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 17,101–17,200 of 31,469
Passaro, A., Soavi, C., Marušič, U., Rejc, E., Sanz, J. M., Morieri, M. L., Nora, E. D., Kavcic, V., Narici, M. V., Reggiani, C., et al. (2017) · Aging · doi:10.18632/aging.101166 · Cited by 16
Castillo-Hernández, J., Cervantes, M. I. M., Reyes, J. P., Marín, N. P., Cervantes, E. M., Solorzano‐Rodriguez, C., Mendoza, E. D. L. C., & Alvarado‐Sánchez, B. (2017) · Revista Brasileira de Reumatologia (English Edition) · doi:10.1016/j.rbre.2017.01.008 · Cited by 17
Riediger, N. D., Clark, K. O., Lukianchuk, V., Roulette, J., & Bruce, S. G. (2017) · International Journal of Circumpolar Health · doi:10.1080/22423982.2017.1310444 · Cited by 16
Bae, J. C., Kim, S. K., Han, J. M., Kwon, S. H., Lee, W., Kim, J., Park, S. E., Park, C., Lee, W., Oh, K.-W., et al. (2017) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2017.03.037 · Cited by 16
Features of glycemic variations in drug naïve type 2 diabetic patients with different HbA1c values
Li, F.-F., Liu, B.-L., Yan, R., Zhu, H., Zhou, P., Li, H., Su, X., Wu, J.-D., Zhang, D., Ye, L., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-01719-y · Cited by 16
Dujić, T., Bego, T., Mlinar, B., Semiz, S., Malenica, M., Prnjavorac, B., Ostanek, B., Marc, J., & Čaušević, A. (2012) · Biochemia Medica · doi:10.11613/bm.2012.008 · Cited by 16
Murata, M., Adachi, H., Oshima, S., & Kurabayashi, M. (2017) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2017.07.007 · Cited by 17
Garber, A., Csizmadi, I., Friedenreich, C. M., Sajobi, T. T., Longman, R. S., Tyndall, A. V., Drogos, L. L., Davenport, M. H., & Poulin, M. J. (2017) · Clinical Nutrition · doi:10.1016/j.clnu.2017.07.011 · Cited by 18
Stygar, D. M., Sawczyn, T., Skrzep-Poloczek, B., Owczarek, A. J., Matysiak, N., Michalski, M., Mielańczyk, Ł., Bażanów, B., Ziora, P., Choręza, P. S., et al. (2017) · Obesity Surgery · doi:10.1007/s11695-017-2883-3 · Cited by 16
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Cartolano, F. D. C., Dias, G. D. F., de Freitas, M. C. P., Neto, A. M. F., & Damasceno, N. R. T. (2017) · Journal of Diabetes Research · doi:10.1155/2017/1018796 · Cited by 17
Teich, T., Pivovarov, J. A., Porras, D. P., Dunford, E. C., & Riddell, M. C. (2017) · Journal of Applied Physiology · doi:10.1152/japplphysiol.01115.2016 · Cited by 16
Mena, P. R., Tassotti, M., Martini, D., Rosi, A., Brighenti, F., & Rio, D. D. (2017) · Trials · doi:10.1186/s13063-017-2271-2 · Cited by 17
Zaki, M. E., Kamal, S., Kholoussi, S., El-Bassyouni, H. T., Yousef, W., Reyad, H., Mohamed, R., & Basha, W. A. (2017) · EXCLI journal · doi:10.17179/excli2017-275 · Cited by 16
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Torabi, F., Farahani, A., Safakish, S., et al. (2017) · Psychiatry Research · doi:10.1016/j.psychres.2017.12.053 · Cited by 16
Xu, H., Wang, Q., Sun, Q., Qin, Y., Han, A., Cao, Y., Yang, Q., Yang, P., Lü, J., Liu, Q., et al. (2018) · Environmental Science and Pollution Research · doi:10.1007/s11356-018-1337-3 · Cited by 17
Neck circumference, cardiometabolic risk, and Framingham risk score: Population-based study
