The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index; 1 of the 6 sources its claims cite were checked for support.
Homeostasis model assessment: insulin resistance and ?-cell function from fasting plasma glucose and insulin concentrations in man
Open accessAbstract
The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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Claims the paper makes
15 Own result · 7 Own interpretation · 3 No full text of the sources · 1 Partly supported · 1 No basis given · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The fasting plasma insulin concentration is largely determined by the glucose concentration [3]”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
What the sources say
- Partly supportedcites [3] McCarthy ST, Harris F, Turner RC (1977) Glucose control of basal insulin secretion in diabetes. Diabetologia 13: 93?97 sourceExperimental — accuracy not yet measured
The Results and Discussion (page unavailable in our copy) show that changing plasma glucose affects basal insulin secretion. But they also report markedly impaired responses in diabetic subjects, including cases where insulin remained nearly constant despite a substantial glucose decrease. The paper therefore supports glucose as an influence, but not the unqualified claim that it largely determines fasting insulin concentration. (LLM, conf=0.84)
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).
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The degree of the hyperglycaemia does not correlate significantly with either the degrees of abnormal fl-cell function or insulin resistance.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Discussion
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
The authors state this as an assumption.
Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
“Assuming that normal-weight normal subjects aged < 35 years have 100% r-cell function, and an insulin resistance of 1, the values for a patient can be assessed from the insulin and glucose concentrations by the formulae: r-cell”
The paper relies on this without support.
The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
“The plasma glucose:insulin response for normal subjects, equivalent to the C-peptide response, is insulin = 5 (glucose-3.5).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The 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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Munhoz, A. C., Vilas‐Boas, E. A., Panveloski-Costa, A. C., Leite, J. S. M., Lucena, C. F., Riva, P., Emilio, H. R. D. O., & Carpinelli, Â. R. (2020) · Nutrients · doi:10.3390/nu12041029 · Cited by 37
Gujral, U. P., Narayan, K. M. V., Kandula, N. R., Liu, K., & Kanaya, A. M. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-001063 · Cited by 36
Emma, M. R., Giannitrapani, L., Cabibi, D., Porcasi, R., Pantuso, G., Augello, G., Giglio, R. V., Re, N. L., Capitano, A., Montalto, G., et al. (2020) · Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids · doi:10.1016/j.bbalip.2020.158792 · Cited by 34
Passari, A. K., Leo, V. V., Singh, G., Samanta, L., Ram, H., Siddaiah, C. N., Hashem, A., Al-Arjani, A.-B. F., Alqarawi, A. A., Allah, E. F. A., et al. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21197364 · Cited by 34
Li, A., Mei, Y., Zhao, M., Xu, J., Seery, S., Li, R., Zhao, J., Zhou, Q., Ge, X., & Xu, Q. (2020) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2020.143308 · Cited by 34
