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 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
“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]
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
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Boza, C., Muñoz, R., Salinas, J., C, C. G., Klaassen, J., Escalona, A., Pérez, G., Ibañez, L. A., & Guzmán, S. (2011) · Obesity Surgery · doi:10.1007/s11695-011-0463-5 · Cited by 60
Choi, K. M., Han, K. A., Ahn, H. J., Lee, S. Y., Hwang, S. Y., Kim, B. C., Hong, H. C., Choi, H. Y., Yang, S. J., Yoo, H. J., et al. (2012) · Clinical Endocrinology · doi:10.1111/cen.12076 · Cited by 60
Dervaux, N., Wubuli, M., Mégnien, J. L., Chironi, G. N., & Simon, A. C. (2008) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2007.11.032 · Cited by 59
Shabrova, E. V., Olga, T., Singh, A. P., Plutzky, J., Vorsa, N., & Quadro, L. (2011) · PLoS ONE · doi:10.1371/journal.pone.0024634 · Cited by 62
Sridhar, S. B., Xu, F., Darbinian, J., Quesenberry, C. P., Ferrara, A., & Hedderson, M. M. (2014) · Diabetes Care · doi:10.2337/dc13-2229 · Cited by 59
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Borel, A., Nazare, J., Smith, J. D., Aschner, P. J., Barter, P. J., Gaal, L. V., Tan, C., Wïttchen, H., Matsuzawa, Y., Kadowaki, T., et al. (2014) · International Journal of Obesity · doi:10.1038/ijo.2014.163 · Cited by 58
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Kuo, F.-C., Lee, C.-H., Hsieh, C., Kuo, P., Chen, Y.-C., & Hung, Y. (2012) · Psychiatry Research · doi:10.1016/j.psychres.2012.11.020 · Cited by 59
Celi, F., Bini, V., Papi, F., Santilli, E., Ferretti, A., Mencacci, M., Berioli, M. G., Giorgi, G. D., & Falorni, A. (2005) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2005.02313.x · Cited by 59
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Lang, C. H., Pruznak, A. M., Nystrom, G. J., & Vary, T. C. (2009) · Nutrition & Metabolism · doi:10.1186/1743-7075-6-4 · Cited by 60
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Shams, M. E. E. (2011) · Scientia Pharmaceutica · doi:10.3797/scipharm.1104-21 · Cited by 59
Kelly, A. S., Thelen, A. M., Kaiser, D. R., González-Campoy, J. M., & Bank, A. J. (2007) · Vascular Medicine · doi:10.1177/1358863x07084200 · Cited by 59
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Goel, A., Chiu, H. K., Felton, J. L., Palmer, J. P., & Brooks‐Worrell, B. (2007) · Diabetes · doi:10.2337/db06-0552 · Cited by 59
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Elena Carrasco, P., Francisco Pérez, B., Burwinkel, B., Cecilia Albala, B., José Luis Santos, M., Gladys Larenas, Y., & Montalvo, D., V (2004) · Revista médica de Chile · doi:10.4067/s0034-98872004001000005 · Cited by 59
Nishida, Y., Matsubara, T., Tobina, T., Shindo, M., Tokuyama, K., Tanaka, K., & Tanaka, H. (2010) · International Journal of Endocrinology · doi:10.1155/2010/452820 · Cited by 59
Rijpert, M., Evers, I. M., Vroede, M. D., de Valk, H. W., Heijnen, C. J., & Visser, G. H. A. (2009) · Diabetes Care · doi:10.2337/dc09-0652 · Cited by 58
Maiztegui, B., Borelli, M. I., Raschia, M. A., Zotto, H. H. D., & Gagliardino, J. J. (2008) · Journal of Endocrinology · doi:10.1677/joe-08-0386 · Cited by 61
EL-Mesallamy, H. O., Hamdy, N. Μ., Mostafa, D., & Amin, A. I. (2013) · Journal of Interferon & Cytokine Research · doi:10.1089/jir.2013.0059 · Cited by 59
