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)
plainstates a cause- 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.
plainstates an association- 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.
plainstates an association- 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.
plainstates an association- 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
plainstates an association- 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.
plainstates an association- 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.
plainstates an association- 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 source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
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]
Experimental — accuracy not yet measuredThe 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
“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 11,101–11,200 of 31,469
Taniguchi, A., Fukushima, M., Ohya, M., Nakai, Y., Yoshii, S., Nagasaka, S., Matsumoto, K., Taki, Y., Kuroe, A., Nishimura, F., et al. (2006) · Metabolism · doi:10.1016/j.metabol.2005.08.020 · Cited by 32
Insulin Resistance in HIV-Infected Men and Women in the Nutrition for Healthy Living Cohort
Jones, C. Y., Wilson, I. B., Greenberg, A. S., Shevitz, A., Knox, T. A., Gorbach, S. L., Spiegelman, D. L., Jacobson, D. L., & WANKE, C. A. (2005) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/01.qai.0000165910.89462.2f · Cited by 32
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Araki, T., Emoto, M., Yokoyama, H., Maeno, T., Hatsuda, S., Mori, K., Koyama, H., Shoji, T., Inaba, M., & Nishizawà, Y. (2006) · Metabolism · doi:10.1016/j.metabol.2005.11.014 · Cited by 32
A‐FABP—A Biomarker Associated With the Metabolic Syndrome and/or an Indicator of Weight Change?
Engl, J., Ciardi, C., Tatarczyk, T., Kaser, S., Laimer, M. W., Laimer, E., Weiss, H. G., Aigner, F., Molnar, C., Tilg, H., et al. (2008) · Obesity · doi:10.1038/oby.2008.273 · Cited by 32
Jones, M. R., Mathur, R., Cui, J., Guo, X., Azziz, R., & Goodarzi, M. O. (2009) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2009-0931 · Cited by 32
Tanaka, Y., Kikuchi, T., Nagasaki, K., Hiura, M., Ogawa, Y., & Uchiyama, M. (2005) · Hypertension Research · doi:10.1291/hypres.28.529 · Cited by 32
Familial factors in diabetic nephropathy: an offspring study
Agius, E., Attard, G., Shakespeare, L., Clark, P., Mohamed‐Ali, V., Hattersley, A. T., & Fava, S. (2006) · Diabetic Medicine · doi:10.1111/j.1464-5491.2006.01803.x · Cited by 32
Godsland, I. F., Elkeles, R. S., Feher, M. D., Nugara, F., Rubens, M., Richmond, W., Khan, M. N., Donovan, J., Anyaoku, V., Flather, M. D., et al. (2006) · Diabetic Medicine · doi:10.1111/j.1464-5491.2006.01950.x · Cited by 32
RAGE polymorphisms and the heritability of insulin resistance: the Leeds Family Study
Sullivan, C. M., Futers, T. S., Barrett, J. H., et al. (2005) · Diabetes and Vascular Disease Research · doi:10.3132/dvdr.2005.005 · Cited by 32
Wang, H., Chu, W. S., Hemphill, C., Hasstedt, S. J., & Elbein, S. C. (2002) · Molecular Genetics and Metabolism · doi:10.1016/s1096-7192(02)00016-1 · Cited by 32
Iellamo, F., Tesauro, M., Rizza, S., Aquilani, S., Cardillo, C., Iantorno, M., Turriziani, M., & Lauro, R. (2006) · Hypertension · doi:10.1161/01.hyp.0000234648.62994.ab · Cited by 32
