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
1 of 6 cited sources checked · 0 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The fasting plasma insulin concentration is largely determined by the glucose concentration [3]”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
What the sources say
- Partly supportedcites [3] McCarthy ST, Harris F, Turner RC (1977) Glucose control of basal insulin secretion in diabetes. Diabetologia 13: 93?97 sourceExperimental — accuracy not yet measured
The Results and Discussion (page unavailable in our copy) show that changing plasma glucose affects basal insulin secretion. But they also report markedly impaired responses in diabetic subjects, including cases where insulin remained nearly constant despite a substantial glucose decrease. The paper therefore supports glucose as an influence, but not the unqualified claim that it largely determines fasting insulin concentration. (LLM, conf=0.84)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimate of deficient/3cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (R~ = 0.61, p< 0.01)”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (R~=0.88,p<0.0001)”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“There was no correlation with any aspect of insulin-receptor binding.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the fasting insulin concentration (P~ = 0.81, p < 0.0001)”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mean coefficient of variation over the two studies was 32%.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Reproducibility of estimate of /3-cell function by HOMA
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“in both groups together (1% 0.61, p < 0.01 : Fig. 3).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Comparisons of methods of assessing ~3-cell function
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and with the estimate from the intravenous glucose tolerance test (R~ = 0.64, p < 0.05).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and in both groups together: 1% = 0.64, p< 0.05; Fig. 3).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > HOMA compared with intravenous glucose tolerance test.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Neither receptor numbers or any aspect of high or low affinity binding correlated with other measures of insulin resistance (HOMA, clamp, basal insulin or CIGMA: ~ all < 0.55; NS).”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > HOMA compared with the hyperglycaemic clamp. The
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
- It may state two separate claims as one. 2
- The quote only partly states this claim. 1
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for/3-cell deficit) limits its use”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The mean coefficient of variation over the two studies was 31%.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Hyperglycaemic clamp : ~-cell function (%)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The degree of the hyperglycaemia does not correlate significantly with either the degrees of abnormal fl-cell function or insulin resistance.”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Discussion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- The authors state this as an assumption: Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
- Rests on an assumption: The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
The authors state this as an assumption.
Normal-weight normal subjects aged <35 years have 100% beta-cell function and an insulin resistance of 1.
“Assuming that normal-weight normal subjects aged < 35 years have 100% r-cell function, and an insulin resistance of 1, the values for a patient can be assessed from the insulin and glucose concentrations by the formulae: r-cell”
The paper relies on this without support.
The plasma glucose:insulin response for normal subjects is equivalent to the C-peptide response and is insulin = 5 (glucose-3.5).
“The plasma glucose:insulin response for normal subjects, equivalent to the C-peptide response, is insulin = 5 (glucose-3.5).”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“After the unprocessed diet, fasting glucose and insulin levels tended to decrease compared to baseline and the homeostasis model assessment of insulin resistance (HOMA-IR) (Matthews et al., 1985) was significantly decreased compared to baseline.”
From Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake (2019) · cites this paper as Matthews et al., 1985
The cited source says what the claim attributes to it.
In the citing paper
“The Homeostasis Model Assessment of Insulin Resistance (HoMA-IR) was calculated according to Matthews et al. [25].”
From Chronotype: A Tool to Screen Eating Habits in Polycystic Ovary Syndrome? (2022) · cites this paper as [25]
The cited source says what the claim attributes to it.
In the citing paper
“AAV9-mTERT treated mice also showed a trend for an improved glucose uptake and a better homeostatic model assessment (HOMA (Heikkinen et al, 2007; Matthews et al, 1985)) compared to the AAV9-eGFP controls, although differences did not reach statistical significance (Supporting Information Fig S4D and E).”
From Telomerase gene therapy in adult and old mice delays aging and increases longevity without increasing cancer (2012) · cites this paper as Matthews et al, 1985
In the citing paper
“Surrogate markers of peripheral insulin activity, such as homeostasis model assessment and whole-body insulin sensitivity indexes, could be useful as well in the early identification of patients with IR, before overt DM sets in ().”
