The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index; 1 of the 6 sources its claims cite were checked for support.
Homeostasis model assessment: insulin resistance and ?-cell function from fasting plasma glucose and insulin concentrations in man
Open accessAbstract
The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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Claims the paper makes
15 Own result · 7 Own interpretation · 3 No full text of the sources · 1 Partly supported · 1 No basis given · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The fasting plasma insulin concentration is largely determined by the glucose concentration [3]”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
What the sources say
- Partly supportedcites [3] McCarthy ST, Harris F, Turner RC (1977) Glucose control of basal insulin secretion in diabetes. Diabetologia 13: 93?97 sourceExperimental — accuracy not yet measured
The Results and Discussion (page unavailable in our copy) show that changing plasma glucose affects basal insulin secretion. But they also report markedly impaired responses in diabetic subjects, including cases where insulin remained nearly constant despite a substantial glucose decrease. The paper therefore supports glucose as an influence, but not the unqualified claim that it largely determines fasting insulin concentration. (LLM, conf=0.84)
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimate of deficient/3cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (R~ = 0.61, p< 0.01)”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (R~=0.88,p<0.0001)”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There was no correlation with any aspect of insulin-receptor binding.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the fasting insulin concentration (P~ = 0.81, p < 0.0001)”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mean coefficient of variation over the two studies was 32%.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Reproducibility of estimate of /3-cell function by HOMA
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“in both groups together (1% 0.61, p < 0.01 : Fig. 3).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Comparisons of methods of assessing ~3-cell function
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and with the estimate from the intravenous glucose tolerance test (R~ = 0.64, p < 0.05).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and in both groups together: 1% = 0.64, p< 0.05; Fig. 3).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > HOMA compared with intravenous glucose tolerance test.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Neither receptor numbers or any aspect of high or low affinity binding correlated with other measures of insulin resistance (HOMA, clamp, basal insulin or CIGMA: ~ all < 0.55; NS).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > HOMA compared with the hyperglycaemic clamp. The
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
- It may state two separate claims as one. 2
- The quote only partly states this claim. 1
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for/3-cell deficit) limits its use”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mean coefficient of variation over the two studies was 31%.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Hyperglycaemic clamp : ~-cell function (%)
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
The quote only partly states this claim.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The degree of the hyperglycaemia does not correlate significantly with either the degrees of abnormal fl-cell function or insulin resistance.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
The authors state this as an assumption.
Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
“Assuming that normal-weight normal subjects aged < 35 years have 100% r-cell function, and an insulin resistance of 1, the values for a patient can be assessed from the insulin and glucose concentrations by the formulae: r-cell”
The paper relies on this without support.
The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
“The plasma glucose:insulin response for normal subjects, equivalent to the C-peptide response, is insulin = 5 (glucose-3.5).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“After the unprocessed diet, fasting glucose and insulin levels tended to decrease compared to baseline and the homeostasis model assessment of insulin resistance (HOMA-IR) (Matthews et al., 1985) was significantly decreased compared to baseline.”
From Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake (2019) · cites this paper as Matthews et al., 1985
The cited source says what the claim attributes to it.
In the citing paper
“The Homeostasis Model Assessment of Insulin Resistance (HoMA-IR) was calculated according to Matthews et al. [25].”
From Chronotype: A Tool to Screen Eating Habits in Polycystic Ovary Syndrome? (2022) · cites this paper as [25]
The cited source says what the claim attributes to it.
In the citing paper
“AAV9-mTERT treated mice also showed a trend for an improved glucose uptake and a better homeostatic model assessment (HOMA (Heikkinen et al, 2007; Matthews et al, 1985)) compared to the AAV9-eGFP controls, although differences did not reach statistical significance (Supporting Information Fig S4D and E).”
From Telomerase gene therapy in adult and old mice delays aging and increases longevity without increasing cancer (2012) · cites this paper as Matthews et al, 1985
In the citing paper
“Surrogate markers of peripheral insulin activity, such as homeostasis model assessment and whole-body insulin sensitivity indexes, could be useful as well in the early identification of patients with IR, before overt DM sets in ().”
