The Mediating Effect of Metabolic Score for Insulin Resistance on the Link Between Cardiometabolic Index and Obstructive Sleep Apnea

doi:10.21203/rs.3.rs-5162330/v1

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The Mediating Effect of Metabolic Score for Insulin Resistance on the Link Between Cardiometabolic Index and Obstructive Sleep Apnea

https://doi.org/10.21203/rs.3.rs-5162330/v1

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Background

Although there is considerable evidence that obstructive sleep apnea (OSA) is associated with cardiovascular and metabolic risks, such as insulin resistance and visceral fat accumulation, limited studies have explored how these risks are mediated by specific metabolic markers. The purpose of this study was to explore the relationship between Cardiometabolic Index (CMI) and OSA, and to pay special attention to the mediating role of insulin resistance metabolic score (METS-IR).

Methods

Data from 12,703 participants aged 20 and above were analyzed from the National Health and Nutrition Examination Survey (NHANES). CMI was the primary exposure variable, METS-IR served as the mediating variable, and OSA was the outcome. Multivariate logistic regression models were used to adjust for confounders and assess the relationship between CMI and OSA. Mediation analysis determined the proportion of CMI’s effect on OSA explained by METS-IR.

Results

Among 12,703 subjects, the prevalence of OSA was 30.88%. CMI was significantly associated with increased OSA risk, and participants with the highest quartile of CMI were significantly more likely to have OSA (OR = 2.06, 95% CI : 1.70–2.49). Mediation analysis showed that METS-IR accounted for 89.8% of the association between CMI and OSA, highlighting the key role of insulin resistance in this pathway.

Conclusions

CMI is a key predictor of OSA risk, which is mainly mediated by its effect on insulin resistance, as shown by METS-IR. These findings highlight the importance of addressing metabolic dysfunction in the prevention and management of OSA, and CMI and METS-IR provide valuable insights into clinical risk assessment in high-risk populations.

Health sciences/Endocrinology

Health sciences/Diseases

Health sciences/Diseases/Respiratory tract diseases

Cardiometabolic index

Metabolic score for Insulin resistance

Obstructive sleep apnea

Obstructive sleep apnea syndrome (OSA) is a prevalent and clinically significant sleep disorder, characterized by repeated collapse of the upper respiratory tract during sleep, leading to intermittent apnea and hypoxemia[1]. This condition severely impacts quality of life and is strongly associated with several metabolic and cardiovascular diseases, including diabetes, hypertension, myocardial infarction, and stroke[2][3]. Studies have shown that individuals with OSA face a significantly higher risk of developing these comorbidities compared to the general population[4].

With the global increase in obesity and sedentary lifestyles, the incidence of OSA has risen dramatically[5]. In some countries, OSA prevalence exceeds 20%, and this trend continues to increase[6]. Given the complex etiology of OSA, involving multiple pathophysiological mechanisms, identifying its related risk factors and potential predictors is crucial for early detection and intervention[7].

The cardiometabolic index (CMI), which integrates waist circumference, triglyceride (TG), and high-density lipoprotein cholesterol (HDL-C), has emerged as a significant marker for assessing cardiovascular and metabolic health[8]. CMI is closely linked to insulin resistance and visceral fat accumulation, making it a robust predictor of metabolic syndrome and cardiovascular diseases[9][10]. Numerous studies have demonstrated a strong association between CMI and the risk of metabolic syndrome and cardiovascular diseases[11][12]. However, its relationship with OSA remains underexplored. Given the overlap between the pathophysiological mechanisms of OSA and metabolic syndrome, it is plausible that CMI could serve as a potential predictor for OSA[13][14].

In recent years, the metabolic score for insulin resistance (METS-IR) has been proposed as an effective indicator of insulin resistance, which plays a central role in metabolic syndrome[15]. METS-IR incorporates easily obtainable clinical measures, such as waist circumference, TG, and fasting glucose, providing a reliable surrogate for direct insulin resistance measures. As insulin resistance has been implicated in the pathogenesis of OSA, METS-IR offers a promising pathway for understanding how metabolic dysfunction mediates the relationship between CMI and OSA[16]. Exploring this mediation mechanism will provide valuable insights into the metabolic processes that contribute to OSA risk.

