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 10,801–10,900 of 31,469
Hayashi, Y., Okumura, K., Matsui, H., Imamura, A., Miura, M., Takahashi, R., Murakami, R., Ogawa, Y., Numaguchi, Y., & Murohara, T. (2007) · Metabolism · doi:10.1016/j.metabol.2007.01.001 · Cited by 35
Lara, M., Bustos, P., Amigo, H., Silva, C., & Rona, R. J. (2012) · BMC Public Health · doi:10.1186/1471-2458-12-638 · Cited by 33
Ernst, B., Thurnheer, M., & Schultes, B. (2009) · Obesity Surgery · doi:10.1007/s11695-009-9856-0 · Cited by 33
Insulin Resistance and Depressive Symptoms in Older Men: The Health in Men Study
Ford, A. H., Flicker, L. A., Hankey, G. J., Yeap, B. B., Chubb, S. A. P., Golledge, J., & Almeida, O. P. (2014) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2014.10.010 · Cited by 33
Sevillano, J., de Castro, J., Bocos, C., Herrera, E., & Ramos, P. (2007) · Endocrinology · doi:10.1210/en.2007-0352 · Cited by 33
Vignera, S. L., Condorelli, R. A., Bellanca, S., et al. (2011) · PubMed · Cited by 33
Association of Vaspin with Metabolic Syndrome: The Pivotal Role of Insulin Resistance
Esteghamati, A., Noshad, S., Mousavizadeh, M., Zandieh, A., & Nakhjavani, M. (2014) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2014.38.2.143 · Cited by 33
Hu, C., Jia, W., Fang, Q., Zhang, R., Wang, C., Lu, J., & Xiang, K. (2006) · Diabetic Medicine · doi:10.1111/j.1464-5491.2006.02001.x · Cited by 33
Ma, Y., Gao, J., Yin, J., Gu, L., Liu, X., Su, C., Huang, Q., Lu, H., Yang, Y., Zhou, H., et al. (2016) · Journal of Proteome Research · doi:10.1021/acs.jproteome.5b01030 · Cited by 33
Take, K., Mochida, T., Maki, T., Satomi, Y., Hirayama, M., Nakakariya, M., Amano, N., Adachi, R., Sato, K., Kitazaki, T., et al. (2016) · PLoS ONE · doi:10.1371/journal.pone.0150976 · Cited by 35
Bervoets, L., Mewis, A., & Massa, G. (2014) · Hormone and Metabolic Research · doi:10.1055/s-0034-1395551 · Cited by 31
Monfort‐Pires, M., Folchetti, L. D., Previdelli, Á. N., Siqueira-Catania, A., de Barros, C. R., & Ferreira, S. R. G. (2013) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2013-0279 · Cited by 34
Korkmaz, H., Sözen, M., & Kebapçılar, L. (2015) · European Journal of Gastroenterology & Hepatology · doi:10.1097/meg.0000000000000437 · Cited by 33
Kanbay, M., Afşar, B., Siriopol, D. C., et al. (2016) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2016.06.033 · Cited by 33
Pointer, S. D., Rickstrew, J. J., Slaughter, J. C., Vaezi, M. F., & Silver, H. J. (2016) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.13784 · Cited by 33
Lepsch, J., Eshriqui, I., Farias, D., Vaz, J., Figueiredo, A. C. C., Adegboye, A., Brito, A., Mokhtar, R. R., Allen, L., Holick, M., et al. (2017) · Metabolism: Clinical and Experimental · doi:10.1016/j.metabol.2017.02.004 · Cited by 30
Moon, J. H., Roh, E., Oh, T. J., Kim, K. M., Moon, J. H., Lim, S. K., Jang, H. C., & Choi, S. H. (2017) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-017-0210-8 · Cited by 35
Mooij, C. F., van Herwaarden, A. E., Sweep, F. C. G. J., Roeleveld, N., de Korte, C. L., Kapusta, L., & der Grinten, H. L. C. (2017) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2017-0068 · Cited by 32
