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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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
Cited by
Sorted by citations
Items 16,901–17,000 of 31,469
Mnif, M. F., Kamoun, M., Mnif, F., Charfi, N., Naceur, B. B., Kallel, N., Rekik, N. M., Mnif, Z., Sfar, M. H., Sfar, M., et al. (2012) · Indian Journal of Endocrinology and Metabolism · doi:10.4103/2230-8210.102995 · Cited by 17
Insulin levels in low birth weight neonates.
Yada, K. K., Gupta, R., Gupta, A., et al. (2003) · PubMed · Cited by 17
Jellema, A. (2003) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2003.08.002 · Cited by 17
The Impact of Insulin Resistance on Proinsulin Secretion in Pregnancy
Retnakaran, R. R., Hanley, A. J., Sermer, M., & Zinman, B. (2005) · Diabetes Care · doi:10.2337/diacare.28.11.2710 · Cited by 17
Hormone replacement therapy and risk for coronary heart disease
Windler, E. E. T., Zyriax, B., Eidenmüller, B., & Boeing, H. (2007) · Maturitas · doi:10.1016/j.maturitas.2007.01.004 · Cited by 17
Yun, K. H., Jeong, M. H., Kim, K. H., Hong, Y. J., Park, H. W., Kim, J. H., Ahn, Y. K., Cho, J. G., Park, J. C., Kim, N., et al. (2006) · Journal of Korean Medical Science · doi:10.3346/jkms.2006.21.2.212 · Cited by 17
Ezenwaka, C. E., & Offiah, N. V. (2002) · PubMed · Cited by 17
Miyawaki, K., Inoue, H., Keshavarz, P., Mizuta, K., Sato, A., Sakamoto, Y., Moritani, M., Kunika, K., Tanahashi, T., & Itakura, M. (2008) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2008.06.014 · Cited by 17
Perugini, R. A., Quarfordt, S. H., Baker, S. P., Czerniach, D. R., Litwin, D., & Kelly, J. J. (2007) · Journal of Gastrointestinal Surgery · doi:10.1007/s11605-007-0158-3 · Cited by 17
Kokaze, A., Ishikawa, M., Matsunaga, N., et al. (2005) · Mitochondrion · doi:10.1016/j.mito.2005.09.001 · Cited by 17
Ebetjen, M., egura, B., Videenik, V., & Keber, I. (2005) · Coronary Artery Disease · doi:10.1097/00019501-200505000-00007 · Cited by 17
Pérez‐Bravo, F., Fuentes, M., Bárbara Angel, B., et al. (2006) · Acta Diabetologica · doi:10.1007/s00592-006-0221-7 · Cited by 17
Nauck, M. A., Meier, J. J., Wolfersdorff, A. V., TILLIL, H., Creutzfeldt, W., & K�bberling, J. (2003) · Acta Diabetologica · doi:10.1007/s00592-003-0106-y · Cited by 17
Caixàs, A., Giménez‐Palop, O., Giménez‐Pérez, G., Potau, N., Berlanga, E., González-Clemente, J. M., Arroyo, J., Laferrère, B., & Mauricio, D. (2005) · Hormone Research in Paediatrics · doi:10.1159/000090513 · Cited by 17
Koenigsberg, J., Boyd-Woschinko, G. S., Gidding, S. S., Hassink, S. G., & Falkner, B. E. (2006) · Journal of the CardioMetabolic Syndrome · doi:10.1111/j.1559-4564.2006.05695.x · Cited by 17
Prognostic factors for successful insulin therapy in subjects with type 2 diabetes
Wolffenbuttel, B. H. R. (1999) · The Netherlands Journal of Medicine · doi:10.1016/s0300-2977(98)00145-4 · Cited by 17
Alcohol and arterial wave reflections in middle aged and elderly men
van Trijp, M. J. C. A., Bos, W. J. W., van der Schouw, Y. T., Muller, M., Grobbee, D. E., & Bots, M. L. (2005) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.2005.01560.x · Cited by 17
Marked Hyperinsulinaemia in Postmenopausal, Healthy Indian (Asian) Women
Berger, G. M. B., Naidoo, J., Gounden, N., & Gouws, E. (1995) · Diabetic Medicine · doi:10.1111/j.1464-5491.1995.tb02081.x · Cited by 17
Genetic Influences on Insulinemia in Normotensive Twins
Narkiewicz, K. (1997) · American Journal of Hypertension · doi:10.1016/s0895-7061(96)00451-7 · Cited by 17
Is HCV infection associated with liver steatosis also in children?