Zanuncio, V. V., Pessoa, M. C., Pereira, P. F., & Longo, G. Z. (2017) · Revista de Nutrição · doi:10.1590/1678-98652017000600009 · Cited by 16
Pérez‐Martínez, L., Ochoa‐Callejero, L., Rubio-Mediavilla, S., Narro-Íñiguez, J., Bernardo, I., Oteo, J. A., & Blanco, J. R. (2018) · Translational research · doi:10.1016/j.trsl.2018.01.004 · Cited by 17
Rausch, J. C., Lavine, J. E., Chalasani, N., et al. (2018) · Journal of Pediatric Gastroenterology and Nutrition · doi:10.1097/mpg.0000000000001926 · Cited by 16
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da Silva, C. D. C., Vasques, A. C. J., Zambon, M. P., Camilo, D. F., Rodrigues, A. M. D. B., Antônio, M. Â. R. D. G. M., Geloneze, B., & On Behalf of the Brazilian Metabolic Syndrome Study (BRAMS) Investigators (2018) · Pediatric Diabetes · doi:10.1111/pedi.12664 · Cited by 17
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Kondo, M., Nagao, Y., Mahbub, M., Tanabe, T., & Tanizawa, Y. (2018) · Diabetic Medicine · doi:10.1111/dme.13657 · Cited by 17
Serum leptin levels in gastric cancer patients and the relationship with insulin resistance
Beğenik, H., Aslan, M., Dülger, A. C., Emre, H., Kemık, A. S., Kemik, Ö., & Esen, R. (2015) · Archives of Medical Science · doi:10.5114/aoms.2015.50967 · Cited by 16
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McAlister, K. L., Fisher, K. L., Dumont-Driscoll, M. C., et al. (2018) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2017-0539 · Cited by 16
Lind, L., Ingelsson, E., Ärnlöv, J., Sundström, J., Zethelius, B., & Reaven, G. M. (2018) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2018.0058 · Cited by 16
Yuan, X., Chen, J., Cheng, Q., Zhao, Y., Zhang, P., Shao, X., Bi, Y., Shi, X., Ding, Y., Sun, X., et al. (2018) · BMC Gastroenterology · doi:10.1186/s12876-018-0871-2 · Cited by 16
Ding, H., Huang, J., Xie, H., Wang, B., Lin, T., Zhao, J., & Lin, Q. (2018) · Sleep And Breathing · doi:10.1007/s11325-018-1744-1 · Cited by 15
Popa, S. G., Moța, M., Popa, A., Moța, E., Timar, R. Z., Serafinceanu, C., Cheţa, D., Graur, M., & Hâncu, N. (2018) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2018.11.033 · Cited by 16
Hoyos, C. M., Murugan, S. M., Melehan, K. L., Yee, B. J., Phillips, C. L., Killick, R., Cayanan, E. A., WONG, K. K. H., Liu, P. Y., Grunstein, R. R., et al. (2018) · Journal of Sleep Research · doi:10.1111/jsr.12788 · Cited by 16
Ferritin levels throughout childhood and metabolic syndrome in adolescent stage
Suárez‐Ortegón, M. F., Blanco, E., McLachlan, S., Fernández‐Real, J. M., Burrows, R. A., Wild, S. H., Lozoff, B., & Gahagan, S. (2018) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2018.11.008 · Cited by 16
Park, C. Y., Moon, J., Jo, G., Lee, J., Kim, O. Y., Oh, H., Lim, H., & Shin, M. (2019) · Scientific Reports · doi:10.1038/s41598-018-36581-z · Cited by 17
Sex-Specific Associations of Blood-Based Nutrient Profiling With Body Composition in the Elderly
Konz, T., Santoro, A., Goulet, L., Bazzocchi, A., Battista, G., Nicoletti, C., Kadi, F., Ostan, R., Goy, M., Monnard, C., et al. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2018.01935 · Cited by 16
