The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index; 1 of the 6 sources its claims cite were checked for support.
Homeostasis model assessment: insulin resistance and ?-cell function from fasting plasma glucose and insulin concentrations in man
Open accessAbstract
The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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Claims the paper makes
15 Own result · 7 Own interpretation · 3 No full text of the sources · 1 Partly supported · 1 No basis given · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The fasting plasma insulin concentration is largely determined by the glucose concentration [3]”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Summary.
What the sources say
- Partly supportedcites [3] McCarthy ST, Harris F, Turner RC (1977) Glucose control of basal insulin secretion in diabetes. Diabetologia 13: 93?97 sourceExperimental — accuracy not yet measured
The Results and Discussion (page unavailable in our copy) show that changing plasma glucose affects basal insulin secretion. But they also report markedly impaired responses in diabetic subjects, including cases where insulin remained nearly constant despite a substantial glucose decrease. The paper therefore supports glucose as an influence, but not the unqualified claim that it largely determines fasting insulin concentration. (LLM, conf=0.84)
plaindirect- 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.
plaincorrelational- 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.
plaincorrelational- 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.
plaincorrelational- 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
plaincorrelational- 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.
plaincorrelational- 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.
plaincorrelational- 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 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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > Subjects and procedures
“The same 12 normal subjects and 11 Type 2 diabetic patients came to the hospital fasting on a separate day for a continuous infusion”
§ Homeostasis model assessment: insulin resistance and fl-cell function from fasting plasma glucose and insulin concentrations in man > HOMA compared with the hyperglycaemic clamp. The
“HOMA compared with CIGMA.The HOMA estimate of insulin resistance from the mean of 15 stressed fasting samples before the CIGMA correlated with that measured by CIGMA in 11 normal subjects (R~ = 0.69, p < 0.02), in 12 diabetic subjects (R~ 0.97, p < 0.0001)”
Citations
Cites
Glucose clamp technique: a method for quantifying insulin secretion and resistance.
DeFronzo, R. A., Tobin, J. D., & Andres, R. (1979) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1979.237.3.e214 · Cited by 7,425
Quantitative estimation of insulin sensitivity.
Bergman, R. N., Ider, Y. Z., Bowden, C. R., & Cobelli, C. (1979) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.1979.236.6.e667 · Cited by 1,874 · cited 1×
“This use of modelling to provide a reference graph contrasts with other models where an individual patient's data have to be entered into a computer to determine the relationships between rapidly changing variables, such as occur after an intravenous glucose test [19].”
Method[WHO Expert Committee on diabetes mellitus].
