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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Evaluation of leptin as a marker of insulin resistance in type 2 diabetes mellitus
Manjuladevi, M. T., Nanda, S. K., Shamraj, M., et al. (2017) · International Journal of Applied and Basic Medical Research · doi:10.4103/ijabmr.ijabmr_278_16 · Cited by 63
Tsai, S., Wu, H., Chen, P., Chen, Y., Yu, M., Wang, T.-F., Wu, S.-Y., Tzeng, S., & Kuo, Y. (2018) · Brain Research · doi:10.1016/j.brainres.2018.07.017 · Cited by 66
Chou, Y.-C., Ho, C., & Pan, M. (2018) · Journal of Agricultural and Food Chemistry · doi:10.1021/acs.jafc.8b02719 · Cited by 63
Correa, P. J., Venegas, P., Palmeiro-Silva, Y. K., Albers, D., Rice, G. E., Roa, J., Cortez, J., Mönckeberg, M., Manuel Schepeler, S., Osorio, E., et al. (2018) · Journal of Perinatal Medicine · doi:10.1515/jpm-2018-0120 · Cited by 65
Monk, J. M., Wu, W., Lepp, D., Wellings, H. R., Hutchinson, A. L., Liddle, D. M., Gräf, D., Pauls, K. P., Robinson, L. E., & Power, K. A. (2019) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2019.04.009 · Cited by 66
Sajoux, I., Lorenzo, P. M., Gómez-Arbeláez, D., Zulet, M. Á., Abete, I., Castro, A. I., Baltar, J., Portillo, M. P., Tinahones, F. J., Martínez, J. A., et al. (2019) · Nutrients · doi:10.3390/nu11102368 · Cited by 63
Pureza, I. R. D. O. M., de Melo, I. S. V., Macêna, M. D. L., Praxedes, D. R. S., Vasconcelos, L. G. L., Júnior, A. E. D. S., Florêncio, T. M. D. M. T., & Bueno, N. B. (2020) · Nutrition · doi:10.1016/j.nut.2020.110796 · Cited by 66
Persistence of skin-deep resilience in African American adults.
Brody, G. H., Yu, T., Chen, E., & Miller, G. E. (2020) · Health Psychology · doi:10.1037/hea0000945 · Cited by 64
Wang, X., Mukherjee, B., Karvonen-Gutierrez, C. A., Herman, W. H., Batterman, S., Harlow, S. D., & Park, S. K. (2020) · Environment International · doi:10.1016/j.envint.2020.106109 · Cited by 63
LIJNEN, H. R., Alessi, M., HOEF, B. V., Collen, D., & Juhan-Vague, I. (2005) · Journal of Thrombosis and Haemostasis · doi:10.1111/j.1538-7836.2005.01390.x · Cited by 64
Smoking as a Risk Factor for Visceral Fat Accumulation in Japanese Men
Komiya, H., Mori, Y., YOKOSE, T., & Tajima, N. (2006) · The Tohoku Journal of Experimental Medicine · doi:10.1620/tjem.208.123 · Cited by 62
Hjelmesaeth, J. S., Hagen, M., Hartmann, A., Midtvedt, K., Egeland, T., & Jenssen, T. G. (2002) · Clinical Transplantation · doi:10.1034/j.1399-0012.2002.02059.x · Cited by 63
Bosy‐Westphal, A., Onur, S., Geisler, C., Wolf, A., Korth, O., Pfeuffer, M., Schrezenmeir, J., Krawczak, M., & Müller, M. J. (2006) · International Journal of Obesity · doi:10.1038/sj.ijo.0803481 · Cited by 64
Bajraktari, G., Koltai, M. Z., Ademaj, F., Rexhepaj, N., Qirko, S., Ndrepepa, G., & Elezi, S. (2005) · International Journal of Cardiology · doi:10.1016/j.ijcard.2005.08.033 · Cited by 63
The Effect of Alcohol Intake on Insulin Sensitivity in Men