Arenaza, L., Medrano, M., Osés, M., Huybrechts, I., López, I. D., Henriksson, H., & Labayen, I. (2019) · British Journal Of Nutrition · doi:10.1017/s0007114519000436 · Cited by 16
Markers of bone turnover are reduced in patients with CF related diabetes; the role of glucose
Mathiesen, I. H. M., Hitz, M. F., Katzenstein, T. L., Oturai, P., Skov, M., Jørgensen, N. R., Jensen, P. O., Mikkelsen, C. R., Krogh‐Madsen, R., Pressler, T., et al. (2019) · Journal of Cystic Fibrosis · doi:10.1016/j.jcf.2019.01.004 · Cited by 16
Glycemic Markers and Subclinical Cardiovascular Disease: The Jackson Heart Study
Echouffo-Tcheugui, J. B., Chen, H., Kalyani, R. R., Sims, M., Simpson, S. L., Effoe, V. S., Correa, A., Bertoni, A. G., & Golden, S. H. (2019) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.118.008641 · Cited by 18
Mak, L., Lee, C., Cheung, K. S., Wong, D. K.-H., Liu, F., Hui, R. W., Fung, J., Xu, A., Lam, K. S. L., Yuen, M., et al. (2019) · Liver International · doi:10.1111/liv.14104 · Cited by 16
Ninomiya, T., Kanzaki, N., Hirakawa, Y., Yoshinari, M., Higashioka, M., Honda, T., Shibata, M., Sakata, S., Yoshida, D., Teramoto, T., et al. (2019) · Diabetes Care · doi:10.2337/dc18-2655 · Cited by 16
Motta, V. F., Bargut, T. C. L., Souza‐Mello, V., Águila, M. B., & Mandarim‐de‐Lacerda, C. A. (2019) · The Journal of Nutritional Biochemistry · doi:10.1016/j.jnutbio.2019.05.008 · Cited by 17
Joost, U., Villa, I., Comasco, E., Oreland, L., Veidebaum, T., & Harro, J. (2019) · International Journal of Obesity · doi:10.1038/s41366-019-0396-y · Cited by 16
Ruíz, D., Regnier, S. M., Kirkley, A. G., Hara, M., Haro, F., Aldirawi, H., Dybala, M. P., & Sargis, R. M. (2019) · Reproductive Toxicology · doi:10.1016/j.reprotox.2019.06.010 · Cited by 16
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Park, H., Lim, J. S., & Lim, S.-K. (2019) · Yonsei Medical Journal · doi:10.3349/ymj.2019.60.8.742 · Cited by 17
Guarnotta, V., Mineo, M., Radellini, S., Pizzolanti, G., & Giordano, C. (2019) · Therapeutic Advances in Endocrinology and Metabolism · doi:10.1177/2042018819871169 · Cited by 11
Fryk, E., Strindberg, L., Lundqvist, A., et al. (2019) · Metabolism Open · doi:10.1016/j.metop.2019.100017 · Cited by 16
Bicer, M., Alarslan, P., Guler, A., et al. (2019) · Journal of Endocrinological Investigation · doi:10.1007/s40618-019-01108-4 · Cited by 16
Simpson, E. J., Clark, M., Razak, A. A., & Salter, A. M. (2019) · Food & Function · doi:10.1039/c9fo00758j · Cited by 16
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Biagetti, B., Herance, J. R., Ferrer, R., Aulinas, A., Palomino‐Schätzlein, M., Mesa, J., Castano, J. P., Luque, R. M., & Simó, R. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8101549 · Cited by 16
Yan, W., Zhang, Y., Wang, L., Yang, W., Li, C., Wang, L., Gu, P., Xia, Y., Yan, J., Shen, Y., et al. (2019) · Genes & Nutrition · doi:10.1186/s12263-019-0634-x · Cited by 16
Mitsou, E. K., Detopoulou, M., Kakali, A., et al. (2019) · Beneficial Microbes · doi:10.3920/bm2019.0033 · Cited by 16
Are Circulating Mg2+ Levels Associated with Glucose Tolerance Profiles and Incident Type 2 Diabetes?
