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 10,301–10,400 of 31,469
Papakonstantinou, E., Kechribari, I., Mitrou, P., Trakakis, E., Vassiliadi, D., Georgousopoulou, E., Zampelas, A., Kontogianni, M. D., & Dimitriadis, G. (2016) · European Journal of Clinical Nutrition · doi:10.1038/ejcn.2015.225 · Cited by 34
Carstens, M. T., Goedecke, J. H., Dugas, L., Evans, J., Kroff, J., Levitt, N. S., & Lambert, E. V. (2013) · Nutrition & Metabolism · doi:10.1186/1743-7075-10-8 · Cited by 34
Grarup, N., Andreasen, C. H., Andersen, M. K., Albrechtsen, A., Sandbæk, A., Lauritzen, T., Borch-Johnsen, K., Jørgensen, T., Schmitz, O., Hansen, T., et al. (2008) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2007-2815 · Cited by 34
Willis, S. A., Sargeant, J. A., Yates, T., Takamura, T., Takayama, H., Gupta, V., Brittain, E., Crawford, J., Parry, S. A., Thackray, A. E., et al. (2020) · The Journal of Nutrition · doi:10.1093/jn/nxz333 · Cited by 34
Khoudary, S. R. E., Chen, H.-Y., Barinas‐Mitchell, E., McClure, C. K., Selzer, F., Karvonen-Gutierrez, C. A., Jackson, E. A., Ylitalo, K. R., & Sternfeld, B. (2014) · International Journal of Cardiology · doi:10.1016/j.ijcard.2014.12.042 · Cited by 34
Sun, S., He, D., Luo, C., Lin, X., Wu, J., Yin, X., Jia, C., Pan, Q., Dong, X., Zheng, F., et al. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.795044 · Cited by 36
Eldesoqui, M. B., Eldken, Z. H., Mostafa, S. A., Al‐Serwi, R. H., El‐Sherbiny, M., Elsherbiny, N. M., Mohammedsaleh, Z. M., & Sakr, N. H. (2022) · Metabolites · doi:10.3390/metabo12070635 · Cited by 34
Yang, C. D., Pan, W. Q., Feng, S., Quan, J. W., Chen, J. W., Shu, X. Y., Aihemaiti, M., Ding, F. H., Shen, W. F., Lu, L., et al. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.122.026184 · Cited by 37
Chen, L., Mir, S. A., Bendt, A. K., Chua, E. W. L., Narasimhan, K., Tan, K. M. L., Loy, S. L., Tan, K. H., Shek, L. P., Chan, J. K. Y., et al. (2023) · BMC Medicine · doi:10.1186/s12916-023-02740-x · Cited by 39
Biernacka-Bartnik, A., Kocełak, P., Owczarek, A. J., Choręza, P. S., Markuszewski, L., Madej, P., Puzianowska‐Kuźnicka, M., Chudek, J. T., & Olszanecka‐Glinianowicz, M. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1100547 · Cited by 34
Tian, N., Fa, W., Dong, Y., Liu, R., Liu, C., Liu, K., Mao, M., Zhu, M., Liang, X., Wang, N., et al. (2023) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12426 · Cited by 33
Busch, C. B. E., Meiring, S., van Baar, A. C. G., Holleman, F., Nieuwdorp, M., & Bergman, J. J. G. H. M. (2024) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2024.04.2904 · Cited by 36
Serum alkaline phosphatase, body composition, and risk of metabolic syndrome in middle-aged Korean
Kim, M. K., Baek, K. H., Kang, M. I., Park, S. E., Rhee, E., Park, C., Lee, W., & Oh, K. W. (2012) · Endocrine Journal · doi:10.1507/endocrj.ej12-0331 · Cited by 35
Adipocyte chemerin release is induced by insulin without being translated to higher levels in vivo
Bauer, S., Bala, M., Kopp, A., Eisinger, K., Schmid, A., Schneider, S., Neumeier, M., & Buechler, C. (2012) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.2012.02713.x · Cited by 34
