Introduction

Obstructive sleep apnoea syndrome (OSAS) is a clinical disorder characterized by recurrent apnoea and hypoventilation events during sleep, which may be complicated by nocturnal hypoxaemia. Prolonged nocturnal hypoxia leads to autonomic dysfunction, activation of oxidative stress, and chronic inflammatory responses1. With the obesity epidemic, the prevalence of OSAS is increasing every year2, but it is usually overlooked by patients and physicians because its clinical features are not clearly specific. Its main clinical manifestations are disrupted sleep structure, snoring or breath-holding at night, and daytime drowsiness. Undetected and untreated OSAS patients are highly susceptible to cardiovascular, neurological, and endocrine diseases3. Studies have reported that OSAS is strongly associated with a number of cardiovascular conditions, including coronary heart disease, heart failure, arrhythmias and stroke4,5,6. Studies have shown that OSAS can increase the risk of hypertension and that positive pressure ventilation therapy has a significant clinical benefit in patients with OSAS combined with hypertension7,8. Intermittent hypoxia in OSAS is independently associated with insulin resistance, and the prevalence of OSAS is significantly higher in patients with type 2 diabetes9,10. Studies have revealed that the metabolic disorders associated with insulin resistance are linked to hypertension and other cardiovascular diseases11.

There is an established relationship between OSAS and hypertension, but few studies have investigated the mediators of this relationship. Based on the above research evidence, the present study proposes the following hypothesis: insulin resistance may be a mediator of the association between OSAS and hypertension. The triglyceride-glucose (TyG) index is a valid and simple surrogate for insulin resistance and can be calculated using the formula Ln [triglycerides (mg/dl) × fasting glucose (mg/dl)/2]12. Considering that body mass index (BMI) is an independent risk factor for OSAS and hypertension, this study used the BMI-modified TyG index to investigate the value of insulin resistance in assessing hypertension risk in OSAS patients. TyG-BMI is derived by multiplying BMI by the TyG index, which several studies have shown to be superior to the TyG index in predicting insulin resistance13,14. It has an excellent ability to identify patients with hypertension combined with hyperuricaemia15. Therefore, the purpose of this study was to examine the association between OSAS, TyG-BMI, and hypertension and to verify whether TyG-BMI mediates the association between OSAS and the risk of developing hypertension.

Methods

Research population

This was a retrospective cross-sectional study. The study collected clinical data on adult OSAS patients with routine health check-ups and completed polysomnography in the General Medicine Department between January 2020 and December 2023 using the electronic medical record system of the Wuhan Union Medical College Hospital. Exclusion criteria included subjects with any of the following conditions: (1) patients whose blood counts, liver and kidney functions were outside the normal range due to long-term use of hormones, antibiotics or chemotherapy drugs. (2) patients with heart failure, kidney failure, liver failure, respiratory failure or malignant tumors. (3) Previously diagnosed hypertension due to acute or chronic renal disease, pheochromocytoma, renal artery stenosis, primary aldosteronism, Cushing’s syndrome. (4) patients with missing results of any of the complete blood count, liver function tests, renal function tests, fasting blood glucose or lipid tests. In the end, a total of 840 patients with OSAS were enrolled in the analysis. This study was performed in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Wuhan Union Medical College Hospital (UHCT-IEC-SOP-016-03-01), and subjects were exempted from informed consent as clinical data of all participants were analyzed retrospectively.

Basic information and laboratory measurements

General information such as age, sex, smoking history, alcohol consumption were recorded, and the history of hypertension, diabetes mellitus, and coronary heart disease was taken. Height and weight were measured and BMI was calculated. Venous blood samples were collected from patients in the fasting state and sent to the central laboratory of Wuhan Union Medical College Hospital for testing. Measurements included: white blood cells, haemoglobin, platelets, neutrophils, lymphocytes, monocytes; alanine aminotransferase, aspartate aminotransferase, creatinine, uric acid, fasting blood glucose, triglycerides and other markers. The collection of all samples and the testing of biological indicators in the blood were completed by professional technicians in the laboratory. The neutrophil-lymphocyte ratio (NLR) was calculated from the neutrophil and lymphocyte counts. The TyG index was calculated using the above formula12 and then the TyG-BMI value was calculated.

