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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Items 6,401–6,500 of 31,469
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Zhang, P., Chen, Y., Guo, H., Wang, J., Lin, S., Li, H., Yang, Y., & Ling, W. (2017) · Journal of clinical lipidology · doi:10.1016/j.jacl.2017.12.006 · Cited by 63
Yan, P., Xu, Y., Wan, Q., Feng, J., Li, H., Yang, J., Zhong, H., & Zhang, Z. (2018) · Disease Markers · doi:10.1155/2018/6974191 · Cited by 56
Morakinyo, A., Samuel, T. A., & Adekunbi, D. A. (2018) · Endocrine Regulations · doi:10.2478/enr-2018-0002 · Cited by 56
Heat shock protein 72 regulates hepatic lipid accumulation
Archer, A. E., Rogers, R. S., Schulze, A. T. V., Wheatley, J. L., Morris, E. M., McCoin, C. S., Thyfault, J. P., & Geiger, P. C. (2018) · American Journal of Physiology-Regulatory, Integrative and Comparative Physiology · doi:10.1152/ajpregu.00073.2018 · Cited by 56
A community-based study on the association between Helicobacter pylori Infection and obesity
Chen, L., Kuo, S., Chen, C., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-28792-1 · Cited by 56
Božić, J., Borovac, J. A., Galić, T., Kurir, T. T., Šupe‐Domić, D., & Đogaš, Z. (2018) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.7204 · Cited by 56
Oh, M., Kim, S., An, K.-Y., Min, J., Yang, H. I., Lee, J., Lee, M. K., Kim, D.-I., Lee, J., Lee, J., et al. (2018) · BMC Public Health · doi:10.1186/s12889-018-6009-1 · Cited by 57
Fang, Y., Ma, Y., Mo, D., Zhang, S., Xiang, M., & Zhang, Z. (2019) · BMC Public Health · doi:10.1186/s12889-019-6992-x · Cited by 56
Mohammadi, S., Karimzadeh, L., Hojati, V., Ghorbani, A. G., & Nabiuni, M. (2017) · PubMed Central · doi:10.22074/cellj.2017.4415 · Cited by 56
Marlatt, K. L., Redman, L. M., Beyl, R. A., Smith, S. R., Champagne, C. M., Yi, F., & Lovejoy, J. C. (2019) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2019.09.051 · Cited by 58
Genovesi, S., Antolini, L., Orlando, A., Gilardini, L., Bertoli, S., Giussani, M., Invitti, C., Nava, E., Mariagrazia, B., Leone, A., et al. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.00027 · Cited by 58
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Matboli, M., Saad, M., Hasanin, A. H., Saleh, L. A., Baher, W., Bekhet, M. M., & Eissa, S. (2021) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2020.111176 · Cited by 58
Onat, A., Hergenç, G., Türkmen, S., Yazıcı, M., Sarı, İ., & Can, G. (2006) · Metabolism · doi:10.1016/j.metabol.2005.10.005 · Cited by 56
Frajese, G. V., Taylor, N., Jenkins, P. J., BESSER, G. M., & Monson, J. P. (2004) · Hormone Research in Paediatrics · doi:10.1159/000077135 · Cited by 56
Effect of Glucose Tolerance Status on PAI‐1 Plasma Levels in Overweight and Obese Subjects
Pannacciulli, N., Mitrio, V. D., Marino, R., GIORGINO, R., & Pergola, G. D. (2002) · Obesity Research · doi:10.1038/oby.2002.98 · Cited by 55
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Henry, R. R. (2003) · Clinical Therapeutics · doi:10.1016/s0149-2918(03)80242-4 · Cited by 55
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Serum levels of hepatoprotective peptide adiponectin in non-alcoholic fatty liver disease
Aygün, C., Şentürk, Ö., Hülagü, S., Uraz, S., Çelebi, A., Konduk, B. T., Mutlu, B., & Cantürk, Z. (2006) · European Journal of Gastroenterology & Hepatology · doi:10.1097/00042737-200602000-00010 · Cited by 57
Antiretroviral Therapy Exposure and Insulin Resistance in the Women's Interagency HIV Study