From Glucose Metabolism Abnormalities in Cushing Syndrome: From Molecular Basis to Clinical Management (2017) · cites this paper as [240]
In the citing paper
“ndirect estimates of insulin resistance, such as HOMA-IR 63 and QUICKI, 64 are derived from fasting S-insulin and glucose concentrations and have been widely used in both nondiabetic and type 2 diabetic subjects as surrogates of insulin sensitivity.65,66”
From Increased liver fat, impaired insulin clearance, and hepatic and adipose tissue insulin resistance in type 2 diabetes. (2008) · cites this paper as [63]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Subjects and procedures
“The same 12 normal subjects and 11 Type 2 diabetic patients came to the hospital fasting on a separate day for a continuous infusion”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > HOMA compared with the hyperglycaemic clamp. The
“HOMA compared with CIGMA.The HOMA estimate of insulin resistance from the mean of 15 stressed fasting samples before the CIGMA correlated with that measured by CIGMA in 11 normal subjects (R~ = 0.69, p < 0.02), in 12 diabetic subjects (R~ 0.97, p < 0.0001)”
Citations
Cites
Glucose clamp technique: a method for quantifying insulin secretion and resistance.
DeFronzo, R. A., Tobin, J. D., & Andres, R. (1979) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1979.237.3.e214 · Cited by 7,425
Quantitative estimation of insulin sensitivity.
Bergman, R. N., Ider, Y. Z., Bowden, C. R., & Cobelli, C. (1979) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1979.236.6.e667 · Cited by 1,874 · cited 1×
“This use of modelling to provide a reference graph contrasts with other models where an individual patient's data have to be entered into a computer to determine the relationships between rapidly changing variables, such as occur after an intravenous glucose test [19].”
Method[WHO Expert Committee on diabetes mellitus].
Reitsma, W. D., & Terpstra, J. (1981) · PubMed · Cited by 556
Turner, R. C., Holman, R. R., Matthews, D., Hockaday, T. D. R., & Peto, J. (1979) · Metabolism · doi:10.1016/0026-0495(79)90146-x · Cited by 388
Self-citationCharacteristics of Human Erythrocyte Insulin Receptors
Gambhir, K. K., Archer, J. A., & BRADLEY, C. J. (1978) · Diabetes · doi:10.2337/diab.27.7.701 · Cited by 290
The Insulin Receptor: Its Role in Insulin Resistance of Obesity and Diabetes
Olefsky, J. M. (1976) · Diabetes · doi:10.2337/diab.25.12.1154 · Cited by 289
Hosker, J. P., Matthews, D. R., Rudenski, A. S., Burnett, M. A., Darling, P., Bown, E. G., & Turner, R. C. (1985) · Diabetologia · doi:10.1007/bf00280882 · Cited by 287
Self-citationPulsatile Insulin Has Greater Hypoglycemic Effect Than Continuous Delivery
Matthews, D. R., Naylor, B. A., Jones, R., Ward, G. M., & Turner, R. C. (1983) · Diabetes · doi:10.2337/diab.32.7.617 · Cited by 256
Self-citationControl of pulsatile insulin secretion in man
Matthews, D. R., Lang, D. A., Burnett, M. A., & Turner, R. C. (1983) · Diabetologia · doi:10.1007/bf00282705 · Cited by 175
Self-citationTHE BASAL PLASMA GLUCOSE: A SIMPLE RELEVANT INDEX OF MATURITY‐ONSET DIABETES
HOLMAN, R. R., & TURNER, R. C. (1981) · Clinical Endocrinology · doi:10.1111/j.1365-2265.1981.tb00196.x · Cited by 64