Kim, S.-M., & Imm, J. (2020) · Molecules · doi:10.3390/molecules25235503 · Cited by 34
The association between obesity and weight loss after bariatric surgery on the vaginal microbiota
Raglan, O., MacIntyre, D. A., Mitra, A., Lee, Y. S., Smith, A., Assi, N., Nautiyal, J., Purkayastha, S., Gunter, M. J., Gabra, H., et al. (2021) · Microbiome · doi:10.1186/s40168-021-01011-2 · Cited by 39
Mayer, C., Richard, L., Côme, M., Ulmann, L., Nazih, H., Chénais, B., Ouguerram, K., & Mimouni, V. (2021) · Nutrients · doi:10.3390/nu13020430 · Cited by 39
Hypoglycemic effect of lupinus mutabilis in healthy volunteers and subjects with dysglycemia
astro, J. C. (2012) · Cited by 34
Rodríguez‐Mañas, L., Angulo, J., Carnicero, J. A. C., Assar, M. E., García‐García, F. J., & Sinclair, A. J. (2021) · GeroScience · doi:10.1007/s11357-021-00384-4 · Cited by 35
Subtypes of gestational diabetes and future risk of pre-diabetes or diabetes
Retnakaran, R. R., Chang, Y. R., Hanley, A. J., Connelly, P. W., Sermer, M., & Zinman, B. (2021) · EClinicalMedicine · doi:10.1016/j.eclinm.2021.101087 · Cited by 34
Zhu, Y., Li, N., Huang, M., Bartels, M. D., Dogné, S., Zhao, S., Chen, X., Crewe, C., Straub, L. G., Vishvanath, L., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-25025-4 · Cited by 34
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Ciardullo, S., Ballabeni, C., Trevisan, R., & Perseghin, G. (2021) · American Journal of Nephrology · doi:10.1159/000518111 · Cited by 34
Th1/Th2 Balance in Young Subjects: Relationship with Cytokine Levels and Metabolic Profile
Matia‐García, I., Vadillo, E., Pelayo, R., Muñóz-Valle, J. F., García‐Chagollán, M., Loaeza‐Loaeza, J., Vences-Velázquez, A., Salgado-Goytia, L., García‐Arellano, S., & Parra‐Rojas, I. (2021) · Journal of Inflammation Research · doi:10.2147/jir.s342545 · Cited by 36
Kim, H. S., Cho, Y. K., Kim, E. H., Lee, M. J., Jung, C. H., Park, J., Kim, H., & Lee, W. J. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm11010041 · Cited by 35
Lejawa, M., Osadnik, K., Czuba, Z. P., Osadnik, T., & Pawlas, N. (2021) · Life · doi:10.3390/life11121350 · Cited by 35
Morbidity and mortality in PCOS: A prospective follow-up up to a mean age above 80 years
Forslund, M., Schmidt, J., Brännström, M., Landin‐Wilhelmsen, K., & Dahlgren, E. (2022) · European Journal of Obstetrics & Gynecology and Reproductive Biology · doi:10.1016/j.ejogrb.2022.02.020 · Cited by 34
Barthow, C. A., Hood, F. E., Crane, J., Huthwaite, M., Weatherall, M., Parry‐Strong, A., & Krebs, J. D. (2022) · BMJ Open · doi:10.1136/bmjopen-2021-055214 · Cited by 33
Association of Longitudinal Trajectories of Insulin Resistance With Adverse Renal Outcomes
Yang, S., Kwak, S., Song, Y.-H., Han, S. S., Lee, H. S., Kang, S., & Lee, S. (2022) · Diabetes Care · doi:10.2337/dc21-2521 · Cited by 39
Physiologically assessed hot flashes and endothelial function among midlife women
Thurston, R. C., Chang, Y., Barinas‐Mitchell, E., Jennings, J. R., von Känel, R., Landsittel, D., & Matthews, K. A. (2018) · Menopause The Journal of The North American Menopause Society · doi:10.1097/gme.0000000000001239 · Cited by 34
Kubo, H., Sawada, S., Satoh, M., Asai, Y., Kodama, S., Sato, T., Tomiyama, S., Seike, J., Takahashi, K., Kaneko, K., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-23521-1 · Cited by 34
