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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Plasma Levels of Triglycerides and IL-6 Are Associated With Weight Regain and Fat Mass Expansion
Qiao, Q., Bouwman, F. G., van Baak, M. A., Roumans, N. J. T., Vink, R., & Mariman, E. C. M. (2022) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgac198 · Cited by 23
Ferrocino, I., Ponzo, V., Pellegrini, M., Goitre, I., Papurello, M., Franciosa, I., D’Eusebio, C., Ghigo, E., Cocolin, L., & Bo, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-13438-0 · Cited by 22
Abdulkhalikova, D., Sustarsic, A., Bokal, E. V., Jančar, N., Jensterle, M., & Papler, T. B. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.888460 · Cited by 23
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Yi, J., Wang, L., Guo, J., & Ren, X. (2023) · Hepatology Communications · doi:10.1097/hc9.0000000000000016 · Cited by 22
Lindqvist, C., Holmer, M., Hagström, H., Petersson, S., Tillander, V., Brismar, T. B., & Stal, P. I. (2023) · Nutrition · doi:10.1016/j.nut.2023.111982 · Cited by 25
Zhao, H., Zhou, D., Liu, C., & Zhang, L. (2023) · International Journal of Women s Health · doi:10.2147/ijwh.s393594 · Cited by 25
Yang, Z., Gong, D., He, X., Huang, F., Sun, Y., & Hu, Q. (2023) · Frontiers in Nutrition · doi:10.3389/fnut.2023.1113789 · Cited by 23
Burger, P. M., Koudstaal, S., Dorresteijn, J. A. N., Savarese, G., van der Meer, M. G., de Borst, G. J., Mosterd, A., & Visseren, F. L. J. (2023) · International Journal of Cardiology · doi:10.1016/j.ijcard.2023.03.024 · Cited by 25
Martins, B. C., Soares, A. C., Martins, F. F., Resende, Â. C., Inada, K. O. P., Souza‐Mello, V., Moura‐Nunes, N., & Daleprane, J. B. (2023) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2023.109336 · Cited by 23
Tadiotto, M. C., Corazza, P. R. P., de Menezes-Junior, F. J., de Moraes-Junior, F. B., Tozo, T. A. A., Purim, K. S. M., Mota, J. A. P. S., & Leite, N. (2023) · European Journal of Pediatrics · doi:10.1007/s00431-023-04974-6 · Cited by 25
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Foghsgaard, S., Vedtofte, L., Andersen, E. S., Bahne, E., Andreasen, C. H., Sørensen, A. L., Forman, J. L., Mathiesen, E. R., Svare, J., Clausen, T. D., et al. (2023) · Diabetes Obesity and Metabolism · doi:10.1111/dom.15306 · Cited by 27
Deery, H. A., Liang, E. X., Paolo, R. D., Voigt, K., Murray, G., Siddiqui, M. N., Egan, G. F., Moran, C., & Jamadar, S. D. (2024) · Scientific Reports · doi:10.1038/s41598-024-65396-4 · Cited by 24
Ravanini, G. D. A. G., Portari, P., Luna, R. A., & de Oliveira, V. A. (2015) · Nutrición Hospitalaria · doi:10.3305/nh.2015.32.2.8944 · Cited by 24
Abd-Elbaky, A. E., Abo–Elmatty, D. M., Mesbah, N. M., & Ibrahim, S. M. (2015) · International Journal of Diabetes in Developing Countries · doi:10.1007/s13410-015-0416-y · Cited by 23
Damjanovic, S. S., Antic, J. A., Ilic, B. B., Čokić, B. B., Ivović, M., Ognjanovic, S. I., Isailovic, T. V., Popovic, B. M., Antić, I. B., Tatic, S. B., et al. (2012) · Molecular Medicine · doi:10.2119/molmed.2012.00261 · Cited by 24