Reitsma, W. D., & Terpstra, J. (1981) · PubMed · Cited by 556
Turner, R. C., Holman, R. R., Matthews, D., Hockaday, T. D. R., & Peto, J. (1979) · Metabolism · doi:10.1016/0026-0495(79)90146-x · Cited by 388
Self-citationCharacteristics of Human Erythrocyte Insulin Receptors
Gambhir, K. K., Archer, J. A., & BRADLEY, C. J. (1978) · Diabetes · doi:10.2337/diab.27.7.701 · Cited by 290
The Insulin Receptor: Its Role in Insulin Resistance of Obesity and Diabetes
Olefsky, J. M. (1976) · Diabetes · doi:10.2337/diab.25.12.1154 · Cited by 289
Hosker, J. P., Matthews, D. R., Rudenski, A. S., Burnett, M. A., Darling, P., Bown, E. G., & Turner, R. C. (1985) · Diabetologia · doi:10.1007/bf00280882 · Cited by 287
Self-citationPulsatile Insulin Has Greater Hypoglycemic Effect Than Continuous Delivery
Matthews, D. R., Naylor, B. A., Jones, R., Ward, G. M., & Turner, R. C. (1983) · Diabetes · doi:10.2337/diab.32.7.617 · Cited by 256
Self-citationControl of pulsatile insulin secretion in man
Matthews, D. R., Lang, D. A., Burnett, M. A., & Turner, R. C. (1983) · Diabetologia · doi:10.1007/bf00282705 · Cited by 175
Self-citationTHE BASAL PLASMA GLUCOSE: A SIMPLE RELEVANT INDEX OF MATURITY‐ONSET DIABETES
HOLMAN, R. R., & TURNER, R. C. (1981) · Clinical Endocrinology · doi:10.1111/j.1365-2265.1981.tb00196.x · Cited by 64
Self-citation
Research works printed but not linked: 2
- Metropolitan Life Assurance Co (1959) Net weight standard for men and women. Stat Bull Metrol Life Found 40: 1?4
- Matthews DR, Hosker JP (1984) An unbiased, flexible computer programme for glucose clamping, with graphics and running statistics. Diabetologia 27: 308?309 A
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Sorted by citations
Items 7,501–7,600 of 31,469
Korish, A. A., Gader, A. G. A., Korashy, H. M., Al-Drees, A. M., Alhaider, A. A., & Arafah, M. (2015) · Chemico-Biological Interactions · doi:10.1016/j.cbi.2015.01.013 · Cited by 48
Goldberg, Y., Boaz, M., Matas, Z., Goldberg, I., & Shargorodsky, M. (2008) · Clinical Nutrition · doi:10.1016/j.clnu.2008.10.001 · Cited by 48
Ahlsson, F., Diderholm, B., Jönsson, B., et al. (2010) · Hormone Research in Paediatrics · doi:10.1159/000295710 · Cited by 48
Mean platelet volume in biopsy-proven non-alcoholic fatty liver disease
Çelikbilek, M., Gürsoy, Ş., Denız, K., et al. (2012) · Platelets · doi:10.3109/09537104.2012.688898 · Cited by 48
Georgoulis, M., Kontogianni, M. D., Tileli, N., Margariti, A., Fragopoulou, E., TINIAKOS, D. G., Zafiropoulou, R., & Papatheodoridis, G. (2014) · European Journal of Nutrition · doi:10.1007/s00394-014-0679-y · Cited by 48
Fine-mapping diabetes-related traits, including insulin resistance, in heterogeneous stock rats
Woods, L. C. S., Holl, K. L., Oreper, D., Xie, Y., Tsaih, S., & Valdar, W. S. J. (2012) · Physiological Genomics · doi:10.1152/physiolgenomics.00040.2012 · Cited by 48
Penn-Marshall, M., Holtzman, G. I., & Barbeau, W. E. (2010) · Journal of Medicinal Food · doi:10.1089/jmf.2009.0195 · Cited by 48
Makhoul, Z., Kristal, A. R., Gulati, R., Luick, B. R., Bersamin, A., Boyer, B. B., & Mohatt, G. V. (2010) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.2009.28820 · Cited by 48
Johnstone, A. M., Lobley, G. E., Horgan, G. W., Bremner, D. M., Fyfe, C., Morrice, P. C., & Duthie, G. G. (2011) · British Journal Of Nutrition · doi:10.1017/s0007114511000092 · Cited by 52