Self-citation
Research works printed but not linked: 2
- Metropolitan Life Assurance Co (1959) Net weight standard for men and women. Stat Bull Metrol Life Found 40: 1?4
- Matthews DR, Hosker JP (1984) An unbiased, flexible computer programme for glucose clamping, with graphics and running statistics. Diabetologia 27: 308?309 A
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Muñoz‐Hernando, J., Escribano, J., Ferré, N., Closa‐Monasterolo, R., Grote, V., Koletzko, B. V., Gruszfeld, D., ReDionigi, A., Verduci, E., Xhonneux, A., et al. (2021) · Clinical Nutrition · doi:10.1016/j.clnu.2021.12.008 · Cited by 52
Yadav, J., Liang, T., Qin, T., Nathan, N., Schwenger, K. J. P., Pickel, L., Xie, L., Lei, H., Winer, D. A., Maughan, H., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101051 · Cited by 49
Hong, S., Sung, Y., Hong, Y. S., Song, D. K., Jung, H., Jeong, K., Chung, H., & Lee, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-39428-4 · Cited by 44
Nucci, S. D., Bonfiglio, C., Donvito, R., Chito, M. D., Cerabino, N., Rinaldi, R., Sila, A., Shahini, E., Giannuzzi, V., Pesole, P. L., et al. (2023) · Nutrients · doi:10.3390/nu15204468 · Cited by 45
Li, L., Huang, L., Lei, R., Zhang, P., Yang, Y., Liu, H., & Zhang, Y. (2023) · Environmental Pollution · doi:10.1016/j.envpol.2023.122948 · Cited by 45
Lee, J., Kim, M., Jang, J., & Oh, C. (2023) · Clinical Diabetes and Endocrinology · doi:10.1186/s40842-023-00156-3 · Cited by 60
Witham, M. D., McDonald, C., Wilson, N., Rennie, K., Bardgett, M., Bradley, P., Clegg, A. P., Connolly, S., Hancock, H. C., Hiu, S. K. W., et al. (2025) · The Lancet Healthy Longevity · doi:10.1016/j.lanhl.2025.100695 · Cited by 46
Dongxia, L., Hua, Q., Lisong, L., & Jincheng, G. (2007) · Circulation Journal · doi:10.1253/circj.72.551 · Cited by 43
Wang, F., Han, X., Ren, Q., Zhang, X., Han, L., Luo, Y., Zhou, X., & Ji, L. (2009) · PubMed · Cited by 44
Iba, A., Kohjimoto, Y., Mori, T., et al. (2010) · British Journal of Urology · doi:10.1111/j.1464-410x.2010.09216.x · Cited by 44
Berberoğlu, Z., Yazıcı, A. C., & Demirağ, N. G. (2010) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2010.03784.x · Cited by 44
Hurliman, A. K., Brown, J. K., Maille, N., Mandalà, M., Casson, P. R., & Osol, G. (2015) · Endocrinology · doi:10.1210/en.2015-1159 · Cited by 44
Gómez‐Zorita, S., Fernández‐Quintela, A., Aguirre, L., Macarulla, M. T., Rimando, A. M., & Portillo, M. P. (2015) · Food & Function · doi:10.1039/c5fo00151j · Cited by 46
Kim, H.-N., & Song, S.-W. (2014) · Biological Trace Element Research · doi:10.1007/s12011-014-9934-6 · Cited by 44
Perticone, F., Maio, R., Tassone, J. E., Perticone, M., Pascale, A., Sciacqua, A., & Sesti, G. (2012) · International Journal of Cardiology · doi:10.1016/j.ijcard.2011.12.065 · Cited by 44
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Choi, J. S., Koh, I.-U., & Song, J. (2012) · Nutrition Research · doi:10.1016/j.nutres.2012.10.002 · Cited by 46
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Timonen, M., Salmenkaita, I., Jokelainen, J. J., Laakso, M., Härkönen, P. K., Koskela, P. U., Meyer‐Rochow, V. B., Peitso, A., & Keinanen-Kiukaanniemi, S. M. (2007) · Psychosomatic Medicine · doi:10.1097/psy.0b013e318157ad2e · Cited by 45