This study utilizes data from the National Health and Nutrition Examination Survey (NHANES) to systematically assess the impact of CMI on OSA risk, focusing on the mediating role of METS-IR. By analyzing this large-scale, representative dataset, we aim to uncover the potential pathways linking CMI to OSA through insulin resistance. This study not only addresses a gap in the current literature but also offers important insights for the clinical and public health management of OSA.

2.1 Study design and participants

The National Health and Nutrition Examination Survey (NHANES) is an ongoing national program designed to gather comprehensive data regarding the dietary patterns and overall health of the U.S. population. Before beginning data collection, participants provided written informed consent, and all study procedures received approval from the ethical review board of the National Center for Health Statistics. Additional details about the NHANES program are available on its official website.

This cross-sectional study utilized data from the NHANES (2005–2008 and 2015–2020) and included patients aged 20 years and above (n = 29,763). Participants with missing data required to calculate CMI and METS-IR were excluded (n = 17,059). Additionally, data for patients diagnosed with obstructive sleep apnea syndrome (n = 1) were excluded from the analysis. The final number of subjects included in the analysis was 12,703.

2.2 Definitions of the exposure variable, mediating variable and outcome variable

The exposure variable in this study is the Cardiometabolic Index (CMI), which serves as a predictor for metabolic health. Data for calculating the CMI—specifically triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and waist-to-height ratio (WHtR)—were obtained from the NHANES database. The formula used is:\(\:\text{C}\text{M}\text{I}=\frac{\text{T}\text{G}}{\text{H}\text{D}\text{L}-\text{C}}\times\:\text{W}\text{H}\text{t}\text{R}\), which is commonly applied to assess cardiometabolic risk factors.

The mediating variable of this study was the metabolic score for insulin resistance (METS-IR), which used easily available clinical variables such as fasting blood glucose, triglycerides, body mass index (BMI) and high-density lipoprotein cholesterol (HDL-C) to quantify insulin resistance. METS-IR has been proved to be a reliable indicator of insulin resistance[17]. In this study, METS-IR was used to test its mediating role in the relationship between CMI and OSA.

In accordance with earlier studies, the outcome variable OSA is diagnosed when a person answers 'yes' to at least one of the following three NHANES questions[18]: (1) feeling excessively sleepy during the day despite getting at least 7 hours of sleep per night, as reported 16–30 times; (2) experiencing episodes of gasping, snorting, or stopping their breath on three or more occasions per week; (3) snoring on three or more occasions every week.

2.3 Potential covariates

The self-reported sociodemographic characteristics included age,sex(male/female), race/ethnicity (non-Hispanic White, Mexican American, non-Hispanic Black, other Hispanic, or other Race/multiple Races), education level (less than high school, completed high school, or more than high school), marital status (married/living with a partner, never married, widowed, divorced, or separated) and family poverty index ratio (PIR, a ratio of family income to the poverty threshold).Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. Physical activity was assessed by asking participants to report the types of physical activities they engaged in regularly, categorized into vigorous physical activities (e.g., running, playing basketball) and moderate physical activities (e.g., brisk walking, swimming, cycling). METS-IR, a calculated score based on waist circumference, triglyceride levels, and fasting blood glucose, was used as a measure of insulin resistance in this analysis. It was included as a covariate to explore its mediating role between CMI and OSA.Alcohol consumption and smoking status were also treated as categorical variables.

