Volume 30, Issue 8 pp. 1549-1563

REVIEW

Open Access

Measuring and estimating insulin resistance in clinical and research settings

Amalia Gastaldelli,

Corresponding Author

Amalia Gastaldelli

[email protected]

orcid.org/0000-0003-2594-1651

National Research Council (CNR), Institute of Clinical Physiology (IFC), Pisa, Italy

Correspondence

Amalia Gastaldelli, Head of Cardiometabolic Risk Laboratory, Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Via Moruzzi 1, 56100 Pisa, Italy.

Email: [email protected]

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Amalia Gastaldelli,

Corresponding Author

Amalia Gastaldelli

[email protected]

orcid.org/0000-0003-2594-1651

National Research Council (CNR), Institute of Clinical Physiology (IFC), Pisa, Italy

Correspondence

Amalia Gastaldelli, Head of Cardiometabolic Risk Laboratory, Institute of Clinical Physiology, Consiglio Nazionale delle Ricerche, Via Moruzzi 1, 56100 Pisa, Italy.

Email: [email protected]

Search for more papers by this author

First published: 27 July 2022

https://doi.org/10.1002/oby.23503

Citations: 14

Funding information: European Union H2020 Marie Skłodowska-Curie Actions, Grant/Award Number: mtFOIE GRAS No. 734719; Innovative Medicines Initiative-2, Grant/Award Number: SOPHIA No. 875534

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Abstract

The article discusses how to measure insulin resistance in muscle, liver, and adipose tissue in human participants. The most frequently used methodologies to evaluate insulin resistance are described in detail starting from the gold standard, that is, the euglycemic hyperinsulinemic clamp, to the intravenous glucose tolerance test, surrogate indices based on fasting measurements, or dynamic tests (such as oral glucose or mixed meal tolerance tests). The accuracy, precision, and reproducibility of the tests as well as cutoff values are reported.

Study Importance

What is already known?

The gold standard for measuring insulin resistance is the euglycemic hyperinsulinemic clamp; however, given its complexity, several surrogate indices have been developed and validated.
These indices are based on insulin and glucose concentrations obtained either during fasting or in response to glucose challenges (oral glucose or meal tolerance test).

What does this study add?

The alteration of insulin action, which is an important determinant of hyperglycemia, is usually not limited to one organ, e.g., the muscle, but is observed in many organs, the most important being the liver and adipose tissue.
Reduced insulin clearance is an indication of insulin resistance not yet sufficiently recognized.

How might these results change the direction of research or the focus of clinical practice?

It is important to assess insulin resistance status in all organs using validated methods.
The measurement of insulin concentration in the fasting state and/or in response to glucose challenges, which is not common in clinical practice, is determined to evaluate the degree of insulin resistance using validated indices.

DEFINITION OF INSULIN RESISTANCE

Insulin is an essential hormone secreted by the pancreas that regulates the homeostasis of glucose, lipids, and protein. Insulin stimulates glucose uptake and glycolysis in muscle, and in the liver, it decreases endogenous glucose production (EGP) and stimulates glycogen synthesis (Figure 1). Insulin action occurs through the binding to membrane receptors and it is transmitted through the cell by a series of protein–protein interactions. Its action starts with the phosphorylation of insulin receptor substrates (IRS-1 and -2) that leads to the activation of PI3K (phosphatidylinositol 3-kinase) and phosphorylation of AKT (serine/threonine-protein kinase), which are the main signals involved in the metabolic effects of insulin [(1, 2)]. In the liver, forkhead box protein (FOXO1) stimulates gluconeogenesis (GNG) and suppresses glycolysis. Insulin mediates the phosphorylation of AKT, which then phosphorylates FOXO1, leading to its inactivation with subsequent suppression of GNG and EGP (Figure 1). In the adipose tissue, insulin suppresses lipolysis and release of nonesterified fatty acids and stimulates fatty acid re-esterification and triglyceride (TG) synthesis (Figure 1).

Details are in the caption following the image — **FIGURE 1**
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Insulin is secreted by the pancreas in the portal vein and then passes through the liver, which degrades 60% to 70% of secreted insulin, so only 30% to 40% reaches peripheral tissues. Insulin acts at the level of the liver by suppressing endogenous glucose production (EGP), at the level of the muscle by promoting glucose disposal (Rd), and at the level of the adipose tissue by suppressing lipolysis and free fatty acid (FFA) release.

Insulin resistance (IR) is defined as an impairment in insulin action that results in reduced glucose uptake by the muscle and increased hepatic glucose production (HGP) and in the adipose tissue by increased lipolysis (Figure 1). Thus, if in animal studies, IR is evaluated by measuring the activation/suppression of the genes involved in insulin action (e.g., insulin receptors and insulin signaling cascade), in humans, the degree of IR is evaluated by measuring the metabolic effects of insulin, i.e., peripheral glucose uptake and glucose production, during fasting or insulin stimulated conditions.

THE EUGLYCEMIC HYPERINSULINEMIC CLAMP: THE GOLD STANDARD METHOD FOR MEASURING IR

The gold standard method for the measurement of peripheral (muscle) IR is the euglycemic hyperinsulinemic clamp (EHC), a technique that was first proposed by DeFronzo et al. in 1979 [(3)]. During the test, insulin is infused at a physiological rate, usually 40 mU/min·m², but higher doses also have been tested such as 50, 60, 80, 100, 120, or 160 mU/min·m² [(4-11)]. More details on the protocol are given in the online Supporting Information. Because insulin's main effect is the decrease in glucose concentrations by stopping EGP (mainly hepatic) and stimulating peripheral glucose disposal (Figure 2), glucose (or dextrose 20%) is infused alongside (at variable infusion rates, glucose infusion rates [GIR]) to maintain plasma glucose concentration at constant levels throughout the study (around 5.5 mmoL/L or ~100 mg/dL). Thus, when glucose concentration is constant, the rate of glucose appearance (Ra = EGP + GIR) equals the rate of glucose disappearance (Rd), i.e., muscle glucose uptake (Figure 2). Because these insulin infusion rates lead to insulin concentrations that almost completely suppresses EGP, GIR is used as an estimate of glucose appearance and the index of insulin sensitivity (M value) calculated as follows:

M {value}_{(mg / kg \min)} = {GIR}_{(mg / kg \min)} - [G_{120 (mg / dL)} - G_{90 (mg / dL)}] / 30_{\min} \times V_{glucose (dL / kg)} - UC

()

where GIR is the glucose infusion rate expressed in mg/kg min (or as mg/min per kg of fat free mass [FFM]), G is the glucose concentration measured at 90 and 120 minutes of the clamp and expressed in mg/dL, V_glucose is the volume of distribution of glucose that is usually considered 2.5 dL/kg of body weight, and UC is the correction factor for urinary loss of glucose [(3)] that is important for participants with diabetes and high fasting glucose or if the clamp is performed in individuals treated with the SGLT2 inhibitors that increase urinary loss of glucose.

If the glucose concentration is stable during the last 30 minutes of the clamp and UC is negligible, then the M value is equivalent to the rate of glucose/dextrose infusion:

M {value}_{(mg / kg \min)} = {GIR}_{(mg / kg \min)}

()

If the glucose concentration is not stable during the last 30 minutes of the clamp, i.e., it is still decreasing from fasting levels, this can be taken into account by calculating a correction factor that is in Equation (1).

[G_{120 (mg / dL)} - G_{90 (mg / dL)}] / 30_{\min} \times V_{glucose (dL / kg)}

However, the combined used of tracers measured throughout the clamp and mathematical modeling (e.g., using the Steele equation) will allow a more precise measurement of glucose fluxes [(12)].

Whole-body glucose disposal (M value) should be normalized to kilograms of lean body mass rather than to body weight because glucose is taken up mainly by muscle and participants with obesity are often sarcopenic. Moreover, because participants with different degrees of IR often have very different values of insulin concentration, also because of different insulin clearance, the M values should be normalized to the insulin concentration measured at the end of the test (I_cl).

{Insulin sensitivity}_{(mg / kg \min \cdot L / mU)} = {GIR}_{(mg / kg \min)} / I_{cl (mU / L)}

()

The M/I_cl ratio is a measure of the insulin sensitivity as the amount of glucose metabolized per unit of plasma insulin concentration [(3)]. Insulin concentration is expected to rise almost 10 times during a 40 mU/min·m² clamp (from 6 ± 1 to 75 ± 5 mU/L and from 7 ± 1 to 82 ± 5 mU/L in healthy participants without obesity and participants with diabetes, respectively [(4)]).

However, several studies have shown that insulin doses of 40 mU/min·m² are often not sufficient to suppress EGP in participants with severe IR, such as many participants with obesity [(4-6)], thus requiring the infusion of tracer for an accurate measurement of peripheral glucose disposal. Alternatively, higher insulin doses could be used, e.g., 50 to 160 mU/min/m² [(4, 6, 7, 9-11)], which, in most of the participants, will allow the suppression of EGP. However, a residual EGP (2.5 μmol/min·kg FFM) was measured in insulin-resistant participants with diabetes who were studied with insulin doses of 160 mU/min/m² [(7)].

Cutoff value for M value

The cutoff for IR as M value (whole-body glucose disposal) was defined in a large cohort that included 2,321 participants (2,138 nondiabetic) from 17 centers among Europe (the EGIR-RISC study [n = 1,436], the Pima Indian Study [n = 597], and studies performed in San Antonio, Texas (n = 288, of whom 99 were Mexican American) all studied with a 40 mU/min·m² EHC [(13)]. The distribution of the M value appeared to be bimodal with an optimal cut point of 5 mg/min·kg_FFM (28 μmol/min·kg_FFM) [(13)]. Tam et al. studied a smaller number of participants (116 without and 51 with diabetes) with a 120 mU/min·m² EHC and defined IR as those with a glucose disposal rate less than 4.9 mg/min·kg (212.2 mg/min·m² or 7.3 mg/min·kg_FFM) [(11)].

