The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index; 1 of the 6 sources its claims cite were checked for support.
Homeostasis model assessment: insulin resistance and ?-cell function from fasting plasma glucose and insulin concentrations in man
Open accessAbstract
The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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Claims the paper makes
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
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Items 4,401–4,500 of 31,469
Koot, B. G. P., van der Baan‐Slootweg, O. H., Bohte, A. E., Nederveen, A. J., van Werven, J. R., Tamminga-Smeulders, C. L. J., Merkus, M. P., Schaap, F. G., Jansen, P. L. M., Stoker, J., et al. (2013) · Obesity · doi:10.1002/oby.20173 · Cited by 78
Shin, J. Y., Shin, Y. G., & Chung, C. H. (2006) · Diabetes Care · doi:10.2337/dc06-0631 · Cited by 77
The accelerator hypothesis and increasing incidence of type 1 diabetes
Fourlanos, S., Harrison, L. C., & Colman, P. G. (2008) · Current Opinion in Endocrinology Diabetes and Obesity · doi:10.1097/med.0b013e3283073a5a · Cited by 76
Plum, L., Ahmed, L., Febres, G., Bessler, M., Inabnet, W., Kunreuther, E., McMahon, D., & Korner, J. (2011) · Obesity · doi:10.1038/oby.2011.134 · Cited by 82
Prevalence and trends of insulin resistance, impaired fasting glucose, and diabetes
Ioannou, G. N., Bryson, C. L., & Boyko, E. J. (2007) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2006.07.005 · Cited by 77
Kim, M.-H., Park, J.-S., Jung, J.-W., Byun, K.-W., Kang, K.-S., & Lee, Y. S. (2010) · International Journal of Obesity · doi:10.1038/ijo.2010.256 · Cited by 78
Ndisang, J. F., & Jadhav, A. (2009) · Endocrinology · doi:10.1210/en.2008-1370 · Cited by 77
Viardot, A., Lord, R. V., & Samaras, K. (2010) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2009-2371 · Cited by 79
BANK, A., Kelly, A. S., Thelen, A. M., Kaiser, D. R., & González-Campoy, J. M. (2007) · American Journal of Hypertension · doi:10.1016/j.amjhyper.2007.01.019 · Cited by 77
Macis, D., Gandini, S., Guerrieri‐Gonzaga, A., Johansson, H. A., Magni, P., Ruscica, M., Lazzeroni, M., Serrano, D., Cazzaniga, M., Mora, S., et al. (2011) · Journal of Clinical Oncology · doi:10.1200/jco.2011.35.2237 · Cited by 77
Is Ghrelin the Culprit for Weight Loss after Gastric Bypass Surgery? A Negative Answer
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Novel Metabolic Markers for the Risk of Diabetes Development in American Indians
Zhao, J., Zhu, Y., Hyun, N., Zeng, D., Uppal, K., Tran, V., Yu, T., Jones, D. P., He, J., Lee, E. T., et al. (2014) · Diabetes Care · doi:10.2337/dc14-2033 · Cited by 77
Catov, J. M., Newman, A. B., Sutton-Tyrrell, K., Harris, T. B., Tylavsky, F., Visser, M., Ayonayon, H. N., & Ness, R. B. (2008) · Annals of Epidemiology · doi:10.1016/j.annepidem.2008.09.009 · Cited by 77
Isomaa, B., Forsén, B., Lahti, K., Holmström, N., Wadén, J., Matintupa, O., Almgren, P., Eriksson, J. G., Lyssenko, V., Taskinen, M., et al. (2010) · Diabetologia · doi:10.1007/s00125-010-1776-y · Cited by 79
Yılmaz, M. İ., Axelsson, J., Sönmez, A., Carrero, J. J., Sağlam, M., Eyileten, T., Çağlar, K., Kırkpantur, A., Celık, T., Oğuz, Y., et al. (2009) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.04330808 · Cited by 76
Gasparetti, A. L., de Souza, C. T., Pereira‐da‐Silva, M., Oliveira, R. L. G. S., Saad, M. J. A., Carneiro, E. M., & Velloso, L. A. (2003) · The Journal of Physiology · doi:10.1113/jphysiol.2003.050369 · Cited by 77