Tien, P. C., Schneider, M. F., Cole, S. R., Levine, A. M., Cohen, M. H., DeHovitz, J. A., Young, M., & Justman, J. E. (2008) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0b013e318189a780 · Cited by 56
Postexercise insulin sensitivity is not impaired after an overnight lipid infusion
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Anfossi, G., Russo, I., Massucco, P., Mattiello, L., Doronzo, G., Salve, A. D., & Trovati, M. (2004) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.2004.01370.x · Cited by 55
Lind, L. (2007) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2007.01.014 · Cited by 56
Giménez‐Palop, O., Giménez‐Pérez, G., Mauricio, D., González-Clemente, J. M., Potau, N., Berlanga, E., Trallero, R., Laferrère, B., & Caixàs, A. (2006) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2006.02707.x · Cited by 56
Altınova, A. E., Törüner, F. B., Bukan, N. C., Yaşar, D. G., Aktürk, M., Çakır, N., & ARSLAN, M. (2007) · Endocrine Journal · doi:10.1507/endocrj.k06-021 · Cited by 56
Fogari, R., Derosa, G., Zoppi, A., Rinaldi, A., Lazzari, P., Fogari, E., Mugellini, A., & Preti, P. S. (2005) · Hypertension Research · doi:10.1291/hypres.28.209 · Cited by 55
Lü, B., Yang, Y., Song, X., Dong, X., Zhang, Z., Zhou, L., Li, Y., Zhao, N., Zhu, X., & Hu, R. (2006) · Metabolism · doi:10.1016/j.metabol.2006.04.003 · Cited by 56
Buono, P., Pasanisi, F., Nardelli, C., Ieno, L., Capone, S., Liguori, R., Finelli, C., Oriani, G., Contaldo, F., & Sacchetti, L. (2005) · Clinical Chemistry · doi:10.1373/clinchem.2005.047886 · Cited by 56
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Topp, B., Atkinson, L. L., & Finegood, D. (2007) · American Journal of Physiology. Endocrinology and Metabolism · doi:10.1152/ajpendo.00572.2007 · Cited by 60
Clinical Characteristics of Subjects with a Missense Mutation in Glucokinase
Page, R. C., Lévy, J., Barrow, B. A., Turner, R. C., Patel, P., Hattersley, A. T., Lo, D., Wainscoat, J. S., Permutt, M. A., & Bell, G. I. (1995) · Diabetic Medicine · doi:10.1111/j.1464-5491.1995.tb00460.x · Cited by 56
Self-citationWang, A. Y.-H., Hickman, I. J., Richards, A. A., Whitehead, J. P., Prins, J. B., & Macdonald, G. A. (2005) · The American Journal of Gastroenterology · doi:10.1111/j.1572-0241.2005.00311.x · Cited by 55
Barbieri, M., Bonafè, M., Rizzo, M. R., Ragno, E., Olivieri, F., Marchegiani, F., Franceschi, C., & Paolisso, G. (2004) · Experimental Gerontology · doi:10.1016/j.exger.2004.03.034 · Cited by 56
Östgren, C. J., Lindblad, U., Melander, O., Melander, A., Groop, L., & Råstam, L. (2003) · PubMed · doi:10.1097/01.hjh.0000084734.53355.0d · Cited by 56
The euglycemic hyperinsulinemic clamp examination: variability and reproducibility
L. Bokemark, A. F. (2000) · Scandinavian Journal of Clinical and Laboratory Investigation · doi:10.1080/00365510050185010 · Cited by 56
Physical activity is negatively associated with the metabolic syndrome in the elderly
Bianchi, G., Rossi, V., Muscari, A., Magalotti, D., Zoli, M., & the Pianoro Study Group (2008) · QJM · doi:10.1093/qjmed/hcn084 · Cited by 55
Littlejohn, E. E., Weiss, R. E., Deplewski, D., Edidin, D. V., & Rosenfield, R. (2007) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem.2007.20.1.41 · Cited by 55
Bremer, A. A., Auinger, P., & Byrd, R. S. (2010) · Journal of Nutrition and Metabolism · doi:10.1155/2010/196476 · Cited by 55
“5) [39] values from individuals in these cohorts could not be calculated.”