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Kotani (2011) · Journal of Clinical Medicine Research · doi:10.4021/jocmr704w · Cited by 32
Guffanti, M., Caumo, A., Galli, L., Bigoloni, A., Galli, A., Dagba, G., Danise, A., Luzi, L., Lazzarin, A., & Castagna, A. (2007) · European Journal of Endocrinology · doi:10.1530/eje-06-0648 · Cited by 32
Sun, L. D., Yang, Z., Jin, F., Zhu, X., Qu, Y. C., Shi, X., & Wang, L. (2006) · Diabetic Medicine · doi:10.1111/j.1464-5491.2006.01949.x · Cited by 33
Beysen, C., Murphy, E. J., McLaughlin, T., Riiff, T. J., Lamendola, C. A., Turner, H., Awada, M., Turner, S., Reaven, G. M., & Hellerstein, M. K. (2007) · Diabetes Care · doi:10.2337/dc06-1809 · Cited by 33
Insulin resistance as a modifier of the relationship between dietary fat intake and weight gain
Mosca, C. L., Marshall, J. A., Grunwald, G. K., Cornier, M., & Baxter, J. (2004) · International Journal of Obesity · doi:10.1038/sj.ijo.0802621 · Cited by 32
The role of PPARγ as a thrifty gene both in mice and humans
Hara, K., Kubota, N., Tobe, K., Terauchi, Y., Miki, H., Komeda, K., Tamemoto, H., Yamauchi, T., Hagura, R., Ito, C., et al. (2000) · British Journal Of Nutrition · doi:10.1079/096582197388608 · Cited by 32
Hodge, A. M., Dowse, G. K., Alberti, K. G. M. M., Tuomilehto, J. O., Gareeboo, H., & Zimmet, P. (1996) · Metabolism · doi:10.1016/s0026-0495(96)90035-9 · Cited by 32
Lu, J., Chuang, L., Yang, W., Tai, T., Lai, M., Chen, P., Kao, J., Lee, C., & Lee, M. (2005) · Liver International · doi:10.1111/j.1478-3231.2005.1007.x · Cited by 32
Chen, L., Lin, M.-H., Chen, Z.-J., Hwang, S., Tsai, S., & Chiou, S.-T. (2005) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2005.05.010 · Cited by 32
Insulin resistance affects endothelium-dependent acetylcholine-induced coronary artery response
Inoue, T. (2000) · European Heart Journal · doi:10.1053/euhj.1999.1872 · Cited by 32
Gough, S. C. L., Rice, P. J., McCormack, L. J., et al. (1993) · Diabetic Medicine · doi:10.1111/j.1464-5491.1993.tb00137.x · Cited by 32
Kriketos, A. D., Carey, D., Jenkins, A. B., Chisholm, D. J., Furler, S. M., & Campbell, L. V. (2003) · Diabetic Medicine · doi:10.1046/j.1464-5491.2003.00938.x · Cited by 31
Fundamentals of computer organization and architecture
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Capristo, E., Panunzi, S., De Gaetano, A., Raffaelli, M., Guidone, C., Iaconelli, A., L’Abbate, L., Birkenfeld, A. L., Bellantone, R., Bornstein, S. R., et al. (2018) · Diabetes & Metabolism · doi:10.1016/j.diabet.2017.12.007 · Cited by 31
Ouguerram, K., Maugeais, C., Gardette, J., Magot, T., & Krempf, M. (2006) · British Journal of Nutrition · doi:10.1079/bjn20061806 · Cited by 31
Aksnes, T. A., Reims, H. M., Guptha, S., Moan, A., Os, I., & Kjeldsen, S. E. (2006) · Journal of Human Hypertension · doi:10.1038/sj.jhh.1002087 · Cited by 31
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Circulating sclerostin and Dickkopf-1 levels in patients with nonalcoholic fatty liver disease
Polyzos, S. A., Anastasilakis, A. D., Kountouras, J., Makras, P., Papatheodorou, A., Kokkoris, P., Sakellariou, G. T., & Terpos, E. (2016) · Journal of Bone and Mineral Metabolism · doi:10.1007/s00774-015-0687-x · Cited by 32
