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 8,301–8,400 of 31,469
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Soliman, E., Essmat, N., Mahmoud, M. F., & Mahmoud, A. A. A. (2020) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-020-01838-w · Cited by 45
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Luo, H., Lan, C., Fan, C., Gong, X., Chen, C., Yu, C., Wang, J., Luo, X., Hu, C., José, P. A., et al. (2021) · Cardiovascular Research · doi:10.1093/cvr/cvab280 · Cited by 45
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Leuner, B., Max, M., Thamm, K., Kausler, C., Yakobus, Y., Bierhaus, A., Sel, S., Hofmann, B., Silber, R.-E., Simm, A., et al. (2012) · Zeitschrift für Gerontologie und Geriatrie · doi:10.1007/s00391-011-0279-x · Cited by 46
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Silva, M. S. D., Julien, P., Përusse, L., Vohl, M., & Rudkowska, I. (2015) · Lipids · doi:10.1007/s11745-015-4055-3 · Cited by 45
Onishi, Y., Hayashi, T., Satō, K., Ogihara, T., Kuzuya, N., Anai, M., Tsukuda, K., Boyko, E. J., Fujimoto, W. Y., & Kikuchi, M. (2010) · Journal of Diabetes Investigation · doi:10.1111/j.2040-1124.2010.00041.x · Cited by 44
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Parillo, M., Licenziati, M. R., Vacca, M., de Marco, D., & Iannuzzi, A. (2012) · PubMed · doi:10.3275/7909 · Cited by 45
Chan, D. C., Watts, G. F., Barrett, P. H. R., Martins, I. J., James, A. P., Mamo, J. C. L., Mori, T. A., & Redgrave, T. G. (2002) · Journal of Lipid Research · doi:10.1016/s0022-2275(20)30112-7 · Cited by 44
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Shin, M.-J., Jang, Y., Koh, S. J., Chae, J. S., Kim, O. Y., Lee, J. E., Ordovás, J. M., & Lee, J. H. (2006) · International Journal of Obesity · doi:10.1038/sj.ijo.0803338 · Cited by 44
Ogawa, T., Hirose, H., Miyashita, K., Saito, I., & Saruta, T. (2005) · Metabolism · doi:10.1016/j.metabol.2004.09.008 · Cited by 44
Samaras, K., Botelho, N. K., Chisholm, D. J., & Lord, R. V. (2009) · Obesity Surgery · doi:10.1007/s11695-009-9952-1 · Cited by 44
Tai, E. S., Collins, D. E., Robins, S. J., O’Connor, J. J., Bloomfield, H. E., Ordovás, J. M., Schaefer, E. J., & Brousseau, M. E. (2005) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2005.08.034 · Cited by 44
Derosa, G., SALVADEO, S. A. T., D’Angelo, A., Ferrari, I., Mereu, R., Palumbo, I., Maffioli, P., Randazzo, S., & Cicero, A. F. G. (2009) · Current Medical Research and Opinion · doi:10.1185/03007990802711024 · Cited by 44
Perticone, F., Sciacqua, A., Perticone, M., Laino, I., Miceli, S., Carè, I., Leone, G. G., Andreozzi, F., Maio, R., & Sesti, G. (2008) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2008-0646 · Cited by 44
Śladowska-Kozłowska, J., Litwin, M., Niemirska, A., Wierzbicka, A., Wawer, Z. T., & Janas, R. M. (2011) · Pediatric Nephrology · doi:10.1007/s00467-011-1916-8 · Cited by 44
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Satoh, A., Adachi, H., Tsuruta, M., Hirai, Y., Hiratsuka, A., Enomoto, M., Furuki, K., Hino, A., Takeuchi, T., & Imaizumi, T. (2005) · Diabetes Care · doi:10.2337/diacare.28.10.2514 · Cited by 45
Kreider, R. B., Rasmussen, C. J., Kerksick, C. M., Wilborn, C., Taylor, L. W., Campbell, B. I., Magrans-Courtney, T., Fogt, D. L., Ferreira, M. P., Li, R., et al. (2011) · The Physician and Sportsmedicine · doi:10.3810/psm.2011.05.1893 · Cited by 44
