Association between age-adjusted visceral adiposity index and obstructive sleep apnea: a study based on NHANES 2015–2018

Introduction

Obstructive sleep apnea (OSA), a prevalent sleep disorder, features recurrent upper airway collapse in sleep, causing episodes of apnea or hypopnea. These interruptions in breathing typically last for several seconds to minutes, occurring frequently and severely compromising sleep quality and daytime alertness (1). Epidemiological studies estimate the global prevalence of OSA ranging from 9% to 38%, with a significantly higher incidence in males than females. Additionally, its prevalence rises with age, and the rates are as high as 90% in old men and 78% in old women (2). Notably, there is a strong correlation of obesity with OSA, with the prevalence of OSA being approximately 2.7 times higher in obese populations compared to the general population (3). Despite its public health significance, OSA often remains underdiagnosed, with many patients suffering from its symptoms long before receiving a formal diagnosis (4). The nonspecific nature of OSA symptoms, which are frequently misattributed to other conditions such as fatigue or insomnia, contributes to diagnostic delays. Undiagnosed and untreated OSA lowers patients’ quality of life and elevates the likelihood of cardiovascular disease (CVD), metabolic syndrome, diabetes, and stroke (5). Therefore, identifying high-risk populations and implementing effective prevention and management strategies are crucial for improving public health outcomes.

To better predict and assess the risk of OSA, researchers have sought effective biomarkers reflecting body fat distribution, particularly abdominal fat accumulation. Visceral obesity has emerged as a critical factor closely associated with the development and progression of OSA. Excess visceral fat may compress the airway, increase respiratory resistance, and alter upper airway anatomy, exacerbating OSA symptoms (6). Furthermore, visceral fat may indirectly influence the development of OSA through systemic inflammatory responses and oxidative stress, among other factors (7). However, traditional obesity measures like body mass index (BMI), although widely used, cannot help differentiate between muscle and fat tissue proportions nor effectively reflect the accumulation of visceral fat (8). To address this limitation, Amato et al. introduced and defined the visceral adiposity index (VAI), a parameter calculated based on metabolic factors, which aims to better reflect body fat distribution, particularly abdominal fat accumulation (9). Recent research has proved a significant relation of VAI to OSA (10).

Nevertheless, with aging, changes in metabolism, hormone levels, and lifestyle can lead to alterations in body fat distribution (11). Although VAI offers a novel perspective for assessing visceral fat function and insulin resistance, it does not thoroughly accommodate age-associated differences in visceral fat distribution, which may limit its applicability in certain population groups. To overcome this limitation, this study utilizes the age-adjusted visceral adiposity index (AVAI). AVAI not only retains the advantages of VAI but also incorporates the impact of age, providing a more accurate reflection of visceral fat status in individuals across different age groups. This adjustment enhances the sensitivity and specificity of AVAI, particularly in assessing old populations or cross-age groups (12).

Therefore, this study aims to elucidate the correlation of AVAI with OSA, based on the National Health and Nutrition Examination Survey (NHANES) database, and explore whether AVAI serves as an independent factor influencing OSA. The findings may offer a new perspective for the risk assessment of OSA, facilitating more effective early intervention and disease management. We present this article in accordance with the STROBE reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1337/rc).

Methods

Data and sample sources

Our data were from NHANES, a nationally representative cross-sectional survey designed and implemented by the National Center for Health Statistics (NCHS) through a stratified, multistage probability sampling method to sample the U.S. population and offer health and nutrition information for noninstitutionalized American civilians. The study was authorized by the NCHS Institutional Review Board, which verified that informed consent was gained from every participant. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was authorized by the NCHS Institutional Review Board, which verified that informed consent was gained from every participant. Detailed statistical data are available at http://www.cdc.gov/nhanes.

Adults from the 2015–2018 NHANES cycle were encompassed. The exclusion criteria were: (I) age <20 years; (II) absence of questionnaire data related to OSA; (III) missing data on BMI, high-density lipoprotein cholesterol (HDL), waist circumference (WC), triglycerides (TG), or age; (IV) missing data on pre-existing hypertension, diabetes, CVD, smoking, alcohol consumption, education, or poverty-to-income ratio (PIR). A detailed exclusion flowchart is presented in Figure 1.

Figure 1 Flowchart of participant selection from NHANES 2015–2018. A total of 19,225 participants were initially identified from NHANES 2015–2018. Participants were sequentially excluded due to: age <20 years (n=7,937), missing data on OSA questionnaire (n=3), missing data on AVAI parameters (n=6,866), and missing data on covariates (n=3,255). The final analytic sample included 3,611 adults. AVAI, age-adjusted visceral adiposity index; NHANES, National Health and Nutrition Examination Survey; OSA, obstructive sleep apnea.