Piché, M.-È., Lemieux, S., Corneau, L., Nadeau, A., Bergeron, J., & Weisnagel, S. J. (2007) · Metabolism · doi:10.1016/j.metabol.2007.04.002 · Cited by 31
Heemstra, K. A., Smit, J. W. A., Eustatia‐Rutten, C. F. A., Heijboer, A. C., Frölich, M., Romijn, J. A., & Corssmit, E. P. M. (2006) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2006.02660.x · Cited by 31
Rosmond, R., & Holm, G. (2008) · Journal of the CardioMetabolic Syndrome · doi:10.1111/j.1559-4572.2008.00008.x · Cited by 41
Kliewer, W. L., Robins, J. L. W., & Borré, A. (2019) · International Journal of Behavioral Medicine · doi:10.1007/s12529-019-09801-7 · Cited by 31
Ghorbani, Y., Schwenger, K. J. P., Sharma, D., Jung, H. J., Yadav, J., Xu, W., Lou, W. Y. W., Poutanen, S. M., Hota, S. S., Comelli, E. M., et al. (2022) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14891 · Cited by 36
Li, X., Xue, Y., Dang, Y., et al. (2022) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s385906 · Cited by 31
Justice, J. N., Leng, X., LeBrasseur, N. K., Tchkonia, T., Kirkland, J. L., Mitin, N., Liu, Y., Kritchevsky, S. B., Nicklas, B. J., & Ding, J. (2023) · The Journals of Gerontology Series A · doi:10.1093/gerona/glad214 · Cited by 30
Xu, Y., Pu, S., Zhang, Y., Tong, X., Sun, X., Shan, Y., & Gao, X. (2023) · Clinical and Experimental Ophthalmology · doi:10.1111/ceo.14344 · Cited by 31
Li, X.-M., Liu, S.-L., He, Y., & Shu, J.-C. (2024) · BMC Gastroenterology · doi:10.1186/s12876-024-03190-2 · Cited by 30
Prognostic role of serum albumin levels in patients with chronic heart failure
Armentaro, G., Condoleo, V., Pastura, C. A., et al. (2024) · Internal and Emergency Medicine · doi:10.1007/s11739-024-03612-9 · Cited by 31
Chen, Y., Xie, K., Han, Y., Ju, H., Sun, J., & Zhao, X. (2024) · Lipids in Health and Disease · doi:10.1186/s12944-024-02277-9 · Cited by 36
Groenewegen, B., Ruissen, M. M., Crossette, E. M., Menon, R., Prince, A., Norman, J. M., Ballieux, B. E. P. B., Lamb, H. J., Terveer, E. M., Keller, J. J., et al. (2025) · Gut Microbes · doi:10.1080/19490976.2025.2541035 · Cited by 42
Effect of diet and omega-3 fatty acid intervention on asymmetric dimethylarginine
Eid, H. M. A., Arnesen, H., Hjerkinn, E. M., Lyberg, T., Ellingsen, I., & Seljeflot, I. (2006) · Nutrition & Metabolism · doi:10.1186/1743-7075-3-4 · Cited by 31
The effects of thyroid hormones on brown adipose tissue in humans: a PET‐CT study
Zhang, Q., Miao, Q., Ye, H., Zhang, Z., Zuo, C., Hua, F., Guan, Y., & Li, Y. (2014) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.2556 · Cited by 31
Kelly, A. S., González-Campoy, J. M., Rudser, K. D., Katz, H., Metzig, A. M., Thalin, M., & Bank, A. J. (2011) · Journal of Clinical Hypertension · doi:10.1111/j.1751-7176.2011.00569.x · Cited by 31
Talukdar, A., Sonthalia, N., Saha, M., et al. (2015) · The Indian Journal of Medical Research · doi:10.4103/0971-5916.154504 · Cited by 31
Ovarian volume and gluco‐insulinaemic markers in the diagnosis of PCOS during adolescence
Villa, P., Rossodivita, A. N., Sagnella, F., Moruzzi, M. C., Mariano, N., Lassandro, A. P., Pontecorvi, A., Scambia, G., & Lanzone, A. (2012) · Clinical Endocrinology · doi:10.1111/j.1365-2265.2012.04475.x · Cited by 31
Ogura, M., Kusaka, I., Nagasaka, S., Yatagai, T., Shinozaki, S., Itabashi, N., Nakamura, T., Yokoyama, M., Ishikawa, S., & Ishibashi, S. (2003) · Endocrine Journal · doi:10.1507/endocrj.50.715 · Cited by 31