Kato, K., Takeshita, Y., Misu, H., Zen, Y., Kaneko, S., & Takamura, T. (2014) · Journal of Diabetes Investigation · doi:10.1111/jdi.12271 · Cited by 49
Noto, A. D., Zahradka, P. C., Yurkova, N., Xie, X., Truong, H., Nitschmann, E. P., Ogborn, M. R., & Taylor, C. G. (2007) · Metabolism · doi:10.1016/j.metabol.2007.06.025 · Cited by 48
von Schnurbein, J., Heni, M., Moss, A., Nagel, S. A., Machann, J., Muehleder, H., Debatin, K., Farooqi, I. S., & Wabitsch, M. (2013) · Hormone Research in Paediatrics · doi:10.1159/000348541 · Cited by 48
Lee, J. H., Lee, S., Kim, B., Seo, W. D., Jia, Y., Wu, C., Jun, H., & Lee, S. (2015) · Food Research International · doi:10.1016/j.foodres.2015.03.041 · Cited by 48
Fayh, A. P. T., Lopes, A. L., Fernandes, P. R., Reischak-Oliveira, Á., & Friedman, R. (2013) · British Journal Of Nutrition · doi:10.1017/s0007114512005442 · Cited by 51
Independent Effect of Ethnicity on Glycemia in South Asians and White Europeans
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Alemzadeh, R., & Kichler, J. (2012) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2012.0056 · Cited by 48
Miyajima, I., Kawaguchi, T., Fukami, A., et al. (2012) · Journal of Gastroenterology · doi:10.1007/s00535-012-0610-3 · Cited by 48
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Magkos, F., Brennan, A., Sweeney, L. L., Kang, E. S., Doweiko, J. P., Karchmer, A. W., & Mantzoros, C. S. (2010) · Metabolism · doi:10.1016/j.metabol.2010.10.002 · Cited by 48
da Silva, D. F., Bianchini, J. A. A., Antonini, V. D. S., et al. (2013) · Pediatric Cardiology · doi:10.1007/s00246-013-0838-6 · Cited by 48
Reinehr, T., Woelfle, J., & Roth, C. L. (2011) · Metabolism · doi:10.1016/j.metabol.2011.02.005 · Cited by 48
Strong association between insulin resistance in liver and skeletal muscle in non‐diabetic subjects
Abdul‐Ghani, M. A., Matsuda, M., & DeFronzo, R. A. (2008) · Diabetic Medicine · doi:10.1111/j.1464-5491.2008.02597.x · Cited by 48
Gupta, S. K., Mi, D., Moe, S. M., Dubé, M. P., & Liu, Z. (2013) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0b013e3182a97c39 · Cited by 48
Cho, J., Han, T., & Kang, H. (2009) · European Journal of Applied Physiology · doi:10.1007/s00421-009-1238-8 · Cited by 48
Protective and ameliorative effects of maté (Ilex paraguariensis) on metabolic syndrome in TSOD mice
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Kyrgios, I., Galli‐Τsinopoulou, A., Stylianou, C., Papakonstantinou, E., Arvanitidou, M., & Haidich, A. (2011) · Metabolism · doi:10.1016/j.metabol.2011.09.004 · Cited by 49
Zheng, Y., Zhang, D., Zhang, L., Fu, M., Zeng, Y., & Russell, R. (2013) · Gene · doi:10.1016/j.gene.2013.07.082 · Cited by 47
Moran, L., Noakes, M., Clifton, P. M., Norman, R. J., & Fenech, M. F. (2007) · Mutation research. Fundamental and molecular mechanisms of mutagenesis · doi:10.1016/j.mrfmmm.2007.11.007 · Cited by 48
Raghupathy, P., Antonisamy, B., Geethanjali, F. S., Saperia, J., Leary, S. D., Priya, G., Richard, J., Barker, D. J. P., & Fall, C. H. D. (2010) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2009.11.015 · Cited by 48
Liao, S., Mei, J., Song, W., Liu, Y., Tan, Y. ‐., Chi, S., Li, P., Chen, X., & Deng, S. (2013) · Diabetic Medicine · doi:10.1111/dme.12349 · Cited by 48
Oxidative stress and endothelium influenced by metformin in type 2 diabetes mellitus
Škrha, J., Prázný, M., Hilgertová, J., Kvasnička, J., Kalousová, M., & Zima, T. (2007) · European Journal of Clinical Pharmacology · doi:10.1007/s00228-007-0378-1 · Cited by 48
