Triglyceride-Glucose Index in Non-Diabetic, Non-Obese Patients with Obstructive Sleep Apnoea

Bikov, András; Frent, Stefan; Mészáros, Martina; Kunos, László; Mathioudakis, Alexander G.; Negru, Alina Gabriela; Gaiță, Laura; Mihăicuţă, Ştefan

doi:10.3390/jcm10091932

Cited by 18 publications

(20 citation statements)

References 50 publications

(72 reference statements)

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“…TyG was used as a predictor in this work, and the findings parallel the results of [71] where TyG had a noticeable independent correlation with OSA in both non-obese and non-diabetic patients.…”

Section: Discussionsupporting

confidence: 77%

Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning

et al. 2021

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Obstructive sleep apnea (OSA) is a common, chronic, sleep-related breathing disorder characterized by partial or complete airway obstruction in sleep. The gold standard diagnosis method is polysomnography, which estimates disease severity through the Apnea-Hypopnea Index (AHI). However, this is expensive and not widely accessible to the public. For effective screening, this work implements machine learning algorithms for classification of OSA. The model is trained with routinely acquired clinical data of 1479 records from the Wisconsin Sleep Cohort dataset. Extracted features from the electronic health records include patient demographics, laboratory blood reports, physical measurements, habitual sleep history, comorbidities, and general health questionnaire scores. For distinguishing between OSA and non-OSA patients, feature selection methods reveal the primary important predictors as waist-to-height ratio, waist circumference, neck circumference, body-mass index, lipid accumulation product, excessive daytime sleepiness, daily snoring frequency and snoring volume. Optimal hyperparameters were selected using a hybrid tuning method consisting of Bayesian Optimization and Genetic Algorithms through a five-fold cross-validation strategy. Support vector machines achieved the highest evaluation scores with accuracy: 68.06%, sensitivity: 88.76%, specificity: 40.74%, F1-score: 75.96%, PPV: 66.36% and NPV: 73.33%. We conclude that routine clinical data can be useful in prioritization of patient referral for further sleep studies.

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Section: Discussionsupporting

confidence: 77%

Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning

et al. 2021

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“…To the best of our knowledge, this is the first study to evaluate the association between the TyG index and sleep quality in ostensibly healthy adults. The two previous cross-sectional studies have associated TyG with OSA (25,26) but did not consider sleep quality. We found that subjects with poor sleep quality had a 1.44-fold higher risk of having a TyG index above the cutoff than those with good sleep quality, regardless of sex and total cholesterol, LDL/HDL ratio, insulin, complement C3, CRP, and adiponectin levels.…”

Section: Discussionmentioning

confidence: 98%

“…A meta-analysis showed a significant association between higher TyG values and T2D risk (24). Recently, two cross-sectional studies have associated TyG with obstructive sleep apnea (OSA), a sleep breathing disorder that often involves IR (25,26). However, the relationship between TyG and sleep quality has not been previously studied.…”

Section: Introductionmentioning

confidence: 99%

Triglyceride-glucose index is associated with poor sleep quality in apparently healthy subjects: A cross-sectional study

Avelino¹,

Silva²,

Chaves³

et al. 2022

Archives of Endocrinology and Metabolism

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Objectives:We aimed to evaluate the association between the triglyceride glucose index (TyG index) and sleep quality and to establish a cut-off value for the TyG index based on the prevalence of subjects with insulin resistance (IR). Materials and methods: This cross-sectional study involved Brazilian health professionals (20-59 years). A total of 138 subjects answered the Pittsburgh Sleep Quality questionnaire to evaluate sleep quality. They were categorized into two groups: good sleep quality (global score ≤ 5 points) and poor sleep quality (global score ≥ 6 points). Also, we classified the subjects as having a high (>8.08 or >4.38) or low TyG index (≤8.08 or ≤4.38). Results: The majority of the subjects (70%) with high TyG index values (>8.08 or >4.38) reported poor sleep quality (p ≤ 0.001). Those with poor sleep quality had a 1.44-fold higher prevalence of IR (TyG index >8.08 or >4.38) compared to those with good sleep quality, regardless of sex, total cholesterol, LDL/HDL ratio, insulin, complement C3, CRP, and adiponectin (p ≤ 0.001). Conclusion: Our data showed a positive and significant association between the TyG index and poor sleep quality. Thus, these findings support the association between poor sleep quality and IR.

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“…So far, sparse data have evaluated the associations between sleep and novel subclinical indicators, most of which focused on OSA. In a case-control study conducted in non-diabetic and non-obese Europeans, the TyG levels were significantly higher in participants with OSA after adjustment for covariates [ 22 ]. Only a Korean study evaluated the associations of sleep duration with the novel subclinical indicators.…”

Section: Discussionmentioning

confidence: 99%

Sex-specific association of sleep duration with subclinical indicators of metabolic diseases among asymptomatic adults

Huang

Long

et al. 2022

Lipids Health Dis

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Background Accumulating evidence suggests sleep duration may be involved in metabolic regulation. However, studies regarding the association with the early stage of the metabolic disease are limited, and the findings were inconsistent. Methods A study among 4922 asymptomatic adults was conducted based on a Chinese national survey in 2009. The early stage of metabolic diseases was evaluated using three proxies: triglyceride to high-density lipoprotein cholesterol ratio (TG/HDL-C), the product of triglyceride and fasting glucose (TyG), and lipid accumulation product (LAP). Multivariable linear and logistic regression models were used to explore the associations of sleep duration with the three indicators. Results The linear regression models revealed that, among females, sleep duration <7 h per day, compared with 7-9 h, was associated with an increased value of LAP and TyG by 25.232% (95%CI: 10.738%, 41.623%) and 0.104 (95%CI: 0.024, 0.185), respectively, in the crude model. The effects were attenuated but remained significant for LAP (11.405%; 95%CI: 1.613%, 22.262%). Similarly, the logistic regression models further found that sleep duration <7 h per day could increase the risk of elevated LAP (OR: 1.725, 95CI%:1.042, 2.856) after adjusting for multiple covariates. By contrast, no associations were found among males. Conclusions Short sleep duration was associated with subclinical indicators of metabolic diseases, and females were more susceptible to the association.

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Triglyceride-Glucose Index in Non-Diabetic, Non-Obese Patients with Obstructive Sleep Apnoea

Cited by 18 publications

References 50 publications

Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning

Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning

Triglyceride-glucose index is associated with poor sleep quality in apparently healthy subjects: A cross-sectional study

Sex-specific association of sleep duration with subclinical indicators of metabolic diseases among asymptomatic adults

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