BackgroundMild Sleep Restriction Acutely Reduces Plasma Glucagon Levels in Healthy Men
Schmid, S. M., Jauch-Chara, K., Hallschmid, M., & Schultes, B. (2009) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2009-0969 · Cited by 55
Xu, X. J., Apovian, C. M., Hess, D. T., Carmine, B. J., Saha, A. K., & Ruderman, N. B. (2015) · Diabetes · doi:10.2337/db14-1765 · Cited by 55
Insulin resistance, central obesity, and risk of colorectal adenomas
Ortiz, A. P., Thompson, C. L., Chak, A., Berger, N. A., & Li, L. (2011) · Cancer · doi:10.1002/cncr.26454 · Cited by 55
de Sá, R. D. C. D. C., Crisma, A. R., Cruz, M. M., Martins, A. R., Masi, L. N., do Amaral, C. L., Curi, R., & Alonso‐Vale, M. I. C. (2016) · The Journal of Physiology · doi:10.1113/jp272541 · Cited by 58
Huang, C., Wang, C., Lin, C., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0189065 · Cited by 55
Carbohydrate status in patients with phenylketonuria
Couce, M. L., Sánchez‐Pintos, P., Vitoria, I., de Castro, M.-J., Aldamiz-Echevarria, L. J., Medina, P. C., Marmiesse, A. F., Roca, I., Ameijeiras, Á. H., Olmos, M. Á. M., et al. (2018) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-018-0847-x · Cited by 55
Barrett, E. S., Vitek, W., Mbowe, O. B., Thurston, S. W., Legro, R. S., Alvero, R. J., Baker, V. L., Bates, G. W., Casson, P. R., Coutifaris, C., et al. (2018) · Human Reproduction · doi:10.1093/humrep/dey261 · Cited by 60
Alves, M. F. D. A., Barreto, F. K. D. A., de Vasconcelos, M. A., Neto, L. G. D. N., Carneiro, R. F., da Silva, L. T., Nagano, C. S., Sampaio, A. H., & Teixeira, E. H. (2020) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2020.04.238 · Cited by 55
Association of the triglyceride and glucose index with low muscle mass: KNHANES 2008–2011
Kim, J. A., Hwang, S. Y., Yu, J. H., Roh, E., Hong, S., Lee, Y., Kim, N. H., Yoo, H. J., Seo, J. A., Kim, N. H., et al. (2021) · Scientific Reports · doi:10.1038/s41598-020-80305-1 · Cited by 58
Narayan, K. M. V., Kondal, D., Daya, N. R., Gujral, U. P., Mohan, D., Patel, S. A., Shivashankar, R., Anjana, R. M., Staimez, L. R., Ali, M. K., et al. (2021) · BMJ Open Diabetes Research & Care · doi:10.1136/bmjdrc-2020-001927 · Cited by 59
Said, M. A., Nafeh, N. Y., & Abdallah, H. A. (2021) · Archives of Physiology and Biochemistry · doi:10.1080/13813455.2021.1899242 · Cited by 55
Lischka, J., Schanzer, A., Hojreh, A., Ba‐Ssalamah, A., de Gier, C., Valent, I., Item, C. B., Greber‐Platzer, S., & Zeyda, M. (2021) · International Journal of Obesity · doi:10.1038/s41366-021-00842-1 · Cited by 55
Characteristics of gestational diabetes subtypes classified by oral glucose tolerance test values
Kotzaeridi, G., Blätter, J., Eppel, D., Rosicky, I., Linder, T., Geissler, F., Huhn, E. A., Hösli, I., Tura, A., & Göbl, C. S. (2021) · European Journal of Clinical Investigation · doi:10.1111/eci.13628 · Cited by 55
Wamil, M., Coleman, R. L., Adler, A., McMurray, J. J. V., & Holman, R. R. (2021) · Diabetes Care · doi:10.2337/dc21-0429 · Cited by 57