Pannexin 1 is required for full activation of insulin-stimulated glucose uptake in adipocytes
Adamson, S. E., Meher, A. K., Chiu, Y., Sandilos, J. K., Oberholtzer, N. P., Walker, N. N., Hargett, S. R., Seaman, S. A., Peirce, S. M., Isakson, B. E., et al. (2015) · Molecular Metabolism · doi:10.1016/j.molmet.2015.06.009 · Cited by 81
Meier, J. J., Holst, J. J., Schmidt, W. E., & Nauck, M. A. (2007) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00289.2007 · Cited by 79
Small for gestational age status is associated with metabolic syndrome in overweight children
Reinehr, T., Kleber, M., & Toschke, A. M. (2009) · European Journal of Endocrinology · doi:10.1530/eje-08-0914 · Cited by 77
Faeh, D., William, J., Yerly, P., Paccaud, F. M., & Bovet, P. (2007) · Cardiovascular Diabetology · doi:10.1186/1475-2840-6-32 · Cited by 77
Relation of Cumulative Weight Burden to Vascular Endothelial Dysfunction in Obesity
Arkin, J. M., Alsdorf, R., Bigornia, S., et al. (2007) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2007.07.055 · Cited by 77
Ganie, M., Marwaha, R., Aggarwal, R., & Singh, S. (2010) · European Journal of Endocrinology · doi:10.1530/eje-09-1012 · Cited by 78
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Chen, D., Chen, Y., Hsieh, T., Hsieh, C., Lin, C., & Lan, J. (2015) · Arthritis Research & Therapy · doi:10.1186/s13075-015-0559-8 · Cited by 77
Dursun, P., Demirtaş, E., Bayrak, A., & Yaralı́, H. (2005) · Human Reproduction · doi:10.1093/humrep/dei284 · Cited by 78
Differential effects of conjugated linoleic acid isomers in insulin-resistant female C57Bl/6J mice
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Impact of successful transsphenoidal surgery on cardiovascular risk factors in acromegaly
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Lü, M., Tang, Y., Li, M., Yang, P., Liu, Z., Yuan, J., & Zheng, P. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138078 · Cited by 78
Hwang, Y., Fujimoto, W. Y., Hayashi, T., Kahn, S. E., Leonetti, D. L., & Boyko, E. J. (2015) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2015-3246 · Cited by 79
Does accelerated reproductive aging underlie premenopausal risk for cardiovascular disease?
Bleil, M. E., Gregorich, S. E., McConnell, D. S., Rosen, M. P., & Cedars, M. I. (2013) · Menopause The Journal of The North American Menopause Society · doi:10.1097/gme.0b013e31828950fa · Cited by 80
Popa, S. G., Moța, M., Popa, A., Moţa, E., Serafinceanu, C., Guja, C., Catrinoiu, D., Hâncu, N., Lichiardopol, R., Bala, C. G., et al. (2016) · Journal of Endocrinological Investigation · doi:10.1007/s40618-016-0470-4 · Cited by 83
PREVENT-DM Comparative Effectiveness Trial of Lifestyle Intervention and Metformin
O’Brien, M. J., Perez, A., Scanlan, A. B., Alos, V. A., Whitaker, R. C., Foster, G. D., Ackermann, R. T., Ciolino, J. D., & Homko, C. J. (2017) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2017.01.008 · Cited by 79
Cai, J., Wu, C., Zhang, Y., Wang, Y., Xu, W., Lin, T., Li, S. X., Wang, L. H., Zheng, J., Sun, Y. W., et al. (2017) · International Journal of Obesity · doi:10.1038/ijo.2017.116 · Cited by 78
Madhuri, K., & Naik, P. R. (2017) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2017.09.122 · Cited by 77
Guided bone regeneration using bone grafts and collagen membranes.
