The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index; 1 of the 6 sources its claims cite were checked for support.
Homeostasis model assessment: insulin resistance and ?-cell function from fasting plasma glucose and insulin concentrations in man
Open accessAbstract
The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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Claims the paper makes
15 Own result · 7 Own interpretation · 3 No full text of the sources · 1 Partly supported · 1 No basis given · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The fasting plasma insulin concentration is largely determined by the glucose concentration [3]”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
What the sources say
- Partly supportedcites [3] McCarthy ST, Harris F, Turner RC (1977) Glucose control of basal insulin secretion in diabetes. Diabetologia 13: 93?97 sourceExperimental — accuracy not yet measured
The Results and Discussion (page unavailable in our copy) show that changing plasma glucose affects basal insulin secretion. But they also report markedly impaired responses in diabetic subjects, including cases where insulin remained nearly constant despite a substantial glucose decrease. The paper therefore supports glucose as an influence, but not the unqualified claim that it largely determines fasting insulin concentration. (LLM, conf=0.84)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimate of deficient/3cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (R~ = 0.61, p< 0.01)”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (R~=0.88,p<0.0001)”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There was no correlation with any aspect of insulin-receptor binding.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the fasting insulin concentration (P~ = 0.81, p < 0.0001)”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mean coefficient of variation over the two studies was 32%.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Reproducibility of estimate of /3-cell function by HOMA
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“in both groups together (1% 0.61, p < 0.01 : Fig. 3).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Comparisons of methods of assessing ~3-cell function
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and with the estimate from the intravenous glucose tolerance test (R~ = 0.64, p < 0.05).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and in both groups together: 1% = 0.64, p< 0.05; Fig. 3).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > HOMA compared with intravenous glucose tolerance test.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Neither receptor numbers or any aspect of high or low affinity binding correlated with other measures of insulin resistance (HOMA, clamp, basal insulin or CIGMA: ~ all < 0.55; NS).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > HOMA compared with the hyperglycaemic clamp. The
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
- It may state two separate claims as one. 2
- The quote only partly states this claim. 1
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for/3-cell deficit) limits its use”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mean coefficient of variation over the two studies was 31%.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Hyperglycaemic clamp : ~-cell function (%)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The degree of the hyperglycaemia does not correlate significantly with either the degrees of abnormal fl-cell function or insulin resistance.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
The authors state this as an assumption.
Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
“Assuming that normal-weight normal subjects aged < 35 years have 100% r-cell function, and an insulin resistance of 1, the values for a patient can be assessed from the insulin and glucose concentrations by the formulae: r-cell”
The paper relies on this without support.
The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
“The plasma glucose:insulin response for normal subjects, equivalent to the C-peptide response, is insulin = 5 (glucose-3.5).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Surrogate markers of peripheral insulin activity, such as homeostasis model assessment and whole-body insulin sensitivity indexes, could be useful as well in the early identification of patients with IR, before overt DM sets in ().”
From Glucose Metabolism Abnormalities in Cushing Syndrome: From Molecular Basis to Clinical Management (2017) · cites this paper as [240]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“After the unprocessed diet, fasting glucose and insulin levels tended to decrease compared to baseline and the homeostasis model assessment of insulin resistance (HOMA-IR) (Matthews et al., 1985) was significantly decreased compared to baseline.”
From Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake (2019) · cites this paper as Matthews et al., 1985
The cited source says what the claim attributes to it.
In the citing paper
“The Homeostasis Model Assessment of Insulin Resistance (HoMA-IR) was calculated according to Matthews et al. [25].”
From Chronotype: A Tool to Screen Eating Habits in Polycystic Ovary Syndrome? (2022) · cites this paper as [25]
The cited source says what the claim attributes to it.
In the citing paper
“AAV9-mTERT treated mice also showed a trend for an improved glucose uptake and a better homeostatic model assessment (HOMA (Heikkinen et al, 2007; Matthews et al, 1985)) compared to the AAV9-eGFP controls, although differences did not reach statistical significance (Supporting Information Fig S4D and E).”