Alam, S., Kabir, J., Mustafa, G., Gupta, U. D., Hasan, S., & Alam, A. K. (2016) · Saudi Journal of Gastroenterology · doi:10.4103/1319-3767.173762 · Cited by 61
Strong and persistent effect on liver fat with a Paleolithic diet during a two-year intervention
Otten, J., Mellberg, C., Ryberg, M., Sandberg, S., Kullberg, J., Lindahl, B., Larsson, C. L., Hauksson, J. B., & Olsson, T. (2016) · International Journal of Obesity · doi:10.1038/ijo.2016.4 · Cited by 60
Bandarra, N. M., Lopes, P. A., Martins, S. V., Ferreira, J., Alfaia, C. M., Rolo, E. A., Correia, J., Pinto, R. M. A., Ramos‐Bueno, R. P., Batista, I., et al. (2016) · Nutrition Research · doi:10.1016/j.nutres.2015.12.015 · Cited by 59
Cerbone, M., Capalbo, D., Wasniewska, M. G., Alfano, S., Raso, G. M., Oliviero, U., Cittadini, A., Luca, F. D., & Salerno, M. (2016) · European Journal of Endocrinology · doi:10.1530/eje-15-0833 · Cited by 59
Okamoto, A., Yokokawa, H., Sanada, H., & Naito, T. (2016) · Drugs in R&D · doi:10.1007/s40268-016-0137-9 · Cited by 59
Molecular characterization of insulin resistance and glycolytic metabolism in the rat uterus
Zhang, Y., Sun, X., Sun, X., Meng, F., Hu, M., Li, X., Li, W., Wu, X., Brännström, M., Shao, R., et al. (2016) · Scientific Reports · doi:10.1038/srep30679 · Cited by 59
Petta, S., Eslam, M., Valenti, L., Bugianesi, E., Barbara, M., Cammá, C., Porzio, M., Rosso, C., Fargion, S., George, J., et al. (2017) · Liver international (Print) · doi:10.1111/liv.13397 · Cited by 53
Chung, G. E., Lee, J., Lee, H., Kim, M. K., Yim, J. Y., Choi, S., Kim, Y. J., Yoon, J., & Kim, D. (2018) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2018.03.027 · Cited by 59
Sundaram, S. S., Halbower, A. C., Klawitter, J., Pan, Z., Robbins, K. N., Capocelli, K. E., & Sokol, R. J. (2018) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2018.03.028 · Cited by 59
Cremona, A., O’Gorman, C. S., Cotter, A. M., Saunders, J. A., & Donnelly, A. (2018) · Obesity Science & Practice · doi:10.1002/osp4.283 · Cited by 63
Sleep-disordered breathing, circulating exosomes, and insulin sensitivity in adipocytes
Khalyfa, A., Gozal, D., Masa, J. F., Marín, J. M., Qiao, Z., Corral, J., González, M., Martí, S., Gozal, D., Egea, C., et al. (2018) · International Journal of Obesity · doi:10.1038/s41366-018-0099-9 · Cited by 61
Meta-Analysis of Adiponectin as a Biomarker for the Detection of Metabolic Syndrome
Liu, Z., Liang, S., Que, S., Zhou, L., Zheng, S., & Mardinoğlu, A. (2018) · Frontiers in Physiology · doi:10.3389/fphys.2018.01238 · Cited by 61
SHIP-AHOY (Study of High Blood Pressure in Pediatrics: Adult Hypertension Onset in Youth)
Mendizábal, B., Urbina, E. M., Becker, R. C., Daniels, S. R., Falkner, B. E., Hamdani, G., Hanevold, C. D., Hooper, S. R., Ingelfinger, J. R., Lande, M. B., et al. (2018) · Hypertension · doi:10.1161/hypertensionaha.118.11434 · Cited by 59
Gao, X., Tian, Y., Randell, E. W., Zhou, H., & Sun, G. (2019) · Frontiers in Endocrinology · doi:10.3389/fendo.2019.00168 · Cited by 59
Barata, L., Feitosa, M. F., Bielak, L. F., Halligan, B. D., Baldridge, A. S., Guo, X., Yerges‐Armstrong, L. M., Smith, A. V., Yao, J., Palmer, N. D., et al. (2019) · Hepatology Communications · doi:10.1002/hep4.1353 · Cited by 61
