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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Cercato, C., Roizenblatt, V. A., Leança, C. C., Segal, A., Lopes Filho, A. P., Mancini, M. C., & Halpern, A. (2009) · International Journal of Obesity · doi:10.1038/ijo.2009.124 · Cited by 100
Gopal, S., Vijapurkar, U., Lim, P., Ma, M., Eerdekens, M., & Hough, D. W. (2010) · Journal of Psychopharmacology · doi:10.1177/0269881110372817 · Cited by 103
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Longo, K., Charoenthongtrakul, S., Giuliana, D. J., Govek, E., McDonagh, T., Qi, Y., Distefano, P., & Geddes, B. (2008) · Regulatory Peptides · doi:10.1016/j.regpep.2008.03.011 · Cited by 105
Sundaram, S. S., Sokol, R. J., Capocelli, K. E., Pan, Z., Sullivan, J. S., Robbins, K. N., & Halbower, A. C. (2013) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2013.10.072 · Cited by 105
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Park, S., Ham, J.-O., & Lee, B.-K. (2013) · Clinical Nutrition · doi:10.1016/j.clnu.2013.09.016 · Cited by 103
Ellington, A. A., Malik, A. R., Klee, G. G., Turner, S. T., Rule, A. D., Mosley, T. H., & Kullo, I. J. (2007) · Hypertension · doi:10.1161/hypertensionaha.107.095257 · Cited by 103
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Olza, J., Aguilera, C. M., Gil‐Campos, M., Leis, R., Bueno, G., Martínez‐Jiménez, M. D. C., Valle, M., Cañete, R., Tojo, R., Moreno, L. A., et al. (2012) · Diabetes Care · doi:10.2337/dc12-0614 · Cited by 103
Weiss, E. P., Albert, S. G., Reeds, D. N., Kress, K. S., McDaniel, J. L., Klein, S., & Villareal, D. T. (2016) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.116.131391 · Cited by 107
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Low-sodium DASH reduces oxidative stress and improves vascular function in salt-sensitive humans
Al-Solaiman, Y., Jesri, A., Zhao, Y., Morrow, J. D., & Egan, B. M. (2009) · Journal of Human Hypertension · doi:10.1038/jhh.2009.32 · Cited by 102
Muldoon, M. F., Mackey, R. H., Korytkowski, M. T., Flory, J. D., Pollock, B. G., & Manuck, S. B. (2006) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2005-1654 · Cited by 102
Functional Significance of Polycystic-Size Ovaries in Healthy Adolescents
Mortensen, M., Rosenfield, R. L., & Littlejohn, E. (2006) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2006-0835 · Cited by 103
Effect of Cabergoline on Metabolism in Prolactinomas
Auriemma, R. S., Granieri, L., Galdiero, M., Simeoli, C., Perone, Y., Vitale, P., Pivonello, C., Negri, M., Mannarino, T., Giordano, C. S. D. S., et al. (2013) · Neuroendocrinology · doi:10.1159/000357810 · Cited by 102
Cold‐Induced Adaptive Thermogenesis in Lean and Obese
Wijers, S. L. J., Saris, W. H. M., & Lichtenbelt, W. D. V. M. (2010) · Obesity · doi:10.1038/oby.2010.74 · Cited by 103
Wang, Q., Kangas, A. J., Soininen, P., Tiainen, M., Tynkkynen, T., Puukka, K. S., Ruokonen, A., Viikari, J. S. A., Kähönen, M. A. P., Lehtimäki, T. J., et al. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv093 · Cited by 102
Ryan, M. C., Wilson, A., Slavin, J., Best, J. D., Jenkins, A. J., & Desmond, P. V. (2005) · Diabetes Care · doi:10.2337/diacare.28.5.1222 · Cited by 100
Gebauer, S. K., Destaillats, F., Dionisi, F., Krauss, R. M., & Baer, D. J. (2015) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.116129 · Cited by 108
Association between markers of glucose metabolism and risk of colorectal cancer
