Comparative Analysis of Anthropometric Indices for  Predicting Obstructive Sleep Apnea among U.S. Adults

doi:10.21203/rs.3.rs-4904818/v1

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Comparative Analysis of Anthropometric Indices for Predicting Obstructive Sleep Apnea among U.S. Adults

https://doi.org/10.21203/rs.3.rs-4904818/v1

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Background: Obstructive sleep apnea syndrome (OSA) is a significant public health issue with adverse outcomes such as cardiovascular disease and metabolic disorders. Obesity is a primary risk factor for OSA, and traditional obesity indicators like BMI may not accurately reflect fat distribution, which is crucial in OSA development. Other anthropometric indicators like waist circumference (WC), body mass index (BMI),body roundness index(BRI), visceral adiposity index (VAI),lipid accumulation product(LAP),a body shape index(ABSI) and weight-to-waist index (WWI) have been proposed for better assessment of obesity-related health risks.

Methods:A cross-sectional study was conducted using data from the 2005-2008 and 2015-2018 NHANES, which included a total of 8,966 participants. We used restricted cubic spline (RCS) analysis, logistic regression analysis, and subgroup analysis to assess the associations of anthropometric indices with OSA. We assessed the ability of the anthropometric indices to identify OSA using receiver operating curve (ROC) analysis and area under the curve (AUC)and compared them using the Delong test.

Results:Based on NHANES data, this study revealed that waist circumference (WC) and body mass index (BMI) were the strongest predictors of obstructive sleep apnea (OSA) risk, with their associations remaining significant after adjusting for all covariates. Subgroup analysis revealed that this positive correlation was particularly strong in the 40-60 years age group, non-Hispanic whites, inactive individuals, and those with higher education. ROC analysis confirmed that WC and BMI had the highest AUC values of 0.637 and 0.633, respectively, making them the most effective in distinguishing OSA cases. Although the predictive power of the BRI, LAP, VAI, and WWI was weaker, the BRI still showed some predictive ability. Bonferroni correction and DeLong tests further validated WC and BMI as the most reliable indicators for predicting OSA.

Conclusions: WC, BMI, BRI, LAP, VAI and WWI were significantly correlated with OSA. It is worth noting that WC and BMI, two traditional anthropometric indicators, show the best predictive ability compared with other new measurement indicators. Among them, WC has the characteristics of high efficiency and simple measurement in predicting OSA in clinical application, and is the best choice for early detection and treatment strategies.

Health sciences/Risk factors

Health sciences/Diseases/Respiratory tract diseases

obstructive sleep apnea

obesity

anthropometric indices

National Health and Nutrition Examination Survey(NHANES)

Obstructive sleep apnea syndrome (OSA) is an increasingly serious public health problem that has a significant impact on personal health and global health systems. OSA is characterized by recurrent upper respiratory tract obstruction during sleep and is closely related to many adverse health outcomes, such as cardiovascular disease, metabolic disorders, and decreased quality of life. The increased prevalence of OSA highlights the need for effective prediction and early intervention strategies [1] [2] [3]. Obesity is one of the main risk factors for OSA. Traditional obesity indicators such as body mass index (BMI) have been widely used to assess the risk of OSA. However, BMI cannot accurately reflect the distribution of body fat, and body fat is a key factor in the pathogenesis of OSA [4] [5] [6]. Therefore, researchers have proposed other anthropometric indicators, such as waist circumference (WC), a body shape index (ABSI), the body roundness index (BRI), the visceral adiposity index (VAI), the lipid accumulation product (LAP) and the weight-to-height ratio (WWI), to assess obesity and its associated health risks more comprehensively [7].

Waist circumference (WC) is a direct indicator of abdominal fat accumulation and is closely related to a variety of health outcomes, including cardiovascular disease and metabolic syndrome [8]. The weight-to-Waist Index (WWI) is a new index that consider the ratio of body weight to waist circumference and aims to better reflect the influence of visceral fat [9]. The body roundness index (BRI) is an index that estimates body fat distribution by considering waist circumference and height [10]. The visceral Adiposity Index (VAI) is used to evaluate visceral fat by combining waist circumference, BMI, triglyceride and high-density lipoprotein levels [11]. The lipid Accumulation Product (LAP) reflects fat accumulation through waist circumference and triglyceride levels [12]. These indicators may be superior to BMI alone in predicting obesity-related health risks.

This study used data from the National Health and Nutrition Survey (NHANES) to compare the predictive effects of different anthropometric measures, including BMI, BRI, VAI, LAP, WC, ABSI and WWI, on OSA in American adults. By evaluating the relationships between these indicators and OSA risk, this study aims to identify the most accurate predictors, strengthen the early detection and management of OSA, and ultimately improve the health of patients, thereby reducing the burden on the health care system [13] [14].