Oh, M., Jang, H.-Y., Lee, S.-Y., Jung, S., Chae, S., Lee, S., & Park, B. (2021) · Nutrients · doi:10.3390/nu13072337 · Cited by 56
Quesada, O., Claggett, B. L., Rodríguez, F., Cai, J., Moncrieft, A. E., Garcia, K. A., Rios, M. D., Hanna, D. B., Daviglus, M. L., Talavera, G. A., et al. (2021) · Hypertension · doi:10.1161/hypertensionaha.120.16905 · Cited by 58
El‐Dakroury, W. A., Zewail, M. B., & Amin, M. M. (2022) · Journal of Drug Delivery Science and Technology · doi:10.1016/j.jddst.2022.104040 · Cited by 55
Worldwide trends in prediabetes from 1985 to 2022: A bibliometric analysis using bibliometrix R-tool
Zhao, J., & Li, M. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1072521 · Cited by 61
Zuraikat, F. M., Laferrère, B., Cheng, B., Scaccia, S. E., Cui, Z., Aggarwal, B., Jelić, S., & St‐Onge, M. (2023) · Diabetes Care · doi:10.2337/dc23-1156 · Cited by 55
Prevalence of Extremely Severe Obesity and Metabolic Dysfunction Among US Children and Adolescents
Münte, E., Zhang, X., Khurana, A., & Hartmann, P. (2025) · JAMA Network Open · doi:10.1001/jamanetworkopen.2025.21170 · Cited by 57
Choi, Y., Nam, Y., Yun, J. M., Park, J. H., Cho, B. L., Son, H. ‐., Kim, J. I., & Yun, J. W. (2015) · Diabetic Medicine · doi:10.1111/dme.12808 · Cited by 56
Risk factors for type 2 diabetes: Lessons learned from Japanese Americans in Seattle
Fujimoto, W. Y., Boyko, E. J., Hayashi, T., Kahn, S. E., Leonetti, D. L., McNeely, M. J., & Shuman, W. P. (2012) · Journal of Diabetes Investigation · doi:10.1111/j.2040-1124.2012.00195.x · Cited by 58
Islam, S. A., Seo, M., Lee, Y., & Moon, S. (2015) · Endocrine Journal · doi:10.1507/endocrj.ej15-0350 · Cited by 58
Saely, C. H., Leiherer, A., Muendlein, A., Vonbank, A., Rein, P., Geiger, K. D., Malin, C., & Drexel, H. (2015) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2015.10.100 · Cited by 55
Jayanthy, G., & Subramanian, S. P. (2014) · Biomedicine & Preventive Nutrition · doi:10.1016/j.bionut.2014.03.006 · Cited by 57
Rho, Y. H., Chung, C. P., Oeser, A. M., Solus, J. F., Gebretsadik, T. S., Shintani, A., Raggi, P., Milne, G. L., & Stein, C. (2010) · Arthritis Care & Research · doi:10.1002/acr.20237 · Cited by 55
Hordern, M., Cooney, L. M., Beller, E., Prins, J. B., Marwick, T. H., & Coombes, J. S. (2008) · Clinical Science · doi:10.1042/cs20070422 · Cited by 56
Gambineri, A., Fanelli, F., Prontera, O., Repaci, A., Dalmazi, G. D., Zanotti, L., Pagotto, U., Flacco, M. E., Guidi, J., Fava, G. A., et al. (2013) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2012-3537 · Cited by 54
Busetto, L., Bassetto, F., Zocchi, M. L., Zuliani, F., Nolli, M. L., Pigozzo, S., Coin, A., Mazza, M., Sergi, G., Mazzoleni, F., et al. (2007) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2006.09.009 · Cited by 55
Sesti, G., Fiorentino, T., Arturi, F., Perticone, M., Sciacqua, A., & Perticone, F. (2014) · PLoS ONE · doi:10.1371/journal.pone.0088569 · Cited by 60
de Oliveira-Junior, S. A., Martinez, P. F., Guizoni, D. M., Campos, D. H. S., Fernandes, T., de Oliveira, E. M., Okoshi, M. P., Okoshi, K., Padovani, C. R., & Cicogna, A. C. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086447 · Cited by 55