PTPRT Regulates High-Fat Diet-Induced Obesity and Insulin Resistance
Feng, X., Scott, A. J., Wang, Y., Wang, L., Zhao, Y., Doerner, S. K., Satake, M., Croniger, C. M., & Wang, Z. (2014) · PLoS ONE · doi:10.1371/journal.pone.0100783 · Cited by 28
Melzi, R., Battaglia, M., Draghici, E., Bonifacio, E. E., & Piemonti, L. (2007) · Transplantation · doi:10.1097/01.tp.0000250659.24286.43 · Cited by 31
Danielsen, I., Granström, C., Halldorsson, T. I., Rytter, D., Bech, B. H., Henriksen, T. B., Vaag, A. A., & Olsen, S. F. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064887 · Cited by 32
Visceral obesity is associated with microalbuminuria in nondiabetic Asians
Kim, H. S., Kim, H. J., Shin, N., et al. (2014) · Hypertension Research · doi:10.1038/hr.2014.47 · Cited by 31
Eom, C.-S., Park, J., Cho, B., Choi, H.-C., Oh, M.-J., & Kwon, H.-T. (2011) · The Journal of Urology · doi:10.1016/j.juro.2011.03.025 · Cited by 31
Piché, M., Martin, J., Cianflone, K., Bastien, M., Marceau, S., Biron, S., Hould, F., & Poirier, P. (2013) · Metabolism · doi:10.1016/j.metabol.2013.09.004 · Cited by 31
Böhm, A., Ordelheide, A., Machann, J., Heni, M., Ketterer, C., Machicao, F. E., Schick, F., Stefan, N., Fritsche, A., Häring, H., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0034035 · Cited by 32
Pattullo, V., Duarte‐Rojo, A., Soliman, W. T., Vargas-Vorácková, F., Sockalingam, S., Fantus, I. G., Allard, J. P., & Heathcote, J. (2012) · Liver International · doi:10.1111/liv.12041 · Cited by 31
Samaras, K., Viardot, A., Lee, P. N., Jenkins, A. J., Botelho, N. K., Bakopanos, A., Lord, R. V., & Hayward, C. (2012) · Diabetes and Vascular Disease Research · doi:10.1177/1479164112443375 · Cited by 30
Korkmaz, H., Sahin, F., İpekçi, S. H., et al. (2014) · European Journal of Gastroenterology & Hepatology · doi:10.1097/meg.0000000000000104 · Cited by 31
Relationship of betatrophin with youth onset type 2 diabetes among Asian Indians
Gokulakrishnan, K., Manokaran, K., Pandey, G. K., Amutha, A., Ranjani, H., Anjana, R. M., & Mohan, V. (2015) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2015.04.028 · Cited by 31
Kim, U.-H., Yoon, J.-H., Li, H., Kang, J., Ji, H.-S., Park, K. H., Shin, D., Park, H.-Y., & Jeong, T. (2014) · Molecules · doi:10.3390/molecules191118493 · Cited by 31
[6]-Gingerol dampens hepatic steatosis and inflammation in experimental nonalcoholic steatohepatitis
Tzeng, T., Liou, S., Chang, C. J., & Liu, I.-M. (2015) · Phytomedicine · doi:10.1016/j.phymed.2015.01.015 · Cited by 34
Yıldızhan, R., İlhan, G. A., Yıldızhan, B., & Çim, N. (2015) · Gynecological Endocrinology · doi:10.3109/09513590.2015.1006187 · Cited by 32
Sun, X., Zhang, R., Jiang, F., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0116714 · Cited by 31
Pietzner, M., Lehmphul, I., Friedrich, N., Schurmann, C., Ittermann, T., Dörr, M., Nauck, M., Laqua, R., Völker, U., Brabant, G., et al. (2015) · Thyroid · doi:10.1089/thy.2014.0262 · Cited by 31
Goday, A., Benaiges, D., Parri, A., Ramón, J. M., Roux, J. A. F., & Pedro‐Botet, J. (2014) · Surgery for Obesity and Related Diseases · doi:10.1016/j.soard.2014.04.022 · Cited by 32
Lee, E. Y., Hwang, S., Lee, S. H., Lee, Y., Choi, A. R., Lee, Y., Lee, B., Kang, E. S., Ahn, C. W., Soo, B., et al. (2014) · Journal of Diabetes Investigation · doi:10.1111/jdi.12187 · Cited by 32