2.1 Study design and participants

The National Health and Nutrition Examination Survey (NHANES) is an ongoing national study that provides extensive data on the dietary habits and general health of Americans. Prior to data collection, written informed consent was obtained from all participants, and all study protocols were approved by the National Center for Health Statistics' ethical review board. Further information about the NHANES survey can be found at NHANES.

This cross-sectional study utilized data from the NHANES (2005–2008 and 2015–2018) and included patients aged 20 years and above (n = 22,202). Participants with missing data required to calculate BMI, WC, BRI, LAP, VAI, ABSI, or WWI were excluded (n = 13,235). 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 8,966.

2.2 Definitions of the exposure and outcome variables

The exposure variables include body mass index (BMI), waist circumference (WC), body roundness index (BRI), lipid accumulation product (LAP), visceral adiposity index (VAI), a body shape index (ABSI), and weight-to-waist index (WWI). The data pertaining to these variables were obtained from the body measurements section of the NHANES database. The formulas for these indices are as follows:

In accordance with earlier studies, OSA is diagnosed when a person answers 'yes' to at least one of the following three NHANES questions[22]: (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), and marital status (married/living with a partner, never married, widowed, divorced, or separated). 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). Alcohol consumption and smoking status were also treated as categorical variables.

Alcohol consumption was classified into two categories, "Yes" and "No," based on respondents' answers to the question: "Have you consumed at least 12 alcoholic drinks in the past year?" 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 [23]. Additionally, total cholesterol concentrations were quantified using the enzymatic method. This method utilizes a single-reagent endpoint reaction, which is highly specific for cholesterol, ensuring accurate and reliable measurements of cholesterol levels in the samples.

To address missing values in the dataset, the mean values for total cholesterol (TC) and the ratio of family income to poverty (PIR) were imputed. 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 seven anthropometric indices 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 metabolic syndrome and total cholesterol. A further assessment of the heterogeneity between seven anthropometric indices and OSA was conducted through subgroup analysis, which included the following variables: age, sex, race/ethnicity, smoking status, alcohol consumption, marital status, physical activity and metabolic syndrome.

Moreover, we conducted a receiver operating characteristic (ROC) curve analysis to illustrate the connection between the indices and the probability of developing OSA, with the reference value set at the median. Furthermore, the capacity of the indices to identify OSA was evaluated via receiver operating characteristic (ROC) analysis and area under the curve (AUC) values. The AUC values of the BRI and BMI were compared via the Bonferroni correction and DeLong test[24][25].

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 8,966, with a mean age of 47.64(46.96,48.33) years. Of these, 48.79% were male, and 51.03% were female. The majority of participants (67.32%) identified as non-Hispanic white, whereas 10.81% were non-Hispanic black, 8.49% were Mexican American, 5.44% were of other Hispanic origin, and 7.93% belonged to other racial groups or had multiple racial identities.Overall, 29.99% of the participants were categorized as having OSA. Individuals in the OSA group were more likely to be older, male, non-Hispanic white, more educated, non-drinkers, current smokers, less physically active, and had higher values for BMI, BRI, VAI, LAP, ABSI, WC and WWI. They were also more likely to have metabolic syndrome compared to those in the non-OSA group (P < 0.0001) (Table 1).

[Table 1 near here]

3.2 Association between Anthropometric Indices and Obstructive Sleep Apnea

In the RCS, anthropometric indices (WC, BMI, BRI, ABSI, LAP, VAI, and WWI) were positively associated with OSA(Fig. 1). Table. 2 illustrates the associations between the anthropometric indices and the risk of obstructive sleep apnea (OSA) across different models. The results show a significant positive correlation between higher values of these indices and an increased risk of OSA. Waist Circumference (WC): Each unit increase in WC was associated with a 3% greater risk of OSA (OR = 1.03, 95% CI: 1.02–1.03, P < 0.0001). The highest quartile (≥109.4 cm) had an OR of 3.36 (95% CI: 2.66–4.25, P < 0.0001). Body Mass Index (BMI): A unit increase in BMI was correlated with a 7% increased risk of OSA (OR = 1.07, 95% CI: 1.06–1.08, P < 0.0001). The participants in the highest quartile (≥32.65 kg/m²) had an OR of 3.54 (95% CI: 2.79–4.48, P < 0.0001).Body Roundness Index (BRI): Each unit increase in BRI is linked to a 21% greater risk of OSA (OR = 1.21, 95% CI: 1.17–1.25, P < 0.0001). The highest quartile (≥6.77) had an OR of 3.57 (95% CI: 2.98–4.29, P < 0.0001). A Body Shape Index (ABSI): The ABSI was not significantly associated with OSA, indicating an OR close to 1 (OR = 0.00, 95% CI: 0.00–48993.61, P = 0.50). Lipid Accumulation Product (LAP): A unit increase in LAP is associated with a slightly increased risk of OSA (OR = 1.01, 95% CI: 1.00–1.01, P < 0.0001). The highest quartile (≥74.84) had an OR of 2.91 (95% CI: 2.39–3.54, P < 0.0001). Visceral Adiposity Index (VAI): Each unit increase in VAI corresponds to a 4% greater risk of OSA (OR = 1.04, 95% CI: 1.01–1.07, P = 0.005). The highest quartile (≥2.442) had an OR of 1.63 (95% CI: 1.32–2.02, P < 0.0001).Weight-to-Waist Index (WWI): A unit increase in WWI was associated with a 38% higher risk of OSA (OR = 1.38, 95% CI: 1.25–1.52, P < 0.0001). The highest quartile (≥11.67) had an OR of 2.01 (95% CI: 1.63–2.48, P < 0.0001).

