Home | About us | Editorial board | Search | Ahead of print | Current issue | Archives | Submit article | Instructions | Subscribe | Contacts | Advertise | Login 
 
Search Article 
  
Advanced search 
  Users Online: 3345 Home Print this page Email this page Small font sizeDefault font sizeIncrease font size  

 
Table of Contents
BRIEF COMMUNICATION
Year : 2015  |  Volume : 19  |  Issue : 1  |  Page : 160-164

Assessment of insulin sensitivity/resistance


1 Department of Endocrinology, LLRM Medical College, Meerut, Uttar Pradesh, India
2 Department of Radiodiagnosis, SGPGI, Lucknow, Uttar Pradesh, India

Date of Web Publication12-Dec-2014

Correspondence Address:
Manish Gutch
D-15, LLRM Medical College, Meerut - 250 004, Uttar Pradesh
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2230-8210.146874

Rights and Permissions
   Abstract 

Insulin resistance is one pretty troublesome entity which very commonly aggravates metabolic syndrome. Many methods and indices are available for the estimation of insulin resistance. It is essential to test and validate their reliability before they can be used as an investigation in patients. At present, hyperinsulinemic euglycemic clamp and intravenous glucose tolerance test are the most reliable methods available for estimating insulin resistance and are being used as a reference standard. Some simple methods, from which indices can be derived, have been validated e.g. homeostasis model assessment (HOMA), quantitative insulin sensitivity check index (QUICKI). For the clinical uses HOMA-insulin resistance, QUIKI, and Matsuda are suitable, while HES, McAuley, Belfiore, Cederholm, Avignon and Stumvoll index are suitable for epidemiological/research purposes. With increasing number of these available indices of IR, it may be difficult for clinicians to select the most appropriate index for their studies. This review provides guidelines that must be considered before performing such studies.

Keywords: Homeostasis model assessment, hyperinsulinemic euglycemic clamp, insulin sensitivity, quantitative insulin sensitivity check index


How to cite this article:
Gutch M, Kumar S, Razi SM, Gupta KK, Gupta A. Assessment of insulin sensitivity/resistance. Indian J Endocr Metab 2015;19:160-4

How to cite this URL:
Gutch M, Kumar S, Razi SM, Gupta KK, Gupta A. Assessment of insulin sensitivity/resistance. Indian J Endocr Metab [serial online] 2015 [cited 2019 Dec 11];19:160-4. Available from: http://www.ijem.in/text.asp?2015/19/1/160/146874


   Introduction Top


Hyperinsulinemic euglycemic clamp (HEC) is known to be the "gold standard" for the measurement of insulin sensitivity. However, the realization that it is time and money consuming led to the development of a simplified approach in quantification of insulin sensitivity. Various indices of insulin sensitivity/resistance using the data from oral glucose tolerance test (OGTT) were proposed in last 20 years.

There are two groups of insulin sensitivity indices: (1) Indices calculated by using fasting plasma concentrations of insulin, glucose and triglycerides, (2) indices calculated by using plasma concentrations of insulin and glucose obtained during 120 min of a standard (75 g glucose) OGTT [Table 1] and [Table 2].
Table 1: The indices for insulin sensitivity/resistance for clinical purpose

Click here to view
Table 2: The indices for insulin sensitivity/resistance for epidemiological purpose

Click here to view


Former group include homeostasis model assessment-insulin resistance (HOMA-IR), QUIKI INDEX, and McAuley index while latter include, Matsuda, Belfiore, Cederholm, Avignon and Stumvoll index [Table 1] and [Table 2].

These indices are conveniently used in epidemiological and clinical studies to predict diabetes development in a non-diabetic population. Their use in clinical practice is limited because of the absence of reference values for normal and impaired insulin sensitivity.

For the clinical uses HOMA-IR, QUIKI, and Matsuda are suitable while HES, McAuley, Belfiore, Cederholm, Avignon and Stumvoll index are suitable for epidemiological/research purposes [Table 1] and [Table 2].

Insulin resistance is accepted to be a major risk factor in the etiology of type 2 diabetes mellitus, hypertension, dyslipidemia, atherosclerotic vascular disease, and may be a risk factor for coronary heart disease and stroke as well. [1]

Several risk factors (e.g. obesity, physical inactivity, body fat distribution, age and hyperinsulinemia) may be considered markers of insulin resistance. Insulin resistance is a predictor for the development of Type 2 diabetes mellitus even in individuals with normal glucose tolerance. Therefore, it is important to recognize insulin resistance in the pre-disease stage when therapeutic intervention is likely to be more successful than in manifest disease. [2]

Several authors proposed various indices of insulin sensitivity based on the interrelations between the concentration of insulin, glucose and other parameters obtained either in the fasting state or during OGTT and correlated the indices with the data obtained during a HEC. [3]

The HEC-derived index of insulin sensitivity (ISI HEC , ml/kg/min/μIU ml) is obtained during a steady state period of HEC.

ISI HEC = MCR/I mean

where,
Imean - average steady state plasma insulin response (μIU/ml),
MCR: Metabolic clearance rate of glucose (ml/kg/min).

MCR = M mean/ (G mean × 0.18), where
M mean : Metabolized glucose expressed as average steady state glucose infusion rate per kg of body weight (mg/kg/min)
G mean :Average steady state blood glucose concentration (mmol/l)
0.18 -conversion factor to transform blood glucose concentration from mmol/l into mg/ml.

Correct application of the indices in their proposed form and with the proposed concentration units is of high importance.