Association of lean body mass with cardiometabolic risk factors in healthy postmenopausal women
Peppa, Μ., Koliaki, C. C., Boutati, E. I., Garoflos, E., Papaefstathiou, A., Siafakas, N., Katsilambros, N. L., Raptis, S., Hadjidakis, D. I., & Dimitriadis, G. (2013) · Obesity · doi:10.1002/oby.20389 · Cited by 40
Yang, D., Liu, Z., & Yang, H. (2012) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.2301 · Cited by 34
Araújo, J., Severo, M., Barros, H., Mishra, G. D., Guimarães, J. T., & Ramos, E. (2015) · International Journal of Obesity · doi:10.1038/ijo.2015.128 · Cited by 34
Song, B., Kim, H. C., Rhee, Y., Youm, Y., & Kim, C. O. (2015) · Journal of Affective Disorders · doi:10.1016/j.jad.2015.09.043 · Cited by 35
Estimates of hepatic glyceroneogenesis in type 2 diabetes mellitus in humans
Kalhan, S. C., Bugianesi, E., McCullough, A. J., Hanson, R. W., & Kelley, D. E. (2008) · Metabolism · doi:10.1016/j.metabol.2007.10.003 · Cited by 34
Resistência insulínica e sua relação com os componentes da síndrome metabólica
Medeiros, C. C. M., Ramos, A. T., Cardoso, M. A. A., de França, I. S. X., Cardoso, A. D. S., & Gonzaga, N. C. (2011) · Arquivos Brasileiros de Cardiologia · doi:10.1590/s0066-782x2011005000089 · Cited by 34
Ryu, S., Chang, Y., Woo, H., Yoo, S.-H., Choi, N., Lee, W., Kim, I., & Song, J. (2009) · Metabolism · doi:10.1016/j.metabol.2009.08.024 · Cited by 33
Anastasiou, C. A., Kavouras, S. A., Lentzas, Y., Gova, A., Sidossis, L. S., & Melidonis, A. (2009) · Metabolism · doi:10.1016/j.metabol.2009.05.019 · Cited by 34
Eckard, A. R., Judd, S. E., Ziegler, T. R., Camacho-González, A., Fitzpatrick, A. M., Hadley, G. R., Grossmann, R. E., Thomas-Seaton, L., Seydafkan, S., Mulligan, M. J., et al. (2012) · Antiviral Therapy · doi:10.3851/imp2318 · Cited by 34
Atmaca, A. A., Bilgici, B., Ecemiş, G. C., & Tunçel, Ö. K. (2013) · Endocrine · doi:10.1007/s12020-013-9931-0 · Cited by 35
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Petta, S., Marchesini, G., Caracausi, L., Macaluso, F. S., Cammà, C., Ciminnisi, S., Cabibi, D., Porcasi, R., Craxı̀, A., & Marco, V. D. (2013) · Journal of Hepatology · doi:10.1016/j.jhep.2013.07.037 · Cited by 35
Kim, S. M., Cho, G. J., Yannakoulia, M., Hwang, T. G., Kim, I. H., Park, E., & Mantzoros, C. S. (2011) · Metabolism · doi:10.1016/j.metabol.2011.01.013 · Cited by 34
Vlaar, E. M., van Valkengoed, I. G. M., Nierkens, V., Nicolaou, M., Middelkoop, B. J., & Stronks, K. (2012) · BMC Public Health · doi:10.1186/1471-2458-12-371 · Cited by 34
Patterns, levels and correlates of self-reported physical activity in urban black Soweto women
Gradidge, P. J.-L., Crowther, N. J., Chirwa, E. D., Norris, S. A., & Micklesfield, L. K. (2014) · BMC Public Health · doi:10.1186/1471-2458-14-934 · Cited by 37
Relationship between serum osteocalcin levels and non‐alcoholic fatty liver disease in Chinese men
Dou, J., Ma, X., Fang, Q., Hao, Y., Yang, R., Wang, F., Zhu, J., Bao, Y., & Jia, W. (2013) · Clinical and Experimental Pharmacology and Physiology · doi:10.1111/1440-1681.12063 · Cited by 34
