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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
16 Own result · 7 Own interpretation · 3 No full text of the sources · 1 Partly supported · 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)
plainstates a cause- 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.
plainstates an association- 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.
plainstates an association- 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.
plainstates an association- 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
plainstates an association- 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.
plainstates an association- 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.
plainstates an association- 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).
A result the paper states — it cites no source for it, so there is none to check. 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 14,701–14,800 of 31,469
Chamberlin, M. L., Wilson, S. M. G., Gaston, M. E., Kuo, W., & Miles, M. P. (2024) · Nutrients · doi:10.3390/nu16030419 · Cited by 24
Magaña, A. I. C., Vázquez‐Moreno, M. A., Rocha-Aguado, A., Mejía, S. Á., Valladares‐Salgado, A., Díaz-Flores, M., López‐Díazguerrero, N. E., & Cruz, M. (2024) · Antioxidants · doi:10.3390/antiox13040457 · Cited by 22
High-fiber basil seed flour reduces insulin resistance and hepatic steatosis in high-fat diet mice
Farías, C., Cisternas, C., Paz, A. V. C., Mercado, L., Valenzuela, R., Calderón, H., Espinosa, A., Videla, L. A., & Muñoz, L. A. (2024) · npj Science of Food · doi:10.1038/s41538-024-00329-z · Cited by 22
Mager, D. R., Ling, S. C., & Roberts, E. A. (2008) · Paediatrics & Child Health · doi:10.1093/pch/13.2.111 · Cited by 21
Mercado, M. V.-D., Guzmán-Ornelas, M.-O., Corona-Meraz, F.-I., Ríos-Ibarra, C. P., Reyes‐Serratos, E., Castro-Albarrán, J., Ruíz-Quezada, S. L., & Navarro-Hernández, R. E. (2015) · BioMed Research International · doi:10.1155/2015/285491 · Cited by 23
Wang, Yu, Zhang, Floyd, Z. E., Lian, Cefalu, & Boudreau (2012) · International Journal of Nanomedicine · doi:10.2147/ijn.s30887 · Cited by 21
Predictive factors of non-alcoholic steatohepatitis: relationship with metabolic syndrome.
Aller, R., Izaola, O., Ruiz-Rebollo, L., et al. (2015) · PubMed · doi:10.3305/nh.2015.31.6.8908 · Cited by 21
Yang, Y., Zhang, X., Bao, M., et al. (2015) · Journal of Diabetes Investigation · doi:10.1111/jdi.12381 · Cited by 21
de Luis, D. A., Díaz-Soto, G., Conde, R., Izaola, O., & de la Fuente, B. (2012) · Journal of Clinical Laboratory Analysis · doi:10.1002/jcla.21541 · Cited by 21
Racial discrimination and metabolic control in women with type 2 diabetes.
Wagner, J. A., Tennen, H. A., Feinn, R., & Finan, P. H. (2013) · PubMed · Cited by 21
Akbaba, G., Berker, D. S., Işık, S., Tuna, M. M., Koparal, S., Vural, M., Yılmaz, F. M., Topçuoğlu, C., & Güler, S. (2015) · Wiener klinische Wochenschrift · doi:10.1007/s00508-015-0865-9 · Cited by 22
Bayram, F., Elbüken, G., Korkmaz, C., Aydoğdu, A., Karaca, Z., & Çakır, I. (2015) · Hormone and Metabolic Research · doi:10.1055/s-0035-1564252 · Cited by 22
Nunes, E. A., Gonçalves-Neto, L. M., Ferreira, F. B. D., dos Santos, C., Fernandes, L. C., Boschero, A. C., Calder, P. C., & Rafacho, A. (2013) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2012-0456 · Cited by 22