Younes, R., Rosso, C., Petta, S., Cucco, M., Marietti, M., Caviglia, G. P., Ciancio, A., Abate, M. L., Cammà, C., Smedile, A., et al. (2017) · Liver International · doi:10.1111/liv.13612 · Cited by 33
Kang, S.-J., Kim, J.-H., Gang, Z., Yook, Y.-S., Yoon, J.-R., Ha, G.-C., & Ko, K.-J. (2018) · Journal of Physical Therapy Science · doi:10.1589/jpts.30.169 · Cited by 34
Objectively measured physical activity and cognitive function in urban‐dwelling older adults
Umegaki, H., Makino, T., Uemura, K., Shimada, H., Cheng, X. W., & Kuzuya, M. (2018) · Geriatrics and gerontology international/Geriatrics & gerontology international · doi:10.1111/ggi.13284 · Cited by 36
Identification of human skeletal muscle miRNA related to strength by high-throughput sequencing
Mitchell, C. J., D’Souza, R. F., Schierding, W. S., Zeng, N., Ramzan, F., O'Sullivan, J. M., Poppitt, S. D., & Cameron‐Smith, D. (2018) · Physiological Genomics · doi:10.1152/physiolgenomics.00112.2017 · Cited by 34
Somodi, S., Seres, I., Lőrincz, H., Harangi, M., Fülöp, P., & Paragh, G. (2018) · International Journal of Endocrinology · doi:10.1155/2018/9596054 · Cited by 33
Kogure, G. S., Silva, R. C., Furtado, C. L. M., Ribeiro, V. B., Pedroso, D. C. C., de Melo, A. S., Ferriani, R. A., & dos Reis, R. M. (2018) · The Journal of Strength and Conditioning Research · doi:10.1519/jsc.0000000000002714 · Cited by 33
Yu, X., Zhao, Y., Song, X., & Song, Z. (2014) · Journal of Zhejiang University SCIENCE B · doi:10.1631/jzus.b1400028 · Cited by 34
Castro, G. F. M., Escobar-Morreale, H. F., Ortega, H., et al. (2000) · Clinical Endocrinology · doi:10.1046/j.1365-2265.2000.01092.x · Cited by 33
Vascular Structure and Function in Women
Olson, T. P., Schmitz, K. H., Leon, A. S., & Dengel, D. R. (2006) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2006.02.006 · Cited by 33
Metabolic syndrome in obese adolescents
Bustos, P., Sáez, K. L., Gleisner, A., Natalia Ulloa, M., Calvo, C., & Sylvia Asenjo, M. (2009) · Pediatric Diabetes · doi:10.1111/j.1399-5448.2009.00531.x · Cited by 33
Crowther, N. J., Cameron, N., Trusler, J., Toman, M., Norris, S. A., & Gray, I. P. (2008) · PEDIATRICS · doi:10.1542/peds.2007-3147 · Cited by 33
Serum lipids in children 3 to 5 years after kidney, liver, and heart transplantation
Siirtola, A., Antikainen, M., Ala‐Houhala, M., et al. (2004) · Transplant International · doi:10.1007/s00147-003-0677-0 · Cited by 33
Koba, S. (2003) · Atherosclerosis · doi:10.1016/s0021-9150(03)00245-4 · Cited by 33
Components of Metabolic Syndrome Predicting Diabetes: No Role of Inflammation or Dyslipidemia
Norberg, M., Stenlund, H. C., Lindahl, B., Andersson, C., Weinehall, L., Hallmans, G., & Eriksson, J. W. (2007) · Obesity · doi:10.1038/oby.2007.222 · Cited by 34
Kerstens, M. N., van der Kleij, F. G. H., Boonstra, A. H., Sluiter, W. J., Koerts, J., Navis, G. J., & Dullaart, R. P. F. (2003) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2002-021625 · Cited by 33
Mondy, K. E., Fuentes, L. D. L., Waggoner, A. D., Önen, N. F., Bopp, C., Lassa‐Claxton, S., Powderly, W. G., Dávila‐Román, V. G., & Yarasheski, K. E. (2008) · AIDS · doi:10.1097/qad.0b013e3282f70694 · Cited by 33
R??os-V??zquez, R., Marzoa‐Rivas, R., Gil-Ortega, I., & Kaski, J. C. (2006) · American Journal of Cardiovascular Drugs · doi:10.2165/00129784-200606040-00003 · Cited by 33