From Telomerase gene therapy in adult and old mice delays aging and increases longevity without increasing cancer (2012) · cites this paper as Matthews et al, 1985
In the citing paper
“ndirect estimates of insulin resistance, such as HOMA-IR 63 and QUICKI, 64 are derived from fasting S-insulin and glucose concentrations and have been widely used in both nondiabetic and type 2 diabetic subjects as surrogates of insulin sensitivity.65,66”
From Increased liver fat, impaired insulin clearance, and hepatic and adipose tissue insulin resistance in type 2 diabetes. (2008) · cites this paper as [63]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Subjects and procedures
“The same 12 normal subjects and 11 Type 2 diabetic patients came to the hospital fasting on a separate day for a continuous infusion”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > HOMA compared with the hyperglycaemic clamp. The
“HOMA compared with CIGMA.The HOMA estimate of insulin resistance from the mean of 15 stressed fasting samples before the CIGMA correlated with that measured by CIGMA in 11 normal subjects (R~ = 0.69, p < 0.02), in 12 diabetic subjects (R~ 0.97, p < 0.0001)”
Citations
Cites
Glucose clamp technique: a method for quantifying insulin secretion and resistance.
DeFronzo, R. A., Tobin, J. D., & Andres, R. (1979) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1979.237.3.e214 · Cited by 7,425
Quantitative estimation of insulin sensitivity.
Bergman, R. N., Ider, Y. Z., Bowden, C. R., & Cobelli, C. (1979) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1979.236.6.e667 · Cited by 1,874 · cited 1×
“This use of modelling to provide a reference graph contrasts with other models where an individual patient's data have to be entered into a computer to determine the relationships between rapidly changing variables, such as occur after an intravenous glucose test [19].”
Method[WHO Expert Committee on diabetes mellitus].
Reitsma, W. D., & Terpstra, J. (1981) · PubMed · Cited by 556
Turner, R. C., Holman, R. R., Matthews, D., Hockaday, T. D. R., & Peto, J. (1979) · Metabolism · doi:10.1016/0026-0495(79)90146-x · Cited by 388
Self-citationCharacteristics of Human Erythrocyte Insulin Receptors
Gambhir, K. K., Archer, J. A., & BRADLEY, C. J. (1978) · Diabetes · doi:10.2337/diab.27.7.701 · Cited by 290
The Insulin Receptor: Its Role in Insulin Resistance of Obesity and Diabetes
Olefsky, J. M. (1976) · Diabetes · doi:10.2337/diab.25.12.1154 · Cited by 289
Hosker, J. P., Matthews, D. R., Rudenski, A. S., Burnett, M. A., Darling, P., Bown, E. G., & Turner, R. C. (1985) · Diabetologia · doi:10.1007/bf00280882 · Cited by 287
Self-citationPulsatile Insulin Has Greater Hypoglycemic Effect Than Continuous Delivery
Matthews, D. R., Naylor, B. A., Jones, R., Ward, G. M., & Turner, R. C. (1983) · Diabetes · doi:10.2337/diab.32.7.617 · Cited by 256
Self-citationControl of pulsatile insulin secretion in man
Matthews, D. R., Lang, D. A., Burnett, M. A., & Turner, R. C. (1983) · Diabetologia · doi:10.1007/bf00282705 · Cited by 175
Self-citationTHE BASAL PLASMA GLUCOSE: A SIMPLE RELEVANT INDEX OF MATURITY‐ONSET DIABETES
HOLMAN, R. R., & TURNER, R. C. (1981) · Clinical Endocrinology · doi:10.1111/j.1365-2265.1981.tb00196.x · Cited by 64
Self-citation
Research works printed but not linked: 2
- Metropolitan Life Assurance Co (1959) Net weight standard for men and women. Stat Bull Metrol Life Found 40: 1?4
- Matthews DR, Hosker JP (1984) An unbiased, flexible computer programme for glucose clamping, with graphics and running statistics. Diabetologia 27: 308?309 A
Cited by
Sorted by citations
Items 19,501–19,600 of 31,469