Serum osteocalcin in subjects with metabolic syndrome and central obesity
Bador, K. M., Wee, L. D., Halim, S. A. A., et al. (2015) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2015.09.009 · Cited by 21
Determinants of dyslipidaemia in probands with polycystic ovary syndrome and their sisters
Joharatnam, J., Barber, T. M., Webber, L. J., Conway, G. S., McCarthy, M. I., & Franks, S. (2011) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2011.03983.x · Cited by 21
Lao, X. Q., Thompson, A. J. V., McHutchison, J. G., et al. (2010) · Journal of Viral Hepatitis · doi:10.1111/j.1365-2893.2010.01347.x · Cited by 21
Exercise training improves heart rate recovery in women with breast cancer
Giallauria, F., Maresca, L., Vitelli, A., de Magistris, M. S., Chiodini, P., Mattiello, A., Gentile, M., Mancini, M. E., Grieco, A., Russo, A., et al. (2015) · SpringerPlus · doi:10.1186/s40064-015-1179-0 · Cited by 24
Mitochondrial DNA content and function, childhood obesity, and insulin resistance
Shoar, Z., Goldenthal, M. J., Luca, F. D., & Suarez, E. A. (2015) · Endocrine Research · doi:10.3109/07435800.2015.1068797 · Cited by 21
Influence of gastrointestinal factors on glucose metabolism in patients with cirrhosis
Junker, A. E., Gluud, L. L., Holst, J. J., Knop, F. K., & Vilsbøll, T. (2015) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.12981 · Cited by 21
Serum visfatin levels and metabolic syndrome criteria in obese female subjects
de Luis, D. A., Aller, R., Sagrado, M. G., Conde, R., Izaola, O., & de la Fuente, B. (2013) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.2430 · Cited by 21
Circulating adropin levels in patients with endometrium cancer
Nergiz, S., Altınkaya, S. Ö., Ömürlü, İ. K., et al. (2015) · Gynecological Endocrinology · doi:10.3109/09513590.2015.1065480 · Cited by 21
Impaired coronary collateral vessel development in patients with proliferative diabetic retinopathy
Celık, T., Berdan, M. E., Iyısoy, A., Kurşaklıoğlu, H., Turhan, H., Kılıç, S., Güleç, M., Öztürk, S., & Işık, E. (2005) · Clinical Cardiology · doi:10.1002/clc.4960280808 · Cited by 21
Biochemical effect of a ketogenic diet on the brains of obese adult rats
Mohamed, H. E., El-Swefy, S. E., Rashed, L. A., & Hammad, S. K. (2010) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2009.11.005 · Cited by 22
Sokup, A., Ruszkowska-Ciastek, B., Góralczyk, B., Góralczyk, K., Szymański, M., Grabiec, M., & Rość, D. (2012) · International Journal of Endocrinology · doi:10.1155/2012/278050 · Cited by 21
Eckard, A. R., Tangpricha, V., Seydafkan, S., O’Riordan, M. A., Storer, N. J., Labbato, D. E., & McComsey, G. A. (2013) · The Pediatric Infectious Disease Journal · doi:10.1097/inf.0b013e318286c793 · Cited by 21
Second Trimester Insulin Resistance, Early Pregnancy Body Mass Index and Gestational Weight Gain
Stuebe, A. M., McElrath, T. F., Thadhani, R., & Ecker, J. L. (2009) · Maternal and Child Health Journal · doi:10.1007/s10995-009-0449-2 · Cited by 21
Kim, Y. A., Ku, E. J., Khang, A. R., Hong, E. S., Kim, K. M., Moon, J. H., Choi, S. H., Park, K. S., Jang, H. C., & Lim, S. K. (2014) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2014.08.014 · Cited by 20
O’Sullivan, T. A., Ambrosini, G. L., Mori, T. A., Beilin, L. J., & Oddy, W. H. (2010) · Lipids · doi:10.1007/s11745-010-3499-8 · Cited by 21
