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

Triglyceride-glucose index and combined indicators: effective indicators for screening NAFLD in snoring patients

AIMS

Nonalcoholic fatty liver disease (NAFLD) is a common complication in snoring patients, especially in patients with obstructive sleep apnea syndrome (OSA). Triglyceride-glucose (TyG) index was a simple indicator of metabolic status and a surrogate marker of insulin resistance. This study aimed to explore the relationship between NAFLD and TyG index in snoring patients.

METHODS

A retrospective study was conducted. The successive snoring patients enrolled in the Sleep Center of the First Affiliated Hospital of Fujian Medical University and had abdominal ultrasonography were included. The clinical characteristics of patients in different quartile TyG groups were compared. The relationship of the TyG index and NAFLD were valued via logistic regression models and restricted cubic spline analysis. The value of TyG index in predicting NAFLD was determined by receiver operating characteristic curve (ROC curve).

RESULTS

A total of 463 NAFLD cases were found among the 654 snoring patients. TyG index was a risk factor of NAFLD in snoring patients (OR = 2.38, 95% CI = 1.71-3.36). The risk of NAFLD was much higher in patients with the highest quartile of TyG index (OR = 5.12, 95% CI = 2.85-9.22), compared with the lowest quartile group. Restricted cubic spline (RCS) analysis showed a significant dose-response relationship between TyG index and risk of NAFLD (p for non-linearity < 0.001). A combination of TyG, neck circumference and ESS score presented the acceptable AUC for the detection of NAFLD in snoring patients (0.746, 95% CI 0.701-0.790, p < 0.001).

CONCLUSION

The TyG index was a risk factor of NAFLD in snoring patients. A combination of TyG, neck circumferences and ESS score could act as a convenient and effective indicator for screening NAFLD in snoring patients.

Hypoxia-Induced Insulin Resistance Mediates the Elevated Cardiovascular Risk in Patients with Obstructive Sleep Apnea: A Comprehensive Review

Patients with obstructive sleep apnea (OSA) experience insulin resistance and its clinical consequences, including hypertriglyceridemia, reduced high density lipoprotein-associated cholesterol (HDL-c), visceral adiposity, hepatic steatosis, increased epicardial fat thickness, essential hypertension, glucose intolerance, increased risk for type 2 diabetes, chronic kidney disease, subclinical vascular damage, and increased risk for cardiovascular events. Obesity is a major contributor to OSA. The prevalence of OSA is almost universal among patients with severe obesity undergoing bariatric surgery. However, insulin resistance and its clinical complications occur in OSA patients irrespective of general obesity (body mass index). In OSA patients, apnea episodes during sleep induce oxyhemoglobin desaturation and tissue hypoxia. Insulin resistance is an adaptive response to tissue hypoxia and develops in conditions with limited tissue oxygen supply, including healthy subjects exposed to hypobaric hypoxia (high altitude) and OSA patients. Indicators of oxyhemoglobin desaturation have been robustly and independently linked to insulin resistance and its clinical manifestations in patients with OSA. Insulin resistance mediates the elevated rate of type 2 diabetes, chronic kidney disease, and cardiovascular disease unexplained with traditional cardiovascular risk factors present in OSA patients. Pathophysiological processes underlying hypoxia-induced insulin resistance involve hypoxia inducible factor-1 upregulation and peroxisome proliferator-activated receptor-gamma (PPAR- γ ) downregulation. In human adipose tissue, PPAR- γ activity promotes glucose transport into adipocytes, lipid droplet biogenesis, and whole-body insulin sensitivity. Silencing of PPAR- γ in the adipose tissue reduces glucose uptake and fat accumulation into adipocytes and promotes insulin resistance. In conclusion, tissue hypoxia drives insulin resistance and its clinical consequences in patients with OSA, regardless of body mass index.

Correlation between the triglyceride-glucose index and left ventricular global longitudinal strain in patients with chronic heart failure: a cross-sectional study

BACKGROUND

Left ventricular global longitudinal strain (GLS) holds greater diagnostic and prognostic value than left ventricular ejection fraction (LVEF) in the heart failure (HF) patients. The triglyceride-glucose (TyG) index serves as a reliable surrogate for insulin resistance (IR) and is strongly associated with several adverse cardiovascular events. However, there remains a research gap concerning the correlation between the TyG index and GLS among patients with chronic heart failure (CHF).

