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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Kawano, M., Shono, N., Yoshimura, T., Yamaguchi, M., Hirano, T., & Hisatomi, A. (2009) · Internal Medicine · doi:10.2169/internalmedicine.48.1527 · Cited by 21
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Takezako, T., Saku, K., Zhang, B., Shirai, K., & Arakawa, K. (1999) · Japanese Circulation Journal-english Edition · doi:10.1253/jcj.63.666 · Cited by 21
Rizza, S., Clementi, F., Porzio, O., Cardellini, M., Savo, A., Sérino, M., Chiricolo, G., Romeo, F., Lauro, R., & Federici, M. (2008) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2007.12.001 · Cited by 21
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Graci, S., Baratta, R., Degano, C., et al. (1999) · Journal of Endocrinological Investigation · doi:10.1007/bf03343592 · Cited by 21
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Keim, N. L., & Horn, W. F. (2004) · Obesity Research · doi:10.1038/oby.2004.19 · Cited by 21
McComsey, G. A., O'Riordan, M., Setzer, B., et al. (2007) · European Journal of Clinical Nutrition · doi:10.1038/sj.ejcn.1602793 · Cited by 21
Yuen, K. C. J., Wareham, N. J., Frystyk, J., Hennings, S., Mitchell, J., Fryklund, L., & Dunger, D. B. (2004) · European Journal of Endocrinology · doi:10.1530/eje.0.1510039 · Cited by 21
Jeng, J., Shieh, S., Harn, H., Lee, M. M.-S., Sheu, W. H., & Jeng, C.-Y. (1997) · Journal of Hypertension · doi:10.1097/00004872-199715090-00006 · Cited by 21
Chronotype: A Tool to Screen Eating Habits in Polycystic Ovary Syndrome?
Barrea, L., Verde, L., Vetrani, C., Savastano, S., Colao, A., & Muscogiuri, G. (2022) · Nutrients · doi:10.3390/nu14050955 · Cited by 20
Breuer, T. G. K., Menge, B. A., Banasch, M., Uhl, W., Tannapfel, A., Schmidt, W. E., Nauck, M. A., & Meier, J. J. (2010) · European Journal of Endocrinology · doi:10.1530/eje-10-0330 · Cited by 20
Stine, J. G., Schreibman, I., Navabi, S., Kang, M., Dahmus, J., Soriano, C., Rivas, G., Hummer, B., Beyer, M., Tressler, H., et al. (2020) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2020.100560 · Cited by 19
“Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) [60] is calculated to evaluate insulin resistance as (Fasting insulin)* (Fasting glucose)/405.”
MethodInfluentialKabisch, S., Meyer, N. M. T., Honsek, C., Gerbracht, C., Dambeck, U., Kemper, M., Osterhoff, M. A., Birkenfeld, A. L., Arafat, A. M., Weickert, M. O., et al. (2019) · Nutrients · doi:10.3390/nu11112726 · Cited by 20
“Abbreviations ALAT alanine-amino transferase ASAT aspartate-amino transferase AUC area under the curve BCAA branched-chain amino acid BIA bioelectric impedance analysis CRP C-reactive protein EPAQ European Physical Activity Questionnaire FLI fatty liver index GGT gamma-glutamyl transferase GLP-1 glucagon-like peptide…”
MethodInfluentialHuybrechts, I., De Vriendt, T., Breidenassel, C., Rogiers, J., Vanaelst, B., Cuenca-García, M., Moreno, L. A., González-Gross, M., Roccaldo, R., Kafatos, A., et al. (2014) · Nutrition, Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2014.04.014 · Cited by 20
