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)
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
Cited by
Sorted by citations
Items 4,901–5,000 of 31,469
Long-term Protective Changes in Adipose Tissue After Gastric Bypass
Hoffstedt, J., Andersson, D. P., Hogling, D. E., Theorell, J., Näslund, E. B., Thorell, A., Ehrlund, A., Rydén, M., & Arner, P. (2016) · Diabetes Care · doi:10.2337/dc16-1072 · Cited by 74
Metabolic Syndrome and Hypertension Resulting from Fructose Enriched Diet in Wistar Rats
Dupas, J., Féray, A., Goanvec, C., Guernec, A., Samson, N., Bougaran, P., Guerrero, F., & Mansourati, J. (2017) · BioMed Research International · doi:10.1155/2017/2494067 · Cited by 73
Myonectin Predicts the Development of Type 2 Diabetes
Li, K., Liao, X., Wang, K., Mi, Q., Zhang, T., Jia, Y., Xu, X., Luo, X., Zhang, C., Liu, H., et al. (2017) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2017-01604 · Cited by 73
Tsochatzis, E. A., Papatheodoridis, G., Manesis, E. K., Kafiri, G., TINIAKOS, D. G., & Archimandritis, A. J. (2007) · Alimentary Pharmacology & Therapeutics · doi:10.1111/j.1365-2036.2007.03538.x · Cited by 71
Han, Y., Kwon, E.-Y., Yu, M. R., Lee, S., Kim, H., Kim, S.-B., Kim, Y., & Choi, M. (2018) · Nutrients · doi:10.3390/nu10020160 · Cited by 72
Ebrahimpour‐Koujan, S., Gargari, B. P., Mobasseri, M., Valizadeh, H., & Jafarabadi, M. A. (2018) · Phytomedicine · doi:10.1016/j.phymed.2018.03.050 · Cited by 72
Zhu, Y., Tsai, M. Y., Sun, Q., Hinkle, S. N., Rawal, S., Mendola, P., Ferrara, A. M., Albert, P. S., & Zhang, C. (2018) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqy051 · Cited by 71
Phillips, C. M., Harrington, J. M., & Perry, I. J. (2018) · Clinical Nutrition · doi:10.1016/j.clnu.2018.08.028 · Cited by 76
Helicobacter pylori and non‐alcoholic fatty liver disease: A new enigma?
Abdel‐Razik, A., Mousa, N., Shabana, W., Refaey, M., Elhelaly, R. M., Elzehery, R. R., Abdelsalam, M., Elgamal, A., Nassar, M. R., Soud, A. A. E., et al. (2018) · Helicobacter · doi:10.1111/hel.12537 · Cited by 71
Ilyas, A., Hübel, C., Stahl, D. R., Stadler, M., Ismail, K., Breen, G. D., Treasure, J. L., & Kan, C. (2018) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2018.10.005 · Cited by 71
Borel, A., Coumes, S., Reche, F., Ruckly, S., Pépin, J., Tamisier, R., Wion, N., & Arvieux, C. C. (2018) · PLoS ONE · doi:10.1371/journal.pone.0206617 · Cited by 78
Differential expression analysis for RNAseq using Poisson mixed models
Sun, S., Hood, M. M., Scott, L. J., Peng, Q., Mukherjee, S., Tung, J., & Zhou, X. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx204 · Cited by 75
Yao, F., Jia, R., Huang, H., Yu, Y. Q., Lü, M., Bai, L., Ding, Y., & Zheng, P. (2019) · Archives of Medical Science · doi:10.5114/aoms.2019.86611 · Cited by 71
Grădinaru, D., Borșa, C., Ionescu, C., Margina, D. M., Prada, G. I., & Jansen, E. H. J. M. (2012) · Aging Clinical and Experimental Research · doi:10.1007/bf03654842 · Cited by 71
Dhuper, S., Cohen, H. W., Daniel, J., Gumidyala, P., Agarwalla, V., Victor, R. S., & Dhuper, S. (2007) · Cardiovascular Diabetology · doi:10.1186/1475-2840-6-4 · Cited by 71
Sturrock, N. D. C., Lannon, B. M., & Fay, T. N. (2002) · British Journal of Clinical Pharmacology · doi:10.1046/j.1365-2125.2002.01575.x · Cited by 70
Sidhu, J. S., Cowan, D., Tooze, J. A., & Kaski, J. C. (2004) · American Heart Journal · doi:10.1016/j.ahj.2003.12.035 · Cited by 71
Pickavance, L. C., Brand, C. L., Wassermann, K., & Wilding, J. P. H. (2005) · British Journal of Pharmacology · doi:10.1038/sj.bjp.0706041 · Cited by 71
Weight Reduction Decreases Soluble Cellular Adhesion Molecules In Obese Women
Ito, H., Ohshima, A., Inoue, M., Ohto, N., Nakasuga, K., Kaji, Y., Maruyama, T., & Nishioka, K. (2002) · Clinical and Experimental Pharmacology and Physiology · doi:10.1046/j.1440-1681.2002.03672.x · Cited by 74
Revised concept for the estimation of insulin sensitivity from a single sample.
