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Download references. The author C. Ghosh acknowledges S. Bose Centre for PhD fellowship. We are also extremely grateful to all volunteers for participating in this study.

Department of Chemical, Biological and Macromolecular Sciences, S. Bose National Centre for Basic Sciences, JD Block, Sector III, Kolkata, , Salt Lake, India.

Department of Medicine, Vivekananda Institute of Medical Sciences, 99 Sarat Bose Road, Kolkata, , India. Department of Medicine, Raipur Institute of Medical Sciences, Raipur, , Chhattisgarh, India. You can also search for this author in PubMed Google Scholar.

provided the funding; M. and C. designed the whole study and provided the conception; M. and S. supervised the overall study; C.

collected and analysed the samples; All authors drafted the manuscript with critical revision. This work is licensed under a Creative Commons Attribution 4.

Reprints and permissions. Sci Rep 5 , Download citation. Current approaches for assessing insulin sensitivity and resistance in vivo: advantages, limitations, and appropriate usage.

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The oral minimal model method. Dalla Man C. Insulin sensitivity by oral glucose minimal models: validation against clamp. Dube S. Assessment of insulin action on carbohydrate metabolism: physiological and non-physiological methods. Diabet Med. Matthews D. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.

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QUICKI is a useful index of insulin sensitivity in subjects with hypertension. Mather K. Repeatability characteristics of simple indices of insulin resistance: implications for research applications. Hanley A. 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.

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Thompson D. In this study, we compare various insulin sensitivity indices derived from the OGTT with whole-body insulin sensitivity measured by the euglycemic insulin clamp technique.

After a h overnight fast, all subjects underwent, in random order, a g OGTT and a euglycemic insulin clamp, which was performed with the infusion of [H]glucose. The indices of insulin sensitivity derived from OGTT data and the euglycemic insulin clamp were compared by correlation analysis. Our results demonstrate the limitations of such an approach.

We have derived a novel estimate of insulin sensitivity that is simple to calculate and provides a reasonable approximation of whole-body insulin sensitivity from the OGTT. Sign In or Create an Account. Search Dropdown Menu. header search search input Search input auto suggest.

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In such instances, the rebound glucose concentration was not included in the regression. I represents the mean plasma insulin concentration during the OGTT. Therefore, we validated the proposed skeletal muscle insulin sensitivity index during the OGTT against the rate of whole-body insulin-mediated glucose disposal measured with the euglycemic insulin clamp.

All data are expressed as means ± SD. Correlation analyses were performed with JNC software package version 5. Insulin-stimulated total-body glucose disposal, i.

Other OGTT-derived indexes of insulin sensitivity had a lower correlation coefficient with total-body insulin sensitivity Table 1. The skeletal muscle insulin sensitivity index during the OGTT had a significant but much weaker correlation with the liver insulin sensitivity index measured as the product of EGP and FPI Table 1.

The product of basal EGP measured with tritiated glucose and FPI concentration provides a direct measure of hepatic insulin resistance under postabsorptive conditions.

The lower correlation in the lean group most likely is explained by the rather limited range of hepatic insulin resistance 3. The proposed hepatic insulin sensitivity index was correlated with the whole-body insulin sensitivity index measured with the insulin clamp, but the correlation coefficient was lower than that of the proposed muscle insulin sensitivity index Table 1.

Insulin resistance is a characteristic feature of type 2 diabetes 1 , is present in multiple tissues 12 , is evident long before the onset of overt diabetes 24 , and is associated with obesity and atherosclerotic cardiovascular disease 2. Recent clinical trials have demonstrated that amelioration of insulin resistance by lifestyle intervention in subjects with IGT reduces their risk for conversion to type 2 diabetes by more than one-half 5 , 6 and reduces the prevalence of cardiovascular risk factors 7.

Furthermore, pharmacological treatments that improve insulin sensitivity in subjects with type 2 diabetes reduce the incidence of cardiovascular events, independent of glycemic control 10 , Because of the clinical benefit derived by treating the insulin resistance, there has been widespread interest in the development of techniques to assess insulin sensitivity in vivo.