Mohammadzadeh, G., & Zarghami, N. (2011) · Europe PMC (PubMed Central) · Cited by 23
Masuda, T., Ogura, M. N., Moriya, T., et al. (2010) · Cardiovascular Therapeutics · doi:10.1111/j.1755-5922.2009.00126.x · Cited by 23
Vaggopoulos, V., Trakakis, E., Chrelias, C. G., Panagopoulos, P., Basios, G., MAKRIDIMA, S., Sioulas, V. D., SIMEONIDES, G., Labos, G., Boutati, E. I., et al. (2013) · PubMed · doi:10.3275/8771 · Cited by 23
Inagaki, N., Goda, M., Yokota, S., Maruyama, N., & Iijima, H. (2015) · Expert Opinion on Pharmacotherapy · doi:10.1517/14656566.2015.1055250 · Cited by 23
Serra, M. C., Ryan, A. S., Sorkin, J. D., Favor, K. H., & Goldberg, A. P. (2015) · Obesity · doi:10.1002/oby.20998 · Cited by 24
Fjeldborg, K., Pedersen, S. B., Møller, H. J., Rask, P., Danielsen, A. V., Stødkilde‐Jørgensen, H., & Richelsen, B. (2014) · Obesity · doi:10.1002/oby.20942 · Cited by 23
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Mardones, F., Arnaíz, P., Salesa Barja, Y., et al. (2013) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.3305/nh.2013.28.6.6770 · Cited by 23
Ipavec-Levasseur, S., Croci, I., Choquette, S., Byrne, N. M., Cowin, G., O'Moore‐Sullivan, T. M., Prins, J. B., & Hickman, I. J. (2015) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2015-0258 · Cited by 22
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Mancina, R. M., Burza, M. A., Maglio, C., Pirazzi, C., Sentinelli, F., Incani, M., Montalcini, T., Pujia, A., Congiu, T., Loche, S., et al. (2013) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.2408 · Cited by 23
Huang, J., Yeh, M., Yu, M., Huang, C., Dai, C., Hsieh, M., Hsieh, M., Huang, C., Lin, Z., Chen, S., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0139796 · Cited by 23
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Rummo, P. E., Meyer, K. A., Howard, A. G., Shikany, J. M., Guilkey, D. K., & Gordon‐Larsen, P. (2015) · Health & Place · doi:10.1016/j.healthplace.2015.06.010 · Cited by 24
Pothuraju, R., Sharma, R. K., Chagalamarri, J., Kavadi, P. K., & Jangra, S. (2015) · Food & Function · doi:10.1039/c5fo00781j · Cited by 23
Rasgon, N., Kenna, H. A., Reynolds-May, M. F., Stemmle, P. G., Vemuri, M., Marsh, W., Wang, P. W., & Ketter, T. A. (2010) · Bipolar Disorders · doi:10.1111/j.1399-5618.2010.00839.x · Cited by 23
Taha, A. Y., Gao, F., Ramadan, E., Cheon, Y., Rapoport, S. I., & Kim, H. (2012) · BMC Neuroscience · doi:10.1186/1471-2202-13-131 · Cited by 23
High prevalence of vitamin D insufficiency/deficiency in Dutch multi-ethnic obese children
Radhakishun, N. N. E., van Vliet, M., von Rosenstiel, I., et al. (2014) · European Journal of Pediatrics · doi:10.1007/s00431-014-2378-3 · Cited by 23
Khalili, P., Flyvbjerg, A., Frystyk, J., Lundin, F., JENDLE, J. H., Engström, G., & Nilsson, P. M. (2010) · Diabetes & Metabolism · doi:10.1016/j.diabet.2009.10.004 · Cited by 23
Sharma, A. K., Raikwar, S. K., Kurmi, M. K., & Srinivasan, B. P. (2012) · Inflammopharmacology · doi:10.1007/s10787-012-0154-4 · Cited by 23