Stabile, G., Borrielli, I., Artenisio, A. C., Bruno, L. M., Benvenga, S., Giunta, L., Marca, A. L., Volpe, A., & Pizzo, A. (2014) · Journal of Pediatric and Adolescent Gynecology · doi:10.1016/j.jpag.2013.09.015 · Cited by 34
Mulligan, K. M., Harris, D. R., Monte, D., Stoszek, S., Emmanuel, P. J., Hardin, D. S., Kapogiannis, B. G., Worrell, C. J., Meyer, W. A., Sleasman, J. W., et al. (2009) · Clinical Infectious Diseases · doi:10.1086/648728 · Cited by 34
Yang, S. J., Kim, S., Hwang, S. Y., Kim, T. N., Choi, H. Y., Yoo, H. J., Seo, J. A., Kim, S. G., Kim, N. H., Baik, S. H., et al. (2011) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2011.11.008 · Cited by 34
Michels, N., Henauw, S. D., Breidenassel, C., Censi, L., Cuenca‐García, M., González‐Gross, M., Gottrand, F., Hällström, L., Kafatos, A., Kersting, M., et al. (2014) · European Journal of Nutrition · doi:10.1007/s00394-014-0805-x · Cited by 35
Effect of a Soluble Cocoa Fiber-Enriched Diet in Zucker Fatty Rats
Sánchez‐Infantes, D., Moulay, L., Muguerza, B., Quiñones, M., Miguel, M., & Aleixandre, A. (2010) · Journal of Medicinal Food · doi:10.1089/jmf.2009.0127 · Cited by 34
Jeon, J.-Y., Han, J., Kim, H.-J., Park, M. S., Seo, D. Y., & Kwak, Y. (2013) · Integrative Medicine Research · doi:10.1016/j.imr.2013.10.001 · Cited by 38
Circulating Adipokines in Healthy versus Unhealthy Overweight and Obese Subjects
Alfadda, A. A. (2014) · International Journal of Endocrinology · doi:10.1155/2014/170434 · Cited by 34
Shadyab, A. H., Kritz‐Silverstein, D. C., Laughlin, G. A., Wooten, W. J., Barrett-Connor, E. L., & Araneta, M. R. G. (2014) · Sleep Medicine · doi:10.1016/j.sleep.2014.11.010 · Cited by 35
Corripio, R., González-Clemente, J. M., Pérez, J., Näf, S., Gallart, L., Nosas, R., Vendrell, J. J., & Caixàs, A. (2010) · European Journal of Endocrinology · doi:10.1530/eje-10-0408 · Cited by 34
Tan, B. K., Chen, J., Hu, J., et al. (2014) · Clinical Endocrinology · doi:10.1111/cen.12438 · Cited by 34
Santos-Silva, A. P., Oliveira, E., Pinheiro, C. R., Nunes‐Freitas, A. L., Abreu‐Villaça, Y., Santana, A. C., do Nascimento‐Saba, C. C. A., Nogueira-Neto, J. F., dos Reis, A. M., de Moura, E. G., et al. (2011) · Journal of Endocrinology · doi:10.1530/joe-10-0410 · Cited by 35
Hou, X., Liu, J., Song, J., Wang, C., Liang, K., Sun, Y. S., Ma, Z., Yang, W., Li, C., Zhang, X., et al. (2015) · Journal of Diabetes Research · doi:10.1155/2016/8797316 · Cited by 35
Association of Cortisol and the Metabolic Syndrome in Korean Men and Women
Park, S. B., Blumenthal, J. A., Lee, S. Y., & Georgiades, A. (2011) · Journal of Korean Medical Science · doi:10.3346/jkms.2011.26.7.914 · Cited by 34
Insulin resistance and lichen planus in patients with HCV‐infectious liver diseases
Nagao, Y., Kawasaki, K., & Sata, M. (2007) · Journal of Gastroenterology and Hepatology · doi:10.1111/j.1440-1746.2007.04835.x · Cited by 34
Hong, E. S., Kim, E. K., Kang, S. M., Khang, A. R., Choi, S. H., Park, K. S., Jang, H. C., & Lim, S. K. (2014) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.12536 · Cited by 34
Disconnect between adipose tissue inflammation and cardiometabolic dysfunction in Ossabaw pigs
