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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
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
Cited by
Sorted by citations
Items 16,401–16,500 of 31,469
Conjugated linoleic acids (CLAs) and health
Yaquoob, P., Tricon, S., Burdge, G. C., & Calder, P. C. (2006) · Elsevier eBooks · doi:10.1533/9781845691073.1.182 · Cited by 18
Lack of association between central adiposity and lipaemia in UK Sikh men
Lovegrove, J. A., Brady, L. M., Lesauvage, S. V., Lovegrove, S. S., Minihane, A. M., & Williams, C. M. (2003) · International Journal of Obesity · doi:10.1038/sj.ijo.0802384 · Cited by 18
Pender, C., Goldfine, I. D., Kulp, J. L., Tanner, C. J., Maddux, B. A., MacDonald, K. G., Houmard, J. A., & YOUNGREN, J. F. (2005) · Metabolism · doi:10.1016/j.metabol.2004.11.018 · Cited by 18
Karelis, A. D., Henry, J., St‐Pierre, D. H., Prud’homme, D., & RABASA-LHORET, R. P. R. (2005) · Diabetes Obesity and Metabolism · doi:10.1111/j.1463-1326.2005.00503.x · Cited by 18
Prediction of the Effect of Metformin Treatment in Patients with Polycystic Ovary Syndrome
Vrbíková, J., Hill, M., Stárka, L., & Vondra, K. (2002) · Gynecologic and Obstetric Investigation · doi:10.1159/000053002 · Cited by 18
Fetal origins of cardiovascular risk: evidence from studies in children
Whincup, P. H. (1998) · Proceedings of The Nutrition Society · doi:10.1079/pns19980018 · Cited by 18
Is high-sensitivity C-reactive protein associated with carotid atherosclerosis in healthy Koreans?
Choi, S. H., Kim, H. C., Ahn, C. W., Cho, H. K., Soo, B., Chung, Y., Lee, K. W., Lee, H. C., Huh, K. B., & Kim, D. J. (2005) · European Journal of Cardiovascular Prevention & Rehabilitation · doi:10.1097/00149831-200512000-00006 · Cited by 18
Hyperhomocysteinemia in Type 2 Diabetes
Buysschaert, M., Dramais, A.-S., Wallemacq, P. E., et al. (2000) · Cited by 18
Index of glucose effectiveness derived from oral glucose tolerance test
Nagasaka, S., Kusaka, I., Yamashita, K., Funase, Y., Yamauchi, K., Katakura, M., Ishibashi, S., & Aizawa, T. (2012) · Acta Diabetologica · doi:10.1007/s00592-012-0417-y · Cited by 18
Stress, serotonergic function, and mood in users of oral contraceptives
Tuiten, A., Panhuysen, G. E. M., Koppeschaar, H. P. F., et al. (1995) · Psychoneuroendocrinology · doi:10.1016/0306-4530(94)00063-g · Cited by 18
Thermogenic genes are blunted whereas brown adipose tissue identity is preserved in human obesityPreprint
Jespersen, N. Z., Andersen, M. W., Hirschberg, V., Stærkær, T. W., Severinsen, M. C. K., Peijs, L., Soares, R., Forss, I., Andersen, E. S., Hahn, C. H., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.07.082057 · Cited by 18
Dilimulati, D., Shao, X., Wang, L., Cai, M., Zhang, Y., Lu, J., Wang, Y., Liu, H., Kuang, M., Chen, H., et al. (2024) · Journal of Medical Internet Research · doi:10.2196/55883 · Cited by 17
“(Fasting blood glucose [mmol/L]×fasting insulin [mU/L])/22.5 was used to calculate the HOMA-IR levels [19].”