The EHC with tracers

One of the hypotheses of the EHC is that the insulin dose completely suppresses EGP. With the dose of 40 mU/min·m², this is not often true, especially in insulin-resistant participants [(4-8)], i.e., participants with obesity or type 2 diabetes (Figure 3 shows the fasting and insulin suppressed EGP during EHC with different doses of insulin).

If the EGP is not suppressed, the rate of glucose disposal (Rd) is underestimated (for residual EGP). To overcome this pitfall, a glucose tracer should be infused, or the studies should be carried out using a higher dose of insulin. If EGP is not 0, then Rd will be underestimated by the residual EGP.

{Rd}_{(mg / kg \min)} = {GIR}_{(mg / kg \min)} + Clamp - {EGP}_{(mg / kg \min)} - [G_{120 (mg / dL)} - G_{90 (mg / dL)}] / 30_{\min} \times V_{glucose (dL / kg)} - UC

()

EGP is measured by glucose tracer infusion that starts at least 2 hours before starting the clamp to reach an isotopic steady state. For a 6,6-2H-glucose tracer, an optimal tracer infusion rate (TIR) for nondiabetic participants is (0.22 μmol/min·kg of body weight) preceded by a priming dose (22 μmol/kg) that should achieve an enrichment of 2% as the tracer to tracee ratio (TTR) at the end of the equilibration period (usually 120 minutes) (Figure 4). A few studies [(14, 15)] have shown that the M value measured at 120 minutes is lower than during prolonged insulin infusion (up to 8 hours), indicating that hyperinsulinemia over one or several hours may in itself enhance insulin action.

For individuals with diabetes, given that they often show fasting hyperglycemia and have a slower glucose turnover, the equilibration period is extended to 180 minutes (instead of 120 minutes), and both the constant infusion and the priming dose are slightly increased to 0.28 μmol/min·kg of body weight for the TIR and 0.28 μmol/kg· fasting glucose (mg/dL)/100 for the priming dose (i.e., 0.28 μmol/kg for someone with fasting glycemia of 100 mg/dL) in order to obtain a TTR of approximately 2% at the end of the equilibration period (180 minutes) (see online Supporting Information for more details). In the isotopic steady state (i.e., constant TTR), EGP can be estimated as follows:

Fasting_EGP (μmol / kg / \min) = TIR / TTR

()

During the clamp, the glucose tracer (4 mg) is added to the glucose infusate (GIR, 20% glucose or dextrose) to reach a 2% (TTR_GIR) of the labeled to unlabeled glucose ratio, i.e., similar to the plasma TTR (HOT-GINF approach) [(8, 16)]. Thus, the intravenous TIR is slightly reduced during the clamp to match the decrease in endogenous EGP, while the plasma TTR is maintained constant around 2% because the tracer is infused along with GIR.

Clamp_EGP (μmol / kg / \min) = ({TTR}_{GIR} \cdot GIR + TIR) / TTR

()

The ratio of Rd, calculated from Equation (4), and insulin, i.e., Rd/I_cl, is a measure of the quantity of glucose metabolized per unit of plasma insulin concentration [(3)].

Advantages, disadvantages, and potential risks

The euglycemic hyperinsulinemic test is the gold standard for the measurement of IR. It allows the amount of glucose metabolized per unit of plasma insulin concentration, which is the best way to assess muscle insulin sensitivity in humans. However, only trained operators should perform this test, which is very complex and which requires the use of at least three pumps (two for insulin and glucose infusion and one for saline) and a glucometer at the bedside (Figure 4).

The protocol is explained in detail in the online Supporting Information. It requires several blood samples from arterialized blood (the preferred sampling site) to monitor the possible changes in glycemia. Bedside plasma glucose is sampled every 5 to 10 minutes (a few microliters, depending on the glucometer) only for immediate plasma glucose determination; possible changes in GIR should be done quickly after blood sampling to avoid large variation in glucose concentrations (i.e., they should not exceed ± 10% from the desired goal level of 5.5 mmoL/L or 100 mg/dL). This is important especially in insulin-sensitive participants because changes in glucose concentrations are much slower.

Only if EGP is completely suppressed is the M value calculated using Equation (1) an accurate measure of peripheral glucose uptake (Rd). Otherwise, the M value underestimates Rd by the residual EGP. However, if a tracer is infused, the test will be 2 to 3 hours longer because it is necessary to wait for tracer equilibration before starting the EHC (Figure 4).

There is a potential risk of hypoglycemic events after discharge in nondiabetic participants who are very sensitive, especially if, after stopping insulin infusion, they will discharged before glucose concentrations reached a stable value.

Post-clamp glycemic profile

After cessation of insulin infusion, at the end of the test, GIR is gradually reduced while measuring glucose concentrations [(3)]. Owei et al. suggested decreasing infusion rates by 25% if two consecutive bedside plasma glucose values remained higher than 120 mg/dL [(17)]. If the plasma glucose value is around 100 mg/dL or lower, dextrose infusion should be increased by 25% to 50%, depending on the exact value. The iterative process should continue until dextrose infusion could be tapered and discontinued. The time for discontinuation of dextrose infusion can vary between 30 and 120 minutes depending on age, glucose tolerance, and insulin sensitivity, being lower in participants who are more insulin resistant [(17)].

After the clamp, participants should be discharged from the research center only if glucose concentration is stable around 120 mg/dL to avoid post-clamp hypoglycemic events.

At the end of the clamp, participants should eat a meal rich in carbohydrate to avoid potential hypoglycemia after discharge. However, not only the amount of food consumed but also the time for gastric emptying, digestion, and absorption of ingested nutrients could vary considerably among individuals. The participant should be allowed to go only if the glycemia is stable [(17)].

Quality parameters: Accuracy, precision, and reproducibility

The accuracy and precision of the EHC within each laboratory depend on several factors, but they are mainly due to technical factors, e.g., the operator-dependent decision on the magnitude of adjustment of GIR in response to a given plasma glucose level can result in overshooting and undershooting if the operator is not an expert and this occurs especially in insulin-sensitive participants in whom the effect of insulin is more marked. The interlaboratory variability may also be high if different methods are used for measuring glucose and insulin concentrations. Glucose clamp protocols usually use a laboratory-quality glucose measurement device (Figure 4), such as the Yellow Springs Instrument or the Beckman glucose oxidase analyzer [(3, 18)]. Cohen and colleagues [(19)] used the FreeStyle Mini glucose meter and concluded that is accurate enough for glucose clamp studies, based on clamp procedures conducted on seven volunteers with type 2 diabetes. Hompesch and Rave [(20)], however, argued that the systematic bias of 6% would result in substantial underestimation or overestimation of glucose requirements that would affect the comparison of the absolute amount of glucose infused between different studies.

The use of a tracer to measure glucose turnover during the clamp allows improved accuracy.

Radioactive and stable isotope tracers provide similar estimates of EGP and glucose disposal. However, the measurement of stable isotopic enrichment (i.e., TTR) is less variable because it is directly measured by mass spectrometry, whereas for radioactive tracers, the measure that corresponds to TTR (i.e., specific activity) is given by the ratio of two concentrations (i.e., labeled to unlabeled glucose in disintegrations per minute [dpm]/mL divided by μmol/mL).

Another source of error might be attributed to pump calibration, an operation that should be performed regularly (i.e., every month).

Several factors may affect the reproducibility of the measurement of insulin sensitivity by the EHC. Besides the operator and the technical issues, several other factors may influence insulin sensitivity values measured at different times, e.g., changes in body weight, health status, heavy exercise the day before the study, lack of sleep, stress (catecholamine levels), other environmental exposures. EHC reproducibility was tested by DeFronzo et al. [(3)] in the first paper on the EHC that restudied four study participants 4 to 6 weeks apart, showing high reproducibility (M value 8.43 vs. 8.53 mg/kg min). Soop et al. [(14)] performed three EHCs 48 hours and 14 days apart in seven healthy study participants (median age of 50 years; BMI 23.1 kg/m²) using an insulin dose of 0.8 mU/kg min (i.e., around 32 mU/min m²) for 2 hours. The GIRs during the three 2-hour clamps were 7.41, 7.26, and 6.63 mg/kg min, respectively, with a median intraindividual coefficient of variation of 5.8%, indicating high reproducibility at a midphysiological range of hyperinsulinemia.

James et al. [(21)] investigated the stability of insulin sensitivity in the long term by comparing the results of EHC studies conducted at Year 1 and Year 3 in 90 healthy participants participating in the Pathobiology of Prediabetes in a Biracial Cohort (POP-ABC) study. There was a strong correlation between the repeated studies (r = 0.81, p < 0.0001), and the coefficient of variation ([SD/mean] × 100) was 13.6% and repeatability coefficient was ± 0.025. Intraparticipant differences in serial insulin sensitivity clamp were less than the repeatability coefficients and within the 95% limits of agreement.

INTRAVENOUS GLUCOSE TOLERANCE TEST

The frequently sampled intravenous glucose tolerance test (FS-IVGTT) is a highly used test to measure insulin sensitivity [(22)]. The standard protocol (STANDARD-IVGTT) consists of a single injection of glucose (0.3 g/kg) and frequent blood sampling during the first minutes after the injection until glucose returns to fasting concentration. Because in study participants who are insulin resistant and/or have diabetes, the decay in glucose concentration is slow, a modified test (MODIFIED-IVGTT) was designed [(23, 24)], and either tolbutamide or insulin is administered at 20 minutes to allow a rapid decay of glucose concentration (Figure 5).

