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
1 of 6 cited sources checked · 0 supported · 0 flagged · 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)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- 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
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- 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.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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 (%)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe 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
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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.
Normal and diabetic group sizes are reversed for the CIGMA comparison
MinorThe methods say the CIGMA comparison used 12 normal subjects and 11 diabetic patients, but the results report 11 normal subjects and 12 diabetic subjects.
“The same 12 normal subjects and 11 Type 2 diabetic patients came to the hospital fasting on a separate day for a continuous infusion”
“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
Flagged first, then by citations
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
Cited by
Sorted by citations
Items 27,901–28,000 of 31,469
Al-Naemi, A. H. (2022) · Open Access Macedonian Journal of Medical Sciences · doi:10.3889/oamjms.2022.7928 · Cited by 2
FMBA of Russia, M., Frolkov, V. K., Benkov, A. A., et al. (2022) · Fizioterapevt (Physiotherapist) · doi:10.33920/med-14-2202-07 · Cited by 1
Gestasyonel Diyabetes Mellituslu Gebelerde Serum YKL-40 Düzeyi
Tüten, N., Gök, K., Kücür, M., Açıkgöz, A. S., Öncül, M., & Tüten, A. (2021) · Sakarya Medical Journal · doi:10.31832/smj.948949 · Cited by 3
Pharmacometrics Approaches and its Applications in Diabetes
Aziz, S., Harun, S. N., Sulaiman, S. A. S., & Ghadzi, S. M. S. (2021) · Journal of Pharmacy And Bioallied Sciences · doi:10.4103/jpbs.jpbs_399_21 · Cited by 1
Kırmızıbekmez, H., Demirkol, Y. K., Doğan, Ö. A., Karabulut, G. S., Balcı, E. İ., Atla, P., & Dursun, F. (2022) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2021-0756 · Cited by 1
Serum asymmetric dimethylarginine (ADMA) level and cognitive dysfunction in diabetic patients
Arab, A., Zakeri, A., Nadermohammadi, M., & Mostafalou, S. (2022) · International Journal of Diabetes in Developing Countries · doi:10.1007/s13410-022-01064-5 · Cited by 1
da Cruz, V. L., Mediano, M. F. F., Paravidino, V. B., Neves, F. A., Malafaia, T. D. O., Rodrigues, L. F., Moura, A. S., & Sichieri, R. (2022) · American Journal of Human Biology · doi:10.1002/ajhb.23745 · Cited by 1
[Correlational Study of Thyroid-Stimulating Hormone and Salivary Microecology].
Dong, T., Luo, J.-Y., & Huang, Z. (2022) · PubMed · doi:10.12182/20220360502 · Cited by 1
Moderating effect of bone maturation on the relationship between body fat and insulin resistance
Olivares, L. A. F., Fragoso, I. J., Fierro, L. G. D. L., Ortiz-Rodríguez, B., Luján, R. C., & Legleu, C. E. C. (2022) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2021-0663 · Cited by 1
Tasgoz, F. N., Erturk, N. K., & Temur, M. (2022) · Biomarkers · doi:10.1080/1354750x.2022.2064549 · Cited by 1
Meyhöfer, S., Wilms, B., Chamorro, R., Knaak, A., Pappa, E., Schulz, A. S., Pagels, A., Schröder, M., Kaluzny, N., Grein, H., et al. (2022) · Experimental and Clinical Endocrinology & Diabetes · doi:10.1055/a-1831-0265 · Cited by 1