Assessment of OSA

OSA was assessed based on a previous study (13). A diagnosis of OSA was made when a participant answered “yes” to one or more of three NHANES questions: (I) in spite of sleeping at least 7 hours every night, feeling excessively sleepy during the day on more than 16–30 occasions; (II) experiencing snoring, gasping, or cessation of breathing three or more times per week; (III) snoring three or more times each week.

Although the definition of OSA in this study was based on participants’ self-reported symptoms from the NHANES questionnaires, this approach is widely adopted in large-scale epidemiological studies due to its practicality and cost-effectiveness. Recent research utilizing NHANES data has demonstrated that self-reported OSA symptoms are significantly associated with a range of cardiometabolic outcomes, supporting the validity of this method in population-based analyses.

Assessment of AVAI

AVAI was calculated based on a set of metabolic parameters and body measurements, designed to more accurately reflect visceral fat status and its impact on health across different age groups. In this study, the AVAI calculation formula is as follows (14):

AVAI=−16.186−1.369×HDL+0.038×WC+0.144×age−0.013×BMI−0.151×TG[1]

HDL: measured in mmol/L; WC: measured in cm; age: measured in years; BMI: weight (kg) divided by height (m) squared; TG: measured in mmol/L.

Covariates

This study included 10 potential confounding variables: gender, race, PIR, education, marital status, smoking and alcohol use, hypertension, diabetes, as well as CVD. Racial categories included Mexican American, non-Hispanic White, non-Hispanic Black, and other races. Education was categorized as less than high school, high school graduate, or some college and higher. Smoking status was categorized based on lifetime smoking history, using a threshold of fewer than 100 cigarettes smoked and over 100 cigarettes smoked. Alcohol use was classified as never drinking in the past year or consuming ≥12 drinks per year. Hypertension was determined through a doctor’s diagnosis, with average systolic blood pressure (SBP) ≥140 mmHg or diastolic blood pressure (DBP) ≥90 mmHg (15). Diabetes was defined as having been told by a doctor that the participant had diabetes (10). Being diagnosed with congestive heart failure, coronary artery disease, angina, myocardial infarction, or stroke by a physician was deemed CVD (16).

Statistical analysis

In data analysis, our study adhered to the guidelines of NHANES and included the morning fasting weight variable (WTINT2YRs) to ensure nationally representative estimates. To account for the standard errors (SEs) related to the intricate survey design, primary sampling unit (SDMVPSU) and stratum (SDMVSTRA) variables were adopted.

For categorical variables, frequency or percentage was utilized for description, and the Chi-squared test was employed for group comparison. Regarding continuous variables, normality was first checked via the Kolmogorov-Smirnov test. Continuous variables in normal distribution were described using means and standard deviations, while those not in normal distribution were presented as medians and interquartile ranges (IQRs). The Kruskal-Wallis H test was applied to examine the differences between groups for non-normally distributed continuous variables.

A multivariable logistic regression model was leveraged to unravel the relationship of AVAI with OSA. AVAI was treated as a continuous variable and categorized based on its quartiles. Specifically, Model 1 represented the unadjusted raw model; Model 2 adjusted for sociodemographic factors such as gender, race, education, marital status, PIR, smoking, and alcohol use; Model 3 further adjusted for health-correlated factors: diabetes, hypertension, and CVD on the basis of Model 2. The degree of multicollinearity between covariates was evaluated via the generalized variance inflation factor (GVIF). All GVIFs in the regression models were below 4, indicating no marked multicollinearity across covariates (Table S1).

To explore the potential heterogeneity of the relationship between AVAI and OSA across different populations, subgroup analyses were planned based on factors such as gender, diabetes, hypertension, CVD, smoking, alcohol consumption, and menopausal status. Furthermore, to assess the potential moderating effects of these key variables, interaction terms were included in the multivariable logistic regression models to examine whether gender, diabetes, hypertension, CVD, smoking, alcohol consumption, or menopausal status influenced the association between AVAI and OSA. Statistical significance was evaluated using the Wald test. All statistical analyses were performed using R version 4.3.3, and a two-sided P<0.05 was deemed statistical significance.