Giannattasio, A., Spagnuolo, M. I., Sepe, A. O., et al. (2006) · Journal of Hepatology · doi:10.1016/j.jhep.2006.03.010 · Cited by 17
Han, S. J., Kim, S.-K., Fujimoto, W. Y., Kahn, S. E., Leonetti, D. L., & Boyko, E. J. (2017) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2017.10.003 · Cited by 17
“Insulin sensitivity was estimated by homeostasis model assessment insulin resistance (HOMA-IR) index calculated as [fasting serum insulin (μIU/mL) × fasting serum glucose (mg/dl)]/405 [24].”
MethodPerry, C. A., Van Guilder, G. P., & Butterick, T. A. (2022) · BMC Nutrition · doi:10.1186/s40795-022-00516-9 · Cited by 16
Amblee, A., Lious, D., & Fogelfeld, L. (2016) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2015-4097 · Cited by 16
Castro-Quezada, I., Flores-Guillén, E., Núñez-Ortega, P. E., Irecta-Nájera, C. A., Sánchez-Chino, X. M., Mendez-Flores, O. G., Olivo-Vidal, Z. E., García-Miranda, R., Solís-Hernández, R., & Ochoa-Díaz-López, H. (2019) · Nutrients · doi:10.3390/nu11123066 · Cited by 16
“The homeostasis model assessment of insulin resistance (HOMA-IR) was calculated using the formula [28]: [fasting insulin (μU/mL)*fasting glucose (mg/dL)]/405.”
MethodEvaluation of insulin sensitivity and secretion in primary aldosteronism
Watanabe, D., Yatabe, M., & Ichihara, A. (2016) · Clinical and Experimental Hypertension · doi:10.1080/10641963.2016.1182176 · Cited by 16
Validation of a simple index (SIisOGTT) of insulin sensitivity in a population of sedentary men
Pigeon, E., Riou, M. E., St-Onge, J., Couillard, E., Tremblay, A., Marette, A., Weisnagel, S. J., & Joanisse, D. R. (2009) · Diabetes & Metabolism · doi:10.1016/j.diabet.2009.04.007 · Cited by 16
Increases in muscle blood flow after a mixed meal are impaired at all stages of type 2 diabetes
Lambadiari, V., Mitrou, P., Maratou, E., Raptis, A., Raptis, S. A., & Dimitriadis, G. (2012) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2011.04211.x · Cited by 16
Ha, J., Chung, S. T., Springer, M., Kim, J. Y., Chen, P., Chhabra, A., Cree‐Green, M., Behn, C. D., Sumner, A. E., Arslanian, S. A., et al. (2023) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00189.2023 · Cited by 18
Gijbels, A., Erdõs, B., Trouwborst, I., Jardon, K. M. C., Adriaens, M. E., Goossens, G. H., Blaak, E. E., Feskens, E. J. M., & Afman, L. A. (2024) · Cardiovascular Diabetology · doi:10.1186/s12933-024-02188-0 · Cited by 21
Cho, Y., Park, H., Seo, D. H., Ahn, S. H., Hong, S., Suh, Y. J., Chon, S., Woo, J., Baik, S. H., Lee, K. W., et al. (2024) · Yonsei Medical Journal · doi:10.3349/ymj.2023.0355 · Cited by 18
Montoya-Estrada, A., García-Cortés, A. Y., Romo‐Yáñez, J., Ortíz-Luna, G. F., Arellano‐Eguiluz, A., Belmont-Gómez, A., López-Ugalde, León‐Reyes, G., Pliego, A. F., Espejel-Núñez, A., et al. (2024) · Nutrients · doi:10.3390/nu16213775 · Cited by 17
Patt, M., Karkossa, I., Krieg, L., Massier, L., Makki, K., Tabei, S., Karlas, T., Dietrich, A., Gericke, M. T., Stümvoll, M., et al. (2024) · EBioMedicine · doi:10.1016/j.ebiom.2024.105458 · Cited by 16
Yu, Y., Fu, Y.-H., Chen, Y., Fang, Y.-W., & Tsai, M. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1458353 · Cited by 17