Muldoon, M. F., Mackey, R. H., Sutton‐Tyrrell, K., Flory, J. D., Pollock, B. G., & Manuck, S. B. (2007) · Stroke · doi:10.1161/strokeaha.106.477638 · Cited by 43
Influence of Magnesium on Insulin Resistance in Obese Women
Cruz, K. J. C., de Oliveira, A. R. S., Pinto, D. P., et al. (2014) · Biological Trace Element Research · doi:10.1007/s12011-014-0044-2 · Cited by 44
Ross, A. C., Storer, N. J., O’Riordan, M. A., Dogra, V. S., & McComsey, G. A. (2010) · The Pediatric Infectious Disease Journal · doi:10.1097/inf.0b013e3181d770c4 · Cited by 44
Pedrosa, C. A., Oliveira, B. M. P. M., Albuquerque, I., et al. (2010) · European Journal of Pediatrics · doi:10.1007/s00431-010-1316-2 · Cited by 44
Li, Y., Ji, D.-F., Zhong, S. S., Tian-bao, L. I. N., & Lv, Z.-Q. (2014) · Chemico-Biological Interactions · doi:10.1016/j.cbi.2014.11.003 · Cited by 45
Oka, R., Yagi, K., Sakurai, M., Nakamura, K., Moriuchi, T., Miyamoto, S., Nohara, A., Kawashiri, M., Takeda, Y., & Yamagishi, M. (2011) · Endocrine Journal · doi:10.1507/endocrj.ej11-0157 · Cited by 43
Huang, J., Chuang, W., Yu, M., Yu, S., Huang, C., Huang, C., Yeh, M., Hsieh, M., Yang, J., Lin, Z., et al. (2009) · The Kaohsiung Journal of Medical Sciences · doi:10.1016/s1607-551x(09)70520-0 · Cited by 44
Derosa, G., Ragonesi, P. D., Fogari, E., Cicero, A. F. G., Bianchi, L., Bonaventura, A., Romano, D., & Maffioli, P. (2012) · Fundamental and Clinical Pharmacology · doi:10.1111/fcp.12001 · Cited by 47
Alanbay, İ., Ercan, C. M., Sakıncı, M., Çoksüer, H., Ozturk, M., & Tapan, S. (2012) · Archives of Gynecology and Obstetrics · doi:10.1007/s00404-012-2425-0 · Cited by 45
Shimaoka, I., Kamide, K., Ohishi, M., Katsuya, T., Akasaka, H., Saitoh, S., Sugimoto, K., Oguro, R., Congrains, A., Fujisawa, T., et al. (2010) · Hypertension Research · doi:10.1038/hr.2009.215 · Cited by 44
ISHIZAKI, N., Okushi, N., Yano, T., & Yamamura, Y. (2009) · Metabolism · doi:10.1016/j.metabol.2009.05.001 · Cited by 44
Pardina, E., Baena‐Fustegueras, J. A., Catalán, R., Galard, R., Lecube, A., Fort, J. M., Allende, H., Vargas, V., & Peinado‐Onsurbe, J. (2008) · Obesity Surgery · doi:10.1007/s11695-008-9739-9 · Cited by 44
Yu, Y., Zhang, X. H., Ebersole, B., Ribnicky, D., & Wang, Z. Q. (2013) · Scientific Reports · doi:10.1038/srep03142 · Cited by 46
Effects of aging on basal fat oxidation in obese humans
Solomon, T. P. J., Marchetti, C. M., Krishnan, R. K., González, F., & Kirwan, J. P. (2008) · Metabolism · doi:10.1016/j.metabol.2008.03.021 · Cited by 44
Locher, L., Häussler, S., Laubenthal, L., SINGH, S. P., Winkler, J., Kinoshita, A., Kenéz, Á., Rehage, J., Huber, K., Sauerwein, H., et al. (2014) · Journal of Dairy Science · doi:10.3168/jds.2014-8710 · Cited by 47
Oh, J.-Y., Sung, Y.-O.-A., & Lee, H. (2013) · Diabetic Medicine · doi:10.1111/dme.12052 · Cited by 45
Lenart-Lipińska, M., Matyjaszek‐Matuszek, B., Gernand, W., Nowakowski, A. M., & Solski, J. (2013) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2013.04.010 · Cited by 44
Metabolic Syndrome in Obese Children and Adolescents: Dichotomous or Continuous?