Diagnosis of OSAS

All participants underwent polysomnographic respiratory monitoring in a quiet, comfortable sleep monitoring room. Sleep time, respiratory events, and changes in oxygen saturation during the participant’s overnight sleep were recorded using a model YH-600B polysomnography monitor (BMC Medical Co., Ltd., China). Instruments were regularly quality checked and calibrated by engineers. The data obtained from the monitoring were recorded and interpreted by a sleep monitoring technician, and the results were reviewed by a sleep physician. According to the American Academy of Sleep Medicine16, apnoea is defined as the loss or significant reduction of oro-nasal airflow (≥ 90% decrease from baseline amplitude) during sleep for a duration of ≥ 10 s, and hypopnoea is defined as a decrease in oro-nasal airflow of ≥ 30% from baseline, accompanied by a decrease in oxygen saturation of ≥ 3%, for a duration of ≥ 10 s. Sleep efficiency, apnoea-hypopnoea index (AHI), snoring, electrocardiogram, mean oxygen saturation and lowest oxygen saturation were routinely reported in the sleep apnoea monitoring results. The AHI is calculated by dividing the sum of the number of apnoeas and hypoventilations by the total sleep time. The severity of OSAS was graded according to AHI, with 5 < AHI ≤ 15 considered mild, 15 < AHI ≤ 30 considered moderate, and AHI > 30 considered severe. Oxygen desaturation index (ODI) was defined as the number of hourly decreases in oxygen desaturation of ≥ 3% from baseline17.

Diagnosis of hypertension

Patients with previously diagnosed hypertension and new-onset hypertension diagnosed according to the 2023 ESH guidelines18.

Statistical analysis

A normality test for all continuous variables showed that the variables followed a normal distribution and were therefore described by the mean (standard deviation), and differences between groups were compared using the t-test. Categorical variables were expressed as percentages, and the chi-square test was used to compare whether the differences between groups were statistically significant. The relationship between OSAS severity and hypertension was analyzed using binary logistic regression models. The relationship between AHI and TyG-BMI was analyzed using Pearson’s correlation, considering AHI and TyG-BMI as continuous variables. The study used restricted cubic spline (RCS) regression to analyze the potential non-linear relationship between TyG-BMI and risk of hypertension. Quartiles were used to divide TyG-BMI into four intervals, and multifactorial logistic regression was used to explore the association between TyG-BMI and hypertension. The variables in the multifactor logistic regression were tested for multicollinearity and the variance inflation factors (VIF) for all independent variables were less than 5. Finally, mediation analysis was used to calculate the percentage mediation effect of TyG-BMI between OSAS and hypertension, and the mediation effect was tested using the bootstrap method. The mediation effect was found to be significant when the 95% confidence interval (CI) did not include zero. Statistical analyses and graphs were performed using R 4.3.1 software for Windows. Significant results were defined as those with a two-tailed p-value less than 0.05.

Results

General clinical characteristics

The OSAS patients were divided into hypertensive and non-hypertensive groups, a total of 573 (68.21%) had hypertension and 267 (31.79%) in the non-hypertensive group. Table 1 showed that the hypertensive group was older, had more overweight and obesity, diabetes and coronary heart disease; higher levels of neutrophils, NLR, uric acid, fasting glucose, AHI, ODI and TyG-BMI; and had lower mean oxygen saturation and lowest oxygen saturation. Variables that differed between the two groups were included in the regression model to be analyzed. Table 2 showed that age, TyG-BMI, uric acid, diabetes and coronary heart disease were risk factors for the development of hypertension in patients with OSAS.

Table 1 General clinical characteristics of the participants.
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Table 2 Logistic regression analysis of risk factors for hypertension in OSAS patients.
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Multifactorial logistic regression analysis of OSAS severity and hypertension

OSAS was categorized into mild, moderate and severe according to AHI values and three logistic regression models were constructed in Table 3, with OSAS severity as the independent variable and the presence of hypertension as the dependent variable. Compared with mild OSAS as reference, model 1 found a significantly increased risk of hypertension in patients with severe OSAS (OR = 1.667, 95% CI: 1.138–2.442, p = 0.009); models 2 and 3 progressively included adjustments for the remaining confounders and finally found that this risk remained (OR = 1.752, 95% CI: 1.125–2.728, p = 0.013).

