The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index; 1 of the 6 sources its claims cite were checked for support.
Homeostasis model assessment: insulin resistance and ?-cell function from fasting plasma glucose and insulin concentrations in man
Open accessAbstract
The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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Claims the paper makes
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
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Chen, Y., Huang, H., He, X., Duan, W., & Mo, X. (2021) · Environmental Health and Preventive Medicine · doi:10.1186/s12199-021-00966-w · Cited by 28
Stasi, V. D., Maseroli, E., Rastrelli, G., Scavello, I., Cipriani, S., Todisco, T., Marchiani, S., Sorbi, F., Fambrini, M., Petraglia, F., et al. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.641446 · Cited by 30
Beyazıt, F., Hiz, M. M., Türkön, H., & Unsal, M. A. (2021) · Ginekologia Polska · doi:10.5603/gp.a2020.0176 · Cited by 28
Park, S., Yang, J., & Song, Y. (2021) · Nutrients · doi:10.3390/nu13072164 · Cited by 28
A new way for punicalagin to alleviate insulin resistance: regulating gut microbiota and autophagy
Cao, Y., Ren, G., Zhang, Y., Qin, H., An, X., Yi, L., Chen, J., & Yang, L. (2021) · Food & Nutrition Research · doi:10.29219/fnr.v65.5689 · Cited by 28
Metabolic syndrome and stone disease
Soligo, M., Morlacco, A., Zattoni, F., Valotto, C., Giorgi, G. D., & Beltrami, P. (2021) · Panminerva Medica · doi:10.23736/s0031-0808.21.04517-1 · Cited by 28
Vladu, I. M., Forţofoiu, M., Clenciu, D., Forțofoiu, M.-C., Pădureanu, R., Radu, L., Cojan, Ș., Rădulescu, P. M., & Pădureanu, V. (2021) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2021.10996 · Cited by 34
Queathem, E. D., Welly, R. J., Clart, L., Rowles, C. C., Timmons, H., Fitzgerald, M., Eichen, P. A., Lubahn, D. B., & Vieira‐Potter, V. J. (2021) · Cells · doi:10.3390/cells10123453 · Cited by 32
Zhai, T., Chen, Q., Xu, J., Jia, X., & Xia, P. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.825448 · Cited by 28
Montoro-García, S., Velasco-Soria, Á., Mora, L., et al. (2022) · Nutrients · doi:10.3390/nu14020298 · Cited by 28
Association between Metabolic Dysfunction-associated Fatty Liver Disease and Cognitive Impairment
Yu, Q., He, R., Jiang, H., Wu, J., Xi, Z., He, K., Liu, Y., Zhou, T., Feng, M., Wan, P., et al. (2022) · Journal of Clinical and Translational Hepatology · doi:10.14218/jcth.2021.00490 · Cited by 29
Wang, Z., Xie, J., Wang, J., Feng, W. Q., Liu, N., & Liu, Y. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.895609 · Cited by 28
Tan, H. C., Hsu, J. W.-C., Tai, E. S., Chacko, S. K., Wu, V., Lee, C. F., Kovalik, J., & Jahoor, F. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.900343 · Cited by 31
Gonzalo Labarca, T., Henríquez‐Beltrán, M., Lamperti, L., Nova‐Lamperti, E., Sanhueza, S. A., Cabrera, C., Quiroga, R. A., Antilef, B., Ormazábal, V., Zuñiga, F. A., et al. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.884218 · Cited by 29
Huwiler, V. V., Schönenberger, K. A., von Brunegg, A. S., Reber, E., Mühlebach, S., Stanga, Z. G., & Balmer, M. L. (2022) · Nutrients · doi:10.3390/nu14132627 · Cited by 31