Parry, S. A., Rosqvist, F., Mózes, F. E., Cornfield, T., Hutchinson, M., Piché, M., Hülsmeier, A. J., Hornemann, T., Dyson, P. A., & Hodson, L. (2020) · Diabetes Care · doi:10.2337/dc19-2331 · Cited by 64
Yang, N., Yao, Z. J., Li, M., Liu, J., Gao, X., Fan, H. J., Hu, Y., Zhang, H., Xu, Y., Qu, A., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0125922 · Cited by 58
Liu, K. H., Chan, Y. L., Chan, J. C. N., et al. (2004) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2004.10.038 · Cited by 59
Comparison of Different Insulin Regimens in Elderly Patients With NIDDM
Wolffenbuttel, B. H. R., Sels, J.-P. J. E., Rondas-Colbers, G. J., Menheere, P. P. C. A., & Kruseman, A. C. N. (1996) · Diabetes Care · doi:10.2337/diacare.19.12.1326 · Cited by 58
Lotz, T., Chase, J. G., McAuley, K. A., Lee, D. S., Lin, J., Hann, C. E., & Mann, J. I. (2006) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2006.8.338 · Cited by 59
The common PPAR-γ2 Pro12Ala variant is associated with greater insulin sensitivity
Buzzetti, R., Petrone, A., Ribaudo, M. C., Alemanno, I., Zavarella, S., Mein, C. A., Maiani, F., Tiberti, C., Baroni, M. G., Vecci, E., et al. (2004) · European Journal of Human Genetics · doi:10.1038/sj.ejhg.5201283 · Cited by 58
Anan, F., Takahashi, N., Ooie, T., Yufu, K., Hara, M., Nakagawa, M., Yonemochi, H., Saikawa, T., & Yoshimatsu, H. (2005) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-005-0931-8 · Cited by 59
David‐Neto, E., Lemos, F. C., Fadel, L. M., Agena, F., Sato, M. Y., Coccuza, C., Pereira, L. M., de Castro, M. C. R., Lando, V. S., Nahas, W. C., et al. (2007) · Transplantation · doi:10.1097/01.tp.0000267647.03550.22 · Cited by 59
Weerakiet, S., Bunnag, P., Phakdeekitcharoen, B., Wansumrith, S., Chanprasertyothin, S., Jultanmas, R., & Thakkinstian, A. (2007) · Gynecological Endocrinology · doi:10.1080/09513590701214158 · Cited by 59
Gelernter‐Yaniv, L., Feng, N., Sebring, N. G., Hochberg, Z., & Yanovski, J. A. (2003) · International Journal of Obesity · doi:10.1038/sj.ijo.0802327 · Cited by 60
Chambers, J. C., McGregor, A., Jean-Marie, J., & Kooner, J. S. (1999) · Heart · doi:10.1136/hrt.81.5.501 · Cited by 59
Majali, K. A., Cooper, M. B., Staels, B., Luc, G., Taskinen, M., & Betteridge, D. J. (2006) · Diabetologia · doi:10.1007/s00125-005-0092-4 · Cited by 58
Tebas, P., Zhang, J., Yarasheski, K. E., et al. (2007) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0b013e318042e204 · Cited by 59
Zhu, Q., Yamagata, K., Miura, A., Shihara, N., Horikawa, Y., Takeda, J., Miyagawa, J.-I., & Matsuzawa, Y. (2003) · Diabetologia · doi:10.1007/s00125-003-1067-y · Cited by 59
Glueck, C. J., Sieve, L., Zhu, B., & Wang, P. (2006) · Metabolism · doi:10.1016/j.metabol.2005.09.008 · Cited by 59
Vascular Function in Women with Previous Gestational Diabetes mellitus
Hannemann, M. M., Liddell, W. G., Shore, A. C., Clark, P. M. S., & Tooke, J. E. (2002) · Journal of Vascular Research · doi:10.1159/000065543 · Cited by 59
Hitti, J. E., Andersen, J., McComsey, G. A., Liu, T., Melvin, A. J., Smith, L., Stek, A. M., Aberg, J. A., Hull, A., Alston‐Smith, B., et al. (2007) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2006.11.037 · Cited by 58
Matsubara, M., Katayose, S., & Maruoka, S. (2003) · European Journal of Endocrinology · doi:10.1530/eje.0.1480343 · Cited by 59