de Mauverger, É. R., Pérez‐Martin, A., Brun, J. ‐., Benhaddad, A. A., & Mercier, J. G. (1999) · Diabetes Care · doi:10.2337/diacare.22.6.1003 · Cited by 71
Shiwaku, K., Anuurad, E., Enkhmaa, B., Nogi, A., Kitajima, K., Yamasaki, M., Yoneyama, T., Oyunsuren, T., & Yamane, Y. (2004) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2004.11.001 · Cited by 71
Energy Expenditure Is Very High in Extremely Obese Women
Das, S. K., Saltzman, E. J., McCrory, M. A., Hsu, L. K. G., Shikora, S. A., Dolnikowski, G. G., Kehayias, J. J., & Roberts, S. B. (2004) · Journal of Nutrition · doi:10.1093/jn/134.6.1412 · Cited by 71
Li, J., Nanayakkara, A., Jun, J. C., Savransky, V., & Polotsky, V. Y. (2007) · Physiological Genomics · doi:10.1152/physiolgenomics.00082.2007 · Cited by 74
Poulsen, P., Andersen, G., Fenger, M., et al. (2003) · Diabetes · doi:10.2337/diabetes.52.1.194 · Cited by 71
Heald, A. H., Anderson, S. G., Ivison, F., Laing, I., Gibson, M., & Cruickshank, K. J. (2003) · Atherosclerosis · doi:10.1016/s0021-9150(03)00235-1 · Cited by 71
Vrbíková, J., Hill, M., Stárka, L., Cibula, D., Bendlová, B., Vondra, K., Šulcová, J., & Šnajderová, M. (2001) · European Journal of Endocrinology · doi:10.1530/eje.0.1440619 · Cited by 71
The Cat as a Model for Human Obesity and Diabetes
Hoenig, M. E. (2012) · Journal of Diabetes Science and Technology · doi:10.1177/193229681200600306 · Cited by 81
Association of inflammation with worsening HOMA-insulin resistance
Park, K., Steffes, M., Lee, D.-H., Himes, J. H., & Jacobs, D. R. (2009) · Diabetologia · doi:10.1007/s00125-009-1486-5 · Cited by 72
Monnier, L., Benichou, M., Charra-Ebrard, S., Boegner, C., & Colette, C. (2005) · Diabetes & Metabolism · doi:10.1016/s1262-3636(07)70174-x · Cited by 69
Palomba, S., Falbo, A., Chiossi, G., Muscogiuri, G., Fornaciari, E., Orio, F., Tolino, A., Colao, A., La Sala, G. B., & Zullo, F. (2014) · Steroids · doi:10.1016/j.steroids.2014.06.005 · Cited by 69
Boehncke, S., Fichtlscherer, S., Salgo, R., Garbaravičienė, J., Beschmann, H. A., Diehl, S., Hardt, K., Thaçi, D., & Boehncke, W. (2010) · Archives of Dermatological Research · doi:10.1007/s00403-010-1108-6 · Cited by 70
Sarkar, P. D., & Choudhury, A. B. (2013) · PubMed · Cited by 70
Belligoli, A., Compagnin, C., Sanna, M., Favaretto, F., Fabris, R., Busetto, L., D, M. F. M., Prà, C. D., Serra, R., Prevedello, L., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-47719-y · Cited by 73
Rahimi, G. R. M., Bijeh, N., & Rashidlamir, A. (2019) · Experimental Physiology · doi:10.1113/ep088036 · Cited by 69
Alshehri, A. S., El-kott, A. F., El-Kenawy, A. E.-M., Khalifa, H. S., & Al‐Ramlawy, A. M. (2021) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2021.147182 · Cited by 73
A pathway level analysis of PFAS exposure and risk of gestational diabetes mellitus
Birru, R. L., Liang, H., Farooq, F., Bedi, M., Feghali, M. N., Haggerty, C. L., Méndez, D. D., Catov, J. M., Ng, C. A., & Adibi, J. J. (2021) · Environmental Health · doi:10.1186/s12940-021-00740-z · Cited by 73