Sex-steroid milieu determines diabetes rescue in pttg-null mice
Fraenkel, M., Caloyeras, J., Ren, S.-G., & Melmed, S. (2006) · Journal of Endocrinology · doi:10.1677/joe.1.06656 · Cited by 20
Noordam, R., Ramkisoensing, A., Loh, N. Y., Neville, M. J., Rosendaal, F. R., Willems van Dijk, K., van Heemst, D., Karpe, F., Christodoulides, C., & Kooijman, S. (2019) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2018-02532 · Cited by 20
Body weight reduction and metformin: Roles in polycystic ovary syndrome
AL-Nozha, O., Habib, F., Mojaddidi, M., & El-Bab, M. F. (2013) · Pathophysiology · doi:10.1016/j.pathophys.2013.03.002 · Cited by 20
Indicators of insulin resistance in clinical practice
Sasaki, N., Ueno, Y., & Higashi, Y. (2024) · Hypertension Research · doi:10.1038/s41440-023-01566-7 · Cited by 21
Zhu, M., Li, P., Xu, T., Zhang, G., Xu, Z., Wang, X., Zhao, L., & Yang, H. (2024) · Environmental Toxicology · doi:10.1002/tox.24125 · Cited by 20
Chen, L., Tan, K. M. L., Xu, J., Mishra, P., Mir, S. A., Gong, M., Narasimhan, K., Ng, B., Lai, J. S., Tint, M. T., et al. (2024) · Genome Medicine · doi:10.1186/s13073-024-01403-7 · Cited by 22
da Silva, T. R., Marchesan, L. B., Rampelotto, P. H., Longo, L., de Oliveira, T. F., Landberg, R., de Mello, V. D., & Spritzer, P. M. (2024) · Journal of Ovarian Research · doi:10.1186/s13048-024-01550-w · Cited by 25
Kim, J. A., Shin, C., Jung, I., Park, S. Y., Lee, D. Y., Yu, J. H., Cho, H., Lee, S. K., Kim, K. J., Song, E., et al. (2025) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.13760 · Cited by 24
Decker, D. D., Patel, R., Cheavens, J. S., Hayes, S. M., Whitted, W. M., Lee, A. J., Buga, A., Robinson, B. T., Crabtree, C. D., Kackley, M. L., et al. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03544-8 · Cited by 22
McCormick, K. G., Scorletti, E., Bhatia, L. S., Calder, P. C., Griffin, M. J., Clough, G. F., & Byrne, C. D. (2015) · Diabetologia · doi:10.1007/s00125-015-3628-2 · Cited by 20
Tryggestad, J. B., Wang, J. J., Zhang, S. X., Thompson, D. M., & Short, K. R. (2014) · Pediatric Diabetes · doi:10.1111/pedi.12226 · Cited by 21
Lin, J. J., Khetarpal, S. A., Terembula, K., Reilly, M. P., & Wilson, F. P. (2015) · BMC Nephrology · doi:10.1186/s12882-015-0122-5 · Cited by 22
Factors affecting impairment of blood rheology in obese subjects
Hitsumoto, T. (2012) · Journal of Cardiology · doi:10.1016/j.jjcc.2012.06.012 · Cited by 20
Auinger, A., Helwig, U., Pfeuffer, M., Rubin, D., Luedde, M., Rausche, T., Mokhtari, N. E. E., Fölsch, U. R., Schreiber, S., Frey, N., et al. (2011) · British Journal Of Nutrition · doi:10.1017/s0007114511004727 · Cited by 20
Belo, V. D. A., Luizon, M. R., Lacchini, R., de Miranda, J. A., Lanna, C. M. M., Souza-Costa, D. C., & Tanus-Santos, J. E. (2013) · International Journal of Obesity · doi:10.1038/ijo.2013.173 · Cited by 20
Real Incidence of Diabetes Mellitus in a Coronary Disease Population
de la Hera, J. M., García-Ruiz, J. M., Martínez‐Camblor, P., Martín, M. A. A., Tellería, A. L., Corros, C., Torres, F., Fernández-Cimadevilla, O. C., Pichel, I. A., Capín, E., et al. (2012) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2012.10.008 · Cited by 20