Ducray, P. S., D’ardhuy, X. L., Jamois, C., & Banken, L. (2010) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2009.259 · Cited by 31
Habitual fish intake and clinically silent carotid atherosclerosis
Buscemi, S., Nicolucci, A., Lucisano, G., Galvano, F., Grosso, G., Belmonte, S., Sprini, D., Migliaccio, S., Cianferotti, L., Brandi, M. L., et al. (2014) · Nutrition Journal · doi:10.1186/1475-2891-13-2 · Cited by 31
Petta, S., Grimaudo, S., Tripodo, C., Cabibi, D., Calvaruso, M., Cristina, A. D., Guarnotta, C., Macaluso, F. S., Minissale, M. G., Marchesini, G., et al. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2014-2741 · Cited by 30
Impaired beta cell function is present in nondiabetic rheumatoid arthritis patients
Ferraz‐Amaro, I., García-Dopico, J. A., Medina-Vega, L., González-Gay, M. Á., & Díaz‐González, F. (2013) · Arthritis Research & Therapy · doi:10.1186/ar4149 · Cited by 33
Walsh, J. M., Mahony, R. M., Culliton, M., Foley, M. E., & McAuliffe, F. M. (2014) · Reproductive Sciences · doi:10.1177/1933719114525275 · Cited by 31
A sustained increase in plasma NEFA upregulates the Toll-like receptor network in human muscle
Hussey, S. E., Lum, H., Alvarez, A. V., Cipriani, Y., Garduño‐García, J. D. J., Anaya, L., Dubé, J. J., & Musi, N. (2013) · Diabetologia · doi:10.1007/s00125-013-3111-x · Cited by 32
Arshadi, S., Azarbayjani, M. A., Hajiaghaalipour, F., Yusof, A., Peeri, M., Bakhtiyari, S., Stannard, R. S., Osman, N. A. A., & Dehghan, F. (2015) · Food & Nutrition Research · doi:10.3402/fnr.v59.29717 · Cited by 31
Yi, M., Chen, R., Yang, R., Guo, X., Zhang, J.-C., & Chen, H. (2015) · International Journal of Endocrinology · doi:10.1155/2015/479157 · Cited by 31
Karimifar, M., Aminorroaya, A., Amini, M., Mirfendereski, T., Iraj, B., Feizi, A., & Noroozi, A. (2014) · Europe PMC (PubMed Central) · Cited by 31
Mo, Y., Ma, X., Li, H., Ran, X., Yang, W., Li, Q., Peng, Y., Li, Y., Gao, X., Luan, X., et al. (2016) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2016.03.003 · Cited by 31
Grassi, G. M., Seravalle, G., Brambilla, G., Buzzi, S., Volpe, M., Cesana, F., Dell’Oro, R., & Mancia, G. (2014) · Journal of Hypertension · doi:10.1097/hjh.0000000000000034 · Cited by 29
Panidis, D., Τζιόμαλος, Κ., Papadakis, E. P., Chatzis, P., Kandaraki, E., Tsourdi, E. A., Μacut, D., Bjekić-Macut, J., Marthopoulos, A., & Katsikis, I. (2015) · HORMONES · doi:10.14310/horm.2002.1593 · Cited by 32
Velija-Ašimi, Z. (2014) · PubMed · Cited by 31
Park, S. H., Ha, E., Hong, Y. S., & Park, H. (2016) · Environmental Health Perspectives · doi:10.1289/ehp147 · Cited by 31
Association of Genetic Variants of SIRT1 With Type 2 Diabetes Mellitus
Han, J., Wei, M., Wang, Q., et al. (2015) · Gene Expression · doi:10.3727/105221615x14399878166195 · Cited by 31
Association of Serum Ferritin Levels with Metabolic Syndrome and Insulin Resistance
Padwal, M. K. (2015) · JOURNAL OF CLINICAL AND DIAGNOSTIC RESEARCH · doi:10.7860/jcdr/2015/13480.6564 · Cited by 32
Diniz, M. D. F. H. S., Beleigoli, A. M. R., Ribeiro, A. L. P., Vidigal, P. G., Benseñor, I. M., Lotufo, P. A., Duncan, B. B., Schmidt, M. I., & Barreto, S. M. (2016) · Medicine · doi:10.1097/md.0000000000004010 · Cited by 31