InfluentialOh, T. J., Kim, Y. G., Kang, S., Moon, J. H., Kwak, S. H., Choi, S. H., Lim, S., Park, K. S., Jang, H. C., Hong, J.-S., et al. (2019) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2018.0086 · Cited by 17
Ramos, F. M. M., Ribeiro, C. B., Cesar, T. B., Milenkovic, D., Cabral, L., Noronha, M. F., & Sivieri, K. (2023) · Food Science & Nutrition · doi:10.1002/fsn3.3654 · Cited by 17
Glucose Transporter Expression in Skeletal Muscle of Endurance-Trained Individuals
SEKI, Y., BERGGREN, J. R., HOUMARD, J. A., & CHARRON, M. J. (2006) · Medicine & Science in Sports & Exercise · doi:10.1249/01.mss.0000222837.74015.f1 · Cited by 18
Lemay, A., Turcot, L., Déchêne, F., Dodin, S., & Forest, J.-C. (2010) · Menopause · doi:10.1097/gme.0b013e3181b7c521 · Cited by 17
Haverinen, A., Kangasniemi, M., Luiro, K., Piltonen, T., Heikinheimo, O., & Tapanainen, J. S. (2021) · Contraception · doi:10.1016/j.contraception.2020.10.014 · Cited by 17
Vázquez-Bourgon, J., Ortiz-García de la Foz, V., Gómez-Revuelta, M., Mayoral-van Son, J., Juncal-Ruiz, M., Garrido-Torres, N., & Crespo-Facorro, B. (2022) · International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyac033 · Cited by 18
Peréz‐Beltrán, Y. E., González‐Becerra, K., Rivera‐Íñiguez, I., Martínez‐López, E., Ramos-López, O., Alcaraz-Mejia, M. I., Rodriguez‐Echevarría, R., Sáyago‐Ayerdi, S. G., & Mendivil, E. J. (2023) · Nutrients · doi:10.3390/nu15204324 · Cited by 18
Krzymień, J., & Ładyżyński, P. (2023) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2023.12.004 · Cited by 20
Hafez, M., Elarab, S. M. E., Tohamy, H. G., & El‐Far, A. H. (2024) · Scientific Reports · doi:10.1038/s41598-024-62780-y · Cited by 23
Madsen, A. L., Bonàs‐Guarch, S., Gheibi, S., Prasad, R. B., Vangipurapu, J., Ahuja, V., Cataldo, L. R., Dwivedi, O. P., Hatem, G., Atla, G., et al. (2024) · Nature Metabolism · doi:10.1038/s42255-024-01140-6 · Cited by 20
Tian, J., Cao, Y.-T., Zhang, W., Wang, A., Yang, X., Dong, Y., & Zhou, X. (2024) · BMC Endocrine Disorders · doi:10.1186/s12902-024-01794-z · Cited by 18
El‐Sehrawy, A. A. M. A., Khachatryan, L., Kubaev, A., Rekha, M. M., Rab, S. O., Kaur, M., Singh, M., Srivastava, M., KADHIM, A. J., & Zwamel, A. H. (2025) · BMC Endocrine Disorders · doi:10.1186/s12902-025-01887-3 · Cited by 18
One-Hour Plasma Glucose Predicts the Progression From Normal Glucose Tolerance to Prediabetes
Abdul‐Ghani, M. A., Abu‐Farha, M., Abdul‐Ghani, T., Chavez-Velazquez, A., Merovci, A., DeFronzo, R. A., Al-Ajmi, F. A., Stern, M. P., & Al‐Mulla, F. (2025) · Diabetes Care · doi:10.2337/dc24-2832 · Cited by 18
Azarboo, A., Fallahtafti, P., Jalali, S., Shirinezhad, A., Assempoor, R., & Raeini, A. G. (2025) · BMC Endocrine Disorders · doi:10.1186/s12902-025-01957-6 · Cited by 19
Machine learning-based stratification of prediabetes and type 2 diabetes progression
Matboli, M., Khaled, A., Ahmed, M. F., Ahmed, M. Y., Khaled, R., Elmakromy, G. M., Ghani, A. M. A., ElShafei, M. M., Abdelhalim, M. R. M., & ElGwad, A. M. A. (2025) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-025-01786-6 · Cited by 18
Osinga, J. A. J., Derakhshan, A., Karachaliou, M., Poppe, K. G., Warringa, L., Verdonk, K., Vaidya, B., Männistö, T. I., López‐Bermejo, A., Bassols, J., et al. (2025) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(25)00068-3 · Cited by 20