Matteis, C. D., Cariello, M., Graziano, G., Battaglia, S., Suppressa, P., Piazzolla, G., Sabbà, C., & Moschetta, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-94277-3 · Cited by 76
Metwally, A. A., Perelman, D., Park, H., Wu, Y., Jha, A., Sharp, S. A., Celli, A., Ayhan, E. M., Abbasi, F., Gloyn, A. L., et al. (2024) · Nature Biomedical Engineering · doi:10.1038/s41551-024-01311-6 · Cited by 98
Oral magnesium supplementation in type II diabetic patients.
Solati, M., Ouspid, E., Hosseini, S., Soltani, N., Keshavarz, M., & Dehghani, M. (2014) · Europe PMC (PubMed Central) · Cited by 71
Ma, S. W., Benzie, I. F. F., Chu, T. T. W., Fok, B. S. P., Tomlinson, B., & Critchley, L. A. H. (2008) · Diabetes Obesity and Metabolism · doi:10.1111/j.1463-1326.2008.00858.x · Cited by 70
Kim, D., Lee, H., Lee, B.-S., Kim, J., & Jeon, J. Y. (2015) · Archives of Physical Medicine and Rehabilitation · doi:10.1016/j.apmr.2015.07.010 · Cited by 72
Farrell, N., Norris, G. H., Ryan, J. A., Porter, C. M., Jiang, C., & Blesso, C. N. (2015) · British Journal Of Nutrition · doi:10.1017/s0007114515002962 · Cited by 72
Impaired renal function in morbid obese patients with nonalcoholic fatty liver disease
Machado, M. V., Gonçalves, S., Carepa, F., Coutinho, J., Costa, A., & Cortez‐Pinto, H. (2011) · Liver International · doi:10.1111/j.1478-3231.2011.02623.x · Cited by 71
Russo, M. D., Ahrens, W., Vriendt, T. D., Mårild, S., Molnár, D., Moreno, L. A., Reeske, A., Veidebaum, T., Kourides, Y. A., Barba, G., et al. (2013) · International Journal of Obesity · doi:10.1038/ijo.2013.35 · Cited by 68
Plasma Visfatin Concentration as a Surrogate Marker for Visceral Fat Accumulation in Obese Children
Araki, S., Dobashi, K., Kubo, K., Kawagoe, R., Yamamoto, Y., Kawada, Y., Asayama, K., & Shirahata, A. (2008) · Obesity · doi:10.1038/oby.2007.54 · Cited by 70
Tahara, A., Matsuyama‐Yokono, A., & Shibasaki, M. (2011) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2011.01.015 · Cited by 73
IL-12 serum levels in patients with type 2 diabetes treated with sulphonylureas
Wegner, M., Winiarska, H., Bobkiewicz-Kozłowska, T., & Dworacka, M. (2008) · Cytokine · doi:10.1016/j.cyto.2008.02.014 · Cited by 71
White blood cell count and abdominal fat distribution in female obese adolescents
Kim, J. A., & Park, H. S. (2008) · Metabolism · doi:10.1016/j.metabol.2008.05.005 · Cited by 70
Chiba, Y., Saitoh, S., Takagi, S., Ohnishi, H., Katoh, N., Ohata, J., Nakagawa, M., & Shimamoto, K. (2007) · Hypertension Research · doi:10.1291/hypres.30.229 · Cited by 70
Cheung, B. M. Y., Wat, N. M. S., Tam, S., Thomas, G. N., Leung, G. M., Cheng, C. H., Woo, J., Janus, E. D., Lau, C., Lam, T. H., et al. (2007) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2007.03110.x · Cited by 70
Impact of bariatric surgery–induced weight loss on heart rate variability
Nault, I., Nadreau, É., Paquet, C., Brassard, P., Marceau, P., Marceau, S., Biron, S., Hould, F. S., Lebel, S., Richard, D., et al. (2007) · Metabolism · doi:10.1016/j.metabol.2007.06.006 · Cited by 74
Rodríguez‐Morán, M., & Guerrero‐Romero, F. (2014) · Archives of Medical Research · doi:10.1016/j.arcmed.2014.05.003 · Cited by 70