The participants' alcohol consumption status was categorized into five groups: never (fewer than 12 drinks in a lifetime), former (12 or more drinks in a lifetime but none in the previous year), mild (1 + drinks/day for women, 2 + drinks/day for men), moderate (2 + drinks/day for women, 3 + drinks/day for men), and heavy (5 + drinks/day for women, 4 + drinks/day for men). This categorization was based on the number of alcoholic beverages consumed per day, with the following thresholds applied:≥2 drinks/day for women and ≥ 3 drinks/day for men, binge drinking ≥ 2 days/month, and heavy drinking (≥ 3 drinks/day for women and ≥ 4 drinks/day for men, or binge drinking ≥ 5 days/month). The participants were divided into three smoking categories: never smokers (smoked fewer than 100 cigarettes), former smokers (smoked at least 100 cigarettes but not currently smoking), and current smokers (smoked at least 100 cigarettes and currently smoking daily or some days). The definition of metabolic syndrome (MetS) was in accordance with the updated National Cholesterol Education Program/Adult Treatment Panel III criteria for Americans [19]. Additionally, cardiovascular disease (CVD) was determined through interviews where participants reported diagnoses from health professionals for conditions such as congestive heart failure, coronary heart disease, angina, heart attack, or stroke.

All missing values in this study were handled using appropriate imputation methods: continuous variables were imputed with mean values, while categorical variables were assigned dummy variables.The confounding variables and their detailed definitions are collated in Table S1 for reference.

2.4 Statistical analyses

The analyses presented in this study were conducted via the R statistical computing environment (version 4.4.0). The statistical analyses were conducted in accordance with the recommendations and guidelines set forth by the NHANES, utilizing the appropriate sampling weights. This approach accounted for the complex multistage entire cohort survey design. A p value of less than 0.05 was considered to indicate a statistically significant result. The associations between CMI and OSA were investigated via multivariate logistic regression models in four different models. The crude model was not adjusted for covariates, whereas Model 1 was adjusted for age, sex, and race/ethnicity. Model 2 was adjusted for the variables in Model 1, in addition to education level, marital status, physical activity, smoking status, and alcohol status. Model 3 was adjusted for the variables in Model 2, in addition to PIR,BMI,metabolic syndrome and cardiovascular disease. A further assessment of the heterogeneity between CMI and OSA was conducted through subgroup analysis, which included the following variables: age, sex, race/ethnicity, smoking status, alcohol consumption, marital status, physical activity, metabolic syndrome and cardiovascular disease.

With METS-IR as the mediating factor, the indirect relationship between Cardiometabolic Index (CMI) and the risk of obstructive sleep apnea (OSA) was explored by mediating effect analysis. Through causal mediation analysis, we evaluated the proportion of CMI 's impact on OSA through METS-IR. The indirect effect (mediating effect) indicates to what extent the effect of CMI on OSA can be attributed to METS-IR, while the direct effect is assessed by a non-intermediary pathway. Non-parametric bootstrap re-sampling method was used to estimate the confidence interval and significance level of mediating effect and direct effect. The significance of METS-IR mediated ratio was evaluated, and how METS-IR explained the pathway between CMI and OSA was revealed.

3.1 Baseline characteristics

The weighted baseline characteristics of the participants included in the study are presented in Table 1. The total number of participants included in the analysis was 12,703, with a mean age of 47.70(47.09,48.31) years. Of these, 49.04% were male, and 50.96% were female. The majority of participants (66.38%) identified as non-Hispanic white, whereas 10.82% were non-Hispanic black, 8.60% were Mexican American, 5.76% were of other Hispanic origin, and 8.44% belonged to other racial groups or had multiple racial identities.Overall, 30.88% of the participants were categorized as having OSA.In the four CMI groups, all variables were statistically significant. Compared to participants in the lower CMI group, those in the CMI Q4 group were more likely to be male, older, and non-Hispanic white. They were also more likely to have higher METS-IR and BMI levels, be never-smokers or mild drinkers, and have lower physical activity and PIR (poverty income ratio). Additionally, they had higher educational attainment, were more often married or living with a partner, and had a greater prevalence of metabolic syndrome and cardiovascular disease.

