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
1 of 6 cited sources checked · 0 supported · 0 flagged · 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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- 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.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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 (%)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.
Normal and diabetic group sizes are reversed for the CIGMA comparison
MinorThe methods say the CIGMA comparison used 12 normal subjects and 11 diabetic patients, but the results report 11 normal subjects and 12 diabetic subjects.
“The same 12 normal subjects and 11 Type 2 diabetic patients came to the hospital fasting on a separate day for a continuous infusion”
“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
Flagged first, then by citations
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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Sorted by citations
Items 2,501–2,600 of 31,469
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Baum, P., Petroff, D., Claßen, J., Kieß, W., & Blüher, S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0054546 · Cited by 118
Madjd, A., Taylor, M. A., Mousavi, N., Delavari, A., Malekzadeh, R., Macdonald, I. A., & Farshchi, H. R. (2015) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.120170 · Cited by 120
Association between serum uric acid level and metabolic syndrome components
Nejatinamini, S., Ataie‐Jafari, A., Qorbani, M., Nikoohemat, S., Kelishadi, R., Asayesh, H., & Hosseini, S. (2015) · Journal of Diabetes & Metabolic Disorders · doi:10.1186/s40200-015-0200-z · Cited by 118
Insulin resistance in Alzheimer's disease
Diehl, T. C., Mullins, R. J., & Kapogiannis, D. (2016) · Translational research · doi:10.1016/j.trsl.2016.12.005 · Cited by 124
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Jing, J., Pan, Y., Zhao, X., Zheng, H., Jia, Q., Mi, D., Chen, W., Li, H., Liu, L., Wang, C., et al. (2017) · Stroke · doi:10.1161/strokeaha.116.015613 · Cited by 119
Samsulrizal, N., Goh, Y. M., Ahmad, H., Dom, S. M., Azmi, N. S., Zin, N. S. N. M., & Ebrahimi, M. M. (2017) · BMC Complementary and Alternative Medicine · doi:10.1186/s12906-017-1762-8 · Cited by 118
Lee, S. M., Kwak, S. H., Koo, J. N., Oh, I. H., Kwon, J. E., Kim, B. J., Kim, S. M., Kim, S. Y., Kim, G. M., Joo, S. K., et al. (2018) · Diabetologia · doi:10.1007/s00125-018-4779-8 · Cited by 121
Indirect insulin resistance detection: Current clinical trends and laboratory limitations
Płaczkowska, S., Pawlik-Sobecka, L., Kokot, I., & Piwowar, A. (2019) · Biomedical Papers · doi:10.5507/bp.2019.021 · Cited by 118
Barnard, N. D., Alwarith, J., Rembert, E., Brandon, L., Nguyen, M., Goergen, A., Horne, T., do Nascimento, G. F., Lakkadi, K., Tura, A., et al. (2022) · Journal of the American Nutrition Association · doi:10.1080/07315724.2020.1869625 · Cited by 119
Gunji, T., Matsuhashi, N., Satō, H., Fujibayashi, K., Okumura, M., Sasabe, N., & Urabe, A. (2009) · Helicobacter · doi:10.1111/j.1523-5378.2009.00705.x · Cited by 118
Fasting Plasma Glucose and Serum Lipids in Patients With Primary Aldosteronism
Matrozova, J., Steichen, O., Amar, L., Zacharieva, S., Jeunemaı̂tre, X., & Plouin, P. (2009) · Hypertension · doi:10.1161/hypertensionaha.108.122002 · Cited by 121