Changes in gut hormone profile and glucose homeostasis after laparoscopic sleeve gastrectomy
Papamargaritis, D., le Roux, C. W., Sioka, E., KOUKOULIS, G. N., Tzovaras, G. A., & Zacharoulis, D. (2012) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2012.08.007 · Cited by 72
SIMONE, G. D., Devereux, R. B., Chinali, M., Roman, M. J., Lee, E. T., Resnick, H. E., & Howard, B. V. (2008) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2008.04.001 · Cited by 72
Roth, C. L., Reinehr, T., Schernthaner, G.-H., Kopp, H.-P., Kriwanek, S., & Schernthaner, G. (2008) · Obesity Surgery · doi:10.1007/s11695-008-9568-x · Cited by 70
van Hecke, M. V., Dekker, J. M., Nijpels, G., Stolk, R. P., Henry, R. M. A., Heine, R. J., Bouter, L. M., Stehouwer, C. D. A., & Polak, B. C. P. (2006) · Clinical Science · doi:10.1042/cs20050270 · Cited by 70
Methods of Measuring Insulin Sensitivity
Trout, K. K., Homko, C. J., & Tkacs, N. C. (2007) · Biological Research For Nursing · doi:10.1177/1099800406298775 · Cited by 72
Sexual dysfunction assessment and hormonal correlations in patients with polycystic ovary syndrome
Ercan, C. M., Çoksüer, H., Aydoğan, Ü., Alanbay, İ., Keskin, U., Karaşahin, K. E., & Basȩr, Ï. (2013) · International Journal of Impotence Research · doi:10.1038/ijir.2013.2 · Cited by 71
Subramanyam, M. A., Roux, A. V. D., Hickson, D. A., Sarpong, D. F., Sims, M., Taylor, H. A., Williams, D. R., & Wyatt, S. B. (2012) · Social Science & Medicine · doi:10.1016/j.socscimed.2011.12.042 · Cited by 70
Visceral obesity is characterized by impaired nitric oxide-independent vasodilation
de Kreutzenberg, S. V. (2003) · European Heart Journal · doi:10.1016/s0195-668x(03)00206-9 · Cited by 70
Qi, X., Li, L., Yang, G., et al. (2007) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2007.02776.x · Cited by 70
The effect of puberty on insulin resistance in obese children
Pilia, S., Casini, M. R., Foschini, M. L., Minerba, L., Musiu, M. C., Marras, V., Civolani, P., & Loche, S. (2009) · Journal of Endocrinological Investigation · doi:10.1007/bf03346475 · Cited by 72
Circulating preptin levels in normal, impaired glucose tolerance, and type 2 diabetic subjects
Yang, G., Li, L., Chen, W., Liu, H., Boden, G. H., & Li, K. (2008) · Annals of Medicine · doi:10.1080/07853890802244142 · Cited by 68
Buscemi, S., Nicolucci, A., Mattina, A., Rosafio, G., Massenti, F. M., Lucisano, G., Galvano, F., Amodio, E., Pellegrini, F., Barile, A. M., et al. (2013) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2013.172 · Cited by 71
Mostoufi‐Moab, S., Magland, J. F., Isaacoff, E. J., Sun, W., Rajapakse, C. S., Zemel, B. S., Wehrli, F. W., Shekdar, K. V., Baker, J. F., Long, J., et al. (2015) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.2512 · Cited by 71
Grau, K., Hansen, T. F., Holst, J. J., Astrup, A. V., Saris, W. H. M., Arner, P., Rössner, S., Macdonald, I. A., Polak, J., Oppert, J., et al. (2009) · International Journal of Obesity · doi:10.1038/ijo.2009.159 · Cited by 73
Semba, R. D., Sun, K., Egan, J. M., Crasto, C., Carlson, O. D., & Ferrucci, L. (2012) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2011-2823 · Cited by 70
