Triglyceride-glucose index and obstructive sleep apnea: a systematic review and meta-analysis

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Abstract

Background

Obstructive sleep apnea (OSA) has a bidirectional association with metabolic syndrome, and insulin resistance (IR). The triglyceride-glucose (TyG) index could be a simply calculated marker of IR in OSA. However, its clinical application appears still limited. Hence, this systematic review and meta-analysis aimed to respond to this question by analyzing all the existing studies showing an association between OSA and the TyG index.

Methods

Four online databases, including PubMed, Scopus, the Web of Science, and Embase were searched for studies evaluating the TyG index in OSA. After screening and data extraction, a random-effect meta-analysis was performed to compare the TyG index in OSA patients vs. healthy controls by calculating standardized mean difference (SMD) and 95% confidence interval (CI) and pooling the area under the curves (AUCs) for diagnosis of OSA based on this index.

Results

Ten studies involving 16,726 individuals were included in the current systematic review. Meta-analysis indicated that there was a significantly higher TyG index in patients with OSA, compared with the healthy controls (SMD 0.856, 95% CI 0.579 to 1.132, P < 0.001). Also, TyG had a diagnostic ability for OSA representing a pooled AUC of 0.681 (95% CI 0.627 to 0.735). However, based on the two studies’ findings, no difference between different severities of OSA was observed. Finally, our data showed that the TyG index is a good potential predictor of adverse outcomes in these patients.

Conclusion

Our study revealed that the TyG index is an easy-to-measure marker of IR for assessing OSA, both in diagnosis and prognosis. Our study supports its implementation in routine practice to help clinicians in decision-making and patient stratification.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12944-024-02005-3.

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Most cited references 56

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Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses.

Andreas Stang (2010)

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The meaning and use of the area under a receiver operating characteristic (ROC) curve.

J A Hanley, B J McNeil, Marnix van Holsbeeck (1982)

A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.

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Is Open Access

Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range

Xiang Wan, Wenqian Wang, Jiming Liu … (2014)

Background In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. Methods In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al.’s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials. Results We demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. For the third scenario, our method still performs very well for both normal data and skewed data. Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications. Conclusions In this paper, we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new estimation methods to improve the existing literature. We conclude our work with a summary table (an Excel spread sheet including all formulas) that serves as a comprehensive guidance for performing meta-analysis in different situations. Electronic supplementary material The online version of this article (doi:10.1186/1471-2288-14-135) contains supplementary material, which is available to authorized users.

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Author and article information

Contributors

Amirmohammad Khalaji: amirm.khalaji@gmail.com

Journal

Journal ID (nlm-ta): Lipids Health Dis

Journal ID (iso-abbrev): Lipids Health Dis

Title: Lipids in Health and Disease

Publisher: BioMed Central (London )

ISSN (Electronic): 1476-511X

Publication date (Electronic): 8 January 2024

Publication date PMC-release: 8 January 2024

Publication date Collection: 2024

Volume: 23

Electronic Location Identifier: 4

Affiliations

[1 ]School of Medicine, Tehran University of Medical Sciences, ( https://ror.org/01c4pz451) Poursina St., Keshavarz Blvd, Tehran, 1417613151 Iran

[2 ]Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, ( https://ror.org/01c4pz451) Tehran, Iran

[3 ]Undergraduate Program in Neuroscience, University of British Columbia, ( https://ror.org/03rmrcq20) Vancouver, BC Canada

[4 ]Department of Translational Medicine Sciences, Federico II University of Naples, ( https://ror.org/05290cv24) Naples, Italy

Article

Publisher ID: 2005

DOI: 10.1186/s12944-024-02005-3

PMC ID: 10773018

PubMed ID: 38185682

SO-VID: 0d64138e-b6b6-4c9b-b839-e3796cc4a27f

License:

Open Access 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 25 October 2023

Date accepted : 1 January 2024

Custom metadata

ScienceOpen disciplines: Biochemistry

Keywords: obstructive sleep apnea,tyg,triglyceride-glucose index,systematic review,meta-analysis

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ScienceOpen disciplines: Biochemistry

Keywords: obstructive sleep apnea, tyg, triglyceride-glucose index, systematic review, meta-analysis

Triglyceride-glucose index and obstructive sleep apnea: a systematic review and meta-analysis

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Supplementary Information

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Most cited references 56

Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses.

The meaning and use of the area under a receiver operating characteristic (ROC) curve.

Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range

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