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
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)
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).
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 18,501–18,600 of 31,469
Aranaz, P., Zabala, M., Romo‐Hualde, A., Navarro‐Herrera, D., Lópeź‐Yoldi, M., Vizmanos, J. L., Martínez, J. A., Milagro, F. I., & González-Navarro, C. J. (2020) · Food & Function · doi:10.1039/d0fo00504e · Cited by 15
Hyposalivation is prevalent in bariatric patients but improves after surgery.
Schwenger, K., Alghamdi, M., Ghorbani, Y., Jackson, T., Okrainec, A., & Allard, J. (2020) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2020.06.005 · Cited by 9
Metabolic syndrome detection with biomarkers in childhood cancer survivors
Pluimakers, V. G., van Waas, M., Looman, C. W. N., de Maat, M. P. M., de Jonge, R., Delhanty, P. J. D., Huisman, M., Mattace-Raso, F. U. S., van den Heuvel‐Eibrink, M. M., & Neggers, S. J. C. M. M. (2020) · Endocrine Connections · doi:10.1530/ec-20-0144 · Cited by 14
Exploring Metabolic Signature of Protein Energy Wasting in Hemodialysis Patients
Pauzi, F. A., Sahathevan, S., Khor, B., Narayanan, S. S., Zakaria, N. F., Abas, F., Karupaiah, T., & Daud, Z. A. M. (2020) · Metabolites · doi:10.3390/metabo10070291 · Cited by 15
Khodarahmi, M., Jafarabadi, M. A., & Farhangi, M. A. (2020) · Genes & Nutrition · doi:10.1186/s12263-020-00672-2 · Cited by 14
Šimunović, M., Šupe‐Domić, D., Karin, Ž., Degoricija, M., Paradžik, M., Škrabić, R., Jukic, A., Božić, J., & Škrabić, V. (2020) · Journal of Medicinal Food · doi:10.1089/jmf.2020.0032 · Cited by 14
Liu, M., Zhang, K., Wang, L., Yang, H., Yan, K., Pan, H., Zhu, H., & Gong, F. (2020) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s257643 · Cited by 14
Naiki‐Ito, A., Kato, H., Naiki, T., Yeewa, R., Aoyama, Y., Nagayasu, Y., Suzuki, S., Inaguma, S., & Takahashi, S. (2020) · Archives of Toxicology · doi:10.1007/s00204-020-02873-5 · Cited by 14
Impact of diets rich in olive oil, palm oil or lard on myokine expression in rats
Gauze-Gnagne, C., Raynaud, F., Djohan, Y. F., Lauret, C., Feillet‐Coudray, C., Coudray, C., Monde, A., Koffi, G., Morena-Carrere, M., Camara-Cissé, M., et al. (2020) · Food & Function · doi:10.1039/d0fo01269f · Cited by 13
Zwack, C. C., McDonald, R. L., Tursunalieva, A., Cooray, A., Lambert, G. W., & Lambert, É. (2020) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00807.2020 · Cited by 14
Wu, C. H., Han, Y., Wang, Y., et al. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-001215 · Cited by 14
Bartlette, K., Carreau, A., Xie, D., Garcia‐Reyes, Y., Rahat, H., Pyle, L. L., Nadeau, K. J., Cree‐Green, M., & Behn, C. D. (2021) · Metabolism Open · doi:10.1016/j.metop.2021.100078 · Cited by 14
Chao, H.-H., Liao, Y., & Chou, C. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18031255 · Cited by 20
Shi, M., Liu, P., Li, J., Su, Y., Zhou, X., Wu, C., Chen, X., & Zheng, C. (2021) · Journal of Diabetes · doi:10.1111/1753-0407.13169 · Cited by 14