Therefore, the aim of this review is to introduce several insulin sensitivity indices, their formulas and units as proposed by their authors, and to evaluate critically the use of some of the suggested indices in insulin sensitivity estimation.

Some of the indices for insulin sensitivity/resistance are given below:

Homeostasis model assessment-insulin resistance

Homeostasis model assessment was first developed in 1985 by Matthews et al. It is a method used to quantify insulin resistance and beta-cell function from basal (fasting) glucose and insulin (or C-peptide) concentrations. HOMA is a model of the relationship of glucose and insulin dynamics that predicts fasting steady-state glucose and insulin concentrations for a wide range of possible combinations of insulin resistance and β-cell function. Insulin levels depend on the pancreatic β-cell response to glucose concentrations while, glucose concentrations are regulated by insulin-mediated glucose production via the liver. Thus, deficient β-cell function will echo a diminished response of β-cell to glucose-stimulated insulin secretion. Similarly, insulin resistance is reflected by the diminished suppressive effect of insulin on hepatic glucose production. The HOMA model has proved to be a robust clinical and epidemiological tool for the assessment of insulin resistance. HOMA describes this glucose-insulin homeostasis by means of a set of simple, mathematically-derived nonlinear equations. The approximating equation for insulin resistance has been simplified; it uses a fasting blood sample. It is derived from the use of the insulin-glucose product, divided by a constant. The product of FPG × FPI is an index of hepatic insulin resistance. [4]

The equation proposed by Matthews et al.:



It is appropriate to apply this index in large epidemiological studies where only fasting insulin and glucose values are available.

Homeostasis model assessment-IR for Indian children's are: Boys: Normal weight 1.70 ± 1.44 (95%CI: 1.46-1.94) versus overweight 2.67 ± 1.41 (95%CI: 2.40-2.94) versus obese 4.39 ± 2.14 (95%CI: 3.95-4.83), P < 0.0001 between all groups); Girls: Normal weight 1.21 ± 1.10 (95% CI 1.73-2.12) versus overweight 3.19 ± 2.02 (95% CI 2.79-3.60) versus obese 4.19 ± 2.52 (95% CI 3.69-4.69), P < 0.0001 between all groups).

Quantitative insulin sensitivity check index

Quantitative insulin sensitivity check index (QUICKI) is an empirically-derived mathematical transformation of fasting blood glucose and plasma insulin concentrations that provide a consistent and precise ISI with a better positive predictive power. It is simply a variation of HOMA equations, as it transforms the data by taking both the logarithm and the reciprocal of the glucose-insulin product, thus slightly skewing the distribution of fasting insulin values. QUICKI has been seen to have a significantly better linear correlation with glucose clamp determinations of insulin sensitivity than minimal-model estimates, especially in obese and diabetic subjects. It employs the use of fasting values of insulin and glucose as in HOMA calculations. QUICKI is virtually identical to the simple equation form of the HOMA model in all aspects, except that a log transform of the insulin glucose product is employed to calculate QUICKI. The QUICKI can be determined from fasting plasma glucose (mg/dl) and insulin (μIU/ml) concentrations. [5]

QUICKI = 1/(logI 0 + logG 0 )

The reported values of QUICKI were 0.382 ± 0.007 for non-obese, 0.331 ± 0.010 for obese and 0.304 ± 0.007 for diabetic individuals.

McAuley index

It is used for predicting insulin resistance in normoglycemic individuals. Regression analysis was used to estimate the cut-off points and the importance of various data for insulin resistance (fasting concentrations of insulin, triglycerides, aspartate aminotransferase, basal metabolic rate (BMI), waist circumference). [6] A bootstrap procedure was used to find an index most strongly correlating with insulin sensitivity index, corrected for fat-free mass obtained by HEC (Mffm/I).

Mffm/I = e (2,63-0,28 ln (I 0 ) - 0,31 ln (TAG 0 )

Matsuda index

Several methods have been described that derive an ISI from the OGTT. In these methods, the ratio of plasma glucose to insulin concentration during the OGTT is used. A novel assessment of insulin sensitivity that is simple to calculate and provides a reasonable approximation of whole-body insulin sensitivity from the OGTT was developed by Matsuda and Defronzo, and is referred to as the Matsuda index. Here the OGTT ISI (composite) was calculated using both the data of the entire 3 h OGTT and the first 2 h of the test. The composite whole-body insulin sensitivity index (WBISI), developed by Matsuda and DeFronzo is based on insulin values given in microunits per milliliter (μU/mL) and those of glucose, in milligrams per deciliter (mg/L) obtained from the OGTT and the corresponding fasting values The index of whole-body insulin sensitivity combines both hepatic and peripheral tissue insulin sensitivity. This index is calculated from plasma glucose (mg/dl) and insulin (mIU/l) concentrations in the fasting state and during OGTT. [7]



I 0 - Fasting plasma insulin concentration (mIU/l),
G 0 - Fasting plasma glucose concentration (mg/dl),
G mean - Mean plasma glucose concentration during OGTT (mg/dl),
I mean - Mean plasma insulin concentration during OGTT (mU/l),
10,000- Simplifying constant to get numbers from 0 to 12.
√- Correction of the nonlinear values distribution.