Szkodziak, P. R., Szkodziak, F., Trzeciak, K., Woźniak, S., Mlynarczyk, M., & Paszkowski, T. (2025) · Scientific Reports · doi:10.1038/s41598-025-26718-2 · Cited by 25
Ashour, E., Samy, N., Sayed, M., & Imam, A. (2010) · PubMed · Cited by 16
Association between liver insulin resistance and cardiovascular risk factors
Vangipurapu, J., Stančáková, A., Kuulasmaa, T., Soininen, P., Kangas, A. J., Ala‐Korpela, M., Kuusisto, J., & Laakso, M. (2012) · Journal of Internal Medicine · doi:10.1111/j.1365-2796.2012.02540.x · Cited by 17
Ösby, U., Olsson, E., Edman, G., Hilding, A., Eriksson, S. V., & Östenson, C. G. (2013) · Nordic Journal of Psychiatry · doi:10.3109/08039488.2013.797022 · Cited by 17
Sloboda, N., Fève, B., Thornton, S. N., Nzietchueng, R., Regnault, V., Simon, G., Labat, C., Louis, H., Max, J., Muscat, A., et al. (2012) · The Journals of Gerontology Series A · doi:10.1093/gerona/glr236 · Cited by 17
Xiao, X., Liu, Y., Sun, C., Gang, X., Cheng, J., Tian, S., Gao, Y., Lv, Y., Sun, Z., Li, Y., et al. (2014) · Journal of Diabetes · doi:10.1111/1753-0407.12201 · Cited by 17
The association between serum uric acid and metabolic syndrome among adolescents in northeast China.
Zn, W., Li, P., Rh, J., et al. (2015) · PubMed · Cited by 17
Li, F.-F., Liu, B.-L., Zhu, H., Li, T., Zhang, W., Su, X., Wu, J.-D., Wang, X., Xu, N., Yu, W., et al. (2017) · Journal of Diabetes Research · doi:10.1155/2017/2740372 · Cited by 17
Owei, I., Jain, N., Jones, D., Umekwe, N., & Dagogo-Jack, S. (2018) · Journal of Clinical Endocrinology and Metabolism · doi:10.1210/jc.2018-01569 · Cited by 18
L’hadj, I., Azzi, R., LAHFA, F. B., Koceir, E.-A., & Omari, N. (2018) · Journal of Food Biochemistry · doi:10.1111/jfbc.12725 · Cited by 17
Malipatil, N. S., Fachim, H., Siddals, K. W., Geary, B., Wark, G., Porter, N., Anderson, S. G., Donn, R. P., Harvie, M., Whetton, A. D., et al. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8020141 · Cited by 17
Placental 11β-HSD2 and Cardiometabolic Health Indicators in Infancy
Chen, L., Guilmette, J. M., Luo, Z., Cloutier, A., Wang, W., Yang, M., Fraser, W. D., Dubois, J., & Nuyt, A. M. (2019) · Diabetes Care · doi:10.2337/dc18-2041 · Cited by 17
Choi, M., Lee, Y., Heo, Y., Hyun, T. H., Lyu, E.-S., Park, H., Ro, H.-K., & Bae, Y. (2019) · Ecology of Food and Nutrition · doi:10.1080/03670244.2019.1644327 · Cited by 17
Doğan, K., Helvacıoğlu, Ç., Baghaki, H. S., & Ekin, M. (2020) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2020.01.008 · Cited by 17
Schinzari, F., Tesauro, M., Campia, U., & Cardillo, C. (2020) · Vascular Pharmacology · doi:10.1016/j.vph.2020.106676 · Cited by 17
Gómez, J. J. L., Izaola-Jáuregui, O., Primo, D., Torres-Torres, B., Hoyos, E. G., Ortolá-Buigues, A., Martín-Ferrero, M. Á., & de Luis, D. A. (2020) · Nutrients · doi:10.3390/nu12040976 · Cited by 17
Wang, X., Wang, X., Shi, J., et al. (2020) · BMC Cardiovascular Disorders · doi:10.1186/s12872-020-01456-7 · Cited by 17
Mittag‐Roussou, V., Wagenpfeil, S., Lammert, F., & Stokes, C. S. (2020) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.131 · Cited by 17
Vine, D. F., Beilin, L. J., Burrows, S., Huang, R.-C., Hickey, M., Hart, R., Proctor, S. D., & Mori, T. A. (2020) · Journal of the Endocrine Society · doi:10.1210/jendso/bvaa061 · Cited by 17