Karczewska-Kupczewska, M., Nikołajuk, A., Kondraciuk, M., Stachurska, Z., Dubatówka, M., Szpakowicz, A., Strączkowski, M., Kowalska, I., & Kamiński, K. (2022) · Cardiovascular Diabetology · doi:10.1186/s12933-022-01491-y · Cited by 1
Tsai, P., Wang, C.-H., Hunt, C. W., Watts, S. O., & Ware, K. (2022) · Topics in Geriatric Rehabilitation · doi:10.1097/tgr.0000000000000358 · Cited by 1
Prediabetes: From diagnosis to prognosis
Živković, T. B. (2022) · Galenika Medical Journal · doi:10.5937/galmed2201057b · Cited by 1
Wongniyomkaset, W., Rungraung, N., Muangpracha, N., Winuprasith, T., & Trachootham, D. (2022) · Journal of Nutritional Science · doi:10.1017/jns.2022.23 · Cited by 1
Protocol for a Longitudinal Study of the Determinants of Metabolic Syndrome Risk in Young Adults
Pomeroy, A., Bates, L., Stoner, L. R., Weaver, M. A., Moore, J. B., Nepocatych, S., & Higgins, S. (2022) · Translational Journal of the American College of Sports Medicine · doi:10.1249/tjx.0000000000000197 · Cited by 2
Mishra, N., & Mishra, R. (2022) · Journal of South Asian Federation of Obstetrics and Gynaecology · doi:10.5005/jp-journals-10006-2000 · Cited by 1
Haschka, S. J., Gar, C., Potzel, A. L., Sacco, V., Kern-Matschilles, S., Benz, I., Then, C., Seißler, J., & Lechner, A. (2022) · Cureus · doi:10.7759/cureus.23916 · Cited by 1
Serum Leptin as a Biomarker in Diabetes
Ahmed, H. S. (2022) · Biomarkers in disease · doi:10.1007/978-3-030-81303-1_50-1 · Cited by 1
Park, W.-B., Cho, H., & Lee, M.-G. (2019) · Korean Journal of Sport Science · doi:10.24985/kjss.2019.30.2.236 · Cited by 1
Glucose Metabolism and Visceral Fat in GH Deficient Adults: Two Years of GH-Replacement
Spina, L. D. C., Soares, D. V., Brasil, R. R. L. O., Lobo, P. M., Conceição, F. L., & Vaisman, M. (2005) · Pituitary · doi:10.1007/s11102-005-0009-8 · Cited by 1
Lima-Martínez, M. M., Colmenares, L., Campanelli, Y., Paoli, M., Rodney, M., Santos, R. D., & Iacobellis, G. (2019) · Clínica e Investigación en Arteriosclerosis (English Edition) · doi:10.1016/j.artere.2018.06.004 · Cited by 1
Tanyanskiy, D. A., & Denisenko, A. D. (2012) · Biomeditsinskaya Khimiya · doi:10.18097/pbmc20125804457 · Cited by 1
Insulin Resistance and the Metabolic Syndrome
Chaiken, R. L. (2004) · Springer eBooks · doi:10.1007/978-1-4757-6260-0_39 · Cited by 1
Improvement of glucose metabolism after a pancreatoduodenectomy
Ohtsuka, T., Kitahara, K., Kohya, N., et al. (2009) · Suizo · doi:10.2958/suizo.24.749 · Cited by 1
de Luis, D. A., Izaola, O., Primo, D., et al. (2017) · Endocrinología Diabetes y Nutrición (English ed ) · doi:10.1016/j.endien.2017.06.002 · Cited by 1
Park, S., Ham, J. O., & Lee, B. K. (2014) · The journal of nutrition health & aging · doi:10.1007/s12603-014-0516-2 · Cited by 1
de la Fuente, R. A., Cuadrado, N. M., Sánchez, C. N. T., et al. (2018) · Endocrinología Diabetes y Nutrición (English ed ) · doi:10.1016/j.endien.2018.05.009 · Cited by 1
Long-term maintenance of weight loss after lifestyle intervention in frail, obese older adults
Waters, D. L., Vawter, R., Qualls, C., Chode, S., Armamento‐Villareal, R., & Villareal, D. T. (2012) · The journal of nutrition health & aging · doi:10.1007/s12603-012-0429-x · Cited by 1
Schreiber, C. D. S., Rafacho, A., Silvério, R., Betti, R., Lerário, A. C., Lotenberg, A. M. P., Rahmann, K., de Oliveira, C. P., WAJCHENBERG, B. L., & da Luz, P. L. (2022) · Archives of Endocrinology and Metabolism · doi:10.20945/2359-3997000000478 · Cited by 2