The results of the IVGTT have been tested versus the EHC in participants with different glucose tolerances (from normal [NGT] to impaired [IGT] to tdiabetes [T2D]), finding good correlation for the MODIFIED-IVGTT, whereas the standard test is not as good (STANDARD-IVGTT r = 0.55, MODIFIED-IVGTT r = 0.84, p < 0.001)) [(23-26)]. Saad et al. found that in the population with diabetes, only the modified test was correlated with the EHC (better if with insulin than tolbutamide) [(23, 26)].

The test is much simpler than the EHC, but it is also quite long (3 hours), and it requires several blood samples. Typical blood draws occur at time 0, 3, 4, 5, 6, 8, 10, 14, 19, 22, 27, 30, 35, 40, 50, 70, 100, 140, and 180 minutes for the measurement of glucose and insulin concentrations (Figure 5). Moreover, a mathematical analysis of the data (i.e., by using theminimal model [(25, 27)]) is necessary for the quantification of insulin sensitivity index (SI) and glucose effectiveness, i.e., the insulin-dependent and insulin-independent processes of glucose disappearance (Figure 5).

The cutoff for IR is log(insulin sensitivity) < 0.000107 min⁻¹·pmol/L (Table 1), and it was estimated in a population of 204 healthy study participants who underwent the insulin-modified IVGTT [(28)].

TABLE 1. Most common indices for muscle insulin resistance/insulin sensitivity based on dynamic studies

Index	Metabolic state	Condition (units)	Formula	Cutoff for IR	Duration	Number of samples	Validation (R value)	Reference
EHC	Fasting	Sensitivity (μmol kg_FFM⁻¹ min⁻¹)	M = GIR − [G_{120 mg/dL} − G_{90 mg/dL}]/30_min × V_{glucose dL/kg} − UC	< 28	2 h	15–20		[(3, 11, 13)]
EHC	Fasting	Sensitivity (μmol kg_FFM⁻¹ min⁻¹ pM⁻¹)	M/I	< 0.08	2 h	15–20		[(3, 11, 13)]
IVGTT	Fasting	Sensitivity (min⁻¹·pM)	SI (from minimal model)	< 0.000107 for log(SI)	3 h	20	R = 0.89, p < 0.001 vs. EHC	[(22-24)]
OGIS	OGTT or MMT	Sensitivity mL/kg/min (or mL/m²/min)	f (G₀, G₉₀, G₁₂₀, I₀, I₉₀, D)^a	<9.8 (or <390)	2–3 h (OGTT) or 4-6 h (MMT)	3	R = 0.77, p < 0.0001 vs. EHC	[(31, 32)]
ISI Matsuda	OGTT or MMT	Sensitivity	10⁴/√ [(G_{0 mg/dL} × I_{0 mU/mL}) × (G_mean × I_mean)]	<4.3	2-3 h (OGTT) or 4-6 h (MMT)	3–5	R = 0.73, p < 0.0001 vs. EHC	[(33)]
ISI Stumvoll	OGTT	Sensitivity (μmol · kg⁻¹ · min⁻¹· pM⁻¹)	ISI^nodem = 0.157–0.00004576 × I_{120 pmol/L} − 0.000299 × I_{0 pmol/L} − 0.00519 × G_120 mmol/L	< 0.08	2 h	3	R = 0.707, p < 0.00005 vs. EHC	[(35)]
ISI^dem Stumvoll	OGTT	Sensitivity (μmol · kg⁻¹ · min⁻¹· pM⁻¹)	ISI^dem = 0.226−0.0032 ×BMI − 0.0000645 × I_{120 pmol/L} − 0.000375 × G_90 mmol/L	<0.08	2h	2	R = 0.79, p < 0.00005 vs. EHC	[(35)]
eMCR Stumvoll	OGTT	Sensitivity (mL · kg⁻¹ · min⁻¹)	eMCR^nodem = 13–0.0042 × I_{120 pmol/L} – 0.384 × G_90 mmol/L – 0.0209 × I_{0 pmol/L}	<9.8	2h	3	R = 0.686, P< 0.00005 vs. EHC	[(35)]
eMCR^dem Stumvoll	OGTT	Sensitivity (mL · kg⁻¹ · min⁻¹)	eMCR^dem = 18.8–0.271 × BMI − 0.0052 × I_{120 pmol/L} – 0.27 × G_90 mmol/L	<9.8	2h	2	R = 0.80, P< 0.00005 vs. EHC	[(35)]
Gutt	OGTT	Sensitivity (mL/kg/min)	[75,000 + (G₀-G₁₂₀) _mg/dL x 0.19 x BW_kg/m²] / (120 x LOG[(I₀ + I₁₂₀) _mU/L/2] x [(G₀ + G₁₂₀)/2])	<9.8	2 h	2	R = 0.63, p < 0.001 vs. EHC	[(37)]
SIOGTT	OGTT	Sensitivity	1/[LOG(G₀ + G₃₀ + G₉₀ + G₁₂₀) _mg/dL + LOG(I₀ + I₃₀ + I₉₀ + I₁₂₀) _mU/mL]	NA	2 h	4	R = 0.65, p < 0.0001 vs. EHC	[(87)]
BIGTT-SI	OGTT	Sensitivity (min⁻¹·pM)	SI = EXP (4.9 − (0.00402 × I_{0 pmol/L})) – (0.000556 × I_{30 pmol/L}) – (0.00127 × I_{120 pmol/L}) – (0.152 × G_0 mmol/L) – (0.00871 × G_30 mmol/L) – (0.0373 × G_120 mmol/L) – (0.145 × Gender) – (0.0376 × BMI)	< 0.000107 For Log(SI)	2 h	3	R = 0.88, p < 0.0001 vs. IVGTT	[(34)]
Cederholm	OGTT	Sensitivity	[75,000 + (G₀-G₁₂₀) _mg/dL × 1.15 × 180 × 0.19 × BW _kg/m²]/(120 × LOG I_mean _mU/L × G_mean _mg/dL)	NA	2 h	5	R = 0.75, p < 0.001 vs. EHC	[(88)]
Belfiore ISI_gly	OGTT or MMT	Sensitivity	2/[(AUC-I × AUC-G) + 1]	NA	2-3 h (OGTT) or 4-6 h (MMT)	3–5	R = 0.93, p < 0.05 vs. EHC	[(30)]

Abbreviations: BW, body weight; EHC, euglycemic hyperinsulinemic clamp; FFM, fat-free mass; OGTT, oral glucose tolerance test; MMT, mixed meal test; eMCR^dem, metabolic clearance rate estimation including demographic parameters; eMCR^nodem, metabolic clearance rate estimation without demographic parameters; G, glucose; GIR, glucose infusion rate; I, insulin; IR, insulin resistance; IVGTT, intravenous glucose tolerance test; ISI, insulin sensitivity index; M value, glucose disposal measured during the EHC; OGIS, oral glucose insulin sensitivity index.
^a G₀, G₉₀, and G₁₂₀ are the plasma concentration of glucose measured at baseline, 90, and 120 minutes during OGTT, respectively; I₀, and I₉₀ are the plasma concentration of insulin measured at baseline and 90 minutes during OGTT, respectively. D is the oral glucose dose (g/m² body surface area). The formula can be found at the following website: http://webmet.pd.cnr.it/ogis/.

ALTERNATIVE TESTS FOR THE ASSESSMENT OF PERIPHERAL IR

The clamp and the IVGTT are very complex and long; they require intravenous infusion and several blood samples, meaning that they cannot be applied on a large population or outside research centers. Thus, several indexes have been developed based either on dynamic data (e.g., oral glucose tolerance test [OGTT] or mixed meal tolerance test) or based on fasting plasma concentrations.

Beside the EHC and the IVGTT, other dynamic tests are used to estimate peripheral IR. Surrogate indexes of peripheral IR have been derived from the OGTT and the mixed meal tolerance test and validated either versus the EHC or the IVGTT.

Table 1 reports the most-used indexes derived from OGTT measurements with the derived cutoff. Several indexes use only fasting values (Table 2). Fasting indexes are based on glucose and insulin values such as HOMA-IR and quantitative insulin sensitivity check index (QUICKI), whereas others use TG.