From Glucose Metabolism Abnormalities in Cushing Syndrome: From Molecular Basis to Clinical Management (2017) · cites this paper as [240]
In the citing paper
“ndirect estimates of insulin resistance, such as HOMA-IR 63 and QUICKI, 64 are derived from fasting S-insulin and glucose concentrations and have been widely used in both nondiabetic and type 2 diabetic subjects as surrogates of insulin sensitivity.65,66”
From Increased liver fat, impaired insulin clearance, and hepatic and adipose tissue insulin resistance in type 2 diabetes. (2008) · cites this paper as [63]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Normal and diabetic group sizes are reversed for the CIGMA comparison
MinorThe methods say the CIGMA comparison used 12 normal subjects and 11 diabetic patients, but the results report 11 normal subjects and 12 diabetic subjects.
“The same 12 normal subjects and 11 Type 2 diabetic patients came to the hospital fasting on a separate day for a continuous infusion”
“HOMA compared with CIGMA.The HOMA estimate of insulin resistance from the mean of 15 stressed fasting samples before the CIGMA correlated with that measured by CIGMA in 11 normal subjects (R~ = 0.69, p < 0.02), in 12 diabetic subjects (R~ 0.97, p < 0.0001)”
Citations
Cites
Glucose clamp technique: a method for quantifying insulin secretion and resistance.
DeFronzo, R. A., Tobin, J. D., & Andres, R. (1979) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1979.237.3.e214 · Cited by 7,425
Quantitative estimation of insulin sensitivity.
Bergman, R. N., Ider, Y. Z., Bowden, C. R., & Cobelli, C. (1979) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1979.236.6.e667 · Cited by 1,874 · cited 1×
“This use of modelling to provide a reference graph contrasts with other models where an individual patient's data have to be entered into a computer to determine the relationships between rapidly changing variables, such as occur after an intravenous glucose test [19].”
Method[WHO Expert Committee on diabetes mellitus].
Reitsma, W. D., & Terpstra, J. (1981) · PubMed · Cited by 556
Turner, R. C., Holman, R. R., Matthews, D., Hockaday, T. D. R., & Peto, J. (1979) · Metabolism · doi:10.1016/0026-0495(79)90146-x · Cited by 388
Self-citationCharacteristics of Human Erythrocyte Insulin Receptors
Gambhir, K. K., Archer, J. A., & BRADLEY, C. J. (1978) · Diabetes · doi:10.2337/diab.27.7.701 · Cited by 290
The Insulin Receptor: Its Role in Insulin Resistance of Obesity and Diabetes
Olefsky, J. M. (1976) · Diabetes · doi:10.2337/diab.25.12.1154 · Cited by 289
Hosker, J. P., Matthews, D. R., Rudenski, A. S., Burnett, M. A., Darling, P., Bown, E. G., & Turner, R. C. (1985) · Diabetologia · doi:10.1007/bf00280882 · Cited by 287
Self-citationPulsatile Insulin Has Greater Hypoglycemic Effect Than Continuous Delivery
Matthews, D. R., Naylor, B. A., Jones, R., Ward, G. M., & Turner, R. C. (1983) · Diabetes · doi:10.2337/diab.32.7.617 · Cited by 256
Self-citationControl of pulsatile insulin secretion in man
Matthews, D. R., Lang, D. A., Burnett, M. A., & Turner, R. C. (1983) · Diabetologia · doi:10.1007/bf00282705 · Cited by 175
Self-citationTHE BASAL PLASMA GLUCOSE: A SIMPLE RELEVANT INDEX OF MATURITY‐ONSET DIABETES