Aktürk, M., Törüner, F. B., Aslan, S., Altınova, A. E., Çakır, N., Elbeğ, Ş., & Arslan, M. (2013) · Gynecological Endocrinology · doi:10.3109/09513590.2013.769512 · Cited by 15
Tung, T., Shih, H., Tsai, S., Chou, P., Chen, S., Lee, F.-L., Chuang, S., & Liu, J. (2007) · Ophthalmic Epidemiology · doi:10.1080/09286580601139220 · Cited by 13
Martins, C. S., Fernandes-Rosa, F. L., Espineira, A. R., et al. (2014) · Growth Hormone & IGF Research · doi:10.1016/j.ghir.2014.04.005 · Cited by 12
Li, H., Wang, X., Mao, Y., Hu, R., Xu, W., Lei, Z., Zhou, N., Jin, L., Guo, T., Li, Z., et al. (2014) · Cardiovascular Diabetology · doi:10.1186/1475-2840-13-24 · Cited by 12
IJzerman, R. G., Stehouwer, C. D. A., Serné, E. H., Voordouw, J. J., Smulders, Y. M., de Waal, H. A. D., & van Weissenbruch, M. M. (2008) · European Journal of Endocrinology · doi:10.1530/eje-08-0699 · Cited by 12
Kamoi, K., Ohara, N., Ikarashi, T., Shinozaki, Y., & Furukawa, K. (2011) · Journal of Diabetes & Metabolism · doi:10.4172/2155-6156.1000147 · Cited by 12
Ybarra, J., Planas, F., Navarro-López, F., Pujadas, S., Pujadas, J., Jurado, J., & Pou, J. M. (2008) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2008.01.019 · Cited by 12
Stookey, J. D., Klein, A., Hamer, J., Chi, C., Higa, A., Ng, V., Arieff, A. I., Kuypers, F. A., Larkin, S. K., Perrier, E. T., et al. (2013) · Physiological Reports · doi:10.1002/phy2.117 · Cited by 12
Mohammadzadeh, G., Zarghami, N., & Mobasseri, M. (2008) · Intern J Endocrinol Metab · Cited by 12
Långberg, E., Gu, H. F., Nordman, S., et al. (2007) · European Journal of Endocrinology · doi:10.1530/eje-07-0114 · Cited by 12
Ram, J., Snehalatha, C., Nanditha, A., Selvam, S., Shetty, S., Godsland, I. F., Johnston, D. G., & Ramachandran, A. (2014) · Diabetic Medicine · doi:10.1111/dme.12517 · Cited by 12
[Triglycerides/HDL-cholesterol ratio: in adolescents without cardiovascular risk factors].
Soutelo, J., Graffigna, M., Honfi, M., et al. (2012) · PubMed · Cited by 12
Association of Insulin Resistance Indexes to Carotid Intima–Media Thickness
Sciacqua, A., Marini, M. A., Hribal, M. L., Perticone, F., & Sesti, G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0053968 · Cited by 12
Oh, Y. T., Tran, D., Buchanan, T. A., Selsted, M. E., & Youn, J. H. (2015) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00131.2015 · Cited by 12
Grasso, A. (2013) · World Journal of Gastroenterology · doi:10.3748/wjg.v19.i41.6947 · Cited by 12
Konishi, K., Nakano, S., Seto, H., et al. (2009) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2009.04.021 · Cited by 12
Mei, J., Liao, S., Liu, Y., Tan, Y.-D., Wang, H., Liang, Y., Dong, X., Song, W., Gan, L., & Deng, S. (2014) · Journal of Diabetes Investigation · doi:10.1111/jdi.12315 · Cited by 12
Lu, F.-H., Ou, H., Wu, H., Hung, H., Wu, J., Yang, Y., & Chang, C. (2014) · Diabetes Management · doi:10.2217/dmt.14.17 · Cited by 12
de Barros, C. R., Cezaretto, A., Curti, M. L. R., Monfort‐Pires, M., Folchetti, L. D., Siqueira-Catania, A., & Ferreira, S. R. G. (2014) · Diabetology & Metabolic Syndrome · doi:10.1186/1758-5996-6-136 · Cited by 12
Bhansali, S., Shafiq, N., Pandhi, P., et al. (2013) · PubMed · Cited by 12
Mills, J. L., Irving, R. R., Choo-Kang, E. G., et al. (2010) · Revista Panamericana de Salud Pública · doi:10.1590/s1020-49892010000600004 · Cited by 12
Jeong, S. J., Song, J. E., Kim, S. B., et al. (2013) · AIDS Research and Human Retroviruses · doi:10.1089/aid.2013.0048 · Cited by 12
Effects of sleeve gastrectomy on insulin resistance
Cătoi, A. F., Pârvu, A. E., Mironiuc, A. I., et al. (2015) · Medicine and Pharmacy Reports · doi:10.15386/cjmed-576 · Cited by 12
Decreased coronary flow reserve in obese women.