Thivel, D., Malina, R. M., Isacco, L., Aucouturier, J., Meyer, M., & Duché, P. (2009) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2008.0085 · Cited by 44
Agnoux, A. M., Antignac, J., Simard, G., Poupeau, G., Darmaun, D., Parnet, P., & Alexandre-Gouabau, M. C. (2014) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.00015.2014 · Cited by 45
Knop, F. K., Vilsbøll, T., Larsen, S., Madsbad, S., Holst, J. J., & Krarup, T. (2003) · Diabetes Care · doi:10.2337/diacare.26.9.2581 · Cited by 44
The Association of Insulin Resistance with Subclinical Thyrotoxicosis
Rezzónico, J., Nièpomniszcze, H., Rezzónico, M., Pusiol, E., Alberto, M., & Brenta, G. (2011) · Thyroid · doi:10.1089/thy.2010.0402 · Cited by 44
Shen, J., Ma, L. N., Han, Y., Liu, J. X., Yang, W. Q., Chen, L., Liu, Y.-C., Hu, Y., & Jin, M. W. (2012) · Diabetologia · doi:10.1007/s00125-012-2519-z · Cited by 44
Pacifico, L. (2013) · World Journal of Gastroenterology · doi:10.3748/wjg.v19.i25.4007 · Cited by 44
Cardiometabolic risk factors among HIV patients on antiretroviral therapy
Kiage, J. N., Heimburger, D. C., Nyirenda, C. K., Wellons, M., Bagchi, S., Chi, B. H., Koethe, J. R., Arnett, D. K., & Kabagambe, E. K. (2013) · Lipids in Health and Disease · doi:10.1186/1476-511x-12-50 · Cited by 44
Jariyapongskul, A., Areebambud, C., Suksamrarn, S., & Mekseepralard, C. (2015) · BioMed Research International · doi:10.1155/2015/785826 · Cited by 46
Mendoza-Núñez, V. M., García, Á. S., Sánchez-Rodrı́guez, M. A., Galván-Duarte, R. E., & Fonseca‐Yerena, M. E. (2002) · Obesity Research · doi:10.1038/oby.2002.34 · Cited by 44
Vadstrup, E. S., Frølich, A., Perrild, H., Borg, E., & Røder, M. E. (2009) · BMC Public Health · doi:10.1186/1471-2458-9-166 · Cited by 44
Abnormal glucose metabolism among older men with or at risk of HIV infection
Howard, A., Floris-Moore, M., Lo, Y., Arnsten, J. H., Fleischer, N. L., & Klein, R. (2006) · HIV Medicine · doi:10.1111/j.1468-1293.2006.00398.x · Cited by 44
Kretowski, A. J., Adamska-Patruno, E., Maliszewska, K., Wawrusiewicz‐Kurylonek, N., Citko, A., Gościk, J., Bauer, W., Wilk, J., Golonko, A., Waszczeniuk, M., et al. (2015) · Genes & Nutrition · doi:10.1007/s12263-015-0454-6 · Cited by 44
Hivert, M., SULLIVAN, L. M., Shrader, P. J., Fox, C. S., Nathan, D. M., D’Agostino, R. B., Wilson, P. W. F., Kowall, B., Herder, C., Meisinger, C., et al. (2011) · Diabetologia · doi:10.1007/s00125-011-2067-y · Cited by 45
Lipid and glucose alterations in perinatally-acquired HIV-infected adolescents and young adults
Blázquez‐Gamero, D., Ramos-Amador, J. T., Sainz, T., Mellado, M. J., García‐Ascaso, M. T., de José, M. I., Rojo, P., Navarro, M. L., Muñoz‐Fernández, M. Á., Saavedra, J., et al. (2015) · BMC Infectious Diseases · doi:10.1186/s12879-015-0853-8 · Cited by 44
Savage, T., Derraik, J. G. B., Miles, H. L., Mouat, F., Hofman, P. L., & Cutfield, W. S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058869 · Cited by 45
Evaluation of the hypothalamic‐pituitary‐gonadal axis in eugonadal men with type 2 diabetes mellitus
Costanzo, P., Suárez, S. M., Scaglia, H. E., Zylbersztein, C., Litwak, L., & Knoblovits, P. (2013) · Andrology · doi:10.1111/j.2047-2927.2013.00163.x · Cited by 44