Belfiore index

The Belfiore index is mainly used for calculation of the Belfiore formulas in defining the normal values for basal glucose and insulin concentrations and mean normal value for glucose and insulin areas during OGTT. The main point of the Belfiore formulas is the comparison of insulin and glucose values measured (fasting, 0-1-2 h areas or 0-2 h areas) with the defined normal reference values. [8]

Cederholm index

The insulin sensitivity index proposed by CEDER-HOLM and Cederholm and Wibell represents mainly peripheral insulin sensitivity and muscular glucose uptake, due to the dominant role of peripheral tissues in glucose disposal after an oral glucose load. [9]

Avignon index

The authors (Avignon et al. 1999) proposed 3 insulin sensitivity indices: Sib (derived from fasting plasma insulin and glucose concentrations), Si2h (derived from plasma insulin and glucose concentrations in the 120 th min of OGTT) and SiM (derived by averaging Sib and Si2h after balancing Sib by a coefficient of 0.137 to give the same weight to both indices). [10]

It was observed that the results obtained by computation of sensitivity indices from glucose and insulin concentrations in the basal state and during a conventional 2 h OGTT were useful for blending both the determination of glucose tolerance and an estimate of insulin sensitivity in a single and simple test.

Stumvoll index

It is possible to calculate insulin sensitivity and insulin release from simple demographic parameters and values obtained during an OGTT with practical precision. Stumvoll and Gerich proposed use of demographic data such as age, sex and BMI in addition to plasma glucose (mmol/L) and insulin (pmol/L) responses during the OGTT to predict insulin sensitivity and beta cell function. The equations were generated using the multiple linear regression analysis and adapted to the availabilities of sampling times during OGTT and of demographic parameters (BMI, age). [11]


   Example Top


A 35-year-old male person, weighing 68 kg, with a height of 164 cm, and thus a BMI of 25.28 kg/m 2 . He is non-diabetic, non-hypertensive and has a normal lipid profile. He does not have any family history of coronary artery disease, diabetes mellitus or hypertension. His fasting blood sugar is 100 mg/dl, and fasting insulin level of 4.6 μU/ml. An OGTT was done with 75 g of anhydrous glucose [Table 3]. His blood sugar and insulin levels are as follows.
Table 3: Comparison of insulin sensitivity through different methods

Click here to view


Using the above values, we calculated various insulin sensitivity indices, the values obtained are as follows:

Parameters derived from above mentioned example: HOMA-IR-1.23 [Normal < 2.5], QUICKI-0.39 [Normal < 0.4], MATSUDA-12.34 [Normal < 4.5].

From the above-derived values, we can conclude that patient does not have insulin resistance as all the three values are within normal limits.

Thus, we see that, there are various tools used for quantifying insulin sensitivity and resistance directly (hyperinsulinemic euglycemic glucose clamping and insulin suppression tests) and indirectly (frequently sampled intravenous glucose tolerance test, OGTT, meal tolerance test, and HOMA-IR). The utility of HOMA-IR in assessment of IR has been validated in children and adolescents. HOMA-IR is a simple method for evaluation of insulin sensitivity and correlates with the results of glucose clamp test in subjects with mild diabetes without significant hyperglycemia. Nevertheless it is difficult to apply to patients with poor glycemic control, those with severe β cell dysfunction or those treated with insulin. [12]

Insulin resistance, earlier thought to be a rare complication of the treatment of diabetes, is now recognized as a component of several disorders, including the following: [13]

  • Extreme insulin-resistance syndromes, such as the type B syndrome with autoantibodies against the insulin receptor, and rare inherited disorders, such as Leprechaunism with insulin-receptor mutations and the lipodystrophic states
  • Impaired glucose tolerance and type 2 diabetes mellitus.
  • Obesity, stress, infection, uremia, acromegaly, glucocorticoid excess, and pregnancy, which cause secondary insulin resistance
  • Common disorders such as the metabolic syndrome, hypertension, hyperlipidemia, coronary artery disease, the polycystic ovary syndrome, and ovarian hyperthecosis, in which the mechanism of the associated hyperinsulinemia is unknown.



   Conclusion Top


Estimation of impaired insulin sensitivity should be given importance mainly in individuals with risk factors. The importance of the indices lies in their use in large epidemiological studies for assessment of relations between selected variables. For fasting values, insulin resistance is defined by WHO as the highest quartile of the IR HOMA index in non-diabetic subjects. Insulin resistance is also defined as the lowest decile of insulin sensitivity in the lean subgroup of non-diabetic population. In clinical practice, however, their application is limited due to the lack of exact reference values.

 
   References Top

1.
Bray GA. Medical consequences of obesity. J Clin Endocrinol Metab 2004;89:2583-9.  Back to cited text no. 1
    
2.
Boden G. Pathogenesis of type 2 diabetes. Insulin resistance. Endocrinol Metab Clin North Am 2001;30:801-15, v.  Back to cited text no. 2
    
3.
DeFronzo RA, Tobin JD, Andres R. Glucose clamp technique: A method for quantifying insulin secretion and resistance. Am J Physiol 1979;237:E214-23.  Back to cited text no. 3
    
4.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985;28:412-9.  Back to cited text no. 4
    
5.
Chen H, Sullivan G, Yue LQ, Katz A, Quon MJ. QUICKI is a useful index of insulin sensitivity in subjects with hypertension. Am J Physiol Endocrinol Metab 2003;284:E804-12.  Back to cited text no. 5
    
6.
McAuley KA, Williams SM, Mann JI, Walker RJ, Lewis-Barned NJ, Temple LA, et al. Diagnosing insulin resistance in the general population. Diabetes Care 2001;24:460-4.  Back to cited text no. 6
    
7.
Matsuda M, DeFronzo RA. Insulin sensitivity indices obtained from oral glucose tolerance testing: Comparison with the euglycemic insulin clamp. Diabetes Care 1999;22:1462-70.  Back to cited text no. 7
    