Caviglia, G. P., Casalone, E., Rosso, C., Aneli, S., Allione, A., Carli, F., Grange, C., Armandi, A., Catalano, C., Birolo, G., et al. (2025) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.70150 · Cited by 16
Berezin, A. Е., & Kremzer, A. A. (2015) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2015.04.003 · Cited by 16
Lee, H.-O., Yim, J., Kim, Y.-S., & Choue, R. (2014) · Nutrition Research and Practice · doi:10.4162/nrp.2014.8.4.469 · Cited by 16
Effect of nutrition and atherogenic index on the occurrence and intensity of insulin resistance
Ostrowska, L., Witczak-Sawczuk, K., & Adamska-Patruno, E. (2013) · Polskie Archiwum Medycyny Wewnętrznej · doi:10.20452/pamw.1774 · Cited by 16
PDE5A Polymorphisms Influence on Sildenafil Treatment Success
Marchal, C., Herrera‐Imbroda, B., Clemente‐Postigo, M., Alcaide‐Torres, J., Quiñonero, A., Marchal, M., Queipo‐Ortuño, M. I., Aragón, I. M., Martín‐Morales, A., Lara, M. F., et al. (2016) · The Journal of Sexual Medicine · doi:10.1016/j.jsxm.2016.04.075 · Cited by 16
Mitchell, C. J., Zeng, N., D’Souza, R. F., Mitchell, S. M., Aasen, K. M. M., Fanning, A. C., Poppitt, S. D., & Cameron‐Smith, D. (2017) · Journal of the International Society of Sports Nutrition · doi:10.1186/s12970-017-0175-x · Cited by 17
Wang, X.-D., Zhou, L., Zhu, C., Chen, B., Chen, Z., & Li, W. (2018) · Clinical Interventions in Aging · doi:10.2147/cia.s149920 · Cited by 15
Vitamin D deficiency aggravates the liver metabolism and inflammation in ovariectomized mice
Borges, C. C., Bringhenti, I., Mandarim‐de‐Lacerda, C. A., & Águila, M. B. (2018) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2018.08.075 · Cited by 16
Serum irisin levels in polycystic ovary syndrome after ovarian drilling
Foda, A. A., Foda, E. A., & El-Said, Z. H. (2019) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2019.02.019 · Cited by 16
Shojasaadat, F., Ayremlou, P., Hashemi, A., Mehdizadeh, A., & Zarrin, R. (2019) · Journal of Research in Medical Sciences · doi:10.4103/jrms.jrms_282_18 · Cited by 16
Neighborhood walkability and risk of gestational diabetes
Kew, S., Ye, C., Mehmood, S., Hanley, A. J., Sermer, M., Zinman, B., & Retnakaran, R. R. (2020) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2019-000938 · Cited by 18
Liu, Y., Ma, C., Li, P., Ma, C., He, S., Ping, F., Zhang, H., Li, W., Xu, L., & Li, Y. (2020) · Oxidative Medicine and Cellular Longevity · doi:10.1155/2020/9256107 · Cited by 16
Dirajlal‐Fargo, S., Yu, J., Albar, Z., Sattar, A., Mahtab, S., Jao, J. K., Myer, L., Zar, H. J., & McComsey, G. A. (2020) · AIDS · doi:10.1097/qad.0000000000002615 · Cited by 16
Eating Duration throughout a Rotating Shift Schedule: A Case Study
Marot, L. P., Rosa, D. E., Lopes, T. D. V. C., Moreno, C. R. D. C., & Crispim, C. A. (2020) · Journal of the American College of Nutrition · doi:10.1080/07315724.2020.1814899 · Cited by 17
González-Aldaco, K., Román, S., Torres-Reyes, L. A., & Panduro, A. (2020) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s268329 · Cited by 17
Fernström, M., Fernberg, U., & Hurtig‐Wennlöf, A. (2020) · BMC Public Health · doi:10.1186/s12889-020-09801-3 · Cited by 19