Mylonopoulou, M., Tentolouris, N., Antonopoulos, S., Mikros, S., Katsaros, K., Melidonis, A. J., Sevastos, N., & Katsilambros, N. L. (2010) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfq226 · Cited by 44
Parekh, N., Lin, Y., Marcella, S., Kant, A. K., & Lu‐Yao, G. L. (2008) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-08-0059 · Cited by 44
Physical Exercise, Fasting Glucose, and Renal Hyperfiltration in the General Population
Melsom, T., Mathisen, U. D., Eilertsen, B.-A. W., Ingebretsen, O. C., Jenssen, T. G., Njølstad, I., Solbu, M. D., Toft, I., & Eriksen, B. O. (2012) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.02980312 · Cited by 45
Maternal and Cord Blood Adiponectin Multimeric Forms in Gestational Diabetes Mellitus
Ballesteros, M., Simón, I., Vendrell, J. J., Ceperuelo‐Mallafré, V., Miralles, R. M., Albaigés, G., Tinahones, F. J., & Megía, A. (2011) · Diabetes Care · doi:10.2337/dc11-0788 · Cited by 45
Turchiano, M., Sweat, V., Fierman, A. H., & Convit, A. J. (2012) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/archpediatrics.2012.1263 · Cited by 43
Hamer, H. M., Wall, B. T., Kiskini, A., de Lange, A., Groen, B. B. L., Bakker, J. A., Gijsen, A. P., Verdijk, L. B., & van Loon, L. J. C. (2013) · Nutrition & Metabolism · doi:10.1186/1743-7075-10-15 · Cited by 44
Tybor, D. J., Lichtenstein, A. H., Dallal, G. E., Daniels, S. R., & Must, A. (2010) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.110.001719 · Cited by 44
Intra-renal hemodynamics and carotid intima-media thickness in the metabolic syndrome
Buscemi, S., Verga, S., Batsis, J. A., Cottone, S., Mattina, A., Re, A., Arnone, M., Citarda, S., & Cerasola, G. (2009) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2009.09.015 · Cited by 44
Association of chronic viral hepatitis B with insulin resistance
Lee, J. G. (2012) · World Journal of Gastroenterology · doi:10.3748/wjg.v18.i42.6120 · Cited by 44
Severe hypoglycemia after gastric bypass surgery for morbid obesity
Galiana, P. A., Cámara, R., Merino-Torres, J. F., Pérez-Lázaro, A., Olmo-García, M. I. D., Ponce, J. L., Rayón, J. M., & Piñón, F. (2007) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2007.07.017 · Cited by 44
Panici, J. A., Wang, F., Bonkowski, M. S., Spong, A., Bartke, A., Pawlikowska, L., Kwok, P., & Masternak, M. M. (2009) · The Journals of Gerontology Series A · doi:10.1093/gerona/glp111 · Cited by 44
Calcaterra, V., Brambilla, P., Maffè, G. C., Klersy, C., Albertini, R., Introzzi, F., Bozzola, E., Bozzola, M., & Larizza, D. (2014) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2013.0075 · Cited by 44
Andersen, A. F., Carson, C., Watt, H., Lawlor, D. A., Avlund, K., & Ebrahim, S. B. J. (2008) · Diabetic Medicine · doi:10.1111/j.1464-5491.2008.02594.x · Cited by 44
Longenecker, C. T., Hileman, C. O., Funderburg, N., & McComsey, G. A. (2014) · Clinical Infectious Diseases · doi:10.1093/cid/ciu523 · Cited by 44
Chapman, M. J., Orsoni, A., Robillard, P., Hounslow, N. J., Sponseller, C. A., & Giral, P. (2013) · Current Medical Research and Opinion · doi:10.1185/03007995.2013.874989 · Cited by 44
Eslam, M., Kawaguchi, T., Campo, J. A. D., Sata, M., Khattab, M. A., & Romero‐Gómez, M. (2011) · Journal of Viral Hepatitis · doi:10.1111/j.1365-2893.2011.01474.x · Cited by 44