HOLMAN, R. R., & TURNER, R. C. (1981) · Clinical Endocrinology · doi:10.1111/j.1365-2265.1981.tb00196.x · Cited by 64
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- Metropolitan Life Assurance Co (1959) Net weight standard for men and women. Stat Bull Metrol Life Found 40: 1?4
- Matthews DR, Hosker JP (1984) An unbiased, flexible computer programme for glucose clamping, with graphics and running statistics. Diabetologia 27: 308?309 A
Cited by
Sorted by citations
Items 26,301–26,400 of 31,469
Bang, K., Jun, J. E., Jeong, I., Ahn, K. J., Chung, H. Y., & Hwang, Y. (2021) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2020.0208 · Cited by 2
González-Salazar, L. E., Granados‐Portillo, O., Vera, I., Pichardo‐Ontiveros, E., Vigil‐Martínez, A., Guizar-Heredia, R., Palacios‐González, B., Arteaga-Sánchez, L., Hernández-Gómez, K. G., Ávila-Nava, A., et al. (2021) · British Journal Of Nutrition · doi:10.1017/s0007114521002920 · Cited by 3
Nonalcoholic fatty liver disease in adolescents with phenylketonuria
Silveira, A. M., Lima, P. L., Alves, M. R. A., et al. (2022) · Jornal de Pediatria · doi:10.1016/j.jped.2022.08.003 · Cited by 2
McKay, A. J., Jones, M. T., & Gallo, S. (2023) · Southern Medical Journal · doi:10.14423/smj.0000000000001506 · Cited by 2
Ahmed, A., & Ahmed, M. S. M. (2023) · Isokinetics and Exercise Science · doi:10.3233/ies-220117 · Cited by 3
Kim, H. J., Shin, J. S., Kim, W. G., & Lee, S. Y. (2023) · Food & Function · doi:10.1039/d3fo00589e · Cited by 2
The correct formula for computing the triglyceride-glucose index
Mahat, R. K., Panda, S., & Rathore, V. (2023) · Diabetes Epidemiology and Management · doi:10.1016/j.deman.2023.100156 · Cited by 2
Effect of Hypoenergetic Diet Combined With Pumpkin Seed Flour Consumption on Obese Women
Carvalho, A. P. S. D. O., Aranha, L. N., Soares, L. A., Luiz, R. R., Nogueira, J. F., de Oliveira, G. M. M., & Rosa, G. (2023) · International Journal of Cardiovascular Sciences · doi:10.36660/ijcs.20220134 · Cited by 3
Wen, S., Ni, Y., Dai, Y., Liu, Z., Wang, X., Zhang, J., Yu, W., & Hu, W. (2023) · Eating and Weight Disorders - Studies on Anorexia Bulimia and Obesity · doi:10.1007/s40519-023-01609-5 · Cited by 3
Krysta, K., Trędzbor, B., Martyniak, E., Cieślik, A., Koźmin-Burzyńska, A., Piekarska-Bugiel, K., Skałacka, K., Bieś, R., & Krzystanek, M. (2023) · Pharmaceuticals · doi:10.3390/ph16121633 · Cited by 2
Fasting hepatic insulin clearance reflects postprandial hepatic insulin clearance: a brief report
Okura, T., Nakamura, R., Kitao, S., et al. (2023) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-023-01241-4 · Cited by 2
Liu, Y., Hao, L., Yin, C., et al. (2024) · Annals of Nutrition and Metabolism · doi:10.1159/000536433 · Cited by 2
Fayed, A., Mohamed, A., Ahmed, R. M., et al. (2023) · Saudi Journal of Kidney Diseases and Transplantation · doi:10.4103/1319-2442.395446 · Cited by 2
Metabolic Profiles of Offspring Born From Biopsied Embryos from Toddlerhood to Preschool Age
Zhao, J., Li, S., Ban, M., et al. (2024) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgae315 · Cited by 2
Jiang, W., Yang, L., & Liang, S. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1429154 · Cited by 2