Table 1

Baseline characteristics of participants in the NHANES 2005–2008 and 2015–2020 (n = 12,703)
Characteristic	Overall	CMI Quartiles				p value
Characteristic	Overall	Q1(< 0.277)	Q2(0.277to < 0.496)	Q3(0.496to < 0.886)	Q4(≥ 0.886)	p value
Age, years, mean(95% CI)	47.70(47.09,48.31)	43.98(42.99,44.96)	48.26(47.39,49.13)	49.21(48.32,50.11)	49.61(48.89,50.34)	< 0.0001
PIR,mean(95% CI)	3.05(2.99,3.11)	3.19(3.11,3.27)	3.07(2.98,3.17)	3.00(2.90,3.10)	2.92(2.83,3.01)	< 0.0001
BMI,mean(95% CI)	29.13(28.91,29.35)	24.74(24.51,24.98)	28.23(27.92,28.54)	30.80(30.47,31.14)	33.05(32.68,33.42)	< 0.0001
METS-IR,mean(95% CI)	43.18(42.77,43.59)	32.48(32.16,32.80)	39.86(39.41,40.30)	46.23(45.70,46.76)	54.80(54.15,55.44)	< 0.0001
Sex, n (%)						< 0.0001
Female	6520(50.96)	1909(61.18)	1717(54.17)	1592(49.07)	1302(38.86)
Male	6183(49.04)	1275(38.82)	1451(45.83)	1586(50.93)	1871(61.14)
Race/ethnicity, n (%)						< 0.0001
Non-Hispanic White	4988(66.38)	1134(65.05)	1214(67.33)	1208(63.11)	1432(69.87)
Other Race - Including Multi-Racial	1630( 8.44)	486(9.02)	368(7.05)	397(9.50)	379(8.22)
Non-Hispanic Black	2830(10.82)	1046(15.73)	826(12.16)	612( 9.95)	346( 5.17)
Mexican American	1999( 8.60)	294( 5.56)	471( 8.26)	569( 9.79)	665(11.00)
Other Hispanic	1256( 5.76)	224(4.64)	289(5.19)	392(7.65)	351(5.74)
Educational level, n (%)						< 0.0001
<High school	1254( 5.11)	159(2.86)	279(4.69)	365(5.67)	451(7.36)
Completed high school	4711(34.61)	1088(29.57)	1154(33.31)	1228(37.19)	1241(38.74)
>High school	6731(60.27)	1934(67.54)	1735(62.00)	1582(57.13)	1480(53.89)
Marital status, n (%)						< 0.0001
Married/Living with partner	7732(64.31)	1763(60.67)	1896(62.31)	1999(65.74)	2074(68.75)
Widowed/Divorced/Separated/Never married	4965(35.68)	1418(39.29)	1271(37.68)	1179(34.26)	1097(31.24)
Alcohol consumption, n (%)						< 0.0001
Former	1239( 8.78)	202( 5.11)	306( 9.14)	327( 8.86)	404(12.16)
Heavy	2264(19.10)	551(18.70)	566(19.73)	552(19.74)	595(18.31)
Mild	4068(35.73)	1088(37.04)	983(32.97)	1011(36.51)	986(36.37)
Moderate	1839(16.29)	572(21.59)	472(16.84)	431(14.31)	364(12.07)
Never	1437( 8.89)	337( 8.07)	404(10.53)	324( 7.55)	372( 9.35)
Smoking status, n (%)						< 0.0001
Never smoking	6958(53.62)	1954(59.83)	1818(56.83)	1675(51.58)	1511(45.85)
Former smoker	3179(26.46)	656(23.39)	736(24.53)	819(25.88)	968(32.10)
Current smoker	2554(19.88)	571(16.74)	610(18.58)	681(22.50)	692(21.98)
Physical activity, n (%)						< 0.0001
Inactive	5658(37.13)	1215(29.39)	1410(37.88)	1481(38.18)	1552(43.48)
Moderate	2593(22.44)	656(21.48)	656(22.15)	659(23.91)	622(22.36)
Vigorous	731( 6.33)	244(8.07)	189(6.98)	149(5.07)	149(5.06)
Both moderate and vigorous	1657(15.54)	649(25.64)	416(14.66)	340(12.76)	252( 8.47)
Metabolic Syndrome, n (%)						< 0.0001
No	7685(63.99)	2889(93.27)	2433(80.91)	1746(58.60)	617(21.64)
Yes	4758(34.83)	243( 5.85)	684(17.74)	1350(40.02)	2481(77.19)
Cardiovascular disease, n (%)						< 0.0001
No	11229(91.07)	2970(94.59)	2797(91.65)	2774(90.33)	2688(87.52)
Yes	1474( 8.93)	214( 5.41)	371( 8.35)	404( 9.67)	485(12.48)
OSA, n (%)						< 0.0001
No	8758(69.12)	2493(80.08)	2294(73.11)	2069(64.16)	1902(58.34)
Yes	3945(30.88)	691(19.92)	874(26.89)	1109(35.84)	1271(41.66)
Abbreviations: NHANES, National Health and Nutrition Examination Survey; Q, quantile;PIR, ratio of family income to poverty;BMI,body mass index;OSA,Obstructive Sleep Apnea;METS-IR,metabolic Score for Insulin Resistance