Luo, B., Yang, Y., Nieman, D. C., Zhang, Y., Wang, J., Wang, R., & Chen, P. (2013) · Journal of sport and health science/Journal of Sport and Health Science · doi:10.1016/j.jshs.2013.05.001 · Cited by 21
Young, L. R., Kurzer, M. S., Thomas, W., Redmon, J. B., & Raatz, S. K. (2013) · Nutrition Research · doi:10.1016/j.nutres.2013.04.011 · Cited by 21
Alves, R. D. M., de Oliveira, F. C. E., Hermsdorff, H. H. M., Abete, I., Zulet, M. Á., Martínez, J. A., & Bressan, J. (2013) · European Journal of Nutrition · doi:10.1007/s00394-013-0497-7 · Cited by 24
Tian, G., Wei, W., Zhang, W., Zhang, L., You, H., Liu, W., Sun, Z., Wang, X., & Wu, X. T. (2013) · Journal of International Medical Research · doi:10.1177/0300060513477290 · Cited by 21
Song, S., Paik, H. Y., & Song, Y. (2012) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2012.09.038 · Cited by 22
Pelusi, C., Fanelli, F., Pariali, M., Zanotti, L., Gambineri, A., & Pasquali, R. (2013) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2013-1107 · Cited by 21
Shen, C., Yu, T., Zhang, T., et al. (2013) · Hormone Research in Paediatrics · doi:10.1159/000351464 · Cited by 21
Tzeng, T., Liou, S., Chang, C. J., & Liu, I.-M. (2013) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2013/303061 · Cited by 21
Guimarães, M. M. M., Júnior, A. R. D. O., Penido, M. G., de Queiróz, L. C., Goulart, E. M. A., Greco, D. B., & Machado, L. J. D. C. (2007) · Annals of Nutrition and Metabolism · doi:10.1159/000100819 · Cited by 21
Khalyfa, A., Kheirandish‐Gozal, L., Capdevila, Ó. S., et al. (2012) · Pediatric Pulmonology · doi:10.1002/ppul.22560 · Cited by 21
Ma, D., Shuler, J. M., Raider, K. D., Rogers, R. S., Wheatley, J. L., Geiger, P. C., & Stanford, J. A. (2015) · Brain Research · doi:10.1016/j.brainres.2015.03.053 · Cited by 21
Muras-Szwedziak, K., Masajtis‐Zagajewska, A., & Nowicki, M. P. (2013) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2013-2418 · Cited by 21
Insulin resistance following thermal injury: An animal study
Chen, X.-L., Xia, Z., Ben, D., Tian, J., & Wei, D. (2007) · Burns · doi:10.1016/j.burns.2006.08.017 · Cited by 21
Huang, C., Acevedo, E. O., Mari, D. C., Randazzo, C., & Shibata, Y. (2013) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2013.10.004 · Cited by 21
BMI Is the Main Determinant of the Circulating Leptin in Women after Vertical Banded Gastroplasty
Zabrocka, L. K., Raczyńska, S., Goyke, E., Śledziński, Z., & Świerczyński, J. (2004) · Obesity Research · doi:10.1038/oby.2004.57 · Cited by 21
Dias, J. P., & Couture, R. (2012) · Journal of Cardiovascular Pharmacology · doi:10.1097/fjc.0b013e3182576277 · Cited by 21
Feigh, M., Andreassen, K. V., Hjuler, S. T., Nielsen, R. H., Christiansen, C., Henriksen, K., & Karsdal, M. A. (2013) · Menopause The Journal of The North American Menopause Society · doi:10.1097/gme.0b013e31827c58ab · Cited by 21
Leelayuwat, N., Tunkumnerdthai, O., Donsom, M., Punyaek, N., Manimmanakorn, A., Kukongviriyapan, U., & Kukongviriyapan, V. (2008) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2008.08.010 · Cited by 21
Khadir, A., Tiss, A., Kavalakatt, S., Behbehani, K., Dehbi, M., & Elkum, N. B. (2015) · Mediators of Inflammation · doi:10.1155/2015/512603 · Cited by 21
Jans, A., Sparks, L. M., van Hees, A. M. J., Gjelstad, I. M. F., Tierney, A. C., Risérus, U., Drevon, C. A., Roche, H. M., Schrauwen, P., & Blaak, E. E. (2011) · Obesity · doi:10.1038/oby.2011.149 · Cited by 21
