
==== Front
Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39256533
72218
10.1038/s41598-024-72218-0
Article
Association of visceral adiposity index with sarcopenia based on NHANES data
Li Jianzhao 13413690017@163.com

Lin Yuning
Deng Haitang
Su Xiaoen
Feng Wenjie
Shao Qingfeng
Zou Kai
https://ror.org/04gcfwh66 grid.502971.8 0000 0004 1758 1569 Department of Orthopaedics, The First People’s Hospital of Zhaoqing, Zhaoqing, 526060 China
10 9 2024
10 9 2024
2024
14 2116920 6 2024
4 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
The Visceral Adiposity Index (VAI) assesses visceral fat and related metabolic risks. However, its precise correlation with sarcopenia is unclear. This study aimed to examine this correlation. A cross-sectional analysis was conducted using NHANES data from 2011 to 2018. To correct VAI skewness, a logarithmic transformation was applied. Multiple covariates were included, and logistic regression was employed to explore the relationship between VAI and sarcopenia. Restricted cubic spline (RCS) and threshold saturation analyses were used to investigate the nonlinear relationship. Subgroup analyses evaluated the effects of various stratification factors. Sensitivity and additive analyses tested the robustness of the findings. The study included 4688 individuals. Participants with sarcopenia had significantly higher VAI values. Logistic regression revealed a significant positive connection between Log VAI and sarcopenia (OR 2.09, 95% CI 1.80–2.43) after adjusting for variables. RCS analysis showed a nonlinear correlation, identifying a breakpoint at VAI = 1.51. To the left of this breakpoint, each unit increase in VAI significantly correlated with a higher likelihood of sarcopenia (OR 2.54, 95% CI 1.74–3.79); to the right, increases in VAI did not significantly affect prevalence. Subgroup analyses suggested VAI as an independent risk factor. Sensitivity and additive analyses confirmed the main findings’ robustness. Among American adults, the VAI is significantly associated with sarcopenia, with higher VAI values potentially increasing the prevalence of sarcopenia. Monitoring VAI is critical for early identification of high-risk individuals and interventions to delay or minimize the onset and progression of sarcopenia.

Keywords

Visceral adiposity index
Sarcopenia
Obesity
NHANES
Cross-sectional study
Subject terms

Risk factors
Epidemiology
Disease prevention
Geriatrics
Public health
Weight management
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pmcIntroduction

Sarcopenia is defined by a notable reduction in both the amount and strength of skeletal muscle, resulting in diminished physical capabilities and a decline in overall quality of life1,2. While the commencement of muscle decline is mainly associated with age, occurring normally between the ages of 30 and 40 when muscle mass and strength reach their highest point, there are other factors that can also trigger its occurrence at a younger age3. Sarcopenia is estimated to affect 10%–16% of elderly people worldwide according to a recent study4. This condition increases the risk of falls and fractures and is closely associated with functional impairment, disability, and increased all-cause mortality5,6. Due to the increasing number of elderly people worldwide, the occurrence of sarcopenia is predicted to increase, presenting a substantial obstacle to public health systems. Hence, it is crucial to identify and evaluate the risk factors associated with sarcopenia in order to enhance health and quality of life.

Current studies have been progressively emphasizing the connection between obesity and sarcopenia. Obesity is characterized by excessive fat accumulation, often caused by unhealthy diets, a lack of exercise, and metabolic diseases7.Body mass index (BMI) is frequently employed as a tool to assess weight problems and identify persons who are obese8. However, BMI has limitations as an assessment tool, as it overlooks the heterogeneity of fat distribution, particularly the specific impact of visceral fat on health risks9,10. The Visceral Adiposity Index (VAI) was proposed by Amato et al. in 2010, and is calculated from the values of waist circumference, body mass index (BMI), triglycerides, and high-density lipoprotein cholesterol (HDL)11. VAI provides a non-invasive method of estimating visceral adiposity, which indirectly assesses the amount of visceral fat and its function, thus reflecting the metabolic health of an individual. Numerous studies have shown that visceral adiposity is strongly associated with the development of a variety of metabolic diseases (diabetes and hypertension) and is widely used in clinical and epidemiologic studies12,13. A study suggests that VAI is positively associated with the prevalence of type 2 diabetes and that VAI can be used as an indicator for the clinical assessment of type 2 diabetes14. Furthermore, a separate study showed that a high VAI is correlated with a heightened susceptibility to CHD and poor survival, underscoring the need to effectively address this risk factor15. The specific relationship between VAI and sarcopenia remains a topic of ongoing research. Previous studies have examined the association between visceral adipose tissue (VAT) mass and sarcopenic obesity in obese populations16. However, there are no definitive conclusions regarding the specific relationship between VAI and sarcopenia at present. This study utilized NHANES survey data from 2011 to 2018 to explore the association between the VAI and sarcopenia. The aim of this study was to provide new data suggesting that VAI can be used as an early identifier of people at risk for sarcopenia and to provide a scientific basis for future public health interventions.