TABLE 2. Fasting indices for muscle insulin resistance/insulin sensitivity

Index	Metabolic state	Condition (units)	Formula	Cutoff for IR	Validation (R value)	Reference
HOMA-IR	Fasting	Resistance	(I_{0 mU/mL} × G_{0 mg/dL})/405	>4.65 or >3.6 if BMI > 27.5	R = 0.60–0.88, p < 0.0001 vs. EHC	[(13, 29, 44, 48)]
HOMA-IR	Fasting	Resistance	(I_{0 mU/mL} × G_{0 mg/dL})/405	>2 if NAFLD		[(13, 29, 44, 48)]
QUICKI	Fasting	Sensitivity	1/(LOG₁₀ I_{0 mU/mL} + LOG₁₀ G_{0 mg/dL})	<0.33	R = 0.43–0.78, p < 0.0001 vs. EHC	[(48, 87)]
Revised QUICKI	Fasting	Sensitivity	1/(LOG₁₀ I_{0 mU/mL} + LOG₁₀ G_{0 mg/dL} + LOG₁₀ FFA_0 mmol/L)	<0.37	R = 0.51, p < 0.001 vs. EHC	[(49)]
Insulin	Fasting	Resistance	I_{0 mU/L}	>12.2	R = 0.59, p < 0.001 vs. EHC	[(35, 51)]
IGR	Fasting	Resistance	I_{0 mU/L}/G_0 mmol/L	>2.4	R = −0.56, p < 0.001 vs. EHC	[(89)]
ISI Bennett	Fasting	Sensitivity	1/(ln G_{0 mg/dL} × ln I_{0 mU/L})	<0.089	R = 0.48, p < 0.0001 vs. EHC	[(51, 90)]
VAI	Fasting	Resistance	Men: (Waist_cm/39.68 + [1.88 × BMI]) × (TG_mmol/L/ 1.03) × (1.31/HDL-C_mmol/L);	>0.34	R = −0.39, p < 0.0001 vs. EHC	[(53, 56)]
VAI	Fasting	Resistance	Women: (Waist_cm 36.58 + [1.89 × BMI]) × (TG_mmol/L 0.81) × (1.52/ HDL-C_mmol/L)	>0.34	R = −0.39, p < 0.0001 vs. EHC	[(53, 56)]
TG/HDL-C	Fasting	Resistance	TG_mg/dL/HDL-C_mg/dL	>0.57	R = −0.41, p < 0.0001 vs. EHC	[(52, 56)]
TyG	Fasting	Resistance	Ln[TG_mg/dL × glucose_mg/dL/2]	>9.36	R = −0.38, p < 0.0001 vs. EHC	[(55, 56)]
LAP	Fasting	Resistance	Men: (Waist_cm - 65) × (TG_mmol/L) Women: (Waist_cm - 58) × (TG_mmol/L)	>3.84	R = −0.47, p < 0.0001 vs. EHC	[(54, 56)]
McAuley index	Fasting	Sensitivity	exp [2.63–0.28xln (I_{0 mU/L}) − 0.31x ln (TG_mmol/L)]	<6.07	R = −0.39, p < 0.0001 vs. EHC	[(51)]

Note: Cutoffs were either published or calculated considering the limit published by McAuley for insulin 12.2 mU/L, glucose 90 mg/dL, and TG 1.5 mmoL/L [(51)].
Abbreviations: EHC, euglycemic hyperinsulinemic clamp; G, glucose; HDL-C, high-density lipoprotein cholesterol; HOMA, homeostasis model of assessment; I, insulin; IGR, insulin to glucose ratio; IR, insulin resistance; ISI, insulin sensitivity index; LAP, lipid accumulation product; QUICKI, quantitative insulin sensitivity check index; TG, triglycerides; TG/HDL-C, triglyceride to high-density lipoprotein cholesterol ratio; TyG, triglycerides × fasting glucose; VAI, visceral adiposity index.

Advantages and limitations

The surrogate indexes are easier to calculate and less expensive than the clamp or IVGTT. It is important to note that there is large interlaboratory variation in the measurement of insulin concentrations (25%) [(29)]; the measurements of other variables such as glucose or TG are standardized among laboratories and they have a lower range of variability (between 5% and 10%).

Cutoff values for surrogate OGTT indexes

Several indexes of peripheral IR, or insulin sensitivity, were derived from glucose and insulin concentrations measured during the OGTT (Table 1) and validated versus the glucose disposal rate, or the glucose metabolic clearance rate measured during the clamp [(30-37)]. The number of blood samples depends on the indexes, and they range from 3 to 5 samples (compared with 12–20 samples for EHC or IVGTT).

Among the most used indexes, there are the Matsuda [(33)], the oral glucose insulin sensitivity index (OGIS) [(31)], and the Stumvoll [(35)] indexes that were also validated in populations with different characteristics and ethnicities [(38-43)]. The Matsuda index and OGIS, together with the Belfiore index, can also be used for the OGTT longer than 2 hours or for the mixed meal tolerance test. A meta-analysis showed that the Stumvoll-metabolic clearance rate, OGIS, Matsuda, Stumvoll, and Gutt indexes were also those that exhibited the strongest correlations with the insulin sensitivity measured by the EHC [(42)].

Cutoff values for surrogate fasting indexes

Among the best predictors of peripheral IR measured with the clamp (M values) are fasting glucose and plasma insulin concentrations, BMI, waist circumference, and low-density lipoprotein (LDL) cholesterol [(13)]. Fasting glucose and plasma insulin are used in many surrogate fasting indexes (Table 2). The most used is the HOMA-IR index that was first proposed in 1985 and then updated in 1998 [(44, 45)]. Stern et al. analyzed a large cohort (2,138 without and 193 with diabetes) studied with a 40 mU/min·m² EHC and defined presence of IR if any of the following conditions were met: (1) HOMA-IR > 4.65, (2) BMI > 28.9 kg/m², or (3) HOMA-IR > 3.60 and BMI > 27.5 kg/m² [(13)]. Tam et al. analyzed White participants (116 without and 51 with diabetes) studied with a 120 mU/min·m² EHC and defined presence of IR if HOMA-IR >5.9 or if HOMA-IR >2.8 when high-density lipoprotein (HDL) < 51 mg/dL [(11)]. Isokuortti et al. determined that HOMA-IR >2.0 identified participants with nonalcoholic fatty liver disease (NAFLD) [(29)]. This low cutoff for HOMA-IR also defined IR in studies on general populations involving both Caucasian [(46)] and Asian [(47)] participants.

The QUICKI is very similar to the inverse of HOMA, but it takes into account the skewness of insulin because it uses log-transformed values [(48)]. The revised QUICKI also uses the concentration of nonesterified fatty acids and has a better correlation with the M value, especially in participants without type 2 diabetes [(42, 49, 50)]. The meta-analysis by Otten et al. [(42)] showed that among the fasting indexes of IR/sensitivity, the revised QUICKI had the strongest correlations with the insulin sensitivity measured by the EHC.

Other fasting indexes of peripheral IR use serum TG instead of plasma glucose as a surrogate marker of IR; among these indexes, there is the insulin-TG index by McAuley et al. [(51)], the TG to HDL cholesterol (TG/HDL-C) ratio [(52)], the visceral adiposity index [(53)], the lipid accumulation product (LAP), [(54)] and triglycerides × fasting glucose (TyG) index [(55)] (Table 2). The score for insulin sensitivity developed by McAuley et al. with fasting insulin and TG showed good sensitivity and specificity versus the M/I measured during the EHC with insulin infused at 40 mU/min m² [(51)]; of interest is that the addition of BMI to the formula did not improve sensitivity and specificity. Other indexes such as as LAP, TyG, TG/HDL-C, and visceral adiposity index were tested versus the EHC by Fiorentino et al. in a cohort of 631 study participants with different degrees of glucose tolerance [(56)]; all indexes were well correlated with the insulin sensitivity measured by the EHC, and the cutoff for IR was derived (Table 2). The area under the receiver operating characteristic curve (AUROC) to detect individuals with IR (defined as the bottom quartile of M value) was ≥ 0.688 for all indexes, with LAP showing the highest AUROC (0.728).

ASSESSMENT OF EGP AND HEPATIC IR

Another main action of insulin is the modulation of EGP. Hepatic insulin resistance (Hep-IR) is defined as a reduced capacity of insulin to suppress EGP.

Glucose is produced mainly by the liver (90%) and only in minimal part by the kidney (maximum 10%) [(57)]. EGP is in part due to glycogenolysis and in part to GNG (which was shown to be around 47% in lean control participants but was significantly increased in association with both obesity and diabetes [(58)]). The kidney contributes to EGP with GNG as renal glycogen stores are minimal (except in people with severe hyperglycemia) [(59)].

EGP is measured noninvasively by infusing glucose labeled with either a radioactive (³H or ¹⁴C) or a stable (²H or ¹³C) isotope (i.e., glucose tracer). As mentioned previously, the glucose tracer is infused using a primed continuous infusion for at least 2 hours in normoglycemic participants and 3 hours in diabetic participants (see online Supporting Information for more details on the protocol). Insulin exerts its main effect by suppressing mainly glycogenolysis rather than GNG and, during a 40 mU/min·m² hyperinsulinemia, suppresses GNG by approximately 20% while completely blocking glycogenolysis [(60, 61)].

The gold standard for the measurement of Hep-IR is the insulin-mediated EGP suppression during the EHC with the infusion of tracers (to measure EGP). Insulin suppresses EGP following a hyperbolic relationship as shown by Groop et al. [(4)] who measured the dose response insulin-EGP during the EHC with five different insulin infusion rates, i.e., first study, three-step insulin infusion of 4, 20, and 100 mU/min·m² ; second study, two-step insulin infusion of 10 and 40 mU/min·m²; each step lasted about 2 hours) in study participants either with or without diabetes and BMI equal to 23.8 kg/m² and 22.7 kg/m², respectively.

Hep-IR is measured during the EHC by low insulin doses (e.g., 10 or 20 mU/min·m²). Because the kidney contributes minimally to EGP during the clamp, the changes in EGP mainly reflect changes in HGP. The liver “senses” portal rather than peripheral insulin. During the five insulin steps (4, 10, 20, 40, and 100 mU/min·m²), peripheral plasma insulin concentrations ranged from 10 to 240 mU/L at the end of each step, whereas the estimated portal insulin concentrations, i.e., the prehepatic insulin, were 30% higher (Figure 3). Groop et al. estimated that in study participants without diabetes, an increment in portal insulin level of only 5 mU/L (e.g., from 20 to 25 mU/L) was associated with a 50% reduction in HGP, and that at a portal insulinemia of 45 mU/L, HGP was suppressed by more than 90%; in contrast, for study participants with diabetes, insulin suppression required higher portal insulin levels, and for complete suppression of HGP, portal insulin > 100 mU/L was required [(4)]. Mean portal insulinemia of 17 ± 2 mU/L was required to achieve half-maximal suppression of HGP in study participants without diabetes compared with those with diabetes [(4)].