METHOD

427 CHF patients were included in the final analysis. Patient demographic information, along with laboratory tests such as blood glucose, lipids profiles, and echocardiographic data were collected. The TyG index was calculated as Ln [fasting triglyceride (TG) (mg/dL) × fasting plasma glucose (FPG) (mg/dL)/2].

RESULTS

Among CHF patients, GLS was notably lower in the higher TyG index group compared to the lower TyG index group. Following adjustment for confounding factors, GLS demonstrated gradual decrease with increasing TyG index, regardless of the LVEF level and CHF classification.

CONCLUSION

Elevated TyG index may be independently associated with more severe clinical left ventricular dysfunction in patients with CHF.

Multimodal data integration for enhanced longitudinal prediction for cardiac and cerebrovascular events following initial diagnosis of obstructive sleep apnea syndrome

Background

Obstructive sleep apnea syndrome (OSAS), a prevalent condition, often coexists with intricate metabolic issues and is frequently associated with negative cardiovascular outcomes. We developed a longitudinal prediction model integrating multimodal data for cardiovascular risk stratification of patients with an initial diagnosis of OSAS.

Methods

We reviewed the data of patients with new-onset OSAS who underwent diagnostic polysomnography between 2018-19. Patients were treated using standard treatment regimens according to clinical practice guidelines.

Results

Over a median follow-up of 32 months, 98/729 participants (13.4%) experienced our composite outcome. At a ratio of 7:3, cases were randomly divided into development (n = 510) and validation (n = 219) cohorts. A prediction nomogram was created using six clinical factors - sex, age, diabetes mellitus, history of coronary artery disease, triglyceride-glucose index, and apnea-hypopnea index. The prediction nomogram showed excellent discriminatory power, based on Harrell's C-index values of 0.826 (95% confidence interval (CI) = 0.779-0.873) for the development cohort and 0.877 (95% CI = 0.824-0.93) for the validation cohort. Moreover, comparing the predicted and observed major adverse cardiac and cerebrovascular events in both development and validation cohorts indicated that the prediction nomogram was well-calibrated. Decision curve analysis demonstrated the good clinical applicability of the prediction nomogram.

Conclusions

Our findings demonstrated the construction of an innovative visualisation tool that utilises various types of data to predict poor outcomes in Chinese patients diagnosed with OSAS, providing accurate and personalised therapy.

Registration

Chinese Clinical Trial Registry ChiCTR2300075727.

Association between the triglyceride glucose index and obstructive sleep apnea and its symptoms: results from the NHANES

BACKGROUND

Certain studies have indicated a link between obstructive sleep apnea and insulin resistance in specific populations. To gain more clarity, extensive research involving a broad sample of the overall population is essential. The primary objective of this study was to investigate this correlation by utilizing data from the National Health and Nutrition Examination Survey database.

METHODS

The analysis incorporated data from the National Health and Nutrition Examination Survey database spanning the time periods from 2005 to 2008 and from 2015 to 2018, with a focus on American adults aged 18 years and older after applying weight adjustments. Key variables such as obstructive sleep apnea, triglyceride glucose index, and various confounding factors were considered. A generalized linear logistic regression model was used to investigate the association between obstructive sleep apnea and the triglyceride glucose index, with additional exploration of the consistency of the results through hierarchical analysis and other techniques.

RESULTS

The study included participants aged between 18 and 90 years, with an average age of 46.75 years. Among the total sample, 50.76% were male. The triglyceride glucose index demonstrated a diagnostic capability for obstructive sleep apnea, with an AUC of 0.701 (95% CI: 0.6619-0.688). According to the fully adjusted model, individuals in the fourth quartile of the triglyceride glucose index showed an increased likelihood of having obstructive sleep apnea compared to those in the first quartile (OR: 1.45; 95% CI: 1.02-2.06; P < 0.05). Subgroup analysis indicated that male sex (OR: 2.09; 95% CI: 1.76-2.45; P < 0.05), younger age (OR: 2.83; 95% CI: 2.02-3.96; P < 0.05), white ethnicity (OR: 2.29; 95% CI: 1.93-2.73; P < 0.05), and obesity (OR: 1.54; 95% CI: 1.28-1.85; P < 0.05) were correlated with an elevated risk of OSA.