Duo, Y., Song, S., Zhang, Y., Qiao, X., Xu, J., Zhang, J., Peng, Z., Chen, Y., Nie, X., Sun, Q., et al. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm13010060 · Cited by 35
Lee, W., Jung, I., Park, S. Y., Yu, J. H., Seo, J. A., Kim, K. J., Kim, N. H., Yoo, H. J., Kim, S. G., Choi, K. M., et al. (2023) · Endocrinology and Metabolism · doi:10.3803/enm.2022.1582 · Cited by 31
Martínez‐Montoro, J. I., Núñez‐Sánchez, M. Á., Martínez‐Sánchez, M. A., Román, A. B., Fernández-Ruiz, V. E., Ferrer‐Gómez, M., Śledziński, T., Frutos, M. D., Fernández‐García, J. C., Mika, A., et al. (2023) · Obesity · doi:10.1002/oby.23711 · Cited by 29
Castro, L., Brant, L. C. C., Diniz, M. D. F. H. S., Lotufo, P. A., Benseñor, I. M., Chor, D., Griep, R. H., Barreto, S. M., & Ribeiro, A. L. P. (2023) · Scientific Reports · doi:10.1038/s41598-023-35298-y · Cited by 30
Hariharan, R., Cameron, J., Menon, K., Mesinovic, J., Jansons, P., Scott, D. S., Lu, Z. X., de Courten, M., Feehan, J., & de Courten, B. V. (2023) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2023.10.012 · Cited by 31
Rahman, A., Cope, M. B., Sarker, S. A., et al. (2009) · Journal of Life Sciences · doi:10.1080/09751270.2009.11885133 · Cited by 28
Zhang, R., Hu, C., Wang, C.-R., et al. (2009) · PubMed · Cited by 28
Çelebi, G., Genç, H., Gürel, H., Sertoğlu, E., Kara, M., Tapan, S., Açıkel, C., Karslıoğlu, Y., Erçin, C. N., & Doğru, T. (2015) · Saudi Journal of Gastroenterology · doi:10.4103/1319-3767.157556 · Cited by 28
Maternal serum resistin concentrations in gestational diabetes mellitus and normal pregnancies
Vitoratos, N., Deliveliotou, A. E., Dimitrakaki, A., Hassiakos, D., Panoulis, C., Deligeoroglou, E., & Creatsas, G. K. (2010) · Journal of obstetrics and gynaecology research · doi:10.1111/j.1447-0756.2010.01327.x · Cited by 27
Yoo, K., Ko, S., Park, J., Ahn, Y., Yim, H. W., Lee, W., & Park, Y. (2011) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2011.04248.x · Cited by 28
Serum Leptin is Associated With Metabolic Syndrome in Obese Mexican Subjects
García‐Jiménez, S., Bernal-Fernández, G., Martínez-Salazar, M. F., et al. (2014) · Journal of Clinical Laboratory Analysis · doi:10.1002/jcla.21718 · Cited by 28
Calpain-5 gene variants are associated with diastolic blood pressure and cholesterol levels
Sáez, M. E., Martínez-Larrad, M. T., Ramírez‐Lorca, R., et al. (2007) · BMC Medical Genetics · doi:10.1186/1471-2350-8-1 · Cited by 28
Hirsch, S., Barrera, G., Leiva, L., de la Maza, M. P., & Bunout, D. C. (2013) · PubMed · doi:10.3305/nh.2013.28.2.6161 · Cited by 28
The DECODE Study Group (2005) · Diabetic Medicine · doi:10.1111/j.1464-5491.2005.01655.x · Cited by 28
Cermak, N. M., Hansen, D., Kouw, I. W. K., van Dijk, J., Blackwell, J. R., JONES, A. M., Gibala, M. J., & van Loon, L. J. C. (2015) · Nutrition Research · doi:10.1016/j.nutres.2015.05.017 · Cited by 28
Weyrich, P., Staiger, H., Stančáková, A., Machicao, F. E., Machann, J., Schick, F., Stefan, N., Kuusisto, J., Laakso, M., Schäfer, S., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0013980 · Cited by 28