The decay of EGP at different doses of insulin is hyperbolic (Figure 3); thus, Hep-IR can also be estimated in fasting conditions as the product of fasting insulin times fasting EGP [(4, 62)].

Hep - {IR}_{tracer} = {EGP}_{fast} \times I_{fast}

()

Several factors can induce Hep-IR such as the excess free fatty acids (FFA, e.g., from adipose tissue lipolysis) that impair insulin action in the liver [(61)]. Moreover, hepatic and visceral fat accumulation is associated to impaired insulin-mediated suppression of EGP [(62, 63)], but only visceral fat is related to increased GNG [(62)].

Indirect indexes of Hep-IR

Several scores have been developed to estimate Hep-IR (Table 3). Indexes that do not use a glucose tracer have been derived using OGTT data. Abdul-Ghani et al. proposed to use glucose and insulin concentrations at fasting and 30 minutes after a standard 75-g OGTT to estimate insulin-mediated suppression of EGP [(64)] and named it the hepatic insulin resistance index (HIRI).

HIRIOGTT = \frac{G 0 (mg / mL) + G 30 (mg / mL)}{2} ∙ \frac{I 0 (mU / L) + l 30 (mU / L)}{2}

()

Vangipurapu et al. estimated Hep-IR using mean OGTT insulin (mean_I_OGTT), percent fat mass (FM%, HDL cholesterol concentration, and BMI [(65)].

Hep - {IR}_{OGTT - VA} = - 0.091 + {LOG}_{10} (mean_I_{OGTT}, mU / mL) \times 6) \times 0.4 + LOG {(FM %)}^{*} 0.346 - {LOG}_{10} (HDL, mg / dL) \times 0.408 + {LOG}_{10} (BMI, kg / m^{2}) \times 0.435

()

TABLE 3. Most common indices for liver and adipose tissue insulin resistance/insulin sensitivity

Index	Metabolic state	Condition	Tissue	Formula	Reference
Hep-IR	Fasting	Resistance	Liver	EGP × I₀ _mU_/_L	[(4, 62, 64)]
TG/HDL-C	Fasting	Resistance	Liver	TG/HDL-C	[(91)]
Hep-IR OGTT (HIRI)	OGTT	Resistance	Liver	(G_{0 mg/dL} + G_{30 mg/dL})/100/2 × (I_{0 mU/mL} + I_{30 mU/mL})/2	[(64)]
LIRI	OGTT	Resistance	Liver	− 0.091 + LOG₁₀ (I_mean × 6) × 0.4 + LOG₁₀ (FM _kg/weight_kg × 100) × 0.346 – LOG₁₀ HDL-C _mg/dL × 0.408 + LOG₁₀ BMI × 0.435	[(65)]

Adipo-IR	Fasting	Resistance	Adipose	FFA × I₀ mU/L	[(4, 74)]
Lipo-IR	Fasting	Resistance	Adipose	RaGly × I_{0 mU/L}	[(75)]
ATIRI	Fasting	Resistance	Adipose	RaPalmitate × I_{0 mU/L}	[(76)]
Belfiore ISI_FFA	OGTT	Sensitivity	Adipose	2/[(AUC-I × AUC-FFA₎ + 1]	[(30)]

Abbreviations: EGP, endogenous glucose production; FFA, free fatty acid; FM, fat mass; G, glucose ; HDL-C, high-density lipoprotein cholesterol; Hep-IR, hepatic insulin resistance; HIRI, hepatic insulin resistance index; I, insulin; ISI, insulin sensitivity index; LIRI, liver insulin resistance index; OGTT, oral glucose tolerance test; RaGly, rate of appearance of glycerol measured by tracer infusion; RaPalmitate, rate of appearance of palmitate measured by tracer infusion; TG, triglycerides.

Both indexes were validated, with good correlations with fasting Hep-IR measured by tracer infusion (r = 0.64, p < 0.0001) [(64)] and (r = 0.65, p < 0.001) [(65)] for HIRI and Vangipurapu index, respectively. The Vangipurapu index performed better in participants with abnormal glucose tolerance (r = 0.69, p < 0.001) than in those with normal glucose tolerance (r = 0.62, p < 0.001). Fasting Hep-IR estimated with tracers and with indexes were tested in a population of IR study participants with NAFLD and mean BMI of 27.4 kg/m² and it was found that they had increased Hep-IR compared with study participants without NAFLD (p = 0.008 for Hep-IR_tracer and HIRI_OGTT and p = 0.012 for Hep-IR_OGTT-VA) [(66)].

Because Hep-IR is estimated as the product of EGP x I₀ and increased fasting glucose is highly correlated to increased EGP [(67)], then the HOMA-IR, which is the product of fasting glucose and insulin, also can be considered a surrogate index of Hep-IR. It is interesting to note that diabetic hyperglycemia and increased EGP are due mainly to increased GNG rather than glycogenolysis [(67)].

ASSESSMENT OF LIPOLYSIS AND ADIPOSE TISSUE IR

Lipolysis, i.e., the rate of adipose tissue TG hydrolysis, is measured by infusing labeled glycerol and calculating the rate of appearance (Ra-glycerol) because the FFA can be retained and re-esterified to TG [(12, 68, 69)]. On the other hand, glycerol cannot be reused within the adipocytes for TG synthesis because they lack the enzyme glycerol kinase [(12, 68, 69)]. The hydrolysis of one mole of TG results in the release of a mole of glycerol into the systemic circulation while the rate of release of FFA may vary because of intracellular re-esterification [(69)]. Studies by Wolfe and colleagues using glycerol and palmitate as tracers [(69)] and by Frayn and colleagues [(70, 71)] using the abdominal vein catheterization technique have shown that during fasting conditions, the ratio of FFA to glycerol released from abdominal adipose is close to 3:1, suggesting minimal re-esterification, but during other conditions, such as epinephrine infusion, TG cycling is increased [(69)]. Thus, the rate of FFA release (measured by the infusion of labeled fatty acids, e.g., palmitate or oleate) may reflect lipolysis only in part. Table 3 reports the most common indices for adipose tissue IR.

The gold standard for measuring adipose tissue IR is the EHC with a low dose of insulin (e.g., 10 or 20 mU/min·m²) and this is quantified by the degree of suppression of FFA or lipolysis. Similar to EGP, the dose–response insulin-FFA concentrations or insulin-lipolysis follows a hyperbolic curve [(4, 72-74)]. Thus, the products of fasting insulin times either FFA (Adipo-IR) [(4, 74)] or Ra-glycerol (Lipo-IR) [(75)] or Ra-palmitate (AT-IRI) [(76)] are surrogate measures of adipose tissue IR in the fasting state [(4, 74, 75)].

Adipo - IR = {FFA}_{fast} \times I_{fast}

()

Lipo - {IR}_{tracer} = Ra - {Glycerol}_{fast} \times I_{fast}

()

{AT−IRI}_{tracer} = Ra - {Palmitate}_{fast} \times I_{fast}

()

All indexes using fasting values of insulin have been validated externally in participants with different BMI, with or without type 2 diabetes, although data on participants with type 1 diabetes are lacking. The fasting indexes are widely used [(77-79)], especially Adipo-IR because it does not require tracer infusion. The indexes that use OGTT values (Table 3) should be employed with caution especially in participants with diabetes, because during the OGTT, the curve FFA-insulin does not necessarily follow a hyperbolic relationship [(72, 74)]. It is of note that the contribution of plasma FFA spillover and hydrolysis of very LDL-TG to the plasma pool of FFA and glycerol might contribute to the overestimation of adipose tissue lipolysis and of the indexes of adipose tissue IR in participants with high TG. Lipoprotein lipase-induced hydrolysis of circulating very LDL-TG releases FFA, which are mainly taken up by the adipose tissue, whereas the glycerol remains in the circulation [(70, 71)]; however, the contribution becomes relevant compared with tissue lipolysis mainly in the postprandial state [(70)]. Another limitation is associated with the fact that the measurement of insulin concentration is not standardized, as discussed earlier.

REDUCED INSULIN CLEARANCE, ANOTHER MARKER OF IR

Peripheral insulin concentrations are the balance between pancreatic insulin secretion and insulin degradation. Insulin is secreted into the portal vein and in great part (~60%–70%) taken up by hepatocytes (Figure 1) by endocytosis, binding to specific receptors; the rest appears in the systemic circulation and it is cleared by extrasplanchnic organs (mainly kidney ~80%, muscle ~6%, adipose tissue and other organs 7-14% [(80-82)]).

In conditions of IR, the need for more insulin in peripheral tissues is associated with increased insulin secretion and reduced insulin clearance mainly in the liver [(26, 83, 84)]. IR in muscle, liver, and adipose tissue is strongly correlated with whole-body insulin clearance, although there is a debate on which dysfunction is driving the other [(85, 86)].

It is now established that postprandial insulin clearance is not constant throughout the day, but it decreases in response to a meal both during the rise and the decay in insulin secretion and glucose concentration [(83, 84)], more in participants with obesity than in nonobese participants but similarly in participants with normal and impaired glucose tolerance [(83)].

Similarly, insulin infusion during the clamp is associated with a reduction in insulin clearance [(83)] and higher steady-state plasma insulin in conditions of IR [(26)].