Sharma, P., Han, S. M., Gillies, N. A., et al. (2020) · Nutrients · doi:10.3390/nu12113529 · Cited by 16
Abd-Allah, H., Nasr, M., Ahmed-Farid, O. A.-H., El‐Marasy, S. A., Bakeer, R. M., & Ahmed, R. F. (2021) · ACS Omega · doi:10.1021/acsomega.0c05096 · Cited by 16
West, J. A., Ghosh, S. S., Parkes, D. G., Tsakmaki, A., Grønlund, R. V., Pedersen, P. J., MAGGS, D. G., Rajagopalan, H., & Bewick, G. A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0249239 · Cited by 19
Wongdokmai, R., Shantavasinkul, P. C., Chanprasertyothin, S., Panpunuan, P., Matchariyakul, D., Sritara, P., & Sirivarasai, J. (2021) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s303146 · Cited by 15
Abaj, F., Saeedy, S. A. G., & Mirzaei, K. (2021) · BMC Research Notes · doi:10.1186/s13104-021-05597-6 · Cited by 16
Lingaiah, S., Arffman, R. K., Morin-Papunen, L. C., Tapanainen, J. S., & Piltonen, T. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-045324 · Cited by 18
Li, X.-J., Wang, H., Lu, D.-Y., Yu, T., Ullah, K., Shi, X., Shen, Y., Fei, X.-Y., Lin, Z., Huang, H., et al. (2021) · Hormone and Metabolic Research · doi:10.1055/a-1499-7718 · Cited by 16
LaBarre, J. L., Hirschfeld, E., Soni, T., Kachman, M. T., Wigginton, J. E., Duren, W. L., Fleischman, J. Y., Karnovsky, A., Burant, C., & Lee, J. M. (2021) · Nutrients · doi:10.3390/nu13103365 · Cited by 14
Gezer, E., Piro, B., Cantürk, Z., Çetinarslan, B., Sözen, M., Selek, A., IŞIK, A. Ö. P., & Seal, L. (2021) · Transgender Health · doi:10.1089/trgh.2021.0061 · Cited by 16
Tsaban, G., Meir, A. Y., Zelicha, H., Rinott, E., Kaplan, A., Shaleṿ, A., Katz, A., Brikner, D., Blüher, M., Ceglarek, U., et al. (2021) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgab681 · Cited by 16
Wang, T., Zuo, G., Yao, L., Yuan, C., Li, H., Lai, Y., Chen, Z., Zhang, J., Jin, Y., Yamahara, J., et al. (2021) · Experimental and Therapeutic Medicine · doi:10.3892/etm.2021.10901 · Cited by 18
Geraldi, M. V., Cazarin, C. B. B., Cristianini, N., Vasques, A. C. J., Geloneze, B., & Júnior, M. R. M. (2021) · British Journal Of Nutrition · doi:10.1017/s0007114521004530 · Cited by 17
Luo, P., Cao, Y., Li, P., Wang, G., Song, Z. J., Li, W., Su, Z., Zhou, H., Yi, X., Fu, Z., et al. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.772577 · Cited by 16
Prognostic Significance of Insulin Resistance in Pulmonary Hypertension
Zare, E., Kafshbani, P., Chenaghlou, M., Noori, M., Ghaemmaghami, Z., Amin, A., Taghavi, S., & Naderi, N. (2021) · ESC Heart Failure · doi:10.1002/ehf2.13752 · Cited by 16
Geng, J., Qiu, Y., Li, Y., et al. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2021.716878 · Cited by 16
Sabi, E. M., Dahman, L. S. B., Mohammed, A. K., Sumaily, K. M., & Al‐Daghri, N. M. (2022) · Medicina · doi:10.3390/medicina58030346 · Cited by 18
Hajhashemy, Z., Mirzaei, S., Asadi, A., Akhlaghi, M., & Saneei, P. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.821089 · Cited by 17
Xu, N., Liu, H., Wang, Y., et al. (2022) · BMC Endocrine Disorders · doi:10.1186/s12902-022-00943-6 · Cited by 16
Insulin secretion and action affect glucose variability in the early stages of glucose intolerance