To control possible confounders and further validate the robustness of the findings, propensity score matching (PSM) was also applied in this study. Propensity scores were calculated based on covariates such as gender, age, race, BMI, smoking, alcohol use, hypertension, diabetes, and CVD. The 1:1 nearest neighbor matching method (caliper =0.1) was employed for matching OSA and non-OSA groups and balancing the baseline characteristics between them. The balance was assessed via the standardized mean difference (SMD), with a value of less than 0.1 suggesting balanced baseline characteristics. Subsequently, multivariable logistic regression models were refitted using the matched samples to investigate the relationship between AVAI and OSA. Finally, to evaluate the clinical applicability of AVAI, receiver operating characteristic (ROC) curve analysis was performed. The area under the curve (AUC) with 95% confidence interval (CI) was calculated to assess the discriminative ability of AVAI for OSA.

Results

Demographic characteristics

3,611 participants from the NHANES dataset were included. Table 1 presents the baseline characteristics of participants based on the presence of OSA. Overall, 49% were male and 51% were female. The median age was 48 (range, 34–61) years, with the median age for the OSA group being 50 (range, 37–62) years and the mean age for the non-OSA group being 46 (range, 31–61) years. The majority of participants were non-Hispanic whites. Statistically significant differences existed between the OSA and non-OSA cohorts in gender, age, BMI, marital status, AVAI index, smoking, hypertension, and diabetes. However, significant variations were not noted in terms of race, education, PIR, alcohol consumption, or CVD.

AVAI and OSA correlation analysis

To unveil the link of OSA to AVAI, three stepwise-adjusted multivariable logistic regression models were created (Table 2). In the unadjusted Model 1, the initial analysis showed a significant positive relation of OSA to AVAI [odds ratio (OR) =1.15, 95% CI: 1.11, 1.19]. In Model 2, after adjusting for demographic characteristics and smoking/alcohol consumption, their significant positive correlation between OSA and AVAI remained (OR =1.17, 95% CI: 1.12, 1.22). In the Model 3, after all covariates were adjusted, the significant correlation persisted (OR =1.15, 95% CI: 1.09, 1.21). Furthermore, when AVAI was categorized based on quartiles for analysis, the results indicated that, in all three models, participants in higher quartiles had a markedly risen likelihood of OSA in comparison to those in the lowest quartile, with differences being statistically significant.

Subgroup analysis

To further explore the heterogeneity of the correlation of AVAI with OSA across cohorts, subgroup analyses were built based on gender, hypertension, diabetes, CVD, smoking status, and alcohol consumption. Figure 2 demonstrated a significant positive link of AVAI to OSA in the subgroups of females, non-hypertensive, non-diabetic individuals, or non-CVD patients, and across both premenopausal and postmenopausal women. However, no significant association was found in the subgroups of males, hypertensive patients, diabetic patients, or those with CVD. Interaction tests proved notable differences in the relation of AVAI to OSA with respect to gender, hypertension, diabetes, CVD and menopausal status (P_interaction <0.05), suggesting that these factors significantly modified the association. Additionally, a notable positive correlation of AVAI with OSA was found regardless of smoking or drinking status. Interaction tests showed no significant interaction between smoking or drinking habits and this association (P>0.05).

Figure 2 Subgroup analysis of the association between AVAI and OSA using multivariable logistic regression. Adjusted ORs and 95% CIs are presented for the association between AVAI and OSA across predefined subgroups. Multivariable logistic regression models were adjusted for relevant covariates. Statistically significant interaction effects were observed for sex, hypertension, diabetes, and CVD (P for interaction <0.05), indicating effect modification. AVAI, age-adjusted visceral adiposity index; CI, confidence interval; CVD, cardiovascular disease; OR, odds ratio; OSA, obstructive sleep apnea.

Sensitivity analysis

To control for baseline imbalances, PSM was used to match OSA and non-OSA groups in a 1:1 nearest-neighbor fashion. After matching, the differences in baseline characteristics across groups were significantly reduced. The matched population characteristics are shown in Table S2. In the matched sample, there were 1,521 participants in each group, and the SMD were all less than 0.1, indicating a good balance in baseline characteristics. In the matched sample, three stepwise-adjusted multivariable logistic regression models were fitted, and the results confirmed that the positive correlation between OSA and AVAI remained significant across all models, as detailed in Table 3.

ROC curve analysis

To further evaluate the clinical applicability of AVAI in predicting OSA, ROC curve analysis was performed. The overall AUC was 0.589 (95% CI: 0.571, 0.608), indicating limited discriminative ability when AVAI was used as a single marker, as detailed in Figure 3.