[Table 2 near here]

3.3 Subgroup analysis

Table 3. Subgroup analysis showed that there was a significant interaction between demographic and lifestyle factors and anthropometric indicators ( LAP, BRI, VAI, WC, BMI, WWI ) on OSA risk ( interaction P < 0.05). A consistent positive correlation was observed in all subgroups, especially in the 40-60 years age group, non-Hispanic whites, inactive participants, and participants with higher education. The analysis of non-smokers and non-drinkers also showed a slightly stronger correlation. The presence of metabolic syndrome further expands these correlations. Overall, these results suggest that different demographic and lifestyle factors may regulate the relationships between these anthropometric indicators and OSA risk. Therefore, when studying and intervening OSA risks, these factors need to be considered in order to develop more targeted and effective prevention strategies.

[Table 3 near here]

3.4 Comparison of the Ability of Anthropometric Indicators to Predict OSA

Fig. 2 show the results of the ROC curve. ROC analysis revealed that the AUC value of waist circumference (WC) was the highest (0.637), followed by BMI (0.633). The AUC values of BRI, LAP, VAI and WWI were 0.622, 0.611, 0.572 and 0.568, respectively, indicating that WC and BMI had the best performance in predicting obstructive sleep apnea (OSA). To further evaluate the predictive performance of these indicators, we used Bonferroni correction (Supplementary Table.S1) and DeLong test(Supplementary Table.S2). The results of Bonferroni correction revealed that BMI was significantly correlated with BRI, LAP, VAI and WWI (P < 0.05). Similarly, the DeLong test also revealed that the AUC differences between BMI and BRI, LAP, VAI, and WWI were significant (P < 0.05), but the difference between BMI and WC was not significant (P = 0.2393). These results consistently show that WC and BMI are the most reliable indicators for predicting OSA, while BRI still shows moderate predictive ability although it is not as good as WC and BMI. Clinicians should give priority to these indicators when assessing OSA risk.

[Table 4 near here]

This study used NHANES data to assess the relationships between multiple predictors and the risk of obstructive sleep apnea (OSA). The results revealed that waist circumference (WC) and body mass index (BMI) were the strongest predictors of OSA, which was consistent with our research hypothesis. Even after adjusting for all covariates, these associations were still statistically significant. In addition, group analysis also revealed consistent positive correlations, especially in the 40-60 age group, non-Hispanic whites, inactive participants, and highly educated participants. Non-smokers and non-drinkers also showed a strong correlation, and the presence of metabolic syndrome further expanded this association. To further evaluate the potential of these indicators in predicting OSA, we performed ROC curve analysis and compared the predictive power of six anthropometric indicators. The results showed that WC and BMI had the highest AUC values in predicting OSA, which were 0.637 and 0.633, respectively, indicating that they had high accuracy in distinguishing OSA and non-OSA individuals.

These results were further verified by Bonferroni correction and DeLong test. The AUC difference between BRI and BMI was significant (P < 0.0001), indicating that BRI was superior to BMI in predicting OSA. Although the AUC values of BRI, LAP, VAI and WWI are low, they are still associated with OSA risk to some extent. These findings highlight the importance of WC and BMI as predictors of OSA and provide directions for further research. Although BRI, LAP, VAI and WWI are not as effective as WC and BMI in predicting OSA, they can still be used as auxiliary indicators, especially in specific populations.

A number of studies have explored the relationship between obesity and OSA. Peppard et al. found a significant association between obesity and the incidence of OSA [26]. Jordan et al. noted out that obesity significantly increased the risk of OSA by increasing the deposition of fat around the upper respiratory tract and affecting the respiratory regulatory mechanism [27]. In addition, Gami et al. reported that systemic inflammation and metabolic disorders caused by obesity play important roles in the pathogenesis of OSA [28]. Vgontzas et al. further noted that obesity, especially the excessive accumulation of visceral fat, directly compresses the airway and increases the risk of OSA [29]. Mokhlesi and Tjaden emphasized that systemic inflammation and insulin resistance caused by obesity can affect respiratory regulation and sleep structure [30].Ryan and McNicholas discussed the role of intermittent hypoxia and inflammatory molecular pathways in OSA [31]. These reviews further support the findings of this study and highlight the importance of obesity management in the prevention and treatment of OSA [32].