CONCLUSIONS

This study demonstrated a strong association between an elevated TG index and OSA. Additionally, the triglyceride glucose index could serve as an independent predictor of obstructive sleep apnea.

A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators

Objective

Obstructive sleep apnea (OSA) is a common and potentially fatal sleep disorder. The purpose of this study was to construct an objective and easy-to-promote model based on common clinical biochemical indicators and demographic data for OSA screening.

Methods

The study collected the clinical data of patients who were referred to the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University from December 1, 2020, to July 31, 2023, including data for demographics, polysomnography (PSG), and 30 biochemical indicators. Univariate and multivariate analyses were performed to compare the differences between groups, and the Boruta method was used to analyze the importance of the predictors. We selected and compared 10 predictors using 4 machine learning algorithms which were "Gaussian Naive Bayes (GNB)", "Support Vector Machine (SVM)", "K Neighbors Classifier (KNN)", and "Logistic Regression (LR)". Finally, the optimal algorithm was selected to construct the final prediction model.

Results

Among all the predictors of OSA, body mass index (BMI) showed the best predictive efficacy with an area under the receiver operating characteristic curve (AUC) = 0.699; among the predictors of biochemical indicators, triglyceride-glucose (TyG) index represented the best predictive performance (AUC = 0.656). The LR algorithm outperformed the 4 established machine learning (ML) algorithms, with an AUC (F1 score) of 0.794 (0.841), 0.777 (0.827), and 0.732 (0.788) in the training, validation, and testing cohorts, respectively.

Conclusion

We have constructed an efficient OSA screening tool. The introduction of biochemical indicators in ML-based prediction models can provide a reference for clinicians in determining whether patients with suspected OSA need PSG.

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

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.

Triglyceride-glucose index as a predictor of obstructive sleep apnoea severity in the absence of traditional risk factors

OBJECTIVE

We evaluated the association between the triglyceride-glucose (TG) index, a marker of insulin resistance, and obstructive sleep apnoea (OSA) severity in patients without diabetes mellitus, obesity, and metabolic syndrome.

METHODS

This retrospective cohort study included 1,527 patients. We used univariate and multivariate analyses to identify the independent predictors associated with OSA.

RESULTS

Most patients were males (81.5%) with a mean age of 43.9 ± 11.1 (15-90) years. Based on the apnoea-hypopnea index (AHI), 353 (23.1%) patients were included in the control group, whereas 32.4%, 23.5%, and 21% had mild, moderate, and severe OSA, respectively. The TG index values demonstrated significant associations with OSA patients compared with the control group (p = 0.001). In addition, the mean values of the oxygen desaturation index (ODI), AHI, minimum oxygen saturation, and total sleep time percentage with saturation below 90% demonstrated statistically significant differences among the TG index groups (p: 0.001; p:0.001; p:0.001; p:0.003). The optimal TG index cutoff value to predict OSA was 8.615 (AUC = 0.638, 95% CI = 0.606-0.671, p = 0.001). In multivariate logistic regression analysis, after adjusting for age, sex, and body mass index, the TG index was independently associated with OSA patients.

CONCLUSION

The TG index is independently associated with increased risk for OSA. This indicates that this index, a marker for disease severity, can be used to identify severe OSA patients on waiting lists for PSG.

The association of nocturnal hypoxemia with dyslipidemia in sleep-disordered breathing population of Chinese community: a cross-sectional study

BACKGROUND

Currently, there is limited and controversial clinical research on the correlation between sleep-disordered breathing (SDB) and dyslipidemia. This discrepancy in findings may be because studies that primarily focused on hospital-based populations may not be applicable to community-based populations. Therefore, the primary objective of this research endeavor is to scrutinize the correlation between nocturnal hypoxemia and blood lipid concentrations among adult individuals residing in the community who exhibit symptoms of SDB. Additionally, this study aimed to identify the nocturnal hypoxia parameters having the strongest correlation with this relationship.

