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)
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
Cited by
Sorted by citations
Items 22,901–23,000 of 31,469
Ar, K., Kınık, S. T., Verdi, H., Ataç, F. B., Ac, Y., & Özbek, N. Y. (2011) · The Turkish Journal of Pediatrics · doi:10.24953/turkjpediatr.2011.1835 · Cited by 7
Lys656Asn polymorphism of leptin receptor gene and metabolic syndrome in obese patients.
de Luis, D. A., Sagrado, M. G., Aller, R., et al. (2011) · PubMed · Cited by 7
Li, C., Ford, E. S., Zhao, G., & Mokdad, A. H. (2008) · Cited by 7
Tian, H.-S., Zhou, G., & Zhu, Z. (2015) · Tropical Journal of Pharmaceutical Research · doi:10.4314/tjpr.v14i11.10 · Cited by 7
Serum adipocyte fatty acid binding protein in liver transplant recipients and the metabolic syndrome
Schmilovitz‐Weiss, H., Laish, I., Levi, Z., et al. (2012) · Annals of Hepatology · doi:10.1016/s1665-2681(19)30930-5 · Cited by 7
Metabolic factors affect the occurrence of colorectal neoplasm on surveillance colonoscopies
So, H., Han, S., Park, H. W., Kim, E. H., Lee, J. Y., Lee, H., Chang, H., Kim, H., Choe, J., Park, S. H., et al. (2016) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.13286 · Cited by 6
Hyperhomocysteinemia is associated with decreased apolipoprotein AI levels in normal healthy people
Wang, Y., Liu, J., Jiang, Y., et al. (2016) · BMC Cardiovascular Disorders · doi:10.1186/s12872-016-0186-6 · Cited by 7
Heidari, B., Fotouhi, A., Sharifi, F., et al. (2015) · PubMed · Cited by 7
Relationship between Abdominal Aortic Intima Media Thickness and Central Obesity in Children
Aylanç, H., Aylanç, N., Yıldırım, Ş., Tekin, M., Battal, F., Kaymaz, N., Binnetoğlu, F. K., Topaloğlu, N., Türkön, H., & Sürecek, F. E. (2015) · Hormone Research in Paediatrics · doi:10.1159/000442156 · Cited by 7
Bhatt, A. B., Mulvey, C. K., Qasim, A., Nair, J., Rickels, M. R., Prenner, S. B., Iqbal, N., & Reilly, M. P. (2016) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.2794 · Cited by 7
Hayashi, T., Ogawa, E., Furusyo, N., et al. (2016) · Infectious Agents and Cancer · doi:10.1186/s13027-016-0056-y · Cited by 7
Calzado, C. M. A., Suárez, R. M. G., Álvarez, A. Á., Pendás, B. V. R., & Durán, A. R. (2010) · Revista cubana de endocrinología · Cited by 7
Heden, T. D., Liu, Y., Park, Y., Winn, N. C., & Kanaley, J. A. (2014) · Journal of Physical Activity and Health · doi:10.1123/jpah.2014-0105 · Cited by 7
Insulin Resistance and Interleukin-1β During Normal Pregnancy
Jahromi, A. S., Zareian, P., & Madani, A. (2011) · Asian Journal of Biochemistry · doi:10.3923/ajb.2011.366.372 · Cited by 7
Gallo‐Villegas, J., Pérez-Idárraga, A., Valencia-Gómez, K., Castro, D. A. P., Arenas-Sosa, M. M., Velásquez, M. A. Q., & Aristizábal-Ocampo, D. (2016) · Revista Colombiana de Cardiología · doi:10.1016/j.rccar.2016.01.033 · Cited by 7
Clinical characteristics of OGTT-derived hepatic- and muscle insulin resistance in healthy young men
Kim, M. K. (2014) · Physical Activity and Nutrition · doi:10.5717/jenb.2014.18.4.385 · Cited by 7