Liu, W., Eslam, M., Zheng, K. I., Ma, H., Rios, R. S., Lv, M., Li, G., Tang, L., Zhu, P., Wang, X., et al. (2021) · Journal of Clinical and Translational Hepatology · doi:10.14218/jcth.2020.00151 · Cited by 14
Kouwenhoven, S. M. P., Fleddermann, M., Finken, M. J. J., et al. (2021) · Nutrients · doi:10.3390/nu13041159 · Cited by 14
Chen, Q., Wang, W., Xia, M., Lu, Y., Bian, H., Chen, Y., Li, X.-Y., Vadas, M. A., Gao, X., Lin, H., et al. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-021-03066-z · Cited by 14
Negrati, M., Razza, C., Biasini, C., Nunzio, C. D., Vancini, A., Dall’Asta, M., Lovotti, G., Trevisi, E., Rossi, F., & Cavanna, L. (2021) · Nutrients · doi:10.3390/nu13103482 · Cited by 17
Ginis, K. A. M., van der Scheer, J. W., Todd, K. R., et al. (2020) · Spinal Cord · doi:10.1038/s41393-020-0478-7 · Cited by 14
Miura, N., Ikezaki, A., Iwama, S., Matsuoka, H., Ito, K., & Sugihara, S. (2006) · Acta Paediatrica · doi:10.1111/j.1651-2227.2006.tb02202.x · Cited by 14
Resino, S., Micheloud, D., Miralles, P., et al. (2009) · HIV Medicine · doi:10.1111/j.1468-1293.2009.00745.x · Cited by 14
Park, H., Kim, J., Han, E. J., Park, S. E., Park, C., Lee, W., Oh, K.-W., Park, S., & Rhee, E. (2015) · Clinical Endocrinology · doi:10.1111/cen.12946 · Cited by 14
Milewicz, A., Krzyżanowska‐Świniarska, B., Miazgowski, T., Jędrzejuk, D., Arkowska, A., Mieszczanowicz, U., & Bar‐Andziak, E. (2013) · PubMed · Cited by 14
Evidence for altered control of glucose disposal after total colectomy
Robertson, M. D., Livesey, G., Hampton, S. M., & Mathers, J. C. (2000) · British Journal Of Nutrition · doi:10.1017/s0007114500002427 · Cited by 14
Does hyperandrogenism have an effect on hearing loss in patients with polycystic ovary syndrome?
Oğhan, F., & Çoksüer, H. (2011) · Auris Nasus Larynx · doi:10.1016/j.anl.2011.06.006 · Cited by 15
Zhang, Z., Gong, F., & Lu, G. (2012) · Peptides · doi:10.1016/j.peptides.2012.01.018 · Cited by 14
Insulin Sensitivity in Women with Coronary Heart Disease during Hormone Replacement Therapy
Os, I., Os, A., Abdelnoor, M., Larsen, A., Birkeland, K. I., & Westheim, A. S. (2005) · Journal of Women s Health · doi:10.1089/jwh.2005.14.137 · Cited by 13
Prediction of Weight Loss and Regain Following Dietary, Lifestyle, and Pharmacologic Intervention
Napolitano, A., Miller, S. R., Murgatroyd, P. R., Delafont, B., Brooke, A. K., Elkhawad, M., Tan, C. Y., Virtue, S., Vidal‐Puig, A., & Nunez, D. J. (2012) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2011.333 · Cited by 14
Hitze, B., Bosy‐Westphal, A., Plachta‐Danielzik, S., et al. (2009) · Acta Paediatrica · doi:10.1111/j.1651-2227.2009.01573.x · Cited by 14
Comparison of Analgesic Methods for Total Knee Arthroplasty: Metabolic Effect of Exogenous Glucose
Mistraletti, G., DELACUADRAFONTAINE, J., Asenjo, J. F., et al. (2006) · Regional Anesthesia & Pain Medicine · doi:10.1016/j.rapm.2006.02.005 · Cited by 14
Macaluso, F. S., Maida, M. F., Cammà, C., et al. (2013) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2013.09.059 · Cited by 14