The relationship between OSA and obesity is achieved mainly through a variety of physiological and metabolic pathways. In obese individuals, excessive accumulation of visceral fat, in particular, can lead to increased fat around the respiratory tract, directly compressing the airway and increasing the risk of OSA [33]. In addition, obesity can cause systemic inflammation and insulin resistance, which affect respiratory regulation and sleep architecture [34] [35]. In particular, the accumulation of visceral fat results in the release of many inflammatory factors, such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), which affect the respiratory regulation function of the central nervous system and further increase the risk of OSA [36]. As a widely used obesity index, BMI can reflect total fat mass but cannot accurately distinguish fat distribution. Waist circumference (WC) is more likely to reflect the accumulation of abdominal fat, especially visceral fat. The strong correlation between increased abdominal fat and OSA may be related to the direct effect of visceral fat on respiratory structure and function [37] [38]. An increase in visceral fat not only mechanically compresses the respiratory tract but also affects respiratory regulation and central nervous system function by releasing a variety of metabolic and inflammatory factors [39].Therefore, compared with BMI, waist circumference (WC) may be more suitable as the most direct and accurate predictor for predicting OSA.

This study has several advantages. First, the study samples were from the National Health and Nutrition Survey (NHANES), providing a large and reliable data set. Secondly, we adjusted multiple confounding variables and performed group analysis to ensure that the results were applicable to a wider population. Third, this study explored the relationship between multiple predictors and OSA risk for the first time, used new anthropometric indicators to enhance the understanding of the relationship between obesity and OSA, and identified the potential of waist circumference (WC) in predicting OSA risk. However, our research also has several limitations. First, the cross-sectional design of this study could not prove the causal relationship between predictors and OSA. Secondly, despite the consideration of relevant variables, the complete elimination of confounding factors remains a major challenge.

The results of this study revealed that multiple predictors (including LAP, BRI, VAI, WC, BMI and WWI) were positively correlated with the risk of obstructive sleep apnea (OSA) in American adults. Among them, WC and BMI have the greatest predictive value for OSA risk. We corrected for multiple confounding variables and performed subgroup analysis to ensure that the results were applicable to a wider population. Compared with newly developed indicators such as the BRI, VAI, LAP and WWI, traditional obesity indicators such as WC and BMI have greater sensitivity and specificity in assessing OSA risk, and WC is simpler and more accurate than BMI is. The purpose of this study was to improve the public's understanding of these obesity indicators and provide a reference for the clinical prediction of OSA. To verify our results, further comprehensive and detailed prospective studies are needed in the future.

NHANES: National Health and Nutrition Examination Survey; CDC: Centers for Disease Control and Prevention; BMI: body mass index;WC:waist circumference;BRI: body roundness index;ABSI:a body shape index;VAI:visceral adiposity index;LAP:lipid accumulation product;WWI:weight-to-waist index;

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 the provincial key fund project (No. GJB1319137).

Authors’ contributions: WJL and SYS participated in the literature search, study design, data collection, data analysis, and data interpretation and wrote the manuscript. HGZ conceived the study and participated in its design, coordination, data collection and analysis. CPA 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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Table 1.Baseline characteristics of participants in the NHANES 2005-2008 and 2015-2018 (n=8966)