Role of Insulin Resistance in Familial Combined Hyperlipidemia
Veerkamp, M. J., Graaf, J. D., & Stalenhoef, A. F. H. (2005) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/01.atv.0000160612.18065.29 · Cited by 59
Prediction models for insulin resistance in the polycystic ovary syndrome
Gennarelli, G. (2000) · Human Reproduction · doi:10.1093/humrep/15.10.2098 · Cited by 59
Gender differences in diurnal triglyceridemia in lean and overweight subjects
Halkes, C. J. M., Cabezas, M. C., van Wijk, J., & Erkelens, D. W. (2001) · International Journal of Obesity · doi:10.1038/sj.ijo.0801831 · Cited by 59
Ibáñez, L., Ong, K. K., Potau, N., Marcos, M. V., de Zegher, F., & Dunger, D. B. (2001) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.86.12.8093 · Cited by 56
Insulin Resistance Syndrome and Fibrinolytic Activity: The Northern Sweden MONICA Study
Lindahl, B., Asplund, K., Eliasson, M., & Evrin, P. (1996) · International Journal of Epidemiology · doi:10.1093/ije/25.2.291 · Cited by 59
Inverse association between adiponectin and C-reactive protein in substantially healthy Japanese men
Matsushita, K., Yatsuya, H., Tamakoshi, K., Wada, K., Otsuka, R., Zhang, H., Sugiura, K., Kondo, T., Murohara, T., & Toyoshima, H. (2005) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2005.10.031 · Cited by 59
Central obesity predicts the worsening of glycemia in southern Chinese
Wat, N., Lam, T., Janus, E., & Lam, K. (2001) · International Journal of Obesity · doi:10.1038/sj.ijo.0801834 · Cited by 59
Dilley, J., Ganesan, A., Deepa, R., Deepa, M., Sharada, G., Williams, O. D., & Mohan, V. (2007) · Diabetes Care · doi:10.2337/dc06-2414 · Cited by 58
Inancli, S. S., Usluogullari, A., Ustu, Y., Caner, S., Tam, A. A., Ersoy, R., & Cakir, B. (2013) · Endocrine · doi:10.1007/s12020-012-9857-y · Cited by 59
Moran, L. J., Noakes, M., Clifton, P., Buckley, J., Brinkworth, G., Thomson, R., & Norman, R. J. (2019) · Nutrients · doi:10.3390/nu11030492 · Cited by 57
“5] [21] and FAI was calculated by [(testosterone/SHBG)] × 100] [22].”
MethodCuevas-Ramos, D., Almeda-Valdes, P., Gómez-Pérez, F. J., Meza-Arana, C. E., Cruz-Bautista, I., Arellano-Campos, O., Navarrete-López, M., & Aguilar-Salinas, C. A. (2010) · European Journal of Endocrinology · doi:10.1530/eje-10-0454 · Cited by 58
Gracia‐Marco, L., Moreno, L. A., Ruiz, J. R., Ortega, F. B., de Moraes, A. C. F., Gottrand, F., Roccaldo, R., Marcos, A., Gómez‐Martínez, S., Dallongeville, J., et al. (2015) · Progress in Cardiovascular Diseases · doi:10.1016/j.pcad.2015.11.002 · Cited by 58
Berezin, A. Е., Kremzer, A. A., Samura, T. A., et al. (2015) · Journal of Endocrinological Investigation · doi:10.1007/s40618-015-0273-z · Cited by 58
EL-Mesallamy, H. O., Hamdy, N. Μ., Zaghloul, A. S., & Sallam, A.-A. M. (2012) · Scandinavian Journal of Clinical and Laboratory Investigation · doi:10.3109/00365513.2012.723135 · Cited by 58
Metabolic Surgery for Non-Obese Type 2 Diabetes
Geloneze, B., Geloneze, S. R., Chaim, É. A., Hirsch, F. F., Felici, A. C., Lambert, G., Tambascia, M. A., & Pareja, J. C. (2012) · Annals of Surgery · doi:10.1097/sla.0b013e3182592c62 · Cited by 56
Khripun, I., Vorobyev, S., Белоусов, И. И., Коган, М. И., & Zitzmann, M. (2018) · The Aging Male · doi:10.1080/13685538.2018.1506918 · Cited by 59