Huh, J. H., Song, M., Park, K. H., Kim, K.-R., Kim, J. E., Rhee, Y., & Lim, S.-K. (2013) · Osteoporosis International · doi:10.1007/s00198-013-2531-2 · Cited by 23
Kim, H., Lee, S.-Y., Kwon, H., Lee, S., Jung, M. H., Han, K., Yim, H. W., Lee, W., & Park, Y. (2012) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2012.10.011 · Cited by 23
Russo, G. T., Benedetto, A. D., Alessi, E., et al. (2006) · Journal of Endocrinological Investigation · doi:10.1007/bf03345540 · Cited by 23
Dehydroepiandrosterone prevents age-associated alterations, increasing insulin sensitivity
Sánchez, J., de Heredia, F. P., Priego, T., Portillo, M. P., Zamora, S., Garaulet, M., & Palou, A. (2008) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2007.10.005 · Cited by 23
Fujiwara, F., Ishii, M., Taneichi, H., Miura, M., Toshihiro, M., Takebe, N., Ishida, W., Kaneko, Y., Kato, A., Suzuki, K., et al. (2005) · The Tohoku Journal of Experimental Medicine · doi:10.1620/tjem.205.327 · Cited by 23
Metabolic dysfunction in obese Hispanic women with polycystic ovary syndrome
Sam, S., Scoccia, B., Yalamanchi, S. K., & Mazzone, T. (2015) · Human Reproduction · doi:10.1093/humrep/dev073 · Cited by 23
Schmieder, A., Poppe, M., Hametner, C., Meyer-Schraml, H., Schaarschmidt, M., Findeisen, P., Benoit, S., Bauer, B., Schmid, S., Goebeler, M., et al. (2015) · Archives of Dermatological Research · doi:10.1007/s00403-015-1541-7 · Cited by 23
Barbieri, M., Esposito, A., Angellotti, E., Rizzo, M. R., Marfella, R., & Paolisso, G. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071391 · Cited by 23
Direct renin inhibition modulates insulin resistance in caveolin-1-deficient mice
Chuengsamarn, S., Garza, A., Krug, A. W., Romero, J. R., Adler, G. K., Williams, G. H., & Pojoga, L. H. (2012) · Metabolism · doi:10.1016/j.metabol.2012.07.013 · Cited by 23
Sarkar, P. D., & Choudhury, A. B. (2012) · Archives of Physiology and Biochemistry · doi:10.3109/13813455.2012.715651 · Cited by 23
Tura, A., Pacini, G., Kautzky‐Willer, A., Gastaldelli, A., DeFronzo, R. A., Ferrannini, E., & Mari, A. (2011) · Metabolism · doi:10.1016/j.metabol.2011.08.001 · Cited by 23
Agouridis, A. P., Tsimihodimos, V., Filippatos, T. D., Tselepis, A. D., & Elisaf, M. (2011) · Lipids · doi:10.1007/s11745-011-3538-0 · Cited by 23
Woodman, R. J., Playford, D. A., & Watts, G. F. (2005) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2005.05.008 · Cited by 23
Chauhan, G., Kaur, I., Tabassum, R., Dwivedi, O. P., Ghosh, S., Tandon, N., & Bharadwaj, D. (2011) · Experimental Diabetes Research · doi:10.1155/2012/960318 · Cited by 23
Meza, S. H., Mendoza-López, M. R., García‐Barradas, O., Sánchez‐Otero, M. G., Silva-Hernández, E., Angulo, J. O., & Oliart‐Ros, R. M. (2013) · International Journal of Food Sciences and Nutrition · doi:10.3109/09637486.2013.763908 · Cited by 23
Iwanaga, S., Sakano, N., Taketa, K., Takahashi, N. K., Wang, D.-H., Takahashi, H., Kubo, M., Miyatake, N., & Ogino, K. (2013) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2013.01.003 · Cited by 23