RetractedRamakrishnan, G., Unni, U. S., Raj, T. D. S., et al. (2009) · PubMed · Cited by 7
Wang, J., Lee, I., Lee, W., Lin, S., Su, S., Tu, S., Tseng, Y., Lin, S., & Sheu, W. H. (2016) · Journal of Diabetes · doi:10.1111/1753-0407.12406 · Cited by 7
Li, P., Jiang, R., Li, L., Li, L., Wang, Z.-N., Li, X., & Liu, C. (2012) · Journal of Investigative Medicine · doi:10.2310/jim.0b013e31825a47fe · Cited by 8
de Luis, D. A., Aller, R., Izaola, O., et al. (2013) · Journal of Investigative Medicine · doi:10.2310/jim.0b013e31827c2e4e · Cited by 7
Bernat-Karpińska, M., Piątkiewicz, P., Czech, A., & Wierzbicki, P. (2012) · PubMed · Cited by 5
Andreoli, M. F., Stoker, C., Lazzarino, G. P., Canesini, G., Luque, E. H., & Ramos, J. G. (2016) · British Journal Of Nutrition · doi:10.1017/s0007114516002865 · Cited by 7
Abdelkarem, H. M., Fadda, L. M., & Hassan, A. A. G. (2016) · Journal of Dietary Supplements · doi:10.1080/19390211.2016.1203856 · Cited by 8
Crispim, F. G. S., ELIAS, M. C., & Parise, E. R. (2016) · Revista de Nutrição · doi:10.1590/1678-98652016000400005 · Cited by 7
Rashidi, H., Toolabi, M., Najafian, M., Sadrian, E., Safapoor, N., & Nazari, P. (2016) · Middle East Fertility Society Journal · doi:10.1016/j.mefs.2016.06.003 · Cited by 7
Matsubara, F., Nagai, Y., Tsukiyama, H., Shimizu, H., Yamanouchi, E., Iwamoto, T., Sada, Y., Kato, H., Ohta, A., & Tanaka, Y. (2016) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2016.07.005 · Cited by 7
Mabchour, A. E., Delisle, H. F., Vilgrain, C., Larco, P., & Sodjinou, R. (2016) · Pan African Medical Journal · doi:10.11604/pamj.2016.24.306.8530 · Cited by 7
Blahová, T., Peterková, L., Leníček, M., et al. (2016) · Physiological Research · doi:10.33549/physiolres.933276 · Cited by 7
Zhong, M., Liu, S., Zhang, G., Zhang, X., & Hu, S. (2016) · World Journal of Gastroenterology · doi:10.3748/wjg.v22.i32.7332 · Cited by 7
Barnard, S. A., Pieters, M., Nienaber‐Rousseau, C., & Kruger, H. S. (2016) · Thrombosis Research · doi:10.1016/j.thromres.2016.09.003 · Cited by 7
Oxidized LDL , insulin resistance and central blood pressure after gestational diabetes mellitus
Vilmi‐Kerälä, T., Palomäki, O., Kankkunen, P., Juurinen, L., Uotila, J. T., & Palomäki, A. (2016) · Acta Obstetricia Et Gynecologica Scandinavica · doi:10.1111/aogs.13029 · Cited by 7
Kružliak, P., Berezin, A. Е., Kremzer, A. A., et al. (2016) · Folia Medica · doi:10.1515/folmed-2016-0021 · Cited by 7
Insulin Sensitivity and Secretion in Obese Type 2 Diabetic Women after Various Bariatric Operations
Vrbíková, J., Kunešová, M., Kyrou, I., Tura, A., Hill, M., Grimmichová, T., Dvořáková, K., Šrámková, P., Dolezalova, K., Lischkova, O., et al. (2016) · Obesity Facts · doi:10.1159/000453000 · Cited by 7
Ahmad, I., Zelnick, L. R., Robinson, N., Hung, A. M., Kestenbaum, B. R., Utzschneider, K. M., Kahn, S. E., & de Boer, I. H. (2017) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00394.2016 · Cited by 8