de Luis, D. A., Aller, R., Izaola, O., Sagrado, M. G., Conde, R., & Bellido, D. (2007) · Archives of Medical Research · doi:10.1016/j.arcmed.2007.06.009 · Cited by 13
Insulin Resistance in Patients with Stroke is Related to Visceral Fat Obesity and Adipocytokines
Hishinuma, A., Majima, M., & Kurabayashi, H. (2008) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2008.01.004 · Cited by 14
Ersözlü, E. D., Bozkırlı, E., & Yücel, E. (2013) · Modern Rheumatology · doi:10.3109/14397595.2013.843752 · Cited by 14
C-type natriuretic peptide plasma levels are reduced in obese adolescents
Ry, S. D., Cabiati, M., Bianchi, V., Storti, S., Caselli, C., Prescimone, T., Clerico, A., Saggese, G., Giannessi, D., & Federico, G. (2013) · Peptides · doi:10.1016/j.peptides.2013.09.013 · Cited by 14
Piché, M., Després, J., Pascot, A., Nadeau, A., Tremblay, A., Weisnagel, S. J., Bergeron, J., & Lemieux, S. (2004) · Diabetic Medicine · doi:10.1111/j.1464-5491.2004.01234.x · Cited by 14
Anan, F., Masaki, T., Yonemochi, H., Takahashi, N., Nakagawa, M., Eshima, N., Saikawa, T., & Yoshimatsu, H. (2007) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-007-0421-6 · Cited by 14
Ghattas, M. H., & Amer, M. A. (2012) · Endocrine · doi:10.1007/s12020-012-9656-5 · Cited by 14
Satirapoj, B., YINGWATANADEJ, P., Chaichayanon, S., & Patumanond, J. (2007) · Nephrology · doi:10.1111/j.1440-1797.2007.00770.x · Cited by 14
Potatoes, Glycemic Index, and Weight Loss in Free-Living Individuals: Practical Implications
Randolph, J., Edirisinghe, I., Masoni, A. M., Kappagoda, T. C., & Burton‐Freeman, B. M. (2014) · Journal of the American College of Nutrition · doi:10.1080/07315724.2013.875441 · Cited by 14
Falkner, B. E., & Kushner, H. (2008) · Journal of Clinical Hypertension · doi:10.1111/j.1524-6175.2007.07458.x · Cited by 14
Genome-wide scan of resistin mRNA expression in omental adipose tissue of baboons
Tejero, M. E., Cole, S. A., Cai, G., et al. (2004) · International Journal of Obesity · doi:10.1038/sj.ijo.0802699 · Cited by 14
Taniguchi, A., Fukushima, M., Nakai, Y., et al. (2005) · Metabolism · doi:10.1016/j.metabol.2004.10.006 · Cited by 14
Kullberg, J., Sundbom, M., Haenni, A., Fredén, S., Johansson, L., Börnert, P., Ahlström, A., Ahlström, H. K., & Karlsson, A. (2010) · Journal of Obesity · doi:10.1155/2011/959601 · Cited by 14
Daskalova, D. C., Kolovou, G. D., Panagiotakos, D. B., Pilatis, N. D., & Cokkinos, D. V. (2005) · Clinical Cardiology · doi:10.1002/clc.4960281208 · Cited by 14
Galgani, J. E., de Jonge, L., Rood, J. C., Smith, S. R., Young, A. A., & Ravussin, É. (2010) · Obesity · doi:10.1038/oby.2010.70 · Cited by 14
Ok, E. S., Ok, E. S., Aşçı, G., et al. (2012) · International Urology and Nephrology · doi:10.1007/s11255-012-0138-8 · Cited by 14
Shin, H. D., Park, B. L., Kim, L. H., et al. (2005) · Diabetologia · doi:10.1007/s00125-005-1917-x · Cited by 14