Davis, T. M. E., Wright, A. D., Mehta, Z., Cull, C. A., STRATTON, I. M., Bottazzo, G. F., Bosi, E., Mackay, I. R., & Holman, R. R. (2005) · Diabetologia · doi:10.1007/s00125-005-1690-x · Cited by 118
Peptide YY Is Secreted after Oral Glucose Administration in a Gender-Specific Manner
Kim, B.-J., Carlson, O. D., Jang, H.-J., Elahi, D., Berry, C., & Egan, J. M. (2005) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2005-0409 · Cited by 122
Hosojima, H., Togo, T., Odawara, T., Hasegawa, K., Miura, S., Kato, Y., Kanai, A., Kase, A., Uchikado, H., & Hirayasu, Y. (2005) · Journal of Psychopharmacology · doi:10.1177/0269881105056647 · Cited by 117
Lasikiewicz, N., Hendrickx, H., Talbot, D., & Dye, L. (2007) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2007.10.013 · Cited by 122
Ciampelli, M., Leoni, F., Cucinelli, F., Mancuso, S., Panunzi, S., Gaetano, A. D., & Lanzone, A. (2005) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2004-0410 · Cited by 120
Aquilani, R., Opasich, C., Gualco, A., Verri, M., Testa, A., Pasini, E., Viglio, S., Iadarola, P., Pastoris, O., Dossena, M., et al. (2008) · European Journal of Heart Failure · doi:10.1016/j.ejheart.2008.09.002 · Cited by 118
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Muscari, A., Antonelli, S., Bianchi, G. P., Cavrini, G., Dapporto, S., Ligabue, A., Ludovico, C., Magalotti, D., Poggiopollini, G., Zoli, M., et al. (2007) · Diabetes Care · doi:10.2337/dc07-0637 · Cited by 119
Reinehr, T., Stoffel‐Wagner, B., Roth, C. L., & Andler, W. (2005) · Metabolism · doi:10.1016/j.metabol.2005.03.022 · Cited by 119
The Ubiquitin-Proteasome System and Inflammatory Activity in Diabetic Atherosclerotic Plaques
Marfella, R., D’Amico, M., Esposito, K., Baldi, A., Filippo, C. D., Siniscalchi, M., Sasso, F. C., Portoghese, M., Cirillo, F., Cacciapuoti, F., et al. (2006) · Diabetes · doi:10.2337/diabetes.55.03.06.db05-0832 · Cited by 117
Carlsson, M., Wessman, Y., Almgren, P., & Groop, L. (2000) · Arteriosclerosis, Thrombosis, and Vascular Biology · doi:10.1161/01.atv.20.6.1588 · Cited by 95
Thöni, G., Fédou, C., Brun, J. ‐., Fabre, J., Renard, É., Reynes, J., Varray, A. L., & Mercier, J. G. (2002) · PubMed · Cited by 118
Frederiksen, L., Brødbæk, K., Fenger, M., Jørgensen, T., Borch‐Johnsen, K., Madsbad, S., & Urhammer, S. A. (2002) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.87.8.8732 · Cited by 118
Meijnikman, A. S., De Block, C. E. M., Dirinck, E., Verrijken, A., Mertens, I., Corthouts, B., & Van Gaal, L. F. (2017) · International Journal of Obesity · doi:10.1038/ijo.2017.165 · Cited by 112
De Feyter, H. M., van den Broek, N. M. A., Praet, S. F. E., Nicolay, K., van Loon, L. J. C., & Prompers, J. J. (2008) · European Journal of Endocrinology · doi:10.1530/eje-07-0756 · Cited by 116
Markers of triglyceride-rich lipoprotein remnant metabolism in visceral obesity
Chan, D. C., Watts, G. F., Barrett, P. H., Mamo, J. C., & Redgrave, T. G. (2002) · Clinical Chemistry · doi:10.1093/clinchem/48.2.278 · Cited by 117
Gunderson, E. P., Sun, B., Catov, J. M., Carnethon, M., Lewis, C. E., Allen, N. B., Sidney, S., Wellons, M., Rana, J. S., Hou, L., et al. (2021) · Circulation · doi:10.1161/circulationaha.120.047320 · Cited by 117
“Low-density lipoprotein cholesterol, mg/dL 105 (30) 108 (33) 111 (35) 0.”