Wang, H., & Carroll, M. J. (2001) · PubMed · Cited by 80
Jia, A., Xu, S., Xing, Y., Zhang, W., Yu, X., Zhao, Y., Ming, J., & Ji, Q. (2018) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2018.06.015 · Cited by 78
Paolisso, G. (2001) · Experimental Gerontology · doi:10.1016/s0531-5565(01)00148-6 · Cited by 77
Huang, T. T. ‐., Johnson, M. S., & Goran, M. I. (2002) · Diabetes Care · doi:10.2337/diacare.25.7.1203 · Cited by 76
Fat depot-specific expression of adiponectin is impaired in zucker fatty rats
Altomonte, J., Harbaran, S., Richter, A. M., & Dong, H. H. (2003) · Metabolism · doi:10.1016/s0026-0495(03)00092-1 · Cited by 77
Yamamoto, Y., Hirose, H., Miyashita, K., Nishikai, K., Saito, I., Taniyama, M., Tomita, M., & Saruta, T. (2002) · Metabolism · doi:10.1053/meta.2002.35586 · Cited by 77
Birth weight of offspring and insulin resistance in late adulthood: cross sectional survey
Lawlor, D. A., Smith, G. D., & Ebrahim, S. (2002) · BMJ · doi:10.1136/bmj.325.7360.359 · Cited by 77
Sarafidis, P. A., Lasaridis, A. N., Nilsson, P. M., Pagkalos, E., Hitoglou-Makedou, A., Pliakos, C. I., Kazakos, K. A., Yovos, J. G., Zebekakis, P. E., Tziolas, I. M., et al. (2004) · Journal of Hypertension · doi:10.1097/00004872-200409000-00022 · Cited by 77
Rajala, U., Laakso, M., Päivänsalo, M. J., Pelkonen, O., Suramo, I., & Keinanen-Kiukaanniemi, S. M. (2002) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2002-020703 · Cited by 76
Fabbri, E., Chia, C. W., Spencer, R. G., Fishbein, K. W., Reiter, D. A., Cameron, D., Zane, A. C., Moore, Z. A., Gonzalez-Freire, M., Zoli, M., et al. (2017) · Diabetes · doi:10.2337/db16-0754 · Cited by 76
LESMANA, C. R. A., HASAN, I., BUDIHUSODO, U., GANI, R. A., KRISNUHONI, E., AKBAR, N., & LESMANA, L. A. (2009) · Journal of Digestive Diseases · doi:10.1111/j.1751-2980.2009.00386.x · Cited by 76
Oikawa, S. Y., McGlory, C., D'Souza, L. K., Morgan, A. K., Saddler, N. I., Baker, S. K., Parise, G., & Phillips, S. M. (2018) · The American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqy193 · Cited by 76
Garekani, E. T., Mohebbi, H., Kraemer, R. R., & Fathi, R. (2011) · Peptides · doi:10.1016/j.peptides.2011.01.027 · Cited by 77
Kubota, Y., Miyamoto, M., Takagi, G., Ikeda, T., Kirinoki‐Ichikawa, S., Tanaka, K., & Mizuno, K. (2012) · Journal of Korean Medical Science · doi:10.3346/jkms.2012.27.11.1364 · Cited by 76
Verbeek, J., Lannoo, M., Pirinen, E., Ryu, D., Spincemaille, P., Elst, I. V., Windmolders, P., Thevissen, K., Cammue, B. P. A., van Pelt, J. F., et al. (2014) · Gut · doi:10.1136/gutjnl-2014-306748 · Cited by 79
Salord, N., Fortuna, A. M., Monasterio, C., Gasa, M., Pérez, A., Bonsignore, M. R., Vilarrasa, N., Montserrat, J. M., & Mayos, M. (2015) · SLEEP · doi:10.5665/sleep.5312 · Cited by 79
Nouwen, A., Chambers, A., Chechlacz, M., Higgs, S., Blissett, J. M., Barrett, T. G., & Allen, H. A. (2017) · NeuroImage Clinical · doi:10.1016/j.nicl.2017.07.004 · Cited by 77
Prolonged, Uninterrupted Sedentary Behavior and Glycemic Biomarkers Among US Hispanic/Latino Adults
Diaz, K. M., Goldsmith, J., Greenlee, H., Strizich, G., Qi, Q., Mossavar‐Rahmani, Y., Vidot, D. C., Buelna, C., Brintz, C., Elfassy, T., et al. (2017) · Circulation · doi:10.1161/circulationaha.116.026858 · Cited by 80