Thus, reduced insulin clearance rate is an indication of IR, although not yet sufficiently recognized.

CONCLUSION

The EHC remains the gold standard for the measurement of IR in muscle, liver, and adipose tissue, although it requires trained operators and several blood samples and, for very insulin-resistant participants, the dose of 40 mU/min·m² does not completely suppress the HGP, thus underestimating the total glucose disposal. Several surrogate indexes have been derived and validated and they use either fasting insulin, glucose, fatty acids, and TG levels or insulin and glucose concentrations during the OGTT.

ACKNOWLEDGMENTS

AG gratefully acknowledge the financial support from the European Union's Horizon 2020 Research and Innovation Programme for the projects “Non-invasive Profiling of Mitochondrial Function in Non-Alcoholic Fatty Liver Disease” (mtFOIE GRAS) and “Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy” (SOPHIA).

mtFOIE GRAS has received funding from the H2020-MSCA-RISE (Horizon 2020 Marie Skłodowska-Curie Research and Innovation Staff Exchange [RISE]) program under grant agreement No. 734719. SOPHIA has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 875534. This Joint Undertaking received support from the European Union's Horizon 2020 Research and Innovation program, EFPIA, T1D Exchange, JDRF, and Obesity Action Coalition.

The communication reflects the author's view. Neither the Innovative Medicines Initiative nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.

CONFLICT OF INTEREST

The author declares no conflict of interest for this paper.

