
  31. Quon MJ, Cochran C, Taylor SI, Eastman RC 1994 Direct comparison of standard and insulin modified protocols for minimal model estimation of insulin sensitivity in normal subjects. Diabetes Res 25:139-149
  32. Saad MF, Steil GM, Kades WW, Ayad MF, Elsewafy WA, Boyadjian R, Jinagouda SD, Bergman RN 1997 Differences between the tolbutamide-boosted and the insulin-modified minimal model protocols. Diabetes 46:1167-1171
  33. Yang YJ, Youn JH, Bergman RN 1987 Modified protocols improve insulin sensitivity estimation using the minimal model. Am J Physiol 253:E595-602
  34. Beard JC, Bergman RN, Ward WK, Porte D, Jr. 1986 The insulin sensitivity index in nondiabetic man. Correlation between clamp-derived and IVGTT-derived values. Diabetes 35:362-369
  35. Bergman RN, Prager R, Volund A, Olefsky JM 1987 Equivalence of the insulin sensitivity index in man derived by the minimal model method and the euglycemic glucose clamp. J Clin Invest 79:790-800
  36. Martin BC, Warram JH, Krolewski AS, Bergman RN, Soeldner JS, Kahn CR 1992 Role of glucose and insulin resistance in development of type 2 diabetes mellitus: results of a 25-year follow-up study. Lancet 340:925-929
  37. Howard G, O'Leary DH, Zaccaro D, Haffner S, Rewers M, Hamman R, Selby JV, Saad MF, Savage P, Bergman R 1996 Insulin sensitivity and atherosclerosis. The Insulin Resistance Atherosclerosis Study (IRAS) Investigators. Circulation 93:1809-1817
  38. Muniyappa R, Lee S, Chen H, Quon MJ 2008 Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol Endocrinol Metab 294:E15-26
  39. 2007 Diagnosis and classification of diabetes mellitus. Diabetes Care 30 Suppl 1:S42-47
  40. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC 1985 Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28:412-419
  41. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC 1985 Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28:412-419
  42. Levy JC, Matthews DR, Hermans MP 1998 Correct homeostasis model assessment (HOMA) evaluation uses the computer program. Diabetes Care 21:2191-2192
  43. Wallace TM, Levy JC, Matthews DR 2004 Use and abuse of HOMA modeling. Diabetes Care 27:1487-1495
  44. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, Quon MJ 2000 Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 85:2402-2410
  45. Wallace TM, Levy JC, Matthews DR 2004 Use and abuse of HOMA modeling. Diabetes Care 27:1487-1495
  46. Haffner SM, Miettinen H, Stern MP 1997 The homeostasis model in the San Antonio Heart Study. Diabetes Care 20:1087-1092
  47. Dabelea D, Pettitt DJ, Hanson RL, Imperatore G, Bennett PH, Knowler WC 1999 Birth weight, type 2 diabetes, and insulin resistance in Pima Indian children and young adults. Diabetes Care 22:944-950
  48. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, Quon MJ 2000 Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 85:2402-2410
  49. Chen H, Sullivan G, Yue LQ, Katz A, Quon MJ 2003 QUICKI is a useful index of insulin sensitivity in subjects with hypertension. Am J Physiol Endocrinol Metab 284:E804-812
  50. Chen H, Sullivan G, Quon MJ 2005 Assessing the predictive accuracy of QUICKI as a surrogate index for insulin sensitivity using a calibration model. Diabetes 54:1914-1925
  51. Mather KJ, Hunt AE, Steinberg HO, Paradisi G, Hook G, Katz A, Quon MJ, Baron AD 2001 Repeatability characteristics of simple indices of insulin resistance: implications for research applications. J Clin Endocrinol Metab 86:5457-5464
  52. Hanley AJ, Williams K, Gonzalez C, D'Agostino RB, Jr., Wagenknecht LE, Stern MP, Haffner SM 2003 Prediction of type 2 diabetes using simple measures of insulin resistance: combined results from the San Antonio Heart Study, the Mexico City Diabetes Study, and the Insulin Resistance Atherosclerosis Study. Diabetes 52:463-469
  53. Katz A, Nambi SS, Mather K, Baron AD, Follmann DA, Sullivan G, Quon MJ 2000 Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab 85:2402-2410
  54. Chen H, Sullivan G, Yue LQ, Katz A, Quon MJ 2003 QUICKI is a useful index of insulin sensitivity in subjects with hypertension. Am J Physiol Endocrinol Metab 284:E804-812
  55. Mather KJ, Hunt AE, Steinberg HO, Paradisi G, Hook G, Katz A, Quon MJ, Baron AD 2001 Repeatability characteristics of simple indices of insulin resistance: implications for research applications. J Clin Endocrinol Metab 86:5457-5464