Kuwabara, W. M. T., Panveloski-Costa, A. C., Yokota, C. N. F., Pereira, J. N. B., Filho, J. M., Torres, R. P., Hirabara, S. M., Curi, R., & Alba‐Loureiro, T. C. (2017) · PLoS ONE · doi:10.1371/journal.pone.0189622 · Cited by 76
Ortiz, R., Kluwe, B., Odei, J. B., Echouffo-Tcheugui, J. B., Sims, M., Kalyani, R. R., Bertoni, A. G., Golden, S. H., & Joseph, J. J. (2018) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2018.12.237 · Cited by 81
Marinho, T. D. S., Ornellas, F., Barbosa‐da‐Silva, S., Mandarim‐de‐Lacerda, C. A., & Águila, M. B. (2019) · Nutrition · doi:10.1016/j.nut.2019.02.020 · Cited by 81
Pellegrini, M., Ippolito, M., Monge, T., Violi, R., Cappello, P., Ferrocino, I., Cocolin, L., Francesco, A. D., Bo, S., & Finocchiaro, C. (2020) · Nutrition · doi:10.1016/j.nut.2020.110749 · Cited by 79
Goss, A. M., Dowla, S. A., Pendergrass, M., Ashraf, A. P., Bolding, M., Morrison, S. A., Amerson, A., Soleymani, T., & Gower, B. A. (2020) · Pediatric Obesity · doi:10.1111/ijpo.12630 · Cited by 78
Doulberis, M., Srivastava, S., Pοlyzos, S. A., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9040933 · Cited by 76
Kim, D., Vazquez‐Montesino, L. M., Escober, J. A., Fernandes, C. T., Cholankeril, G. T., Loomba, R. S., Harrison, S. A., Younossi, Z. M., & Ahmed, A. (2020) · The American Journal of Gastroenterology · doi:10.14309/ajg.0000000000000654 · Cited by 78
Zeinali, F., Zarch, S. M. A., Jahan-Mihan, A., Kalantar, S. M., Mehrjardi, M. Y. V., Fallahzadeh, H., Hosseinzadeh, M., Rahmanian, M., & Mozaffari–Khosravi, H. (2021) · PLoS ONE · doi:10.1371/journal.pone.0251697 · Cited by 77
Hammad, R., Aglan, R. B., Mohammed, S. A., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232012541 · Cited by 76
The Role of Hormones in Hidradenitis Suppurativa: A Systematic Review
Rached, N. A., Gambichler, T., Dietrich, J. W., Ocker, L., Seifert, C., Stockfleth, E., & Bechara, F. G. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232315250 · Cited by 84
Xiang, Q., Zhang, J., Li, C., Wang, Y., Zeng, M.-J., Cai, Z.-X., Tian, R.-B., Jia, W. P., & Li, X. (2015) · Neuropeptides · doi:10.1016/j.npep.2015.08.009 · Cited by 80
Yokoyama, H., Okazaki, K., Imai, D., Yamashina, Y., Takeda, R., Naghavi, N., Ota, A., Hirasawa, Y., & Miyagawa, T. (2015) · BMC Geriatrics · doi:10.1186/s12877-015-0058-4 · Cited by 76
Arginase inhibition alleviates hypertension in the metabolic syndrome
El‐Bassossy, H. M., El‐Fawal, R., Fahmy, A., & Watson, M. (2013) · British Journal of Pharmacology · doi:10.1111/bph.12144 · Cited by 75
Incidence of type 2 diabetes in southern Spain (Pizarra Study)
Soriguer, F., Rojo‐Martínez, G., Almaraz, M. C., Esteva, I., de Adana, M. S. R., Morcillo, S., Valdés, S., García‐Fuentes, E., García‐Escobar, E., Cardona, I., et al. (2008) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.2007.01910.x · Cited by 74
Wang, C., Lin, H., Shin, S., Yu, M., Lin, Z., Dai, C., Huang, J., Chen, S., Li, S. S., & Chuang, W. (2011) · Liver International · doi:10.1111/j.1478-3231.2011.02526.x · Cited by 76
Ryu, O., Lee, S., Yu, J., Choi, M. G., Yoo, H. J., & Mantero, F. (2013) · Endocrine Journal · doi:10.1507/endocrj.ej13-0356 · Cited by 76