8.
Belfiore F, Iannello S, Volpicelli G. Insulin sensitivity indices calculated from basal and OGTT-induced insulin, glucose, and FFA levels. Mol Genet Metab 1998;63:134-41.  Back to cited text no. 8
    
9.
Cederholm J, Wibell L. Insulin release and peripheral sensitivity at the oral glucose tolerance test. Diabetes Res Clin Pract 1990;10:167-75.  Back to cited text no. 9
    
10.
Avignon A, Boegner C, Mariano-Goulart D, Colette C, Monnier L. Assessment of insulin sensitivity from plasma insulin and glucose in the fasting or post oral glucose-load state. Int J Obes Relat Metab Disord 1999;23:512-7.  Back to cited text no. 10
    
11.
Stumvoll M, Gerich J. Clinical features of insulin resistance and beta cell dysfunction and the relationship to type 2 diabetes. Clin Lab Med 2001;21:31-51.  Back to cited text no. 11
    
12.
Mari 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;24:539-48.   Back to cited text no. 12
    
13.
Singh B, Saxena A. Surrogate markers of insulin resistance: A review. World J Diabetes 2010;1:36-47.  Back to cited text no. 13
    



 
 
    Tables

  [Table 1], [Table 2], [Table 3]


This article has been cited by
1 Rising Glucagon-Like Peptide 1 Concentrations After Parathyroidectomy in Patients With Primary Hyperparathyroidism
Vasiliki Antonopoulou,Spyridon N. Karras,Theocharis Koufakis,Maria Yavropoulou,Niki Katsiki,Spyridon Gerou,Theodosios Papavramidis,Kalliopi Kotsa
Journal of Surgical Research. 2020; 245: 22
[Pubmed] | [DOI]
2 Optimized fasting and OGTT-based simple surrogate methods for assessing insulin sensitivity
Miguel Altuve,Erika Severeyn,Sara Wong
Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2019; 13(4): 2683
[Pubmed] | [DOI]
3 Pathophysiology and Individualized Treatment of Hypothalamic Obesity Following Craniopharyngioma and Other Suprasellar Tumors: A Systematic Review
Laura van Iersel,Karen E Brokke,Roger A H Adan,Lauren C M Bulthuis,Erica L T van den Akker,Hanneke M van Santen
Endocrine Reviews. 2019; 40(1): 193
[Pubmed] | [DOI]
4 Differential Effects of Alternate-Day Fasting Versus Daily Calorie Restriction on Insulin Resistance
Kelsey Gabel,Cynthia M. Kroeger,John F. Trepanowski,Kristin K. Hoddy,Sofia Cienfuegos,Faiza Kalam,Krista A. Varady
Obesity. 2019;
[Pubmed] | [DOI]
5 Task-related fMRI BOLD response to hyperinsulinemia in healthy older adults
Victoria J. Williams,Bianca A. Trombetta,Rabab Z. Jafri,Aaron M. Koenig,Chase D. Wennick,Becky C. Carlyle,Laya Ekhlaspour,Rexford S. Ahima,Steven J. Russell,David H. Salat,Steven E. Arnold
JCI Insight. 2019; 4(14)
[Pubmed] | [DOI]
6 Late life insulin resistance and Alzheimerćs disease and dementia: The Kuakini Honolulu heart program
Thomas H. Lee,Eric L. Hurwitz,Robert V. Cooney,Yan Yan Wu,Chen-Yen Wang,Kamal Masaki,Andrew Grandinetti
Journal of the Neurological Sciences. 2019; 403: 133
[Pubmed] | [DOI]
7 Indirect insulin resistance detection: Current clinical trends and laboratory limitations
Sylwia Placzkowska,Lilla Pawlik-Sobecka,Izabela Kokot,Agnieszka Piwowar
Biomedical Papers. 2019;
[Pubmed] | [DOI]
8 Metformin paradoxically worsens insulin resistance in SHORT syndrome
Krzysztof C. Lewandowski,Katarzyna Dabrowska,Maria Brzozowska,Joanna Kawalec,Andrzej Lewinski
Diabetology & Metabolic Syndrome. 2019; 11(1)
[Pubmed] | [DOI]
9 Are plasma 25-hydroxyvitamin D and retinol levels and one-carbon metabolism related to metabolic syndrome in patients with a severe mental disorder?
Belén Arranz,Mónica Sanchez-Autet,Luis San,Gemma Safont,Lorena De La Fuente-Tomás,Carla Hernandez,José Luis Bogas,María Paz García-Portilla
Psychiatry Research. 2019; 273: 22
[Pubmed] | [DOI]
10 Diosgenin and Its Fenugreek Based Biological Matrix Affect Insulin Resistance and Anabolic Hormones in a Rat Based Insulin Resistance Model
Rita Kiss,Georgina Pesti-Asbóth,Mária Magdolna Szarvas,László Stündl,Zoltán Cziáky,Csaba Hegedus,Diána Kovács,Andrea Badale,Endre Máthé,Zoltán Szilvássy,Judit Remenyik
BioMed Research International. 2019; 2019: 1
[Pubmed] | [DOI]
11 Effect of pomegranate seed oil supplementation on the GLUT-4 gene expression and glycemic control in obese people with type 2 diabetes: A randomized controlled clinical trial