Eroğlu, S., Sade, L. E., Bozbaş, H., & Müderrisoğlu, H. (2009) · PubMed · Cited by 12
Obesity-related leptin receptor polymorphisms and gallstones disease
Méndez‐Sánchez, N., Bermejo-Martínez, L., Chávez-Tapia, N. C., Zamora‐Valdés, D., Sánchez-Lara, K., Uribe-Ramos, M. H., Ponciano‐Rodríguez, G., González, H. A. B., Ramos, M. H., & Uribe, M. (2006) · Annals of Hepatology · doi:10.1016/s1665-2681(19)32025-3 · Cited by 12
Identification of Insulin Resistance in Subjects with Normal Glucose Tolerance
Lin, J. D., Chang, J., Wu, C., Pei, D., Hsieh, C. H., Hsieh, A., Chen, Y., Hsu, C. H., & Liu, C. C. (2014) · Annals of the Academy of Medicine Singapore · doi:10.47102/annals-acadmedsg.v43n2p113 · Cited by 12
Μacut, D., Antić, I. B., Nestorov, J. M., Topalović, V., Macut, J. B., Panidis, D., Kotlica, B. K., Papadakis, E. P., Matić, G. M., & Milutinović, D. V. (2014) · HORMONES · doi:10.14310/horm.2002.1526 · Cited by 12
Assessment of Chronic Stress: Comparison of Hair Biomarkers and Allostatic Load Indices
Wippert, P., Honold, J., Wang, V., & Kirschbaum, C. (2014) · Journal of Psychology Research · doi:10.17265/2159-5542/2014.07.001 · Cited by 12
Paleczny, B., Siennicka, A. E., Zacharski, M., Jankowska, E. A., Ponikowska, B., & Ponikowski, P. P. (2016) · Clinical Autonomic Research · doi:10.1007/s10286-015-0338-2 · Cited by 12
Kepper, M. M., SOTHERN, M. S., Zabaleta, J., Ravussin, É., Velasco‐Gonzalez, C., Leonardi, C., Griffiths, L., Park, C., Estrada, J. J., & Scribner, R. A. (2016) · Obesity · doi:10.1002/oby.21462 · Cited by 12
Tripodo, C., Petta, S., Guarnotta, C., et al. (2011) · Antiviral Therapy · doi:10.3851/imp1957 · Cited by 12
Aqueous Extracts of Purple Sweet Potato Attenuate Weight Gain in High Fat-fed Mice
Shin, S. J., Bae, U., Ahn, M., Ka, S. O., woo, S., Noh, S.-Y., Kwon, Y. S., Jung, K., Wee, J., & Park, B. H. (2012) · International Journal of Pharmacology · doi:10.3923/ijp.2013.42.49 · Cited by 12
Başyığıt, S., Özkan, S., Uzman, M., Ertuğrul, D. T., Kefeli, A., Aktaş, B., Yeniova, A. Ö., Asiltürk, Z., Nazlıgül, Y., Simsek, H., et al. (2015) · Medicine · doi:10.1097/md.0000000000000793 · Cited by 12
Yıldırım, M., Türkyılmaz, E., Neşeli̇oğlu, S., Alışık, M., & Avşar, A. F. Y. (2016) · Gynecologic and Obstetric Investigation · doi:10.1159/000445744 · Cited by 12
Tyndall, A., Argourd, L., Sajobi, T., Davenport, M., Forbes, S. C., Gill, S., Parboosingh, J., Anderson, T., Wilson, B. J., Smith, E., et al. (2016) · Physiological Reports · doi:10.14814/phy2.12733 · Cited by 14
Soy-derived Isoflavones Treatment in Children with Hypercholesterolemia: A Pilot Study
Zung, A., Shachar, S., Zadik, Z., & Kerem, Z. (2010) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem.2010.23.1-2.133 · Cited by 12
Wójcik, M., Mac‐Marcjanek, K., Woźniak, L. A., Nadel, I., Lewiñski, A., & Cypryk, K. (2014) · Endokrynologia Polska · doi:10.5603/ep.2014.0003 · Cited by 12
Feng, N., Ma, X., Xiao-wei, W., et al. (2014) · Chinese Medical Journal · doi:10.3760/cma.j.issn.0366-6999.20140521 · Cited by 12