Dimova, R. B., Chakarova, N. Y., Daniele, G., Bianchi, C., Dardano, A., Prato, S. D., & Tankova, T. I. (2022) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.3531 · Cited by 17
Huang, R., Song, C., Li, T., et al. (2022) · PLoS ONE · doi:10.1371/journal.pone.0265645 · Cited by 16
Dominoni, L. A. D. C., Gabiatti, M. P., Piazza, F. R. G., et al. (2022) · Nutrition Research · doi:10.1016/j.nutres.2022.06.006 · Cited by 16
Berezin, A. A., Fushtey, I. M., & Berezin, A. Е. (2022) · Biomedicines · doi:10.3390/biomedicines10071751 · Cited by 17
Heart rate thresholds for cardiovascular risk and sympathetic activation in the metabolic syndrome
Seravalle, G., Vanoli, J., Molisano, C., Merati, V., & Grassi, G. M. (2022) · Acta Diabetologica · doi:10.1007/s00592-022-01945-5 · Cited by 16
Reda, D., Elshopakey, G. E., Mahgoub, H. A., Risha, E., Khan, A. A., Rajab, B. S., El‐Boshy, M., & Abdelhamid, F. (2022) · Evidence-based Complementary and Alternative Medicine · doi:10.1155/2022/3362005 · Cited by 16
Ji, X., Chen, J., Xu, P., Shen, S., & Bi, Y. (2022) · Gynecological Endocrinology · doi:10.1080/09513590.2022.2119219 · Cited by 16
Michael, N., Gupta, V., Fogel, A. M., Huang, J. Y., Chen, L., Sadananthan, S. A., Ong, Y. Y., Aris, I. M., Pang, W. W., Yuan, W. L., et al. (2022) · International Journal of Epidemiology · doi:10.1093/ije/dyac177 · Cited by 18
Félix‐Soriano, E., Sáinź, N., Fernández‐Galilea, M., Gil‐Iturbe, E., Celay, J., Martinez-Climent, J. A., & Moreno-Aliaga, M. J. (2022) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2022.109153 · Cited by 18
Deischinger, C., Sluková, D., Just, I., Kaufmann, U., Harreiter, J., van Trotsenburg, M., Trattnig, S., Krššák, M., Kautzky‐Willer, A., Klepochová, R., et al. (2022) · International Journal of Transgender Health · doi:10.1080/26895269.2022.2123425 · Cited by 18
Salus, M., Tillmann, V., Remmel, L., Unt, E., Mäestu, E., Parm, Ü., Mägi, A., Tali, M., & Jürimäe, J. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph191912672 · Cited by 16
Liu, F., Chen, S., Li, X., Li, S., Xiao, Y., Han, J., Tu, Y., Bao, Y., Bai, W., & Yu, H. (2022) · Obesity Facts · doi:10.1159/000527595 · Cited by 17
Gioxari, A., Grammatikopoulou, M. G., Katsarou, C., Panagiotakos, D. B., Toutouza, M. G., Kavouras, S. A., Sidossis, L. S., & Maraki, M. I. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph192215347 · Cited by 26
Liu, Q., Huang, Y., Wang, M., Jiang, H., & Zhang, X. (2022) · Andrology · doi:10.1111/andr.13355 · Cited by 16
Ting, Y., Jalaludin, M. Y., Zaini, A. A., & Mohamed, R. B. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1071350 · Cited by 17
Giannouli, A., Efthymiou, V., Konidari, M., Mani, I., Aravantinos, L., Dourakis, S. P., Antoniou, A., Deligeoroglou, E., & Bacopoulou, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12020557 · Cited by 17
Amirkhizi, F., Khademi, Z., Shahraki, S. H., et al. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1080138 · Cited by 16
Wanders, L., Gijbels, A., Bakker, E. A., Trouwborst, I., Jardon, K. M. C., Manusama, K., Hul, G. B. J., Feskens, E. J. M., Afman, L. A., Blaak, E. E., et al. (2023) · Acta Physiologica · doi:10.1111/apha.13945 · Cited by 16