Yaser Khajebishak,Laleh Payahoo,Mohammadreza Alivand,Hamed Hamishehkar,Majid Mobasseri,Vahide Ebrahimzadeh,Mahdiye Alipour,Beitollah Alipour
Journal of Cellular Physiology. 2019;
[Pubmed] | [DOI]
12 Associations of Adiposity and Diet Quality with Serum Ceramides in Middle-Aged Adults with Cardiovascular Risk Factors
Margaret A. Drazba,Ida Holásková,Nadine R. Sahyoun,Melissa Ventura Marra
Journal of Clinical Medicine. 2019; 8(4): 527
[Pubmed] | [DOI]
13 Human Visceral Adipose Tissue Macrophages Are Not Adequately Defined by Standard Methods of Characterization
Alecia M. Blaszczak,Anahita Jalilvand,Joey Liu,Valerie P. Wright,Andrew Suzo,Bradley Needleman,Sabrena Noria,William Lafuse,Willa A. Hsueh,David Bradley
Journal of Diabetes Research. 2019; 2019: 1
[Pubmed] | [DOI]
14 The association between plasma proneurotensin and glucose regulation is modified by country of birth
A. Fawad,P. M. Nilsson,J. Struck,A. Bergmann,O. Melander,L. Bennet
Scientific Reports. 2019; 9(1)
[Pubmed] | [DOI]
15 Endocrine Challenges and Metabolic Profile in Recipients of Allogeneic Haematopoietic Stem Cell Transplant: A Cross-Sectional Study from Southern India
Kripa Elizabeth Cherian,Nitin Kapoor,Anup J. Devasia,Vikram Mathews,Alok Srivastava,Nihal Thomas,Biju George,Thomas V. Paul
Indian Journal of Hematology and Blood Transfusion. 2019;
[Pubmed] | [DOI]
16 Plasma cathepsin D activity is negatively associated with hepatic insulin sensitivity in overweight and obese humans
Lingling Ding,Gijs H. Goossens,Yvonne Oligschlaeger,Tom Houben,Ellen E. Blaak,Ronit Shiri-Sverdlov
Diabetologia. 2019;
[Pubmed] | [DOI]
17 Visceral fat, cardiometabolic risk factors, and nocturnal blood pressure fall in young adults with primary hypertension
Tomasz Miazgowski,Aleksandra Taszarek,Bartosz Miazgowski
The Journal of Clinical Hypertension. 2019;
[Pubmed] | [DOI]
18 Exercise training attenuates insulin resistance and improves ß-cell function in patients with systemic autoimmune myopathies: a pilot study
Diego Sales de Oliveira,Isabela Bruna Pires Borges,Jean Marcos de Souza,Bruno Gualano,Rosa Maria Rodrigues Pereira,Samuel Katsuyuki Shinjo
Clinical Rheumatology. 2019;
[Pubmed] | [DOI]
19 Fasting triglycerides and glucose index: a useful screening test for assessing insulin resistance in patients diagnosed with rheumatoid arthritis and systemic lupus erythematosus
Betsabe Contreras-Haro,Sandra Ofelia Hernandez-Gonzalez,Laura Gonzalez-Lopez,Maria Claudia Espinel-Bermudez,Leonel Garcia-Benavides,Edsaul Perez-Guerrero,Maria Luisa Vazquez-Villegas,Jose Antonio Robles-Cervantes,Mario Salazar-Paramo,Diana Mercedes Hernandez-Corona,Arnulfo Hernan Nava-Zavala,Jorge I. Gamez-Nava
Diabetology & Metabolic Syndrome. 2019; 11(1)
[Pubmed] | [DOI]
20 The effect of hydroalcoholic Saffron ( Crocus sativus L .) extract on fasting plasma glucose, HbA1c, lipid profile, liver, and renal function tests in patients with type 2 diabetes mellitus: A randomized double-blind clinical trial
Armaghan Moravej Aleali,Reza Amani,Hajieh Shahbazian,Frough Namjooyan,Seyed Mahmoud Latifi,Bahman Cheraghian
Phytotherapy Research. 2019;
[Pubmed] | [DOI]
21 Late-Evening Snack with Branched-Chain Amino Acid-Enriched Nutrients Does Not Always Inhibit Overt Diabetes in Patients with Cirrhosis: A Pilot Study
Keisuke Nakanishi,Tadashi Namisaki,Tsuyoshi Mashitani,Kosuke Kaji,Kuniaki Ozaki,Soichiro Saikawa,Shinya Sato,Takashi Inoue,Yasuhiko Sawada,Kou Kitagawa,Hiroaki Takaya,Naotaka Shimozato,Hideto Kawaratani,Kei Moriya,Takemi Akahane,Akira Mitoro,Hitoshi Yoshiji
Nutrients. 2019; 11(9): 2140
[Pubmed] | [DOI]
22 Homeostasis Model Assessment cut-off points related to metabolic syndrome in children and adolescents: a systematic review and meta-analysis
Paola Arellano-Ruiz,Antonio García-Hermoso,Iván Cavero-Redondo,Diana Pozuelo-Carrascosa,Vicente Martínez-Vizcaíno,Monserrat Solera-Martinez
European Journal of Pediatrics. 2019;
[Pubmed] | [DOI]
23 Sesame oil and vitamin E co-administration may improve cardiometabolic risk factors in patients with metabolic syndrome: a randomized clinical trial