The hyperinsulinemic-euglycemic clamp technique is considered the most definitive method to quantitate whole-body insulin sensitivity When combined with radiolabeled glucose, one can quantify the individual contributions of hepatic and muscle insulin sensitivity to whole-body insulin-mediated glucose disposal Although the insulin clamp is the most accurate method for quantifying insulin sensitivity, it is complicated and cannot be used easily in routine clinical practice or large-scale epidemiological studies.

Therefore, there has been considerable interest in developing simpler methods to quantitate insulin sensitivity from the OGTT 15 — 18 , which is the most commonly used test to assess glucose homeostasis in clinical practice and epidemiological studies.

All OGTT-derived indexes rely upon the measurement of plasma glucose and insulin concentrations, either from fasting values e. Since insulin resistance occurs in multiple organs and with varying degrees, and since the interventions that improve insulin resistance are organ dependent physical activity for muscle insulin resistance, metformin for hepatic insulin resistance, and weight loss and thiazolidinediones for muscle and hepatic insulin resistance , it is important to have a simple method that can assess the contribution of each organ to the whole-body insulin resistance.

In this study, we describe a very simple method to quantitate separately hepatic and muscle insulin resistance from measurements of plasma glucose and insulin concentrations during the OGTT.

The proposed indexes were compared with measures of hepatic and muscle insulin resistance quantitated directly with the euglycemic insulin clamp technique. The proposed index for muscle insulin sensitivity during the OGTT correlated strongly with insulin-stimulated total glucose disposal during the euglycemic clamp, and the correlation coefficient was greater than all other OGTT-derived indexes of insulin sensitivity Table 1.

Furthermore, it had a much weaker correlation with hepatic insulin resistance measured with tritiated glucose, suggesting that this index specifically reflected insulin sensitivity of the skeletal muscle. Indexes derived from measurements of fasting plasma glucose and insulin concentrations HOMA and QUICKI primarily reflect hepatic insulin resistance.

The proposed hepatic insulin resistance index derived from plasma glucose and insulin concentrations during the OGTT correlates more strongly with the HGP × FPI index than HOMA and QUICKI.

The better correlation observed with the proposed hepatic insulin resistance index may be explained the fact that the HOMA and QUICKI indexes are based only on fasting plasma glucose and insulin concentrations, while the proposed index takes into consideration both the basal measurement of HGP and the suppression of HGP during the OGTT.

Our results also shed light on the course of plasma glucose concentration during glucose load e. They suggest that the initial rate of rise in plasma glucose concentration is mainly determined by hepatic insulin resistance and by the suppression of HGP in response to the insulin that is secreted in response to hyperglycemia.

The greater the hepatic insulin resistance, the smaller the suppression of the HGP, and the greater is the initial rise in plasma glucose concentration.

Obviously, the β-cell response is an important determinant of the rate of rise in plasma glucose, but our proposed measure of hepatic insulin resistance glucose 0—30 [AUC] × insulin 0—30 [AUC] takes this into account.

Thus, worsening hepatic insulin resistance or impaired β-cell function would result in a greater initial increase in plasma glucose concentration following the glucose load.

Approximately 60 min after the ingestion of the glucose load, HGP is maximally suppressed and remains suppressed at a constant level for the subsequent 60— min Therefore, the rate of decline in plasma glucose concentration from its peak value to its nadir primarily reflects glucose uptake by peripheral tissues, muscle, and the insulin secretory response to hyperglycemia.

In the face of increased muscle insulin resistance, the decline in plasma glucose concentration will be reduced. In subjects with type 2 diabetes, the plasma glucose concentration often rises continuously during the last hour 60— min of the OGTT.

Therefore, determination of muscle insulin sensitivity using the current approach is not feasible. Although the improvement in the correlation over the current indexes of insulin sensitivity is modest, the proposed indexes have greater selectivity in detecting changes in muscle and hepatic insulin sensitivity separately and are easily calculated.