Schellong, K., Melchior, K., Ziska, T., Rancourt, R. C., Henrich, W., & Plagemann, A. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17124229 · Cited by 20
Isocaloric Ketogenic Diet in Adults with High-Grade Gliomas: A Prospective Metabolic Study
Foppiani, A., Amicis, R. S. D., Lessa, C., Leone, A., Ravella, S., Ciusani, E., Silvani, A., Zuccoli, G., Battezzati, A., Lamperti, E., et al. (2020) · Nutrition and Cancer · doi:10.1080/01635581.2020.1779759 · Cited by 16
Sleep Duration, Lipid Profile and Insulin Resistance: Potential Role of Lipoprotein(a)
Коростовцева, Л. С., Alieva, A. S., Rotar, О. P., Викторович, Б. М., Boyarinova, M. A., Sviryaev, Y. V., Конради, А. О., & Shlyakhto, E. V. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21134680 · Cited by 17
Shin, Y., Moon, J. H., Chin, H. J., Ferrannini, E., & Lim, S. K. (2020) · Endocrinology and Metabolism · doi:10.3803/enm.2020.35.2.329 · Cited by 17
Larsen, A. H., Wiggers, H., Dollerup, O. L., Jespersen, N. R., Hansson, N. H., Frøkiær, J., Brøsen, K., Nørrelund, H., Bøtker, H. E., Møller, N., et al. (2020) · Cardiovascular Drugs and Therapy · doi:10.1007/s10557-020-07050-5 · Cited by 17
Moncayo, S., Insenser, M. R., Martínez-García, M. Á., et al. (2020) · Clinical Nutrition · doi:10.1016/j.clnu.2020.08.006 · Cited by 17
Pichavaram, P., Shawky, N. M., Hartney, T. J., Jun, J. Y., & Segar, L. (2020) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2020.173666 · Cited by 17
Suvan, J. E., Masi, S., Harrington, Z., Santini, E., Raggi, F., D’Aiuto, F., & Solini, A. (2020) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgaa757 · Cited by 17
Mašek, T., Barišić, J., Micek, V., et al. (2020) · Nutrients · doi:10.3390/nu12113339 · Cited by 17
Kocatürk, E., Kar, E., Kiraz, Z. K., & Alataş, Ö. (2020) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2020.11.008 · Cited by 17
Metabolism of testosterone during weight loss in men with obesity
Van de Velde, F., Deventer, K., Van Gansbeke, W., Van Eenoo, P., Van Renterghem, P., Fiers, T., Reyns, T., Kaufman, J. M., Van Nieuwenhove, Y., & Lapauw, B. (2021) · The Journal of Steroid Biochemistry and Molecular Biology · doi:10.1016/j.jsbmb.2021.105851 · Cited by 17
Taharboucht, S., Guermaz, R., Brouri, M., & Chibane, A. (2021) · JMV-Journal de Médecine Vasculaire · doi:10.1016/j.jdmv.2021.03.008 · Cited by 17
Short-Term Physical Inactivity Induces Endothelial Dysfunction
Davies, K. A. B., Norman, J. A., Thompson, A., Mitchell, K. L., Harrold, J. A., Halford, J. C. G., Wilding, J. P. H., Kemp, G. J., Cuthbertson, D. J., & Sprung, V. S. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.659834 · Cited by 21
Wang, Y., Zou, X., Cai, X., Liu, W., Chen, L., Zhang, R., Zhou, L., Gong, S., Gao, Y., Li, S., et al. (2021) · Journal of Diabetes · doi:10.1111/1753-0407.13203 · Cited by 17
Wade, A. N., Crowther, N. J., Abrahams‐Gessel, S., Berkman, L. F., George, J. A., Gómez-Olivé, F. X., Manne‐Goehler, J., Salomon, J. A., Wagner, R. G., Gaziano, T. A., et al. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-046060 · Cited by 17
A New Analysis Tool for Continuous Glucose Monitor Data
OLAWSKY, E. A., Zhang, Y., Eberly, L. E., Helgeson, E. S., & Chow, L. S. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211028909 · Cited by 19