Characteristic	Overall(N=8966)	Non-OSA(N= 6277)	OSA(N= 2689)	P value
Continuous variables, mean(95% CI)
Age, years	47.64(46.96,48.33)	46.97(46.21,47.73)	49.17(48.29,50.06)	< 0.0001
Total cholesterol, mmol/L	5.00(4.96,5.04)	4.98(4.94,5.03)	5.04(4.99,5.10)	0.04
PIR	3.04(2.96,3.11)	3.07(2.98,3.15)	2.98(2.87,3.08)	0.12
BRI	5.39(5.29,5.49)	5.06(4.96,5.17)	6.12(5.99,6.25)	< 0.0001
VAI	1.98(1.92,2.05)	1.81(1.75,1.88)	2.36(2.23,2.48)	< 0.0001
LAP	57.65(55.74,59.57)	51.08(49.24,52.92)	72.50(69.22,75.77)	< 0.0001
ABSI	0.08(0.08,0.08)	0.08(0.08,0.08)	0.08(0.08,0.08)	< 0.0001
BMI,(kg/m2)	29.01(28.75,29.27)	28.02(27.72,28.31)	31.26(30.92,31.60)	< 0.0001
WC,(cm)	99.38(98.69,100.06)	96.80(96.07, 97.54)	105.18(104.28,106.09)	< 0.0001
WWI	10.97(10.93,11.01)	10.89(10.85,10.93)	11.14(11.08,11.19)	< 0.0001
Categorical variables,n(%)
Sex,n (%)				< 0.0001
Female	4598(51.03)	3342(53.55)	1256(45.34)
Male	4368(48.97)	2935(46.45)	1433(54.66)
Race/ethnicity, n (%)				0.39
Non-Hispanic White	3723(67.32)	2577(67.15)	1146(67.70)
Other Race - Including Multi-Racial	962(7.93)	672(7.72)	290(8.43)
Non-Hispanic Black	1884(10.81)	1316(10.78)	568(10.87)
Mexican American	1522(8.49)	1087(8.63)	435(8.18)
Other Hispanic	875(5.44)	625(5.71)	250(4.82)
Metabolic Syndrome, n (%)				< 0.0001
No	5408(63.85)	4036(68.47)	1372(53.44)
Yes	3333(34.88)	2051(29.97)	1282(45.95)
Marital status, n (%)				< 0.0001
Married/Living with Partner	5521(64.99)	3737(62.23)	1784(71.22)
Widowed/Divorced/Separated/Never married	3443(35.01)	2539(37.77)	904(28.77)
Educational level, n (%)				< 0.001
>High school	4591(59.58)	3234(61.36)	1357(55.54)
Completed high school	3396(34.86)	2334(32.90)	1062(39.28)
<High school	974(5.55)	706(5.73)	268(5.16)
Physical activity, n (%)				< 0.0001
Inactive	3718(35.23)	2463(33.35)	1255(39.46)
Moderate	1680(21.00)	1149(19.94)	531(23.41)
Both moderate and vigorous	1025(13.99)	773(15.71)	252(10.11)
Vigorous	476(6.00)	367(6.61)	109(4.62)
Smoking status, n (%)				< 0.0001
Never smoking	4832(52.89)	3542(55.33)	1290(47.40)
Former smoker	2269(26.27)	1541(25.89)	728(27.12)
Current smoker	1855(20.78)	1186(18.71)	669(25.44)
Alcohol status, n (%)				0.02
No	1906(17.36)	1405(18.33)	501(15.15)
Yes	4429(51.94)	3110(51.55)	1319(52.81)

Abbreviations: NHANES, National Health and Nutrition Examination Survey; Q, quantile;PIR, ratio of family income to poverty;BRI,body roundness index;VAI,visceral adiposity index;LAP,lipid accumulation product;ABSI,a body shape index;BMI, body mass index;WC,waist circumference;WWI,weight-to-waist index

Table 2. Associations of WC,BMI,BRI,ABSI,LAP,VAI and WWI with obstructive sleep apnea.