Supporting Information

REFERENCES

1Postic C, Girard J. Contribution of de novo fatty acid synthesis to hepatic steatosis and insulin resistance: lessons from genetically engineered mice. J Clin Invest. 2008; 118: 829-838.
10.1172/JCI34275
CAS PubMed Web of Science® Google Scholar
2Samuel VT, Shulman GI. Mechanisms for insulin resistance: common threads and missing links. Cell. 2012; 148: 852-871.
10.1016/j.cell.2012.02.017
CAS PubMed Web of Science® Google Scholar
3DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: a method for quantifying insulin secretion and resistance. Am J Physiol. 1979; 237: E214-E223.
10.1152/ajpendo.1979.237.3.E214
CAS PubMed Web of Science® Google Scholar
4Groop LC, Bonadonna RC, DelPrato S, et al. Glucose and free fatty acid metabolism in non-insulin-dependent diabetes mellitus. Evidence for multiple sites of insulin resistance. J Clin Invest. 1989; 84: 205-213.
10.1172/JCI114142
CAS PubMed Web of Science® Google Scholar
5Bonadonna RC, Groop L, Kraemer N, Ferrannini E, Del Prato S, DeFronzo RA. Obesity and insulin resistance in humans: a dose-response study. Metabolism. 1990; 39: 452-459.
10.1016/0026-0495(90)90002-T
CAS PubMed Web of Science® Google Scholar
6Conte C, Fabbrini E, Kars M, Mittendorfer B, Patterson BW, Klein S. Multiorgan insulin sensitivity in lean and obese subjects. Diabetes Care. 2012; 35: 1316-1321.
10.2337/dc11-1951
CAS PubMed Web of Science® Google Scholar
7Gastaldelli A, Miyazaki Y, Mahankali A, et al. The effect of pioglitazone on the liver: role of adiponectin. Diabetes Care. 2006; 29: 2275-2281.
10.2337/dc05-2445
CAS PubMed Web of Science® Google Scholar
8Fernandez M, Gastaldelli A, Triplitt C, et al. Metabolic effects of muraglitazar in type 2 diabetic subjects. Diabetes Obes Metab. 2011; 13: 893-902.
10.1111/j.1463-1326.2011.01429.x
CAS PubMed Web of Science® Google Scholar
9Heilbronn LK, Rood J, Janderova L, et al. Relationship between serum resistin concentrations and insulin resistance in nonobese, obese, and obese diabetic subjects. J Clin Endocrinol Metab. 2004; 89: 1844-1848.
10.1210/jc.2003-031410
CAS PubMed Web of Science® Google Scholar
10Karelis AD, Faraj M, Bastard JP, et al. The metabolically healthy but obese individual presents a favorable inflammation profile. J Clin Endocrinol Metab 2005; 90: 4145-4150.
10.1210/jc.2005-0482
CAS PubMed Web of Science® Google Scholar
11Tam CS, Xie W, Johnson WD, Cefalu WT, Redman LM, Ravussin E. Defining insulin resistance from hyperinsulinemic-euglycemic clamps. Diabetes Care. 2012; 35: 1605-1610.
10.2337/dc11-2339
CAS PubMed Web of Science® Google Scholar
12Wolfe RR. Radioactive and Stable Isotope Tracers in Biomedicine: Principles and Practice of Kinetic Analysis. Wiley; 1992.
Google Scholar
13Stern SE, Williams K, Ferrannini E, DeFronzo RA, Bogardus C, Stern MP. Identification of individuals with insulin resistance using routine clinical measurements. Diabetes. 2005; 54: 333-339.
10.2337/diabetes.54.2.333
CAS PubMed Web of Science® Google Scholar
14Soop M, Nygren J, Brismar K, Thorell A, Ljungqvist O. The hyperinsulinaemic-euglycaemic glucose clamp: reproducibility and metabolic effects of prolonged insulin infusion in healthy subjects. Clin Sci (Lond). 2000; 98: 367-374.
10.1042/CS19990268
CAS PubMed Web of Science® Google Scholar
15Karelis AD, Henry JF, Malita F, et al. Comparison of insulin sensitivity values using the hyperinsulinemic euglycemic clamp: 2 vs 3 hours. Diabetes Metab. 2004; 30: 413-414.
10.1016/S1262-3636(07)70135-0
CAS PubMed Web of Science® Google Scholar
16Finegood DT, Bergman RN, Vranic M. Estimation of endogenous glucose production during hyperinsulinemic-euglycemic glucose clamps. Comparison of unlabeled and labeled exogenous glucose infusates. Diabetes. 1987; 36: 914-924.
10.2337/diab.36.8.914
CAS PubMed Web of Science® Google Scholar
17Owei I, Jain N, Jones D, Umekwe N, Dagogo-Jack S. Physiology of glycemic recovery and stabilization after hyperinsulinemic euglycemic clamp in healthy subjects. J Clin Endocrinol Metab. 2018; 103: 4155-4162.
10.1210/jc.2018-01569
PubMed Web of Science® Google Scholar
18Nowotny B, Nowotny PJ, Strassburger K, Roden M. Precision and accuracy of blood glucose measurements using three different instruments. Diabet Med. 2012; 29: 260-265.
10.1111/j.1464-5491.2011.03406.x
CAS PubMed Web of Science® Google Scholar
19Cohen O, Shaklai S, Gabis E, Pani MA. FreeStyle mini blood glucose results are accurate and suitable for use in glycemic clamp protocols. J Diabetes Sci Technol. 2008; 2: 890-895.
10.1177/193229680800200521
PubMed Google Scholar
20Hompesch M, Rave K. An analysis of how to measure glucose during glucose clamps: are glucose meters ready for research? J Diabetes Sci Technol. 2008; 2: 896-898.
10.1177/193229680800200522
PubMed Google Scholar
21James D, Umekwe N, Edeoga C, Nyenwe E, Dagogo-Jack S. Multi-year reproducibility of hyperinsulinemic euglycemic clamp-derived insulin sensitivity in free-living adults: association with incident prediabetes in the POP-ABC study. Metabolism. 2020; 109:154263. doi: 10.1016/j.metabol.2020.154263
10.1016/j.metabol.2020.154263
CAS PubMed Web of Science® Google Scholar
22Bergman RN. Toward physiological understanding of glucose tolerance: minimal-model approach. Diabetes. 1989;(38): 1512-1527.
Google Scholar
23Saad MF, Anderson RL, Laws A, et al. A comparison between the minimal model and the glucose clamp in the assessment of insulin sensitivity across the spectrum of glucose tolerance. Insulin Resistance Atherosclerosis Study. Diabetes. 1994; 43: 1114-1121.
10.2337/diab.43.9.1114
CAS PubMed Web of Science® Google Scholar
24Bergman RN, Prager R, Volund A, Olefsky JM. Equivalence of the insulin sensitivity index in man derived by the minimal model method and the euglycemic glucose clamp. J Clin Invest. 1987; 79: 790-800.
10.1172/JCI112886
CAS PubMed Web of Science® Google Scholar
25Pacini G, Tonolo G, Sambataro M, et al. Insulin sensitivity and glucose effectiveness: minimal model analysis of regular and insulin-modified FSIGT. Am J Physiol. 1998; 274: E592-E599.
10.1152/ajpendo.1998.274.4.E592
CAS PubMed Web of Science® Google Scholar
26Bergman RN, Finegood DT, Ader M. Assessment of insulin sensitivity in vivo. Endocr Rev. 1985; 6: 45-86.
10.1210/edrv-6-1-45
CAS PubMed Web of Science® Google Scholar
27Bergman RN, Ider YZ, Bowden CR, Cobelli C. Quantitative estimation of insulin sensitivity. Am J Physiol. 1979; 236: E667-E677.
10.1152/ajpendo.1979.236.6.E667
CAS PubMed Web of Science® Google Scholar
28Denti P, Bertoldo A, Vicini P, Cobelli C. IVGTT glucose minimal model covariate selection by nonlinear mixed-effects approach. Am J Physiol Endocrinol Metab. 2010; 298: E950-E960.
10.1152/ajpendo.00656.2009
CAS PubMed Web of Science® Google Scholar
29Isokuortti E, Zhou Y, Peltonen M, et al. Use of HOMA-IR to diagnose non-alcoholic fatty liver disease: a population-based and inter-laboratory study. Diabetologia. 2017; 60: 1873-1882.
10.1007/s00125-017-4340-1
PubMed Web of Science® Google Scholar
30Belfiore F, Iannello S, Volpicelli G. Insulin sensitivity indices calculated from basal and OGTT-induced insulin, glucose, and FFA levels. Mol Genet Metab. 1998; 63: 134-141.
10.1006/mgme.1997.2658
CAS PubMed Web of Science® Google Scholar
31Mari A, Pacini G, Murphy E, Ludvik B, Nolan JJ. A model-based method for assessing insulin sensitivity from the oral glucose tolerance test. Diabetes Care. 2001; 24: 539-548.
10.2337/diacare.24.3.539
CAS PubMed Web of Science® Google Scholar
32Svegliati-Baroni G, Bugianesi E, et al. Post-load insulin resistance is an independent predictor of hepatic fibrosis in virus C chronic hepatitis and in non-alcoholic fatty liver disease. Gut. 2007; 56: 1296-1301.
10.1136/gut.2006.107946
CAS PubMed Web of Science® Google Scholar
33Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes Care. 1999; 22: 1462-1470.
10.2337/diacare.22.9.1462
CAS PubMed Web of Science® Google Scholar
34Hansen T, Drivsholm T, Urhammer SA, et al. The BIGTT test: a novel test for simultaneous measurement of pancreatic beta-cell function, insulin sensitivity, and glucose tolerance. Diabetes Care. 2007; 30: 257-262.
10.2337/dc06-1240
CAS PubMed Web of Science® Google Scholar
35Stumvoll M, Mitrakou A, Pimenta W, et al. Use of the oral glucose tolerance test to assess insulin release and insulin sensitivity. Diabetes Care. 2000; 23: 295-301.
10.2337/diacare.23.3.295
CAS PubMed Web of Science® Google Scholar
36Bastard JP, Faraj M, Karelis AD, et al. Muscle and liver insulin resistance indexes derived from the oral glucose tolerance test: response to Abdul-Ghani et al. Diabetes Care. 2007; 30: e83;author reply e84. doi:10.2337/dc07-0622
10.2337/dc07?0622
PubMed Web of Science® Google Scholar
37Gutt M, Davis CL, Spitzer SB, et al. Validation of the insulin sensitivity index (ISI(0,120)): comparison with other measures. Diabetes Res Clin Pract. 2000; 47: 177-184.
10.1016/S0168-8227(99)00116-3
CAS PubMed Web of Science® Google Scholar
38Bojsen-Møller KN, Dirksen C, Svane MS, et al. Variable reliability of surrogate measures of insulin sensitivity after Roux-en-Y gastric bypass. Am J Physiol Regul Integr Comp Physiol. 2017; 312: R797-R805.
10.1152/ajpregu.00291.2016
PubMed Web of Science® Google Scholar
39Lorenzo C, Haffner SM, Stancakova A, Kuusisto J, Laakso M. Fasting and OGTT-derived measures of insulin resistance as compared with the euglycemic-hyperinsulinemic clamp in nondiabetic Finnish offspring of type 2 diabetic individuals. J Clin Endocrinol Metab. 2015; 100: 544-550.
10.1210/jc.2014-2299
CAS PubMed Web of Science® Google Scholar
40Trikudanathan S, Raji A, Chamarthi B, Seely EW, Simonson DC. Comparison of insulin sensitivity measures in South Asians. Metabolism. 2013; 62: 1448-1454.
10.1016/j.metabol.2013.05.016
CAS PubMed Web of Science® Google Scholar
41Gniuli D, Castagneto-Gissey G, Iaconelli A, Leccesi L, Mingrone G. Fat mass largely contributes to insulin mediated glucose uptake in morbidly obese subjects. Int J Obes (Lond). 2010; 34: 1726-1732.
10.1038/ijo.2010.99
CAS PubMed Web of Science® Google Scholar
42Otten J, Ahren B, Olsson T. Surrogate measures of insulin sensitivity vs the hyperinsulinaemic-euglycaemic clamp: a meta-analysis. Diabetologia. 2014; 57: 1781-1788.
10.1007/s00125-014-3285-x
CAS PubMed Web of Science® Google Scholar
43Onishi Y, Hayashi T, Sato KK, et al. Comparison of twenty indices of insulin sensitivity in predicting type 2 diabetes in Japanese Americans: the Japanese American community diabetes study. J Diabetes Complications. 2020; 34:107731. doi:10.1016/j.jdiacomp.2020.107731
10.1016/j.jdiacomp.2020.107731
PubMed Web of Science® Google Scholar
44Matthews 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. 1985; 28: 412-419.
10.1007/BF00280883
CAS PubMed Web of Science® Google Scholar
45Levy JC, Matthews DR, Hermans MP. Correct homeostasis model assessment (HOMA) evaluation uses the computer program. Diabetes Care. 1998; 21: 2191-2192.
10.2337/diacare.21.12.2191
CAS PubMed Web of Science® Google Scholar
46Gayoso-Diz P, Otero-González A, Rodriguez-Alvarez MX, et al. Insulin resistance (HOMA-IR) cut-off values and the metabolic syndrome in a general adult population: effect of gender and age: EPIRCE cross-sectional study. BMC Endocr Disord. 2013; 13: 47. doi:10.1186/1472-6823-13-47
10.1186/1472-6823-13-47
PubMed Web of Science® Google Scholar