Spiga, R., Mannino, G. C., Mancuso, E., Averta, C., Paone, C., Rubino, M., Sciacqua, A., Succurro, E., Perticone, F., Andreozzi, F., et al. (2019) · Nutrients · doi:10.3390/nu11102460 · Cited by 17
Yabe, D., Kuwata, H., Fujiwara, Y., Sakaguchi, M., Moyama, S., Noboru, M., Murotani, K., Asano, H., Ito, S., Mishima, H., et al. (2019) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2019.107450 · Cited by 17
Wawrusiewicz‐Kurylonek, N., Gościk, J., Chorąży, M., Siewko, K., Posmyk, R., Zajkowska, A., Citko, A., Maciulewski, R., Szelachowska, M., Myśliwiec, J., et al. (2019) · Immunobiology · doi:10.1016/j.imbio.2019.10.013 · Cited by 16
Sambashivaiah, S., Harridge, S. D. R., Sharma, N., Selvam, S., Rohatgi, P., & Kurpad, A. V. (2019) · Frontiers in Nutrition · doi:10.3389/fnut.2019.00179 · Cited by 16
Wang, J., Dong, Z.-H., Gui, M., et al. (2019) · Bioscience Reports · doi:10.1042/bsr20192909 · Cited by 16
Transmembrane 6 superfamily member 2 167K allele improves renal function in children with obesity
Marzuillo, P., Sessa, A. D., Cirillo, G., Umano, G. R., Pedullà, M., Manna, A. L., Guarino, S., & del Giudice, E. M. (2020) · Pediatric Research · doi:10.1038/s41390-020-0753-5 · Cited by 16
Pang, H., Wang, X., Zhao, S., Wang, X., Lv, J., Qin, J., Zhao, Q. X., Che, Y., Chen, L., & Li, S. J. (2020) · Journal of Biological Chemistry · doi:10.1074/jbc.ra119.010489 · Cited by 16
Trafficking of nonesterified fatty acids in insulin resistance and relationship to dysglycemia
Walker, R. E., Ford, J. L., Boston, R. C., Savinova, O. V., Harris, W. S., Green, M. H., & Shearer, G. C. (2020) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00331.2019 · Cited by 16
Fasipe, O. J., Ayoade, O. G., & Enikuomehin, A. C. (2020) · Canadian Journal of Diabetes · doi:10.1016/j.jcjd.2020.02.003 · Cited by 16
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El‐Fayoumi, S. H., Mahmoud, A. A. A., Fahmy, A., & Ibrahim, I. A. A. E.-H. (2020) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-020-01883-5 · Cited by 16
Lactobacillus plantarum TWK10 ‐fermented soymilk improves cognitive function in type 2 diabetic rats
Liu, T., Lin, W., Cheng, M., & Tsai, T. (2020) · Journal of the Science of Food and Agriculture · doi:10.1002/jsfa.10564 · Cited by 16
Yahia, H., Hassan, A. H. M., El‐Ansary, M. R., Al‐Shorbagy, M. Y., & Elyamany, M. F. (2020) · Naunyn-Schmiedeberg s Archives of Pharmacology · doi:10.1007/s00210-020-01940-z · Cited by 15
Dorenbos, E., Drummen, M., Adam, T. C., Rijks, J. H. J., Winkens, B., Martínez, J. A., Navas‐Carretero, S., Stratton, G., Swindell, N. J., Stouthart, P. J. H. M., et al. (2020) · Pediatric Obesity · doi:10.1111/ijpo.12702 · Cited by 16
Carvalho, A. C., Mota, M. C., Marot, L. P., Mattar, L. A., de Sousa, J. A. G., Araújo, A. C. T., Assis, C. T. D. C., & Crispim, C. A. (2020) · Obesity Surgery · doi:10.1007/s11695-020-04873-x · Cited by 16
Hafez, M., Elblehi, S. S., & El-Sayed, Y. S. (2020) · Environmental Science and Pollution Research · doi:10.1007/s11356-020-10262-9 · Cited by 16
Wardle, S. L., Macnaughton, L. S., McGlory, C., Witard, O. C., Dick, J. R., Whitfield, P. D., Ferrando, A. A., Wolfe, R. R., Kim, I., Hamilton, D. L., et al. (2020) · Physiological Reports · doi:10.14814/phy2.14529 · Cited by 18
Shimizu, Y., Tamura, T., Kemmochi, A., Owada, Y., Ozawa, Y., Hisakura, K., Matsuzaka, T., Shimano, H., Nakano, N., Sakashita, S., et al. (2020) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.15239 · Cited by 15