B : Relation between OGTT-derived index of hepatic insulin resistance and the hepatic insulin resistance index measured on the same day as the insulin clamp. EGP, basal endogenous glucose production; FPI, fasting plasma insulin concentration.

Correlation coefficient between OGTT-derived insulin sensitivity indexes and muscle and hepatic insulin sensitivity measured with the euglycemic insulin clamp.

HOMA-IR, HOMA of insulin resistance; SSPI, steady state plasma insulin concentration during the last 30 min of the insulin clamp; TGD, total glucose disposal during the last 30 min of the insulin clamp.

FPI was measured on the day of the insulin clamp. A table elsewhere in this issue shows conventional and Système International SI units and conversion factors for many substances. The costs of publication of this article were defrayed in part by the payment of page charges. Section solely to indicate this fact.

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Previous Article Next Article. RESEARCH DESIGN AND METHODS—. Article Navigation. Muscle and Liver Insulin Resistance Indexes Derived From the Oral Glucose Tolerance Test Muhammad A.

Abdul-Ghani, MD, PHD ; Muhammad A. Abdul-Ghani, MD, PHD. From the Division of Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, Texas. This Site.

Google Scholar. Masafumi Matsuda, MD ; Masafumi Matsuda, MD. Bogdan Balas, MD ; Bogdan Balas, MD. Ralph A. DeFronzo, MD Ralph A. DeFronzo, MD. Address correspondence and reprint requests to Ralph A. DeFronzo, MD, Diabetes Division, University of Texas Health Science Center, Floyd Curl Dr.

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Figure 1—. View large Download slide. Table 1— Correlation coefficient between OGTT-derived insulin sensitivity indexes and muscle and hepatic insulin sensitivity measured with the euglycemic insulin clamp.

FPI × EGP. View Large. DeFronzo RA: Lilly Lecture: The triumvariate: β-cell, muscle, liver, a collusion responsible for NIDDM. DeFronzo RA, Ferannini E: Insulin resistance: a multifaceted syndrome responsible for NIDDM, obesity, hypertension, dyslipidemia and ASCVD. Diabetes Care. Ford ES: Risks for all-cause mortality, cardiovascular disease, and diabetes associated with the metabolic syndrome: a summary of the evidence.

Ford ES: Prevalence of the metabolic syndrome defined by the International Diabetes Federation among adults in the U. The Diabetes Prevention Program Group: Reduction in the incidence of type 2 diabetes with life style intervention or metformin.

N Engl J Med. Tuomilehto J, Lindstrom J, Eriksson JG, Valle TT, Hamalainen H, Ilanne-Parikka P, Keinanen-Kiukaanniemi S, Laakso M, Louheranta A, Rastas M, Salminen V, Uusitupa M, the Finnish Diabetes Prevention Study Group: Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance.

Orchard TJ, Temprosa M, Goldberg R, Haffner S, Ratner R, Marcovina S, Fowler S, the Diabetes Prevention Program Research Group: The effect of metformin and intensive lifestyle intervention on the metabolic syndrome: the Diabetes Prevention Program randomized trial.

Ann Intern Med. Church TS, LaMonte MJ, Barlow CE, Blair SN: Cardiorespiratory fitness and body mass index as predictors of cardiovascular disease mortality among men with diabetes. Arch Intern Med. Buchanan TA, Xiang AH, Peters RK, Kjos SL, Marroquin A, Goico J, Ochoa C, Tan S, Berkowitz K, Hodis HN, Azen SP: Preservation of pancreatic β-cell function and prevention of type 2 diabetes by pharmacological treatment of insulin resistance in high-risk hispanic women.

Dormandy JA, Charbonnel B, Eckland DJ, Erdmann E, Massi-Benedetti M, Moules IK, Skene AM, Tan MH, Lefebvre PJ, Murray GD, Standl E, Wilcox RG, Wilhelmsen L, Betteridge J, Birkeland K, Golay A, Heine RJ, Koranyi L, Laakso M, Mokan M, Norkus A, Pirags V, Podar T, Scheen A, Scherbaum W, Schernthaner G, Schmitz O, Skrha J, Smith U, Taton J, PROactive investigators: Secondary prevention of macrovascular events in patients with type 2 diabetes in the PROactive study PROspective pioglitAzone Clinical Trial In macroVascular Events : a randomised controlled trial.