Zilkens, R., Burke, V., Watts, G. F., Beilin, L. J., & Puddey, I. B. (2003) · Diabetes Care · doi:10.2337/diacare.26.3.608 · Cited by 63
Wisniacki, N., Taylor, W., Lye, M., & Wilding, J. P. H. (2004) · Heart · doi:10.1136/hrt.2003.029652 · Cited by 62
Insulin Resistance in Liver Cirrhosis Is Not Associated With Circulating Retinol-Binding Protein 4
Yagmur, E., Weiskirchen, R., Gressner, A. M., Trautwein, C., & Tacke, F. (2007) · Diabetes Care · doi:10.2337/dc06-2323 · Cited by 63
Effects of cardiopulmonary bypass on glucose homeostasis after coronary artery bypass surgery
Anderson, R. E., Brismar, K., Barr, G., et al. (2005) · European Journal of Cardio-Thoracic Surgery · doi:10.1016/j.ejcts.2005.05.025 · Cited by 63
Kaitosaari, T., Rönnemaa, T., Viikari, J. S. A., Raitakari, O. T., Arffman, M., Marniemi, J., Kallio, K., Pahkala, K., Jokinen, E. V., & Simell, O. (2006) · Diabetes Care · doi:10.2337/diacare.29.04.06.dc05-1523 · Cited by 65
Petibois, C., CASSAIGNE, A., Gin, H., & Déléris, G. (2004) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2003-031311 · Cited by 64
Raben, A., Holst, J. J., Madsen, J., & Astrup, A. V. (2001) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/73.2.177 · Cited by 63
Albareda, M., Caballero, A., Badell, G., Rodrı́guez-Espinosa, J., Ordóñez‐Llanos, J., de Leiva, A., & Corcoy, R. (2005) · Metabolism · doi:10.1016/j.metabol.2005.03.017 · Cited by 63
Weaver, J. U., Kopelman, P. G., McLoughlin, L., Forslingt, M. L., & Grossman, A. B. (1993) · Clinical Endocrinology · doi:10.1111/j.1365-2265.1993.tb02375.x · Cited by 63
Increase in postprandial serum insulin levels in epileptic patients with valproic acid therapy
Luef, G. J., Abraham, I., Hoppichler, F., Trinka, E., Unterberger, I., Bauer, G. J., & Lechleitner, M. (2002) · Metabolism · doi:10.1053/meta.2002.34708 · Cited by 63
Breakfast and fast food consumption are associated with selected biomarkers in adolescents
Marlatt, K. L., Farbakhsh, K., Dengel, D. R., & Lytle, L. A. (2016) · Preventive Medicine Reports · doi:10.1016/j.pmedr.2015.11.014 · Cited by 63
Holder, T., Giannini, C., Santoro, N., Pierpont, B., Shaw, M., Duran, E., Caprio, S., & Weiss, R. (2014) · Diabetologia · doi:10.1007/s00125-014-3345-2 · Cited by 62
Muscle strength is a marker of insulin resistance in patients with type 2 diabetes: a pilot study
NOMURA, T., IKEDA, Y., NAKAO, S., ITO, K., ISHIDA, K., SUEHIRO, T., & HASHIMOTO, K. (2007) · Endocrine Journal · doi:10.1507/endocrj.k07-055 · Cited by 61
Effect of a 21 day Daniel Fast on metabolic and cardiovascular disease risk factors in men and women
Bloomer, R. J., Kabir, M. M., Canale, R. E., Trepanowski, J. F., Marshall, K. E., Farney, T. M., & Hammond, K. G. (2010) · Lipids in Health and Disease · doi:10.1186/1476-511x-9-94 · Cited by 62
“The homeostasis model assessment (HOMA-IR) was used as an index of insulin resistance [18] and calculated as: [fasting glucose (mg·dL) × fasting insulin (μU·mL)]/405.”