Silbernagel, G., Hoffmann, M. M., Grammer, T. B., et al. (2011) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2011.02.008 · Cited by 48
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Hermsdorff, H. H. M., Puchau, B., Zulet, M. Á., & Martínez, J. A. (2010) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2009.0125 · Cited by 48
Hoofnagle, J. H., Wahed, A. S., Brown, R. S., et al. (2009) · The Journal of Infectious Diseases · doi:10.1086/597384 · Cited by 48
Acute testosterone deprivation reduces insulin sensitivity in men
Rubinow, K. B., Snyder, C. N., Amory, J. K., Hoofnagle, A. N., & Page, S. T. (2011) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2011.04189.x · Cited by 48
Choi, H. Y., Park, J. W., Lee, N., Hwang, S. Y., Cho, G. J., Hong, H. C., Yoo, H. J., Hwang, T. G., Kim, S. M., Baik, S. H., et al. (2013) · Diabetes Care · doi:10.2337/dc13-0178 · Cited by 48
Preserved endothelial function in human obesity in the absence of insulin resistance
Assar, M. E., de Adana, J. C. R., Angulo, J., Martínez, M. L. P., Matías, A. H., & Rodríguez‐Mañas, L. (2013) · Journal of Translational Medicine · doi:10.1186/1479-5876-11-263 · Cited by 46
Mercader, J., Ribot, J., Murano, I., Feddersen, S., Cinti, S., Madsen, L., Kristiansen, K., Bonet, M. L., & Palou, A. (2009) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00163.2009 · Cited by 46
Phillips, C. M., Goumidi, L., Bertrais, S., Field, M. R., Peloso, G. M., Shen, J., McManus, R., Herçberg, S., Lairon, D., Planells, R., et al. (2009) · Journal of Nutrition · doi:10.3945/jn.109.110635 · Cited by 48
Desmosterol in Human Nonalcoholic Steatohepatitis
Simonen, M., Männistö, V., Leppänen, J., Kamińska, D., Kärjä, V. J., Venesmaa, S., Käkelä, P., Kuusisto, J., Gylling, H., Laakso, M., et al. (2013) · Hepatology · doi:10.1002/hep.26342 · Cited by 49
Brouwer, S. I., Stolk, R. P., Liem, E. T., et al. (2012) · Pediatric Diabetes · doi:10.1111/j.1399-5448.2012.00893.x · Cited by 48
Samjoo, I. A., Safdar, A., Hamadeh, M. J., Glover, A. W., Mocellin, N. J., Santana, J. O., Little, J. P., Steinberg, G. R., Raha, S., & Tarnopolsky, M. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066322 · Cited by 56
Measurement of insulin resistance in chronic kidney disease
Pham, H., Utzschneider, K., & de Boer, I. D. (2011) · Current opinion in nephrology and hypertension · doi:10.1097/mnh.0b013e32834b23c1 · Cited by 50
Watanabe, K., Iizuka, T., Adeleke, A., Pham, L., Shlimon, A. E., Yasin, M., Horvath, P., & Unterman, T. G. (2010) · Journal of Periodontal Research · doi:10.1111/j.1600-0765.2010.01304.x · Cited by 49
Abbasi, F., Blasey, C. M., Feldman, D., Caulfield, M. P., Hantash, F. M., & Reaven, G. M. (2015) · Journal of Nutrition · doi:10.3945/jn.114.209171 · Cited by 51
Exercise training promotes cardioprotection through oxygen-sparing action in high fat-fed mice
Lund, J., Hafstad, A. D., Boardman, N. T., Rossvoll, L., Rolim, N. P. L., Ahmed, M. S., Florholmen, G., Attramadal, H., Wisløff, U., Larsen, T. S., et al. (2015) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00734.2014 · Cited by 48
de Queiroz, M. D. S. R., Janebro, D. I., da Cunha, M. A. L., Medeiros, J. D. S., Sabaa‐Srur, A. U. O., Diniz, M. D. F. F. M., & Santos, S. (2012) · Nutrition Journal · doi:10.1186/1475-2891-11-89 · Cited by 48