Shiwaku, K., Nogi, A., Kitajima, K., Anuurad, E., Enkhmaa, B., Yamasaki, M., Kim, J., Kim, I., Lee, S., Oyunsuren, T., et al. (2005) · Journal of Occupational Health · doi:10.1539/joh.47.126 · Cited by 117
Association of Genetic Loci With Glucose Levels in Childhood and Adolescence
Barker, A., Sharp, S. J., Timpson, N. J., Bouatia-Naji, N., Warrington, N. M., Kanoni, S., Beilin, L. J., Brage, S., Deloukas, P., Evans, D. M., et al. (2011) · Diabetes · doi:10.2337/db10-1575 · Cited by 117
Dowling, R. J., Niraula, S., Chang, M. C., Done, S. J., Ennis, M., McCready, D. R., Leong, W. L., Escallon, J. M., Reedijk, M., Goodwin, P. J., et al. (2015) · Breast Cancer Research · doi:10.1186/s13058-015-0540-0 · Cited by 117
Gaborit, B., Kober, F., Jacquier, A., Moro, P. J., Cuisset, T., Boullu, S., Dadoun, F., Alessi, M.-C., Morange, P., Clément, K., et al. (2012) · International Journal of Obesity · doi:10.1038/ijo.2011.117 · Cited by 116
Sun, G., Bishop, J., Khalili, S., Vasdev, S., Gill, V. D., Pace, D., Fitzpatrick, D., Randell, E. W., Xie, Y., & Zhang, H. (2007) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/85.2.399 · Cited by 117
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Vardeny, O., Gupta, D. K., Claggett, B. L., Burke, S., Shah, A. M., Loehr, L. R., Rasmussen‐Torvik, L. J., Selvin, E., Chang, P. P., Aguilar, D. A., et al. (2013) · JACC Heart Failure · doi:10.1016/j.jchf.2013.07.006 · Cited by 122
Gaillard, R., Welten, M., Oddy, W. H., Beilin, L. J., Mori, T. A., Jaddoe, V. W. V., & Huang, R. (2015) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.13700 · Cited by 117
Marked sympathetic activation and baroreflex dysfunction in true resistant hypertension
Grassi, G. M., Seravalle, G., Brambilla, G., Pini, C., Alimento, M., Facchetti, R., Spaziani, D., Cuspidi, C., & Mancia, G. (2014) · International Journal of Cardiology · doi:10.1016/j.ijcard.2014.09.138 · Cited by 118
Anti-hyperglycemic and hypolipidemic effects of Cistanche tubulosa in type 2 diabetic db/db mice
Xiong, W.-T., Gu, L., Wang, C., Sun, H., & Liu, X. (2013) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2013.09.027 · Cited by 117
Efficacy of Turmeric as Adjuvant Therapy in Type 2 Diabetic Patients
Selvi, N. M. K., Sridhar, M. G., Swaminathan, R. P., et al. (2014) · Indian Journal of Clinical Biochemistry · doi:10.1007/s12291-014-0436-2 · Cited by 117
Bianchi, C., Miccoli, R., Trombetta, M., Giorgino, F., Frontoni, S., Faloia, E., Marchesini, G., Dolci, M. A., Cavalot, F. L., Cavallo, M. G., et al. (2013) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2012-3971 · Cited by 120
Johnson, D. W., Armstrong, K. A., Campbell, S. B., Mudge, D. W., Hawley, C. M., Coombes, J. S., Prins, J. B., & Isbel, N. M. (2007) · Nephrology · doi:10.1111/j.1440-1797.2007.00804.x · Cited by 117
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Kabadi, S., Lee, B. K., & Liu, L. (2012) · Diabetes Care · doi:10.2337/dc12-0235 · Cited by 120
Heutling, D., Schulz, H., Nickel, I., Kleinstein, J., Kaltwaßer, P., Westphal, S., Mittermayer, F., Wolzt, M., Krzyżanowska, K., Randeva, H. S., et al. (2007) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2007-0842 · Cited by 117
Selective effects of CPAP on sleep apnoea‐associated manifestations
Vgontzas, A. N., Zoumakis, E., Bixler, E. O., Lin, H., Collins, B., Basta, M., Pejovic, S., & Chrousos, G. P. (2008) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.2008.01984.x · Cited by 118
Dieli-Conwright, C. M., Wong, L. C., Waliany, S., Bernstein, L., Salehian, B., & Mortimer, J. E. (2016) · Cancer · doi:10.1002/cncr.30104 · Cited by 117