[Table 1 near here]

3.2 Association between CMI and OSA

In the RCS, CMI were positively associated with OSA(Fig. 1). Table 2 illustrates the associations between the CMI and the risk of obstructive sleep apnea (OSA) across different models. Regardless of whether the confounding factors were adjusted, CMI was significantly positively correlated with OSA in all participants. In the multivariate regression model, CMI was divided into quartiles, and the Q1 group was used as a reference group to evaluate the relationship between CMI and OSA. After adjusting for age, gender, race, education level, marital status, physical activity, smoking, drinking status, metabolic syndrome, poverty income ratio, body mass index (BMI), and cardiovascular disease, compared with the Q1 reference group, the Q2 group (OR : 1.37 ; 95% CI : 1.12, 1.67), Q3 group (OR : 1.75 ; 95% CI : 1.49, 2.05) and Q4 group (OR : 2.06 ; 95% CI : 1.70,2.49) was significantly increased, and showed a upward trend, P for trend < 0.001. This trend has been verified in different models, indicating that the positive correlation between CMI and OSA risk remains stable no matter how confounding factors are adjusted.

Table 2

Multivariable logistic regression models for the association between CMI and OSA in adults:NHANES 2005–2008 and 2015–2020
CMI	Crude model^a		Model 1^b		Model 2^c		Model 3^d
CMI	OR (95% CI)	p value	OR (95% CI)	p value	OR (95% CI)	p value	OR (95% CI)	p value
Continuous	1.42(1.29,1.56)	< 0.0001	1.38(1.25,1.52)	< 0.0001	1.34(1.22, 1.48)	< 0.0001	1.11(1.03, 1.18)	0.004
Quartiles
Q1(< 0.277)	Reference		Reference		Reference		Reference
Q2(0.277to < 0.496)	1.48(1.22,1.78)	< 0.0001	1.43(1.19,1.73)	< 0.001	1.40(1.16, 1.70)	< 0.001	1.37(1.12,1.67)	0.003
Q3(0.496to < 0.886)	2.25(1.91,2.64)	< 0.0001	2.16(1.83,2.54)	< 0.0001	2.03(1.73, 2.38)	< 0.0001	1.75(1.49,2.05)	< 0.0001
Q4(≥ 0.886)	2.87(2.47,3.34)	< 0.0001	2.72(2.32,3.20)	< 0.0001	2.55(2.16, 3.02)	< 0.0001	2.06(1.70,2.49)	< 0.0001
p for trend	< 0.0001		< 0.0001		< 0.0001		< 0.001
Abbreviations: CI, confidence interval; OR, odds ratio; Q, quantile.
In multivariate regression, samples with missing values for covariates in the model were encoded as dummy variables. The missing values were categorized as "Unknown" and included as a separate category in the analysis.
^aCrude Model: Unadjusted.
^bModel 1: Adjusted for age, sex, and race/ethnicity.
^cModel 2: Adjusted for the variables in Model 1 plus education level, marital status, physical activity,smoking status, and alcohol status.
^dModel 3: Adjusted for the variables in Model 2 plus metabolic syndrome, ratio of family income to poverty,body mass index and cardiovascular disease