Iwanaga, S., Hashida, R., Takano, Y., Bekki, M., Nakano, D., Omoto, M., Nago, T., Kawaguchi, T., Matsuse, H., Torimura, T., et al. (2020) · The Tohoku Journal of Experimental Medicine · doi:10.1620/tjem.252.23 · Cited by 16
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Reijrink, M., de Boer, S. A., Antunes, I. F., Spoor, D. S., Heerspink, H. J. L., Lodewijk, M. E., Mastik, M. F., Boellaard, R., Greuter, M. J. W., Benjamens, S., et al. (2020) · Molecular Imaging and Biology · doi:10.1007/s11307-020-01538-0 · Cited by 16
Song, K., Zhang, Y., Ga, Q., Bai, Z., & Ge, R. (2020) · Obesity Facts · doi:10.1159/000508112 · Cited by 19
von Korn, P., Keating, S. E., Mueller, S., Haller, B., Kränkel, N., Dinges, S. M.-T., Duvinage, A., Scherr, J., Wisløff, U., Tjønna, A. E., et al. (2020) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2020.0105 · Cited by 18
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Morales‐Palomo, F., Moreno‐Cabañas, A., Ramírez-Jiménez, M., Álvarez-Jiménez, L., Valenzuela, P. L., Lucía, A., ORTEGA, J. F., & Mora‐Rodríguez, R. (2021) · Medicine & Science in Sports & Exercise · doi:10.1249/mss.0000000000002591 · Cited by 18
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Hedges, C. P., Boix, J., Jaiswal, J. K., Shetty, B., Shepherd, P. R., & Merry, T. L. (2021) · Biomolecules · doi:10.3390/biom11020150 · Cited by 16
Cardiometabolic Profile of Different Body Composition Phenotypes in Children
Ong, Y. Y., Huang, J. Y., Michael, N., Sadananthan, S. A., Yuan, W. L., Chen, L., Karnani, N., Velan, S. S., Fortier, M. V., Tan, K. H., et al. (2021) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgab003 · Cited by 16
Li, Y., Wang, J., Yang, J., Chen, J., Zhou, W., Qiao, C., Shen, S., & Bi, Y. (2021) · Gynecological Endocrinology · doi:10.1080/09513590.2020.1810228 · Cited by 16
Prasad, R. B., Kristensen, K. H., Katsarou, A., & Shaat, N. (2021) · BMC Medical Genomics · doi:10.1186/s12920-021-01123-6 · Cited by 18
Epperlein, S., Gebhardt, C., Rohde, K., Chakaroun, R., Patt, M., Schamarek, I., Kralisch, S., Heiker, J. T., Scholz, M. R., Stümvoll, M., et al. (2021) · Biomedicines · doi:10.3390/biomedicines9040345 · Cited by 16
Serbis, A., Giapros, V., Paschou, S. Α., & Siomou, E. C. (2021) · Endocrine · doi:10.1007/s12020-021-02762-6 · Cited by 16
Boßlau, T. K., Wasserfurth, P., Krüger, B., Reichel, T., Palmowski, J., Nebl, J., Weyh, C., Schenk, A., Joisten, N., Stahl, F., et al. (2021) · Cells · doi:10.3390/cells10050998 · Cited by 16
Rousseau, M., Horne, J. R., Guénard, F., de Toro‐Martín, J., Garneau, V., Guay, V., Kearney, M., Pilon, G., Roy, D., Couture, P., et al. (2021) · Genes & Nutrition · doi:10.1186/s12263-021-00688-2 · Cited by 16
İdilman, İ. S., Low, H. M., Gidener, T., Philbrick, K. A., Mounajjed, T., Li, J., Allen, A. M., Yin, M., & Venkatesh, S. K. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10122565 · Cited by 16
Cho, A., Lee, S., Hong, K., & Jung, D. (2021) · Acta Diabetologica · doi:10.1007/s00592-021-01755-1 · Cited by 17
Papandreou, C., García-Gavilán, J. F., Camacho‐Barcia, L., Hansen, T. T., Harrold, J. A., Sjödin, A. M., Halford, J. C. G., & Bulló, M. (2021) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.202001154 · Cited by 17
Doulberis, M., Pierre, N. T., Manzini, G., et al. (2021) · Microorganisms · doi:10.3390/microorganisms9071361 · Cited by 16