Molinari, M., Cremaschi, A., Iorio, M. D., et al. (2022) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1111/rssc.12570 · Cited by 1
Osipova, O. A., Гостева, Е. В., Жернакова, Н. И., et al. (2022) · CARDIOVASCULAR THERAPY AND PREVENTION · doi:10.15829/1728-8800-2022-3252 · Cited by 1
Gao, X., Horbal, S. R., Kazé, A. D., et al. (2022) · Obesity Medicine · doi:10.1016/j.obmed.2022.100435 · Cited by 1
Escartín, R., Font, M. L., González-Clemente, J. M., et al. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.909201 · Cited by 1
Maltais‐Payette, I., & Tchernof, A. (2022) · Biomarkers in disease · doi:10.1007/978-3-030-81304-8_56-1 · Cited by 1
Botsi, E., Karatzi, K., Mavrogianni, C., Tsochev, Κ., González‐Gil, E. M., Radó, S. I., Kivelä, J., Wikström, K., Cardon, G., Rurik, I., et al. (2022) · Nutrition · doi:10.1016/j.nut.2022.111805 · Cited by 1
Gu, M., Lin, Y., Gai, X., Wei, X., Lu, C., Wang, Y., Ding, X., Peng, Y., & Ma, Y. (2022) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s374912 · Cited by 1
Göğebakan, H., & Çetin, G. Y. (2022) · DAHUDER Medical Journal · doi:10.56016/dahudermj.1146178 · Cited by 1
Borges, J. H., Camargo, D. A., SEWAYBRICKER, L. E., Santoro, R. I., de Oliveira, D. M., de Lemos‐Marini, S. H. V., Geloneze, B., Guerra‐Júnior, G., & Gonçalves, E. M. (2022) · Archives of Endocrinology and Metabolism · doi:10.20945/2359-3997000000504 · Cited by 1
Metabolic status is not related to dietary acid load in polycystic ovary syndrome
Omma, T., ZENGİN, F. H., AYDOĞMUŞ, S., & Çulha, C. (2022) · Ankara Eğitim ve Araştırma Hastanesi Tıp Dergisi · doi:10.20492/aeahtd.1069778 · Cited by 1
Study of Insulin Resistance in Patients with Subclinical Hypothyroidism
Shehata, A. A., Elfayoumy, K. N., omran, T., et al. (2022) · International Journal of Medical Arts · doi:10.21608/ijma.2022.137559.1453 · Cited by 1
Gümüşsoy, M., Koç, Ö., Karataş, G., Özercan, M., Ellik, Z. M., Duman, S., Kiremitçi, S., Gökcan, H., Elhan, A. H., Savaş, B., et al. (2022) · European Journal of Gastroenterology & Hepatology · doi:10.1097/meg.0000000000002448 · Cited by 1
Yang, X., Chen, X., Xu, H., Deng, H., Yao, B., Xu, W., & Lin, Q. (2022) · Hormone and Metabolic Research · doi:10.1055/a-1933-3009 · Cited by 1
Martino, F., Martino, E., Albanese, C. V., et al. (2022) · Journal of Clinical Densitometry · doi:10.1016/j.jocd.2022.10.001 · Cited by 1
Siri, G., Mahmoudinezhad, M., Alesaeidi, ., Farhangi, M. A., & Ardekani, A. M. (2022) · BMC Nutrition · doi:10.1186/s40795-022-00608-6 · Cited by 1
Michalichen, K. C., Weber, V. M. R., Queiróga, M. R., Fernandes, D. Z., Carreira, C. M., Vieira, E. R., & Venturini, D. (2022) · BMC Women s Health · doi:10.1186/s12905-022-02010-7 · Cited by 2
Yang, J., Zhang, J., Wang, R., Liu, Y., & Chen, Y. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.960843 · Cited by 1
Hamed, N., Soliman, A. T., Sanctis, V. D., Shaat, M., Alaaraj, N., Ahmed, S. J., Qusad, M., Siddiq, K., & Alyafei, F. (2022) · Acta bio-medica dell'Ateneo parmense/Acta biomedica · doi:10.23750/abm.v93i5.12679 · Cited by 1
Exposure to pesticides and overweight - A systematic review
Skonieski, C., Silva, L., Fagundes, K. R., Nunes, A. T., Finhler, S., Roversi, K., Gallina, A. L., Hirata, M. H., Ferreira, G. M., & Benvegnú, D. M. (2022) · Research Society and Development · doi:10.33448/rsd-v11i14.36493 · Cited by 1