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Embleton, N. D., Korada, M., Wood, C. L., Pearce, M. S., Swamy, R. S., & Cheetham, T. D. (2016) · Archives of Disease in Childhood · doi:10.1136/archdischild-2015-310190 · Cited by 116
Delgado‐Borrego, A., Casson, D. R., Schoenfeld, D., Somsouk, M., Terella, A. M., Jordan, S. H., Bhan, A. K., Baid‐Agrawal, S., Cosimi, A. B., Pascual, M. A., et al. (2004) · Transplantation · doi:10.1097/01.tp.0000114283.04840.3a · Cited by 117
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Behall, K. M., Scholfield, D. J., & Hallfrisch, J. G. (2006) · Nutrition Research · doi:10.1016/j.nutres.2006.10.001 · Cited by 117
Osei, K., Rhinesmith, S., Gaillard, T. R., & Schuster, D. (2004) · Diabetes Care · doi:10.2337/diacare.27.6.1439 · Cited by 116
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Goodarzi, M. O., & Korenman, S. G. (2003) · Fertility and Sterility · doi:10.1016/s0015-0282(03)00734-9 · Cited by 118
Liu, I.-M., Tzeng, T., Liou, S., & Lan, T. (2007) · Life Sciences · doi:10.1016/j.lfs.2007.08.045 · Cited by 117
Suliman, M. E., Yılmaz, M. İ., Carrero, J. J., Qureshi, A. R., Sağlam, M., İpçioğlu, O. M., Yenicesu, M., Tong, M., Heimbürger, O., Barany, P. F., et al. (2008) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.03960907 · Cited by 117
Effect of Android to Gynoid Fat Ratio on Insulin Resistance in Obese Youth
Aucouturier, J., Meyer, M., Thivel, D., Taillardat, M., & Duché, P. (2009) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/archpediatrics.2009.148 · Cited by 117
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Orho‐Melander, M., Klannemark, M., Svensson, M., Ridderstråle, M., Lindgren, C. M., & Groop, L. (2002) · Diabetes · doi:10.2337/diabetes.51.8.2658 · Cited by 117
The Association Between Metabolic Syndrome and Restrictive Ventilatory Dysfunction in Older Persons
Fimognari, F. L., Pasqualetti, P., Moro, L., Franco, A., Piccirillo, G., Pastorelli, R., Rossini, P. M., & Incalzi, R. A. (2007) · The Journals of Gerontology Series A · doi:10.1093/gerona/62.7.760 · Cited by 116
Insawang, T., Selmi, C. F., Cha’on, U., et al. (2012) · Nutrition & Metabolism · doi:10.1186/1743-7075-9-50 · Cited by 117
Erdmann, J., Lippl, F., Wagenpfeil, S., & SCHUSDZIARRA, V. (2005) · Diabetes · doi:10.2337/diabetes.54.5.1371 · Cited by 118
Rudenski, A. S., Matthews, D. R., Lévy, J., & Turner, R. C. (1991) · Metabolism · doi:10.1016/0026-0495(91)90065-5 · Cited by 118
Self-citationJensterle, M., Kravos, N. A., Pfeifer, M., Kocjan, T., & Janez, A. (2015) · Hormones · doi:10.1007/bf03401383 · Cited by 117
Effects of resistance versus endurance training on serum adiponectin and insulin resistance index.
Ahmadizad, S., Haghighi, A. H., & Hamedinia, M. R. (2007) · European Journal of Endocrinology · doi:10.1530/eje-07-0223 · Cited by 115
Melatonin improves metabolic syndrome induced by high fructose intake in rats
Kitagawa, A., Ohta, Y., & Ohashi, K. (2012) · Journal of Pineal Research · doi:10.1111/j.1600-079x.2011.00955.x · Cited by 116
Wewer Albrechtsen, N. J., Færch, K., Jensen, T. M., Witte, D. R., Pedersen, J., Mahendran, Y., Jonsson, A. E., Galsgaard, K. D., Winther-Sørensen, M., Torekov, S. S., et al. (2018) · Diabetologia · doi:10.1007/s00125-017-4535-5 · Cited by 119
Inverse relationship between serum osteocalcin levels and visceral fat area in Chinese men
Bao, Y., Ma, X., Yang, R., Wang, F., Hao, Y., Dou, J., He, H., & Jia, W. (2013) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2012-2906 · Cited by 116
Yamada, C., Mitsuhashi, T., Hiratsuka, N., Inabe, F., Araida, N., & Takahashi, E. (2011) · Journal of Diabetes Investigation · doi:10.1111/j.2040-1124.2011.00113.x · Cited by 117