	Crude model^a		Model 1^b		Model 2^c		Model 3^d
	OR (95% CI)	p value	OR (95% CI)	p value	OR (95% CI)	p value	OR (95% CI)	p value
WC
Continuous	1.03(1.03,1.04)	<0.0001	1.03(1.03,1.03)	<0.0001	1.03(1.03, 1.03)	<0.0001	1.03(1.02, 1.03)	<0.0001
Quartiles
Q1(<88.2)	Reference		Reference		Reference		Reference
Q2(88.2to<98.3)	1.85(1.53,2.23)	<0.0001	1.79(1.47,2.17)	<0.0001	1.77(1.45, 2.17)	<0.0001	1.72(1.40, 2.12)	<0.0001
Q3(98.3to<109.4)	2.64(2.23,3.13)	<0.0001	2.53(2.11,3.02)	<0.0001	2.49(2.08, 2.98)	<0.0001	2.34(1.92, 2.84)	<0.0001
Q4(≥109.4)	3.86(3.18,4.69)	<0.0001	3.71(3.05,4.52)	<0.0001	3.68(3.00, 4.52)	<0.0001	3.36(2.66, 4.25)	<0.0001
p for trend	<0.0001		<0.0001		<0.0001		<0.0001
BMI
Continuous	1.07(1.06,1.08)	<0.0001	1.08(1.06,1.09)	<0.0001	1.08(1.07, 1.09)	<0.0001	1.07(1.06, 1.08)	<0.0001
Quartiles
Q1(<24.42)	Reference		Reference		Reference		Reference
Q2(24.42to<28.08)	1.60(1.30,1.98)	<0.0001	1.52(1.22,1.88)	<0.001	1.55(1.25, 1.92)	<0.001	1.51(1.21, 1.88)	<0.0001
Q3(28.08to<32.65)	2.48(2.00,3.07)	<0.0001	2.35(1.89,2.91)	<0.0001	2.41(1.94, 3.01)	<0.0001	2.27(1.80, 2.85)	<0.0001
Q4(≥32.65)	3.70(3.02,4.54)	<0.0001	3.75(3.06,4.59)	<0.0001	3.83(3.10, 4.74)	<0.0001	3.54(2.79, 4.48)	<0.0001
p for trend	<0.0001		<0.0001		<0.0001		<0.0001
BRI
Continuous	1.21(1.18,1.24)	<0.0001	1.23(1.19,1.26)	<0.0001	1.23(1.19, 1.26)	<0.0001	1.21(1.17, 1.25)	<0.0001
Quartiles
Q1(<3.93)	Reference		Reference		Reference		Reference
Q2(3.93to<5.16)	1.85(1.59,2.14)	<0.0001	1.82(1.58,2.11)	<0.0001	1.80(1.55, 2.08)	<0.0001	1.75(1.50, 2.03)	<0.0001
Q3(5.16to<6.77)	2.42(1.99,2.94)	<0.0001	2.42(1.97,2.97)	<0.0001	2.37(1.91, 2.93)	<0.0001	2.21(1.77, 2.77)	<0.0001
Q4(≥6.77)	3.62(3.12,4.20)	<0.0001	3.92(3.37,4.56)	<0.0001	3.92(3.35, 4.58)	<0.0001	3.57(2.98, 4.29)	<0.0001
p for trend	<0.0001		<0.0001		<0.0001		<0.0001
ABSI
Continuous	7.16e+11 (1.21e+06, 4.23e+17)	<0.001	1.29e+06 (2.20e-01, 7.47e+12)	0.08	1.25e+02 (0.00e+00, 8.10e+08)	0.54	0.00(0.00, 48993.61)	0.50
Quartiles
Q1(<0.079)	Reference		Reference		Reference		Reference
Q2(0.079to<0.082)	1.40(1.18,1.67)	<0.001	1.31(1.09,1.57)	0.004	1.24(1.03, 1.50)	0.02	1.17(0.98, 1.41)	0.08
Q3(0.082to<0.085)	1.46(1.24,1.73)	<0.0001	1.31(1.09,1.57)	0.01	1.18(0.99, 1.40)	0.06	1.09(0.91, 1.30)	0.35
Q4(≥0.085)	1.46(1.22,1.76)	<0.001	1.24(1.02,1.52)	0.03	1.12(0.92, 1.37)	0.26	0.98(0.80, 1.21)	0.86
p for trend	<0.0001		0.026		0.293		0.733
LAP
Continuous	1.01(1.01,1.01)	<0.0001	1.01(1.00,1.01)	<0.0001	1.01(1.00, 1.01)	<0.0001	1.01(1.00, 1.01)	<0.0001
Quartiles
Q1(<23.605)	Reference		Reference		Reference		Reference
Q2(23.61to<43.48)	1.52(1.25,1.85)	<0.0001	1.51(1.23,1.85)	<0.001	1.47(1.19, 1.80)	<0.001	1.46(1.19, 1.80)	<0.001
Q3(43.48to<74.84)	2.35(1.95,2.84)	<0.0001	2.29(1.89,2.77)	<0.0001	2.18(1.81, 2.62)	<0.0001	2.14(1.77, 2.59)	<0.0001
Q4(≥74.84)	3.26(2.75,3.87)	<0.0001	3.21(2.68,3.85)	<0.0001	3.07(2.54, 3.71)	<0.0001	2.91(2.39, 3.54)	<0.0001
p for trend	<0.0001		<0.0001		<0.0001		<0.0001
VAI
Continuous	1.11(1.07,1.15)	<0.0001	1.11(1.07,1.15)	<0.0001	1.10(1.06, 1.14)	<0.0001	1.04(1.01, 1.07)	0.005
Quartiles
Q1(<0.854)	Reference		Reference		Reference		Reference
Q2(0.854to<1.429)	1.52(1.27,1.82)	<0.0001	1.53(1.28,1.84)	<0.0001	1.48(1.24, 1.77)	<0.0001	1.41(1.18, 1.69)	<0.001
Q3(1.429to<2.442)	1.93(1.56,2.39)	<0.0001	1.96(1.58,2.43)	<0.0001	1.84(1.49, 2.29)	<0.0001	1.59(1.29, 1.97)	<0.0001
Q4(≥2.442)	2.34(1.95,2.81)	<0.0001	2.35(1.95,2.83)	<0.0001	2.20(1.81, 2.68)	<0.0001	1.63(1.32, 2.02)	<0.0001
p for trend	<0.0001		<0.0001		<0.0001		<0.0001
WWI
Continuous	1.41(1.32,1.52)	<0.0001	1.55(1.43,1.68)	<0.0001	1.52(1.40, 1.65)	<0.0001	1.38(1.25, 1.52)	<0.0001
Quartiles
Q1(<10.51)	Reference		Reference		Reference		Reference
Q2(10.51to<11.077)	1.47(1.28,1.69)	<0.0001	1.53(1.33,1.77)	<0.0001	1.41(1.22, 1.64)	<0.0001	1.31(1.13, 1.52)	<0.001
Q3(11.077to<11.67)	2.09(1.76,2.49)	<0.0001	2.29(1.93,2.72)	<0.0001	2.09(1.76, 2.49)	<0.0001	1.83(1.53, 2.18)	<0.0001
Q4(≥11.67)	2.18(1.86,2.56)	<0.0001	2.58(2.16,3.08)	<0.0001	2.46(2.05, 2.94)	<0.0001	2.01(1.63, 2.48)	<0.0001
p for trend	<0.0001		<0.0001		<0.0001		<0.0001

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, poverty and total cholesterol.