Azizi, M., Tadıbı, V., & Behpoor, N. (2016) · International Journal of Diabetes in Developing Countries · doi:10.1007/s13410-016-0504-7 · Cited by 12
Ling, Y., Gao, X., Lin, H., Ma, H., Pan, B., & Gao, J. (2016) · Journal of Human Hypertension · doi:10.1038/jhh.2016.16 · Cited by 12
The Distribution of Obesity Phenotypes in HIV-Infected African Population
Nguyen, K. A., Peer, N., de Villiers, A., et al. (2016) · Nutrients · doi:10.3390/nu8060299 · Cited by 12
Küçükaydın, Z., Duran, C., Başaran, M., Camlica, F., Erdem, S. S., Basaran, A., Kutlu, O., BURNİK, F. S., Elmas, H., & Gönen, M. S. (2016) · Journal of Endocrinological Investigation · doi:10.1007/s40618-016-0498-5 · Cited by 12
Decreasing Insulin Sensitivity in Women Induces Alterations in LH Pulsatility
Leckwyck, M. V., Kong, W., Burton, K. J., Amati, F., Vionnet, N., & Pralong, F. P. (2016) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2016-1727 · Cited by 12
Erçin, C. N., Doğru, T., Çelebi, G., et al. (2016) · TURKISH JOURNAL OF MEDICAL SCIENCES · doi:10.3906/sag-1502-144 · Cited by 12
Ezz, M. K., Atef, A. A., Hassanein, N., et al. (2016) · International Journal of Biochemistry Research & Review · doi:10.9734/ijbcrr/2016/26651 · Cited by 12
Bhosle, D. (2016) · Journal of Medical Science And clinical Research · doi:10.18535/jmscr/v4i9.65 · Cited by 13
Moreno, L. G., Lidiane, G. O., Dirceu, S. M., Liliane, V. C. P., Costa, K. B., Joao, L. M., Etel, R.-V., Flvio, C. M., Marco, F. D.-P., & Esteves, E. A. (2016) · Journal of Medicinal Plants Research · doi:10.5897/jmpr2016.6222 · Cited by 13
Jaruratanasirikul, S., Thammaratchuchai, S., Puwanant, M., Mo‐suwan, L., & Sriplung, H. (2016) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2016-0195 · Cited by 12
Hillman, S. L., Kubba, T., & Williams, D. J. (2017) · Ultrasound in Obstetrics and Gynecology · doi:10.1002/uog.17342 · Cited by 12
Ikezaki, H., Ai, M., Schaefer, E. J., Otokozawa, S., Asztalos, B. F., Nakajima, K., Zhou, Y., Liu, C., Jacques, P. F., Cupples, L. A., et al. (2016) · Scientific Reports · doi:10.1038/srep36725 · Cited by 12
Impact of short sleep on metabolic variables in obese children with obstructive sleep apnea
Bhushan, B., Ayub, B., Thompson, D. M., Abdullah, F., & Billings, K. R. (2016) · The Laryngoscope · doi:10.1002/lary.26420 · Cited by 12
Farias, D. R., Franco‐Sena, A. B., Rebelo, F. M., Salles, G. F., Struchiner, C. J., Martins, M. C., & Kac, G. (2016) · American Journal of Hypertension · doi:10.1093/ajh/hpw147 · Cited by 12
Chen, M., Zhang, X., Fang, Q., Wang, T., Li, T., & Qiao, H. (2016) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2016.3920 · Cited by 12
Juraschek, S. P., Miller, E. R., Selvin, E., Carey, V. J., Appel, L. J., Christenson, R. H., & Sacks, F. M. (2016) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2016-000276 · Cited by 12
Jung, C., Lee, B., Choi, D., et al. (2016) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2016.12.017 · Cited by 12
van der Aa, M. P., Hoving, V., van de Garde, E. M. W., de Boer, A., Knibbe, C. A. J., & van der Vorst, M. M. J. (2016) · Journal of Obesity · doi:10.1155/2016/7852648 · Cited by 12