Ali Farajbakhsh,Seyed Mohammad Mazloomi,Mohsen Mazidi,Peyman Rezaie,Marzieh Akbarzadeh,Saeedeh Poor Ahmad,G. A. Ferns,Richard Ofori-Asenso,Siavash Babajafari
European Journal of Clinical Nutrition. 2019;
[Pubmed] | [DOI]
24 Utilizing a low-carbohydrate/high-protein diet to improve metabolic health in individuals with spinal cord injury (DISH): study protocol for a randomized controlled trial
Ceren Yarar-Fisher,Jia Li,Amie McLain,Barbara Gower,Robert Oster,Casey Morrow
Trials. 2019; 20(1)
[Pubmed] | [DOI]
25 Silymarin protects against high fat diet-evoked metabolic injury by induction of glucagon-like peptide 1 and sirtuin 1
Kai-Jyun Chang,Jer-An Lin,Sheng-Yi Chen,Ming-Hung Weng,Gow-Chin Yen
Journal of Functional Foods. 2019; 56: 136
[Pubmed] | [DOI]
26 Metabolic Abnormalities in Normal Weight Children Are Associated with Increased Visceral Fat Accumulation, Elevated Plasma Endotoxin Levels and a Higher Monosaccharide Intake
Anika Nier,Annette Brandt,Anja Baumann,Ina Conzelmann,Yelda Özel,Ina Bergheim
Nutrients. 2019; 11(3): 652
[Pubmed] | [DOI]
27 Effects of FABP2 Ala54Thr gene polymorphism on obesity and metabolic syndrome in middle-aged Korean women with abdominal obesity
Tae-Kyung Han,Wi-Young So
Central European Journal of Public Health. 2019; 27(1): 37
[Pubmed] | [DOI]
28 Fat redistribution and accumulation of visceral adipose tissue predicts type 2 diabetes risk in middle-aged black South African women: a 13-year longitudinal study
Asanda Mtintsilana,Lisa K. Micklesfield,Elin Chorell,Tommy Olsson,Julia H. Goedecke
Nutrition & Diabetes. 2019; 9(1)
[Pubmed] | [DOI]
29 Evaluation of the effect of insulin sensitivity-enhancing lifestyle- and dietary-related adjuncts on antidepressant treatment response: protocol for a systematic review and meta-analysis
Olaitan J. Jeremiah,Gráinne Cousins,Finbarr P. Leacy,Brian P. Kirby,Benedict K. Ryan
Systematic Reviews. 2019; 8(1)
[Pubmed] | [DOI]
30 Simple Diagnostic Method for Liver Insulin Resistance by Fasting 13C-glucose Breath Test
Tomokazu Matsuura,Hirotaka Ezaki,Mariko Nakamura,Yoshihiro Mezaki,Takahiro Masaki
RADIOISOTOPES. 2019; 68(2): 59
[Pubmed] | [DOI]
31 Assessment of Hepatic Steatosis in Patients with Chronic Hepatitis B Using Fibroscan and its Relation to Insulin Resistance
Reham M. Gameaa,Nehad Hawash,Rehab Badawi,Sherief Abd-Elsalam,Gamal K. Kasem,El-Sayed A. Wasfy
The Open Biomarkers Journal. 2019; 9(1): 70
[Pubmed] | [DOI]
32 Role of MicroRNAs in the Regulation of Subcutaneous White Adipose Tissue in Individuals With Obesity and Without Type 2 Diabetes
O. Brovkina,A. Nikitin,D. Khodyrev,E. Shestakova,I. Sklyanik,A. Panevina,Iurii Stafeev,M. Menshikov,A. Kobelyatskaya,A. Yurasov,V. Fedenko,Yu Yashkov,M. Shestakova
Frontiers in Endocrinology. 2019; 10
[Pubmed] | [DOI]
33 Functional and systemic effects of whole body electrical stimulation post bariatric surgery: study protocol for a randomized controlled trial
Larissa Delgado André,Renata P. Basso-Vanelli,Luciana Di Thommazo-Luporini,Paula Angélica Ricci,Ramona Cabiddu,Soraia Pilon Jürgensen,Claudio Ricardo de Oliveira,Ross Arena,Audrey Borghi-Silva
Trials. 2018; 19(1)
[Pubmed] | [DOI]
34 Pharmaceutical Impact of Houttuynia Cordata and Metformin Combination on High-Fat-Diet-Induced Metabolic Disorders: Link to Intestinal Microbiota and Metabolic Endotoxemia
Jing-Hua Wang,Shambhunath Bose,Na Rae Shin,Young-Won Chin,Young Hee Choi,Hojun Kim
Frontiers in Endocrinology. 2018; 9
[Pubmed] | [DOI]
35 Artificially Cultivated Ophiocordyceps sinensis Alleviates Diabetic Nephropathy and Its Podocyte Injury via Inhibiting P2X7R Expression and NLRP3 Inflammasome Activation
Chao Wang,Xiao-xia Hou,Hong-liang Rui,Li-jing Li,Jing Zhao,Min Yang,Li-jun Sun,Hong-rui Dong,Hong Cheng,Yi-Pu Chen
Journal of Diabetes Research. 2018; 2018: 1
[Pubmed] | [DOI]
36 A high-protein diet or combination exercise training to improve metabolic health in individuals with long-standing spinal cord injury: a pilot randomized study
Jia Li,Keith F. L. Polston,Mualla Eraslan,C. Scott Bickel,Samuel T. Windham,Amie B. McLain,Robert A. Oster,Marcas M. Bamman,Ceren Yarar-Fisher
Physiological Reports. 2018; 6(16): e13813
[Pubmed] | [DOI]