Hagihara, G. N., Lobato, N. S., Filgueira, F. P., Akamine, E. H., Aragão, D. S., Casarini, D. E., de Carvalho, M. H. C., & Fortes, Z. B. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106029 · Cited by 22
Cornford, A. S., Hinko, A., Nelson, R. K., Barkan, A. L., & Horowitz, J. F. (2012) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2012-0266 · Cited by 21
Genes implicated in insulin resistance are down-regulated in primary aldosteronism patients
Williams, T. A., Monticone, S., Urbanet, R., Bertello, C., Giraudo, G., Vettor, R., Fallo, F., Veglio, F., & Mulatero, P. (2012) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2012.02.007 · Cited by 21
Kim, M., Jeung, S. R., Jeong, T., Lee, S., & Lee, J. H. (2014) · Journal of Lipid Research · doi:10.1194/jlr.m044834 · Cited by 22
de Luis, D. A., Sagrado, M. G., Aller, R., Izaola, O., & Conde, R. (2010) · Metabolism · doi:10.1016/j.metabol.2010.01.028 · Cited by 21
Daoussis, D., Panoulas, V. F., John, H., Toms, T. E., Antonopoulos, I., Treharne, G. J., Nightingale, P., Douglas, K., & Kitas, G. D. (2010) · Clinical Rheumatology · doi:10.1007/s10067-010-1446-y · Cited by 21
Dietary Iron Intake and Cardiovascular Outcome in Italian Women: 10-Year Follow-Up
Casiglia, E., Tikhonoff, V., Bascelli, A., Giordano, N., Caffi, S., Andreatta, E., Mazza, A., Boschetti, G., Grasselli, C., Saugo, M., et al. (2011) · Journal of Women s Health · doi:10.1089/jwh.2011.2780 · Cited by 20
Park, S. H., Kim, D. J., & Lee, H. Y. (2009) · The American Journal of Gastroenterology · doi:10.1038/ajg.2009.6 · Cited by 21
Kim, S. K., Hur, K. Y., Choi, Y. H., Kim, S. W., Chung, J. H., Kim, H. K., Lee, M., Min, Y., Kim, K., & Kim, J. H. (2010) · Korean Diabetes Journal · doi:10.4093/kdj.2010.34.4.253 · Cited by 21
Glucose Intolerance after a Recent History of Gestational Diabetes
Benhalima, K., Leuridan, L., Calewaert, P., Devlieger, R., Verhaeghe, J., & Mathieu, C. (2014) · International Journal of Endocrinology · doi:10.1155/2014/727652 · Cited by 22
Shinoki, A., & Hara, H. (2011) · British Journal Of Nutrition · doi:10.1017/s000711451100167x · Cited by 23
Acceleration training for managing nonalcoholic fatty liver disease: a pilot study
Oh, S., Shida, T., Sawai, A., Maruyama, T., Isobe, T., Okamoto, Y., Someya, N., Tanaka, K., Arai, E., Tozawa, A., et al. (2014) · Therapeutics and Clinical Risk Management · doi:10.2147/tcrm.s68322 · Cited by 22
Dobbins, R. L., Byerly, R., Gaddy, R., Gao, F., Mahar, K. M., Napolitano, A., Ambery, P., & de Gouville, A. L. M. (2015) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2015.03.005 · Cited by 21
Relation of the rs9939609 gene variant in FTO with metabolic syndrome in obese female patients
de Luis, D. A., Aller, R., Conde, R., et al. (2013) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2013.02.003 · Cited by 21
Ambulatory Blood Pressure and Subclinical Cardiovascular Disease in Children With Turner Syndrome
Akyürek, N., Atabek, M. E., Eklioğlu, B. S., & Alp, H. (2013) · Pediatric Cardiology · doi:10.1007/s00246-013-0740-2 · Cited by 21
Maeder, M. T., Ammann, P., Schoch, O. D., Rickli, H., Korte, W. C., Hürny, C., MYERS, J. N., & Münzer, T. (2009) · CHEST Journal · doi:10.1378/chest.09-1424 · Cited by 21