Vieira‐Potter, V. J., Lee, S., Bayless, D. S., Scroggins, R. J., Welly, R. J., Fleming, N. J., Smith, T. N., Meers, G. M.-E., Hill, M. A., Rector, R. S., et al. (2015) · Obesity · doi:10.1002/oby.21252 · Cited by 34
Increased arterial stiffness in mildly‐hypertensive women with polycystic ovary syndrome
Sasaki, A., Emi, Y., 松田, 美., Sharula, Kamada, Y., Chekir, C., Hiramatsu, Y., & Nakatsuka, M. (2011) · Journal of obstetrics and gynaecology research · doi:10.1111/j.1447-0756.2010.01375.x · Cited by 34
Şahin, İ., Serter, R., Karakurt, F., Demirbas, B. T., Çulha, C., Taşkapan, Ç., Koşar, F., & Aral, Y. (2004) · Gynecological Endocrinology · doi:10.1080/09513590400004736 · Cited by 34
Ghrelin and obestatin levels in hypertensive obese patients
Wang, W., Li, S., Du, F.-M., Zhu, Z., Zhang, J., & Li, Y. (2014) · Journal of International Medical Research · doi:10.1177/0300060514543040 · Cited by 35
Effect of Glucocorticoid-Induced Insulin Resistance on Follicle Development and Ovulation
Hackbart, K. S., da Cunha, P. M., MEYER, R. K., & Wiltbank, M. C. (2013) · Biology of Reproduction · doi:10.1095/biolreprod.113.107862 · Cited by 34
Pang, J., Chan, D. C., Hamilton, S. J., Tenneti, V. S., Watts, G. F., & Barrett, P. H. R. (2013) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.113.302019 · Cited by 34
Fibrosis is associated with adiponectin resistance in chronic hepatitis C virus infection
Corbetta, S., Redaelli, A. E., Pozzi, M., Bovo, G., Ratti, L., Redaelli, E., Pellegrini, C., Beck‐Peccoz, P., & Spada, A. (2011) · European Journal of Clinical Investigation · doi:10.1111/j.1365-2362.2011.02498.x · Cited by 33
Ruan, L., Chen, W., Srinivasan, S. R., Xu, J.-H., Toprak, A. C., & Berenson, G. S. (2010) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2010.05.004 · Cited by 34
Plasma Alkylresorcinols, Biomarkers of Whole-Grain Intake, Are Related to Lower BMI in Older Adults
Ma, J., Ross, A. B., Shea, M. K., Bruce, S. J., Jacques, P. F., Saltzman, E. J., Lichtenstein, A. H., Booth, S. L., & McKeown, N. M. (2012) · Journal of Nutrition · doi:10.3945/jn.112.163253 · Cited by 34
Gillberg, L., Jacobsen, S., Ribel‐Madsen, R., Gjesing, A. P., Boesgaard, T. W., Ling, C., Pedersen, O. B., Hansen, T. F., & Vaag, A. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0058384 · Cited by 34
Adiponectin, Insulin Sensitivity and Diabetic Retinopathy in Latinos With Type 2 Diabetes
Kuo, J. Z.-C., Guo, X., Klein, R., Klein, B. E. K., Genter, P. M., Roll, K., Hai, Y., Goodarzi, M. O., Rotter, J. I., Chen, Y.-D. I., et al. (2015) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2015-1221 · Cited by 34
Stanišić, J., Korićanac, G., Ćulafić, T., Romić, S., Stojiljković, M., Kostić, M., Pantelić, M., & Tepavčević, S. (2015) · Molecular and Cellular Endocrinology · doi:10.1016/j.mce.2015.11.032 · Cited by 36
Yu, D., Xu, Z., Yin, X., Zheng, F., Lin, X., Pan, Q., & Li, H. (2015) · PLoS ONE · doi:10.1371/journal.pone.0142848 · Cited by 35
Ukibe, K., Miyoshi, M., & Kadooka, Y. (2015) · British Journal Of Nutrition · doi:10.1017/s0007114515002627 · Cited by 34
Kim, H. J., Bae, J. C., Park, H. K., Byun, D. W., Suh, K., Yoo, M. H., Kim, J. H., Min, Y., Kim, S. W., & Chung, J. H. (2016) · Endocrinology and Metabolism · doi:10.3803/enm.2016.31.2.311 · Cited by 35