Yuen, K. C. J., Cook, D. M., Ong, K. K., Chatelain, P., Fryklund, L., Gluckman, P. D., Ranke, M., Rosenfeld, R. G., & Dunger, D. B. (2002) · Clinical Endocrinology · doi:10.1046/j.1365-2265.2002.01601.x · Cited by 32
Should we quantify insulin resistance in patients with renal disease? (Review Article)
Shen, Y., Peake, P. W., & Kelly, J. J. (2005) · Nephrology · doi:10.1111/j.1440-1797.2005.00490.x · Cited by 33
Insulin Resistance Is Associated with Arterial Stiffness Independent of Obesity in Male Adolescents
Lee, J., Lee, D., Im, J., Shim, J., Kim, S.-M., & Lee, H.-R. (2007) · Hypertension Research · doi:10.1291/hypres.30.5 · Cited by 33
Prehypertension and Insulin Resistance in a Nationally Representative Adult Population
Player, M. S., Mainous, A. G., Díaz, V. A., & Everett, C. J. (2007) · Journal of Clinical Hypertension · doi:10.1111/j.1524-6175.2007.06439.x · Cited by 33
Ahmad, J., Hameed, B., Das, G., Siddiqui, M. A., & Ahmad, I. (2005) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2004.11.012 · Cited by 33
Freel, E. M., Tsorlalis, I. K., Lewsey, J., Latini, R., Maggioni, A. P., Solomon, S., Pitt, B., Connell, J. M. C., & McMurray, J. J. V. (2009) · Heart · doi:10.1136/hrt.2009.173344 · Cited by 35
Moffett, S. P., Feingold, E., Barmada, M. M., et al. (2005) · Metabolism · doi:10.1016/j.metabol.2005.05.025 · Cited by 33
The association of birth weight with arterial stiffness at mid-adulthood: the Bogalusa Heart Study
Mzayek, F., Sherwin, R., Hughes, J., et al. (2009) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2008.084475 · Cited by 33
Erythrocyte Membrane Phosphatidylserine Exposure in Obesity
Solá, E., Vayá, A., Martínez, M., et al. (2008) · Obesity · doi:10.1038/oby.2008.499 · Cited by 33
Zhang, X., Yin, K., Li, C., Jia, E., Li, Y., & Zhao-fang, G. A. O. (2007) · Chinese Medical Journal · doi:10.1097/00029330-200709010-00002 · Cited by 32
McLellan, J. A. S., Barrow, B. A., Lévy, J., Hammersley, M. S., Hattersley, A. T., Gillmer, M. D. G., & Turner, R. C. (1995) · Diabetologia · doi:10.1007/bf00401841 · Cited by 33
Self-citationHypertriglyceridemia is associated with preβ-HDL concentrations in subjects with familial low HDL
Söderlund, S., Soro‐Paavonen, A., Ehnholm, C., Jauhiainen, M., & Taskinen, M. (2005) · Journal of Lipid Research · doi:10.1194/jlr.m400480-jlr200 · Cited by 33
Cheng, C., Campbell, K. L., Kushner, H., & Falkner, B. E. (2004) · Metabolism · doi:10.1016/j.metabol.2004.04.004 · Cited by 33
Decreased fibrinolytic potential in South Asian women with ischaemic cerebrovascular disease
Kain, K., Catto, A. J., Carter, A. M., et al. (2001) · British Journal of Haematology · doi:10.1046/j.1365-2141.2001.02916.x · Cited by 33
Winocour, P. H., Kaluvya, S., Ramaiya, K. L., Brown, L., Millar, J. P., Farrer, M., Neil, H. A. W., Laker, M. F., & Alberti, K. G. M. M. (1992) · Arteriosclerosis and Thrombosis A Journal of Vascular Biology · doi:10.1161/01.atv.12.3.393 · Cited by 33
Halimi, J.-M., Forhan, A., Balkau, B., Novak, M., Wilpart, E., Tichet, J., & Marre, M. (2001) · European Journal of Cardiovascular Prevention & Rehabilitation · doi:10.1177/174182670100800304 · Cited by 33