Rhee, E., Lee, W., Min, K. W., Shivane, V. K., Sosale, A., Jang, H. C., Chung, C. H., Nam‐Goong, I. S., Kim, J. A., Kim, S., et al. (2013) · Diabetes Obesity and Metabolism · doi:10.1111/dom.12060 · Cited by 44
Pentraxin 3 (PTX3) is associated with cardiovascular risk factors: the Health 2000 Survey
Jylhävä, J., Haarala, A., Kähönen, M. A. P., et al. (2011) · Clinical & Experimental Immunology · doi:10.1111/j.1365-2249.2011.04354.x · Cited by 44
Ayoub, J. J., Abou-Samra, M., Hlais, S., Bassil, M. S., & Obeid, O. (2015) · Nutrition and Diabetes · doi:10.1038/nutd.2015.38 · Cited by 46
Chen, Y., Zhang, X., Huang, J., Zhou, S., Liu, F., Jiang, L., Chen, M., Wan, J., & Yang, D. (2016) · Journal of Pharmaceutical and Biomedical Analysis · doi:10.1016/j.jpba.2016.01.025 · Cited by 44
Ahn, M., Yoder, S. M., Wang, Z., Oh, E., Ramalingam, L., Tunduguru, R., & Thurmond, D. C. (2016) · Diabetologia · doi:10.1007/s00125-016-4042-0 · Cited by 44
Dirajlal‐Fargo, S., Moser, C., Brown, T. T., et al. (2016) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofw174 · Cited by 44
Type 2 Diabetes Mellitus Is Associated with More Serious Small Intestinal Mucosal Injuries
Zhong, H., Yu, Y., Xie, W.-R., Chen, M.-H., & He, X. (2016) · PLoS ONE · doi:10.1371/journal.pone.0162354 · Cited by 44
Agostinis‐Sobrinho, C., Moreira, C. M. M., Abreu, S., Lopes, L., Sardinha, L. B., Oliveira‐Santos, J., Oliveira, A., Mota, J. A. P. S., & Santos, R. (2016) · Scandinavian Journal of Medicine and Science in Sports · doi:10.1111/sms.12805 · Cited by 45
Holter, M. M., Dutia, R., Stano, S., Prigeon, R. L., Homel, P., McGinty, J. J., Belsley, S., Ren, C. J., Rosen, D., & Laferrère, B. (2016) · Diabetes Care · doi:10.2337/dc16-1376 · Cited by 45
Effect of aerobic exercise intervention on markers of insulin resistance in breast cancer women
Bruno, E., Roveda, E., Vitale, J. A., Montaruli, A., Berrino, F., Villarini, A., Venturelli, E., Gargano, G., Galasso, L., Caumo, A., et al. (2016) · European Journal of Cancer Care · doi:10.1111/ecc.12617 · Cited by 44
van der Aa, M. P., Knibbe, C. A. J., de Boer, A., & van der Vorst, M. M. J. (2016) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2016-0242 · Cited by 44
TSH increment and the risk of incident type 2 diabetes mellitus in euthyroid subjects
Jun, J. E., Jin, S., Jee, J. H., Bae, J. C., Hur, K. Y., Lee, M., Kim, S. W., & Kim, J. H. (2017) · Endocrine · doi:10.1007/s12020-016-1221-1 · Cited by 44
Jun, J. E., Lee, S., Lee, Y., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0176209 · Cited by 44
Kosuru, R., & Singh, S. (2017) · Life Sciences · doi:10.1016/j.lfs.2017.06.015 · Cited by 44
Zachariah, J. P. V., Quiroz, R., Nelson, K. P., Teng, Z., Keaney, J. F., SULLIVAN, L. M., & Vasan, R. S. (2017) · Journal of the American Heart Association · doi:10.1161/jaha.116.004974 · Cited by 44
IGFBP-1 and IGF-I as markers for advanced fibrosis in NAFLD – a pilot study
Hagström, H., Stal, P. I., Hultcrantz, R. W., Brismar, K., & Ansurudeen, I. (2017) · Scandinavian Journal of Gastroenterology · doi:10.1080/00365521.2017.1379556 · Cited by 45
Reproductive and metabolic features during puberty in sons of women with polycystic ovary syndrome