Assessment of methods and indexes of insulin sensitivity
Patarrão, R. S., Lautt, W. W., & Macedo, M. P. (2014) · Endocrinology Insights · doi:10.1016/j.rpedm.2013.10.004 · Cited by 92
Sakurai, T., Iimuro, S., Araki, A., Umegaki, H., Ohashi, Y., Yokono, K., & Ito, H. (2009) · Gerontology · doi:10.1159/000246970 · Cited by 81
In Central Obesity, Weight Loss Restores Platelet Sensitivity to Nitric Oxide and Prostacyclin
Russo, I., Traversa, M., Bonomo, K., Salve, A. D., Mattiello, L., Mese, P. D., Doronzo, G., Cavalot, F. L., Trovati, M., & Anfossi, G. (2009) · Obesity · doi:10.1038/oby.2009.302 · Cited by 79
Hamza, N., Berke, B., Chèze, C., et al. (2012) · Journal of Ethnopharmacology · doi:10.1016/j.jep.2012.05.028 · Cited by 76
Effects of Meal Frequency on Metabolic Profiles and Substrate Partitioning in Lean Healthy Males
Munsters, M. J. M., & Saris, W. H. M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0038632 · Cited by 83
Effects of GCK, GCKR, G6PC2 and MTNR1B Variants on Glucose Metabolism and Insulin Secretion
Hu, C., Zhang, R., Wang, C., Yu, W., Lu, J., Ma, X., Wang, J., Jiang, F., Tang, S., Bao, Y., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0011761 · Cited by 76
Retinol and α-Tocopherol in Morbid Obesity and Nonalcoholic Fatty Liver Disease
Botella-Carretero, J. I., Balsa, J. A., Vázquez, C., Peromingo, R., Díaz-Enriquez, M., & Escobar-Morreale, H. F. (2008) · Obesity Surgery · doi:10.1007/s11695-008-9686-5 · Cited by 77
Maeda, S., Nakanishi, S., Yoneda, M., Awaya, T., Yamane, K., Hirano, T., & Kohno, N. (2011) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.11445 · Cited by 76
Low serum bilirubin concentration is associated with coronary artery calcification (CAC)
Tanaka, M., Fukui, M., Tomiyasu, K.-I., et al. (2009) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2009.02.010 · Cited by 76
Fatigue and autonomic dysfunction in non-alcoholic fatty liver disease
Newton, J. L., Pairman, J., Wilton, K., Jones, D. E. J., & Day, C. P. (2009) · Clinical Autonomic Research · doi:10.1007/s10286-009-0031-4 · Cited by 77
LEE, Y. Y., Lee, N. S., Cho, Y., Moon, M. K., Jung, H. S., Park, Y. J., Park, H. J., Youn, B., Lee, H. K., Park, K. S., et al. (2005) · Diabetic Medicine · doi:10.1111/j.1464-5491.2005.01460.x · Cited by 76
Kuzu, N., Bahçecioğlu, İ. H., Dağlı, A. F., Özercan, İ. H., Üstündağ, B., & Şahin, K. (2007) · Journal of Gastroenterology and Hepatology · doi:10.1111/j.1440-1746.2007.05052.x · Cited by 77
Kao, A. H., Wasko, M. C., Krishnaswami, S., Wagner, J. A., Edmundowicz, D., Shaw, P., Cunningham, A. L., Danchenko, N., Sutton‐Tyrrell, K., Tracy, R. P., et al. (2008) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2008.04.059 · Cited by 76
Chang, H. M., Lee, H. J., Park, H. S., Kang, J., Kim, K. S., Song, Y. S., & Jang, Y. J. (2010) · Obesity · doi:10.1038/oby.2010.60 · Cited by 76
Interleukin 17 as a novel predictor of vascular function in rheumatoid arthritis
Marder, W., Khalatbari, S. H., Myles, J. D., HENCH, R. P., Yalavarthi, S., Lustig, S., Brook, R. D., & Kaplan, M. J. (2011) · Annals of the Rheumatic Diseases · doi:10.1136/ard.2010.148031 · Cited by 76
Prudent diet and the risk of insulin resistance