Xu, J., Yao, Y., Wu, H., Duerksen-Hughes, P. J., Zhang, H., Li, P., Huang, J., Yang, J. J., Wu, Y., & Xia, D. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-011430 · Cited by 109
Støa, E. M., Meling, S., Nyhus, L.-K., Strømstad, G. B., Mangerud, K. M., Helgerud, J., Bratland‐Sanda, S., & Støren, Ø. (2017) · European Journal of Applied Physiology · doi:10.1007/s00421-017-3540-1 · Cited by 109
Ectopic fat accumulation in the pancreas and its biomarkers: A systematic review and meta‐analysis
Singh, R. G., Yoon, H. D., Poppitt, S. D., Plank, L. D., & Petrov, M. S. (2017) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.2918 · Cited by 104
van Dammen, L., Wekker, V., van Oers, A. M., Mutsaerts, M. A. Q., Painter, R. C., Zwinderman, A. H., Groen, H., van de Beek, C., Kobold, A. C. M., Kuchenbecker, W. K. H., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0190662 · Cited by 102
Lee, Y. B., Ha, Y., Chon, Y. E., Kim, M. N., Lee, J. H., Park, H., Kim, K., Kim, S. H., Rim, K. S., & Hwang, S. G. (2018) · Clinical and Molecular Hepatology · doi:10.3350/cmh.2018.0040 · Cited by 103
Kim, J. H., Park, K., Lee, S. B., Kang, S., Park, J. S., Ahn, C. W., & Nam, J. S. (2019) · Journal of Diabetes Investigation · doi:10.1111/jdi.13002 · Cited by 107
Depression is associated with non‐alcoholic fatty liver disease among adults in the United States
Kim, D., Yoo, E. R., Li, A. A., Tighe, S. P., Cholankeril, G. T., Harrison, S. A., & Ahmed, A. (2019) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.15395 · Cited by 102
Weber, M., Mera, P., Casas, J., Salvador, J., Rodríguez, A., Alonso, S., Sebastián, D., Soler‐Vázquez, M. C., Montironi, C., Recalde, S., et al. (2020) · The FASEB Journal · doi:10.1096/fj.202000678r · Cited by 107
Liu, C., Liang, D., Xiao, K., & Xie, L. (2024) · Cardiovascular Diabetology · doi:10.1186/s12933-024-02269-0 · Cited by 103
Lee, S.-H., Jouihan, H. A., Cooksey, R. C., Jones, D., Kim, H. J., Winge, D. R., & McClain, D. A. (2013) · Endocrinology · doi:10.1210/en.2012-1445 · Cited by 105
Tsunoda, S., Kamide, K., Minami, J., & Kawano, Y. (2002) · American Journal of Hypertension · doi:10.1016/s0895-7061(02)02953-9 · Cited by 101
Sathyapalan, T., Manuchehri, A. M., Thatcher, N. J., Rigby, A. S., Chapman, T., Kilpatrick, E. S., & Atkin, S. L. (2011) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2010-2255 · Cited by 105
Cinnamaldehyde protects from the hypertension associated with diabetes
El‐Bassossy, H. M., Fahmy, A., & Badawy, D. (2011) · Food and Chemical Toxicology · doi:10.1016/j.fct.2011.07.060 · Cited by 103
Martínez-Larrad, M. T., Fernández, C., González‐Sánchez, J. L., López, A., Fernández-Álvarez, J., Riviriego, J., & Serrano‐Ríos, M. (2005) · Medicina Clínica · doi:10.1157/13080210 · Cited by 101
Effect of Dietary Advanced Glycation End Products on Mouse Liver
Patel, R. A., Baker, S. S., Liu, W., Desai, S. S., Alkhouri, R. H., Kozielski, R., Mastrandrea, L. D., Sarfraz, A., Cai, W., Vlassara, H., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0035143 · Cited by 105
Panidis, D., Rousso, D., Koliakos, G., Kourtis, A., Katsikis, I., Farmakiotis, D., Votsi, E., & Diamanti‐Kandarakis, E. (2006) · Fertility and Sterility · doi:10.1016/j.fertnstert.2005.11.044 · Cited by 103
Hioki, C., Yoshimoto, K., & Yoshida, T. (2004) · Clinical and Experimental Pharmacology and Physiology · doi:10.1111/j.1440-1681.2004.04056.x · Cited by 104
Jamurtas, A. Z., Theocharis, V., KOUKOULIS, G. N., Stakias, N., Fatouros, I. G., Kouretas, D., & Koutedakis, Y. (2006) · European Journal of Applied Physiology · doi:10.1007/s00421-006-0169-x · Cited by 102