Carnie, A., Lin, J., Aicher, B. O., Leon, B. M., Courville, A. B., Sebring, N. G., Jesus, J. M. D., DellaValle, D. M., Fitzpatrick, B. D., Zalos, G., et al. (2013) · Nutrition and Diabetes · doi:10.1038/nutd.2013.39 · Cited by 22
The plasma level and gene expression of resistin in polycystic ovary syndrome
Zhang, J., Zhou, L., Tang, L., & Xu, L. (2011) · Gynecological Endocrinology · doi:10.3109/09513590.2011.569794 · Cited by 23
Obesity Associated Inflammation in African American Adolescents and Adults
DeLoach, S. S., Falkner, B. E., Keith, S. W., & Gidding, S. S. (2013) · The American Journal of the Medical Sciences · doi:10.1097/maj.0b013e31829555f0 · Cited by 23
Li, L., Yoshitomi, H., Wei, Y., Qin, L., Zhou, J., Xu, T., Wu, X., Zhou, T., Sun, W., Guo, X., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0122024 · Cited by 23
Marfella, R., Grella, R., Rizzo, M. R., Barbieri, M., Grella, R., Ferraraccio, F., Cacciapuoti, F., Mazzarella, G., Ferraro, N., D’Andrea, F., et al. (2009) · Annals of Plastic Surgery · doi:10.1097/sap.0b013e3181955cdb · Cited by 25
Oflaz, H., Kurt, R., Şen, F., Önür, İ., Çimen, A. O., Elitok, A., Türkmen, K., Pamukçu, B., Kaşıkçıoğlu, E., Buğra, Z., et al. (2007) · Endocrine · doi:10.1007/s12020-008-9037-2 · Cited by 23
Assessment of glucose intolerance and insulin sensitivity in polycystic ovary syndrome.
Yıldız, B., & Gedik, O. (2004) · Reproductive Biomedicine Online · doi:10.1016/s1472-6483(10)61645-8 · Cited by 27
Žák, A., Jáchymová, M., TVRZICKÁ, E., Vecka, M., Duffková, L., Zeman, M., Slaby, A., & Staňková, B. (2008) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2007.09.001 · Cited by 23
Adrenal function in adult long-term survivors of nephroblastoma and neuroblastoma
van Waas, M., Neggers, S. J. C. M. M., van Eck, J. P., van Noesel, M. M., van der Lely, A. J., de Jong, F. H., Pieters, R., & van den Heuvel‐Eibrink, M. M. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2012.02.046 · Cited by 23
Long-term recovery of β-cell function after partial pancreatectomy in humans
Menge, B. A., Breuer, T. G. K., Ritter, P. R., Uhl, W. H., Schmidt, W. E., & Meier, J. J. (2011) · Metabolism · doi:10.1016/j.metabol.2011.09.019 · Cited by 25
Maraki, M. I., Aggelopoulou, N., Christodoulou, N., Anastasiou, C. A., Toutouza, M. G., Panagiotakos, D. B., Kavouras, S. A., Magkos, F., & Sidossis, L. S. (2010) · Obesity · doi:10.1038/oby.2010.218 · Cited by 23
Olson, K. M., Augeri, A. L., Seip, R. L., Tsongalis, G. J., Thompson, P. D., & Pescatello, L. S. (2012) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2012.01.027 · Cited by 23
Shay, C. M., Carnethon, M. R., Church, T. R., Hankinson, A. L., Chan, C., Jacobs, D. R., Lewis, C. E., Schreiner, P. J., Sternfeld, B., & Sidney, S. (2011) · Obesity · doi:10.1038/oby.2011.113 · Cited by 24
Petrov, P., Ribot, J., Palou, A., & Bonet, M. L. (2014) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00308.2014 · Cited by 23
Dietary fat and cardiovascular disease: putting the Women's Health Initiative in perspective.