Relationship Between a Sedentary Lifestyle and Adipose Insulin Resistance
Andersson, D. P., Kerr, A. G., Dahlman, I. A., Rydén, M., & Arner, P. (2022) · Diabetes · doi:10.2337/db22-0612 · Cited by 35
Wang, H., Shen, Q., Fu, Y., Liu, Z., Wu, T., Wang, C., & Zhao, Q. (2023) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.3c02179 · Cited by 34
Santilli, F., Blardi, P., Scapellato, C., Bocchia, M., Guazzi, G., Terzuoli, L., Tabucchi, A., Silvietti, A., Lucani, B., Gioffrè, W. R., et al. (2015) · Vascular Pharmacology · doi:10.1016/j.vph.2015.04.004 · Cited by 34
HOMA‐IR and QUICKI: decide on a general standard instead of making further comparisons
Rössner, S. M., Neovius, M., Mattsson, A., Marcus, C., & Norgren, S. (2010) · Acta Paediatrica · doi:10.1111/j.1651-2227.2010.01911.x · Cited by 34
Genzer, Y., Dadon‐Freiberg, M., Burg, C., Chapnik, N., & Froy, O. (2015) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2015.09.012 · Cited by 35
Klein, M. R. S. T., Francischetti, E. A., Genelhu, V. A., & Sanjuliani, A. F. (2010) · International Journal of Clinical Practice · doi:10.1111/j.1742-1241.2009.02312.x · Cited by 34
Hosseinpanah, F., Barzin, M., Tehrani, F. R., & Azizi, F. (2011) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2011.04113.x · Cited by 34
Weight Loss in Obese African American and Caucasian Adolescents
Budd, G. M., Hayman, L. L., Crump, E., Pollydore, C., Hawley, K. D., Cronquist, J. L., & Berkowitz, R. I. (2007) · The Journal of Cardiovascular Nursing · doi:10.1097/01.jcn.0000278959.75253.1d · Cited by 34
Śledziński, T., Goyke, E., Smolenski, R. T., Śledziński, Z., & Świerczyński, J. (2008) · Obesity Surgery · doi:10.1007/s11695-008-9691-8 · Cited by 34
Syed, A. A., Halpin, C. G., Irving, J. A. E., Unwin, N. C., White, M. J., Bhopal, R. S., Redfern, C. P. F., & Weaver, J. U. (2008) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2008.03175.x · Cited by 33
Schautz, B., Later, W., Heller, M., Müller, M. J., & Bosy‐Westphal, A. (2011) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2011.35 · Cited by 34
Exercise reduces cellular stress related to skeletal muscle insulin resistance
de Matos, M. A., Ottone, V. D. O., Duarte, T. C., Sampaio, P. F. D. M., Costa, K. B., Fonseca, C. A., Neves, M. P. C., Schneider, S. M., Moseley, P. L., Coimbra, C. C., et al. (2013) · Cell Stress and Chaperones · doi:10.1007/s12192-013-0453-8 · Cited by 36
Rs9939609 Variant of the Fat Mass and Obesity-Associated Gene and Trunk Obesity in Adolescents
Mangge, H., Renner, W., Almer, G., Weghuber, D., Möller, R., & Horejsi, R. (2011) · Journal of Obesity · doi:10.1155/2011/186368 · Cited by 34
Bhuiyan, A. R., Srinivasan, S. R., Chen, W., Sultana, A., & Berenson, G. S. (2008) · Metabolism · doi:10.1016/j.metabol.2007.12.003 · Cited by 34
Plaisance, E. P., Grandjean, P. W., Judd, R. L., Jones, K. W., & Taylor, K. J. (2009) · Metabolism · doi:10.1016/j.metabol.2009.04.038 · Cited by 34
HbA1c negatively correlates with LCAT activity in type 2 diabetes
Nakhjavani, M., Esteghamati, A., Esfahanian, F., Ghanei, A., Rashidi, A., & Hashemi, S. (2008) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2008.01.018 · Cited by 34
Maternal serum and cord blood preptin levels in gestational diabetes mellitus
Aslan, M., Çelik, Ö., Karsavuran, N., Çelik, N., Doğan, D. G., Botan, E., & Kafkaslı, A. (2010) · Journal of Perinatology · doi:10.1038/jp.2010.125 · Cited by 33