Sheedfar, F., Sung, M. M. Y., Aparicio-Vergara, M., Kloosterhuis, N., Miquilena-Colina, M. E., Vargas‐Castrillón, J., Febbraio, M., Jacobs, R. L., de Bruin, A., Vinciguerra, M., et al. (2014) · Aging · doi:10.18632/aging.100652 · Cited by 102
Hu, Y., Li, L., Xu, Y., Yu, T., Tong, G., Huang, H., Bi, Y., Weng, J., & Zhu, D. (2011) · Diabetes Care · doi:10.2337/dc10-2105 · Cited by 101
Dyslipidemia and Metabolic Syndrome in the Sisters of Women with Polycystic Ovary Syndrome
Sam, S., Legro, R. S., Bentley–Lewis, R., & Dunaif, A. E. (2005) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2004-2217 · Cited by 101
Development of the Metabolic Syndrome in Black and White Adolescent Girls: A Longitudinal Assessment
Morrison, J. A., Friedman, L. A., Harlan, W. R., Harlan, L. C., BARTON, B. A., Schreiber, G. B., & Klein, D. J. (2005) · PEDIATRICS · doi:10.1542/peds.2004-2358 · Cited by 101
Effect of Aerobic Exercise on Plasma Adiponectin Levels and Insulin Resistance in Type 2 Diabetes
Yokoyama, H., Emoto, M., Araki, T., Fujiwara, S., Motoyama, K., Morioka, T., Koyama, H., Shoji, T., Okuno, Y., & Nishizawà, Y. (2004) · Diabetes Care · doi:10.2337/diacare.27.7.1756 · Cited by 102
Braun, B., Rock, P. B., Zamudio, S., Wolfel, G. E., Mazzeo, R. S., Muza, S. R., Fulco, C. S., MOORE, L. G., & Butterfield, G. E. (2001) · Journal of Applied Physiology · doi:10.1152/jappl.2001.91.2.623 · Cited by 101
Insulin resistance and insulin secretion in chronic hepatitis C virus infection
Narita, R., Abe, S., Kihara, Y., Akiyama, T., Tabaru, A., & Otsuki, M. (2004) · Journal of Hepatology · doi:10.1016/j.jhep.2004.03.020 · Cited by 102
Brucellosis: past, present and future.
Nicoletti, P. (2010) · PubMed · Cited by 100
Effects of weight loss, induced by gastric bypass surgery, on HDL remodeling in obese women
Asztalos, B. F., Swarbrick, M. M., Schaefer, E. J., Dallal, G. E., Horvath, K. V., Ai, M., Stanhope, K. L., Austrheim-Smith, I. T., Wolfe, B. M., Ali, M. R., et al. (2009) · Journal of Lipid Research · doi:10.1194/jlr.p900015-jlr200 · Cited by 102
Mansfield, M. W., Heywood, D. M., & Grant, P. J. (1996) · Circulation · doi:10.1161/01.cir.94.9.2171 · Cited by 100
D’Adamo, M., Perego, L., Cardellini, M., Marini, M. A., Frontoni, S., Andreozzi, F., Sciacqua, A., Lauro, D., Sbraccia, P., Federici, M., et al. (2004) · Diabetes · doi:10.2337/diabetes.53.7.1905 · Cited by 100
Shariatpanahi, Z. V., Shariatpanahi, M. V., Shahbazi, S., Hossaini, A., & Abadi, A. (2008) · British Journal of Nutrition · doi:10.1017/s000711450787231x · Cited by 101
Nguyen, Q. M., Srinivasan, S. R., Xu, J.-H., Chen, W., & Berenson, G. S. (2010) · Archives of Pediatrics & Adolescent Medicine · doi:10.1001/archpediatrics.2009.268 · Cited by 93
Adiponectin and Insulin Sensitivity in Primary Aldosteronism
FALLO, F., DELLAMEA, P., SONINO, N., BERTELLO, C., ERMANI, M., VETTOR, R., VEGLIO, F., & MULATERO, P. (2007) · American Journal of Hypertension · doi:10.1016/j.amjhyper.2007.03.012 · Cited by 101
Robertson, M. D., Russell-Jones, D., Umpleby, A. M., & Dijk, D.-J. (2013) · Metabolism · doi:10.1016/j.metabol.2012.07.016 · Cited by 100
Lee, M.-R., Jung, S. M., Bang, H., Kim, H. S., & Kim, Y. B. (2018) · Medicine · doi:10.1097/md.0000000000010984 · Cited by 95
“HOMA-IR was calculated using the following formula: HOMAIR= [fasting glucose (mg/dL) fasting insulin (mIU/L)]/40.([21]) The cut-off value of HOMA-IR was 2.”