Table3.Stratified analysis of the correlations between LAP,BRI,VAI,WC,BMI,WWI and OSA in adults in the NHANES 2005-2008 and 2015-2018

Subgroup	OR(95%CI),P-value
	LAP	P	P for interaction	BRI	P	P for interaction	VAI	P	P for interaction	WC	P	P for interaction	BMI	P	P for interaction	WWI	P	P for interaction
Age			0.052			0.189			0.306			0.631			0.577			0.034
≤40	1.007(1.005,1.010)	<0.0001		1.210(1.167,1.255)	<0.0001		1.106(1.023,1.196)	0.012		1.031(1.026,1.036)	<0.0001		1.074(1.060,1.088)	<0.0001		1.440(1.275,1.626)	<0.0001
>40,≤60	1.008(1.005,1.010)	<0.0001		1.229(1.171,1.290)	<0.0001		1.130(1.065,1.200)	<0.001		1.032(1.024,1.039)	<0.0001		1.074(1.055,1.093)	<0.0001		1.561(1.369,1.779)	<0.0001
＞60	1.003(1.001,1.006)	0.011		1.165(1.121,1.210)	<0.0001		1.055(1.003,1.109)	0.039		1.027(1.021,1.034)	<0.0001		1.063(1.047,1.078)	<0.0001		1.234(1.086,1.402)	0.002
Sex			0.301			0.885			0.603			0.117			0.792			0.751
Female	1.008(1.005,1.010)	<0.0001		1.225(1.192,1.260)	<0.0001		1.121(1.058,1.189)	<0.001		1.033(1.028,1.037)	<0.0001		1.073(1.062,1.085)	<0.0001		1.491(1.346,1.653)	<0.0001
Male	1.006(1.004,1.008)	<0.0001		1.229(1.182,1.277)	<0.0001		1.101(1.050,1.154)	<0.001		1.028(1.022,1.033)	<0.0001		1.076(1.059,1.093)	<0.0001		1.520(1.387,1.665)	<0.0001
Race/ethnicity			0.134			0.027			0.087			0.104			0.144			0.257
Non-Hispanic White	1.007(1.005,1.009)	<0.0001		1.212(1.175,1.249)	<0.0001		1.125(1.068,1.186)	<0.0001		1.030(1.025,1.036)	<0.0001		1.074(1.061,1.088)	<0.0001		1.403(1.272,1.548)	<0.0001
Other Race - Including Multi-Racial	1.012(1.008,1.016)	<0.0001		1.368(1.236,1.513)	<0.0001		1.223(1.093,1.369)	<0.001		1.046(1.031,1.061)	<0.0001		1.105(1.069,1.143)	<0.0001		1.765(1.392,2.238)	<0.0001
Non-Hispanic Black	1.007(1.003,1.010)	<0.001		1.184(1.140,1.228)	<0.0001		1.129(1.027,1.240)	0.013		1.028(1.023,1.034)	<0.0001		1.063(1.051,1.075)	<0.0001		1.415(1.234,1.623)	<0.0001
Mexican American	1.005(1.002,1.009)	0.002		1.197(1.125,1.275)	<0.0001		1.043(0.985,1.103)	0.143		1.034(1.023,1.045)	<0.0001		1.070(1.048,1.091)	<0.0001		1.428(1.182,1.725)	<0.001
Other Hispanic	1.003(0.997,1.009)	0.294		1.175(1.083,1.274)	<0.001		1.046(0.958,1.142)	0.310		1.033(1.020,1.045)	<0.0001		1.065(1.033,1.098)	<0.001		1.280(1.053,1.555)	0.014
Education level			0.706			0.004			0.841			0.123			0.04			< 0.001
> High school	1.007(1.005,1.009)	<0.0001		1.247(1.200,1.296)	<0.0001		1.119(1.049,1.194)	<0.001		1.034(1.028,1.040)	<0.0001		1.081(1.065,1.097)	<0.0001		1.575(1.427,1.739)	<0.0001
High school	1.006(1.004,1.008)	<0.0001		1.166(1.127,1.207)	<0.0001		1.095(1.042,1.150)	<0.001		1.027(1.022,1.032)	<0.0001		1.061(1.049,1.074)	<0.0001		1.227(1.105,1.362)	<0.001
< High school	1.007(1.003,1.010)	0.001		1.114(1.032,1.204)	0.007		1.098(0.991,1.216)	0.072		1.025(1.011,1.040)	<0.001		1.048(1.017,1.079)	0.003		1.197(0.979,1.464)	0.079
Marrage			0.938			0.606			0.539			0.715			0.698			0.524
Married/Living with Partner	1.007(1.005,1.009)	<0.0001		1.206(1.164,1.250)	<0.0001		1.116(1.062,1.173)	<0.0001		1.031(1.026,1.037)	<0.0001		1.071(1.056,1.086)	<0.0001		1.396(1.279,1.523)	<0.0001