47Yun KJ, Han K, Kim MK, et al. Insulin resistance distribution and cut-off value in Koreans from the 2008-2010 Korean National Health and Nutrition Examination Survey. PloS One. 2016; 11:e0154593. doi:10.1371/journal.pone.0154593
10.1371/journal.pone.0154593
PubMed Web of Science® Google Scholar
48Katz A, Nambi SS, Mather K, et al. Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans. J Clin Endocrinol Metab. 2000; 85: 2402-2410.
10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S
CAS PubMed Web of Science® Google Scholar
49Perseghin G, Caumo A, Caloni M, Testolin G, Luzi L. Incorporation of the fasting plasma FFA concentration into QUICKI improves its association with insulin sensitivity in nonobese individuals. J Clin Endocrinol Metab. 2001; 86: 4776-4781.
10.1210/jcem.86.10.7902
CAS PubMed Web of Science® Google Scholar
50Brady LM, Gower BA, Lovegrove SS, Williams CM, Lovegrove JA. Revised QUICKI provides a strong surrogate estimate of insulin sensitivity when compared with the minimal model. Int J Obes Relat Metab Disord. 2004; 28: 222-227.
10.1038/sj.ijo.0802547
CAS PubMed Web of Science® Google Scholar
51McAuley KA, Williams SM, Mann JI, et al. Diagnosing insulin resistance in the general population. Diabetes Care. 2001; 24: 460-464.
10.2337/diacare.24.3.460
CAS PubMed Web of Science® Google Scholar
52McLaughlin T, Reaven G, Abbasi F, et al. Is there a simple way to identify insulin-resistant individuals at increased risk of cardiovascular disease? Am J Cardiol. 2005; 96: 399-404.
10.1016/j.amjcard.2005.03.085
CAS PubMed Web of Science® Google Scholar
53Amato MC, Giordano C, Galia M, et al. Visceral Adiposity Index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care. 2010; 33: 920-922.
10.2337/dc09-1825
PubMed Web of Science® Google Scholar
54Bedogni G, Kahn HS, Bellentani S, Tiribelli C. A simple index of lipid overaccumulation is a good marker of liver steatosis. BMC Gastroenterol. 2010; 10: 98. doi:10.1186/1471-230X-10-98
10.1186/1471?230X?10?98
CAS PubMed Web of Science® Google Scholar
55Guerrero-Romero F, Simental-Mendía LE, González-Ortiz M, et al. The product of triglycerides and glucose, a simple measure of insulin sensitivity. Comparison with the euglycemic-hyperinsulinemic clamp. J Clin Endocrinol Metab. 2010; 95: 3347-3351.
10.1210/jc.2010-0288
CAS PubMed Web of Science® Google Scholar
56Fiorentino TV, Marini MA, Succurro E, Andreozzi F, Sesti G. Relationships of surrogate indexes of insulin resistance with insulin sensitivity assessed by euglycemic hyperinsulinemic clamp and subclinical vascular damage. BMJ Open Diabetes Res Care. 2019; 7:e000911. doi:10.1136/bmjdrc-2019-000911
10.1136/bmjdrc?2019?000911
PubMed Web of Science® Google Scholar
57Cherrington AD. Banting Lecture 1997. Control of glucose uptake and release by the liver in vivo. Diabetes. 1999; 48: 1198-1214.
10.2337/diabetes.48.5.1198
CAS PubMed Web of Science® Google Scholar
58Gastaldelli A, Baldi S, Pettiti M, et al. Influence of obesity and type 2 diabetes on gluconeogenesis and glucose output in humans: a quantitative study. Diabetes. 2000; 49: 1367-1373.
10.2337/diabetes.49.8.1367
CAS PubMed Web of Science® Google Scholar
59Sullivan MA, Forbes JM. Glucose and glycogen in the diabetic kidney: heroes or villains? EBioMedicine. 2019; 47: 590-597.
10.1016/j.ebiom.2019.07.067
PubMed Web of Science® Google Scholar
60Gastaldelli A, Toschi E, Pettiti M, et al. Effect of physiological hyperinsulinemia on gluconeogenesis in nondiabetic subjects and in type 2 diabetic patients. Diabetes. 2001; 50: 1807-1812.
10.2337/diabetes.50.8.1807
CAS PubMed Web of Science® Google Scholar
61Boden G, Cheung P, Stein TP, Kresge K, Mozzoli M. FFA cause hepatic insulin resistance by inhibiting insulin suppression of glycogenolysis. Am J Physiol Endocrinol Metab. 2002; 283: E12-E19.
10.1152/ajpendo.00429.2001
CAS PubMed Web of Science® Google Scholar
62Gastaldelli A, Cusi K, Pettiti M, et al. Relationship between hepatic/visceral fat and hepatic insulin resistance in nondiabetic and type 2 diabetic subjects. Gastroenterology. 2007; 133: 496-506.
10.1053/j.gastro.2007.04.068
CAS PubMed Web of Science® Google Scholar
63Bril F, Barb D, Portillo-Sanchez P, et al. Metabolic and histological implications of intrahepatic triglyceride content in nonalcoholic fatty liver disease. Hepatology. 2017; 65: 1132-1144.
10.1002/hep.28985
CAS PubMed Web of Science® Google Scholar
64Abdul-Ghani MA, Matsuda M, Balas B, DeFronzo RA. Muscle and liver insulin resistance indexes derived from the oral glucose tolerance test. Diabetes Care. 2007; 30: 89-94.
10.2337/dc06-1519
CAS PubMed Web of Science® Google Scholar
65Vangipurapu J, Stančáková A, Kuulasmaa T, et al.; EGIR-RISC Study Group. A novel surrogate index for hepatic insulin resistance. Diabetologia 2011; 54: 540-543.
10.1007/s00125-010-1966-7
CAS PubMed Web of Science® Google Scholar
66Rosso C, Mezzabotta L, Gaggini M, et al. Peripheral insulin resistance predicts liver damage in nondiabetic subjects with nonalcoholic fatty liver disease. Hepatology. 2016; 63: 107-116.
10.1002/hep.28287
CAS PubMed Web of Science® Google Scholar
67Gastaldelli A, Miyazaki Y, Pettiti M, et al. Separate contribution of diabetes, total fat mass, and fat topography to glucose production, gluconeogenesis, and glycogenolysis. J Clin Endocrinol Metab. 2004; 89: 3914-3921.
10.1210/jc.2003-031941
CAS PubMed Web of Science® Google Scholar
68Saponaro C, Gaggini M, Carli F, Gastaldelli A. The subtle balance between lipolysis and lipogenesis: a critical point in metabolic homeostasis. Nutrients. 2015; 7: 9453-9474.
10.3390/nu7115475
CAS PubMed Web of Science® Google Scholar
69Wolfe RR, Peters EJ, Klein S, Holland OB, Rosenblatt J, Gary H Jr. Effect of short-term fasting on lipolytic responsiveness in normal and obese human subjects. Am J Physiol. 1987; 252: E189-E196.
10.1152/ajpendo.1987.252.2.E189
CAS PubMed Web of Science® Google Scholar
70Karpe F, Dickmann JR, Frayn KN. Fatty acids, obesity, and insulin resistance: time for a reevaluation. Diabetes. 2011; 60: 2441-2449.
10.2337/db11-0425
CAS PubMed Web of Science® Google Scholar
71Frayn KN, Humphreys SM. Metabolic characteristics of human subcutaneous abdominal adipose tissue after overnight fast. Am J Physiol Endocrinol Metab. 2012; 302: E468-E475.
10.1152/ajpendo.00527.2011
CAS PubMed Web of Science® Google Scholar
72Bugianesi E, Gastaldelli A, Vanni E, et al. Insulin resistance in non-diabetic patients with non-alcoholic fatty liver disease: sites and mechanisms. Diabetologia. 2005; 48: 634-642.
10.1007/s00125-005-1682-x
CAS PubMed Web of Science® Google Scholar
73Søndergaard E, Jensen MD. Quantification of adipose tissue insulin sensitivity. J Invest Med. 2016; 64: 989-991.
10.1136/jim-2016-000098
PubMed Web of Science® Google Scholar
74Gastaldelli A, Gaggini M, DeFronzo RA. Role of adipose tissue insulin resistance in the natural history of type 2 diabetes: results from the San Antonio metabolism study. Diabetes. 2017; 66: 815-822.
10.2337/db16-1167
PubMed Web of Science® Google Scholar
75Rosso C, Kazankov K, Younes R, et al. Crosstalk between adipose tissue insulin resistance and liver macrophages in non-alcoholic fatty liver disease. J Hepatol. 2019; 71: 1012-1021.
10.1016/j.jhep.2019.06.031
CAS PubMed Web of Science® Google Scholar
76Fabbrini E, Magkos F, Conte C, et al. Validation of a novel index to assess insulin resistance of adipose tissue lipolytic activity in obese subjects. J Lipid Res. 2012; 53: 321-324.
10.1194/jlr.D020321
CAS PubMed Web of Science® Google Scholar
77Semnani-Azad Z, Connelly PW, Bazinet RP, et al. Adipose tissue insulin resistance is longitudinally associated with adipose tissue dysfunction, circulating lipids, and dysglycemia: the PROMISE cohort. Diabetes Care. 2021; 44: 1682-1691.
10.2337/dc20-1918
CAS PubMed Web of Science® Google Scholar
78Søndergaard E, Espinosa De Ycaza AE, Morgan-Bathke M, Jensen MD. How to measure adipose tissue insulin sensitivity. J Clin Endocrinol Metab. 2017; 102: 1193-1199.
10.1210/jc.2017-00047
PubMed Web of Science® Google Scholar
79Ter Horst KW, van Galen KA, Gilijamse PW, et al. Methods for quantifying adipose tissue insulin resistance in overweight/obese humans. Int J Obes (Lond). 2017; 41: 1288-1294.
10.1038/ijo.2017.110
CAS PubMed Web of Science® Google Scholar
80Ferrannini E, Wahren J, Faber OK, Felig P, Binder C, DeFronzo RA. Splanchnic and renal metabolism of insulin in human subjects: a dose-response study. Am J Physiol. 1983; 244: E517-E527.
10.1152/ajpendo.1983.244.6.E517
CAS PubMed Web of Science® Google Scholar
81Rabkin R, Simon NM, Steiner S, Colwell JA. Effect of renal disease on renal uptake and excretion of insulin in man. N Engl J Med. 1970; 282: 182-187.
10.1056/NEJM197001222820402
CAS PubMed Web of Science® Google Scholar
82Sonksen PH, McCormick JR, Egdahl RH, Soeldner JS. Distribution and binding of insulin in the dog hindlimb. Am J Physiol. 1971; 221: 1672-1680.
10.1152/ajplegacy.1971.221.6.1672
CAS PubMed Web of Science® Google Scholar
83Gastaldelli A, Abdul Ghani M, DeFronzo RA. Adaptation of insulin clearance to metabolic demand is a key determinant of glucose tolerance. Diabetes. 2021; 70: 377-385.
10.2337/db19-1152
CAS PubMed Web of Science® Google Scholar
84Mittendorfer B, Patterson BW, Smith GI, Yoshino M, Klein S. Beta cell function and plasma insulin clearance in people with obesity and different glycemic status. J Clin Invest. 2022; 132:e154068. doi:10.1172/JCI154068
10.1172/JCI154068
CAS PubMed Web of Science® Google Scholar
85Bergman RN, Kabir M, Ader M. The physiology of insulin clearance. Int J Mol Sci. 2022; 23: 1826. doi:10.3390/ijms23031826
10.3390/ijms23031826
PubMed Web of Science® Google Scholar
86Gastaldelli A, DeFronzo RA, Salehi M. Comment on Piccinini and Bergman. The measurement of insulin clearance. Diabetes Care 2020;43:2296-2302. Diabetes Care. 2021; 44: e98-e99.
10.2337/dc20-2911
PubMed Web of Science® Google Scholar
87Bastard JP, Vandernotte JM, Faraj M, et al. Relationship between the hyperinsulinemic-euglycaemic clamp and a new simple index assessing insulin sensitivity in overweight and obese postmenopausal women. Diabetes Metab. 2007; 33: 261-268.
10.1016/j.diabet.2007.02.004
CAS PubMed Web of Science® Google Scholar
88Cederholm J, Wibell L. Insulin release and peripheral sensitivity at the oral glucose tolerance test. Diabetes Res Clin Pract. 1990; 10: 167-175.
10.1016/0168-8227(90)90040-Z
CAS PubMed Web of Science® Google Scholar
89Hanson RL, Pratley RE, Bogardus C, et al. Evaluation of simple indices of insulin sensitivity and insulin secretion for use in epidemiologic studies. Am J Epidemiol. 2000; 151: 190-198.
10.1093/oxfordjournals.aje.a010187
CAS PubMed Web of Science® Google Scholar
90Anderson RL, Hamman RF, Savage PJ, et al. Exploration of simple insulin sensitivity measures derived from frequently sampled intravenous glucose tolerance (FSIGT) tests. The Insulin Resistance Atherosclerosis Study. Am J Epidemiol. 1995; 142: 724-732.
10.1093/aje/142.7.724
CAS PubMed Web of Science® Google Scholar
91Kim-Dorner SJ, Deuster PA, Zeno SA, Remaley AT, Poth M. Should triglycerides and the triglycerides to high-density lipoprotein cholesterol ratio be used as surrogates for insulin resistance? Metabolism. 2010; 59: 299-304.
10.1016/j.metabol.2009.07.027
CAS PubMed Web of Science® Google Scholar

Citing Literature

Volume30, Issue8

August 2022

Pages 1549-1563

Measuring and estimating insulin resistance in clinical and research settings