Figure 3 ROC curve analysis of AVAI for predicting OSA. The ROC curve was used to evaluate the discriminative ability of the AVAI for OSA. The AUC was 0.589 (95% CI: 0.571–0.608), indicating limited predictive performance of AVAI when used as a single marker. AVAI, age-adjusted visceral adiposity index; AUC, area under the curve; CI, confidence interval; OSA, obstructive sleep apnea; ROC, receiver operating characteristic.

Discussion

This study, based on the NHANES database, thoroughly investigates the association of AVAI with OSA. There existed a significant positive relation of AVAI to OSA, both in the overall sample and in the matched sample, with this association remaining robust after adjusting for multiple covariates. This finding provides substantial support for the potential clinical application of AVAI as a tool for assessing OSA risk.

Obesity, particularly abdominal obesity, is one of the biggest risk factors for OSA. The accumulation of visceral fat exacerbates the occurrence of OSA through various mechanisms (17). First, the increase in visceral fat can mechanically compress the airway, alter its anatomical structure, and increase airway resistance, especially during sleep (18). Additionally, the deposition of abdominal fat, particularly in the cervical region, limits airway space, further increasing airway resistance (19). Studies have shown that increased neck fat is directly associated with the severity of OSA. Moreover, the metabolic disturbances commonly associated with obesity, along with increased oxidative stress, are also closely linked to the exacerbation of OSA. Pro-inflammatory cytokines like tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) secreted by visceral fat can amplify airway inflammation, leading to damage of airway epithelial cells and thereby worsening OSA symptoms (20). Furthermore, obesity is closely associated with dysregulation of the autonomic nervous system, and studies have shown that increased sympathetic nerve excitability reduces airway muscle tone, promoting airway collapse and increasing the risk of OSA (21).

Although BMI is a commonly used tool for assessing obesity, it does not adequately reflect the distribution of fat, particularly the accumulation of visceral fat. AVAI, by incorporating age, waist circumference, BMI, TG, and HDL, provides a more accurate representation of an individual’s visceral fat function and metabolic status (12). Therefore, AVAI overcomes the limitations of BMI and enables a more comprehensive evaluation of the influence of visceral fat on OSA. Our study proves a marked link of AVAI to OSA, with AVAI serving as a crucial independent predictor of OSA. This finding aligns with other research, particularly in the context of metabolic abnormalities and obesity, where AVAI, as a novel indicator reflecting abdominal fat function, is more sensitive than reliance solely on BMI. A study by Liu et al. found that AVAI outperformed VAI in predicting CVD mortality, suggesting that AVAI better captures the functional state of abdominal fat (12). However, our ROC curve analysis demonstrated that the discriminative performance of AVAI alone for OSA was limited (AUC <0.6). This suggests that while AVAI provides incremental information on visceral adiposity beyond BMI, it may be more valuable as a supplementary index rather than a standalone diagnostic tool. Future research should consider combining AVAI with other clinical parameters or biomarkers to improve its predictive utility for OSA risk stratification.

The advantage of AVAI lies in its comprehensive consideration of the effects of age on fat distribution and metabolic function. With advancing age, the accumulation of visceral fat not only increases the total amount of fat but also significantly alters its metabolic characteristics. Studies have shown that the accumulation of visceral fat in the old population is closely associated with the development of insulin resistance (22). Furthermore, the pro-inflammatory cytokines secreted by visceral fat, such as TNF-α and IL-6, may trigger a systemic inflammatory response, thereby increasing airway inflammation and the risk of airway collapse and obstruction (23). By integrating these metabolic factors, AVAI can more accurately reflect the impact of visceral fat on OSA, particularly in old patients, where it can identify changes in visceral fat function and indicate potential airway instability (24). Additionally, visceral fat exacerbates OSA symptoms through mechanisms such as mechanical compression of the airway, alterations in lipid metabolism, and increased oxidative stress (25). The increase in visceral fat may trigger the release of fatty acids, pro-inflammatory cytokines, and lipophilic hormones, which not only affect the body’s metabolic balance but also potentially worsen airway instability by altering autonomic nervous system function (26). Research indicates that elevated AVAI levels are closely associated with metabolic disorders such as insulin resistance and high TG, which may increase airway instability and further elevate the risk of apnea during sleep (27). Therefore, AVAI, by combining multiple physiological and metabolic parameters, offers a more comprehensive view of the complex effects of visceral fat on OSA.