These are reliable indices [ 29 ]. Unfortunately, in our group of subjects we found several negative indices in persons with high insulin levels minutes after glucose load so that we excluded these indices from these analyses. The lowest coefficient of variation in the measurement of insulin secretion from fasting parameters was shown for HOMA2-B C-Peptide.

This is most likely due to the above mentioned difficulties in insulin measurements. C-peptide and insulin are secreted in equimolar amounts based on their cleavage out of proinsulin.

It is a more stable parameter than insulin and has insignificant clearance by the liver and a longer half-life than insulin [ 42 ]. Therefore C-peptide levels better approximate pancreatic insulin secretion than insulin levels [ 43 ], and so the use of C-peptide to compute HOMA2-B has clear advantages.

Our data show that this ratio has a comparable coefficient of variation to the other indices of this category. The OGTT had been described as an acceptable compromise to assess ß-cell function [ 44 ].

This is well in line with the noted superiority of C-peptide over direct insulin measurements. In this category of insulin secretion from dynamic parameters during an oral glucose challenge the highest coefficient of variation was found for DI.

This index has advantages as it is an early marker of an inadequate compensation of beta cell function [ 45 ]. The assumption on which the measurement was established is that all measurements of insulin sensitivity or response show a hyperbolic relationship.

But there are discussions if this assumption is sufficient [ 31 ]. Besides, the variability of insulin levels may be a source of high variability as well.

As for the group of indices measuring insulin sensitivity, our study shows the same differences when comparing the computed coefficient of variation and the discriminant ratios in the insulin secretion groups. The group based on fasting values shows a smaller coefficient of variation Utzschneider et al.

already described a high within-subject variability of various indices and proposed integrated measurements using multiple time points and C-peptide levels to reduce variability [ 46 ]. The study population Utzschneider et al.

describe consists of overall 37 persons of whom some had normal glucose tolerance, some had impaired glucose tolerance and some had diabetes—with or without medication. Our study conversely just analyzed people without diabetes to ensure a better comparability.

Moreover, our study population was larger so that the generalisability should be better. One could therefore assume that the variability of the different indices is independent of the glucose levels.

This should be investigated by other studies. Studies comparing the coefficient of variation and the discriminant ratio at the same time are rare.

The only study which we are aware of is by Mather et al. Mather et al. measured the repeatability of different tests of insulin sensitivity.

In their investigation logHOMA-IR and QUICKI provided the best results with a low coefficient of variation and a high discriminant ratio. Similar results were found in our study. QUICKI and also revised QUICKI showed a low coefficient of variation concomitant with a high discriminant ratio.

Additionally, we investigated more indices and also the insulin secretion. In the insulin sensitivity group OGIS and NEFA-ISI are two important indices which seem to be superior to the other OGTT-based insulin sensitivity indices. The discussed indices are commonly used indices.

However we did not cover one group of indices in our work, the indices computed by mathematical modeling. For example Mari et al. established models for beta-cell assessment [ 44 , 47 ]. Most indices based on mathematical modelling require a more frequent glucose sampling, especially a measurement point at 15 minutes.

QUICKI is used very often, most likely as it only requires one fasting blood sampling. Our literature research showed 2, citations, i. the second highest number of citations of all used indices.

Conversely, the original paper of revised QUICKI was cited just times. Although OGIS has shown good agreement with the hyperinsulinemic euglycemic clamp and we now verified a favorable test-retest coefficient of variation, it is not used applied very frequently.

This might be partly due to its complexity, despite the available web-based calculators and Excel-sheets [ 25 ]. With citations of the original paper in our literature research it is less cited than the Matsuda index. Matsuda index with citations is the most cited paper in this category and among all evaluated indices.

Of note, this top-cited index shows the highest coefficient of variation. Our study investigated several indices for reliability during repeated measurements.