DELVIN, E. E., Lambert, M. A., Levy, E. H., O’Loughlin, J. L., Mark, S., Gray‐Donald, K., & Paradis, G. (2010) · Journal of Nutrition · doi:10.3945/jn.109.112250 · Cited by 116
Validity of Cardiorespiratory Fitness Criterion-Referenced Standards for Adolescents
Lobelo, F., Pate, R. R., Dowda, M. C., Liese, A. D., & Ruiz, J. R. (2009) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0b013e318195d491 · Cited by 116
Gozal, D., Peris, E., Wang, Y., Kakazu, M. T., Khalyfa, A., Carreras, A., & Gozal, D. (2013) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2013-3327 · Cited by 117
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Derosa, G., Fogari, E., D’Angelo, A., Bianchi, L., Bonaventura, A., Romano, D., & Maffioli, P. (2013) · Inflammation · doi:10.1007/s10753-013-9620-4 · Cited by 119
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Pugh, C. J. A., Sprung, V. S., Kemp, G. J., Richardson, P., Shojaee-Moradie, F., Umpleby, A. M., Green, D. J., Cable, N. T., Jones, H., & Cuthbertson, D. J. (2014) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00306.2014 · Cited by 116
Lynch, B. M., Friedenreich, C. M., WINKLER, E. A. H., Healy, G. N., Vallance, J. K., Eakin, E., & Owen, N. G. (2011) · Breast Cancer Research and Treatment · doi:10.1007/s10549-011-1559-2 · Cited by 118
Madeira, I. R., Carvalho, C. N. D. M., Gazolla, F. M., de Matos, H. J., Borges, M. A., & Bordallo, M. A. N. (2008) · Arquivos Brasileiros de Endocrinologia & Metabologia · doi:10.1590/s0004-27302008000900010 · Cited by 116
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Zhang, P., Ge, Z., Wang, H., Feng, W., Sun, X., Chu, X., Jiang, C., Wang, Y., Zhu, D., & Bi, Y. (2018) · Journal of Hepatology · doi:10.1016/j.jhep.2018.01.035 · Cited by 116
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Fisac, C., Fumero, E., Crespo, M., et al. (2005) · AIDS · doi:10.1097/01.aids.0000171405.46113.bf · Cited by 116
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Malloy, V. L., Perrone, C. E., Mattocks, D. A. L., Ables, G. P., Caliendo, N. S., Orentreich, D. S., & Orentreich∥, N. (2013) · Metabolism · doi:10.1016/j.metabol.2013.06.012 · Cited by 118
Goldstone, A. P., Patterson, M., Kalingag, N., Ghatei, M. A., Brynes, A. E., Bloom, S. R., Grossman, A. B., & Korbonits, M. (2005) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2003-032209 · Cited by 114
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O’Donovan, G., Kearney, E. M., Nevill, A. M., Woolf‐May, K., & Bird, S. R. (2005) · European Journal of Applied Physiology · doi:10.1007/s00421-005-0040-5 · Cited by 118
Hayashi, T., Boyko, E. J., Satō, K., McNeely, M. J., Leonetti, D. L., Kahn, S. E., & Fujimoto, W. Y. (2012) · Diabetes Care · doi:10.2337/dc12-0246 · Cited by 122
Rave, K., Roggen, K., Dellweg, S., Heise, T., & Dieck, H. T. (2007) · British Journal Of Nutrition · doi:10.1017/s0007114507749267 · Cited by 116
Chen, W., Srinivasan, S. R., Li, S., Xu, J., & Berenson, G. S. (2007) · American Journal of Epidemiology · doi:10.1093/aje/kwm105 · Cited by 116
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Fallo, F., Scarda, A., Sonino, N., Paoletta, A., Boscaro, M., Pagano, C., Federspil, G., & Vettor, R. (2004) · European Journal of Endocrinology · doi:10.1530/eje.0.1500339 · Cited by 119
Sesmilo, G., Fairfield, W. P., Katznelson, L. J., Pulaski, K., Freda, P. U., Bonert, V. S., Dimaraki, E. V., Stavrou, S., Vance, M. L., Hayden, D., et al. (2002) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jcem.87.4.8364 · Cited by 115
Hill, N., Levy, J., & Matthews, D. (2013) · Diabetes Care · doi:10.2337/dc12-0607 · Cited by 123
Self-citationInsulin Resistance After Renal Transplantation