Risk stratification of non-alcoholic fatty liver disease across body mass index in a community basis
Huang, J., Tsai, P., Yeh, M., Huang, C., Huang, C., Hsieh, M., Dai, C., Yang, J., Chen, S., Yu, M., et al. (2019) · Journal of the Formosan Medical Association · doi:10.1016/j.jfma.2019.03.014 · Cited by 59
Correlation of Uric Acid Levels and Parameters of Metabolic Syndrome
Cibičková, Ľ., Langová, K., Vaverková, H., Kubíčková, V., & Karásek, D. (2017) · Physiological Research · doi:10.33549/physiolres.933410 · Cited by 58
Zamani‐Garmsiri, F., Hashemnia, S. M. R., Shabani, M., Bagherieh, M., Emamgholipour, S., & Meshkani, R. (2020) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2020.108505 · Cited by 59
Quan, W., Ye, J., Xue, C., Li, Y., Liu, G., He, Z., Qin, F., Zeng, M., & Chen, J. (2021) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.0c06445 · Cited by 59
Wang, G., Li, X., Zhou, Y. Q., Feng, J., & Zhang, M. (2021) · Animals · doi:10.3390/ani11051286 · Cited by 58
Ren, X., Wang, L., Chen, Z., Hou, D., Xue, Y., Diao, X., & Shen, Q. (2021) · Nutrients · doi:10.3390/nu13061837 · Cited by 61
Margolis, R., & Sant, K. E. (2021) · Journal of Xenobiotics · doi:10.3390/jox11030008 · Cited by 59
Ang, Q. Y., Alba, D., Upadhyay, V., Bisanz, J. E., Cai, J., Lee, H. L., Barajas, E., Wei, G., Noecker, C. A., Patterson, A. D., et al. (2021) · eLife · doi:10.7554/elife.70349 · Cited by 60
Menopausal Vasomotor Symptoms and White Matter Hyperintensities in Midlife Women
Thurston, R. C., Wu, M., Chang, Y., Aizenstein, H. J., Derby, C. A., Barinas‐Mitchell, E., & Maki, P. M. (2022) · Neurology · doi:10.1212/wnl.0000000000201401 · Cited by 67
Zhou, X., & Xu, J. (2023) · Journal of Diabetes Investigation · doi:10.1111/jdi.14086 · Cited by 61
Wang, X., Chen, H., Liu, J., Ouyang, Y., Wang, D., Bao, W., & Liu, L. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms160716483 · Cited by 58
Could dietary seaweed reverse the metabolic syndrome?
Teas, J., Baldeon, M. E., Chiriboga, D. E., Davis, J. R., Sarriés, A. J., & Braverman, L. E. (2009) · PubMed · Cited by 58
Lim, S. M., Choi, D. P., Rhee, Y., & Kim, H. C. (2015) · PLoS ONE · doi:10.1371/journal.pone.0125238 · Cited by 58
Excessively high soluble Klotho in patients with acromegaly
Sze, L., Bernays, R. L., Zwimpfer, C., et al. (2012) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2012.02542.x · Cited by 58
Şengül, C., Çevik, C., Özveren, O., Oduncu, V., Sünbül, A., Akgün, T., Can, M. M., Semiz, E., & Dindar, İ. (2011) · Echocardiography · doi:10.1111/j.1540-8175.2011.01471.x · Cited by 56
Sir‐Petermann, T., de Guevara, A. L., Ethel Codner, D., Preisler, J., Crisosto, N., Echiburú, B., Maliqueo, M. A., Sánchez, F., Pérez‐Bravo, F., & CASSORLA, F. G. (2012) · Reproductive Sciences · doi:10.1177/1933719111424444 · Cited by 58
León‐Mimila, P., Vega‐Badillo, J., Gutiérrez‐Vidal, R., Villamil‐Ramírez, H., Villarreal-Molina, M. T., Larrieta‐Carrasco, E., López-Contreras, B. E., Kauffer, L. R. M., Pintado, D. G. M., Méndez‐Sánchez, N., et al. (2015) · Experimental and Molecular Pathology · doi:10.1016/j.yexmp.2015.01.012 · Cited by 59
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).
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Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
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