- Title
- InsuTAG: a novel physiologically relevant predictor for insulin resistance and metabolic syndrome
- Creator
- Thota, Rohith N.; Abbott, Kylie A.; Ferguson, Jessica J. A.; Veysey, Martin; Lucock, Mark; Niblett, Suzanne; King, Katrina; Garg, Manohar L.
- Relation
- Scientific Reports Vol. 7, Issue 1, no. 15204
- Publisher Link
- http://dx.doi.org/10.1038/s41598-017-15460-z
- Publisher
- Nature Publishing Group
- Resource Type
- journal article
- Date
- 2017
- Description
- The aim of this study was to investigate whether a novel physiologically relevant marker, InsuTAG (fasting insulin × fasting triglycerides) can predict insulin resistance (IR) and metabolic syndrome (MetS). Data of 618 participants from the Retirement Health and Lifestyle Study (RHLS) were evaluated for the current study. IR was defined by homeostatic model assessment (HOMA-IR) scores. Pearson correlations were used to examine the associations of InsuTAG with HOMA-IR and other markers. Predictions of IR from InsuTAG were evaluated using multiple regression models. Receiver operating characteristic curves (ROC) were constructed to measure the sensitivity and specificity of InsuTAG values and to determine the optimum cut-off point for prediction of IR. InsuTAG was positively correlated with HOMA-IR (r = 0.86; p < 0.0001). InsuTAG is a strong predictor of IR accounting for 65.0% of the variation in HOMA-IR values after adjusting for potential confounders. Areas under the ROC curve showed that InsuTAG (0.93) has higher value than other known lipid markers for predicting IR, with a sensitivity and specificity of 84.15% and 86.88%. Prevalence of MetS was significantly (p < 0.0001) higher in subjects with InsuTAG values greater than optimal cut-off value of 11.2. Thus, InsuTAG appears to be a potential feasible marker of IR and metabolic syndrome.
- Subject
- metabolic syndrome; predictive markers; InsuTAG; fasting insulin
- Identifier
- http://hdl.handle.net/1959.13/1351797
- Identifier
- uon:30774
- Identifier
- ISSN:2045-2322
- Rights
- This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
- Language
- eng
- Full Text
- Reviewed
- Hits: 3627
- Visitors: 3430
- Downloads: 339
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | ATTACHMENT02 | Publisher version (open access) | 1 MB | Adobe Acrobat PDF | View Details Download |