Table 3 Logistic regression models of the relationship between OSAS severity and hypertension.
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Correlation analysis between AHI and TyG index, BMI, TyG-BMI

Pearson’s correlation analysis of AHI with TyG index, BMI and TyG-BMI found that TyG-BMI (r = 0.573, p < 0.001) was more closely correlated with AHI compared to TyG index (r = 0.004, p = 0.002) and BMI (r = 0.051, p < 0.001). This indicated that BMI combined with the TyG index is more strongly associated with the severity of OSAS and may be more meaningful in assessing the disease.

RCS regression analysis of TyG-BMI and risk of hypertension

The study used RCS regression to analyze the potential non-linear relationship between TyG-BMI and hypertension. The results are shown in Fig. 1, where TyG-BMI was linearly and positively associated with the risk of hypertension (P-non-linear = 0.535).

Fig. 1
figure 1

Restricted cubic spline plot between TyG-BMI and OR (95% CI) of hypertension.

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Logistic regression analysis of TyG-BMI and risk of hypertension

The relationship between TyG-BMI levels and the risk of hypertension was assessed using logistic regression models and the results are presented in Table 4. TyG-BMI was divided into four levels from Q1 to Q4 from low to high using the quartile method, and Q1 was used as the reference level. The results showed that in model 1 without adjustment for confounders, the risk of hypertension was significantly increased when TyG-BMI was at the Q3 and Q4 levels, and the risk of hypertension was significantly higher at the Q4 level (OR = 2.859, 95% CI: 1.864–4.385, p < 0.001) than at the Q3 level (OR = 2.144, 95% CI: 1.423–3.230, p < 0.001). Model 2 adjusted initially for some of the confounders, and model 3 further adjusted for the remaining confounders based on model 2, finding that the increasing trend in this risk persisted. The ORs at the Q2, Q3 and Q4 levels in model 3 were 1.576(95% CI: 1.015–2.445, p = 0.043), 2.457 (95% CI: 1.527–3.954, p < 0.001) and 3.407 (95% CI: 2.001–5.802, p < 0.001), respectively.

Table 4 Logistic regression analysis of TyG-BMI and hypertension
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Mediation analyses

In this study, mediation analysis was used to investigate the mediating effect of TyG-BMI and NLR in the association between OSAS and hypertension. The results are shown in Fig. 2. In mediation analysis, the sum of the average direct effect and the average causal mediation effect is the total effect, and the average causal mediation effect divided by the total effect is the mediation ratio. Figure 2A showed that after adjustment for the risk factors of age, uric acid, diabetes and coronary heart disease, this mediation ratio was 41.3% (95% CI: 0.184–1.050, p < 0.001). The study was analyzed with the NLR as a mediator, no significant mediation effect was observed as the confidence interval for the mediation effect contained zero (Fig. 2B).

Fig. 2
figure 2

The study presented total effects, average causal mediation effects (ACME) and average direct effects (ADE). (A) and (B) show mediation effect plots for TyG-BMI and NLR as mediators, respectively. Models were adjusted for risk factors.

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Discussion

The findings of this study indicated a strong association between OSAS, TyG-BMI and the risk of hypertension. The risk of hypertension was significantly higher in patients with severe OSAS than in those with mild OSAS, and the severity of OSAS was positively correlated with TyG-BMI. Additionally, the risk of hypertension in patients with OSAS also increased with increasing TyG-BMI. Mediation analysis confirmed that the risk of hypertension in patients with OSAS was partially mediated by TyG-BMI, with a mediation effect of 41.3%. NLR is a novel inflammatory marker19, but the study did not tentatively find a mediating role for NLR in the association between OSAS and hypertension risk. Perhaps future studies could explore the role of other inflammatory markers in the relationship between OSAS and hypertension. This suggests that insulin resistance may explain some of the pathological mechanisms underlying the development of hypertension in OSAS.