MethodAdiponectin and its correlates of cardiovascular risk in young adults: the Bogalusa Heart Study
Patel, D. A., Srinivasan, S. R., Xu, J.-H., Chen, W., & Berenson, G. S. (2006) · Metabolism · doi:10.1016/j.metabol.2006.06.028 · Cited by 62
Kerksick, C. M., Wismann-Bunn, J., Fogt, D., Thomas, A. R., Taylor, L., Campbell, B. I., Wilborn, C. D., Harvey, T., Roberts, M. D., La Bounty, P., et al. (2010) · Nutrition Journal · doi:10.1186/1475-2891-9-59 · Cited by 86
Sharma, N., Castorena, C. M., & Cartee, G. D. (2012) · Nutrition & Metabolism · doi:10.1186/1743-7075-9-90 · Cited by 65
An, X., Yu, D., Zhang, R., Zhu, J., Du, R., Shi, Y., & Xiong, X. (2012) · Cardiovascular Diabetology · doi:10.1186/1475-2840-11-71 · Cited by 62
Association between renin and atherosclerotic burden in subjects with and without type 2 diabetes
Gonçalves, I., Edsfeldt, A., Colhoun, H. M., Shore, A. C., Palombo, C., Natali, A., Fredrikson, G. N., Björkbacka, H., Wigren, M., Bengtsson, E., et al. (2016) · BMC Cardiovascular Disorders · doi:10.1186/s12872-016-0346-8 · Cited by 61
Gagné-Ouellet, V., Houde, A.-A., Guay, S.-P., Perron, P., Gaudet, D., Guérin, R., Jean-Patrice, B., Hivert, M.-F., Brisson, D., & Bouchard, L. (2017) · Epigenetics · doi:10.1080/15592294.2017.1322254 · Cited by 63
Tarantino, G., Porcu, C., Arciello, M., Andreozzi, P., & Balsano, C. (2018) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.14104 · Cited by 64
Bhowmik, B., Siddiquee, T., Majumder, A., Mdala, I., Hossain, I. A., Hassan, Z., Jahan, I., Moreira, N. C. D. V., Alim, A. M., Basit, A., et al. (2019) · BMC Pregnancy and Childbirth · doi:10.1186/s12884-019-2571-5 · Cited by 74
Ferro, Y., Montalcini, T., Mazza, E., FOTI, D. P., Angotti, E., Gliozzi, M., Nucera, S., Paone, S., Bombardelli, E., Aversa, I., et al. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.00494 · Cited by 62
He, S., Mao, X., Lei, H., Dong, B., Guo, D., Zheng, B., & Sun, P. (2020) · Journal of Inflammation Research · doi:10.2147/jir.s260770 · Cited by 65
Tao, T., Zhang, Y., Zhu, Y., Fu, J.-R., Wang, Y., Cai, J., Ma, J., Xu, Y., Gao, Y., Sun, Y. W., et al. (2020) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgaa692 · Cited by 67
The β Cell in Diabetes: Integrating Biomarkers With Functional Measures
Kahn, S. E., Chen, Y., Esser, N., Taylor, A. J., van Raalte, D. H., Zraika, S., & Verchere, C. B. (2021) · Endocrine Reviews · doi:10.1210/endrev/bnab021 · Cited by 75
Abdulmalek, S. A., Eldala, A., Awad, D., & Balbaa, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-00108-w · Cited by 66
Comparing SARC-CalF With SARC-F for Screening Sarcopenia in Adults With Type 2 Diabetes Mellitus
Xu, Z., Zhang, P., Chen, Y., Jiang, J., Zhou, Z., & Zhu, H. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.803924 · Cited by 63
Zheng, M., Yang, X., Wu, Q., Gong, Y., Pang, N., Ge, X., Nagaratnam, N., Jiang, P., Zhou, M., Hu, T., et al. (2022) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.202200597 · Cited by 68
Farahmand, M. A., Daneshzad, E., Fung, T. T., Zahidi, F., Muhammadi, M., Bellissimo, N., & Azadbakht, L. (2023) · BMC Endocrine Disorders · doi:10.1186/s12902-022-01209-x · Cited by 63
Patti, M., Li, P., & Goldfine, A. B. (2015) · Obesity · doi:10.1002/oby.21043 · Cited by 63
Ormseth, M. J., Solus, J. F., Vickers, K. C., Oeser, A. M., Raggi, P., & Stein, C. (2015) · The Journal of Rheumatology · doi:10.3899/jrheum.150232 · Cited by 61
Hwang, Y.-C., Oh, S. W., Park, S., et al. (2013) · International Journal of Obesity · doi:10.1038/ijo.2013.242 · Cited by 62
Roth, C. L., Elfers, C., Gebhardt, U., Muller, H. L., & Reinehr, T. (2012) · Metabolism · doi:10.1016/j.metabol.2012.08.001 · Cited by 60
Pancreatic function, quality of life and costs at long-term follow-up after acute pancreatitis
Andersson, B. (2010) · World Journal of Gastroenterology · doi:10.3748/wjg.v16.i39.4944 · Cited by 62
Curcumin prevents liver fat accumulation and serum fetuin-A increase in rats fed a high-fat diet
Öner-İyidoğan, Y., Koçak, H., Seyithanoğlu, M., et al. (2013) · Journal of Physiology and Biochemistry · doi:10.1007/s13105-013-0244-9 · Cited by 62