Hjelmesaeth, J. S., Midtvedt, K., Jenssen, T. G., et al. (2001) · Diabetes Care · doi:10.2337/diacare.24.12.2121 · Cited by 116
Correlation between the glucose clamp technique and the homeostasis model assessment in hypertension
Lansang, M. C. (2001) · American Journal of Hypertension · doi:10.1016/s0895-7061(00)01229-2 · Cited by 115
Effects of aldosterone on insulin sensitivity and secretion
Luther, J. M. (2014) · Steroids · doi:10.1016/j.steroids.2014.08.016 · Cited by 114
Thomson, P., Santibañez, R., Aguirre, C., Galgani, J. E., & Garrido, D. (2019) · British Journal Of Nutrition · doi:10.1017/s0007114519001570 · Cited by 117
Hahn, S., Benson, S., Elsenbruch, S., Pleger, K., Tan, S., Mann, K., Schedlowski, M., van Halteren, W. B., Kimmig, R., & Janssen, O. E. (2006) · Human Reproduction · doi:10.1093/humrep/del069 · Cited by 115
Khan, U. I., Wang, D., Thurston, R. C., Sowers, M. R., Sutton-Tyrrell, K., Matthews, K. A., Barinas‐Mitchell, E., & Wildman, R. P. (2011) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2011.01.007 · Cited by 115
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Kim, T. N., Kim, S., Yang, S. J., Yoo, H. J., Seo, J. A., Kim, S. G., Kim, N. H., Baik, S. H., Choi, D. S., & Choi, K. M. (2010) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.109.888909 · Cited by 115
Family Dietary Coaching to Improve Nutritional Intakes and Body Weight Control
Paineau, D., Beaufils, F., Boulier, A., Cassuto, D.-A., Chwalow, J., Combris, P., Couet, C., Jouret, B., Lafay, L., Laville, M., et al. (2008) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/archpediatrics.2007.2 · Cited by 115
Finucane, F. M., Sharp, S. J., Purslow, L. R., Horton, K., Horton, J., Savage, D., Brage, S., Besson, H., Rolfe, E. D. L., Sleigh, A., et al. (2010) · Diabetologia · doi:10.1007/s00125-009-1641-z · Cited by 120
Morgan, L., Griffin, B., Millward, D., DeLooy, A., Fox, K., Baic, S., Bonham, M., Wallace, J., MacDonald, I., Taylor, M., et al. (2009) · Public Health Nutrition · doi:10.1017/s1368980008003236 · Cited by 115
High prevalence of polycystic ovary syndrome in women born small for gestational age
de Melo, A. S., Vieira, C. S., Barbieri, M. A., Rosa-e-Silva, A. C. J. S., da Sílva, A. A. M., Cardoso, V. C., dos Reis, R. M., Ferriani, R. A., Silva-de-Sa, M. F., & Bettiol, H. (2010) · Human Reproduction · doi:10.1093/humrep/deq162 · Cited by 114
Wang, D., Zhao, X., & Yulan, L. (2017) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2017.04.056 · Cited by 116
Mindikoglu, A. L., Abdulsada, M. M., Jain, A., Choi, J. M., Jalal, P. K., Devaraj, S., Mezzari, M. P., Petrosino, J. F., Opekun, A. R., & Jung, S. Y. (2020) · Journal of Proteomics · doi:10.1016/j.jprot.2020.103645 · Cited by 121
Effects of Polyphenols on Insulin Resistance
Williamson, G., & Sheedy, K. (2020) · Nutrients · doi:10.3390/nu12103135 · Cited by 126
Co‐based Nanozymatic Profiling: Advances Spanning Chemistry, Biomedical, and Environmental Sciences
Li, J., Cai, X., Jiang, P., Wang, H., Zhang, S., Sun, T., Chen, C., & Fan, K. (2023) · Advanced Materials · doi:10.1002/adma.202307337 · Cited by 116
Wannamethee, S. G., Shaper, A. G., Whincup, P. H., LENNON, L. T., & Sattar, N. A. (2011) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2011.06.057 · Cited by 115
Intra-abdominal Adiposity and Individual Components of the Metabolic Syndrome in Adolescence
Syme, C. A., Abrahamowicz, M., Leonard, G. T., Perron, M., Pitiot, A., Qiu, X., Richer, L., Totman, J. J., Veillette, S., Xiao, Y., et al. (2008) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/archpedi.162.5.453 · Cited by 114
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.
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