Methods

Survey description

The NHANES database used in this study is conducted by the National Center for Health Statistics (NCHS), and the data collection and release have been approved by the NCHS Research Ethics Review Board. This approval covers the collection and public release of NHANES data, ensuring compliance with ethical standards and the protection of participant privacy.

Study population

The data included in this research was taken from the four NHANES cycles that ran from 2011 to 2018. The following were the inclusion criteria: (1) were older than 20 years of age, (2) had comprehensive data on sarcopenia, and (3) had complete data on the VAI.

Calculation of the VAI

The VAI was calculated using formulas from previous studies17, which combines anthropometric data and blood biomarkers, including waist circumference (WC), body mass index (BMI), triglycerides (TG), and high-density lipoprotein (HDL). The formulas are as follows:Male:VAI=WC÷39.68+1.88×BMI×TG÷1.03×(1.31÷HDL)

Female:VAI=WC÷36.58+1.89×BMI×TG÷0.81×(1.52÷HDL)

Definition of sarcopenia

The NHANES employs dual-energy X-ray absorptiometry (DEXA) to quantify nonfat and nonbone tissue (ASM) in the limbs. By dividing the total ASM by the BMI, the sarcopenia index was calculated. The National Institutes of Health Osteoarthritis Biomarkers Consortium defines sarcopenia as having a sarcopenia index below 0.789 for males and 0.512 for females, and previous studies have reported confirmed feasibility18,19.

Covariates

This research covered a variety of confounders, such as food habits, lifestyle factors, health status, and demographic information. Age, sex, race, education level, and poverty index ratio (PIR) were among the demographic factors. The PIR was classified into three categories, < 1, 1 to < 3, and ≥ 3, based on the ratio of household income to the poverty level. Lifestyle factors included smoking behavior and physical activity level. A questionnaire that classified people who had smoked more than 100 cigarettes in their lives as smokers was used to measure smoking behavior. The Global Physical Activity Questionnaire was utilized to evaluate the participants’ levels of physical activity. Metabolic Equivalent (MET) is calculated as follows: MET (min/week) = WET × weekly frequency × duration of each physical activity. Insufficient exercise is defined as a MET of less than 600 min/week20. Health status was determined through physician diagnosis records or self-reports, covering conditions such as diabetes, hypertension, hypercholesterolemia, coronary artery disease (CAD) and chronic kidney disease (CKD). Dietary intake included daily energy, protein, carbohydrate, total sugar, dietary fiber, and total fat consumption.

Statistical analysis

Data from 2011 to 2018, covering four survey cycles, were collected from the NHANES database. An overview of the baseline characteristics of the individuals who were categorized according to their sarcopenia status was given by the descriptive analyses. Categorical variables are given as percentages, while continuous variables are reported as means and standard deviations. A log transformation was performed on the VAI in order to correct for data skewness and normalize the results. Taking into consideration several factors, the researchers used logistic regression analysis to investigate the relationship between the VAI and sarcopenia. The VAI was divided into four levels to investigate the relationships between different VAI scores and sarcopenia. This approach was used to strengthen the reliability of the findings. RCS analysis was used to look at the dose–response association between sarcopenia and the VAI. Furthermore, a research on the threshold effect was carried out to ascertain the crucial point that distinguished the two. The link between the VAI score and sarcopenia was examined using subgroup analyses to explore the possible impact of several factors, including age, sex, race, physical activity, smoking status, diabetes status, hypertension status, and hypercholesterolemia. Ultimately, the researchers conducted sensitivity tests and further analyses to assess the strength and reliability of the findings. Software for conducting the analysis was R (version 4.2.3), and P < 0.05 was considered statistically significant.