Lim, S. Y., Rhee, E., & Sung, K. (2010) · Journal of Korean Medical Science · doi:10.3346/jkms.2010.25.10.1480 · Cited by 55
Bel‐Serrat, S., Mouratidou, T., Santaliestra-Pasías, A. M., Iacoviello, L., Kourides, Y. A., Mårild, S., Molnár, D., Reisch, L. A., Siani, A., Stomfai, S., et al. (2013) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2013.84 · Cited by 54
Improvement of metabolic syndrome markers through altitude specific hiking vacations
Greie, S., Humpeler, E., Gunga, H., Koralewski, E., Klingler, A., Mittermayr, M., Fries, D. R., Lechleitner, M., Hoertnagl, H., HOFFMANN, G. E., et al. (2006) · Journal of Endocrinological Investigation · doi:10.1007/bf03344138 · Cited by 55
The association of adiposity with parathyroid hormone in healthy older adults
Pitroda, A. P., Harris, S. S., & Dawson‐Hughes, B. (2009) · Endocrine · doi:10.1007/s12020-009-9231-x · Cited by 55
Vitamin D levels and obstructive sleep apnoea in children
Gozal, D., Peris, E., & Gozal, D. (2014) · Sleep Medicine · doi:10.1016/j.sleep.2013.12.009 · Cited by 55
Ong, K. L., McClelland, R. L., Rye, K.-A., Cheung, B. M. Y., Post, W. S., Vaidya, D. M., Criqui, M. H., Cushman, M., Barter, P. J., & Allison, M. (2014) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2014.07.015 · Cited by 54
Goldner, W. S., Kraus, V. L., Sivitz, W. I., Hunter, S. K., & Dillon, J. S. (2004) · Diabetes Technology & Therapeutics · doi:10.1089/1520915041705875 · Cited by 55
Resistin Is Closely Related to Systemic Inflammation in Obstructive Sleep Apnea
Yamämoto, Y., Fujiuchi, S., Hiramatsu, M., Nishigaki, Y., Takeda, A., Fujita, Y., & Yamazaki, Y. (2008) · Respiration · doi:10.1159/000141866 · Cited by 55
Telomere length and type 2 diabetes in males, a premature aging syndrome
Murillo-Ortiz, B. O., Albarrán-Tamayo, F., Arenas-Aranda, D. J., Benı́tez-Bribiesca, L., Malacara, J. M., Martı́nez-Garza, S., Hernández-González, M. A., Solorio, S., Garay‐Sevilla, M. E., & Mora-Villalpando, C. (2011) · The Aging Male · doi:10.3109/13685538.2011.593658 · Cited by 55
Palomba, S. (2004) · Human Reproduction · doi:10.1093/humrep/deh296 · Cited by 55
Reduced AMPK-ACC and mTOR signaling in muscle from older men, and effect of resistance exercise
Li, M., Verdijk, L. B., Sakamoto, K., Ely, B. A., van Loon, L. J. C., & Musi, N. (2012) · Mechanisms of Ageing and Development · doi:10.1016/j.mad.2012.09.001 · Cited by 64
Effects of curcuminoid supplement on cardiac autonomic status in high-fat–induced obese rats
Pongchaidecha, A., Lailerd, N., Boonprasert, W., & Chattipakorn, N. (2009) · Nutrition · doi:10.1016/j.nut.2009.02.001 · Cited by 56
Bille, D. S., Banasik, K., Justesen, J. M., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0020640 · Cited by 55
Xiao, S., & Zhao, L. (2013) · FEMS Microbiology Ecology · doi:10.1111/1574-6941.12250 · Cited by 57
Brix, J., Höllerl, F., Kopp, H., Schernthaner, G.-H., & Schernthaner, G.-H. (2012) · International Journal of Obesity · doi:10.1038/ijo.2012.107 · Cited by 56