Villegas, R., Salim, A., Flynn, A., & Perry, I. J. (2004) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/s0939-4753(04)80023-1 · Cited by 76
Tomaro-Duchesneau, C., Saha, S., Malhotra, M., Jones, M. L., Labbé, A., Rodes, L., Kahouli, I., & Prakash, S. (2013) · Applied Microbiology and Biotechnology · doi:10.1007/s00253-013-5252-8 · Cited by 77
Eguchi, Y., Mizuta, T., Sumida, Y., Ishibashi, E., Kitajima, Y., Isoda, H., Horie, H., Tashiro, T., Iwamoto, E., Takahashi, K., et al. (2010) · Journal of Gastroenterology · doi:10.1007/s00535-010-0340-3 · Cited by 76
Lewandowski, K. C., Banach, E., Bieńkiewicz, M., et al. (2011) · Archives of Medical Science · doi:10.5114/aoms.2011.22081 · Cited by 76
Purtell, L., Sze, L., Loughnan, G., Smith, E., Herzog, H., Sainsbury, A., Steinbeck, K. S., Campbell, L. V., & Viardot, A. (2011) · Neuropeptides · doi:10.1016/j.npep.2011.06.001 · Cited by 76
Lee, W., Lee, Y.-C., Ser, K., Chen, J., & Chen, S. C. (2008) · Obesity Surgery · doi:10.1007/s11695-008-9457-3 · Cited by 76
Zuo, H., Shi, Z., Yuan, B., Dai, Y., Hu, G., Wu, G., & Hussain, A. (2012) · BMC Public Health · doi:10.1186/1471-2458-12-247 · Cited by 75
Volynets, V., Machann, J., Küper, M. A., Maier, I. B., Spruss, A., Königsrainer, A., Bischoff, S. C., & Bergheim, I. (2012) · European Journal of Nutrition · doi:10.1007/s00394-012-0355-z · Cited by 76
Caravaggio, F., Borlido, C., Hahn, M., Feng, Z., Fervaha, G., Gerretsen, P., Nakajima, S., Plitman, E., Chung, J. K., Iwata, Y., et al. (2015) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyv014 · Cited by 76
Prior, J. O., Schindler, T. H., Facta, A. D., Hernandez‐Pampaloni, M., Campisi, R., Dahlbom, M. L., & Schelbert, H. R. (2006) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-006-0193-4 · Cited by 76
Matsuhisa, M., Yamasaki, Y., Emoto, M., Shimabukuro, M., Funahashi, T., & Matsuzawa, Y. (2006) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2006.10.005 · Cited by 76
Life Course Influences on Insulin Resistance
Lawlor, D. A., Smith, G. D., & Ebrahim, S. B. J. (2003) · Diabetes Care · doi:10.2337/diacare.26.1.97 · Cited by 76
Jensterle, M., Šebeštjen, M., Janež, A., Preželj, J., Kocjan, T., Keber, I., & Pfeifer, M. (2008) · European Journal of Endocrinology · doi:10.1530/eje-08-0507 · Cited by 76
Daan, N. M. P., Jaspers, L., Koster, M. P. H., Broekmans, F. J., de Rijke, Y. B., Franco, O. H., Laven, J. S. E., Kavousi, M., & Fauser, B. C. J. M. (2015) · Human Reproduction · doi:10.1093/humrep/dev195 · Cited by 76
Florez, J. C., Jablonski, K. A., Taylor, A., Mather, K. J., Horton, E. S., White, N. H., Barrett-Connor, E. L., Knowler, W. C., Shuldiner, A. R., & Pollin, T. I. (2012) · Diabetes Care · doi:10.2337/dc11-2301 · Cited by 78
Rosso, C., Mezzabotta, L., Gaggini, M., Salomone, F., Gambino, R., Marengo, A., Saba, F., Vanni, E., Younes, R., Saponaro, C., et al. (2015) · Hepatology · doi:10.1002/hep.28287 · Cited by 76
Vikøren, L. A. S., Nygard, O. K., Lied, E., Rostrup, E., & Gudbrandsen, O. A. (2012) · British Journal Of Nutrition · doi:10.1017/s0007114512001717 · Cited by 76
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.
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