Seely, E. W., & Grinspoon, S. K. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-802101-9.00002-8 · Cited by 9
Bélan, M., Pelletier, C., & Baillargeon, J. (2017) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2016.0119 · Cited by 7
The Effect of apoM Polymorphism Associated with HDL Metabolism on Obese Korean Adults
Lee, M., Kim, J.-I., Choi, S., Jang, Y., & Sorn, S. R. (2016) · Lifestyle Genomics · doi:10.1159/000455948 · Cited by 7
Sponton, A. C. D. S., Silva, F. H., Araújo, H. N., da Silva, C. P. V., de Moraes, C., Antunes, E., Zanesco, A., & Delbin, M. A. (2017) · Journal of Vascular Research · doi:10.1159/000457800 · Cited by 7
Guarnotta, V., Ciresi, A., Pitrone, M., Pizzolanti, G., & Giordano, C. S. D. S. (2017) · Endocrine · doi:10.1007/s12020-017-1276-7 · Cited by 7
Seghieri, M., Vitolo, E., Giannini, L., et al. (2017) · Acta Diabetologica · doi:10.1007/s00592-017-0988-8 · Cited by 7
Erşahin, A., Acet, M., Erşahin, S. S., et al. (2017) · Daehan saengsik uihak hoeji/Clinical and experimental reproductive medicine · doi:10.5653/cerm.2017.44.1.33 · Cited by 7
Association of Insulin Resistance with Parasitaemia in Rat Malaria
Acquah, S., Ameyaw, E. O., Derkyi‐Kwarteng, L., et al. (2017) · The International Annals of Medicine · doi:10.24087/iam.2017.1.5.130 · Cited by 7
Chaves, G. V., de Souza, D. S., Pereira, S., Saboya, C. J., & Peres, W. A. F. (2012) · Revista da Associação Médica Brasileira (English Edition) · doi:10.1016/s2255-4823(12)70197-2 · Cited by 8
Ciresi, A., Cicciò, F., Radellini, S., Guarnotta, V., Calcaterra, A. M., & Giordano, C. S. D. S. (2017) · International Journal of Endocrinology · doi:10.1155/2017/8469680 · Cited by 7
Deeb, A., Attia, S., Samia, M., et al. (2017) · Journal of Diabetes and Obesity · doi:10.15436/2376-0494.17.1389 · Cited by 7
Antonopoulos, S., Mylonopoulou, M., Angelidi, A. M., et al. (2017) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2017.06.015 · Cited by 7
Dan, Y. A. N. G., Liu, Z., Zhao, Q., & Luo, Q. (2013) · Chinese Medical Journal · doi:10.3760/cma.j.issn.0366-6999.20122769 · Cited by 7
Monnard, C. R., Fellay, B., Scerri, I., & Grasser, E. K. (2017) · Frontiers in Physiology · doi:10.3389/fphys.2017.00507 · Cited by 7
Hwang, Y., Yoon, K., Soo, B., Lee, K. W., Jang, H. C., Min, K. W., Chung, C. H., & Lee, M. (2017) · International Journal of Clinical Practice · doi:10.1111/ijcp.12987 · Cited by 7
Zanetti, M., Cappellari, G. G., Semolic, A., Burekovic, I., Fonda, M., Cattin, L., & Barazzoni, R. (2017) · Archives of Medical Research · doi:10.1016/j.arcmed.2017.09.002 · Cited by 7
Miehle, K., Stümvoll, M., Faßhauer, M., & Hierl, T. (2017) · Endocrine · doi:10.1007/s12020-017-1437-8 · Cited by 7
Duyuler, P. T., Duyuler, S., Demir, M., Elalmış, Ö. U., Güray, Ü., & İleri, M. (2017) · Blood Pressure Monitoring · doi:10.1097/mbp.0000000000000300 · Cited by 7
Yan, Y., Bao, S., Sheng, S., et al. (2017) · Systems Biology in Reproductive Medicine · doi:10.1080/19396368.2017.1378936 · Cited by 7
MRI-measured myocardial iron load in patients with severe diabetic heart failure