METHODS

This cross-sectional study collected data from The Guangdong Sleep Health Study, which included 3829 participants. Type IV sleep monitoring was employed to measure hypoxemia parameters, and lipoproteins were evaluated using fasting blood samples. To understand the association between dyslipidemia and hypoxemia parameters, a multivariable logistic regression model was used. Subgroup analyses were conducted to stratify data according to age, sex, waist circumference, and chronic diseases.

RESULTS

The age of the individuals involved in the study spanned from 20 to 90 years. The average age of the participants was 56.15 ± 13.11 years. Of the total sample size, 55.7% were male. In the fully adjusted model, the meanSpO2 was negatively associated with hyperlipidemia (0.9303 [95% confidence interval 0.8719, 0.9925]). Upon conducting a nonlinearity test, the relationship between the meanSpO2 and hyperlipidemia was nonlinear. The inflection points were determined to be 95. When meanSpO2 ≥ 95%, a difference of 1 in the meanSpO2 corresponded to a 0.07 difference in the risk of hyperlipidemia.

CONCLUSIONS

This study revealed that higher meanSpO2 is significantly and negatively associated with hyperlipidemia in adult community residents with SDB, particularly when the meanSpO2 exceeds 95. This finding emphasizes the importance of close monitoring for dyslipidemia, which is considered an early indicator of atherosclerosis in patients with SDB who experience nocturnal hypoxia.

The association of nocturnal hypoxemia with dyslipidemia in sleep-disordered breathing population of Chinese community: a cross-sectional study

BACKGROUND

Currently, there is limited and controversial clinical research on the correlation between sleep-disordered breathing (SDB) and dyslipidemia. This discrepancy in findings may be because studies that primarily focused on hospital-based populations may not be applicable to community-based populations. Therefore, the primary objective of this research endeavor is to scrutinize the correlation between nocturnal hypoxemia and blood lipid concentrations among adult individuals residing in the community who exhibit symptoms of SDB. Additionally, this study aimed to identify the nocturnal hypoxia parameters having the strongest correlation with this relationship.

METHODS

This cross-sectional study collected data from The Guangdong Sleep Health Study, which included 3829 participants. Type IV sleep monitoring was employed to measure hypoxemia parameters, and lipoproteins were evaluated using fasting blood samples. To understand the association between dyslipidemia and hypoxemia parameters, a multivariable logistic regression model was used. Subgroup analyses were conducted to stratify data according to age, sex, waist circumference, and chronic diseases.

RESULTS

The age of the individuals involved in the study spanned from 20 to 90 years. The average age of the participants was 56.15 ± 13.11 years. Of the total sample size, 55.7% were male. In the fully adjusted model, the meanSpO2 was negatively associated with hyperlipidemia (0.9303 [95% confidence interval 0.8719, 0.9925]). Upon conducting a nonlinearity test, the relationship between the meanSpO2 and hyperlipidemia was nonlinear. The inflection points were determined to be 95. When meanSpO2 ≥ 95%, a difference of 1 in the meanSpO2 corresponded to a 0.07 difference in the risk of hyperlipidemia.

CONCLUSIONS

This study revealed that higher meanSpO2 is significantly and negatively associated with hyperlipidemia in adult community residents with SDB, particularly when the meanSpO2 exceeds 95. This finding emphasizes the importance of close monitoring for dyslipidemia, which is considered an early indicator of atherosclerosis in patients with SDB who experience nocturnal hypoxia.

Dyslipidemia prevalence in nonobese, nondiabetic patients with obstructive sleep apnea: does sex matter?

STUDY OBJECTIVES

Dyslipidemia in obstructive sleep apnea (OSA) has been attributed to confounding obesity and/or diabetes. This study aimed to examine lipid profiles in nondiabetic, nonobese patients with OSA and identify the possible effects of age and sex.

METHODS

We retrospectively evaluated the lipid parameters of 3,050 adults who underwent polysomnography. A total of 2,168 patients were excluded due to obesity (body mass index ≥ 30 kg/m2), diabetes, alcoholism, untreated hypothyroidism, lipid-lowering drug use, missing sleep data, or treatment for suspected OSA.