[Table 2 near here]

3.3 Subgroup analysis

Table 3 Subgroup analyses were performed based on age (≤ 40 years old, 40–60 years old, > 60 years old), gender (male, female), race / ethnicity (non-Hispanic whites, non-Hispanic blacks, Mexican Americans, other races), education level (high school), and other factors (such as smoking status, drinking, marital status, physical activity, metabolic syndrome, and cardiovascular disease) to assess the stability of the association between CMI and OSA. An interaction test was performed on these factors to determine whether the association between CMI and OSA changed due to these variables. The results showed a significant interaction between gender (p-interaction = 0.03), race /ethnicity (p-interaction = 0.002), smoking status (p-interaction = 0.001), alcohol consumption (p-interaction = 0.019) and metabolic syndrome (p-interaction = 0.01), indicating that these factors affect the strength of the association between CMI and OSA. However, no significant interaction was found between age, education, marital status, physical activity or cardiovascular disease, suggesting that the relationship between CMI and OSA was consistent in these subgroups.

Table 3

Associations of CMI with OSA by different factors
character	95% CI	p	p for interaction
Age	1.341(1.306,1.378)	< 0.0001	0.157
≤40	1.461(1.201,1.777)	< 0.001
>40,≤60	1.489(1.277,1.736)	< 0.0001
>60	1.197(1.045,1.370)	0.010
Sex			0.03
Female	1.623(1.373,1.918)	< 0.0001
Male	1.311(1.170,1.468)	< 0.0001
Race/ethnicity			0.002
Non-Hispanic White	1.547(1.365,1.755)	< 0.0001
Other Race - Including Multi-Racial	1.749(1.381,2.214)	< 0.0001
Non-Hispanic Black	1.580(1.190,2.096)	0.002
Mexican American	1.165(1.003,1.352)	0.045
Other Hispanic	1.111(0.954,1.293)	0.172
Education level			0.893
< High school	1.326(1.014,1.735)	0.039
Completed high school	1.411(1.248,1.595)	< 0.0001
> High school	1.447(1.242,1.687)	< 0.0001
Smoking status			0.001
Former smoker	1.302(1.127,1.503)	< 0.001
Never smoking	1.687(1.451,1.961)	< 0.0001
Current smoker	1.186(1.051,1.338)	0.006
Alcohol status			0.019
Former	1.221(1.031,1.445)	0.021
Heavy	1.229(1.055,1.431)	0.009
Mild	1.667(1.327,2.095)	< 0.0001
Moderate	1.236(1.024,1.492)	0.028
Never	1.333(1.010,1.758)	0.042
Marital status			0.82
Married/Living with Partner	1.418(1.252,1.606)	< 0.0001
Widowed/Divorced/Separated/Never married	1.375(1.207,1.568)	< 0.0001
Physical activity			0.931
Both moderate and igorous	1.368(1.004,1.863)	0.047
Moderate	1.387(1.073,1.792)	0.013
Inactive	1.461(1.282,1.665)	< 0.0001
Vigorous	1.439(1.026,2.017)	0.035
Metabolic Syndrome			0.01
No	1.631(1.259,2.115)	< 0.001
Yes	1.131(1.054,1.215)	< 0.001
Cardiovascular disease			0.754
No	1.416(1.270,1.579)	< 0.0001
Yes	1.338(1.123,1.596)	0.001

[Table 3 near here]

3.4 Mediation Analysis

In this study, the metabolic score for insulin resistance (METS-IR) was used as a mediating variable to explore the relationship between cardiovascular metabolic index (CMI) and obstructive sleep apnea (OSA). Figure 2 showed the results of mediating analysis that METS-IR significantly mediated the relationship between CMI and OSA, with a mediating effect of 0.03272 (p = 0.004) and a mediating ratio of 89.8%. This suggests that the association between CMI and OSA is largely explained by the effect of METS-IR, highlighting the central role of insulin resistance in this pathway.