Subgroup analysis results show that the association between AVAI and OSA varies significantly according to factors such as gender, hypertension, diabetes, and CVD. In subgroups of women, non-hypertensive individuals, non-diabetic patients, and non-CVD patients, there was a noteworthy positive association found between OSA and AVAI. However, in subgroups of men, hypertensive patients, diabetic patients, or those with CVD, the relationship between AVAI and OSA was not significant. One possible explanation is that men generally exhibit greater visceral fat accumulation across all levels of AVAI, which may reduce the incremental discriminative value of this index in differentiating OSA risk (28). Similarly, metabolic disorders such as hypertension, diabetes, and CVD are associated with systemic inflammation, endothelial dysfunction, and oxidative stress, which could overshadow or confound the contribution of visceral adiposity reflected by AVAI, thereby attenuating its predictive ability in these subgroups. This finding suggests that the relationship between AVAI and OSA may be significantly influenced by gender and metabolic disease status. Gender differences may correlate with variations in hormone levels, fat distribution, and lipid metabolism (29). Pre-menopausal women typically have a lower proportion of visceral fat (30), and estrogen exerts a protective effect on fat distribution, which may make AVAI more significant in women. In contrast, men generally exhibit greater visceral fat accumulation, which may limit AVAI’s ability to further differentiate the risk of OSA (31). In line with this, our additional analyses stratified by menopausal status showed that the positive association between AVAI and OSA persisted in both premenopausal and postmenopausal women, though the magnitude of association differed. These findings indicate that reproductive status and sex hormones may further modulate the relationship between visceral adiposity and OSA risk (28), underscoring the importance of considering menopausal status in clinical applications of AVAI. Additionally, metabolic diseases like hypertension, diabetes, and CVD are closely linked to the accumulation and dysfunction of visceral fat (32). Studies have shown that these metabolic disorders may interfere with AVAI’s predictive ability for OSA by altering fat metabolism, exacerbating oxidative stress, and promoting inflammation (27). Hypertensive, diabetic, and CVD patients often exhibit stronger systemic inflammatory responses and metabolic imbalances, which could weaken the sensitivity of AVAI to OSA risk in these populations (33). Interaction tests further indicate that the relationship between AVAI and OSA varies significantly under the influence of factors such as gender, hypertension, diabetes, CVD, and menopausal status (P interaction <0.05), suggesting that these factors modulate the correlation of AVAI with OSA. Therefore, an individual’s gender and health status may play an important role when using AVAI to assess OSA risk, and clinical applications of AVAI should be personalized based on these factors.

The primary strength of our study is the utilization of NHANES data, which are nationally representative and reflect the overall health of American adults. Furthermore, the study employed the novel AVAI as a measure of visceral obesity, a metric that offers a more comprehensive and accurate reflection of the functional and metabolic status of visceral fat. Additionally, various statistical models were utilized to ensure the robustness of the method, and subgroup analyses were performed to clarify the relationship of AVAI with OSA, highlighting the modifying effects of gender and metabolic abnormalities. The robustness of the results remained consistent after adjusting for different models, thereby increasing the reliability and generalizability of our conclusions.

Although valuable insights are presented, some limitations exist. First, the cross-sectional design precludes causal inferences between AVAI and OSA; future research should consider longitudinal designs to confirm the long-term relationship between the two. Second, the diagnosis of OSA in this study was based on self-reported symptoms from NHANES questionnaires. Compared with the gold-standard polysomnography, this approach may lead to misclassification, particularly underestimating mild or asymptomatic OSA cases, which could attenuate the observed associations. Nevertheless, the use of self-reported data is common in large-scale epidemiological studies, and prior NHANES-based research has shown acceptable validity, especially regarding associations with metabolic and cardiovascular outcomes. Furthermore, recent studies utilizing NHANES data have demonstrated that self-reported OSA symptoms are significantly associated with both metabolic and cardiovascular outcomes, supporting the validity of this approach in population-level analyses. Lastly, while the study controlled for various potential confounders, some factors, such as genetic background, may not have been thoroughly accounted for, possibly influencing our results.

Conclusions

This study, leveraging the NHANES database, investigates the relationship between AVAI and OSA, revealing a significant association between the two. Elevated AVAI scores are shown to increase the risk of OSA. Subgroup analysis further demonstrates the modifying effects of gender and metabolic disorders on this relationship. Future research should validate the applicability of AVAI in different populations and explore its potential in OSA screening.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1337/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1337/prf

Funding: This work was supported by grants from Natural Science Foundation of Fujian Province (No. 2023J011690), Medical and Health Guidance Project of Xiamen Science and Technology Bureau (No. 3502Z20224ZD1085).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-1337/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was authorized by the NCHS Institutional Review Board, which verified that informed consent was gained from every participant.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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