Due to the single-center design, our highly standardized study settings and laboratory measurements, we potentially reduced variance that can originate from methodological and pre-analytic and analytical differences in routine clinical use. Our work only addressed the coefficient of variation and discrimination ratios of the indices and did not compare how these indices relate to gold-standard measurements.

Most indices based on mathematical modeling require a more frequent glucose sampling. Another limitation may lay in the small number of OGTTs which could restrict the generalisability of our data.

Reliable estimation of insulin sensitivity and secretion is crucial when investigating glucose metabolism in clinical research. Furthermore, measurement of these key metabolic features recently gained clinical importance with the introduction of newly identified subphenotypes of prediabetes and diabetes and their potential impact towards more personalized strategies for diabetes therapy.

Our current work can aid the selection of the appropriate index in upcoming clinical studies on insulin sensitivity or insulin secretion. Coefficients of variation of the analyzed indices in relation to the number of citations of the original article per year calculated by the total number of citations divided by the years since first publication.

Indices that we could not found or differentiate in our literature research or which are not published yet are presented in red using random values for the y axis.

Samples with a time span more than one year between the two OGTTs are already excluded. We thank all research volunteers for their participation. Especially we want to thank Riccardo Bonadonna for his idea of adding the discriminant ratio to our work.

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Abstract Aims Insulin sensitivity and insulin secretion can be estimated by multiple indices from fasting blood samples or blood samples obtained during oral glucose tolerance tests.

Methods We analyzed data of persons without diabetes who underwent two repeated OGTTs. Results 89 persons underwent two OGTTs with a median interval of 86 days IQR 64— Conclusion The data reveal large differences in the reproducibility and the discrimination capability of different indices that assess insulin sensitivity or insulin secretion.

Atkin, Weill Cornell Medical College Qatar, QATAR Received: January 29, ; Accepted: September 24, ; Published: October 22, Copyright: © Hudak et al. Introduction About million people worldwide have diabetes mellitus with numbers expected to rise. Methods Subjects We analyzed data from persons who underwent two or more oral glucose tolerance tests OGTT with a time difference of 4 to days.

Oral glucose tolerance test and laboratory measurements All participants underwent an OGTT with a standardized 75 g glucose solution Accu-Check Dextro, Roche after overnight fasting.

Indices We used the following common indices for insulin sensitivity and insulin secretion for our study. Download: PPT. al [ 32 ] as follows: Where MS B and MS W are the between-subject and within subject mean squares respectively and k is the number of repeated measurements within subject.

Literature research To assess how often different indices are used, we researched the literature via the ISI web of knowledge using the search function title. Results We analyzed data of OGTTs from 89 persons 51 females, 38 males.

Table 3. Coefficient of variation and discriminant ratio index-wise and per group. Fig 1. Boxplot showing coefficients of variation of the analyzed indices of insulin secretion and insulin sensitivity.

Fig 2. Discriminant ratio and corresponding confidence intervals of the analyzed indices of insulin secretion and insulin sensitivity. Discussion With a coefficient of variation range between 2. The results were comparable to the whole group.

Insulin sensitivity Revised QUICKI [ 21 ] and the original QUICKI [ 20 ] showed the smallest coefficients of variation among the indices estimating insulin sensitivity from fasting variables as well as compared to all evaluated indices.

Insulin secretion The lowest coefficient of variation in the measurement of insulin secretion from fasting parameters was shown for HOMA2-B C-Peptide.

Use of indices in the literature QUICKI is used very often, most likely as it only requires one fasting blood sampling. Limitations Our study investigated several indices for reliability during repeated measurements. Summary and outlook Reliable estimation of insulin sensitivity and secretion is crucial when investigating glucose metabolism in clinical research.

Supporting information. S1 Fig. Coefficients of variation in relation to the number of citations per year.

s TIF. S1 Table. Linear regression model between coefficient of variation for each index and time span days. s DOCX. S2 Table. S3 Table. Acknowledgments We thank all research volunteers for their participation. References 1. Bergman RN, Finegood DT, Ader M. Endocr Rev.

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