Hansen, K. B., Vilsbøll, T., Bagger, J. I., Holst, J. J., & Knop, F. K. (2012) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2011-2594 · Cited by 48
Bays, H. E., McElhattan, J., & Bryzinski, B. S. (2007) · Diabetes and Vascular Disease Research · doi:10.3132/dvdr.2007.039 · Cited by 49
Lim, S. H., Kim, D. H., Park, M. J., et al. (2007) · Gut and Liver · doi:10.5009/gnl.2007.1.2.138 · Cited by 48
Peixoto‐Silva, N., Frantz, E. D. C., Mandarim‐de‐Lacerda, C. A., & Mulder, A. D. R. P. (2011) · British Journal Of Nutrition · doi:10.1017/s0007114511001735 · Cited by 48
Circulating adipocyte fatty acid-binding protein induces insulin resistance in micein vivo
Kralisch, S., Klöting, N., Ebert, T., Kern, M., Hoffmann, A., Krause, K., Jeßnitzer, B., Lössner, U., Sommerer, I., Stümvoll, M., et al. (2015) · Obesity · doi:10.1002/oby.21057 · Cited by 49
Nayak, Y., Hillemane, V., Daroji, V. K., Jayashree, B. S., & Unnikrishnan, M. K. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/854267 · Cited by 48
Schwarzwälder, U., Hauk, M., & Zeller, T. (2009) · Trials · doi:10.1186/1745-6215-10-60 · Cited by 48
Tedeschi, S., Pilotti, E., Parenti, E., et al. (2011) · Metabolism · doi:10.1016/j.metabol.2011.05.011 · Cited by 48
Pratt-Phillips, S. E., Owens, K. M., Dowler, L. E., et al. (2010) · Journal of Equine Veterinary Science · doi:10.1016/j.jevs.2010.01.060 · Cited by 48
Park, S. E., Kim, W. J., Park, S. W., Park, J. W., Lee, N., Park, C., & Youn, B. (2012) · European Journal of Endocrinology · doi:10.1530/eje-12-0782 · Cited by 48
Camhi, S. M., Crouter, S. E., Hayman, L. L., Must, A., & Lichtenstein, A. H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138548 · Cited by 48
Ebron, K., Andersen, C. J., Aguilar, D., Blesso, C. N., Barona, J., Dugan, C. E., Jones, J. L., Al‐Sarraj, T., & Fernández, M. L. (2015) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2015.0053 · Cited by 49
de Moraes, T. P., Andreoli, M. C. C., Canziani, M. E. F., et al. (2015) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfv247 · Cited by 48
Physiological Mechanisms of Weight Gain−Induced Steatosis in People With Obesity
Fabbrini, E., Luecking, C. T., Love-Gregory, L. D., Okunade, A. L., Yoshino, M., Fraterrigo, G., Patterson, B. W., & Klein, S. (2015) · Gastroenterology · doi:10.1053/j.gastro.2015.09.003 · Cited by 48
Gad, E. S., Zaitone, S. A., & Moustafa, Y. M. (2015) · Canadian Journal of Physiology and Pharmacology · doi:10.1139/cjpp-2015-0242 · Cited by 48
Tan, H. C., Khoo, C. M., Tan, M. Z.-W., Kovalik, J., Ng, A. C. M., Eng, A. K. H., Lai, O. F., Ching, J. H., Tham, K. W., & Pasupathy, S. (2016) · Obesity Surgery · doi:10.1007/s11695-015-2023-x · Cited by 48
Matrix metalloproteinases 2 and 9 and MMP9/NGAL complex activity in women with PCOS
Ranjbaran, J., Farimani, M. S., Tavilani, H., Ghorbani, M., Karimi, J., Poormonsefi, F., & Khodadadi, I. (2016) · Reproduction · doi:10.1530/rep-15-0340 · Cited by 48
Circulating Galectin‐3 Is Associated With Cardiometabolic Disease in the Community
Nayor, M. G., Wang, N., Larson, M. G., Vasan, R. S., Levy, D. A., & Ho, J. E. (2016) · Journal of the American Heart Association · doi:10.1161/jaha.115.002347 · Cited by 48
Fish oil diet modulates epididymal and inguinal adipocyte metabolism in mice
Bargut, T. C. L., Souza‐Mello, V., Mandarim‐de‐Lacerda, C. A., & Águila, M. B. (2016) · Food & Function · doi:10.1039/c5fo00909j · Cited by 49