Eendebak, R. J. A. H., Święcicka, A., Gromski, P. S., Pye, S. R., O’Neill, T. W., Marshall, A. D., Keevil, B. G., Tampubolon, G., Goodacre, R., Wu, F. C. W., et al. (2017) · Clinical Endocrinology · doi:10.1111/cen.13305 · Cited by 12
Bellatorre, A. C., Jackson, S. H., & Choi, K. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173103 · Cited by 12
HNF1α defect influences post-prandial lipid regulation
St‐Jean, M., Boudreau, F., Carpentier, A. C., & Hivert, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0177110 · Cited by 12
Insulin resistance and associated factors: a cross-sectional study of bank employees
Salaroli, L. B., Cattafesta, M., Molina, M. D. C. B., Zandonade, E., & Bissoli, N. S. (2017) · Clinics · doi:10.6061/clinics/2017(04)06 · Cited by 12
The relationship between insulin resistance and endothelial dysfunction in obese adolescents
Brar, P. C., Patel, P., & Katz, S. D. (2017) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2016-0404 · Cited by 12
Ali, T. M., Elesawy, B. H., & Elaskary, A. A. (2017) · Irish Journal of Medical Science (1971 -) · doi:10.1007/s11845-017-1635-7 · Cited by 12
Association between Auricular Signals and the Risk Factors of Metabolic Syndrome
Suen, L. K. P., Yeh, C. H., Yeung, S. S., et al. (2017) · Medicines · doi:10.3390/medicines4030045 · Cited by 12
Ishikawa, T., Aibe, Y., Matsuda, T., Iwamoto, T., Takami, T., & Sakaida, I. (2017) · American Journal of Roentgenology · doi:10.2214/ajr.16.17307 · Cited by 12
Insulin Resistance in Early Rheumatoid Arthritis Is Associated with Low Appendicular Lean Mass
Müller, R., Kull, M. M., Lember, M., Põlluste, K., Valner, A., & Kallikorm, R. (2017) · BioMed Research International · doi:10.1155/2017/9584720 · Cited by 12
Cho, M. E., Craven, T. E., Cheung, A. K., Glasser, S. P., Rahman, M., Soliman, E. Z., Stafford, R. S., Johnson, K., Bates, J. T., Burgner, A. M., et al. (2017) · Journal of Clinical Hypertension · doi:10.1111/jch.13062 · Cited by 11
Ko, S., Jung, W., Lee, S., et al. (2017) · Nutrition Research and Practice · doi:10.4162/nrp.2017.11.5.396 · Cited by 12
Jung, Y. J., Park, W. S., Nguyen, T. T., Kang, K. P., Jin, H. Y., Kim, S. H., Suh, W., & Kim, W. B. (2017) · Oncotarget · doi:10.18632/oncotarget.21998 · Cited by 12
Genovesi, S., Antolini, L., Orlando, A., Tassistro, E., Giussani, M., Nava, E., Turolo, L., Manolopoulou, J., Parati, G., & Morganti, A. (2017) · Journal of Hypertension · doi:10.1097/hjh.0000000000001571 · Cited by 12
Yang, L., Chen, Z., Li, B., Wang, M., Yu, L., Wan, Y., Gan, J., Zhang, Y., Liu, Z., & Wang, X. (2017) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2017.00355 · Cited by 12
Hayashi, F., Kaibori, M., Sakaguchi, T., Matsui, K., Ishizaki, M., Kwon, A., Iwasaka, J., Kimura, Y., & Habu, D. (2017) · Hepatology Research · doi:10.1111/hepr.13000 · Cited by 12
Effect of neonatal orally administered S-allyl cysteine in high-fructose diet fed Wistar rats
Lembede, B. W., Erlwanger, K. H., Nkomozepi, P., et al. (2017) · Journal of Developmental Origins of Health and Disease · doi:10.1017/s2040174417000940 · Cited by 12