Metabolic syndrome, glucose tolerance categories and the cardiovascular risk in Spanish population
Martínez-Larrad, M. T., Anchuelo, A. C., Fernández, C., Redondo, Y. L., Escobar‐Jiménez, F., & Serrano‐Ríos, M. (2016) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2016.02.003 · Cited by 35
Martínez‐López, E., Curiel-López, F., Hernández-Nazará, A., et al. (2015) · Annals of Hepatology · doi:10.1016/s1665-2681(19)31173-1 · Cited by 34
Mårild, S., Russo, P., Veidebaum, T., Tornaritis, M. J., Henauw, S. D., Bourdeaudhuij, I. D., Molnár, D., Moreno, L. A., Bramsved, R., Peplies, J., et al. (2015) · Obesity Reviews · doi:10.1111/obr.12368 · Cited by 34
Marcelo, K. L., Ribar, T. J., Means, C. R., Tsimelzon, A. I., Stevens, R. D., Ilkayeva, O., Bain, J. R., Hilsenbeck, S. G., Newgard, C. B., Means, A. R., et al. (2016) · Molecular Endocrinology · doi:10.1210/me.2016-1021 · Cited by 34
Božić, J., Galić, T., Šupe‐Domić, D., Ivković, N., Kurir, T. T., Valić, Z., Lesko, J., & Đogaš, Z. (2016) · Endocrine · doi:10.1007/s12020-016-0925-6 · Cited by 36
White, W. B., Cuadra, R. H., Lloyd, E., Bakris, G. L., & Kupfer, S. (2016) · Journal of Hypertension · doi:10.1097/hjh.0000000000000839 · Cited by 37
Goldberg, R. B., Bittner, V., Dunbar, R. L., Fleg, J. L., Grunberger, G., Guyton, J. R., Leiter, L. A., McBride, R., Robinson, J. G., Simmons, D. L., et al. (2016) · The American Journal of Medicine · doi:10.1016/j.amjmed.2016.02.039 · Cited by 34
Huang, S.-S., Yan, Y.-H., Ko, C.-H., et al. (2014) · Journal of Traditional and Complementary Medicine · doi:10.4103/2225-4110.131639 · Cited by 34
Association Between Insulin Resistance and Luminal B Subtype Breast Cancer in Postmenopausal Women
Nam, S., Park, S., Park, H. S., Kim, S., Kim, J. Y., & Kim, S. I. (2016) · Medicine · doi:10.1097/md.0000000000002825 · Cited by 34
Endocrine and metabolic adaptations to pregnancy; impact of obesity
Mouzon, S. H., & Lassance, L. (2015) · Hormone Molecular Biology and Clinical Investigation · doi:10.1515/hmbci-2015-0042 · Cited by 34
Shomaker, L. B., Kelly, N. R., Pickworth, C. K., Cassidy, O. L., Radin, R. M., Shank, L. M., Vannucci, A., Thompson, K. A., Armaiz-Flores, S. A., Brady, S. M., et al. (2016) · Annals of Behavioral Medicine · doi:10.1007/s12160-016-9801-0 · Cited by 36
Bonfigli, A. R., Boemi, M., Festa, R., et al. (2016) · Nutrition Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2016.06.012 · Cited by 34
El-Kader, S. M. A., & Al-Dahr, M. H. S. (2016) · African Health Sciences · doi:10.4314/ahs.v16i3.12 · Cited by 34
Cox, J. M., Chu, H. D., Chelliah, M. V., Debenham, J. S., Eagen, K. A., Lan, P., Lombardo, M., London, C., Plotkin, M. A., Shah, U., et al. (2016) · ACS Medicinal Chemistry Letters · doi:10.1021/acsmedchemlett.6b00360 · Cited by 34
Yoon, J. J., Lee, Y. J., Han, B. H., Choi, E. S., Kho, M. C., Park, J. H., Ahn, Y. M., Kim, H. Y., Kang, D. G., & Lee, H. S. (2016) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2016.11.044 · Cited by 34
Are adipokines associated with gestational diabetes mellitus?