MethodIbáñez, L., Ong, K., Ferrer, A., Amin, R., Dunger, D., & de Zegher, F. (2003) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2002-022002 · Cited by 100
Effects of liraglutide on obesity-associated functional hypogonadism in men
Jensterle, M., Podbregar, A., Goricar, K., Gregoric, N., & Janez, A. (2019) · Endocrine Connections · doi:10.1530/ec-18-0514 · Cited by 101
Greenfield, J. R., Samaras, K., Hayward, C. S., Chisholm, D. J., & Campbell, L. V. (2005) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2004-1511 · Cited by 101
Protective Role of Coffee in Non-alcoholic Fatty Liver Disease (NAFLD)
Catalano, D., Martines, G. F., Tonzuso, A., Pirri, C., Trovato, F. M., & Trovato, G. M. (2010) · Digestive Diseases and Sciences · doi:10.1007/s10620-010-1143-3 · Cited by 101
IL-34 Is Associated with Obesity, Chronic Inflammation, and Insulin Resistance
Chang, E., Lee, S. K., Song, Y. S., Jang, Y. J., Park, H. S., Hong, J. P., Ko, A., Kim, D. Y., Kim, J.-H., Lee, Y. J., et al. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2013-4409 · Cited by 101
Tharaheswari, M., Reddy, N. J., Kumar, R., Varshney, K. C., Kannan, M., & Sadras, S. R. (2014) · Molecular and Cellular Biochemistry · doi:10.1007/s11010-014-2152-x · Cited by 101
Singh, J. B., Robb, A. S., Vijapurkar, U., Nuamah, I. F., & Hough, D. W. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.06.021 · Cited by 101
Srinivasan, S. R., Wang, R., Chen, W., Wei, C. Y., Xu, J.-H., & Berenson, G. S. (2009) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2009.04.037 · Cited by 100
İdilman, İ. S., Tüzün, A. E., Savaş, B., Elhan, A. H., Çelik, A., Idılman, R., & Karçaaltıncaba, M. (2015) · Abdominal Imaging · doi:10.1007/s00261-015-0385-0 · Cited by 104
Møller, J. B., Man, C. D., Overgaard, R. V., Ingwersen, S. H., Tornøe, C. W., Pedersen, M., Tanaka, H., Ohsugi, M., Ueki, K., Lynge, J., et al. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2014-1724 · Cited by 100
Khoudary, S. R. E., Santoro, N. F., Chen, H.-Y., Tepper, P. G., Brooks, M. M., Thurston, R. C., Janssen, İ., Harlow, S. D., Barinas‐Mitchell, E., Selzer, F., et al. (2015) · European Journal of Preventive Cardiology · doi:10.1177/2047487315607044 · Cited by 104
Valenzuela, R., Echeverría, F., Ortíz, M., Rincón‐Cervera, M. Á., Espinosa, A., Hernández‐Rodas, M. C., Illesca, P., Valenzuela, A., & Videla, L. A. (2017) · Lipids in Health and Disease · doi:10.1186/s12944-017-0450-5 · Cited by 101
Nilsson, A. C., Salo, I., Plaza, M., & Björck, I. (2017) · PLoS ONE · doi:10.1371/journal.pone.0188173 · Cited by 105
Pintó, X., Fanlo‐Maresma, M., Corbella, E., Corbella, X., Mitjavila, M. T., Moreno, J. J., Casas, R. M., Estruch, R., Corella, D., Bulló, M., et al. (2019) · Journal of Nutrition · doi:10.1093/jn/nxz147 · Cited by 109
Guo, F., Gong, Z., Fernando, T., Zhang, L., Zhu, X., & Shi, Y. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.892125 · Cited by 110