Cazzola, R., Rondanelli, M., Trotti, R., et al. (2010) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2010.03.007 · Cited by 28
Zazai, R., Wilms, B., Ernst, B., Thurnheer, M., & Schultes, B. (2013) · Obesity Surgery · doi:10.1007/s11695-013-1141-6 · Cited by 31
Kim, S. H., Nikolics, L., Abbasi, F., Lamendola, C. A., Link, J. T., Reaven, G. M., & Lindley, S. E. (2009) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2009.11.007 · Cited by 27
Martins, S. V., Lopes, P. A., Alfaia, C. M., Rodrigues, P., Alves, S. P., Pinto, R. M. A., Castro, M., Bessa, R. J. B., & Prates, J. A. M. (2009) · British Journal Of Nutrition · doi:10.1017/s000711450999256x · Cited by 28
Role of Metabolic Factors in the Association Between Osteocalcin and Testosterone in Chinese Men
Liao, M., Guo, X., Yu, X., Pang, G., Zhang, S., Li, J., Tan, A., Gao, Y., Yang, X., Zhang, H., et al. (2013) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2013-1805 · Cited by 27
Orthostatic Hypotension Does Not Increase Cardiovascular Risk in the Elderly at a Population Level
Casiglia, E., Tikhonoff, V., Caffi, S., Boschetti, G., Giordano, N., Guidotti, F., Segato, F., Mazza, A., Grasselli, C., Saugo, M., et al. (2013) · American Journal of Hypertension · doi:10.1093/ajh/hpt172 · Cited by 28
Increased serum insulin-like growth factor-1 levels in women with gestational diabetes
Matuszek, B., Lenart-Lipińska, M., Burska, A. N., Paszkowski, T., Smoleń, A., & Nowakowski, A. M. (2011) · Advances in Medical Sciences · doi:10.2478/v10039-011-0046-7 · Cited by 29
Changes in Dynamic Insulin and Gastrointestinal Hormone Secretion in Obese Children
Roth, C. L., Bongiovanni, K. D., Gohlke, B. C., & Woelfle, J. (2010) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem.2010.204 · Cited by 28
Domingo, P., Gutiérrez, M. D. M., Gallego-Escuredo, J. M., Torres, F., Mateo, G. M., Villarroya, J., Santos, I. D. L., Domingo, J. C., Villarroya, F., del Río, L., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0089088 · Cited by 28
Zhang, Y., Lee, E. T., Howard, B. V., Best, L. G., Umans, J. G., Yeh, J. L., Wang, W., Yeh, F., Ali, T., Devereux, R. B., et al. (2013) · Diabetes Care · doi:10.2337/dc12-2368 · Cited by 28
Elevated level of C-reactive protein is associated with risk of prediabetes in Indians
Jaiswal, A., Tabassum, R., Podder, A., et al. (2012) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2012.02.034 · Cited by 28
Katayama, T., Ueba, H., Tsuboi, K., Kubo, N., Yasu, T., Kuroki, M., Saito, M., Momomura, S., & Kawakami, M. (2007) · American Heart Journal · doi:10.1016/j.ahj.2007.02.022 · Cited by 28
de Queiroz, K. B., Coimbra, R. S., Ferreira, A. R., Carneiro, C. M., de Paiva, N. C. N., Costa, D. C., Evangelista, E. A., & Guerra‐Sá, R. (2014) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.201400241 · Cited by 28
Franssen, F. M. E., Sauerwein, H. P., Ackermans, M. T., Rutten, E. P. A., Wouters, E. F. �., & Schols, A. M. W. J. (2010) · Metabolism · doi:10.1016/j.metabol.2010.09.004 · Cited by 28
Total ghrelin levels during acute insulin infusion in patients with polycystic ovary syndrome