Wang, B., Zhang, Y., Sun, N., Gu, S., Ding, F., Xu, S., Zhou, H., & Liu, Y. (2017) · Clinical Radiology · doi:10.1016/j.crad.2017.10.012 · Cited by 7
Parental History of Type 2 Diabetes Abrogates Ethnic Disparities in Key Glucoregulatory Indices
Nyenwe, E. A., Owei, I., Wan, J. Y., & Dagogo-Jack, S. E. (2017) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2017-01895 · Cited by 7
Gaitonde, P., Hurtado, F. K., Garhyan, P., Chien, J. Y., & Schmidt, S. (2017) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.998 · Cited by 7
Liu, J., Liu, J., Li, H., Liu, L., Zheng, J., Huang, Z., Cao, X., Xiao, H., & Li, Y. (2017) · International Journal of Endocrinology · doi:10.1155/2017/6180904 · Cited by 7
Atbaşoğlu, C., Akay, G. G., Gülöksüz, S., Saka, M. C., Üçok, A., Alptekın, K., Güllü, S., & van Os, J. (2018) · Psychopharmacology · doi:10.1007/s00213-017-4818-z · Cited by 7
Obert, P., Walther, G., Dutheil, F., et al. (2018) · Heart and Vessels · doi:10.1007/s00380-017-1108-y · Cited by 7
Lee, D., Yoo, J.-H., & Lee, B.-C. (2018) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2018/8159261 · Cited by 7
Bellan, M., Menegatti, M., Ferrari, C., et al. (2018) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2018.01.006 · Cited by 7
Sexual Dimorphism for the Association between Vitamin D and Insulin Resistance in Chinese People
Han, B., Li, Q., Wang, N., Chen, Y., Zhu, C., Chen, Y., Xia, F., Cang, Z., Lu, M., Meng, Y., et al. (2018) · International Journal of Endocrinology · doi:10.1155/2018/1216370 · Cited by 7
Badmus, O. O., Michael, O. S., Rabiu, S., & Olatunji, L. A. (2018) · Environmental Toxicology and Pharmacology · doi:10.1016/j.etap.2018.04.013 · Cited by 7
de Luis, D. A., Izaola, O., Primo, D., Hoyos, E. G., Gómez, J. J. L., Ortolá, A., & Aller, R. (2018) · Endocrinología Diabetes y Nutrición · doi:10.1016/j.endinu.2018.03.006 · Cited by 7
Ibrahim, A. S., Eldeeb, M. K., Salama, O., et al. (2018) · Endocrine Regulations · doi:10.2478/enr-2018-0012 · Cited by 7
Neural, Hormonal, and Cognitive Correlates of Metabolic Dysfunction and Emotional Reactivity
Wolf, T., Tsenkova, V. K., Ryff, C. D., Davidson, R. J., & Willette, A. A. (2018) · Psychosomatic Medicine · doi:10.1097/psy.0000000000000582 · Cited by 7
Langkilde, A., Tavenier, J., Danielsen, A. V., Eugen‐Olsen, J., Therkildsen, C., Jensen, F. K., Henriksen, J. H., Langberg, H., Steiniche, T., Petersen, J., et al. (2018) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiy284 · Cited by 8
Li, F.-F., Liu, B.-L., Yin, G., Yan, R., Zhang, D., Wu, J.-D., Ye, L., Su, X., & Ma, J. (2018) · Scientific Reports · doi:10.1038/s41598-018-27950-9 · Cited by 7
Corsetti, J. P., Love, T. M., Sparks, C. E., Bakker, S. J. L., & Dullaart, R. P. F. (2018) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2018.06.009 · Cited by 7
Umegaki, H., Makino, T., Uemura, K., Shimada, H., Hayashi, T., Cheng, X. W., & Kuzuya, M. (2018) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2018.07.022 · Cited by 7