Howard, B. (2007) · NMCD. Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2006.10.006 · Cited by 20
Kume, A., Miyazaki, T., Kitamura, Y., Oshida, K., Yanagisawab, N., Takizawa, H., Fujii, K., Kiyanagi, T., Sumiyoshi, K., Ohmura, H., et al. (2008) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2007.12.014 · Cited by 23
Lee, B., Aurpibul, L., D, V. S. M., Mangklabruks, A., Sirisanthana, T., & Puthanakit, T. (2008) · HIV Medicine · doi:10.1111/j.1468-1293.2008.00653.x · Cited by 23
Adiponectin is associated with risk of the metabolic syndrome and insulin resistance in women
King, G. A., Deemer, S. E., & Thompson, D. L. (2010) · Acta Diabetologica · doi:10.1007/s00592-010-0192-6 · Cited by 23
Zong, J., Liu, Y., Huang, Y., Chen, J., Gao, L., Zhang, C., Dong, S., & Chen, X. (2013) · International Journal of Pediatric Otorhinolaryngology · doi:10.1016/j.ijporl.2013.01.025 · Cited by 23
Ling, Q., Dong, F., Geng, L., et al. (2013) · Gene · doi:10.1016/j.gene.2013.03.089 · Cited by 23
Interaction of Metabolic Syndrome with Asthma in Postmenopausal Women: Role of Adipokines
Aydın, M., Koca, C., Özol, D., Uysal, S., Yıldırım, Z., Kavaklı, H. Ş., & Yiǧitoǧlu, M. R. (2013) · Inflammation · doi:10.1007/s10753-013-9660-9 · Cited by 22
Eroğlu, S., Sade, L. E., Bozbaş, H., Haberal, A., Özbiçer, S., Demir, O., & Müderrisoğlu, H. (2009) · European Journal of Cardiovascular Prevention & Rehabilitation · doi:10.1097/hjr.0b013e32831f1b8a · Cited by 23
Klein, M. R. S. T., & Sanjuliani, A. F. (2012) · Nutrition · doi:10.1016/j.nut.2012.05.012 · Cited by 23
Metabolic syndrome and erectile dysfunction among obese non-diabetic subjects
Gatti, A., Mandosi, E., Fallarino, M., Radicioni, A. F., Morini, E., Maiani, F., Trischitta, V., Lenzi, A., & Morano, S. (2009) · Journal of Endocrinological Investigation · doi:10.1007/bf03346504 · Cited by 24
da Silva, J. L. T., Barbosa, D. S., de Oliveira, J. A., & Guedes, D. P. (2006) · Arquivos Brasileiros de Endocrinologia & Metabologia · doi:10.1590/s0004-27302006000600009 · Cited by 22
Groups, F. T. D. A. R. S., Bonnet, F., Roussel, R., Natali, A., Cauchi, S., Petrie, J. R., Laville, M., Yengo, L., Froguel, P., Lange, C., et al. (2013) · Diabetologia · doi:10.1007/s00125-013-3021-y · Cited by 23
Lee, M., Jang, Y., Kim, K., Cho, H., Jee, S. H., Park, Y., & Kim, M. K. (2010) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2010.07.056 · Cited by 23
Ramasamy, C., & Pari, L. (2014) · Biomedicine & Aging Pathology · doi:10.1016/j.biomag.2013.12.003 · Cited by 23
Liu, C., Ng, M. C. Y., Rybin, D. V., Adeyemo, A., Bielinski, S. J., Boerwinkle, E., Borecki, I. B., Cade, B. E., Chen, Y. I., Djoussé, L., et al. (2012) · Diabetologia · doi:10.1007/s00125-012-2656-4 · Cited by 23
Calcaterra, V., Muratori, T., Klersy, C., Albertini, R., Caramagna, C., Brizzi, V., & Larizza, D. (2011) · Annals of Nutrition and Metabolism · doi:10.1159/000331573 · Cited by 23
Relationship of Adipocyte Size with Adiposity and Metabolic Risk Factors in Asian Indians
Meena, V. P., Seenu, V., Sharma, M. C., Mallick, S., Bhalla, A. S., Gupta, N., Mohan, A., Guleria, R., Pandey, R. M., Luthra, K., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0108421 · Cited by 24