Dey, P., Mah, E., Li, J., Jalili, T., Symons, J. D., & Bruno, R. S. (2017) · Journal of Functional Foods · doi:10.1016/j.jff.2017.12.007 · Cited by 12
Ramírez, J. C., Soto-Sánchez, J. P., Zbinden‐Foncea, H., González, M. M., Dinamarca, B. L., & González-Rojas, L. (2018) · Nutrición Hospitalaria · doi:10.20960/nh.1279 · Cited by 12
Islam, S. K. M. A., Kim, D., Lee, Y., & Moon, S. (2018) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2018.03.034 · Cited by 12
LHCG receptor polymorphisms in PCOS patients
Robeva, R. N., Andonova, S., Tomova, A., Kumanov, P., & Savov, A. S. (2018) · Biotechnology & Biotechnological Equipment · doi:10.1080/13102818.2017.1423246 · Cited by 12
Gender Differences in the Clinical Features of Sleep Apnea Syndrome
Mieno, Y., Hayashi, M., Sakakibara, H., Takahashi, H., Fujita, S., Isogai, S., Gotô, Y., Uozu, S., Okazawa, M., & Imaizumi, K. (2018) · Internal Medicine · doi:10.2169/internalmedicine.7570-16 · Cited by 13
Bi, X., Loo, Y. T., & Henry, C. J. (2018) · Nutrition · doi:10.1016/j.nut.2018.04.003 · Cited by 12
Szentpéteri, A., Lőrincz, H., Somodi, S., Varga, V. E., Paragh, G., Seres, I., Paragh, G., & Harangi, M. (2018) · Lipids in Health and Disease · doi:10.1186/s12944-018-0691-y · Cited by 13
Lithgow, H., Florida-James, G. D., & Leggate, M. (2018) · Physiological Reports · doi:10.14814/phy2.13684 · Cited by 11
Höbaus, C., Tscharre, M., Herz, C. T., et al. (2018) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2018.05.006 · Cited by 12
Niemczyk, N. A., Bertolet, M. H., Catov, J. M., Desai, M. M., McClure, C. K., Roberts, J. M., Sekikawa, A. M., Tepper, P. G., & Barinas‐Mitchell, E. (2018) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-018-1841-y · Cited by 12
Shiba, T., Takahashi, M., Shiba, C., et al. (2018) · International journal of cardiac imaging · doi:10.1007/s10554-018-1388-z · Cited by 12
Cao, L., Graham, S. L., & Pilowsky, P. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0198432 · Cited by 12
Abdel‐Ghaffar, A., Ghanem, H., Ahmed, E. K., Hassanin, O. A., & Mohamed, R. G. (2018) · Fundamental and Clinical Pharmacology · doi:10.1111/fcp.12385 · Cited by 12
Kamari, V. E., Hileman, C. O., Gholam, P. M., et al. (2018) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2018.05.058 · Cited by 12
Insulinotropic Activity of Standardized Methanolic Extracts of Ficus deltoidea from Seven Varieties
Yahaya, N., Dom, N. S. M., Adam, Z., & Hamid, M. (2018) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2018/3769874 · Cited by 13
Clamp, L. D., Hume, D. J., Lambert, E. V., & Kroff, J. (2018) · Journal of Nutritional Science · doi:10.1017/jns.2018.11 · Cited by 12
Ungethüm, K., Jolink, M., Hippich, M., Lachmann, L., Haupt, F., Winkler, C., Hummel, S., Pitchika, A., Kordonouri, O., Ziegler, A., et al. (2018) · Diabetic Medicine · doi:10.1111/dme.13819 · Cited by 12
Koufakis, T., Karras, S. Ν., Zebekakis, P. E., Ajjan, R., & Kotsa, K. (2018) · Expert Opinion on Biological Therapy · doi:10.1080/14712598.2018.1526278 · Cited by 12
Ko, C., Lin, R., Zeng, Y., et al. (2018) · Food Science & Nutrition · doi:10.1002/fsn3.840 · Cited by 12