Sharma, P. K., Bhansali, A. K., Sialy, R., Malhotra, S., & Pandhi, P. (2006) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2006.02658.x · Cited by 54
Taiwanese vegetarians have higher insulin sensitivity than omnivores
Hung, C.-J., Huang, P., Li, Y.-H., Lu, S., Ho, L.-T., & Chou, H.-F. (2006) · British Journal Of Nutrition · doi:10.1079/bjn20051588 · Cited by 55
Analysis of criteria for metabolic syndrome in a population‐based study of Japanese‐Brazilians
Rosenbaum, P., Gimeno, S. G. A., Sañudo, A., Franco, L. J., Ferreira, S. R. G., & for the Japanese‐Brazilian Diabetes Study Group (2004) · Diabetes Obesity and Metabolism · doi:10.1111/j.1463-1326.2004.00402.x · Cited by 52
Doumatey, A. P., Lashley, K., Huang, H., Zhou, J., Chen, G., Amoah, A. G. B., Agyenim‐Boateng, K., Oli, J., Fasanmade, O. A., Adebamowo, C., et al. (2009) · Obesity · doi:10.1038/oby.2009.322 · Cited by 55
Ozcimen, E., Uçkuyu, A., Çiftçi, F. C., Yanik, F., & Bakar, C. (2008) · Gynecological Endocrinology · doi:10.1080/09513590801948416 · Cited by 56
Vrolix, R., & Mensink, R. P. (2010) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.2009.28339 · Cited by 58
Tessari, P., Kiwanuka, E., Cristini, M., Zaramella, M., Enslen, M., Zurlo, C., & García‐Ródenas, C. L. (2006) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.698 · Cited by 59
Ning, F., Zhang, L., Dekker, J. M., Onat, A., Stehouwer, C. D. A., Yudkin, J. S., Laatikainen, T. K., Tuomilehto, J. O., Pyörälà, K., Qiao, Q., et al. (2012) · Cardiovascular Diabetology · doi:10.1186/1475-2840-11-76 · Cited by 56
Lidfeldt, J., Nerbrand, C., Samsioe, G., & Agardh, C. (2005) · Diabetes Care · doi:10.2337/diacare.28.10.2531 · Cited by 55
Gkourogianni, A., Kosteria, I., Telonis, A. G., Margeli, A., Mantzou, A., Konsta, M. A. P., Loutradis, D., Mastorakos, G. P., Papassotiriou, I., Klapa, M. I., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0094001 · Cited by 56
Norenstedt, S., Pernow, Y., Brismar, K., Sääf, M., Ekip, A., Granath, F., Zedenius, J., & Nilsson, I. (2013) · European Journal of Endocrinology · doi:10.1530/eje-13-0547 · Cited by 55
Park, S., & Lee, B.-K. (2012) · Nutrition Research and Practice · doi:10.4162/nrp.2012.6.2.162 · Cited by 55
Bao, L., Cai, X., Dai, X., Ding, Y., Jiang, Y., Li, Y., Zhang, Z., & Li, Y. (2014) · Food & Function · doi:10.1039/c4fo00340c · Cited by 55
Ghrelin and leptin levels in obese adolescents. Relationship with body fat and insulin resistance
Stylianou, C., Galli‐Τsinopoulou, A., Farmakiotis, D., Rousso, I., Karamouzis, M., Koliakos, G., & Nousia‐Arvanitakis, S. (2007) · HORMONES · doi:10.14310/horm.2002.1111025 · Cited by 55
Panoulas, V. F., Nikas, S. N., Smith, J. P., Douglas, K., Nightingale, P., Milionis, H. J., Treharne, G. J., Toms, T. E., Kita, M. D., & Kitas, G. D. (2008) · Annals of the Rheumatic Diseases · doi:10.1136/ard.2007.082594 · Cited by 55
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 ›
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