Lazich, I., Sarafidis, P. A., Guzman, E., Patel, A. D., Oliva, R. R., & Bakris, G. L. (2011) · Diabetes Obesity and Metabolism · doi:10.1111/j.1463-1326.2011.01510.x · Cited by 20
Pleiotropic Effects of Mitiglinide in Type 2 Diabetes Mellitus
Konya, H., Miuchi, M., Konishi, K., et al. (2009) · Journal of International Medical Research · doi:10.1177/147323000903700628 · Cited by 20
Failure of beta‐cell function to compensate lack of insulin action in hyperuricemic subjects
Simental‐Mendía, L. E., Rodríguez‐Morán, M., & Guerrero‐Romero, F. (2009) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.988 · Cited by 20
Huang, J., Yu, M., Huang, C., Juo, S. H., Dai, C., Hsieh, M., Hou, N., Yeh, M., Hsieh, M., Yang, J., et al. (2012) · Liver International · doi:10.1111/j.1478-3231.2012.02771.x · Cited by 20
Dimitroulas, T., Sandoo, A., van Zanten, J. V., Smith, J., Hodson, J. A., Metsios, G., Stavropoulos‐Kalinoglou, A., & Kitas, G. D. (2013) · Scandinavian Journal of Rheumatology · doi:10.3109/03009742.2012.747627 · Cited by 20
Mendivil, C. O., Robles‐Osorio, L., Horton, E. S., Hamdy, O., & Caballero, A. E. (2013) · Diabetology & Metabolic Syndrome · doi:10.1186/1758-5996-5-37 · Cited by 20
Liao, M., Huang, L., Mao, Y. K., Jiang, Y., Yao, Z., Lin, X., Lu, Z., Wu, C., Qin, X. Y., Zhang, H., et al. (2015) · Mediators of Inflammation · doi:10.1155/2015/683739 · Cited by 20
Early subclinical ventricular dysfunction in patients with insulin resistance
Novo, G., Pugliesi, M., Visconti, C. L., Spatafora, P., Fiore, M., Miceli, R. D., Guarneri, F. P., Vitale, G., & Novo, S. (2014) · Journal of Cardiovascular Medicine · doi:10.2459/jcm.0b013e3283638164 · Cited by 20
for the GDS Group, Weber, K. S., Knebel, B., Straßburger, K., Kotzka, J., Stehle, P., Szendroedi, J., Müssig, K., Buyken, A. E., & Roden, M. (2016) · Cardiovascular Diabetology · doi:10.1186/s12933-016-0455-9 · Cited by 19
Cohen‐Manheim, I., Doniger, G. M., Sinnreich, R., Simon, E. S., Murad, H., Pinchas‐Mizrachi, R., & Kark, J. D. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160843 · Cited by 21
Morrison, S. A., Goss, A. M., Azziz, R., Raju, D. A., & Gower, B. A. (2016) · Human Reproduction · doi:10.1093/humrep/dew279 · Cited by 21
Bhansali, S., Dutta, P. R., Yadav, M., Jain, A., Mudaliar, S. R. D., Hawkins, M. A., Kurpad, A. V., Pahwa, D., Yadav, A. K., Sharma, R. R., et al. (2017) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-017-0248-7 · Cited by 20
Diurnal Variation of Sweet Taste Recognition Thresholds Is Absent in Overweight and Obese Humans
Sanematsu, K., Nakamura, Y., Nomura, M., Shigemura, N., & Ninomiya, Y. (2018) · Nutrients · doi:10.3390/nu10030297 · Cited by 20
Brandl, B., Skurk, T., Lagkouvardos, I., Fischer, S. E., Büttner, J., Lichtenegger, M., Clavel, T., Lechner, A., Rychlik, M., Haller, D., et al. (2018) · Journal of Nutrition · doi:10.1093/jn/nxx020 · Cited by 24
Glucose Homeostasis and Energy Balance in Children With Pseudohypoparathyroidism
Perez, K. M., Curley, K. L., Slaughter, J. C., & Shoemaker, A. H. (2018) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2018-01067 · Cited by 21