  56. Mather KJ, Hunt AE, Steinberg HO, Paradisi G, Hook G, Katz A, Quon MJ, Baron AD 2001 Repeatability characteristics of simple indices of insulin resistance: implications for research applications. J Clin Endocrinol Metab 86:5457-5464
  57. Bastard JP, Robert JJ, Jardel C, Bruckert E, Grimaldi A, Hainque B 2001 Is quantitative insulin sensitivity check index, a fair insulin sensitivity index in humans? Diabetes Metab 27:69-70
  58. Yokoyama H, Emoto M, Fujiwara S, Motoyama K, Morioka T, Komatsu M, Tahara H, Koyama H, Shoji T, Inaba M, Nishizawa Y 2004 Quantitative insulin sensitivity check index and the reciprocal index of homeostasis model assessment are useful indexes of insulin resistance in type 2 diabetic patients with wide range of fasting plasma glucose. J Clin Endocrinol Metab 89:1481-1484
  59. Skrha J, Haas T, Sindelka G, Prazny M, Widimsky J, Cibula D, Svacina S 2004 Comparison of the insulin action parameters from hyperinsulinemic clamps with homeostasis model assessment and QUICKI indexes in subjects with different endocrine disorders. J Clin Endocrinol Metab 89:135-141
  60. 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 2003 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
  61. Katsuki A, Sumida Y, Gabazza EC, Murashima S, Urakawa H, Morioka K, Kitagawa N, Tanaka T, Araki-Sasaki R, Hori Y, Nakatani K, Yano Y, Adachi Y 2002 QUICKI is useful for following improvements in insulin sensitivity after therapy in patients with type 2 diabetes mellitus. J Clin Endocrinol Metab 87:2906-2908
  62. Uwaifo GI, Fallon EM, Chin J, Elberg J, Parikh SJ, Yanovski JA 2002 Indices of insulin action, disposal, and secretion derived from fasting samples and clamps in normal glucose-tolerant black and white children. Diabetes Care 25:2081-2087
  63. Matsuda M, DeFronzo RA 1999 Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 22:1462-1470
  64. Stumvoll M, Mitrakou A, Pimenta W, Jenssen T, Yki-Jarvinen H, Van Haeften T, Renn W, Gerich J 2000 Use of the oral glucose tolerance test to assess insulin release and insulin sensitivity. Diabetes Care 23:295-301
  65. Avignon A, Boegner C, Mariano-Goulart D, Colette C, Monnier L 1999 Assessment of insulin sensitivity from plasma insulin and glucose in the fasting or post oral glucose-load state. Int J Obes Relat Metab Disord 23:512-517
  66. Mari A, Pacini G, Murphy E, Ludvik B, Nolan JJ 2001 A model-based method for assessing insulin sensitivity from the oral glucose tolerance test. Diabetes Care 24:539-548
  67. Gutt M, Davis CL, Spitzer SB, Llabre MM, Kumar M, Czarnecki EM, Schneiderman N, Skyler JS, Marks JB 2000 Validation of the insulin sensitivity index (ISI(0,120)): comparison with other measures. Diabetes Res Clin Pract 47:177-184
  68. Belfiore F, Iannello S, Volpicelli G 1998 Insulin sensitivity indices calculated from basal and OGTT-induced insulin, glucose, and FFA levels. Mol Genet Metab 63:134-141
  69. Cobelli C, Toffolo GM, Man CD, Campioni M, Denti P, Caumo A, Butler P, Rizza R 2007 Assessment of beta-cell function in humans, simultaneously with insulin sensitivity and hepatic extraction, from intravenous and oral glucose tests. Am J Physiol Endocrinol Metab 293:E1-E15
  70. Matsuda M, DeFronzo RA 1999 Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care 22:1462-1470
  71. Mari A, Pacini G, Murphy E, Ludvik B, Nolan JJ 2001 A model-based method for assessing insulin sensitivity from the oral glucose tolerance test. Diabetes Care 24:539-548
  72. Gutt M, Davis CL, Spitzer SB, Llabre MM, Kumar M, Czarnecki EM, Schneiderman N, Skyler JS, Marks JB 2000 Validation of the insulin sensitivity index (ISI(0,120)): comparison with other measures. Diabetes Res Clin Pract 47:177-184
  73. Hanley AJ, Williams K, Gonzalez C, D'Agostino RB, Jr., Wagenknecht LE, Stern MP, Haffner SM 2003 Prediction of type 2 diabetes using simple measures of insulin resistance: combined results from the San Antonio Heart Study, the Mexico City Diabetes Study, and the Insulin Resistance Atherosclerosis Study. Diabetes 52:463-469
  74. Hanson RL, Pratley RE, Bogardus C, Narayan KM, Roumain JM, Imperatore G, Fagot-Campagna A, Pettitt DJ, Bennett PH, Knowler WC 2000 Evaluation of simple indices of insulin sensitivity and insulin secretion for use in epidemiologic studies. Am J Epidemiol 151:190-198
  75. Abdul-Ghani MA, Williams K, DeFronzo RA, Stern M 2007 What is the best predictor of future type 2 diabetes? Diabetes Care 30:1544-1548

Comments (0)

You don't have permission to comment on this page.