37 Ectopic Fat Accumulation in Distinct Insulin Resistant Phenotypes; Targets for Personalized Nutritional Interventions
Inez Trouwborst,Suzanne M. Bowser,Gijs H. Goossens,Ellen E. Blaak
Frontiers in Nutrition. 2018; 5
[Pubmed] | [DOI]
38 Population-based studies of relationships between dietary acidity load, insulin resistance and incident diabetes in Danes
Joachim Gćde,Trine Nielsen,Mia L. Madsen,Ulla Toft,Torben Jřrgensen,Kim Overvad,Anne Tjřnneland,Torben Hansen,Kristine H. Allin,Oluf Pedersen
Nutrition Journal. 2018; 17(1)
[Pubmed] | [DOI]
39 High triglycerides to HDL-cholesterol ratio is associated with insulin resistance in normal-weight healthy adults
Betzi Pantoja-Torres,Carlos J. Toro-Huamanchumo,Diego Urrunaga-Pastor,Mirella Guarnizo-Poma,Herbert Lazaro-Alcantara,Socorro Paico-Palacios,Vitalia del Carmen Ranilla-Seguin,Vicente A. Benites-Zapata
Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2018;
[Pubmed] | [DOI]
40 Serum AMH levels and insulin resistance in women with PCOS
Sezai Sahmay,Begum Aydogan Mathyk,Nigar Sofiyeva,Nil Atakul,Asli Azemi,Tamer Erel
European Journal of Obstetrics & Gynecology and Reproductive Biology. 2018; 224: 159
[Pubmed] | [DOI]
41 Common Inflammatory Markers in Polycystic Ovary Syndrome (PCOS): A BMI (Body Mass Index)-Matched Case–Control Study
Sudhindra Mohan Bhattacharya,Atreyee Basu
The Journal of Obstetrics and Gynecology of India. 2018;
[Pubmed] | [DOI]
42 Insulin-Sensitizer Effects of Fenugreek Seeds in Parallel with Changes in Plasma MCH Levels in Healthy Volunteers
Rita Kiss,Katalin Szabó,Rudolf Gesztelyi,Sándor Somodi,Péter Kovács,Zoltán Szabó,József Németh,Dániel Priksz,Andrea Kurucz,Béla Juhász,Zoltán Szilvássy
International Journal of Molecular Sciences. 2018; 19(3): 771
[Pubmed] | [DOI]
43 The Association between Persistent Hypertriglyceridemia and the Risk of Diabetes Development: The Kangbuk Samsung Health Study
Yu Hyun Kwon,Seul-Ki Kim,Jung Hwan Cho,Hyemi Kwon,Se Eun Park,Hyung-Geun Oh,Cheol-Young Park,Won-Young Lee,Ki-Won Oh,Sung-Woo Park,Eun-Jung Rhee
Endocrinology and Metabolism. 2018; 33(1): 55
[Pubmed] | [DOI]
44 Decaffeinated coffee improves insulin sensitivity in healthy men
Caio E. G. Reis,Cicília L. R. dos S. Paiva,Angélica A. Amato,Adriana Lofrano-Porto,Sara Wassell,Leslie J. C. Bluck,José G. Dórea,Teresa H. M. da Costa
British Journal of Nutrition. 2018; : 1
[Pubmed] | [DOI]
45 Assessment of the Ameliorative Effect of Ruzu Herbal Bitters on the Biochemical and Antioxidant Abnormalities Induced by High Fat Diet in Wistar Rats
Olubanke Olujoke Ogunlana,Oluseyi Ebenezer Ogunlana,Stanley Kelechukwu Ugochukwu,Alaba Oladipupo Adeyemi
International Journal of Pharmacology. 2018; 14(3): 329
[Pubmed] | [DOI]
46 Wheat bran with enriched gamma-aminobutyric acid attenuates glucose intolerance and hyperinsulinemia induced by a high-fat diet
Wenting Shang,Xu Si,Zhongkai Zhou,Padraig Strappe,Chris Blanchard
Food & Function. 2018;
[Pubmed] | [DOI]
47 Intermittent Fasting: Is the Wait Worth the Weight?
Mary-Catherine Stockman,Dylan Thomas,Jacquelyn Burke,Caroline M. Apovian
Current Obesity Reports. 2018;
[Pubmed] | [DOI]
48 Short-term decreased physical activity with increased sedentary behaviour causes metabolic derangements and altered body composition: effects in individuals with and without a first-degree relative with type 2 diabetes
Kelly A. Bowden Davies,Victoria S. Sprung,Juliette A. Norman,Andrew Thompson,Katie L. Mitchell,Jason C. G. Halford,Jo A. Harrold,John P. H. Wilding,Graham J. Kemp,Daniel J. Cuthbertson
Diabetologia. 2018;
[Pubmed] | [DOI]
49 Glucoregulatory and Cardiometabolic Profiles of Almond vs. Cracker Snacking for 8 Weeks in Young Adults: A Randomized Controlled Trial
Jaapna Dhillon,Max Thorwald,Natalie De La Cruz,Emily Vu,Syed Asghar,Quintin Kuse,L. Diaz Rios,Rudy Ortiz
Nutrients. 2018; 10(8): 960
[Pubmed] | [DOI]
50 Momordica charantia Administration Improves Insulin Secretion in Type 2 Diabetes Mellitus
Marisol Cortez-Navarrete,Esperanza Martínez-Abundis,Karina G. Pérez-Rubio,Manuel González-Ortiz,Miriam Méndez-del Villar
Journal of Medicinal Food. 2018;
[Pubmed] | [DOI]
51 Acute High-Intensity Interval Cycling Improves Postprandial Lipid Metabolism