Diels, S., Cuypers, B., Tvarijonaviciute, A., Derudas, B., Dijck, E. V., Verrijken, A., Gaal, L. F. V., Laukens, K., Lefèbvre, P., Cerón, J. M. C., et al. (2021) · Clinical Epigenetics · doi:10.1186/s13148-021-01142-1 · Cited by 16
Beta Cell Function as a Baseline Predictor of Weight Loss After Bariatric Surgery
Borges‐Canha, M., Neves, J. S., Mendonça, F. M., Silva, M. M., Costa, C. F., Cabral, P. M., Guerreiro, V. A., Lourenço, R., Meira, P., Salazar, D. A., et al. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.714173 · Cited by 17
Selenium Intake and Glycemic Control in Young Adults With Normal-Weight Obesity Syndrome
Santos, A. D. C., Passos, A. F. F., Holzbach, L. C., & Cominetti, C. (2021) · Frontiers in Nutrition · doi:10.3389/fnut.2021.696325 · Cited by 15
Lindsay, K. L., Gyllenhammer, L. E., Entringer, S., & Wadhwa, P. D. (2021) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgab655 · Cited by 16
Pancreatic β-Cell Dysfunction Is Associated with Nonalcoholic Fatty Liver Disease
Chen, X., Xiao, J., Pang, J., Chen, S., Wang, Q., & Ling, W. (2021) · Nutrients · doi:10.3390/nu13093139 · Cited by 16
Increased Risk of Nonalcoholic Fatty Liver Disease in Individuals with High Weight Variability
Jung, I., Koo, D.-J., Lee, M. Y., Moon, S. J., Kwon, H., Park, S. E., Rhee, E., & Lee, W. (2021) · Endocrinology and Metabolism · doi:10.3803/enm.2021.1098 · Cited by 16
Poessel, M., Morys, F., Breuer, N., Villringer, A., Hummel, T. R., & Horstmann, A. (2021) · Appetite · doi:10.1016/j.appet.2021.105774 · Cited by 16
Erdoğan, S. S., Gür, T. F., Özkur, E., & Doğan, B. (2021) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2021.0063 · Cited by 16
Burhop, M., Schuchardt, J. P., Nebl, J., Müller, M., Lichtinghagen, R., & Hahn, A. (2022) · Nutrients · doi:10.3390/nu14020396 · Cited by 16
Zheng, H. W., Qiu, Z., Chai, T., et al. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.732122 · Cited by 16
Metadata
- Authors
- Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C.
- Affiliations
- University of Oxford (GB)
- ORCID
- Matthews, D. R.
- Venue
- Diabetologia
- Published
- 1985-07
- Volume
- 28
- Issue
- 7
- Pages
- 412-419
- ISSN
- 0012-186X (ISSN-L)
- 1432-0428
- OpenAlex
- W2160234571
- Cited by
- 31,502
- References
- 21
- Citations by year
- 2026: 933 · 2025: 1060 · 2024: 1240 · 2023: 1291 · 2022: 1489 · 2021: 1528 · 2020: 1424 · 2019: 1427 · 2018: 1456 · 2017: 1440 · 2016: 1481 · 2015: 1682 · 2014: 1693 · 2013: 1621 · 2012: 1518
- FWCI
- 7.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Diabetologia (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Research Council
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- HOMA, insulin resistance, beta-cell function, fasting blood glucose, fasting insulin, hyperglycaemic clamp, euglycaemic clamp, intravenous glucose tolerance test, insulin receptor binding, type 2 diabetes
- MeSH terms
- Adult, Blood Glucose, Computers, Diabetes Mellitus, Type 2, Erythrocytes, Fasting, Homeostasis, Humans, Insulin, Insulin Resistance, Islets of Langerhans, Middle Aged, Monocytes, Receptor, Insulin
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-14, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-14 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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Statistics and arithmetic are recomputed, not judged.
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