Sheu, W. H., Chuang, S., Lee, W., Tsai, S., Chou, P., & Chen, C.-H. (2006) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2006.03.011 · Cited by 44
Halvatsiotis, I., Tsiotra, P. C., Ikonomidis, I., et al. (2010) · Cardiovascular Diabetology · doi:10.1186/1475-2840-9-10 · Cited by 44
Evidence against redox regulation of energy homoeostasis in humans at high altitude
Bailey, D. M., Ainslie, P. N., Jackson, S. K., Richardson, R. S., & Ghatei, M. A. (2004) · Clinical Science · doi:10.1042/cs20040085 · Cited by 44
Insulin resistance in chronic kidney disease is ameliorated by spironolactone in rats and humans
Hosoya, K., Minakuchi, H., Wakino, S., Fujimura, K., Hasegawa, K., Komatsu, M., Yoshifuji, A., Futatsugi, K., Shinozuka, K., Washida, N., et al. (2014) · Kidney International · doi:10.1038/ki.2014.348 · Cited by 43
Rodrı́guez, A., Becerril, S., Valentí, V., Moncada, R., Méndez-Giménez, L., Ramírez, B., Lancha, A., Martín, M., Burrell, M. A., Catalán, V., et al. (2012) · Obesity Surgery · doi:10.1007/s11695-012-0702-4 · Cited by 45
Relationship between Metabolic Syndrome and Early Stage Coronary Atherosclerosis
Hitsumoto, T., Takahashi, M., Iizuka, T., & Shirai, K. (2007) · Journal of Atherosclerosis and Thrombosis · doi:10.5551/jat.e506 · Cited by 43
Martin, R. M., Gunnell, D., Whitley, E., Nicolaides, A. N., GRIFFIN, M. B., Georgiou, N., Smith, G. D., Ebrahim, S. B. J., & Holly, J. M. P. (2008) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2007-2295 · Cited by 44
Diet or exercise: what is more effective in preventing or reducing metabolic alterations?
Bo, S., Ciccone, G., Guidi, S., Gambino, R., Durazzo, M., Gentile, L., Cassader, M., Cavallo‐Perin, P., & Pagano, G. (2008) · European Journal of Endocrinology · doi:10.1530/eje-08-0334 · Cited by 44
von Bibra, H., Diamant, M., Scheffer, P. G., Siegmund, T., & Schumm‐Draeger, P. (2008) · Diabetes and Vascular Disease Research · doi:10.3132/dvdr.2008.045 · Cited by 44
Decreased soluble leptin receptor levels in women with polycystic ovary syndrome
Hahn, S., Haselhorst, U., Quadbeck, B., Tan, S., Kimmig, R., Mann, K., & Janssen, O. E. (2006) · European Journal of Endocrinology · doi:10.1530/eje.1.02078 · Cited by 44
Cystatin C is associated with cardiovascular risk factors and metabolic syndrome in Aboriginal youth
Retnakaran, R. R., Connelly, P. W., Harris, S. B., Zinman, B., & Hanley, A. J. (2007) · Pediatric Nephrology · doi:10.1007/s00467-007-0471-9 · Cited by 44
Zavarella, S., Petrone, A., Zampetti, S., Gueorguiev, M., Spoletini, M., Mein, C. A., Leto, G., Korbonits, M., & Buzzetti, R. (2007) · International Journal of Obesity · doi:10.1038/sj.ijo.0803766 · Cited by 44
Sviridov, D., Hoang, A., Ooi, E. M. M., Watts, G. F., Barrett, P. H. R., & Nestel, P. J. (2007) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2007.07.007 · Cited by 44
Insulin Resistance and the Risk of Stroke and Stroke Subtypes in the Nondiabetic Elderly
Wieberdink, R. G., Koudstaal, P. J., Hofman, A., Witteman, J. C. M., Breteler, M. M. B., & Ikram, M. A. (2012) · American Journal of Epidemiology · doi:10.1093/aje/kws149 · Cited by 47
High Prevalence of Metabolic Syndrome Among HIV–Infected Patients: Link With the Cardiovascular Risk
Bruno, R., Gazzaruso, C., Sacchi, P., Zocchetti, C., Giordanetti, S., Garzaniti, A., Ciappina, V., Maffezzini, E., Maserati, R., & Filice, G. (2002) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/00126334-200211010-00015 · Cited by 44