Wolever, T. M. S., Mehling, C. C., Chiasson, J. L., Josse, R. G., Leiter, L. A., Maheux, P., RABASA-LHORET, R. P. R., Rodger, N. W., & Ryan, E. A. (2008) · Diabetologia · doi:10.1007/s00125-008-1093-x · Cited by 71
Ajjan, R., Carter, A. M., Somani, R., Kain, K. C., & Grant, P. J. (2007) · Journal of Thrombosis and Haemostasis · doi:10.1111/j.1538-7836.2007.02434.x · Cited by 70
Serum Uric Acid Independently Predicts Cardiovascular Events in Advanced Nephropathy
Kanbay, M., Yılmaz, M. İ., Sönmez, A., Solak, Y., Sağlam, M., Çakır, E., Ünal, H. U., Arslan, E., Verim, S., Madero, M., et al. (2012) · American Journal of Nephrology · doi:10.1159/000342390 · Cited by 70
Tracking of clustered cardiovascular disease risk factors from childhood to adolescence
Bugge, A., El-Naaman, B., McMurray, R. G., et al. (2012) · Pediatric Research · doi:10.1038/pr.2012.158 · Cited by 70
Houde, A.-A., St‐Pierre, J., Hivert, M., Baillargeon, J., Perron, P., Gaudet, D., Brisson, D., & Bouchard, L. (2014) · Journal of Developmental Origins of Health and Disease · doi:10.1017/s2040174414000038 · Cited by 70
Lionetti, L., Mollica, M. P., Crescenzo, R., D’Andrea, E., Ferraro, M., Bianco, F., Liverini, G., & Iossa, S. (2007) · International Journal of Obesity · doi:10.1038/sj.ijo.0803636 · Cited by 71
Maeda, H., Yamamoto, R., Hirao, K., & Tochikubo, O. (2004) · Diabetes Obesity and Metabolism · doi:10.1111/j.1463-1326.2004.00370.x · Cited by 70
Maternal serum and fetal cord blood irisin levels in gestational diabetes mellitus
Yüksel, M. A., Öncül, M., Tüten, A., İmamoğlu, M., Açıkgöz, A. S., Kücür, M., & Madazlı, R. (2014) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2013.12.025 · Cited by 71
Clinical spectrum of non-alcoholic fatty liver disease in diabetic and non-diabetic patients
Goh, G. B., Pagadala, M. R., Dasarathy, J., Ünalp–Arida, A., Sargent, R., Hawkins, C., Sourianarayanane, A., Khiyami, A., Yerian, L. M., Pai, R. K., et al. (2014) · BBA Clinical · doi:10.1016/j.bbacli.2014.09.001 · Cited by 70
Andreassen, K. V., Feigh, M., Hjuler, S. T., Gydesen, S., Henriksen, J. E., Beck‐Nielsen, H., Christiansen, C., Karsdal, M. A., & Henriksen, K. (2014) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00121.2014 · Cited by 70
Ruff, J. S., Suchy, A. K., Hugentobler, S. A., Sosa, M. M., Schwartz, B. L., Morrison, L. C., Gieng, J., Shigenaga, M. K., & Potts, W. K. (2013) · Nature Communications · doi:10.1038/ncomms3245 · Cited by 72
Semba, R. D., Arab, L., Sun, K., Nicklett, E. J., & Ferrucci, L. (2011) · Journal of Nutrition · doi:10.3945/jn.111.143172 · Cited by 70
Esmaillzadeh, A., & Azadbakht, L. (2008) · Diabetes Care · doi:10.2337/dc07-1256 · Cited by 70
Gibson, F., & Froguel, P. (2004) · Diabetes · doi:10.2337/diabetes.53.11.2977 · Cited by 70
Wang, Y., Lin, S., Chuang, Y., Chen, C., Tung, K., & Sheu, W. H. (2010) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00301.2010 · Cited by 70
de Luis, D. A., Aller, R., Izaola, O., Sagrado, M. G., & Conde, R. (2010) · PubMed · Cited by 70
Ronchi, C. L., Boschetti, M., Uberti, E. C. D., et al. (2007) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2007.02917.x · Cited by 70