Görkem, Ü., Küçükler, F. K., Toğrul, C., & Gungor, T. (2016) · Journal of the Turkish-German Gynecological Association · doi:10.5152/jtgga.2016.16112 · Cited by 34
Duan, P., Yang, M., Wei, M., Liu, J., & Tu, P. (2017) · International Journal of Endocrinology · doi:10.1155/2017/8724869 · Cited by 34
Xie, P., Yang, L., Ailaiti, T., Wu, J., Yu, J., Yu, T., Wang, H. Y., Huang, B., Wu, Q., Maimaitili, Y., et al. (2017) · Acta Physiologica · doi:10.1111/apha.12874 · Cited by 34
Fanelli, F., Mezzullo, M., Belluomo, I., Lallo, V. D. D., Baccini, M., Gasparini, D. I., Casadio, E., Mastroroberto, M., Vicennati, V., Gambineri, A., et al. (2017) · Molecular Metabolism · doi:10.1016/j.molmet.2017.03.005 · Cited by 34
The prevalence of metabolic syndrome among older adults in Ecuador: Results of the SABE survey
Orces, C. H., & Gavilánez, E. L. (2017) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2017.04.004 · Cited by 35
Cicco, S. D., Immediata, V., Romualdi, D., Policola, C., Tropea, A., Florio, C. D., Tagliaferri, V., Scarinci, E., Casa, S. D., Lanzone, A., et al. (2017) · Gynecological Endocrinology · doi:10.1080/09513590.2017.1313972 · Cited by 37
Jacquinot, Q., Méneveau, N., Chatot, M., et al. (2017) · BMC Cancer · doi:10.1186/s12885-017-3420-4 · Cited by 34
Qarni, A. A. A., Joatar, F. E., Das, N. S., Awad, M., Eltayeb, M., Al-Zubair, A. G., Ali, M. E., Masaud, A. A., Shire, A. M., Gumaa, K. A., et al. (2017) · Endocrinology and Metabolism · doi:10.3803/enm.2017.32.2.230 · Cited by 36
Zhang, Y., Luk, A. O. Y., Chow, E., Ko, G. T. C., Chan, M. H. M., Ng, M. C. Y., Kong, A. P. S., Ma, R. C. W., Ozaki, R., So, W. Y., et al. (2017) · Diabetic Medicine · doi:10.1111/dme.13516 · Cited by 34
Tatsumi, Y., Morimoto, A., Asayama, K., Sonoda, N., Miyamatsu, N., Ohno, Y., Miyamoto, Y., Izawa, S., & Ohkubo, T. (2017) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2017.10.021 · Cited by 35
Draper, C. F., Vassallo, I., Cara, A. D., Milone, C., Comminetti, O., Monnard, I., Godin, J., Scherer, M., Su, M., Jia, W. P., et al. (2017) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.201700703 · Cited by 34
Pregnancy-Associated Hypertension and Offspring Cardiometabolic Health
Rice, M. M., Landon, M. B., Varner, M. W., Casey, B. M., Reddy, U. M., Wapner, R. J., Rouse, D. J., Tita, A., Thorp, J. M., Chien, E. K., et al. (2018) · Obstetrics and Gynecology · doi:10.1097/aog.0000000000002433 · Cited by 34
Giezenaar, C., Luscombe‐Marsh, N. D., Hutchison, A. T., Standfield, S., Feinle‐Bisset, C., Horowitz, M., Chapman, I. M., & Soenen, S. (2018) · Nutrients · doi:10.3390/nu10010078 · Cited by 34
Philouze, C., Obert, P., Nottin, S., Benamor, A., Barthez, O., & Aboukhoudir, F. (2018) · Journal of the American Society of Echocardiography · doi:10.1016/j.echo.2017.12.006 · Cited by 34
Wiik, A., Andersson, D. P., Brismar, T. B., Chanpen, S., Dhejne, C., Ekström, T. J., Flanagan, J. N., Holmberg, M., Kere, J., Lilja, M. R., et al. (2018) · Contemporary Clinical Trials Communications · doi:10.1016/j.conctc.2018.04.005 · Cited by 34