Gavin, T. P., Sloan, R. C., Lukosius, E. Z., Reed, M. A., Pender, J. R., Boghossian, V., Carter, J. J., McKernie, R. D., Parikh, K., Price, J. W., et al. (2010) · Obesity Surgery · doi:10.1007/s11695-010-0304-y · Cited by 23
Mills, J. D., Mansfield, M. W., & Grant, P. J. (2002) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/hq0402.105902 · Cited by 23
Pardina, E., Lecube, A., Llamas, R., Catalán, R., Galard, R., Fort, J. M., Allende, H., Vargas, V., Baena‐Fustegueras, J. A., & Peinado‐Onsurbe, J. (2009) · Obesity Surgery · doi:10.1007/s11695-009-9853-3 · Cited by 23
Metabolic syndrome and ADRB3 gene polymorphism in severely obese patients from South Italy
Bracale, R., Pasanisi, F., Labruna, G., Finelli, C., Nardelli, C., Buono, P., Salvatori, G., Sacchetti, L., Contaldo, F., & Oriani, G. (2007) · European Journal of Clinical Nutrition · doi:10.1038/sj.ejcn.1602640 · Cited by 23
El‐Sakka, A. I., Sayed, H. M., & Tayeb, K. A. (2009) · Urology · doi:10.1016/j.urology.2009.02.077 · Cited by 23
Effects of Resistance versus Endurance Training on Plasma Lipocalin-2 in Young Men.
Moghadasi, M., & Domieh, A. M. (2014) · Europe PMC (PubMed Central) · Cited by 23
López-Suárez, A., Elvira-González, J., Bascuñana-Quirell, A., et al. (2006) · Medicina Clínica · doi:10.1157/13085753 · Cited by 23
Persico, M., Masarone, M., Mura, V. L., Persico, E., Moschella, F., Svelto, M., Bruno, S., & Torella, R. (2009) · World Journal of Gastroenterology · doi:10.3748/wjg.15.462 · Cited by 24
Mancini, F. P., Sabatino, L., Colantuoni, V., Pasanisi, F., Finelli, C., Contaldo, F., Masulli, M., Riccardi, G., & Vaccaro, O. (2003) · Clinical Endocrinology · doi:10.1046/j.1365-2265.2003.01926.x · Cited by 23
Gasparoto, R. C. G. W., Racy, M. D. C. J., & Campos, T. D. (2015) · PubMed · doi:10.6092/1590-8577/2958 · Cited by 23
Kim, J. W., Han, J. E., Kim, Y. S., Won, H. J., Yoon, T. K., & Lee, W. S. (2011) · Gynecological Endocrinology · doi:10.3109/09513590.2011.613967 · Cited by 23
Alvehus, M., Šimonytė, K., Andersson, T., Söderström, I., Burén, J., Rask, E., Mattsson, C., & Olsson, T. (2011) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2011.04322.x · Cited by 24
da Fonseca, L. J. S., Nunes-Souza, V., Guedes, G. D. S., Schettino-Silva, G., Gomes, M. A. M., & Rabelo, L. A. (2014) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2014/898501 · Cited by 23
Lavoie, M.-È., Faraj, M., Strychar, I. M., Doucet, É., Brochu, M., Lavoie, J.-M., & RABASA-LHORET, R. P. R. (2012) · British Journal Of Nutrition · doi:10.1017/s0007114512001699 · Cited by 24
Saito, H., Kato, M., Yoshida, A., & Naito, M. (2014) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.22681 · Cited by 24
de Luis, D. A., Aller, R., Izaola, O., & Bachiller, R. (2014) · Journal of Endocrinological Investigation · doi:10.1007/s40618-014-0161-y · Cited by 24
Pontes, A. G., Rehme, M. F. B., Martins, A. M. V. D. C., Micussi, M. T. A. B. C., Maranhão, T. M. D. O., Pimenta, W. P., & Pontes, A. (2012) · Revista Brasileira Ginecologia e Obstetrícia · doi:10.1590/s0100-72032012000200006 · Cited by 23
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.
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