Counil, É., Julien, P., Lamarche, B., Château‐Degat, M., Ferland, A., & Dewailly, É. (2009) · British Journal Of Nutrition · doi:10.1017/s0007114509297182 · Cited by 34
Tönjes, A., Scholz, M. R., Breitfeld, J., Marzi, C., Grallert, H., Gross, A., Ladenvall, C., Schleinitz, D., Krause, K., Kirsten, H., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004854 · Cited by 34
Albuminuria and insulin resistance in children with biopsy proven non-alcoholic fatty liver disease
Manco, M., Ciampalini, P., Vito, R. D., et al. (2009) · Pediatric Nephrology · doi:10.1007/s00467-009-1134-9 · Cited by 34
Relationships between preptin and osteocalcin in obese, overweight, and normal weight adults
El-Eshmawy, M. M., & Aal, I. A. (2015) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2014-0338 · Cited by 33
Insulin resistance in Nigerians with essential hypertension
Akande, T., Adeleye, J. O., & Kadiri, S. (2013) · African Health Sciences · doi:10.4314/ahs.v13i3.19 · Cited by 34
Association between CAPN10 UCSNP-43 gene polymorphism and polycystic ovary syndrome in Chilean women
Márquez, J. L., Pacheco, A., Valdés, P. R., & Salazar, L. A. (2008) · Clinica Chimica Acta · doi:10.1016/j.cca.2008.07.028 · Cited by 34
Nayak, B. S., Ramsingh, D., Gooding, S., Legall, G., Bissram, S., Mohammed, A., Raychaudhuri, A., Sahadeo, B., Pandohie, V., & Figaro, K. (2010) · Primary care diabetes · doi:10.1016/j.pcd.2010.05.006 · Cited by 34
Goodman, E., Graham, T. E., Dolan, L. M., Daniels, S. R., Goodman, E. R., & Kahn, B. B. (2008) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2008.07.018 · Cited by 34
Association between smoking, insulin resistance and β‐cell function in a North‐western First Nation
Daniel, M., & Cargo, M. (2004) · Diabetic Medicine · doi:10.1046/j.1464-5491.2003.01064.x · Cited by 34
Erdmann, J., Mayr, M., Oppel, U. G., Sypchenko, O., Wagenpfeil, S., & SCHUSDZIARRA, V. (2008) · Regulatory Peptides · doi:10.1016/j.regpep.2008.10.008 · Cited by 33
Kuwahata, S., Hamasaki, S., Ishida, S., et al. (2010) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.1594 · Cited by 34
Somarriba, G. A., Lopez‐Mitnik, G., Ludwig, D. A., Neri, D. A., Schaefer, N., Lipshultz, S. E., Scott, G. B., & Miller, T. L. (2012) · AIDS Research and Human Retroviruses · doi:10.1089/aid.2012.0047 · Cited by 35
Boumaiza, I., Omezzine, A., Rejeb, J., Rebhi, L., Rejeb, N. B., Nabli, N., Abdelaziz, A. B., & Bouslama, A. (2012) · Genetic Testing and Molecular Biomarkers · doi:10.1089/gtmb.2012.0156 · Cited by 34
Wang, Q., Huang, R.-P., Yu, B., Cao, F., Wang, H., Zhang, M., Wang, X., Zhang, B., Zhou, H., & Zhu, Z. (2013) · PLoS ONE · doi:10.1371/journal.pone.0059845 · Cited by 35
Dubé, M. C., Joanisse, D. R., Prud’homme, D., Lemieux, S., Bouchard, C., Përusse, L., Lavoie, C., & Weisnagel, S. J. (2006) · International Journal of Obesity · doi:10.1038/sj.ijo.0803337 · Cited by 34
Kalogeropoulos, A. P., Georgiopoulou, V. V., Harris, T. B., Kritchevsky, S. B., Bauer, D. C., Smith, A. L., Strotmeyer, E. S., Newman, A. B., Wilson, P. W. F., Psaty, B. M., et al. (2009) · Journal of Cardiac Failure · doi:10.1016/j.cardfail.2009.03.001 · Cited by 34
Michishita, R., Shono, N., Kasahara, T., & Tsuruta, T. (2008) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2008.08.013 · Cited by 34
Genetic variants of vitamin D receptor and susceptibility to ischemic stroke