Cunha, M. R., Silva, M. E. R., Machado, H. A., Fukui, R. T., Correia, M. R. S., Santos, R. F., WAJCHENBERG, B. L., Rocha, D. M., Rondon, M. U. P. B., Negrão, C. E., et al. (2007) · Diabetes Obesity and Metabolism · doi:10.1111/j.1463-1326.2006.00690.x · Cited by 14
Differential regulation of insulin action and tumor necrosis factor α system activity by metformin
Biarnés, J., Fernández‐Real, J. M., Fernández-Castañer, M., García-Gil, M., Soler, J., & Ricart, W. (2005) · Metabolism · doi:10.1016/j.metabol.2004.08.018 · Cited by 14
Circulating levels of interleukin-18 in patients with non-alcoholic fatty liver disease
Tapan, S., Doğru, T., Kara, M., Erçin, C. N., Kılcıler, G., Genç, H., Sertoğlu, E., Açıkel, C., Kılıç, S., Karslıoğlu, Y., et al. (2010) · Scandinavian Journal of Clinical and Laboratory Investigation · doi:10.3109/00365513.2010.500675 · Cited by 14
Malita, F. M., Messier, V., Lavoie, J.-M., Bastard, J., RABASA-LHORET, R. P. R., & Karelis, A. D. (2009) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2009.03.005 · Cited by 14
Zhou, Y., Yu, Z., Jia, H., Sun, F., Ma, L., Guo, R., Peng, L., & Cui, T.-G. (2009) · Blood Purification · doi:10.1159/000230810 · Cited by 14
The association of brain natriuretic peptide and insulin resistance in obesity-related hypertension
Serbest, S. T., & Çıkım, A. S. (2007) · Journal of Human Hypertension · doi:10.1038/sj.jhh.1002194 · Cited by 14
Raal, F. J., Panz, V. R., Pilcher, G. J., & Joffe, B. I. (1999) · Journal of Internal Medicine · doi:10.1046/j.1365-2796.1999.00508.x · Cited by 14
Alterations of lipid profile in endurance over-trained subjects
Petibois, C., & Déléris, G. (2004) · Archives of Medical Research · doi:10.1016/j.arcmed.2004.11.013 · Cited by 14
Fenger, M., Linneberg, A. R., Werge, T. M., & Jørgensen, T. (2008) · BMC Genetics · doi:10.1186/1471-2156-9-43 · Cited by 14
Relation between insulin kinetics and insulin sensitivity in pregnancy
Jolly, M. C., Hovorka, R., Godsland, I. F., Amin, R., Lawrence, N., Anyaoku, V., Johnston, D. G., & Robinson, S. (2003) · European Journal of Clinical Investigation · doi:10.1046/j.1365-2362.2003.01193.x · Cited by 14
Martínez-Calatrava, M. J., González‐Sánchez, J. L., Aben, K. K. H., et al. (2007) · Journal of Hypertension · doi:10.1097/hjh.0b013e32803cae09 · Cited by 14
Insulin and HOMA in Spanish prepubertal children: Relationship with lipid profile
Garcés, C., Cano, B., Granizo, J. J., Benavente, M., Viturro, E., Gutiérrez-Guisado, J., de Oya, I., Lasunción, M. A., & de Oya, M. (2005) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2005.07.004 · Cited by 14
Kronenberg, F., Rich, S. S., Sholinsky, P. D., Arnett, D. K., Province, M. A., Myers, R. H., Eckfeldt, J. H., Williams, R., & Hunt, S. C. (2000) · American Journal of Hypertension · doi:10.1016/s0895-7061(99)00177-6 · Cited by 14
Nakamura, T., Adachi, H., Hirai, Y., Satoh, A., Ohuchida, M., & Imaizumi, T. (2003) · Metabolism · doi:10.1053/meta.2003.50027 · Cited by 14
Evaluation of Factors during OGTT to Correlate Insulin Resistance in Non-Diabetic Subjects.