Gregori, G., Celli, A., Barnouin, Y., Paudyal, A., Armamento‐Villareal, R., Napoli, N., Qualls, C., & Villareal, D. T. (2021) · American Journal of Clinical Nutrition · doi:10.1093/ajcn/nqab253 · Cited by 19
Silva, R. S., Fontoura, M., Guimarães, J. T., Barros, H., & Santos, A. C. (2021) · Pediatric Research · doi:10.1038/s41390-021-01706-0 · Cited by 17
Oh, D. J., Jung, J.-J., Shin, S. A., Kim, H., Park, S., Sohn, B. K., Koo, B. K., Moon, M. K., Kim, Y., & Lee, J. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.766216 · Cited by 18
Fernández‐Aparicio, Á., Perona, J. S., Schmidt‐RioValle, J., Padez, C. M. P., & González‐Jiménez, E. (2021) · Biological Research For Nursing · doi:10.1177/10998004211050887 · Cited by 17
Papandreou, C., Harrold, J. A., Hansen, T. T., Halford, J. C. G., Sjödin, A. M., & Bulló, M. (2021) · Nutrients · doi:10.3390/nu13124289 · Cited by 16
Soll, D., Beer, F., Spranger, L., Li, L., Spranger, J., & Mai, K. (2021) · Obesity Facts · doi:10.1159/000520419 · Cited by 17
Li, N., Lu, C., Ma, Y., Wang, X., Ling, Y., Yin, Y., Li, S., Huang, J., Yu, L., Dong, W., et al. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2022.109193 · Cited by 18
Arefhosseini, S., Tutunchi, H., Nomi-Golzar, S., Mahboob, S., Pouretedal, Z., & Ebrahimi‐Mameghani, M. (2022) · Food & Function · doi:10.1039/d1fo03685h · Cited by 19
Wan, F., Ma, F., Wu, J., Qiao, X., Chen, M., Li, W., & Ma, L. (2022) · Analytical Cellular Pathology · doi:10.1155/2022/7847135 · Cited by 18
Effect of Parathyroidectomy on Metabolic Homeostasis in Primary Hyperparathyroidism
Frey, S., Bourgade, R., May, C. L., Croyal, M., Bigot‐Corbel, E., Renaud-Moreau, N., Wargny, M., Caillard, C., Mirallié, É., Cariou, B., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11051373 · Cited by 18
Ghezelbash, B., Shahrokhi, N., Khaksari, M., Asadikaram, G., Shahrokhi, M., & Shirazpour, S. (2022) · Chinese Journal of Integrative Medicine · doi:10.1007/s11655-022-3307-3 · Cited by 17
He, S., Wang, J., Shen, X., Qian, X., An, Y., Gong, Q., Zhang, B., Chen, B., Zhang, L., Chen, X., et al. (2022) · British Journal of Cancer · doi:10.1038/s41416-022-01758-x · Cited by 17
Effects of androgens on glucose metabolism
Umapathysivam, M. M., Grossmann, M., & Wittert, G. (2022) · Best Practice & Research Clinical Endocrinology & Metabolism · doi:10.1016/j.beem.2022.101654 · Cited by 18
Sigala, D. M., Hieronimus, B., Medici, V., Lee, V., Nuñez, M. V., Bremer, A. A., Cox, C. L., Price, C. A., Benyam, Y., Abdelhafez, Y. G., et al. (2022) · Nutrients · doi:10.3390/nu14081648 · Cited by 17
Zhuang, Y.-P., Zhang, Y.-T., Zhang, R.-X., Zhong, H., & He, X. (2022) · International Journal of Clinical Practice · doi:10.1155/2022/4797453 · Cited by 18
Quaresma, L. S., Siais, L. D. O., Grangeiro, É. D., & Rosado, E. L. (2022) · Journal of the American Nutrition Association · doi:10.1080/07315724.2022.2056773 · Cited by 17
Gyllenhammer, L. E., Picard, M. A., McGill, M. A., Boyle, K. E., Vawter, M. P., Rasmussen, J. M., Buß, C., Entringer, S., & Wadhwa, P. D. (2022) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2022.105868 · Cited by 19
Zhai, G., Wang, J., Liu, Y., & Zhou, Y. (2022) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2308263 · Cited by 17