Increased basal coronary blood flow as a cause of reduced coronary flow reserve in diabetic patients
Picchi, A., Limbruno, U., Focardi, M., Cortese, B., Micheli, A., Boschi, L., Severi, S., & Caterina, R. D. (2011) · American Journal of Physiology-Heart and Circulatory Physiology · doi:10.1152/ajpheart.00615.2011 · Cited by 70
Galcheva, S., Iotova, V., Yotov, Y. T., Bernasconi, S., & Street, M. E. (2011) · European Journal of Endocrinology · doi:10.1530/eje-10-1124 · Cited by 70
Effect of glutamine supplementation on cardiovascular risk factors in patients with type 2 diabetes
Mansour, A., Tehrani, M. R. M., Qorbani, M., Heshmat, R., Larijani, B. A., & Hosseini, S. (2014) · Nutrition · doi:10.1016/j.nut.2014.05.014 · Cited by 69
Attinà, T. M., & Trasande, L. (2015) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2015-1686 · Cited by 72
Eilat-Adar, S., Méte, M., Fretts, A. M., Fabsitz, R. R., Handeland, V., Lee, E. T., Loria, C. M., Xu, J., Yeh, J., & Howard, B. V. (2012) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2011.12.005 · Cited by 70
Kanauchi, M., Kimura, K., Kanauchi, K., & Saito, Y. (2005) · International Journal of Clinical Practice · doi:10.1111/j.1368-5031.2005.00422.x · Cited by 70
Yoshimura, E., Kumahara, H., Tobina, T., Matsuda, T., Ayabe, M., Kiyonaga, A., Anzai, K., Higaki, Y., & Tanaka, H. (2014) · Journal of Obesity · doi:10.1155/2014/197216 · Cited by 72
Stearoyl-CoA Desaturase-1 Is Associated with Insulin Resistance in Morbidly Obese Subjects
García‐Serrano, S., Moreno‐Santos, I., Garrido‐Sánchez, L., et al. (2010) · Molecular Medicine · doi:10.2119/molmed.2010.00078 · Cited by 70
Lee, C. J., Wood, G., Lazo, M., Brown, T. T., Clark, J. M., Still, C., & Benotti, P. (2016) · Obesity · doi:10.1002/oby.21479 · Cited by 67
Alhussain, M. H., Macdonald, I. A., & Taylor, M. A. (2016) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.125401 · Cited by 80
Zhao, X. Y., Gang, X., Liu, Y., Sun, C., Han, Q., & Wang, G. (2016) · Journal of Diabetes Research · doi:10.1155/2016/8160545 · Cited by 70
Gao, X., Wang, Y., & Sun, G. (2016) · Nutrition · doi:10.1016/j.nut.2016.08.005 · Cited by 70
Bai, B., Ban, B., Liu, Z., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0172271 · Cited by 70
Güngör, Ö., Ünal, H. U., Güçlü, A., Gezer, M., Eyileten, T., Güzel, F. B., Altunören, O., Erken, E., Oğuz, Y., Kocyigit, I. D., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178939 · Cited by 70
Relationship Between 12 Adipocytokines and Distinct Components of the Metabolic Syndrome
Ebert, T., Gebhardt, C., Scholz, M. R., Wohland, T., Schleinitz, D., Faßhauer, M., Blüher, M., Stümvoll, M., Kovacs, P. L., & Tönjes, A. (2018) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2017-02085 · Cited by 70
Genetic Drivers of Pancreatic Islet Function
Keller, M. P., Gatti, D. M., Schueler, K. L., Rabaglia, M. E., Stapleton, D. S., Šimeček, P., Vincent, M. J., Allen, S. A., Broman, A. T., Bacher, R. L., et al. (2018) · Genetics · doi:10.1534/genetics.118.300864 · Cited by 64
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