Bombardieri, A., Bufano, A., Fralassi, N., Ciuoli, C., Benenati, N., Dalmiglio, C., Voglino, C., Tirone, A., Vuolo, G., & Castagna, M. G. (2024) · Obesity Surgery · doi:10.1007/s11695-024-07426-8 · Cited by 4
Association Between PPARγ Polymorphisms and Neurological Functional Disability of Ischemic Stroke
Yan, R., Qiu, X. Y., Dai, Y., Jiang, Y., Gu, H., Jiang, Y., Ding, L., Cheng, S., Meng, X., Wang, Y., et al. (2022) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1177/0271678x241274681 · Cited by 2
Koyama, K., Sakamaki, A., Morita, S., Nagayama, I., Kudo, M., Tanaka, Y., Kimura, N., Arao, Y., Abé, H., Kamimura, K., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-72217-1 · Cited by 2
Rees, J., Walesiak, D., Thompson, R. B., Mager, D. R., Senior, P. A., & Boulé, N. G. (2024) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000003552 · Cited by 2
Chartrand, D. J., Larose, É., Poirier, P., Mathieu, P., Alméras, N., Pîbarot, P., Lamarche, B., Rhéaume, C., Lemieux, I., Després, J., et al. (2024) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2024.09.010 · Cited by 2
Lingling, W., Zhang, L., Zhao, D., Ma, Y., & Yang, L. (2024) · Endocrine · doi:10.1007/s12020-024-04040-7 · Cited by 2
Fang, C. Y., Rao, A. D., Handorf, E. A., Deng, M., Cheung, P., & Tseng, M. (2024) · Annals of Behavioral Medicine · doi:10.1093/abm/kaae056 · Cited by 2
Amrousy, D. M. E., Ashry, H. E., Maher, S., et al. (2024) · European Journal of Pediatrics · doi:10.1007/s00431-024-05809-8 · Cited by 2
Pointke, M., Strenge, F., Piotrowski, D., Matteikat, A., Meyhöfer, S., Meyhöfer, S. M., Chamorro, R., & Wilms, B. (2024) · Frontiers in Nutrition · doi:10.3389/fnut.2024.1469230 · Cited by 2
Metabolic stress in patients with acute severe ulcerative colitis - a single-center cohort study
Redsted, M., Grønhøj, M., Brøchner, L. D., Fassov, J., Svart, M. V., Andersen, J. R., & Hvas, C. L. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1395686 · Cited by 2
Feng, Y., Yu, Q., Gu, F., Feng, Q., & Zhang, Y. (2024) · Frontiers in Nutrition · doi:10.3389/fnut.2024.1450127 · Cited by 2
Kadowaki, Y., Aoyama, T., Hada, Y., Aihara, M., Sawada, M., Sekine, R., Itoh, H., Kadowaki, T., Kubota, N., & Yamauchi, T. (2024) · Endocrine Journal · doi:10.1507/endocrj.ej24-0315 · Cited by 3
Šimunović, M., Kumrić, M., Rušić, D., Šimunović, M. P., & Božić, J. (2024) · Diagnostics · doi:10.3390/diagnostics14242801 · Cited by 3
Maanaoui, M., Debillon, F., Lenain, R., Defrance, F., Vantyghem, M., Triempont, M. V., Provôt, F., Chetboun, M., Kerr-Conte, J. A., Hamroun, A., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-83817-2 · Cited by 3
Abdelaziz, A. I., El‐Far, Y. M., & Abdel‐Rahman, N. (2025) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.202400654 · Cited by 2
Markussen, L. T., Kivelä, J., Lindström, J., Ollikainen, M., Kytö, M., Heinonen, S. T., Koivusalo, S. B., & Meinilä, J. M. (2025) · BMC Nutrition · doi:10.1186/s40795-024-00988-x · Cited by 2
Venegas, J. J., Weisz, J. M., Choi, C. Y., Herringshaw, R. E., Nabelsi, O. A., & Liang, N. (2025) · Physiological Reports · doi:10.14814/phy2.70184 · Cited by 2
Borel, A., Ngo, T. H. V., Coumes, S., Pépin, J., Abba, J., Tamisier, R., Tonetti, G., Suzeau, P., Reche, F., & Bailly, S. (2025) · Clinical Nutrition · doi:10.1016/j.clnu.2025.09.022 · Cited by 2