In addition, the direct effect of CMI on OSA was not statistically significant (p = 0.818), indicating that the effect of CMI on OSA was mainly mediated by METS-IR, rather than directly related. These findings underscore the importance of addressing insulin resistance in assessing OSA risk in individuals with elevated CMI, as targeting insulin resistance may be a potential strategy to reduce OSA risk.

This study evaluated the role of cardiac metabolic index (CMI) in predicting obstructive sleep apnea (OSA), with particular emphasis on how insulin resistance captured by the metabolic score for insulin resistance (METS-IR) mediates this relationship. Both CMI and METS-IR are recognized markers of metabolic health, reflecting different aspects of cardiovascular risk, which together help to explain the mechanism of OSA.

CMI, including waist-to-height ratio, triglyceride and high-density lipoprotein cholesterol, is a comprehensive index to measure metabolic health, focusing on visceral fat accumulation and lipid metabolism [20].The link between visceral fat and OSA is clear;visceral fat exerts mechanical pressure on the upper airway, increasing the possibility of collapse during sleep, which directly leads to OSA [21]. In addition, visceral fat promotes systemic inflammation, which is also closely related to OSA. Given the sensitivity of CMI to visceral fat accumulation, it can be considered as an early warning marker for individuals at risk of OSA.

However, METS-IR showed that the relationship between CMI and OSA was mainly mediated by insulin resistance. Insulin resistance (METS-IR) is a key metabolic dysfunction in OSA.METS-IR captures insulin resistance by combining fasting blood glucose, triglycerides, and waist circumference. OSA is characterized by intermittent hypoxia, which aggravates oxidative stress and systemic inflammatory response, and further aggravates insulin resistance [22] [23]. With the aggravation of insulin resistance, it will lead to the accumulation of visceral fat and other metabolic disorders, such as dyslipidemia and hypertension, which will aggravate the development of OSA [24].

Our mediation analysis showed that METS-IR mediated more than 89% of the effect between CMI and OSA, emphasizing that the effect of CMI on OSA was mainly through the mechanism of insulin resistance. Although CMI itself may not have a strong direct effect on OSA, its indirect effect through METS-IR makes it a valuable marker of cardiovascular metabolic risk in patients with sleep apnea. This finding highlights the role of insulin resistance as a key mediator between metabolic health and the risk of sleep apnea.

Visceral fat captured by CMI is an important driver of systemic inflammation. It increases the secretion of pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6) and c-reactive protein (CRP), leading to chronic low-grade inflammation, insulin resistance and oxidative stress [25] [26]. In particular, TNF-α has been shown to impair insulin receptor signaling and further aggravate insulin resistance [27]. In addition, IL-6 promotes liver glucose production and aggravates the body 's impaired insulin response [28]. This chronic inflammation plays an important role in metabolic dysfunction associated with OSA and type 2 diabetes.

Adipose tissue dysfunction also plays a central role in metabolic disorders. In addition to the mechanical effects on airway collapse, visceral fat also stimulates the hypothalamic-pituitary-adrenal (HPA) axis, leading to increased cortisol production and central obesity [29]. Elevated cortisol levels not only aggravate insulin resistance, but also reduce sleep quality, leading to the severity of OSA. This periodic interaction indicates that OSA aggravates insulin resistance and further deteriorates metabolic health [30].