Chiang, J., Lin, Y.-L., Chen, C., Ouyang, C., Wu, Y., Chi, Y., Huang, K., & Yang, W. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071799 · Cited by 65
IPF-1/MODY4 gene missense mutation in an Italian family with type 2 and gestational diabetes
Gragnoli, C., Stanojevic, V., Gorini, A., di Preussenthal, G. M., Thomas, M. K., & Habener, J. F. (2005) · Metabolism · doi:10.1016/j.metabol.2005.01.037 · Cited by 63
Nagy, B., Fülöp, T., Varga, Z., Karányi, Z., Magyar, M. T., Oláh, L., Papp, M., Facskó, A., Kappelmayer, J., Paragh, G., et al. (2011) · Thrombosis and Haemostasis · doi:10.1160/th11-01-0030 · Cited by 62
Ateş, S., Yeşil, G., Şevket, O., Molla, T., & Yıldız, Ş. (2014) · Maturitas · doi:10.1016/j.maturitas.2014.07.008 · Cited by 62
Reduced leptin concentrations in subjects with type 2 diabetes mellitus in Sudan
Abdelgadir, M. A., Elbagir, M. N., Eltom, M. A., Berne, C., & Ahr[eacute]n, B. (2002) · Metabolism · doi:10.1053/meta.2002.30504 · Cited by 62
Nallasamy, P., Kannappan, S., & Anuradha, C. V. (2011) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2011.06.011 · Cited by 63
Esteghamati, A., Khalilzadeh, O., Anvari, M., Rashidi, A., Mokhtari, M., & Nakhjavani, M. (2009) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2008.0100 · Cited by 62
Hahn, S., Fingerhut, A., Khomtsiv, U., Khomtsiv, L., Tan, S., Quadbeck, B., Herrmann, B. L., Knebel, B., Müller‐Wieland, D., Mann, K., et al. (2005) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2005.02261.x · Cited by 63
Impaired Endothelium-Dependent Vasodilation in Normotensive and Normoglycemic Obese Adult Humans
Guilder, G. P. V., Hoetzer, G. L., Dengel, D. R., Stauffer, B. L., & DeSouza, C. A. (2006) · Journal of Cardiovascular Pharmacology · doi:10.1097/01.fjc.0000205097.29946.d3 · Cited by 61
Palou, M., Konieczna, J., Torrens, J. M., Sánchez, J., Priego, T., Fernandes, M. L., Palou, A., & Picó, C. (2012) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2011.11.005 · Cited by 62
Blackburn, P., Lemieux, I., Lamarche, B., Bergeron, J., Perron, P., Tremblay, G., Gaudet, D., & Després, J. (2011) · Metabolism · doi:10.1016/j.metabol.2011.05.017 · Cited by 64
Association of Metabolic Syndrome Parameters with TT3 and FT3/FT4 Ratio in Obese Turkish Population
Tarçın, Ö., Abanonu, G. B., Yazıcı, D., & Tarçın, O. (2012) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2011.0098 · Cited by 62
Factors Related to Fatigue in Patients With Cirrhosis Before and After Liver Transplantation
Kalaitzakis, E., Josefsson, A., Castedal, M., Henfridsson, P., Bengtsson, M., Hugosson, I., Andersson, B. A., & Bjornsson, E. S. (2011) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2011.07.029 · Cited by 62
Fasting and daylong triglycerides in obesity with and without type 2 diabetes
van Wijk, J. P. H., Halkes, C. J. M., Erkelens, D. W., & Cabezas, M. C. (2003) · Metabolism · doi:10.1016/s0026-0495(03)00106-9 · Cited by 62
Kawaguchi, T., Shiba, N. N., Maeda, T., Matsugaki, T., Takano, Y., Itou, M., Sakata, M., Taniguchi, E., Nagata, K., & Sata, M. (2011) · Journal of Gastroenterology · doi:10.1007/s00535-011-0378-x · Cited by 61
Sleep duration and insulin resistance in individuals without type 2 diabetes: The PPP-Botnia Study
Pyykkönen, A.-J., Isomaa, B., Pesonen, A., Eriksson, J. G., Groop, L., Tuomilehto, J., & Räikkönen, K. (2014) · Annals of Medicine · doi:10.3109/07853890.2014.902226 · Cited by 59
Ağbaht, K., Gürlek, A., Karakaya, J., & Bayraktar, M. (2009) · Endocrine · doi:10.1007/s12020-009-9158-2 · Cited by 62
Colao, A. A. L., Pivonello, R., Auriemma, R. S., et al. (2007) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2006-2551 · Cited by 62
Insulin resistance and metabolic profile in antipsychotic naïve schizophrenia patients
Dasgupta, A., Singh, O. P., Rout, J. K., Saha, T., & Mandal, S. (2010) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2010.06.011 · Cited by 62
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.
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