Insulin Resistance-related Gray Matter Volume Reduction is Associated with the Default Mode Network
Asano, S., Ogawa, A., Osada, T., OKA, S., Nakajima, K., Oshima, Y., Tanaka, S., Kaga, H., Tamura, Y., Watada, H., et al. (2025) · Juntendō Igaku/Juntendo igaku · doi:10.14789/ejmj.jmj24-0044-ot · Cited by 2
Song, H., Lü, J., Zhang, Y., & Shen, X. (2025) · Chemistry & Biodiversity · doi:10.1002/cbdv.202402446 · Cited by 2
Hu, M., Sun, D., Yang, F., Zheng, X., Wu, N., Zhang, H., Yao, X.-D., Zhang, J.-H., & Zhang, L. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1505050 · Cited by 3
Farrow, M. T., Li, J., Chahande, S., Minarsch, R., Orchard, T., Schwab, J. M., & Yarar‐Fisher, C. (2025) · Topics in Spinal Cord Injury Rehabilitation · doi:10.46292/sci24-00044 · Cited by 2
Bermúdez‐Millán, A., Pérez‐Escamilla, R., Segura‐Pérez, S., Grady, J. J., Feinn, R., Agresta, H. O., Kim, D. Y., & Wagner, J. A. (2025) · JMIR Formative Research · doi:10.2196/66970 · Cited by 2
Association of early onset gestational diabetes mellitus with postpartum glucose intolerance
Morita, A., Tanaka, A., Higeta, D., Sato, T., Yamada, E., & Iwase, A. (2025) · Diabetology International · doi:10.1007/s13340-025-00807-z · Cited by 2
Zhang, K., Huang, C., Li, J., Mai, P., Xu, S., Huang, F., He, W., Zhang, H., Liu, Y., & Feng, W. (2025) · American Journal of Preventive Cardiology · doi:10.1016/j.ajpc.2025.100952 · Cited by 2
Elhag, W., Elgenaied, I., Lock, M., & Ansari, W. E. (2025) · Obesity Surgery · doi:10.1007/s11695-024-07664-w · Cited by 3
Martinez, A. S., Bastian, A. J., Shemirani, F., Titcomb, T. J., Bisht, B., Darling, W. G., Ramanathan, M. K., Shittu, M. O., Gill, C. M., Snetselaar, L., et al. (2025) · Nutrients · doi:10.3390/nu17071163 · Cited by 2
Mira-Martínez, S., Fiol-deRoque, M. A., Malih, N., Zamanillo‐Campos, R., Angullo-Martínez, E., Jiménez, R., Serrano-Ripoll, M. J., Leiva, A., Oña, J. I., Galmes-Panades, A. M., et al. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1524336 · Cited by 4
Sileo, F., Leone, A., Amicis, R. S. D., et al. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14072401 · Cited by 2
Berezin, A. Е., Berezina, T. A., Novikov, E. V., & Berezin, O. O. (2025) · Biomedicines · doi:10.3390/biomedicines13040866 · Cited by 3
Suleman, S., Ängquist, L. H., Linneberg, A. R., Hansen, T., & Grarup, N. (2025) · Scientific Reports · doi:10.1038/s41598-025-98507-w · Cited by 2
McLeod, A., Lamar, M., Schiffer, L. A., Blumstein, L. B., Siegel, L., Berbaum, M. L., Tussing-Humphreys, L. M., & Fitzgibbon, M. (2025) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2025.103099 · Cited by 5
Valder, S., Schick, F., Pietsch, N., Wagner, T., Urban, H., Lindemann, P., Riemer, L., Quenzer, S., Herdegen, V., Diel, P. R., et al. (2025) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2025.104121 · Cited by 3
El-Naby, S. M. A., Khedr, N. F., El‐Ashmawy, N. E., & Ibrahim, A. O. (2025) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-025-04271-z · Cited by 2
Carosso, A. R., Revelli, A., Ruffa, A., Carosso, M., Contangelo, G., Benedetto, C., & Gennarelli, G. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14113848 · Cited by 2