Russo, G. I., Cimino, S., Castelli, T., et al. (2016) · The Prostate · doi:10.1002/pros.23237 · Cited by 31
Regulation of type 2 diabetes by helminth-induced Th2 immune response
Morimoto, M., AZUMA, N., KADOWAKI, H., Abe, T., & Suto, Y. (2016) · Journal of Veterinary Medical Science · doi:10.1292/jvms.16-0183 · Cited by 31
Effect of Resected Gastric Volume on Ghrelin and GLP-1 Plasma Levels: a Prospective Study
Sista, F., Abruzzese, V., Clementi, M., Carandina, S., & Amicucci, G. (2016) · Journal of Gastrointestinal Surgery · doi:10.1007/s11605-016-3292-y · Cited by 31
von Loeffelholz, C., Döcke, S., Lock, J. F., Lieske, S., Horn, P., Kriebel, J., Wahl, S., Singmann, P., Gala, T. D. L. H., Grallert, H., et al. (2016) · Hepatology Research · doi:10.1111/hepr.12825 · Cited by 30
Metabolic dysfunction following weight cycling in male mice
Schofield, S. E., Parkinson, J. R. C., Henley, A. B., Sahuri-Arisoylu, M., Sanchez-Canon, G. J., & Bell, J. D. (2016) · International Journal of Obesity · doi:10.1038/ijo.2016.193 · Cited by 31
Gao, F., Wang, G., Wang, L., & Guo, N. (2017) · Food & Function · doi:10.1039/c6fo01777k · Cited by 31
Li, G.-H., Yin, J., Fu, J., Li, L., Grant, S. F. A., Li, Z., Li, M., Mi, J., Li, M., & Gao, S. (2017) · Diabetes & Metabolism · doi:10.1016/j.diabet.2016.12.003 · Cited by 32
Eisenga, M. F., Zelle, D. M., Sloan, J. H., Gaillard, S., Bakker, S. J. L., & Dullaart, R. P. F. (2017) · Diabetes Care · doi:10.2337/dc16-2258 · Cited by 34
Lazzarino, G. P., Andreoli, M. F., Rossetti, M. F., Stoker, C., Tschopp, M. V., Luque, E. H., & Ramos, J. G. (2017) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2017.05.005 · Cited by 32
Cherif, R., Mahjoub, F., Sahli, H., et al. (2017) · Journal of Clinical Densitometry · doi:10.1016/j.jocd.2017.05.015 · Cited by 31
Wang, X., Xian, T., Jia, X., Zhang, L., Liu, L., Man, F., Zhang, X., Zhang, J., Pan, Q., & Guo, L. (2017) · Medicine · doi:10.1097/md.0000000000007378 · Cited by 31
Bargut, T. C. L., Santos, L. P., Machado, D. G. L., Águila, M. B., & Mandarim‐de‐Lacerda, C. A. (2017) · Prostaglandins Leukotrienes and Essential Fatty Acids · doi:10.1016/j.plefa.2017.07.004 · Cited by 34
Effects of menopause and high-intensity training on insulin sensitivity and muscle metabolism
Mandrup, C. M., Egelund, J., Nyberg, M., Enevoldsen, L. H., Kjær, A., Clemmensen, A., Christensen, A. N., Suetta, C., Frikke‐Schmidt, R., Steenberg, D. E., et al. (2017) · Menopause The Journal of The North American Menopause Society · doi:10.1097/gme.0000000000000981 · Cited by 32
Non-alcoholic fatty liver disease and the development of nephrolithiasis: A cohort study
Kim, S., Chang, Y., Sung, E., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184506 · Cited by 31
Blomquist, C., Chorell, E., Ryberg, M., Mellberg, C., Vorrsjö, E., Makoveichuk, E., Larsson, C. L., Lindahl, B., Olivecrona, G., & Olsson, T. (2017) · European Journal of Nutrition · doi:10.1007/s00394-017-1558-0 · Cited by 35
Lallukka, S., Sädevirta, S., Kallio, M. T., Luukkonen, P. K., Zhou, Y., Hakkarainen, A., Lundbom, N. M. I., Orho‐Melander, M., & Yki‐Järvinen, H. (2017) · Scientific Reports · doi:10.1038/s41598-017-14706-0 · Cited by 31
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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