Palacios, R., Merchante, N., Macı́as, J., González, M., Castillo, J., Ruiz, J., Márquez, M., Gómez-Mateos, J. M., Pineda, J. A., & Santos, J. S. (2006) · Antiviral Therapy · doi:10.1177/135965350601100413 · Cited by 44
Damcott, C., Feingold, E., Moffett, S. P., Barmada, M. M., Marshall, J. A., Hamman, R. F., & Ferrell, R. E. (2003) · Human Genetics · doi:10.1007/s00439-003-0937-1 · Cited by 44
Nasta, P., Gatti, F., Puoti, M., et al. (2008) · AIDS · doi:10.1097/qad.0b013e3282fbd1c4 · Cited by 44
Two measures of insulin sensitivity provided similar information in a U.S. population
Bravata, D. M., Wells, C. K., Concato, J., Kernan, W. N., Brass, L. M., & Gulanski, B. (2004) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2004.05.001 · Cited by 44
Adiponectin in a Native Canadian Population Experiencing Rapid Epidemiological Transition
Hanley, A. J., Connelly, P. W., Harris, S. B., & Zinman, B. (2003) · Diabetes Care · doi:10.2337/diacare.26.12.3219 · Cited by 42
Tavares, V., Hirata, R. D. C., Rodrigues, A. C., Monte, O., Salles, J. E. N., Scalissi, N. M., Speranza, A. C., & Hirata, M. H. (2004) · Diabetes Obesity and Metabolism · doi:10.1111/j.1463-1326.2004.00453.x · Cited by 45
Colao, A., Pivonello, R., Cavallo, L. M., Gaccione, M., Auriemma, R. S., Esposito, F., Cappabianca, P., & Lombardi, G. (2006) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2006.02584.x · Cited by 44
Lindegaard, B., Keller, P., Brüünsgaard, H., Gerstoft, J., & Pedersen, B. K. (2004) · Clinical & Experimental Immunology · doi:10.1111/j.1365-2249.2004.02367.x · Cited by 43
Brady, L. M., Gower, B. A., Lovegrove, S. S., Williams, C. M., & Lovegrove, J. A. (2004) · International Journal of Obesity · doi:10.1038/sj.ijo.0802547 · Cited by 43
Effect of atorvastatin on apolipoprotein B100 containing lipoprotein metabolism in type-2 diabetes
Ouguerram, K., Magot, T., Zäir, Y., Marchini, J. S., Charbonnel, B., Laouenan, H., & Krempf, M. (2003) · The Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.103.048991 · Cited by 44
Thomann, R., Rossinelli, N., Keller, U. B., Tirri, B. F., Geyter, C. D., Ruiz, J. C., Kränzlin, M., & Puder, J. J. (2008) · Gynecological Endocrinology · doi:10.1080/09513590801893398 · Cited by 43
Wolever, T. M. S., Campbell, J. E., Geleva, D., & Anderson, G. H. (2004) · Diabetes Care · doi:10.2337/diacare.27.6.1281 · Cited by 44
Valentino, R., Lupoli, G., Raciti, G. A., Oriente, F., Farinaro, E., Valle, E. D., Salomone, M., Riccardi, G., Vaccaro, O., Donnarumma, G., et al. (2006) · Diabetologia · doi:10.1007/s00125-006-0455-5 · Cited by 45
Reinehr, T., Hinney, A., Nguyen, T. T., & Hebebrand, J. (2007) · Diabetes · doi:10.2337/db07-0408 · Cited by 44
The DD-ACE genotype and cardiovascular disease
Butler, R. J. (2000) · Pharmacogenomics · doi:10.1517/14622416.1.2.153 · Cited by 44
Leiter, L. (2005) · Diabetic Medicine · doi:10.1111/j.1464-5491.2005.01605.x · Cited by 44
Garmendia, F., Pando, R., & Ronceros, G. (2011) · PubMed · Cited by 45
Hron, B. M., Ebbeling, C. B., Feldman, H. A., & Ludwig, D. S. (2015) · Obesity · doi:10.1002/oby.21213 · Cited by 42
Ding, X., Chang, X., Wang, J., Bian, N., An, Y., Wang, G., & Liu, J. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.938341 · Cited by 43
Panidis, D., Tziomalos, K., Papadakis, E., Chatzis, P., Kandaraki, E. A., Tsourdi, E. A., & Katsikis, I. (2014) · Clinical Endocrinology · doi:10.1111/cen.12305 · Cited by 43
Effect of Aerobic Training on Glucose Control and Blood Pressure in T2DDM East African Males