Elsaid, H. H., Elgohary, M. N., & Elshabrawy, A. M. (2018) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2018.10.004 · Cited by 12
Cardiometabolic risk factors in preschool children with abdominal obesity from Medellín, Colombia
Marín-Echeverri, C., Aristizábal, J. C., Gallego-Lopera, N., et al. (2018) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2018-0140 · Cited by 12
Hassan, A. B., Salih, S. F., Hassan, I. I., Saadi, F. S., Abdulah, D. M., Ahmed, I. H., Taher, S. M., & Khaleel, B. B. (2018) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2018.11.016 · Cited by 12
OGTT is recommended for glucose homeostasis assessments in Friedreich ataxia
Azzi, A., Cosentino, C., Kibanda, J., Féry, F., & Cnop, M. (2018) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.686 · Cited by 12
Pergola, G. D., Giagulli, V. A., Guastamacchia, E., Bartolomeo, N., Tatoli, R., Lampignano, L., Silvestris, F., & Triggiani, V. (2019) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2018.12.007 · Cited by 12
Júnior, W. S. D. S., de Souza, M. D. G. C., Neto, J. F. N., Bouskela, E., & Kraemer‐Aguiar, L. G. (2019) · Journal of Diabetes Research · doi:10.1155/2019/5238013 · Cited by 12
Beaudin, A. E., Hanly, P. J., Raneri, J. K., Sajobi, T. T., Anderson, T. J., & Poulin, M. J. (2019) · Experimental Physiology · doi:10.1113/ep086845 · Cited by 13
Metabolic impairments in patients with myotonic dystrophy type 2.
Vujnić, M., Perić, S., Calic, Z., et al. (2018) · PubMed · Cited by 12
Piché, M., Poirier, P., Marette, A., Mathieu, P., Lévesque, V., Bibeau, K., Larose, É., & Després, J. (2019) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2018.0092 · Cited by 12
Metadata
- Authors
- Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C.
- Affiliations
- University of Oxford (GB)
- ORCID
- Matthews, D. R.
- Venue
- Diabetologia
- Published
- 1985-07
- Volume
- 28
- Issue
- 7
- Pages
- 412-419
- ISSN
- 0012-186X (ISSN-L)
- 1432-0428
- OpenAlex
- W2160234571
- Cited by
- 31,502
- References
- 21
- Citations by year
- 2026: 933 · 2025: 1060 · 2024: 1240 · 2023: 1291 · 2022: 1489 · 2021: 1528 · 2020: 1424 · 2019: 1427 · 2018: 1456 · 2017: 1440 · 2016: 1481 · 2015: 1682 · 2014: 1693 · 2013: 1621 · 2012: 1518
- FWCI
- 7.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Diabetologia (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Research Council
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- HOMA, insulin resistance, beta-cell function, fasting blood glucose, fasting insulin, hyperglycaemic clamp, euglycaemic clamp, intravenous glucose tolerance test, insulin receptor binding, type 2 diabetes
- MeSH terms
- Adult, Blood Glucose, Computers, Diabetes Mellitus, Type 2, Erythrocytes, Fasting, Homeostasis, Humans, Insulin, Insulin Resistance, Islets of Langerhans, Middle Aged, Monocytes, Receptor, Insulin
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-14, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-14 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
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