Micic, D., Sumarac-Dumanovic, M., Kendereski, A., Cvijovic, G., Zoric, S., Pejkovic, D., Micic, J., Milic, N., Dieguez, C., & Casanueva, F. F. (2007) · Journal of Endocrinological Investigation · doi:10.1007/bf03349222 · Cited by 28
Serum osteocalcin levels in relation to metabolic syndrome in Chinese postmenopausal women
Yang, R., Ma, X., Pan, X., Wang, F., Luo, Y., Gu, C., Bao, Y., & Jia, W. (2012) · Menopause The Journal of The North American Menopause Society · doi:10.1097/gme.0b013e318271b1da · Cited by 28
Increased Insulin Sensitivity in Mice Lacking Collectrin, a Downstream Target of HNF-1α
Malakauskas, S. M., Kourany, W. M., Zhang, X. Y., Lu, D., Stevens, R. D., Koves, T. R., Hohmeier, H. E., Muoio, D. M., Newgard, C. B., & Le, T. H. (2009) · Molecular Endocrinology · doi:10.1210/me.2008-0274 · Cited by 28
Ateş, S., Şevket, O., Sudolmuş, S., Dane, B., Özkal, F., Uysal, Ö., & Dansuk, R. (2013) · Gynecological Endocrinology · doi:10.3109/09513590.2013.819082 · Cited by 28
Birkenaes, A. B., Birkeland, K., Friis, S., Opjordsmoen, S., & Andreassen, O. (2009) · Journal of Clinical Psychopharmacology · doi:10.1097/jcp.0b013e31819b95fc · Cited by 32
Association Between C-Reactive Protein and Type 2 Diabetes in a Tunisian Population
Belfki, H., Ali, S. B., Bougatef, S., Ahmed, D. B., Haddad, N., Jmal, A., Abdennebi, M., & Romdhane, H. B. (2011) · Inflammation · doi:10.1007/s10753-011-9361-1 · Cited by 28
Casiglia, E., Tikhonoff, V., Caffi, S., et al. (2008) · Journal of Hypertension · doi:10.1097/hjh.0b013e3282f2b90c · Cited by 28
Kitsy, A., Carney, S. T., Vivar, J. C., Knight, M. S., Pointer, M. A., Gwathmey, J. K., & Ghosh, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102615 · Cited by 27
Esteghamati, A., Ashraf, H., Nakhjavani, M., et al. (2008) · Diabetic Medicine · doi:10.1111/j.1464-5491.2008.02653.x · Cited by 28
Janssen, I., Wong, S. L., Colley, R. C., & Tremblay, M. S. (2013) · BMC Public Health · doi:10.1186/1471-2458-13-554 · Cited by 27
Adverse Reactions to Food: Allergies and Intolerances
Montalto, M., Santoro, L., D’Onofrio, F., Curigliano, V., Gallo, A., Visca, D., Cammarota, G., Gasbarrini, A., & Gasbarrini, A. (2008) · Digestive Diseases · doi:10.1159/000116766 · Cited by 28
Expression of TNF-α protein in omental and subcutaneous adipose tissue in obesity
Cao, Y. L., Hu, C., Meng, X., Wang, D. F., & Zhang, J. (2007) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2007.08.030 · Cited by 28
Chang, J.-W., Ou, H. Y., Chen, H. L., Guo, H., Liao, P., & Lee, C. C. (2010) · Chemosphere · doi:10.1016/j.chemosphere.2010.08.050 · Cited by 28
Rosiglitazone improves lipoatrophy in patients receiving thymidine-sparing regimens
Tungsiripat, M., El‐Bejjani, D., Rizk, N., O’Riordan, M. A., Ross, A. C., Hileman, C. O., Storer, N. J., Harrill, D., & McComsey, G. A. (2010) · AIDS · doi:10.1097/qad.0b013e328339e274 · Cited by 28
Lorenzo, C., Hanley, A. J., Rewers, M., & Haffner, S. M. (2013) · Diabetologia · doi:10.1007/s00125-012-2826-4 · Cited by 28
Ormseth, M. J., Swift, L. L., Fazio, S., Linton, M. F., Chung, C. P., Raggi, P., Rho, Y. H., Solus, J. F., Oeser, A. M., Bian, A., et al. (2011) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2011.09.005 · Cited by 28