OSA is characterized by intermittent hypoxia, which stimulates excessive activation of the sympathetic nervous system and increases reactive oxygen species (ROS), thereby disrupting insulin signaling and further exacerbating this vicious cycle [31]. This cascade reaction not only affects glucose metabolism, but also leads to endothelial dysfunction, leading to common cardiovascular complications in OSA patients. The interaction between insulin resistance, chronic inflammation, and intermittent hypoxia forms a feedback loop that promotes fat accumulation and metabolic disorders[32] [33].

METS-IR captures this dysfunction by reflecting insulin resistance, which not only disrupts glucose homeostasis[34], but also accelerates the release of free fatty acids from adipose tissue[35], worsens metabolic control[36], and may lead to nonalcoholic fatty liver disease (NAFLD) - a disease closely associated with OSA [37].

Given these interconnected mechanisms, the relationship between CMI, METS-IR and OSA is multifaceted. CMI is a predictor of insulin resistance and inflammation by capturing visceral fat[38], and METS-IR further magnifies this relationship, directly linking metabolic dysfunction to the severity of OSA[39]. Clinically, these findings suggest that strategies aimed at reducing visceral fat and improving insulin sensitivity can be used as effective interventions to prevent and manage OSA [40].

This study has several significant advantages. The use of NHANES data provides a large nationally representative data set, which enhances the reliability of our research results. In addition, the correction of various confounding factors and the mediating analysis between CMI, METS-IR and OSA increased our understanding of related metabolic pathways. However, the cross-sectional nature of this study limits the ability to determine causality, and further longitudinal studies are needed to verify these findings.

This study showed that Cardiometabolic Index (CMI) and insulin resistance metabolic score (METS-IR) were significantly associated with the risk of obstructive sleep apnea (OSA), and the effect of CMI was mainly mediated by METS-IR. These findings highlight the importance of metabolic health in the development of OSA, suggesting that CMI (reflecting visceral fat accumulation) and METS-IR (as an indicator of insulin resistance) together provide valuable predictive insights into OSA risk. Future studies should further explore the potential of these markers in clinical applications to improve OSA risk assessment and management.

NHANES: National Health and Nutrition Examination Survey; CDC: Centers for Disease Control and Prevention; CMI:Cardiometabolic Index,METS-IR:Metabolic Score for Insulin Resistance,BMI: body mass index;PIR:a ratio of family income to the poverty threshold.

Ethics approval and consent to participate: The National Center for Health Statistics Research Ethics Review Board authorized the NHANES study protocols in compliance with the revised Declaration of Helsinki. All participants provided informed consent.

Consent for publication: Not applicable.

Availability of data and materials: The data used in this study are available on the National Health and Nutrition Examination Survey website: https://www.cdc.gov/nchs/nhanes/index.htm

Competing interests: The authors declare that they have no competing interests.

Funding: This work was supported by National Natural Science Foundation of China (No. 82174491) and Key Project of Shandong Province Traditional Chinese Medicine Science and Technology Program (No. Z–2023019).

Authors’ contributions: SYS participated in the literature search, study design, data collection, data analysis, and data interpretation and wrote the manuscript. XHL,YCL,XXW and WHZ conceived the study and participated in its design, coordination, data collection and analysis. JGY and YXL participated in the study design and provided critical revision. All the authors read and approved the final manuscript.

Acknowledgment: Special thanks to all of the NHANES participants who freely gave their time to make this and other studies possible.

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No competing interests reported.

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The Mediating Effect of Metabolic Score for Insulin Resistance on the Link Between Cardiometabolic Index and Obstructive Sleep Apnea

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Abstract

Background

Methods

Results

Conclusions

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1. Introduction

2. Methods

2.1 Study design and participants

2.2 Definitions of the exposure variable, mediating variable and outcome variable

2.3 Potential covariates

2.4 Statistical analyses

3. Results

3.1 Baseline characteristics

3.2 Association between CMI and OSA

3.3 Subgroup analysis

3.4 Mediation Analysis

4. Discussion

5. Conclusion

Abbreviations

Declarations

References

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