Widowed/Divorced/Separated/Never married	1.007(1.004,1.009)	<0.0001		1.224(1.174,1.275)	<0.0001		1.093(1.040,1.148)	<0.001		1.030(1.024,1.036)	<0.0001		1.075(1.060,1.091)	<0.0001		1.459(1.298,1.640)	<0.0001
Physical activity			0.935			0.18			0.785			0.542			0.457			0.313
Inactive	1.007(1.005,1.009)	<0.0001		1.184(1.143,1.226)	<0.0001		1.112(1.056,1.170)	<0.001		1.029(1.023,1.036)	<0.0001		1.068(1.053,1.083)	<0.0001		1.307(1.163,1.469)	<0.0001
Moderate	1.007(1.004,1.011)	<0.001		1.202(1.127,1.282)	<0.0001		1.131(1.008,1.270)	0.036		1.031(1.021,1.040)	<0.0001		1.067(1.043,1.091)	<0.0001		1.419(1.127,1.788)	0.004
Both moderate and vigorous	1.009(1.002,1.016)	0.015		1.325(1.187,1.478)	<0.0001		1.067(0.979,1.161)	0.135		1.040(1.025,1.054)	<0.0001		1.097(1.059,1.135)	<0.0001		1.681(1.296,2.181)	<0.001
Vigorous	1.008(1.002,1.014)	0.008		1.219(1.093,1.360)	<0.001		1.143(0.987,1.325)	0.073		1.033(1.015,1.051)	<0.001		1.068(1.023,1.115)	0.003		1.577(1.152,2.161)	0.006
Smoking status			0.001			0.07			0.013			0.012			0.179			0.044
Never smoking	1.009(1.007,1.011)	<0.0001		1.248(1.202,1.296)	<0.0001		1.167(1.087,1.253)	<0.0001		1.036(1.030,1.042)	<0.0001		1.082(1.067,1.098)	<0.0001		1.531(1.377,1.701)	<0.0001
Former smoker	1.006(1.004,1.009)	<0.0001		1.223(1.158,1.291)	<0.0001		1.107(1.043,1.175)	0.001		1.033(1.026,1.040)	<0.0001		1.075(1.057,1.094)	<0.0001		1.472(1.232,1.760)	<0.0001
Current smoker	1.003(1.001,1.005)	0.006		1.147(1.084,1.214)	<0.0001		1.030(0.989,1.072)	0.150		1.022(1.014,1.030)	<0.0001		1.059(1.038,1.081)	<0.0001		1.181(1.021,1.365)	0.026
Alcohol status			0.098			0.108			0.166			0.316			0.076			0.281
No	1.004(1.002,1.007)	0.003		1.160(1.103,1.221)	<0.0001		1.056(0.991,1.124)	0.090		1.026(1.017,1.035)	<0.0001		1.056(1.038,1.074)	<0.0001		1.262(1.067,1.492)	0.008
Yes	1.007(1.005,1.009)	<0.0001		1.232(1.186,1.279)	<0.0001		1.118(1.070,1.169)	<0.0001		1.032(1.026,1.038)	<0.0001		1.082(1.065,1.099)	<0.0001		1.407(1.284,1.542)	<0.0001
Metabolic Syndrome			< 0.001			0.016			0.329			0.125			0.165			< 0.001
No	1.010(1.007,1.013)	<0.0001		1.219(1.172,1.267)	<0.0001		1.088(0.995,1.189)	0.063		1.031(1.025,1.037)	<0.0001		1.069(1.054,1.086)	<0.0001		1.395(1.257,1.548)	<0.0001
Yes	1.003(1.001,1.005)	<0.001		1.134(1.083,1.188)	<0.0001		1.039(1.010,1.068)	0.008		1.025(1.018,1.032)	<0.0001		1.055(1.040,1.071)	<0.0001		1.033(0.897,1.189)	0.648

Abbreviations:LAP,lipid accumulation product;BRI,body roundness index;VAI,visceral adiposity index;WC,waist circumference;BMI,body mass index;WWI,weight-to-waist index

Table 4. Comparison of the different indices in predicting obstructive sleep apnea

Test	AUC	95% CI	Cutoff	Sensitivity	Specificity
BMI	0.633	0.621 - 0.646	28.245	0.631	0.571
BRI	0.622	0.610 - 0.634	5.519	0.558	0.615
LAP	0.611	0.598 - 0.623	45.312	0.601	0.574
VAI	0.572	0.559 - 0.585	1.488	0.557	0.557
WWI	0.568	0.556 - 0.581	11.102	0.564	0.543
WC	0.637	0.625 - 0.650	99.45	0.608	0.586

Abbreviations: AUC: area under the curve; CI: confidence interval

No competing interests reported.

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Comparative Analysis of Anthropometric Indices for Predicting Obstructive Sleep Apnea among U.S. Adults

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