Figueiredo, N., Queiroz, M. D. O., Lopes, K. L. S., Oliveira, L. B., Dâmaso, A. R., de Melo, P. R. E., Abreu, V. D. S., Mota, J. F., Horst, M. A., & Corgosinho, F. C. (2022) · Obesities · doi:10.3390/obesities2040030 · Cited by 1
Lee, J., Kim, M., Lee, J., & Chang, S. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11226756 · Cited by 2
Beynon, A. M., Wedderkopp, N., Leboeuf‐Yde, C., Hartvigsen, J., Walker, B. F., & Hébert, J. J. (2022) · PLoS ONE · doi:10.1371/journal.pone.0277991 · Cited by 1
Developmental trajectories at a high risk for childhood overweight/obesity
Yoshinaga, M., Takahashi, H., Ito, Y., Aoki, M., Miyazaki, A., Kubo, T., Shinomiya, M., Horigome, H., Tokuda, M., Lin, L., et al. (2022) · Pediatrics International · doi:10.1111/ped.15425 · Cited by 1
Richter, L. R., Albert, B. I., Zhang, L., Ostropolets, A., Zitsman, J. L., Fennoy, I., Albers, D. J., & Hripcsak, G. M. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.923704 · Cited by 3
Mohammed, N. I., Alzubaidi, Z. F., & Khudhair, M. K. (2022) · Wiadomości Lekarskie · doi:10.36740/wlek202211215 · Cited by 2
Zhang, J., Teng, F., Yuan, Y., et al. (2023) · Heart and Vessels · doi:10.1007/s00380-022-02222-9 · Cited by 1
Islam, M. R., Rahman, S. M., Selling, K. E., Näsänen-Gilmore, P. K., Kippler, M. J., Kajantie, E., Rahman, A. M., Pervin, J., & Ekström, E. (2023) · Frontiers in Nutrition · doi:10.3389/fnut.2023.1058965 · Cited by 3
Costa, J. P., Magalhães, V., Araújo, J., et al. (2023) · Nutrition Research · doi:10.1016/j.nutres.2023.01.002 · Cited by 1
Urakami, T., Kuwabara, R., Suzuki, J., Abe, Y., Hara, M., & Morioka, I. (2023) · Endocrine Journal · doi:10.1507/endocrj.ej22-0449 · Cited by 1
Assessment of Serum Galectin-3 Levels in Patients with Gestational Diabetes Mellitus
Baldane, S., Celik, M., Körez, M. K., et al. (2023) · Journal of Diabetology · doi:10.4103/jod.jod_84_22 · Cited by 1
de Luis, D. A., Primo, D., Izaola, O., & Aller, R. (2023) · Endocrinología Diabetes y Nutrición (English ed ) · doi:10.1016/j.endien.2022.10.004 · Cited by 1
Wu, J., Liu, X., Lu, N., et al. (2023) · Clinical Pediatrics · doi:10.1177/00099228231156751 · Cited by 1
A New Predictor for Insulin Resistance in Polycystic Ovary Syndrome: InsuTAG
Omma, T., Gökçe, A., Çelik, M., Karahan, İ., Çulha, C., & Gülçelik, N. E. (2023) · Current Women s Health Reviews · doi:10.2174/1573404820666230301115706 · Cited by 1
Liu, Q., Hu, F., Zeng, J., Ma, L., Yan, S., Li, C., Tian, H., & Gong, Y. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e14100 · Cited by 1
Association between caffeine intake and liver biomarkers in non-alcoholic fatty liver disease
Uçar, K., Aksoy, E. K., & Göktaş, Z. (2023) · Çukurova medical journal (Online)/Çukurova medical journal · doi:10.17826/cumj.1171396 · Cited by 1
Mirzababaei, A., Shiraseb, F., Hajishizari, S., Farazi, M., Tangestani, H., Khorraminezhad, L., Clark, C. C. T., & Mirzaei, K. (2023) · British Journal of Diabetes · doi:10.15277/bjd.2023.402 · Cited by 1
Racine, F., Chagnon, M., Boudreau, V., et al. (2023) · Diabetes Epidemiology and Management · doi:10.1016/j.deman.2023.100141 · Cited by 1
Effects of Moderate Exercise Training On ApoE And ApoCIII In Metabolic Syndrome
Ertekin, K., Baştuğ, M., & Canpolat, A. G. (2023) · European Journal of Therapeutics · doi:10.58600/eurjther-184 · Cited by 1
Areloegbe, S. E. J., Atuma, C. L., Oyabambi, A. O., & Olaniyi, K. S. (2023) · Nutrire · doi:10.1186/s41110-023-00205-y · Cited by 1
Polycystic ovary syndrome and metabolic syndrome: Risks in later life