Ren, Z., Zhang, P., Zhang, X., Chen, S.-K., Zhang, H., Lv, D.-T., Zhuang, B.-Q., Wen, Y., Hu, H., Ding, W., et al. (2014) · Nutrition · doi:10.1016/j.nut.2014.10.012 · Cited by 21
Keith, M. E., Kuliszewski, M. A., Liao, C., Peeva, V., Ahmed, M., Tran, S., Sorokin, K., Jenkins, D. J. A., Errett, L. E., & Leong-Poi, H. M. (2014) · Clinical Nutrition · doi:10.1016/j.clnu.2014.06.010 · Cited by 21
Hashimoto, S., Ootani, K., Hayashi, S., & Naito, M. (2011) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.8482 · Cited by 21
Characteristics of Polycystic Ovarian Syndrome and Relationship with Ghrelin in Adolescents
Özgen, İ. T., Aydın, M., Güven, A., & Aliyazıcıoğlu, Y. (2010) · Journal of Pediatric and Adolescent Gynecology · doi:10.1016/j.jpag.2010.02.011 · Cited by 22
Impairment of nutritional regulation of adipose triglyceride lipase expression with age
Caimari, A., Oliver, P., & Palou, A. (2008) · International Journal of Obesity · doi:10.1038/ijo.2008.69 · Cited by 21
Contreras, F., Foullioux, C., Pacheco, B., et al. (2008) · American Journal of Therapeutics · doi:10.1097/mjt.0b013e318160c353 · Cited by 21
Juanola‐Falgarona, M., Fernández‐Cao, J. C., Salas‐Salvadó, J., Martínez‐González, M. Á., Estruch, R., Fiol, M., Arija, V., Bulló, M., & for the PREDIMED Study Investigators (2013) · PLoS ONE · doi:10.1371/journal.pone.0076433 · Cited by 21
Akçam, M., Boyacı, A., Pirgon, Ö., et al. (2012) · Clinical Imaging · doi:10.1016/j.clinimag.2012.07.011 · Cited by 21
Insulin-resistance HCV infection-related affects vascular stiffness in normotensives
Perticone, M., Maio, R., Tassone, E. J., Tripepi, G. L., Cello, S. D., Miceli, S., Caroleo, B., Sciacqua, A., Licata, A., Sesti, G., et al. (2014) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2014.11.025 · Cited by 21
Afşar, B., Elsürer, R., Sezer, S., & Özdemir, F. N. (2009) · Metabolism · doi:10.1016/j.metabol.2009.07.025 · Cited by 21
Zhang, Z., Lanza, E., Ross, A. C., Albert, P. S., Colburn, N. H., Rovine, M. J., Bagshaw, D., Ulbrecht, J. S., & Hartman, T. J. (2011) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2010.273 · Cited by 21
Identification of New Biomarkers of Low-Dose GH Replacement Therapy in GH-Deficient Patients
Cruz‐Topete, D., Jørgensen, J. O. L., Christensen, B., Sackmann‐Sala, L., Krusenstjerna-Hafstrøm, T., Jara, A., Okada, S., & Kopchick, J. J. (2011) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2011-0197 · Cited by 21
de Sousa, G., Schlüter, B., Buschatz, D., Menke, T., Trowitzsch, E., Andler, W., & Reinehr, T. (2011) · Sleep And Breathing · doi:10.1007/s11325-010-0469-6 · Cited by 21
Osei, K., Gaillard, T. R., & Schuster, D. (2006) · Metabolism · doi:10.1016/j.metabol.2006.08.018 · Cited by 21
Aboul‐Fotouh, S., & Elgayar, N. (2013) · Canadian Journal of Physiology and Pharmacology · doi:10.1139/cjpp-2013-0185 · Cited by 21
Bae, M. J., Lee, S. Y., Kim, Y. J., et al. (2013) · Journal of Women s Health · doi:10.1089/jwh.2012.4148 · Cited by 21
The Utility of HbA1c as a Diagnostic Criterion of Diabetes
Kim, H., Choi, E. Y., Park, E. W., Cheong, Y. S., Lee, H.-Y., & Kim, J. H. (2011) · Korean Journal of Family Medicine · doi:10.4082/kjfm.2011.32.7.383 · Cited by 21
Iimura, Y., Matsuura, M., Yao, Z., Ito, S., Fujiwara, M., Yoshitsugu, M., Miyauchi, A., & Hiyoshi, T. (2015) · Journal of Diabetes Investigation · doi:10.1111/jdi.12363 · Cited by 21