RESULTS

Of 882 patients (75% males, aged 46.8 ± 12.2 years) included in the study, 88.4% had OSA. Levels of total cholesterol (P = .003), low-density-lipoprotein (LDL) cholesterol (P = .005), non-high-density-lipoprotein (non-HDL) cholesterol (P = .001), and triglycerides (P = .007) were significantly higher in patients with OSA than in those without, whereas HDL-cholesterol levels did not differ. The proportion of patients with hypercholesterolemia and/or elevated non-HDL cholesterol (>  160 mg/dL) was significantly higher in OSA than in non-OSA. Correlation analyses by sex revealed stronger and more significant relationships between lipid parameters and apnea-hypopnea index in women than in men (r = .135, P < .001, vs r = .080, P = .043 for total cholesterol; r = .111, P < .001, vs r = .080, P = .046 for non-HDL cholesterol; r = .122, P < .001, vs r = .061, P = .107 for LDL cholesterol, respectively). In regression analysis, the rate of hypercholesterolemia increased with age (P < .001 for women and P = .031 for men); non-HDL- and LDL-cholesterol levels significantly increased with OSA severity (P = .035 and P = .023, respectively) and age (P = .004 and P = .001, respectively) in women.

CONCLUSIONS

After excluding confounding obesity and diabetes, patients with OSA have an impaired lipid profile including total cholesterol, LDL cholesterol, non-HDL cholesterol, and triglycerides. A significant association between dyslipidemia and OSA severity was observed in women but not in men.

CITATION

Basoglu OK, Tasbakan MS, Kayikcioglu M. Dyslipidemia prevalence in nonobese, nondiabetic patients with obstructive sleep apnea: does sex matter? J Clin Sleep Med. 2023;19(5):889-898.

Triglyceride glucose index and poor sleep patterns in non-diabetic adults: Evidence from NHANES 2005-2016

Introduction

Sleep disorders are commonly encountered in modern populations. This cross-sectional study aimed to investigate the associations between triglyceride glucose (TyG) index and poor sleep patterns in non-diabetic adults.

Methods

Data of non-diabetic adults aged 20-70 years were extracted from the US National Health and Nutrition Examination Survey database 2005-2016. Pregnant women, individuals with diabetes and cancer history, and individuals lacking complete data on sleep patterns or parameters for calculating TyG index were excluded. Poor sleep pattern was defined as having two or more following conditions: (1) abnormal sleep duration, defined as less than 7 h or longer than 9 h; (2) self-reported trouble sleeping; and (3) physician-confirmed sleep disorders. Associations between poor sleep patterns, TyG index, and an additional index incorporating body mass index (BMI), TyGBMI, and other study variables were determined by univariable and multivariable logistic regression analysis.

Results

Among 9,390 included participants, 1,422 had poor sleep patterns and 7,968 did not. The individuals with poor sleep patterns had a higher mean TyG index, were older, had higher BMI, and had higher proportions of hypertension and history of CVD than those without poor sleep pattern (all p < 0.001). Multivariable analysis showed no significant association between poor sleep pattern and TyG index. However, among the components of poor sleep pattern, TyG index in the highest quartile (Q4) was significantly associated with trouble sleeping [adjusted OR (aOR): 1.46, 95%CI: 1.04-2.03) as compared with the lowest TyG quartile (Q1). In addition, TyG-BMI in Q4 was indepently associated with increased likelihood for poor sleep patterns (aOR: 2.18, 95%CI: 1.61-2.95), trouble sleeping (aOR: 1.76, 95%CI: 1.30-2.39), abnormal sleep duration (aOR: 1.41, 95%CI: 1.12-1.78), and sleep disorders (aOR: 3.11, 95%CI: 2.08-4.64) as compared to Q1.

Discussion

Among US adults without diabetes, elevated TyG index is correlated with self-reported trouble sleeping, independent of BMI. Future studies should build upon this preliminary work and examine these associations longitudinally and through treatment trials.

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

Objective

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).

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.

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

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12944-022-01626-w.