Results

Baseline characteristics

The data extracted from the NHANES database included 4688 participants, with 4262 participants without sarcopenia and 426 participants with sarcopenia (Fig. 1). The participant characteristics were categorized according to the presence of sarcopenia, as shown in Table 1. The weighted baseline characteristics are provided in Supplementary Table 1. Patients with sarcopenia tended to be older, mostly of Mexican descent, less well-off, and less educated than those without the condition. In terms of lifestyle, these patients tended to be less active, with higher BMI, waist circumferences, and triglyceride levels but lower energy, protein, and total fat intake. In addition, individuals with sarcopenia had a greater likelihood of having diabetes, hypertension, and hypercholesterolemia. Notably, these patients had higher VAI scores, indicating a correlation with sarcopenia.Fig. 1 Include participants in the process.

Table 1 Baseline characteristics of the study population.

Characteristic	Overall	Non-sarcopenia	Sarcopenia	P-value	
n	4688	4262	426		
Age (%)	< 0.001	
< 50	3624 (77.3)	3358 (78.8)	266 (62.4)		
> 50	1064 (22.7)	904 (21.2)	160 (37.6)		
Sex (%)	0.982	
Female	2363 (50.4)	2149 (50.4)	214 (50.2)		
Male	2325 (49.6)	2113 (49.6)	212 (49.8)		
Race (%)	< 0.001	
Mexican American	704 (15.0)	563 (13.2)	141 (33.1)		
Non-Hispanic black	924 (19.7)	902 (21.2)	22 (5.2)		
Non-Hispanic white	1655 (35.3)	1535 (36.0)	120 (28.2)		
Others	1405 (30.0)	1262 (29.6)	143 (33.6)		
Education level (%)	< 0.001	
Under high school	894 (19.1)	743 (17.4)	151 (35.4)		
High school or equivalent	992 (21.2)	896 (21.0)	96 (22.5)		
Above high school	2801 (59.7)	2622 (61.5)	179 (42.0)		
No record	1 (0.0)	1 (0.0)	0 (0.0)		
PIR (%)	< 0.001	
< 1	943 (22.0)	831 (21.3)	112 (29.3)		
1–3	1750 (40.8)	1584 (40.6)	166 (43.5)		
> 3	1591 (37.1)	1487 (38.1)	104 (27.2)		
Activity status (%)	< 0.001	
Active	2871 (61.2)	2667 (62.6)	204 (47.9)		
Inactive	1817 (38.8)	1595 (37.4)	222 (52.1)		
Smoke (%)	0.902	
No	2786 (59.4)	2532 (59.4)	254 (59.6)		
Yes	1900 (40.5)	1728 (40.5)	172 (40.4)		
No record	2 (0.0)	2 (0.0)	0 (0.0)		
Diabate (%)	< 0.001	
No	4238 (90.4)	3892 (91.3)	346 (81.2)		
Yes	356 (7.6)	291 (6.8)	65 (15.3)		
No record	94 (2.0)	79 (1.9)	15 (3.5)		
CAD (%)	0.002	
No	4575 (97.6)	4169 (97.8)	406 (95.3)		
Yes	113 (2.4)	93 (2.2)	20 (4.7)		
CKD (%)	< 0.001	
No	4589 (97.9)	4184 (98.2)	405 (95.1)		
Yes	97 (2.1)	76 (1.8)	21 (4.9)		
No record	2 (0.0)	2 (0.0)	0 (0.0)		
Hypertension (%)	< 0.001	
No	3541 (75.5)	3260 (76.5)	281 (66.0)		
Yes	1140 (24.3)	997 (23.4)	143 (33.6)		
No record	7 (0.1)	5 (0.1)	2 (0.5)		
Hypercholesterolemia (%)	< 0.001	
No	3499 (74.6)	3231 (75.8)	268 (62.9)		
Yes	1175 (25.1)	1022 (24.0)	153 (35.9)		
No record	14 (0.3)	9 (0.2)	5 (1.2)		
BMI (mean (SD)) (kg/m2)	28.78 (6.79)	28.29 (6.49)	33.69 (7.69)	< 0.001	
WC (mean (SD)) (cm)	97.09 (16.27)	96.06 (15.79)	107.44 (17.36)	< 0.001	
HDL (mean (SD)) (mmol/L)	1.37 (0.39)	1.38 (0.40)	1.26 (0.36)	< 0.001	
TG (mean (SD)) (mmol/L)	1.36 (1.37)	1.32 (1.35)	1.75 (1.50)	< 0.001	
VAI (mean (SD))	1.94 (2.68)	1.87 (2.62)	2.68 (3.17)	< 0.001	
ASM (mean (SD)) (kg)	22.65 (6.35)	22.89 (6.32)	20.34 (6.17)	< 0.001	
SAR (mean (SD))	0.80 (0.20)	0.82 (0.20)	0.61 (0.14)	< 0.001	
Energy (mean (SD)) (kcal)	2112.97 (823.01)	2129.07 (825.99)	1950.37 (774.77)	< 0.001	
Protein (mean (SD)) (g)	83.68 (36.24)	84.37 (36.46)	76.68 (33.14)	< 0.001	
Carbohydrate (mean (SD)) (g)	254.57 (106.87)	255.73 (106.73)	242.76 (107.75)	0.029	
Total sugars (mean (SD)) (g)	108.44 (64.98)	108.81 (63.78)	104.64 (76.11)	0.249	
Dietary fiber (mean (SD)) (g)	17.39 (9.64)	17.44 (9.67)	16.94 (9.26)	0.347	
Total fat (mean (SD)) (g)	80.85 (37.80)	81.61 (38.03)	73.16 (34.47)	< 0.001	
Mean (SD) for continuous variables, % for categorical variables.