van Werven, J. R., Schreuder, T. C. M. A., Aarts, E. O., Nederveen, A. J., Meijer, J. W. R., Berends, F. J., Janssen, I. M. C., Mulder, C. J. J., Jansen, P. L. M., & Stoker, J. (2011) · American Journal of Roentgenology · doi:10.2214/ajr.10.5215 · Cited by 21
Hansen, D., Linsen, L., Verboven, K., Hendrikx, M., Rummens, J., Erum, M., Eijnde, B. O., & Dendale, P. (2015) · Experimental Physiology · doi:10.1113/ep085053 · Cited by 21
Quality of life and metabolic disorders in patients with obstructive sleep apnea
Bulcun, E., Ekici, A., & Ekici, M. S. (2012) · Clinical and investigative medicine · doi:10.25011/cim.v35i2.16294 · Cited by 21
He, L., He, M., Lv, X., Pu, D., Su, P., & Liu, Z. (2010) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2010.06.016 · Cited by 21
Is left ventricular hypertrophy a low-level inflammatory state? A population-based cohort study
Bo, S., Mandrile, C., Milanesio, N., et al. (2011) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2010.11.004 · Cited by 21
Papandreou, D., Karabouta, Z., & Rousso, I. (2012) · Cholesterol · doi:10.1155/2012/572820 · Cited by 21
Sevastianova, K., Sutinen, J., Greco, D., Sievers, M., Salmenkivi, K., Perttilä, J., Olkkonen, V. M., Wågsäter, D., Lidell, M. E., Enerbäck, S., et al. (2011) · Diabetes · doi:10.2337/db11-0075 · Cited by 21
Vos, R. C., Wit, J. M., Pijl, H., Kruyff, C. C., & Houdijk, E. C. A. M. (2011) · Trials · doi:10.1186/1745-6215-12-110 · Cited by 22
Parental Longevity and Diabetes Risk in the Diabetes Prevention Program
Florez, H. J., Ma, Y., Crandall, J. P., Perreault, L., Marcovina, S. M., Bray, G. A., Saudek, C. D., Barrett-Connor, E. L., & Knowler, W. C. (2011) · The Journals of Gerontology Series A · doi:10.1093/gerona/glr114 · Cited by 21
Mertens, I. L., Considine, R. V., van der Planken, M., & Gaal, L. F. V. (2006) · European Journal of Endocrinology · doi:10.1530/eje.1.02239 · Cited by 21
Hariri, A. A., Oliver, N. S., Johnston, D. G., Stevenson, J. C., & Godsland, I. F. (2013) · Disease Markers · doi:10.1155/2013/763907 · Cited by 21
Effect of sleeve gastrectomy on postprandial lipoprotein metabolism in morbidly obese patients
Waldmann, E., Hüttl, T. P., Göke, B., Lang, R. A., & Parhofer, K. G. (2013) · Lipids in Health and Disease · doi:10.1186/1476-511x-12-82 · Cited by 22
Huang, J., Dai, C., Yu, M., et al. (2011) · Liver International · doi:10.1111/j.1478-3231.2011.02545.x · Cited by 21
Espinós, J. J., Corcoy, R., & Calaf, J. (2009) · Gynecological Endocrinology · doi:10.1080/09513590802585597 · Cited by 21
Benefits of pedometer-measured habitual physical activity in healthy women
Graff, S. K., Alves, B. C., Toscani, M. K., & Spritzer, P. M. (2012) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/h11-145 · Cited by 20
Insulinoma accompanied by diabetes mellitus
Nagai, T., Imamura, M., Takai, Y., & Mori, M. (2003) · Diabetes Research and Clinical Practice · doi:10.1016/s0168-8227(02)00252-8 · Cited by 21
Bidulescu, A., Choudhry, S., Musani, S., Buxbaum, S., Liu, J.-K., Rotimi, C., Wilson, J. G., Taylor, H., & Gibbons, G. (2014) · Frontiers in Genetics · doi:10.3389/fgene.2014.00022 · Cited by 14
Vachon, M.-L., Factor, S. H., Branch, A. D., et al. (2010) · Journal of Hepatology · doi:10.1016/j.jhep.2010.06.025 · Cited by 21
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.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review