Wang, J., Young, G., Zhang, M., Salvatore, S. R., Qin, F., Cai, X., Xu, M., Ke, M., Liu, L., Kim, J., et al. (2025) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2025.110327 · Cited by 4
Heimann, A. S., Singh, P., Ferro, E. S., Greenway, F. L., & Krongrad, A. (2025) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.70056 · Cited by 3
Donado-Pestana, C. M., Rodrigues, L. M., Rundle, M., Thomas, E. L., Wopereis, S., Gundersen, T. E., Trezzi, J., Bunzel, D., Kiselova-Kaneva, Y. D., Sonntag, D., et al. (2025) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2025.112081 · Cited by 2
Agoons, D. D., & Agoons, B. B. (2025) · Cureus · doi:10.7759/cureus.85656 · Cited by 2
Asuzu, P., Feto, N. A., Wan, J. Y., Stentz, F. B., Mandal, N. A., & Dagogo-Jack, S. E. (2025) · Experimental Biology and Medicine · doi:10.3389/ebm.2025.10538 · Cited by 2
Bae, J. H., Lee, M. J., Kim, S. H., Kim, J. Y., Khang, A. R., Kang, Y. H., & Yi, D. (2025) · PLoS ONE · doi:10.1371/journal.pone.0327058 · Cited by 2
Association of predicted basal metabolic rate and insulin resistance in a Chinese general population
Wang, L., Lu, T., Wu, P., Chen, K., & Yi-ming, M. (2025) · BMC Endocrine Disorders · doi:10.1186/s12902-025-01976-3 · Cited by 2
Negm, S. H., Eid, W., & El-Masry, H. G. (2025) · Journal of Future Foods · doi:10.1016/j.jfutfo.2025.06.006 · Cited by 2
Epicardial fat tissue in metabolically healthy and unhealthy obesity: culprit or bystander?
de Alteriis, G., Pugliese, G., Somma, C. D., Falco, A., Muscogiuri, G., Barrea, L., Laudisio, D., Zumbolo, F., ESPOSITO, R., Pucci, M., et al. (2025) · Minerva Endocrinology · doi:10.23736/s2724-6507.25.04257-5 · Cited by 2
Mousa, A. M., Taher, R. F., El-Sammad, N. M., Balabel, E. A., Youssef, E. M., Afifi, A. H., Abdelrahman, S. S. M., Anwar, N., & Hassan, S. K. (2025) · BMC Complementary Medicine and Therapies · doi:10.1186/s12906-025-05010-w · Cited by 2
Drakopoulos, M., Nadel, A., Bains, H. K., Bisen, J. B., Sikora, H., Zhang, K. X., Marchese, A., Fahey, J. D., & Mirza, R. G. (2025) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2025.07.005 · Cited by 2
Armentaro, G., Condoleo, V., Francica, M., Severini, G., Pastura, C. A., Divino, M., Panza, A., Panza, M., Capilupi, F., Maruca, F., et al. (2025) · European Journal of Clinical Investigation · doi:10.1111/eci.70098 · Cited by 2
D’Alleva, M., Lazzer, S., Martino, M. D., Mari, L., Rejc, E., Zaccaron, S., Stafuzza, J., Isola, M., Bondesan, A., Caroli, D., et al. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1624696 · Cited by 2
BMI, Sleep Architecture, and Glucose Metabolism: Insights From the Baependi Heart Study
Pessoa, C. M., Taporoski, T. P., Beijamini, F., Alexandria, S. J., Krieger, J. E., von Schantz, M., Pereira, A. C., & Knutson, K. L. (2025) · Obesity · doi:10.1002/oby.24359 · Cited by 2
Quintiero, V., Crisafulli, O., Lacetera, J., Bottoni, G., Negro, M., Tupler, R. G., Lavaselli, E., & D’Antona, G. (2025) · Case Reports in Medicine · doi:10.1155/carm/7873892 · Cited by 2
Mutual Impact of Dietary Antioxidants and TNF-α rs1800629 on Insulin Levels in Adults with Obesity
