The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
The Product of Fasting Glucose and Triglycerides As Surrogate for Identifying Insulin Resistance in Apparently Healthy Subjects
Abstract
BACKGROUND: Because the insulin test is expensive and is not available in most laboratories in the cities of undeveloped countries, we tested whether the product of fasting triglycerides and glucose levels (TyG) is a surrogate for estimating insulin resistance compared with the homeostasis model assessment of insulin resistance (HOMA-IR) index. METHODS: We performed a population-based cross-sectional study. Sampling strategy was based on a randomized two-stage cluster sampling procedure. Only apparently healthy subjects, men and nonpregnant women aged 18-65 years, with newly diagnosed impaired fasting glucose (IFG), impaired glucose tolerance (IGT), or IFG + IGT were enrolled. Renal disease, malignancy, and diabetes were exclusion criteria. Sensitivity, specificity, predictive values, and the probability of disease given a positive test were calculated. The optimal TyG index for estimating insulin resistance was established using a receiver operating characteristic scatter plot analysis. RESULTS: A total of 748 apparently healthy subjects aged 41.4 +/- 11.2 years were enrolled. Insulin resistance was identified in 241 (32.2%) subjects (HOMA-IR index 4.4 +/- 1.6). New diagnoses of IFG, IGT, and IFG + IGT were established in 145 (19.4%), 54 (7.2%), and 75 (10.0%) individuals. respectively. The best TyG index for diagnosis of insulin resistance was Ln 4.65, which showed the highest sensitivity (84.0%) and specificity (45.0%) values. The positive and negative predictive values were 81.1% and 84.8%, and the probability of disease, given a positive test, was 60.5%. CONCLUSIONS: The TyG index could be useful as surrogate to identify insulin resistance in apparently healthy subjects.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Matthews, D. R., Hosker, J. P., Rudenski, A. S., Naylor, B. A., Treacher, D. F., & Turner, R. C. (1985) · Diabetologia · doi:10.1007/bf00280883 · Cited by 31,502
(1997) · Archives of Internal Medicine · doi:10.1001/archinte.1997.00440420033005 · Cited by 3,777
Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease.
Després, J. P., Moorjani, S., Lupien, P. J., Tremblay, A., Nadeau, A., & Bouchard, C. (1990) · Arteriosclerosis: An Official Journal of the American Heart Association, Inc. · doi:10.1161/01.atv.10.4.497 · Cited by 1,248
Skeletal muscle triglyceride levels are inversely related to insulin action
Pan, D. A., Lillioja, S., Kriketos, A. D., Milner, M. R., Baur, L. A., Bogardus, C., Jenkins, A. B., & Storlien, L. H. (1997) · Diabetes · doi:10.2337/diabetes.46.6.983 · Cited by 1,179
Skeletal Muscle Triglyceride Levels Are Inversely Related to Insulin Action
Pan, D. A., Lillioja, S., Kriketos, A. D., Milner, M. R., Baur, L. A., Bogardus, C., Jenkins, A. B., & Storlien, L. H. (1997) · Diabetes · doi:10.2337/diab.46.6.983 · Cited by 1,179
Lemieux, I., Pascot, A., Couillard, C., Lamarche, B., Tchernof, A., Alméras, N., Bergeron, J., Gaudet, D., Tremblay, G., Prud’homme, D., et al. (2000) · Circulation · doi:10.1161/01.cir.102.2.179 · Cited by 937 · cited 1×
“26,27 Among these, the hypertriglyceridemic waist and the triglycerides/HDL-C index show a lower correlation with the HOMA-IR index than the correlation of the TyG index, suggesting that the relation of triglycerides to glycemia could be a surrogate for identification of insulin resistance, without…”
Boden, G., Lebed, B., Schatz, M., Homko, C., & Lemieux, S. (2001) · Diabetes · doi:10.2337/diabetes.50.7.1612 · Cited by 597
Carr, M. C., & Brunzell, J. D. (2004) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2004-0432 · Cited by 493
Ryysy, L., Häkkinen, A. M., Goto, T., Vehkavaara, S., Westerbacka, J., Halavaara, J., & Yki-Järvinen, H. (2000) · Diabetes · doi:10.2337/diabetes.49.5.749 · Cited by 431
Phillips, D. I. W., Caddy, S., Ilic, V., Fielding, B. A., Frayn, K. N., Borthwick, A. C., & Taylor, R. (1996) · Metabolism · doi:10.1016/s0026-0495(96)90260-7 · Cited by 390 · cited 1×
“6 10–13 These findings support the important role that triglycerides play in insulin resistance.”
Cited by
The Gut Microbiota in Prediabetes and Diabetes: A Population-Based Cross-Sectional Study
Wu, H., Tremaroli, V., Schmidt, C., Lundqvist, A., Olsson, L. M., Krämer, M., Gummesson, A., Perkins, R., Bergström, G., & Bäckhed, F. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.06.011 · Cited by 528
Liu, X., Tan, Z., Huang, Y., Zhao, H., Liu, M., Yu, P., Ma, J., Zhao, Y., Zhu, W., & Wang, J. (2022) · Cardiovascular Diabetology · doi:10.1186/s12933-022-01546-0 · Cited by 409
Son, D.-H., Lee, H. S., Lee, Y.-J., Lee, J.-H., & Han, J.-H. (2022) · Nutrition, Metabolism and Cardiovascular Diseases · doi:10.1016/j.numecd.2021.11.017 · Cited by 394
Zhang, S., Du, T., Zhang, J., Lu, H., Lin, X., Xie, J., Yang, Y., & Yu, X. (2017) · Lipids in Health and Disease · doi:10.1186/s12944-017-0409-6 · Cited by 284
Duan, M., Zhao, X., Li, S., Miao, G., Bai, L., Zhang, Q., Yang, W., & Zhao, X. (2024) · Cardiovascular Diabetology · doi:10.1186/s12933-024-02334-8 · Cited by 236
Metadata
- Authors
- Simental-Mendía, L. E., Rodríguez-Morán, M., & Guerrero-Romero, F.
- Venue
- Metabolic Syndrome and Related Disorders
- Publisher
- SAGE Publications
- Published
- 2008-12
- Volume
- 6
- Issue
- 4
- Pages
- 299-304
- ISSN
- 1540-4196 (ISSN-L)
- 1557-8518
- OpenAlex
- W1967999753
- Cited by
- 2,445
- References
- 11
- FWCI
- 1.09
- Type
- journal article
- Language
- en
- Open access
- closed
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.
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: The Product of Fasting Glucose and Triglycerides As Surrogate for Identifying Insulin Resistance in Apparently Healthy Subjects. https://app.cochat.ai/papers/10.1089/met.2008.0034
- The paper
- Simental-Mendía, L. E., Rodríguez-Morán, M., & Guerrero-Romero, F. (2008). The Product of Fasting Glucose and Triglycerides As Surrogate for Identifying Insulin Resistance in Apparently Healthy Subjects. Metabolic Syndrome and Related Disorders. https://doi.org/10.1089/met.2008.0034
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