Sheikh, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-87932-3.00019-0 · Cited by 2
COVID-19 infection causes insulin-resistance between hospitalised patients in Najaf governorate
Alshawi, A., Al-Shukry, A. F., Mohammed, H. A., Majeed, T. R., Jaleel, A.-K. K. A., & Mohammed, M. S. (2023) · AIP conference proceedings · doi:10.1063/5.0135959 · Cited by 1
Rahardjo, T. (2023) · Gaceta Médica de Caracas · doi:10.47307/gmc.2023.131.s2.4 · Cited by 1
Small‐for‐gestational‐age and predictors of HOMA indices, leptin and adiponectin in infancy
Huang, R., He, H., Nuyt, A. M., Julien, P., Marc, I., Levy, E. H., Fraser, W. D., & Luo, Z. (2023) · Acta Paediatrica · doi:10.1111/apa.16816 · Cited by 1
de Luis, D. A., Primo, D., Izaola, O., Hoyos, E. G., & Gómez, J. J. L. (2023) · Endocrinología Diabetes y Nutrición · doi:10.1016/j.endinu.2023.03.004 · Cited by 1
Sparvoli, J. M. H., Sparvoli, A. C., Pereira, A. A., et al. (2023) · Revista da Associação Médica Brasileira · doi:10.1590/1806-9282.20221163 · Cited by 1
Grangeiro, É. D., Trigueiro, M. S., Siais, L. D. O., Paiva, H. M., & Rosado, E. L. (2023) · Revista de Nutrição · doi:10.1590/1678-9865202336e220088 · Cited by 1
Obesity in the Pathophysiology of Diabetes
Paniagua, J. A., & Vidal‐Puig, A. (2023) · Springer eBooks · doi:10.1007/978-3-031-25519-9_14 · Cited by 1
Giulio, F. D., Castellini, C., Tienforti, D., Felzani, G., Baroni, M. G., & Barbonetti, A. (2023) · Journal of Endocrinological Investigation · doi:10.1007/s40618-023-02124-1 · Cited by 2
Banica, T., Verroken, C., Reyns, T., T'Sjoen, G. G., Fiers, T., Kaufman, J., & Lapauw, B. (2023) · European Journal of Endocrinology · doi:10.1093/ejendo/lvad059 · Cited by 1
Marjanović, M., Vuksanović, M., Sagić, D., Radović, I., Soldatović, I., & Živković, T. B. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12123915 · Cited by 1
Lee, H., Kim, H., Provido, S. M. P., Kang, M., Chung, G. H., Lee, J. W., Hong, S., Yu, S. H., Lee, C. B., & Lee, J. E. (2023) · Global Heart · doi:10.5334/gh.1209 · Cited by 1
Zhu, C., Wen, X., You, H., Lu, L., Du, L., & Qian, C. (2023) · Obesity Surgery · doi:10.1007/s11695-023-06686-0 · Cited by 2
Hu, T., Shen, Y., Xu, Y., Wang, Y., Bao, Y., & Ma, X. (2023) · Endocrine · doi:10.1007/s12020-023-03428-1 · Cited by 1
Williams, J. S., Bonafiglia, J. T., King, T. J., et al. (2023) · European Journal of Applied Physiology · doi:10.1007/s00421-023-05209-0 · Cited by 1
Isaksen, V. T., Larsen, M. A., Goll, R., Paulssen, E. J., & Florholmen, J. R. (2023) · Endocrine and Metabolic Science · doi:10.1016/j.endmts.2023.100134 · Cited by 1
Systemic analysis of physiotherapy effects on patients with arterial hypertension
Abdurakhmanova, R. Z., Edelev, D. A., «ArbatKlinik», M., et al. (2023) · Fizioterapevt (Physiotherapist) · doi:10.33920/med-14-2303-02 · Cited by 1
Saucedo, R., Peña-Cano, M. I., García, Y., González-Reynoso, R., Díaz-Velázquez, M. F., & Valencia‐Ortega, J. (2023) · Acta Universitaria · doi:10.15174/au.2023.3711 · Cited by 3
Pan, M., Zhang, J., Zhang, Q., Wang, F., Qu, F., & Jin, M. (2023) · Clinical and Experimental Obstetrics & Gynecology · doi:10.31083/j.ceog5008162 · Cited by 1
Tirani, S. A., Mirzaei, S., Asadi, A., Asjodi, F., Iravani, O., Akhlaghi, M., & Saneei, P. (2023) · Annals of Nutrition and Metabolism · doi:10.1159/000533343 · Cited by 4
Moderation of Physical Activity between Cardiometabolic Risk and Adiponectin in Adolescents