Sierra-Ruelas, É., Vizmanos-Lamotte, B., Gómez, J. J. L., Rico, D., Martínez, J. A., & de Luis, D. A. (2025) · Nutrients · doi:10.3390/nu17142345 · Cited by 2
Priego-Parra, B. A., Román-Calleja, B. M., Gallego‐Durán, R., Gracia‐Sancho, J., Velasco, J. A. V.-R., & Remes-Troche, J. M. (2025) · World Journal of Hepatology · doi:10.4254/wjh.v17.i7.107668 · Cited by 2
Bahçebaşı, S., Açmaz, B., Aydın, E., Biçer, İ., & Yıldız, Ş. (2025) · Current Medical Research and Opinion · doi:10.1080/03007995.2025.2545489 · Cited by 2
Regional and systemic adipose mass and peripheral conduit artery function
Wakeham, D. J., Beckman, B. E., Mashayekhi, M., Luther, J. M., Garner, E. M., Perkins, B., Gottlieb, J., Niswender, K. D., Hearon, C. M., Brown, N. J., et al. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112408 · Cited by 2
Mildly elevated liver lipid content is characterised by reduced insulin sensitivity
Tuomola, N., Rebelos, E., Latva‐Rasku, A., Bucci, M., Immonen, H., Saunavaara, V., Laine, S., Sjöros, T., Garthwaite, T., Raiko, J. R. H., et al. (2025) · JHEP Reports · doi:10.1016/j.jhepr.2025.101535 · Cited by 2
Fernández‐Aparicio, Á., Mohatar-Barba, M., Perona, J. S., et al. (2025) · Metabolites · doi:10.3390/metabo15080535 · Cited by 2
Riedel, S., de Buys, K., Tshivhase, A. M., Mendham, A. E., Venter, P. B., Hoosen, F., Dugas, L. R., D’Alton, C., Rusch, J. A., Southon, B., et al. (2025) · PLoS ONE · doi:10.1371/journal.pone.0328774 · Cited by 2
CEFALO, C. M. A., Fiorentino, T. V., Rubino, M., et al. (2025) · Cardiovascular Diabetology · doi:10.1186/s12933-025-02889-0 · Cited by 2
Sharifi-Rigi, A., Zal, F., Aarabi, M. H., Dehghani, M., Rad, N. R., & Taghiyar, S. (2025) · International Journal of Endocrinology · doi:10.1155/ije/5878361 · Cited by 2
Oh, D. J., Jeong, C., Kim, J., Kim, E. S., Oh, K., Shin, Y. C., Shin, D.-W., Jeon, S. W., & Cho, S. J. (2025) · The Lancet Regional Health - Western Pacific · doi:10.1016/j.lanwpc.2025.101672 · Cited by 4
Wang, Y., Liu, J., Yang, Y., Wang, Q., Sun, Y., Shen, R., & Zhang, M. (2025) · Annals of Medicine · doi:10.1080/07853890.2025.2548976 · Cited by 4
Padilha, C. S., Olean‐Oliveira, T., Figueiredo, C., dos Santos, V. R., Dorneles, G. P., Ribeiro, J. P. J., Deminice, R., Krüger, K., Neto, J. C. R., & Lira, F. S. (2025) · Physiological Reports · doi:10.14814/phy2.70470 · Cited by 2
Choi, W., Moon, J. H., Choe, H. J., Chang, H. H., Kondal, D., Narayan, K. M. V., & Cho, N. H. (2025) · Journal of Internal Medicine · doi:10.1111/joim.70010 · Cited by 4
Wang, J., Liu, L., Liu, H., Qian, Y., Zhang, S., Zheng, S., Jiang, H., Zhou, Y., Cheng, X., Fu, Q., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102327 · Cited by 4
Marzi, G., Verrienti, M., Longo, F., Cattaneo, C. A., Gobbo, A., Vetralla, K., Franzè, G. P., Cossu, A., Culcasi, M., Zatelli, M. C., et al. (2025) · Endocrine · doi:10.1007/s12020-025-04410-9 · Cited by 2
El-Sayed, N. S., Omar, E. M., Habiba, E. S., Harby, S. A., Attaallah, A., Augustyniak, M., Wakil, A. E., El‐Samad, L. M., & Hassan, M. A. (2025) · Journal of Trace Elements in Medicine and Biology · doi:10.1016/j.jtemb.2025.127756 · Cited by 2
Choi, Y. J., Jung, J. I., Lim, S., Kim, C. S., Park, D. W., Lee, S. H., Hur, N., Bae, J., Lee, J. K., & Kim, E. J. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26188950 · Cited by 2