Metabolic Health, Obesity, and Intraocular Pressure
Jung, Y., Kim, G. N., Oh, E. B., Ohn, K., & Moon, J. I. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12052066 · Cited by 16
Ma, Y., Wei, S., Dang, L., Gao, L., Shang, S., Hu, N., Peng, W., Zhao, Y., Yuan, Y., Zhou, R., et al. (2023) · Nutritional Neuroscience · doi:10.1080/1028415x.2023.2193765 · Cited by 16
Shirakawa, R., Nakajima, T., Yoshimura, A., Kawahara, Y., Orito, C., Yamane, M., Handa, H., Takada, S., Furihata, T., Fukushima, A., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-32549-w · Cited by 15
Duan, S., Zhou, M., Fang, L., Chen, C., Chen, S., Geng, L., Zhang, C., Guo, H., Zeng, M., Sun, B., et al. (2023) · Endocrine · doi:10.1007/s12020-023-03357-z · Cited by 16
Inter-organ insulin-leptin signal crosstalk from the liver enhances survival during food shortages
Takahashi, K., Yamada, T., Hosaka, S., Kaneko, K., Asai, Y., Munakata, Y., Seike, J., Horiuchi, T., Kodama, S., Izumi, T., et al. (2023) · Cell Reports · doi:10.1016/j.celrep.2023.112415 · Cited by 17
Ou, Y., Qiu, Z., Geng, T., Lu, Q., Li, R., Li, L., Zhu, K., Chen, X., Lin, X., Liu, S., et al. (2023) · European Journal of Nutrition · doi:10.1007/s00394-023-03173-1 · Cited by 16
Risk factors for insulin resistance related to polycystic ovarian syndrome in Iranian population
Mansour, A., Mirahmad, M., Mohajeri‐Tehrani, M. R., Jamalizadeh, M., Hosseinimousa, S., Rashidi, F., Asili, P., & Sajjadi‐Jazi, S. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-37513-2 · Cited by 27
Sun, X., Yan, T., Li, Z., Zhou, S., Peng, W. H., Cui, W., Xu, J., Cao, Z., Shi, L. S., & Wang, Y. (2023) · Nutrients · doi:10.3390/nu15133027 · Cited by 21
Bao, L., Hao, P., Jiang, M., et al. (2023) · Chemical Biology & Drug Design · doi:10.1111/cbdd.14292 · Cited by 16
Khan, M., Shah, M. A., Kamal, M., Ola, M. S., Ali, M., & Panichayupakaranant, P. (2023) · Metabolites · doi:10.3390/metabo13070863 · Cited by 18
Yeung, R. O., Retnakaran, R. R., Savu, A., Butalia, S., & Kaul, P. (2023) · Diabetic Medicine · doi:10.1111/dme.15205 · Cited by 17
Berberine ameliorates glucocorticoid-induced hyperglycemia: an in vitro and in vivo study
Gupta, M., Rumman, M., Singh, B., Mahdi, A. A., & Pandey, S. (2023) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-023-02703-2 · Cited by 17
Guevara‐Ramírez, P., Paz‐Cruz, E., Cadena-Ullauri, S., Ruiz‐Pozo, V. A., Tamayo‐Trujillo, R., Felix, M. L., Simancas‐Racines, D., & Zambrano, A. K. (2023) · Frontiers in Nutrition · doi:10.3389/fnut.2023.1228703 · Cited by 21
Chang, W., Liu, C., Huang, Y., Wu, J., Tsai, W.-W., Hung, K., Chen, I., & Feng, P.-H. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1282675 · Cited by 17
Abdominal fat depots and their association with insulin resistance in patients with type 2 diabetes
Garg, U., Mathur, N., Sahlot, R., Tiwari, P., Sharma, B., Saxena, A., Jainaw, R. K., Agarwal, L., Gupta, S., & Mathur, S. (2023) · PLoS ONE · doi:10.1371/journal.pone.0295492 · Cited by 16
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review