Yan, H., Prista, A., Ranadive, S. M., Damasceno, A. A. M., Caupers, P., Kanaley, J. A., & Fernhall, B. (2014) · ISRN Endocrinology · doi:10.1155/2014/864897 · Cited by 45
Metabolic Profile in Patients with Mild Obstructive Sleep Apnea
Silva, L. O. E., Guimarães, T. M., Luz, G. C. P., Coelho, G. A., Badke, L., de Almeida, I. R., Millani-Carneiro, A., Tufik, S. B., Bittencourt, L., & Togeiro, S. M. (2017) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2017.0075 · Cited by 44
Yang, P.-K., Chou, C.-H., Chang, C., Chen, S., Ho, H., & Chen, M. (2020) · Reproductive Biology and Endocrinology · doi:10.1186/s12958-020-00629-5 · Cited by 44
Urinary organophosphate esters metabolites, glucose homeostasis and prediabetes in adolescents
Luo, K., Aimuzi, R., Wang, Y., Nian, M., & Zhang, J. (2020) · Environmental Pollution · doi:10.1016/j.envpol.2020.115607 · Cited by 45
Effect of lockdown for COVID-19 on self-reported body weight gain in a sample of obese patients
de Luis, D. A., Izaola, O., Martín, D. P., Hoyos, E. G., Torres, B., & Gómez, J. J. L. (2020) · Nutrición Hospitalaria · doi:10.20960/nh.03307 · Cited by 43
George, E. S., Gavrili, S., Itsiopoulos, C., Μanios, Y., & Moschonis, G. (2021) · Public Health Nutrition · doi:10.1017/s1368980021001701 · Cited by 43
Exposure to perfluorooctane sulfonate reduced cell viability and insulin release capacity of β cells
Qin, W., Ren, X., Zhao, L., & Guo, L. (2021) · Journal of Environmental Sciences · doi:10.1016/j.jes.2021.07.004 · Cited by 46
Wickramasinghe, A. S. D., Attanayake, A. P., & Kalansuriya, P. (2021) · Journal of Pharmacological and Toxicological Methods · doi:10.1016/j.vascn.2021.107144 · Cited by 44
Goh, C. E., Bohn, B., Marotz, C. A., Molinsky, R. L., Roy, S. S., Paster, B. J., Chen, C., Rosenbaum, M. S., yuzefpolskaya, M., Colombo, P. C., et al. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.121.023038 · Cited by 45
Chen, Z., & Wen, J. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.973655 · Cited by 48
Monod, C., Kotzaeridi, G., Linder, T., Eppel, D., Rosicky, I., Filippi, V., Tura, A., Hösli, I., & Göbl, C. S. (2022) · Acta Diabetologica · doi:10.1007/s00592-022-02011-w · Cited by 55
Association between triglyceride glucose index and risk of cancer: A meta-analysis
Wang, H., Yan, F., Cui, Y., Chen, F., Wang, G., & Cui, W. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1098492 · Cited by 48
Steinack, C., Ernst, M., Beuschlein, F., Hage, R., Roeder, M., Schuurmans, M. M., Schmid, C., & Gaisl, T. (2023) · Journal of Cystic Fibrosis · doi:10.1016/j.jcf.2023.01.004 · Cited by 45
Liu, A., Xu, C., Liu, Y., Sun, D., Jiang, L., Tang, L., Zhu, P., Chen, S., Liu, W., Wang, X., et al. (2023) · Alimentary Pharmacology & Therapeutics · doi:10.1111/apt.17362 · Cited by 44
Rinaldi, R., Nucci, S. D., Castellana, F., Chito, M. D., Giannuzzi, V., Shahini, E., Zupo, R., Lampignano, L., Piazzolla, G., Triggiani, V., et al. (2023) · Nutrients · doi:10.3390/nu15040825 · Cited by 45
Liu, Y., Li, D., Wang, S., Peng, Z., Tan, Q., He, Q., & Wang, J. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24076285 · Cited by 50
Fu, C., Li, X., Wang, Y., et al. (2024) · Lipids in Health and Disease · doi:10.1186/s12944-024-02194-x · Cited by 43
Kimura, Y., Hyogo, H., Ishitobi, T., Nabeshima, Y., Arihiro, K., & Chayama, K. (2010) · Journal of Gastroenterology and Hepatology · doi:10.1111/j.1440-1746.2010.06567.x · Cited by 43