Yao, Y., Wang, B., Geng, T., Chen, J., Chen, W., & Li, L. (2024) · Cardiovascular Diabetology · doi:10.1186/s12933-024-02120-6 · Cited by 103
Park, S., Kim, M. Y., Baik, S. H., Woo, J.-T., Kwon, Y. J., Daily, J. W., Park, Y.-M., Jh, Y., & Kim, S.-H. (2013) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2012.207 · Cited by 101
Markers of cardiovascular disease risk after hypertension in pregnancy
Mangos, G., Spaan, J. J., Pirabhahar, S., & Brown, M. A. (2011) · Journal of Hypertension · doi:10.1097/hjh.0b013e32834e5ac7 · Cited by 101
Tentolouris, N., Tsigos, C., Perea, D., Koukou, E., Kyriaki, D., Kitsou, E., Daskas, S., Daifotis, Z., Makrilakis, K., Raptis, S., et al. (2003) · Metabolism · doi:10.1016/s0026-0495(03)00322-6 · Cited by 103
Compound K Enhances Insulin Secretion with Beneficial Metabolic Effects in db/db Mice
Han, G. C., Ko, S. K., Sung, J. H., & Chung, S. H. (2007) · Journal of Agricultural and Food Chemistry · doi:10.1021/jf0722598 · Cited by 101
Magkos, F., Mohammed, B. S., & Mittendorfer, B. (2008) · International Journal of Obesity · doi:10.1038/ijo.2008.164 · Cited by 102
Jørgensen, M. E., Borch‐Johnsen, K., & Bjerregaard, P. (2008) · Diabetologia · doi:10.1007/s00125-008-1066-0 · Cited by 101
Boesgaard, T. W., Grarup, N., Jørgensen, T., Borch‐Johnsen, K., Hansen, T. F., & Pedersen, O. B. (2010) · Diabetologia · doi:10.1007/s00125-010-1753-5 · Cited by 99
Soluble CD163: a biomarker linking macrophages and insulin resistance
Parkner, T., Sørensen, L. P., Nielsen, A. R., Fischer, C. P., Bibby, B. M., Nielsen, S., Pedersen, B. K., & Møller, H. J. (2012) · Diabetologia · doi:10.1007/s00125-012-2533-1 · Cited by 101
Metadata
- Authors
- Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C.
- Affiliations
- University of Oxford (GB)
- ORCID
- Matthews, D. R.
- Venue
- Diabetologia
- Published
- 1985-07
- Volume
- 28
- Issue
- 7
- Pages
- 412-419
- ISSN
- 0012-186X (ISSN-L)
- 1432-0428
- OpenAlex
- W2160234571
- Cited by
- 31,502
- References
- 21
- Citations by year
- 2026: 933 · 2025: 1060 · 2024: 1240 · 2023: 1291 · 2022: 1489 · 2021: 1528 · 2020: 1424 · 2019: 1427 · 2018: 1456 · 2017: 1440 · 2016: 1481 · 2015: 1682 · 2014: 1693 · 2013: 1621 · 2012: 1518
- FWCI
- 7.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Diabetologia (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Research Council
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- HOMA, insulin resistance, beta-cell function, fasting blood glucose, fasting insulin, hyperglycaemic clamp, euglycaemic clamp, intravenous glucose tolerance test, insulin receptor binding, type 2 diabetes
- MeSH terms
- Adult, Blood Glucose, Computers, Diabetes Mellitus, Type 2, Erythrocytes, Fasting, Homeostasis, Humans, Insulin, Insulin Resistance, Islets of Langerhans, Middle Aged, Monocytes, Receptor, Insulin
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-14, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-14 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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