Insulin resistance is associated with Sfrp5 in obstructive sleep apnea
Sun, S., Zhai, H., Zhu, M., Wen, P., He, X., & Wang, H. (2018) · Brazilian Journal of Otorhinolaryngology · doi:10.1016/j.bjorl.2018.07.002 · Cited by 7
Leutner, M., Göbl, C. S., Wolf, P. S., Maruszczak, K., Bozkurt, L., Steinbrecher, H., Kukurová, I. J., Ott, J., Egarter, C., Trattnig, S., et al. (2018) · International Journal of Endocrinology · doi:10.1155/2018/5406128 · Cited by 8
Chung, S. M., Moon, J. S., Yoon, J. S., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0202757 · Cited by 7
Fujita, H., Hosono, A., Shibata, K., Tsujimura, S., Oka, K., Okamoto, N., Kamiya, M., Kondo, F., Wakabayashi, R., Ichikawa, M., et al. (2018) · Journal of Epidemiology · doi:10.2188/jea.je20170180 · Cited by 8
Foschi, D., Sorrentino, L., Tubazio, I., Vecchio, C., Vago, T., Bevilacqua, M. T., Rizzi, A., & Corsi, F. (2018) · Surgical Endoscopy · doi:10.1007/s00464-018-6443-2 · Cited by 8
Crepaldi, L. D., Mariano, I. R., Trondoli, A. J. P. C., Moreno, F. N., Piovan, S., Formigoni, M., Salgueiro‐Pagadigorria, C. L., de Godoi, V. A. F., Brito, M. D. N., & Garcia, R. F. (2018) · Journal of Pharmacy and Pharmacology · doi:10.17265/2328-2150/2018.10.001 · Cited by 7
Tatsumi, Y., Morimoto, A., Asayama, K., Sonoda, N., Miyamatsu, N., Ohno, Y., Miyamoto, Y., Izawa, S., & Ohkubo, T. (2018) · Hypertension Research · doi:10.1038/s41440-018-0121-6 · Cited by 8
Early detection of metabolic dysregulation using water T2 analysis of biobanked samples
Mishra, I., Jones, C., Patel, V., Deodhar, S., & Cistola, D. P. (2018) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s180655 · Cited by 7
AbdelMassih, A. F., Attia, M., Ismail, M., & Samir, M. (2018) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2018-0207 · Cited by 7
Rana, S., Mirza, S., & Rahmani, S. (2018) · Annals of Human Biology · doi:10.1080/03014460.2018.1561947 · Cited by 7
Balakrishnan, P., Vaidya, D. M., Voruganti, V. S., Haack, K., Kent, J. W., North, K. E., Laston, S., Howard, B. V., Umans, J. G., Lee, E. T., et al. (2018) · Frontiers in Genetics · doi:10.3389/fgene.2018.00466 · Cited by 7
Novel Approach to Characterize Heterogeneity in an Aerobic Exercise Intervention
Hall, S. E., Lawal, O. A., Clark, C. M., Tyndall, A. V., Hill, M. D., Sajobi, T. T., & Poulin, M. J. (2019) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001909 · Cited by 7
El-Shamy, F., El-Kholy, S. S., Labib, M., et al. (2018) · Journal of Complementary and Integrative Medicine · doi:10.1515/jcim-2018-0011 · Cited by 7
Berstein, L. M., Iyevleva, A. G., Ivantsov, A. O., et al. (2019) · Future Oncology · doi:10.2217/fon-2018-0687 · Cited by 7
Torquati, L., Coombes, J. S., Murray, L., Hasnain, S. Z., Mallard, A. R., McGuckin, M. A., Fassett, R. G., Croci, I., & Ramos, J. S. (2019) · Nutrients · doi:10.3390/nu11040815 · Cited by 8
Luo, Y., Bajoria, R., Lai, Y., et al. (2019) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s194591 · Cited by 7