Takeuchi, M., Kanazawa, A., Suzuki, M., et al. (2000) · Endocrine Journal · doi:10.1507/endocrj.47.535 · Cited by 14
Mostad, I. L., Qvigstad, E., Bjerve, K. S., & Grill, V. (2004) · Scandinavian Journal of Clinical and Laboratory Investigation · doi:10.1080/00365510410007053 · Cited by 14
Alexander-Aguilera, A., Berruezo, S., Díaz, G. H., Angulo, O., & Oliart‐Ros, R. M. (2011) · Journal of Physiology and Biochemistry · doi:10.1007/s13105-011-0106-2 · Cited by 14
Lenarcik, A., Bidzińska−Speichert, B., & Tworowska-Bardzińska, U. (2010) · Gynecological Endocrinology · doi:10.3109/09513591003686387 · Cited by 15
Macroprolactinemia, like hyperprolactinemia, may promote platelet activation
Anaforoğlu, İ., Ertörer, M. E., Kozanoğlu, İ., et al. (2010) · Endocrine · doi:10.1007/s12020-009-9304-x · Cited by 14
Cazzola, R., Cassani, E., Barichella, M., & Cestaro, B. (2013) · Metabolism · doi:10.1016/j.metabol.2013.01.012 · Cited by 14
Involvement of cholecystokinin in baseline and post-prandial whole body insulin sensitivity in rats
Peitl, B., Döbrönte, R., Drimba, L., Sári, R. Z., Varga, A., Németh, J., Pázmány, T., & Szilvássy, Z. (2010) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2010.06.062 · Cited by 14
de Luis, D. A., Izaola, O., Alonso, M. G., Aller, R., Cabezas, G., & de la Fuente, B. (2012) · PubMed · doi:10.3305/nh.2012.27.5.6053 · Cited by 14
Genç, B. O., Doğan, E. A., Doğan, U., & Genç, E. (2010) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2010.01.045 · Cited by 14
Predictive models for type 2 diabetes onset in middle-aged subjects with the metabolic syndrome
Ozery-Flato, M., Parush, N., El‐Hay, T., Visockienė, Ž., Ryliškytė, L., Badarienė, J., Solovjova, S., Kovaitė, M., Navickas, R., & Laucevičius, A. (2013) · Diabetology & Metabolic Syndrome · doi:10.1186/1758-5996-5-36 · Cited by 13
Chen, P.-H., Tsai, Y.-T., Wang, J., Lin, S.-D., Lee, W.-J., Su, S., Lee, I., Tu, S., Tseng, Y., Sheu, W. H., et al. (2014) · Diabetology & Metabolic Syndrome · doi:10.1186/1758-5996-6-68 · Cited by 14
Ezenwaka, C. E., & Offiah, N. V. (2002) · Scandinavian Journal of Primary Health Care · doi:10.1080/028134302760234654 · Cited by 14
El-Kannishy, G. M., Kamal, S., Mousa, A. A., Saleh, O., Badrawy, A. E., El-Farahaty, R. M., & Shokeir, T. A. (2009) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2009.08.001 · Cited by 13
RetractedFenger, M., Poulsen, P., Beck‐Nielsen, H., & Vaag, A. A. (2000) · Diabetic Medicine · doi:10.1046/j.1464-5491.2000.00377.x · Cited by 14
Bulcão, C., Giuffrida, F. M. A., Ribeiro-Filho, F. F., & Ferreira, S. R. G. (2006) · Brazilian Journal of Medical and Biological Research · doi:10.1590/s0100-879x2006005000042 · Cited by 14
Carey, P. E., Gerrard, J., Cline, G. W., Man, C. D., English, P. T., Firbank, M., Cobelli, C., & Taylor, R. (2005) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00195.2005 · Cited by 14
Tuuri, A., Jauhiainen, M., Ehnholm, C. P., Tikkanen, M. J., Nicholls, M. G., & Kaaja, R. (2013) · Hypertension in Pregnancy · doi:10.3109/10641955.2013.784783 · Cited by 14
Yang, S., Huang, J., Shih, K.-Y., et al. (2004) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfh554 · Cited by 14
Mello, V., Cruz, T., Nuñez, G. R., et al. (2006) · Journal of Medical Virology · doi:10.1002/jmv.20712 · Cited by 14
Fasting Capillary Glucose as a Screening Test for Ruling Out Gestational Diabetes Mellitus
Anderson, V., Ye, C., Sermer, M., Connelly, P. W., Hanley, A. J., Zinman, B., & Retnakaran, R. R. (2013) · Journal of Obstetrics and Gynaecology Canada · doi:10.1016/s1701-2163(15)30909-9 · Cited by 14
Epicardial adipose tissue thickness and NGAL levels in women with polycystic ovary syndrome