El‐Fawal, R., Fayoumi, H. M. E., & Mahmoud, M. F. (2018) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2018.03.162 · Cited by 34
Prokopowicz, Z., Małecka‐Tendera, E., & Matusik, P. (2018) · Canadian Journal of Gastroenterology and Hepatology · doi:10.1155/2018/9465784 · Cited by 34
Guo, Y. M., Musani, S. K., Sims, M., Pearson, T. A., DeBoer, M. D., & Gurka, M. J. (2018) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-018-0344-3 · Cited by 35
Moon, S., Park, J. H., Jang, E.-J., Park, Y.-K., Yu, J. M., Park, J.-S., Ahn, Y.-H., Choi, S. H., & Yoo, H. J. (2018) · Journal of Korean Medical Science · doi:10.3346/jkms.2018.33.e197 · Cited by 34
Methods for Measuring Risk for Type 2 Diabetes in Youth: the Oral Glucose Tolerance Test (OGTT)
Chen, M. E., Aguirre, R. S., & Hannon, T. S. (2018) · Current Diabetes Reports · doi:10.1007/s11892-018-1023-3 · Cited by 36
Hepatic Steatosis Is Associated With Adverse Molecular Signatures in Subjects Without Diabetes
Pietzner, M., Budde, K., Homuth, G., Kastenmüller, G., Henning, A., Artati, A., Krumsiek, J., Völzke, H., Adamski, J., Lerch, M. M., et al. (2018) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2018-00999 · Cited by 32
Karras, S. Ν., Koufakis, T., Petróczi, A., Folkerts, D., Kypraiou, M., Mulrooney, H. M., Naughton, D. P., Persynaki, A., Zebekakis, P. E., Skoutas, D., et al. (2018) · Nutrition · doi:10.1016/j.nut.2018.07.003 · Cited by 34
Król, E., Bogdański, P., Suliburska, J., et al. (2018) · Biological Trace Element Research · doi:10.1007/s12011-018-1470-3 · Cited by 34
Mirmajidi, S., Izadi, A., Saghafi‐Asl, M., Vahid, F., Karamzad, N., Amiri, P., Shivappa, N., & Hébert, J. R. (2018) · Journal of the American College of Nutrition · doi:10.1080/07315724.2018.1504348 · Cited by 34
Metadata
- Authors
- Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C.
- Affiliations
- University of Oxford (GB)
- ORCID
- Matthews, D. R.
- Venue
- Diabetologia
- Published
- 1985-07
- Volume
- 28
- Issue
- 7
- Pages
- 412-419
- ISSN
- 0012-186X (ISSN-L)
- 1432-0428
- OpenAlex
- W2160234571
- Cited by
- 31,502
- References
- 21
- Citations by year
- 2026: 933 · 2025: 1060 · 2024: 1240 · 2023: 1291 · 2022: 1489 · 2021: 1528 · 2020: 1424 · 2019: 1427 · 2018: 1456 · 2017: 1440 · 2016: 1481 · 2015: 1682 · 2014: 1693 · 2013: 1621 · 2012: 1518
- FWCI
- 7.91
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Diabetologia (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Medical Research Council
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- HOMA, insulin resistance, beta-cell function, fasting blood glucose, fasting insulin, hyperglycaemic clamp, euglycaemic clamp, intravenous glucose tolerance test, insulin receptor binding, type 2 diabetes
- MeSH terms
- Adult, Blood Glucose, Computers, Diabetes Mellitus, Type 2, Erythrocytes, Fasting, Homeostasis, Humans, Insulin, Insulin Resistance, Islets of Langerhans, Middle Aged, Monocytes, Receptor, Insulin
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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