Harrela, M., Koistinen, R., Tuomilehto, J. O., Nissinen, A. M., & Seppälä, M. (2000) · Annals of Medicine · doi:10.3109/07853890008995950 · Cited by 33
Lewis, J. G., Shand, B. I., Frampton, C. M., Elder, P. A., & Scott, R. S. (2008) · Clinical Biochemistry · doi:10.1016/j.clinbiochem.2008.06.002 · Cited by 32
Koivunen, R. M., Morin-Papunen, L. C., Ruokonen, A., Tapanainen, J. S., & Martikainen, H. K. (2001) · Human Reproduction · doi:10.1093/humrep/16.12.2546 · Cited by 33
Nagata, C., Shimizu, H., Takami, R., Hayashi, M., Takeda, N., & Yasuda, K. (2000) · Japanese Journal of Cancer Research · doi:10.1111/j.1349-7006.2000.tb01039.x · Cited by 33
Jahansouz, C., Serrot, F. J., Frohnert, B. I., Foncea, R. E., Dorman, R. B., Slusarek, B., Leslie, D. B., Bernlohr, D. A., & Ikramuddin, S. (2015) · Obesity Surgery · doi:10.1007/s11695-015-1708-5 · Cited by 32
The effects of growth hormone status on circulating levels of vascular growth factors
Silha, J. V., Krsek, M., Hana, V., Marek, J., Weiss, V., Jezkova, J., Rosicka, M., Jarkovska, Z., & Murphy, L. J. (2005) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2005.02303.x · Cited by 31
Tsurutani, Y., Sugisawa, C., Ishida, A., Inoue, K., Saito, J., Omura, M., Nagasaka, S., & Nishikawa, T. (2017) · Endocrine Journal · doi:10.1507/endocrj.ej16-0500 · Cited by 32
Liang, S., Cheng, X., Hu, Y., Song, R., & Li, G. (2017) · Acta Paediatrica · doi:10.1111/apa.13685 · Cited by 32
Camajani, E., Persichetti, A., Watanabe, M., Contini, S., Vari, M., Di Bernardo, S., Faro, M., Lubrano, C., Gnessi, L., Caprio, M., et al. (2022) · Nutrients · doi:10.3390/nu14091884 · Cited by 34
de Oliveira, K. M., Figueiredo, L. S., Araújo, T. R., Freitas, I. N., Silva, J. N., Boschero, A. C., & Ribeiro, R. A. (2020) · Steroids · doi:10.1016/j.steroids.2020.108658 · Cited by 32
Panda, V., Deshmukh, A., Singh, S., Shah, T., & Hingorani, L. (2021) · Journal of Ayurveda and Integrative Medicine · doi:10.1016/j.jaim.2021.05.005 · Cited by 33
Hartescu, I., Stensel, D. J., Thackray, A. E., King, J. A., Dorling, J. L., Rogers, E. N., Hall, A. P., Brady, E. M., Davies, M. J., Yates, T. E., et al. (2021) · Journal of Sleep Research · doi:10.1111/jsr.13469 · Cited by 33
Zhou, Y., Lu, Q., Zhang, M., et al. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13030495 · Cited by 32
Miola, A., Álvarez‐Villalobos, N. A., Ruiz-Hernández, F. G., Filippis, E. D., Veldić, M., Prieto, M. L., Singh, B., Sánchez-Ruiz, J. A., Núñez, N. A., Gardea‐Resendez, M., et al. (2023) · Journal of Affective Disorders · doi:10.1016/j.jad.2023.04.068 · Cited by 33
Wen, Q., Fang, S., Liang, Y., Tian, Y., Chen, Y., Yuan, J., & Qiu, C. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1156521 · Cited by 36
The Presence and Severity of NAFLD are Associated With Cognitive Impairment and Hippocampal Damage
Miao, Y., Zhang, B., Sun, X., Ma, X., Fang, D., Zhang, W., Wu, T., Xu, X. M., Yu, C., Hou, Y., et al. (2023) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgad352 · Cited by 37
ANGPTL3 Deficiency and Risk of Hepatic Steatosis
D’Erasmo, L., Martino, M. D., Neufeld, T., Fraum, T. J., Kang, C. J., Burks, K. H., Costanzo, A. D., Minicocci, I., Bini, S., Maranghi, M., et al. (2023) · Circulation · doi:10.1161/circulationaha.123.065866 · Cited by 35