Numerous studies have confirmed the strong association between OSAS and hypertension, and there is a clear dose-dependent relationship between the degree of hypoxia in OSAS and the severity of hypertensive disease20. Repeated intermittent hypoxia during sleep is the main pathological feature of OSAS. Hypoxia induces the production of inflammatory factors such as interleukin-6 and tumor necrosis factor-alpha in adipose tissue, which promotes the conversion of adipocytes to a pro-inflammatory phenotype, leading to adipose tissue dysfunction and insulin resistance9,21. Hypoxia is an independent risk factor for exacerbating the adipose tissue inflammatory response in patients with OSAS22. Rats exposed to intermittent hypoxia have increased levels of liver cytokines, inflammatory factor activity and insulin resistance index23. Intermittent hypoxia accelerates insulin resistance through specific microRNA upregulation of selenoprotein P levels in human hepatocytes24. In addition, intermittent hypoxia may mediate insulin resistance by increasing the secretion of myokines and myonectin25. In summary, intermittent hypoxia induces insulin resistance by affecting the expression and secretion of adipokines, hepatocytokines, muscle factors and other inflammatory factors.

Insulin resistance is an important risk factor for the development of hypertension26,27, and the cause of hypertensive disorders due to insulin resistance is mainly related to abnormal activation of the renin-angiotensin-aldosterone system and the sympathetic nervous system28,29, as well as a decrease in insulin-mediated production of NO, which has a vasodilatory effect30. Various metabolic disorders caused by insulin resistance are also high-risk factors for cardiovascular disease31. In conclusion, the results of many previous studies support the findings of this study.

The TyG index is a classic indicator of insulin resistance, and the TyG-BMI, as a derivative of the TyG index, has been shown in most previous studies to have a significantly higher predictive power for insulin resistance than the TyG index14,32,33. The strong correlation between obesity and insulin resistance could explain these study results. In this study, considering that obesity is an independent risk factor for both OSAS and hypertension, the modified TyG index based on BMI was used as an indicator of insulin resistance and evaluated for its association with both OSAS and hypertension, and the results showed that this combined index is important for assessing the risk of hypertension in OSAS patients. The study results support the application of TyG-BMI values in clinical practice to assess the risk of hypertension in patients with OSAS and to prevent the development of hypertension by controlling body weight, fasting blood glucose and triglyceride levels. Due to the high cost of polysomnographic testing and the low specificity of the clinical presentation of OSAS, this has led to an increased rate of underdiagnosis in patients with OSAS, and the OSAS population is highly susceptible to neglect. However, the metabolic abnormalities associated with OSAS predispose individuals to the development of cardiovascular disease, making the management of this population extremely important. Monitoring and control of body weight and simple biological indicators to reduce the incidence of complications such as hypertension, is very beneficial in the management of chronic diseases. The significance of controlling these indicators should be widely publicized in the daily health education of OSAS patients in order to reduce the risk of developing hypertension.

This study has the following strengths: firstly, there are few studies on the mediators of the association between OSAS and hypertension risk, and exploring the intrinsic link between the two diseases through mediation analyses is the main innovation of this study. Second, the study used the BMI-modified TyG index as a surrogate indicator of insulin resistance, which provided a more comprehensive assessment of the risk of developing hypertension in OSAS by taking into account the additional risk factor of obesity in addition to fasting plasma glucose and triglyceride levels. Finally, compared to some studies that used patients with suspected OSAS who were screened out by snoring or portable home sleep monitors as the study population, this study used the gold standard method of polysomnographic monitoring to diagnose OSAS, which improved diagnostic accuracy and was more rigorous in terms of study design.

Some shortcomings of this study should be noted. Due to the limitations of a cross-sectional study, this study could not establish a causal relationship between OSAS and hypertension. The study was unable to collect information on participants’ dietary preferences, lifestyle and exercise habits, and other information related to hypertension. Finally, this is a single-center study, which lacks a sufficiently large sample size to be representative of the general population and has some limitations in terms of the generalisability of the results. Further cytological and molecular studies are needed to investigate the pathological mechanisms of insulin resistance in the effects of OSAS on hypertension and to reveal the causal relationship between the two diseases. At the same time, multicenter studies with large samples should be conducted to broaden the applicability of the results.

Conclusions

TyG-BMI plays an important mediating role between OSAS and the risk of hypertension, suggesting that 41.3% of the risk of hypertension in OSAS patients is attributable to insulin resistance. Chronic intermittent hypoxia in OSAS patients induces insulin resistance, which promotes abnormal activation of vascular tone regulation systems. The study supports the use of TyG-BMI as an important tool to assess hypertension risk in OSAS populations.