Nagaraju, R., Joshi, A. K. R., & Rajini, P. S. (2014) · Journal of Diabetes · doi:10.1111/1753-0407.12158 · Cited by 43
Self-perceived stress is associated with adiposity and atherosclerosis. The GEA Study
Ortega-Montiel, J., Posadas‐Romero, C., Ocampo-Arcos, W. A., Medina-Urrutia, A. X., Cardoso-Saldaña, G. C., Galarza, E. J., & Posadas‐Sánchez, R. (2015) · BMC Public Health · doi:10.1186/s12889-015-2112-8 · Cited by 49
Lerman, R. H., Minich, D. M., Darland, G. K., Lamb, J. J., Chang, J., Hsi, A., Bland, J. S., & Tripp, M. L. (2009) · Journal of clinical lipidology · doi:10.1016/j.jacl.2009.11.002 · Cited by 43
Ryoo, J., Choi, J., Moon, S. Y., Suh, Y. J., Shin, J., Shin, H. C., & Park, S. K. (2013) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2013.01.002 · Cited by 43
Agrawal, R., Zhuang, Y., Cummings, B. P., Stanhope, K. L., Graham, J. L., Havel, P. J., & Gómez‐Pinilla, F. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2014.05.007 · Cited by 44
Troxerutin suppresses lipid abnormalities in the heart of high-fat–high-fructose diet-fed mice
Geetha, R., Yogalakshmi, B., Sreeja, S., Bhavani, K., & Anuradha, C. V. (2013) · Molecular and Cellular Biochemistry · doi:10.1007/s11010-013-1877-2 · Cited by 44
Ali, F. N., Falkner, B. E., Gidding, S. S., Price, H. E., Keith, S. W., & Langman, C. B. (2014) · The Journal of Pediatrics · doi:10.1016/j.jpeds.2014.06.027 · Cited by 43
Insulin Resistance in Nodular Thyroid Disease
Yilmaz, H., Ertuğrul, Ö., Ertuğrul, B., Ertuğrul, D. T., & Şahi̇n, M. (2011) · Endocrine Research · doi:10.3109/07435800.2011.593011 · Cited by 43
Thromboxane Production in Morbidly Obese Subjects
Graziani, F., Biasucci, L. M., Cialdella, P., Liuzzo, G., Giubilato, S., Bona, R. D., Pulcinelli, F. M., Iaconelli, A., Mingrone, G., & Crea, F. (2011) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2011.01.053 · Cited by 44
Assessment of insulin resistance in young women with polycystic ovary syndrome
Jensterle, M., Weber, M. M., Pfeifer, M., Preželj, J., Pfützner, A., & Janež, A. (2008) · International Journal of Gynecology & Obstetrics · doi:10.1016/j.ijgo.2008.03.017 · Cited by 43
Schierer, A., Been, L. F., Ralhan, S. K., Wander, G. S., Aston, C. E., & Sanghera, D. K. (2011) · Pharmacogenetics and Genomics · doi:10.1097/fpc.0b013e32834dc9ef · Cited by 43
Kawaguchi, Y., Mizuta, T., Oza, N., et al. (2009) · Liver International · doi:10.1111/j.1478-3231.2009.01993.x · Cited by 43
Non-conventional markers of atherosclerosis before and after gastric banding surgery
Hanusch‐Enserer, U., Zorn, G., Wojta, J., Kopp, C. W., Prager, R., Köenig, W., Schillinger, M., Roden, M., & Huber, K. (2009) · European Heart Journal · doi:10.1093/eurheartj/ehp108 · Cited by 43
Amr, N. H., Ahmed, A. Y., & Ibrahim, Y. A. (2014) · Journal of Endocrinological Investigation · doi:10.1007/s40618-014-0148-8 · Cited by 43
Fenger, M., Linneberg, A. R., Torben, J., et al. (2011) · BMC Genetics · doi:10.1186/1471-2156-12-44 · Cited by 43
Knoblovits, P., Costanzo, P., Valzacchi, G. R., Gueglio, G., Layus, A. O., Kozak, A. E., Balzaretti, M., & Litwak, L. (2010) · Journal of Andrology · doi:10.2164/jandrol.109.007757 · Cited by 44
Dysmetabolic signals in “metabolically healthy” obesity
Manu, P., Ionescu-Tirgoviste, C., Tsang, J. C., Napolitano, B. A., LESSER, M. L., & Correll, C. U. (2011) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2011.04.003 · Cited by 43
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).
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