de Luis, D. A., Izaola, O., Primo, D., & Aller, R. (2018) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2018.10.015 · Cited by 20
Insulin resistance: Genetic associations with depression and cognition in population based cohorts
Frangou, S., Shirali, M., Adams, M. J., Howard, D. M., Gibson, J., Hall, L. S., Smith, B. H., Padmanabhan, S., Murray, A. D., Porteous, D. J., et al. (2019) · Experimental Neurology · doi:10.1016/j.expneurol.2019.04.001 · Cited by 19
Ribeiro, F. A., de Resende, E. A. M. R., da Silva, A. P., Tomé, J. M., Palhares, H. M. D. C., & Borges, M. D. F. (2019) · Clinics · doi:10.6061/clinics/2019/e836 · Cited by 20
Caffeine Optimizes HIIT Benefits on Obesity-associated Metabolic Adversity in Women
AlKhatib, A. H., Hsieh, M.-J., Kuo, C., & Hou, C. (2020) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002311 · Cited by 21
Interleukin-38 increases the insulin sensitivity in children with the type 2 diabetes
Liu, Y., Chen, T., Zhou, F., Mu, D., & Liu, S. (2020) · International Immunopharmacology · doi:10.1016/j.intimp.2020.106264 · Cited by 21
Javeres, M. N. L., Raza, S., Judith, N., Anwar, F., Habib, R., Batool, S., & Nurulain, S. M. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.534902 · Cited by 22
Neves, G. S., Lunardi, M. D. S., Lin, K., Rieger, D. K., Ribeiro, L. C., & Moreira, J. D. (2020) · Nutrition Reviews · doi:10.1093/nutrit/nuaa112 · Cited by 20
Sevilla-González, M. D. R., Merino, J., Moreno-Macías, H., Rojas, R., Gómez‐Velasco, D., & Manning, A. K. (2021) · Cardiovascular Diabetology · doi:10.1186/s12933-021-01246-1 · Cited by 21
Relationship between insulin sensitivity and gene expression in human skeletal muscle
Parikh, H. M., Elgzyri, T., Alibegovic, A. C., Hiscock, N. J., Ekström, O., Eriksson, K., Vaag, A. A., Groop, L., Ström, K., & Hansson, O. (2021) · BMC Endocrine Disorders · doi:10.1186/s12902-021-00687-9 · Cited by 20
Das, R. R., Mangaraj, M., Nayak, S., Satapathy, A. K., Mahapatro, S., & Goyal, J. P. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.613594 · Cited by 20
Hannemann, J., Laing, A., Middleton, B., Cridland, J., Staels, B., Marx, N., Grant, P. J., Federici, M., Stenberg, D., Skene, D. J., et al. (2021) · Journal of Hypertension · doi:10.1097/hjh.0000000000002848 · Cited by 20
Sexual assault and white matter hyperintensities among midlife women
Thurston, R. C., Jakubowski, K., Wu, M., Aizenstein, H. J., Chang, Y., Derby, C. A., Koenen, K. C., Barinas‐Mitchell, E., & Maki, P. M. (2021) · Brain Imaging and Behavior · doi:10.1007/s11682-021-00536-2 · Cited by 20
Tsukushi and TSKU genotype in obesity and related metabolic disorders
Li, Y., Jin, L., Yan, J. Y., Huang, Y., Zhang, H., Zhang, R., & Hu, C. (2021) · Journal of Endocrinological Investigation · doi:10.1007/s40618-021-01572-x · Cited by 20
Pennisi, G., Marco, V. D., Buscemi, C., Mazzola, G. C., Randazzo, C., Spatola, F., Craxı̀, A., Buscemi, S., & Petta, S. (2021) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.15523 · Cited by 22