Remission of Recently Diagnosed Type 2 Diabetes Mellitus With Weight Loss and Exercise
Ades, P. A., Savage, P. D., Marney, A. M., Harvey, J., & Evans, K. A. (2015) · Journal of Cardiopulmonary Rehabilitation and Prevention · doi:10.1097/hcr.0000000000000106 · Cited by 50
Assessment of serum chemerin, vaspin and omentin-1 levels in patients with polycystic ovary syndrome
Güvenç, Y., Var, A., Göker, A., & Kuşçu, N. K. (2016) · Journal of International Medical Research · doi:10.1177/0300060516645421 · Cited by 48
Greenlee, H., Gaffney, A. O., Aycinena, A. C., Koch, P., Contento, I. R., Karmally, W., Richardson, J. M., Shi, Z., Lim, E. A., Tsai, W., et al. (2016) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-15-1334 · Cited by 46
Dozio, E., Briganti, S., Delnevo, A., Vianello, E., Ermetici, F., Secchi, F., Sardanelli, F., Morricone, L. F., Malavazos, A. E., & Romanelli, M. M. C. (2016) · European Journal of Nutrition · doi:10.1007/s00394-016-1291-0 · Cited by 48
den Biggelaar, L. J. C. J., Sep, S. J. S., Eussen, S. J. P. M., Mari, A., Ferrannini, E., van Greevenbroek, M. M. J., Kallen, C., Schalkwijk, C. G., Stehouwer, C. D. A., & Dagnelie, P. C. (2016) · Diabetologia · doi:10.1007/s00125-016-4165-3 · Cited by 48
Costanzo, A. D., D’Erasmo, L., Polimeni, L., Baratta, F., Coletta, P., Martino, M. D., Loffredo, L., Perri, L., Ceci, F., Montali, A., et al. (2016) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2016.12.018 · Cited by 48
Papanastasiou, L., Alexandraki, K., Androulakis, I. I., Fountoulakis, S., Kounadi, T., Markou, Α., Tsiavos, V., Samara, C., Papaioannou, T., Piaditis, G. P., et al. (2016) · Clinical Endocrinology · doi:10.1111/cen.13294 · Cited by 49
Shen, X., Zhou, N., Mi, L., Hu, Z., Wang, L., Liu, X., & Zhang, S. (2017) · Drug Design Development and Therapy · doi:10.2147/dddt.s127010 · Cited by 49
Zimdahl, H., Haupt, A., Brendel, M., Bour, L., Machicao, F. E., Salsali, A., Broedl, U. C., Woerle, H., Häring, H., & Staiger, H. (2017) · Pharmacogenetics and Genomics · doi:10.1097/fpc.0000000000000268 · Cited by 54
Zhang, H., Su, S., Yu, X., & Li, Y. (2017) · Journal of Human Nutrition and Dietetics · doi:10.1111/jhn.12470 · Cited by 50
Díaz‐Rúa, R., Keijer, J., Palou, A., van Schothorst, E. M., & Oliver, P. (2017) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2017.04.008 · Cited by 50
Craig, C. M., Liu, L., Nguyen, T. D. T., Price, C. A., Bingham, J., & McLaughlin, T. (2017) · Diabetes Obesity and Metabolism · doi:10.1111/dom.13078 · Cited by 48
Marsche, G., Zelzer, S., Meinitzer, A., Kern, S., Meissl, S., Pregartner, G., Weghuber, D., Almer, G., & Mangge, H. (2017) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2017-00933 · Cited by 48
yaghooblou, F., Siassi, F., Rahimi, A., et al. (2017) · Journal of Diabetes & Metabolic Disorders · doi:10.1186/s40200-017-0320-8 · Cited by 48
Ferjan, S., Janež, A., & Jensterle, M. (2017) · Endocrine Practice · doi:10.4158/ep-2017-0027 · Cited by 50
Castro-Piñero, J., Pérez-Bey, A., Segura-Jiménez, V., Aparicio, V., Gómez-Martínez, S., Izquierdo-Gómez, R., Marcos, A., Ruiz, J., Marcos, A., Castro-Piñero, J., et al. (2017) · Mayo Clinic proceedings · doi:10.1016/j.mayocp.2017.09.003 · Cited by 47
FGF21 and DPP-4 inhibitor equally prevents cognitive decline in obese rats
Sa‐nguanmoo, P., Tanajak, P., Kerdphoo, S., Jaiwongkam, T., Wang, X., Liang, G., Li, X., Jiang, C., Pratchayasakul, W., Chattipakorn, N., et al. (2017) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2017.12.021 · Cited by 48