Crisosto, N., Echiburú, B., Maliqueo, M. A., Luchsinger, M., Rojas, P., Recabarren, S. E., & Sir‐Petermann, T. (2017) · Endocrine Connections · doi:10.1530/ec-17-0218 · Cited by 43
Capetini, V. C., de Vasconcelos, D. A. A., Martins, A. R., Hirabara, S. M., Donato, J., Carpinelli, Â. R., & Abdulkader, F. (2017) · Nutrients · doi:10.3390/nu9101150 · Cited by 44
Predictive Role of Interleukin-18 in Liver Steatosis in Obese Children
Flisiak‐Jackiewicz, M., Bobrus‐Chociej, A., Tarasów, E., Wojtkowska, M., Białokoz‐Kalinowska, I., & Lebensztejn, D. M. (2018) · Canadian Journal of Gastroenterology and Hepatology · doi:10.1155/2018/3870454 · Cited by 44
Hypoxic Training Improves Normoxic Glucose Tolerance in Adolescents with Obesity
Groote, E. D., Britto, F. A., Bullock, L. P., FRANÇOIS, M., Buck, C. D., Nielens, H., & Deldicque, L. (2018) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000001694 · Cited by 45
Kuk, J. L., Rotondi, M. A., Sui, X., Blair, S. N., & Ardern, C. I. (2018) · Clinical Obesity · doi:10.1111/cob.12263 · Cited by 48
Bego, T., Čaušević, A., Dujić, T., Malenica, M., Velija-Ašimi, Z., Prnjavorac, B., Marc, J., Nekvindová, J., Palička, V., & Semiz, S. (2018) · Journal of Medical Biochemistry · doi:10.2478/jomb-2018-0023 · Cited by 47
Adiposity, Insulin Resistance, and Bone Mass in Children and Adolescents
Kindler, J. M., Lobene, A. J., Vogel, K. A., Martin, B. R., McCabe, L. D., Peacock, M., Warden, S. J., McCabe, G. P., & Weaver, C. M. (2018) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2018-00353 · Cited by 44
Anyanwu, G. O., Iqbal, J., Khan, S. U., Zaib, S., Rauf, K., Onyeneke, E. C., Ojo, O. O., & Nisar-ur-Rahman (2018) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2018.10.021 · Cited by 44
Benhalima, K., Crombrugge, P. V., Moyson, C., Verhaeghe, J., Vandeginste, S., Verlaenen, H., Vercammen, C., Maes, T., Dufraimont, E., Block, C. E. M. D., et al. (2018) · Journal of Clinical Medicine · doi:10.3390/jcm7100351 · Cited by 46
Deng, X., Sun, L., Lai, X., Xiang, L., Li, Q., Zhang, W., Zhang, L., & Sun, S. (2018) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.8b04819 · Cited by 44
Korrapati, D., Jeyakumar, S. M., Putcha, U. K., Mendu, V. V. R., Ponday, L. R., Acharya, V., Koppala, S. R., & Vajreswari, A. (2018) · Clinical Nutrition · doi:10.1016/j.clnu.2018.12.026 · Cited by 52
Kahleová, H., Tura, A., Klementová, M., Thieme, L., Haluzík, M., Pavlovičová, R., Hill, M., & Pelikánová, T. (2019) · Nutrients · doi:10.3390/nu11030486 · Cited by 48
Metabolomics of Dynamic Changes in Insulin Resistance Before and After Exercise in PCOS
Halama, A., Aye, M. M., Dargham, S. R., Kuliński, M., Suhre, K., & Atkin, S. L. (2019) · Frontiers in Endocrinology · doi:10.3389/fendo.2019.00116 · Cited by 45
McKendry, J., Joanisse, S., Baig, S., Liu, B., Parise, G., Greig, C. A., & Breen, L. (2019) · The Journals of Gerontology Series A · doi:10.1093/gerona/glz142 · Cited by 46
Tsouristakis, A. I., Febres, G., McMahon, D. J., Tchang, B. G., Conwell, I. M., Tsang, A., Ahmed, L., Bessler, M., & Körner, J. (2019) · Obesity Surgery · doi:10.1007/s11695-019-04111-z · Cited by 44
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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