Ma, N., Bai, L., & Lü, Q. (2024) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s454826 · Cited by 34
Insulin resistance, bone health, and fracture risk
Armutçu, F., & McCloskey, E. V. (2024) · Osteoporosis International · doi:10.1007/s00198-024-07227-w · Cited by 34
Liver enzymes and clustering cardiometabolic risk factors in E uropean adolescents: the HELENA study
Labayen, I., Ruiz, J. R., Ortega, F. B., Davis, C. L., Rodriguez, G. P., González‐Gross, M., Breidenassel, C., Dallongeville, J., Marcos, A., Widhalm, K., et al. (2014) · Pediatric Obesity · doi:10.1111/ijpo.273 · Cited by 32
Kazeem, M. I., Akanji, M. A., & Yakubu, M. T. (2015) · Pathophysiology · doi:10.1016/j.pathophys.2015.08.004 · Cited by 32
Non-linear contribution of serum vitamin D to symptomatic diabetic neuropathy: A case-control study
Esteghamati, A., Fotouhi, A., Faghihi‐Kashani, S., Hafezi‐Nejad, N., Heidari, B., Sheikhbahaei, S., Zandieh, A., & Nakhjavani, M. (2015) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2015.10.018 · Cited by 32
Sesé, M. A., Jiménez‐Pavón, D., Gilbert, C. C., González‐Gross, M., Gottrand, F., Henauw, S. D., Breidenassel, C., Wärnberǵ, J., Widhalm, K., Molnár, D., et al. (2012) · Appetite · doi:10.1016/j.appet.2012.04.011 · Cited by 34
Lagerpusch, M., Enderle, J., Eggeling, B., Braun, W., Johannsen, M., Pape, D., Müller, M. J., & Bosy‐Westphal, A. (2013) · Journal of Nutrition · doi:10.3945/jn.113.179390 · Cited by 32
AR CAG repeat length is not associated with serum gonadal steroids and lipid levels in healthy men
Goutou, M., Sakka, C., Stakias, N., et al. (2008) · International Journal of Andrology · doi:10.1111/j.1365-2605.2008.00908.x · Cited by 32
Serum vitamin B12 and folate levels in patients with non-alcoholic fatty liver disease
Pοlyzos, S. A., Kountouras, J., Patsiaoura, K., et al. (2012) · International Journal of Food Sciences and Nutrition · doi:10.3109/09637486.2011.649249 · Cited by 32
Esteghamati, A., Khalilzadeh, O., Rashidi, A., Meysamie, A., Haghazali, M., Asgari, F., Abbasi, M., Rastad, S., & Gouya, M. M. (2009) · Preventive Medicine · doi:10.1016/j.ypmed.2009.09.005 · Cited by 32
Lindegaard, B., Hvid, T., Grøndahl, T. S., Frøsig, C., Gerstoft, J., Højman, P., & Pedersen, B. K. (2013) · PLoS ONE · doi:10.1371/journal.pone.0055632 · Cited by 32
Cerit, E. T., Aktürk, M., Altınova, A. E., Tavıl, Y., Özkan, Ç., Yayla, Ç., Altay, M., Demirtas, C., & Çakır, N. (2014) · Endocrine · doi:10.1007/s12020-014-0460-2 · Cited by 31
Microalbuminuria in obstructive sleep apnea syndrome
Bulcun, E., Ekici, M. S., Ekici, A., et al. (2015) · Sleep And Breathing · doi:10.1007/s11325-015-1136-8 · Cited by 32
Takeshita, Y., Takamura, T., Ando, H., Hamaguchi, E., Takazakura, A., Nagata, N., & Kaneko, S. (2007) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2007.11.016 · Cited by 32
Shantavasinkul, P. C., Phanachet, P., Puchaiwatananon, O., Chailurkit, L., Lepananon, T., Chanprasertyotin, S., Ongphiphadhanakul, B., & Warodomwichit, D. (2014) · Nutrition · doi:10.1016/j.nut.2014.11.011 · Cited by 32