Retnakaran, R. R., Emery, A., Ye, C., Harris, S. B., Reichert, S. M., McInnes, N., Gerstein, H. C., Thorpe, K. E., Kramer, C. K., & Zinman, B. (2021) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14421 · Cited by 19
Costa, A., Konieczna, J., Reynés, B., Martín, M., Fiol, M., Palou, A., Romaguera, D., & Oliver, P. (2021) · Journal of Nutrition · doi:10.1093/jn/nxab117 · Cited by 21
Impact of protocatechuic acid on high fat diet-induced metabolic syndrome sequelae in rats
Nour, O. A., Ghoniem, H. A., Nader, M. A., & Suddek, G. M. (2021) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2021.174257 · Cited by 19
Luo, X., Cai, W.-Y., Ma, H., Cong, J., Chang, H., Gao, J., Shen, W., Wang, Y., Yang, X., & Wu, X. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.683040 · Cited by 20
Calcium-Deficiency during Pregnancy Affects Insulin Resistance in Offspring
Takaya, J. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22137008 · Cited by 24
Katsiki, N., Kouvari, M., Panagiotakos, D. B., Borghi, C., Chrysohoou, C. A., Mikhailidis, D. P., & Pitsavos, C. S. (2021) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2203108 · Cited by 20
Alteration of serum neuregulin 4 and neuregulin 1 in gestational diabetes mellitus
Zhang, L., Bi, L., Wang, W., Miao, S., Zhou, S., Cheng, X., Zhu, J., & Liu, C.-M. (2021) · Therapeutic Advances in Endocrinology and Metabolism · doi:10.1177/20420188211049614 · Cited by 22
Do Temporal Eating Patterns Differ in Healthy versus Unhealthy Overweight/Obese Individuals?
Mazri, F. H., Manaf, Z. A., Shahar, S., Ludin, A. F. M., Karim, N. A., Hazwari, N., Kek, Q., Basir, S. M. A., & Arifin, A. (2021) · Nutrients · doi:10.3390/nu13114121 · Cited by 21
Allehdan, S., Basha, A. S., Hyassat, D., Nabhan, M., Qasrawi, H., & Tayyem, R. (2021) · Clinical Nutrition · doi:10.1016/j.clnu.2021.11.039 · Cited by 23
Urine tricarboxylic acid cycle signatures of early-stage diabetic kidney disease
Lunyera, J., Diamantidis, C. J., Bosworth, H. B., Patel, U. D., Bain, J. R., Muehlbauer, M. J., Ilkayeva, O., Nguyen, M., Sharma, B., Ma, J. Z., et al. (2021) · Metabolomics · doi:10.1007/s11306-021-01858-4 · Cited by 22
Serhiyenko, V. A., Serhiyenko, L. M., Sehin, V. B., & Serhiyenko, A. A. (2021) · Endocrine Regulations · doi:10.2478/enr-2021-0024 · Cited by 20
Ryckeghem, L. V., Keytsman, C., Brandt, J. D., Verboven, K., Verbaanderd, E., Marinus, N., Franssen, W. M. A., Frederix, I., Bakelants, E., Petit, T., et al. (2022) · European Journal of Applied Physiology · doi:10.1007/s00421-022-04884-9 · Cited by 21
Cosson, E., Nachtergaele, C., Vicaut, É., Tatulashvili, S., Sal, M., Berkane, N., Pinto, S., Fabre, E. E., Benbara, A., Fermaut, M., et al. (2022) · Diabetes & Metabolism · doi:10.1016/j.diabet.2022.101330 · Cited by 21
Contribution of Interleukin-17A to Retinal Degenerative Diseases
Zhong, H., & Sun, X. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.847937 · Cited by 22
Serum Bilirubin Level Is Increased in Metabolically Healthy Obesity
Fu, J., Wang, Q., Zhang, L., Liu, J., & Wang, G. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.792795 · Cited by 21
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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