Basurto, L., Díaz, A., Rodriguez, A., Robledo, A., la Vega, S. M. D., Garcia-Vega, J., & Murillo, C. M. (2019) · Gynecological Endocrinology · doi:10.1080/09513590.2019.1604658 · Cited by 7
Longitudinal examination of pancreatic β‐cell function in Japanese individuals
Fujikawa, R., Ito, C., Kira, S., et al. (2019) · Journal of Diabetes Investigation · doi:10.1111/jdi.13068 · Cited by 7
One-Year Lifestyle Intervention, Muscle Lipids, and Cardiometabolic Risk
Maltais, A., Lemieux, I., Alméras, N., Tremblay, A., Bergeron, J., Poirier, P., & Després, J. (2019) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002030 · Cited by 7
Hussein, S. A., Abdelmagid, A. D., & Fareed, F. (2017) · Benha Veterinary Medical Journal · doi:10.21608/bvmj.2017.31114 · Cited by 7
Yıldırım, E., & Derici, M. K. (2019) · Medical Science Monitor · doi:10.12659/msm.914338 · Cited by 7
Román, C., Maiztegui, B., Mencucci, M. V., Ahrtz, L., Algañarás, M., Zotto, H. H. D., Gagliardino, J. J., & Flores, L. E. (2019) · Peptides · doi:10.1016/j.peptides.2019.05.002 · Cited by 8
NT-proBNP, race and endothelial function in the Multi-Ethnic Study of Atherosclerosis
Yang, S., Huang, S., Daniels, L. B., Yeboah, J., Lima, J. A. C., Cannone, V., Burnett, J. C. J., Beckman, J. A., Carr, J. J., Wang, T. J., et al. (2019) · Heart · doi:10.1136/heartjnl-2019-314707 · Cited by 8
Insulin Sensitivity, Depression/Anxiety, and Physical Fitness in At-Risk Adolescents
Bruggink, S. M., Shomaker, L. B., Kelly, N. R., Drinkard, B. E., Chen, K. Y., Brychta, R. J., Cassidy, O. L., Demidowich, A. P., Brady, S. M., Tanofsky‐Kraff, M., et al. (2019) · Sports Medicine International Open · doi:10.1055/a-0889-8653 · Cited by 9
Ballard, K. D., Berry, C. W., Varty, C. J., Arslain, K. B., & Timmerman, K. L. (2019) · European Journal of Applied Physiology · doi:10.1007/s00421-019-04174-x · Cited by 8
High abdominal adiposity and low phase angle in overweight renal transplant recipients
Ferreira, T. D. S., Silva, M. I. B., da Costa, M. S., Pontes, K. S. D. S., Castro, F. G., Antunes, V. P., Rosina, K. T. D. C., Barreto, A. P. M. M., Souza, E., & Klein, M. R. S. T. (2019) · Clinical Transplantation · doi:10.1111/ctr.13654 · Cited by 7
Larsson, M., Patrone, C., von Euler, M., Holst, J. J., & Nathanson, D. (2019) · Cardiovascular Diabetology · doi:10.1186/s12933-019-0896-z · Cited by 7
Barthow, C. A., Hood, F. E., McKinlay, E. M., Hilder, J., Cleghorn, C., Huthwaite, M., Weatherall, M., Parry‐Strong, A., Pullon, S., Gray, B. V., et al. (2019) · Trials · doi:10.1186/s13063-019-3553-7 · Cited by 7
Mofrad, M. D., Namazi, N., Larijani, B. A., Surkan, P. J., & Azadbakht, L. (2019) · Nutrition & Dietetics · doi:10.1111/1747-0080.12574 · Cited by 7
Influence of waist circumference on blood pressure status in non-obese adolescents
Benmohammed, K., Valensi, P. E., Nguyen, M. T., Benmohammed, F., Benlatreche, M., Benembarek, K., & Lezzar, A. (2018) · International Journal of Adolescent Medicine and Health · doi:10.1515/ijamh-2017-0127 · Cited by 7
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