Saito, E., Okada, T., Abe, Y., Odaka, M., Kuromori, Y., Iwata, F., Hara, M., Mugishima, H., & Kitamura, Y. (2013) · Prostaglandins Leukotrienes and Essential Fatty Acids · doi:10.1016/j.plefa.2013.01.005 · Cited by 32
Associations Between Total Serum Ggt Activity and Metabolic Risk: MESA
Bradley, R. D., Fitzpatrick, A. L., Jenny, N. S., Lee, D., & Jacobs, D. R. (2013) · Biomarkers in Medicine · doi:10.2217/bmm.13.71 · Cited by 32
Imbeault, P., Findlay, C. S., Robidoux, M. A., Haman, F., Blais, J. M., Tremblay, A., Springthorpe, S., Pal, S., Seabert, T., Krümmel, E. M., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039931 · Cited by 33
Ameliorative effect of nicorandil on high fat diet induced non-alcoholic fatty liver disease in rats
Elshazly, S. M. (2014) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2014.12.017 · Cited by 33
King, J. A., Garnham, J. O., Jackson, A. P., Kelly, B. M., Xenophontos, S., & Nimmo, M. A. (2015) · Physiology & Behavior · doi:10.1016/j.physbeh.2014.12.050 · Cited by 35
Obesity, metabolic syndrome, and insulin dynamics in children after craniopharyngioma surgery
Sahakitrungruang, T., Klomchan, T., Supornsilchai, V., & Wacharasindhu, S. (2010) · European Journal of Pediatrics · doi:10.1007/s00431-010-1347-8 · Cited by 32
Zhang, W., Wu, R., Zhang, F., Xu, Y.-Z., Liu, B., Yang, Y., Zhou, H., Wang, L., Wan, K., Xiao, X., et al. (2012) · Clinical and Experimental Pharmacology and Physiology · doi:10.1111/1440-1681.12020 · Cited by 32
Blijdorp, K., van Waas, M., van der Lely, A. J., et al. (2013) · Leukemia Research · doi:10.1016/j.leukres.2012.12.008 · Cited by 32
Esteghamati, A., Noshad, S., Khalilzadeh, O., et al. (2010) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2010.0066 · Cited by 32
Couture, P., Tremblay, A. J., Kelly, I., et al. (2013) · Journal of Lipid Research · doi:10.1194/jlr.m040071 · Cited by 32
Mehta, S. R., Godsland, I. F., Thomas, E. L., Pavitt, D. V., Morin, S. H., Bell, J. D., Taylor-Robinson, S. D., & Johnston, D. G. (2012) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2011-2430 · Cited by 31
Crawford, K. W., Li, X., Xu, X., Abraham, A. G., Dobs, A. S., Margolick, J. B., Palella, F. J., Kingsley, L., Witt, M. D., & Brown, T. T. (2013) · AIDS Research and Human Retroviruses · doi:10.1089/aid.2013.0020 · Cited by 32
Gagnon, C., Brown, C., Couture, C., Kamga-Ngandé, C., Hivert, M., Baillargeon, J., Carpentier, A. C., & Langlois, M. (2011) · Diabetes & Metabolism · doi:10.1016/j.diabet.2011.01.003 · Cited by 33
A Murine Model of Phosphate Nephropathy
Eller, P., Eller, K., Kirsch, A. H., Patsch, J. J., Wolf, A. M., Tagwerker, A., Stanzl, U., Kaindl, R., Kahlenberg, V., Mayer, G. J., et al. (2011) · American Journal Of Pathology · doi:10.1016/j.ajpath.2011.01.024 · Cited by 32
Tam, C. H. T., Ho, J. S. K., Wang, Y., Lam, V. K. L., Lee, H. M., Jiang, G., Lau, E. S. H., Kong, A. P. S., Fan, X., Woo, J., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0083093 · Cited by 32
Su, Y., Wang, D., Xuan, D., Ni, J., Luo, S., Xie, B., & Zhang, J. (2013) · Journal of Periodontology · doi:10.1902/jop.2013.120442 · Cited by 33
Wu, J., Zhang, H., Zheng, H., & Jiang, Y. (2014) · BMC Veterinary Research · doi:10.1186/1746-6148-10-162 · Cited by 32
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