Xiang, Q., Li, Y., Lip, G. Y. H., Cuthbertson, D. J., Huang, W., Zhong, Z., Dong, B., Yue, J., & Isanejad, M. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70113 · Cited by 5
Cannabis improves metabolic dysfunction and macrophage signatures in obese mice
VanderVeen, B. N., Cardaci, T. D., Unger, C. A., NeSmith, M. M., Freeman, J. C., Bastian, A. V., Roark, K. L., Upadhyay, M., Levy, A. G., Bullard, B. M., et al. (2025) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00503.2025 · Cited by 2
Bian, H., Zhang, Y., & Liu, K. (2025) · Sexual Medicine · doi:10.1093/sexmed/qfaf075 · Cited by 2
Masood, S., Khan, I. S., Ihsan, M. N., et al. (2025) · Acta Alimentaria · doi:10.1556/066.2025.00161 · Cited by 2
Liu, H., Wang, L., Zhu, X., et al. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1657912 · Cited by 2
Liu, S., Qin, L., Li, D., Lü, F., Liang, M., & Hao, J. (2025) · Marine Drugs · doi:10.3390/md23100374 · Cited by 3
Tang, X., Li, B., Li, D., & Wang, Y. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1619613 · Cited by 2
Takeshita, M., Matsumura, T., Arimura, A., Takahashi, H., Muta, Y., Kawade, S., Yamagami, D., Deguchi, T., Kondo, T., Yamanaka, M., et al. (2025) · Journal of Diabetes Investigation · doi:10.1111/jdi.70169 · Cited by 2
Yang, Y. J., Hong, M. H., Yoon, J. J., Tai, A. L., Lee, H. S., Park, S. J., Kim, H. Y., & Kang, D. G. (2025) · BMC Complementary Medicine and Therapies · doi:10.1186/s12906-025-05074-8 · Cited by 2
Averta, C., Rubino, M., Mancuso, E., Massimino, M., Coppolino, G., Bolignano, D., Cassano, V., Greco, M., Palummo, A., Sciacqua, A., et al. (2025) · Journal of Translational Medicine · doi:10.1186/s12967-025-07079-w · Cited by 3
Gawron, I. M., Brodowicz, J., Skotniczny, K., & Jach, R. (2025) · Archives of Medical Research · doi:10.1016/j.arcmed.2025.103325 · Cited by 2
Tagliamonte, S., Lumaga, R. B., Filippis, F. D., Valentino, V., Aponte, M., Romano, R., & Vitaglione, P. (2025) · Current Research in Food Science · doi:10.1016/j.crfs.2025.101215 · Cited by 2
Dorling, J. L., Martin, C. K., Das, S. K., Racette, S. B., Redman, L. M., Huffman, K. M., Höchsmann, C., & Kraus, W. E. (2025) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-025-01825-5 · Cited by 3
Günal, A. M., YILMAZ, H. Ö., & Baş, M. (2025) · Food Science & Nutrition · doi:10.1002/fsn3.71066 · Cited by 2
Minno, A. D., Morone, M. V., Buccato, D. G., Lellis, L. F. D., Ullah, H., Borromeo, L., Cerqua, A., Piccinocchi, R., Greco, A., Santonastaso, S., et al. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1671102 · Cited by 2
Ninomiya, T., Konishi, H., Ueda, E., Sakata, S., Oishi, E., Furuta, Y., Ohmae, S., Hoshiko, H., Tateishi, N., Shibata, M., et al. (2025) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2025.109199 · Cited by 3
Brennan, E., Radhi, M. K. E. A., Husain, Z. A. A. H., Sathyapalan, T., Moin, A. S. M., Butler, A. E., & Atkin, S. L. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms262010168 · Cited by 2
Ma, J., Jin, K., Wang, Y., Wang, X., Lou, X., Liu, X., & Cao, L. (2025) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s542653 · Cited by 2
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