CHIA-LUN LEE,YU-HSUAN KUO,CHING-FENG CHENG
Medicine & Science in Sports & Exercise. 2018; 50(8): 1687
[Pubmed] | [DOI]
52 Evaluation of surrogate measures of insulin sensitivity - correlation with gold standard is not enough
Anna Rudvik,Marianne Mĺnsson
BMC Medical Research Methodology. 2018; 18(1)
[Pubmed] | [DOI]
53 The Impact of Intra-articular Depot Betamethasone Injection on Insulin Resistance Among Diabetic Patients With Osteoarthritis of the Knee
George Habib,Mark Chernin,Fahed Sakas,Suheil Artul,Adel Jabbour,Haneen Jabaly-Habib
JCR: Journal of Clinical Rheumatology. 2018; 24(4): 193
[Pubmed] | [DOI]
54 Exercise and insulin resistance in type 2 diabetes mellitus: A systematic review and meta-analysis
A. Sampath Kumar,Arun G. Maiya,B.A. Shastry,K. Vaishali,N. Ravishankar,Animesh Hazari,Shubha Gundmi,Radhika Jadhav
Annals of Physical and Rehabilitation Medicine. 2018;
[Pubmed] | [DOI]
55 Metabolic syndrome in Mexican children: Low effectiveness of diagnostic definitions
Barbara Itzel Peńa-Espinoza,María de los Ángeles Granados-Silvestre,Katy Sánchez-Pozos,María Guadalupe Ortiz-López,Marta Menjivar
Endocrinología, Diabetes y Nutrición (English ed.). 2017; 64(7): 369
[Pubmed] | [DOI]
56 Serum Magnesium Concentrations in the Canadian Population and Associations with Diabetes, Glycemic Regulation, and Insulin Resistance
Jesse Bertinato,Kuan Wang,Stephen Hayward
Nutrients. 2017; 9(3): 296
[Pubmed] | [DOI]
57 Plasminogen Activator Inhibitor-1 Predicts Negative Alterations in Whole-Body Insulin Sensitivity in Chronic HIV Infection
Kamonkiat Wirunsawanya,Loni Belyea,Cecilia Shikuma,Richard M. Watanabe,Lindsay Kohorn,Bruce Shiramizu,Brooks I. Mitchell,Scott A. Souza,Sheila M. Keating,Philip J. Norris,Lishomwa C. Ndhlovu,Dominic Chow
AIDS Research and Human Retroviruses. 2017;
[Pubmed] | [DOI]
58 Methods for quantifying adipose tissue insulin resistance in overweight/obese humans
K W ter Horst,K A van Galen,P W Gilijamse,A V Hartstra,P F de Groot,F M van der Valk,M T Ackermans,M Nieuwdorp,J A Romijn,M J Serlie
International Journal of Obesity. 2017;
[Pubmed] | [DOI]
59 Síndrome metabólico en nińos mexicanos: poca efectividad de las definiciones diagnósticas
Barbara Itzel Peńa-Espinoza,María de los Ángeles Granados-Silvestre,Katy Sánchez-Pozos,María Guadalupe Ortiz-López,Marta Menjivar
Endocrinología, Diabetes y Nutrición. 2017;
[Pubmed] | [DOI]
60 Choosing an optimal input for an intravenous glucose tolerance test to aid parameter identification
Emma C. Martin,James W.T. Yates,Kayode Ogungbenro,Leon Aarons
Journal of Pharmacy and Pharmacology. 2017;
[Pubmed] | [DOI]
61 Validation of HOMA-IR in a model of insulin-resistance induced by a high-fat diet in Wistar rats
Luciana C. Antunes,Jessica L. Elkfury,Manoela N. Jornada,Kelly C. Foletto,Marcello C. Bertoluci
Archives of Endocrinology and Metabolism. 2016; 60(2): 138
[Pubmed] | [DOI]
62 Evaluation of insulin sensitivity and secretion in primary aldosteronism
Daisuke Watanabe,Midori Yatabe,Atsuhiro Ichihara
Clinical and Experimental Hypertension. 2016; 38(7): 613
[Pubmed] | [DOI]
63 Continuous positive airway pressure and diabetes risk in sleep apnea patients: A systemic review and meta-analysis
Liang Chen,Jian Kuang,Jian-Hao Pei,Hong-Mei Chen,Zhong Chen,Zhong-Wen Li,Hua-Zhang Yang,Xiao-Ying Fu,Long Wang,Zhi-Jiang Chen,Shui-Qing Lai,Shu-Ting Zhang
European Journal of Internal Medicine. 2016;
[Pubmed] | [DOI]
64 Association of insulin resistance and coronary artery remodeling: an intravascular ultrasound study
Sang-Hoon Kim,Jae-Youn Moon,Yeong Min Lim,Kyung Ho Kim,Woo-In Yang,Jung-Hoon Sung,Seung Min Yoo,In Jai Kim,Sang-Wook Lim,Dong-Hun Cha,Seung-Yun Cho
Cardiovascular Diabetology. 2015; 14(1)
[Pubmed] | [DOI]



 

Top
 
  Search
 
    Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
    Access Statistics
    Email Alert *
    Add to My List *
* Registration required (free)  

   Abstract Introduction Example Conclusion Article Tables
  In this article
 References

 Article Access Statistics
    Viewed5756    
    Printed45    
    Emailed0    
    PDF Downloaded1769    
    Comments [Add]    
    Cited by others 64    

Recommend this journal