Risk Factors of Nonalcoholic Fatty Liver Disease and Liver Fibrosis in Non-Obese Patients with Obstructive Sleep Apnea

PURPOSE

Liver injury in non-obese obstructive sleep apnea (OSA) patients has received much attention in recent years. This study aimed to investigate risk factors of nonalcoholic fatty liver disease (NAFLD) and liver fibrosis in non-obese patients with OSA.

METHODS

A retrospective study was conducted in the Sleep Center of the First Affiliated Hospital of Fujian Medical University. All consecutive non-obese patients with suspected sleep apnea admitted to the center were enrolled. The clinical characteristics of patients with simple snoring and with different severity OSA were compared. Multivariate logistic regression models were used to analyze the risk factors of NAFLD and liver fibrosis.

RESULTS

A total of 410 patients were enrolled. The levels of triglyceride, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) increased with the aggravation of OSA (All p<0.05). Among non-obese patients with OSA, 17 (5%) were diagnosed with liver fibrosis and 228 (65%) with NAFLD; Apnea‑hyponea index (AHI) was an independent predictor for NAFLD and liver fibrosis [OR (95% CI): 1.02 (1.00-1.03), 1.04 (1.00-1.07), both p<0.05]; hypertriglyceridemia was an independent predictor for NAFLD [OR (95% CI): 1.13 (1.12-1.99), p<0.05].

CONCLUSION

NAFLD and liver fibrosis were common in non-obese OSA patients and the severity of OSA was an independent risk factor for them.

Association Between Triglyceride Glucose Index-Waist Circumference and Risk of First Myocardial Infarction in Chinese Hypertensive Patients with Obstructive Sleep Apnoea: An Observational Cohort Study

PURPOSE

This study aimed to investigate the association between triglyceride glucose index-waist circumference (TyG-WC) and the risk of first myocardial infarction (MI) in Chinese hypertensive patients with obstructive sleep apnoea (OSA).

METHODS

This study was an observational cohort study. A total of 2224 Chinese hypertensive patients with OSA without a history of MI were included in this study. Hazard ratio (HR) and 95% confidence interval (CI) were estimated using multivariable Cox proportion hazard models. A generalized additive model was used to identify nonlinear relationships. Additionally, we performed hierarchical analysis and test for interaction.

RESULTS

During a median follow-up of 7.15 years, 85 incidents of MI developed. Overall, there was a positive association between TyG-WC and the risk of first MI. In the multivariable-adjusted model, the risk of MI increased with quartiles of the TyG-WC, the HR in quartile 4 versus quartile 1 was 4.29. A generalized additive model and a smooth curve fitting showed that there existed a similar J-shaped association between TyG-WC and the risk of first MI, with an inflection point at about 785.

CONCLUSION

Elevated levels of the baseline TyG-WC are associated with an increased risk of first MI. This finding indicates that the TyG-WC might be useful to identify the high risk of first MI in Chinese hypertensive patients with OSA.

Association of Insulin Resistance with Vascular Ageing in a General Caucasian Population: An EVA Study

The data on the relationship between insulin resistance and vascular ageing are limited. The aim of this study was to explore the association of different indices of insulin resistance with vascular ageing in an adult Caucasian population without cardiovascular disease. We selected 501 individuals without cardiovascular disease (mean age: 55.9 years, 50.3% women) through random sampling stratified by sex and age. Arterial stiffness was evaluated by measuring the carotid-to-femoral pulse wave velocity (cfPWV) and brachial-to-ankle pulse wave velocity (baPWV). The participants were classified into three groups according to the degree of vascular ageing: early vascular ageing (EVA), normal vascular ageing (NVA) and healthy vascular ageing (HVA). Insulin resistance was evaluated with the homeostatic model assessment of insulin resistance (HOMA-IR) and another five indices. The prevalence of HVA and EVA was 8.4% and 21.4%, respectively, when using cfPWV, and 7.4% and 19.2%, respectively, when using baPWV. The deterioration of vascular ageing, with both measurements, presented as an increase in all the analysed indices of insulin resistance. In the multiple regression analysis and logistic regression analysis, the indices of insulin resistance showed a positive association with cfPWV and baPWV and with EVA.

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

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.