Prabhakar, P., Majumdar, V., Kulkarni, G. B., & Christopher, R. (2014) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2014.12.007 · Cited by 34
The effect of farmed trout on cardiovascular risk markers in healthy men
Hallund, J., Madsen, B. O., Bügel, S., Jacobsen, C., Jakobsen, J., Krarup, H. B., Holm, J., Nielsen, H. H., & Lauritzen, L. (2010) · British Journal Of Nutrition · doi:10.1017/s0007114510002527 · Cited by 37
Glycaemic variability and inflammation in subjects with metabolic syndrome
Buscemi, S., Verga, S., Cottone, S., Azzolina, V., Buscemi, B., Gioia, D., & Cerasola, G. (2008) · Acta Diabetologica · doi:10.1007/s00592-008-0061-8 · Cited by 34
Adiponectin and waist circumference as predictors of insulin-resistance in women
Bonneau, G. A., Pedrozo, W., & Berg, G. A. (2013) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2013.10.005 · Cited by 34
Lindsay, J. M., Sharma, A. K., Caños, D., et al. (2007) · Cardiovascular revascularization medicine · doi:10.1016/j.carrev.2006.10.002 · Cited by 34
The metabolic syndrome in Chinese, Malays and Asian Indians
Ang, L. W., Ma, S., Cutter, J. L., Chew, S. K., Tan, C. E., & Tai, E. S. (2004) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2004.05.003 · Cited by 34
Serum free testosterone and metabolic syndrome in Japanese men
Katabami, T., Kato, H., Asahina, T., Hinohara, S., Shin, T., Kawata, T., Ohta, A., Iwamoto, T., & Tanaka, Y. (2010) · Endocrine Journal · doi:10.1507/endocrj.k10e-018 · Cited by 34
Obstructive sleep apnoea, insulin resistance and adipocytokines
Lam, D. C.-L., Lam, K. S. L., & Ip, M. (2014) · Clinical Endocrinology · doi:10.1111/cen.12597 · Cited by 33
Rise in Extracellular Fluid Volume During High Sodium Depends on BMI in Healthy Men
Visser, F. W., Krikken, J. A., Muntinga, J. H. J., Dierckx, R. A. J. O., & Navis, G. J. (2009) · Obesity · doi:10.1038/oby.2009.61 · Cited by 33
Karagianis, J. L., Grossman, L. D., Landry, J., Reed, V. A., de Haan, L., Maguire, G. A., Hoffmann, V. P., & Milev, R. V. (2009) · Schizophrenia Research · doi:10.1016/j.schres.2009.05.024 · Cited by 34
Pirgon, Ö., Atabek, M. E., Oran, B., & Güçlü, R. (2009) · Journal of Clinical Research in Pediatric Endocrinology · doi:10.4008/jcrpe.v1i2.9 · Cited by 34
Metabolic syndrome in the offspring of centenarians: focus on prevalence, components, and adipokines
Ostan, R., Bucci, L., Cevenini, E., Palmas, M. G., Pini, E., Scurti, M., Vescovini, R., Caruso, C., Mari, D., Vitale, G., et al. (2012) · AGE · doi:10.1007/s11357-012-9483-x · Cited by 33
McCall, D., McGartland, C. P., McKinley, M. C., Sharpe, P. C., McCance, D. R., Young, I. S., & Woodside, J. V. (2010) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2010.01.009 · Cited by 33
Botella-Carretero, J. I., Luque‐Ramírez, M., Álvarez‐Blasco, F., Peromingo, R., Millán, J. L. S., & Escobar-Morreale, H. F. (2008) · Obesity Surgery · doi:10.1007/s11695-007-9369-7 · Cited by 34
Navas‐Carretero, S., Cuervo, M., Abete, I., Zulet, M. Á., & Martínez, J. A. (2010) · Biological Trace Element Research · doi:10.1007/s12011-010-8831-x · Cited by 34
Thyroid Hormone Levels in Obese Children and Adolescents with Non-Alcoholic Fatty Liver Disease
Torun, E., Özgen, İ. T., Gökçe, S., et al. (2014) · Journal of Clinical Research in Pediatric Endocrinology · doi:10.4274/jcrpe.1155 · Cited by 34
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
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