Rodrigues, P., Martins, S. V., Lopes, P. A., Ramos, C., Miguéis, S., Alfaia, C. M., Pinto, R. M. A., Rolo, E. A., Bispo, P., Batista, I., et al. (2014) · British Journal Of Nutrition · doi:10.1017/s0007114514000853 · Cited by 28
Boggio, A., Muzio, F., Fiscella, M., Sommariva, D., & Branchi, A. (2011) · Internal and Emergency Medicine · doi:10.1007/s11739-011-0743-z · Cited by 28
Yokota, T., Kinugawa, S., Okita, K., Hirabayashi, K., Suga, T., Hattori, M., Nakagawa, Y., Oyama‐Manabe, N., Shirato, H., & Tsutsui, H. (2011) · Hypertension Research · doi:10.1038/hr.2011.78 · Cited by 28
Interplay of overweight and insulin resistance on hypertension development
Lytsy, P., Ingelsson, E., Lind, L., Ärnlöv, J., & Sundström, J. (2013) · Journal of Hypertension · doi:10.1097/hjh.0000000000000081 · Cited by 28
Silvennoinen, M., Rinnankoski‐Tuikka, R., Vuento, M., Hulmi, J. J., Karvinen, S. M., Lehti, M., Kivelä, R., & Kainulainen, H. (2012) · Angiogenesis · doi:10.1007/s10456-012-9315-8 · Cited by 28
Riesco, É., Aubertin‐Leheudre, M., Maltais, M. L., Audet, M., & Dionne, I. J. (2010) · Menopause The Journal of The North American Menopause Society · doi:10.1097/gme.0b013e3181da7915 · Cited by 28
Maliqueo, M. A., Galgani, J. E., Pérez‐Bravo, F., Echiburú, B., de Guevara, A. L., Crisosto, N., & Sir‐Petermann, T. (2012) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2011.12.012 · Cited by 28
Repeated ingestion of the leaf extract from Morus alba reduces insulin resistance in KK-Ay mice
Tanabe, K., NAKAMURA, S., Omagari, K., & Oku, T. (2011) · Nutrition Research · doi:10.1016/j.nutres.2011.09.023 · Cited by 28
Davis, J. N., Ventura, E. E., Shaibi, G. Q., Weigensberg, M. J., Spruijt‐Metz, D., Watanabe, R. M., & Goran, M. I. (2007) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2006.0038 · Cited by 28
Metadata
- Authors
- Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C.
- Affiliations
- University of Oxford (GB)
- ORCID
- Matthews, D. R.
- Venue
- Diabetologia
- Published
- 1985-07
- Volume
- 28
- Issue
- 7
- Pages
- 412-419
- ISSN
- 0012-186X (ISSN-L)
- 1432-0428
- OpenAlex
- W2160234571
- Cited by
- 31,502
- References
- 21
- Citations by year
- 2026: 933 · 2025: 1060 · 2024: 1240 · 2023: 1291 · 2022: 1489 · 2021: 1528 · 2020: 1424 · 2019: 1427 · 2018: 1456 · 2017: 1440 · 2016: 1481 · 2015: 1682 · 2014: 1693 · 2013: 1621 · 2012: 1518
- FWCI
- 7.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Diabetologia (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Research Council
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- HOMA, insulin resistance, beta-cell function, fasting blood glucose, fasting insulin, hyperglycaemic clamp, euglycaemic clamp, intravenous glucose tolerance test, insulin receptor binding, type 2 diabetes
- MeSH terms
- Adult, Blood Glucose, Computers, Diabetes Mellitus, Type 2, Erythrocytes, Fasting, Homeostasis, Humans, Insulin, Insulin Resistance, Islets of Langerhans, Middle Aged, Monocytes, Receptor, Insulin
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-14, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
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