Şahin, S. B., Cüre, M. C., Uğurlu, Y., Ergül, E., Gür, E. U., Alyildiz, N., & Bostan, M. (2014) · Journal of Ovarian Research · doi:10.1186/1757-2215-7-24 · Cited by 14
Mahfouz (2010) · American journal of biochemistry & biotechnology/American journal of biochemistry and biotechnology · doi:10.3844/ajbbsp.2010.11.18 · Cited by 14
Martínez‐López, E., Ruiz‐Madrigal, B., Hernández‐Cañaveral, I., & Panduro, A. (2007) · Diabetes and Vascular Disease Research · doi:10.3132/dvdr.2007.046 · Cited by 14
Olivares-García, V., Torre-Villalvazo, I., Velázquez‐Villegas, L. A., Alemán, G., Lara-Pompa, N. E., López‐Romero, P., Torres, N., Tovar, A. R., & Díaz‐Villaseñor, A. (2015) · Nutrition and Diabetes · doi:10.1038/nutd.2015.34 · Cited by 14
Circulating Omentin-1 in Obesity and Metabolic Syndrome Status Compared to Control Subjects
Hossein‐Nezhad, A. (2012) · Endocrinology & Metabolic Syndrome · doi:10.4172/2161-1017.s1-008 · Cited by 14
de Oliveira, L. P. M., de Jesús, R. P., Boulhosa, R. S. S. B., Freire, T. O., Mendes, C. M. C., Vinhas, L., Waitzberg, D. L., Lemaire, D. C., Cavalcante, L. N., Lyra, A. C., et al. (2016) · Journal of the American College of Nutrition · doi:10.1080/07315724.2015.1072756 · Cited by 14
Lu, Y.-H., Xia, Z.-L., Ma, Y., Chen, H., Yan, L.-P., & Xu, H. (2016) · Gynecological Endocrinology · doi:10.1080/09513590.2016.1183193 · Cited by 14
Lin, Y.-C., & Chen, H.-S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0189047 · Cited by 14
Öztürk, Y. E., Bozbulut, R., Döğer, E., Bıdecı, A., & Köksal, E. (2018) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2017-0271 · Cited by 14
Mice lacking angiotensin type 2 receptor exhibit a sex-specific attenuation of insulin sensitivity
Quiroga, D. T., Miquet, J. G., González, L., Sotelo, A. I., Muñoz, M. C., Geraldes, P., Giani, J. F., & Dominici, F. P. (2019) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2019.110587 · Cited by 14
Shin, H. D., Park, K. S., Park, B. L., Cheong, H. S., Cho, Y. M., Lee, H. K., Lee, J., Lee, J., Kim, H. T., Han, B. G., et al. (2005) · Diabetic Medicine · doi:10.1111/j.1464-5491.2005.01732.x · Cited by 14
Derosa, G., Cicero, A. F. G., D’Angelo, A., Ragonesi, P. D., Ciccarelli, L., Fogari, E., SALVADEO, S. A. T., Ferrari, I., Gravina, A., Fassi, R., et al. (2006) · Clinical Drug Investigation · doi:10.2165/00044011-200626090-00006 · Cited by 14
Perticone, F., Maio, R., Paola, R. D., Sciacqua, A., Marucci, A., Cosmo, S. D., Perticone, M., Sesti, G., & Trischitta, V. (2006) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2005.11.013 · Cited by 14
Association of insulin and insulin propeptides with an atherogenic lipoprotein phenotype
Båvenholm, P., Karpe, F., Proudler, A. J., Tornvall, P., Crook, D., & Hamsten, A. (1995) · Metabolism · doi:10.1016/0026-0495(95)90150-7 · Cited by 14
Metabolic syndrome, insulin resistance, and atherosclerosis in Japanese type 2 diabetic patients
Taniguchi, A., Fukushima, M., Kuroe, A., Sakaguchi, K., Hashimoto, H., Yoshioka, I., Kitatani, N., Tsuji, T., Ohya, M., Ohgushi, M., et al. (2007) · Metabolism · doi:10.1016/j.metabol.2006.07.027 · Cited by 14
The place of pegvisomant in the management of acromegaly
Parkinson, C., & Trainer, P. J. (2001) · Expert Opinion on Investigational Drugs · doi:10.1517/13543784.10.9.1725 · Cited by 14
Itoh, K., Katsumi, I. M. A. I., Masuda, T., Abe, S., Tanaka, M., Koga, R., Hitomi, I. T. O. H., & Nakamura, M. (2001) · Hypertension Research · doi:10.1291/hypres.24.481 · Cited by 14
Xu, M., Li, X., Wang, J.-G., Du, P.-F., Hong, J., Gu, W., Zhang, Y., & Ning, G. (2005) · Molecular Genetics and Metabolism · doi:10.1016/j.ymgme.2005.06.012 · Cited by 17
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