Sun, Z. R., Deng, Z., Wei, X., Wang, N., Yang, J., Li, W., Wu, M., Liu, Y., & He, G. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.903689 · Cited by 17
Williams, R. A., Dring, K. J., Morris, J. G., Sunderland, C. D., Nevill, M. E., & Cooper, S. B. (2022) · Frontiers in Sports and Active Living · doi:10.3389/fspor.2022.884051 · Cited by 19
Diniz, A., Alves, M. G., Candeias, E., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23168912 · Cited by 17
Wang, B., Zhang, W., Chen, C., et al. (2022) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2022.113953 · Cited by 17
Heianza, Y., Wang, X., Rood, J. C., Clish, C. B., Bray, G. A., Sacks, F. M., & Qi, L. (2022) · Metabolism · doi:10.1016/j.metabol.2022.155312 · Cited by 17
Effect of Olive Pomace Oil on Cardiovascular Health and Associated Pathologies
González-Rámila, S., Sarriá, B., Seguido, M. Á., García-Cordero, J., Bravo, L., & Mateos, R. (2022) · Nutrients · doi:10.3390/nu14193927 · Cited by 20
Berezin, A. A., Lichtenauer, M., Boxhammer, E., Stöhr, E. J., & Berezin, A. Е. (2022) · Cells · doi:10.3390/cells11182794 · Cited by 17
Genovesi, S., Tassistro, E., Giussani, M., Lieti, G., Patti, I., Orlando, A., Montemerlo, M., Antolini, L., & Parati, G. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.1006588 · Cited by 17
Ibrahim, S., El‐Tahlawi, S., Mogawer, R. M., et al. (2022) · Journal of Cosmetic Dermatology · doi:10.1111/jocd.15446 · Cited by 17
Ikenoue, S., Waffarn, F., Sumiyoshi, K., Ohashi, M., Ikenoue, C., Tanaka, M., Gillen, D. L., Buß, C., Entringer, S., & Wadhwa, P. D. (2022) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2022.10.015 · Cited by 17
Marcinkiewicz, K., Horodnicka‐Józwa, A., Jackowski, T., Strączek, K., Biczysko‐Mokosa, A., Walczak, M., & Petriczko, E. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.992264 · Cited by 17
Chen, N., Hu, L., Sun, Y., Dong, J., Chu, X., Lu, Y., Liu, Y., Ma, L.-L., & Yan, Y. (2022) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2022.12.005 · Cited by 17
Trim, W. V., Walhin, J., Koumanov, F., Turner, J. E., Shur, N. F., Simpson, E. J., Macdonald, I. A., Greenhaff, P. L., & Thompson, D. (2023) · Clinical Nutrition · doi:10.1016/j.clnu.2023.02.006 · Cited by 17
Past and present: a bibliometric study on polycystic ovary syndrome
Cai, M., Ni, Z., Yuan, Z., Yu, J. M., Zhang, D., Yao, R., Zhou, L., & Yu, C. (2023) · Journal of Ovarian Research · doi:10.1186/s13048-022-01072-3 · Cited by 20
Ayoub‐Charette, S., McGlynn, N. D., Lee, D., Khan, T. A., Mejía, S. B., Chiavaroli, L., Kavanagh, M. E., Seider, M., Taïbi, A., Chen, C. T., et al. (2023) · Nutrients · doi:10.3390/nu15051238 · Cited by 17
Pirchio, R., Auriemma, R. S., Montini, M., et al. (2023) · Journal of Endocrinological Investigation · doi:10.1007/s40618-022-01980-7 · Cited by 17
Ma, M., Jiang, T., Zhen, W., et al. (2023) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s397528 · Cited by 17
Elshinshawy, S., Haddad, H. E. E., Alem, S. A., Shaker, O. G., Salam, R., Yosry, A., & Elebrashy, I. (2023) · Journal of Clinical and Experimental Hepatology · doi:10.1016/j.jceh.2023.03.004 · Cited by 17
Majeed, M. A., Nagabhushanam, K., Devarajan, T. V., Saklecha, S., Reddy, S. V. K., & Mundkur, L. (2023) · Food & Function · doi:10.1039/d2fo03627d · Cited by 18
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review