Spahis, S., Álvarez, F., Ahmed, N., Dubois, J., El-Jalbout, R., Paganelli, M., Grzywacz, K., DELVIN, E. E., Peretti, N., & Levy, E. H. (2018) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2018.03.025 · Cited by 49
Lee, D., Lee, M., & Sung, K. (2018) · Obesity · doi:10.1002/oby.22193 · Cited by 48
Increased liver stiffness in patients with severe sleep apnoea and metabolic comorbidities
Trzépizur, W., Boursier, J., Vaillant, M. L., Ducluzeau, P., Dubois, S. G., Henni, S., Abraham, P., Aubé, C., Calès, P., & Gagnadoux, F. (2018) · European Respiratory Journal · doi:10.1183/13993003.00601-2018 · Cited by 48
Evaluation of Plasma Trace Elements in Different Stages of Nonalcoholic Fatty Liver Disease
Asprouli, E., Kalafati, I. P., Sakellari, A., Karavoltsos, S., Vlachogiannakos, J., Revenas, K., Kokkinos, A. D., Dassenakis, M., Dedoussis, G. V., & Kalogeropoulos, N. (2018) · Biological Trace Element Research · doi:10.1007/s12011-018-1432-9 · Cited by 48
Wong, A., Sanchez‐Gonzalez, M. A., Son, W., Kwak, Y., & Park, S. (2018) · Pediatric Exercise Science · doi:10.1123/pes.2017-0198 · Cited by 54
Østergren, P. B., Kistorp, C., Fode, M. M., Bennedbæk, F. N., Faber, J. O., & Sønksen, J. (2018) · British Journal of Urology · doi:10.1111/bju.14609 · Cited by 48
Licenziati, M. R., Valerio, G., Vetrani, I., Maria, G. D., Liotta, F., & Radetti, G. (2019) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2018-02399 · Cited by 50
Pan, K., Nelson, R. A., Wactawski‐Wende, J., Lee, D. J., Manson, J. E., Aragaki, A. K., Mortimer, J. E., Phillips, L. S., Rohan, T. E., Ho, G. Y. F., et al. (2019) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djz069 · Cited by 49
Chen, A., Jandarov, R., Zhou, L., Calafat, A. M., Zhang, G., Urbina, E. M., Šarac, J., Auguštin, D. H., Carić, T., Bočkor, L., et al. (2019) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2019.05.250 · Cited by 48
Marzuillo, P., Sessa, A. D., Guarino, S., Capalbo, D., Umano, G. R., Pedullà, M., Manna, A. L., Cirillo, G., & del Giudice, E. M. (2019) · Pediatric Obesity · doi:10.1111/ijpo.12539 · Cited by 48
Khusial, R., Cioffi, C. E., Caltharp, S. A., Krasinskas, A. M., Alazraki, A. L., Knight‐Scott, J., Cleeton, R. L., Castillo‐Leon, E., Jones, D. P., Pierpont, B. M., et al. (2019) · Hepatology Communications · doi:10.1002/hep4.1417 · Cited by 49
Aadland, E., Kvalheim, O. M., Anderssen, S. A., Resaland, G. K., & Andersen, L. B. (2019) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-019-0836-z · Cited by 48
Body fat percentage, obesity, and their relation to the incidental risk of hypertension
Park, S. K., Ryoo, J., Oh, C., Choi, J., Chung, P., & Jung, J. Y. (2019) · Journal of Clinical Hypertension · doi:10.1111/jch.13667 · Cited by 49
Zhang, Y., Cao, Y., Chen, J., Qin, H., & Yang, L. (2019) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.9b05910 · Cited by 49
Khat‐udomkiri, N., Toejing, P., Sirilun, S., Chaiyasut, C., & Lailerd, N. (2019) · Food Science & Nutrition · doi:10.1002/fsn3.1327 · Cited by 48
Millar, C. L., Jiang, C., Norris, G. H., Garcia, C., Seibel, S., Anto, L., Lee, J., & Blesso, C. N. (2020) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2020.108351 · Cited by 48
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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