Tadiotto, M. C., Corazza, P. R. P., Menezes, F. J. D., de Moraes-Junior, F. B., Lopes, M. D. F. A., Tozo, T. A. A., Purim, K. S. M., Mota, J. A. P. S., & Leite, N. (2023) · International Journal of Sports Medicine · doi:10.1055/a-2108-6594 · Cited by 1
Álvarez-Jiménez, L., Morales‐Palomo, F., Moreno‐Cabañas, A., ORTEGA, J. F., Mora‐Gonzalez, D., & Mora‐Rodríguez, R. (2023) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgad477 · Cited by 1
Screening and Testing for Prediabetes and Type 2 Diabetes in Asymptomatic Adults
Gustanar, L. A. E., & Fajari, N. M. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.1002298 · Cited by 2
Caracterización de mujeres universitarias con síndrome de ovario poliquístico en Costa Rica
González-Salazar, M., Chinchilla-Monge, R., Holst-Schumacher, I. I., Corella, M. A. R., Calderón-Jiménez, M., & Barrantes-Santamaría, M. (2023) · Acta Médica Costarricense · doi:10.51481/amc.v65i2.1265 · Cited by 1
Rodriguez, R. O., López, M., Pérez, R. E., & Ordaz, G. (2023) · Animal Production Science · doi:10.1071/an23170 · Cited by 1
Defining Outcomes for β-Cell Replacement Therapy
Rickels, M. R. (2023) · Springer eBooks · doi:10.1007/978-3-031-20999-4_65 · Cited by 1
Kumar, M., Kumar, V., Singh, Y., et al. (2023) · Journal of Trace Elements in Medicine and Biology · doi:10.1016/j.jtemb.2023.127313 · Cited by 1
Cheng, Y.-C., Ma, W.-C., Li, Y., Wu, J., Liang, K., Lee, W., Liu, H., Sheu, W. H., & Lee, I. (2023) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-023-01166-y · Cited by 2
Ternhamar, T., Møller, A., Martinussen, C., Svane, M. S., HINDSØ, M. G., Jørgensen, N. B., Dirksen, C., Jensen, J. B., Hartmann, B., Holst, J. J., et al. (2023) · American Journal of Physiology-Endocrinology and Metabolism · doi:10.1152/ajpendo.00176.2023 · Cited by 2
Su, G.-Y., Ning, N. X., Lin, K., & Hwu, C. (2023) · Metabolic Syndrome and Related Disorders · doi:10.1089/met.2023.0061 · Cited by 1
Jafar, I., Ali, H. A., Ali, R. A., et al. (2023) · Current Issues in Pharmacy and Medical Sciences · doi:10.2478/cipms-2023-0030 · Cited by 1
Metadata
- Authors
- Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C.
- Affiliations
- University of Oxford (GB)
- ORCID
- Matthews, D. R.
- Venue
- Diabetologia
- Published
- 1985-07
- Volume
- 28
- Issue
- 7
- Pages
- 412-419
- ISSN
- 0012-186X (ISSN-L)
- 1432-0428
- OpenAlex
- W2160234571
- Cited by
- 31,502
- References
- 21
- Citations by year
- 2026: 933 · 2025: 1060 · 2024: 1240 · 2023: 1291 · 2022: 1489 · 2021: 1528 · 2020: 1424 · 2019: 1427 · 2018: 1456 · 2017: 1440 · 2016: 1481 · 2015: 1682 · 2014: 1693 · 2013: 1621 · 2012: 1518
- FWCI
- 7.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Diabetologia (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Research Council
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- HOMA, insulin resistance, beta-cell function, fasting blood glucose, fasting insulin, hyperglycaemic clamp, euglycaemic clamp, intravenous glucose tolerance test, insulin receptor binding, type 2 diabetes
- MeSH terms
- Adult, Blood Glucose, Computers, Diabetes Mellitus, Type 2, Erythrocytes, Fasting, Homeostasis, Humans, Insulin, Insulin Resistance, Islets of Langerhans, Middle Aged, Monocytes, Receptor, Insulin
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-14, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-14 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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