ASM appendicular skeletal muscle, BMI Body mass index, CAD Coronary artery disease, CKD Chronic kidney disease, TG Triglyceride, VAI Visceral adiposity index, WC Waist circumference.

Associations between the VAI and the prevalence of sarcopenia

To account for the asymmetry of the VAI data, a logarithmic transformation was applied to the VAI variable. The association between the transformed VAI and the prevalence of sarcopenia was then examined using logistic regression, as presented in Table 2. According to the unadjusted Model 1, the prevalence of sarcopenia and the Log VAI showed a significant positive connection (OR 2.41, 95% CI 2.11–2.75). After systematically including various confounders one by one, the fully adjusted Model 3 continued to demonstrate a statistically significant positive correlation between the Log VAI and the occurrence of sarcopenia (OR 2.09, 95% CI 1.80–2.43). To further explore the relationship between the VAI and sarcopenia, the VAI was categorized into quartiles. Higher VAI values were substantially linked to a greater occurrence of sarcopenia compared to the lowest quartile (P-trend < 0.001). Even after adjusting for all covariates, this trend remained for the highest VAI (OR 2.36, 95% CI 1.41–3.96), indicating a robust positive association between the VAI and sarcopenia.Table 2 The relationship between VAI and sarcopenia.

		Model 1 OR (95%CI) P-value	Model 2 OR (95%CI) P-value	Model 3 OR (95%CI) P-value	
Sarcopenia	log VAI	1.79 (1.60, 2.02) < 0.001	1.67 (1.48, 1.88) < 0.001	1.39 (1.20, 1.61) < 0.001	
Q1	[Reference]	[Reference]	[Reference]	
Q2	1.95 (1.12, 3.40) 0.020	1.91 (1.09, 3.37) 0.026	1.52 (0.82, 2.83) 0.200	
Q3	4.12 (2.71, 6.27) < 0.001	3.61 (2.34, 5.59) < 0.001	2.96 (1.64, 5.36) < 0.001	
Q4	4.36 (2.84, 6.69) < 0.001	3.72 (2.44, 5.69) < 0.001	2.36 (1.41, 3.96) 0.002	
P for trend	< 0.001	< 0.001	0.009	
CI Confidence interval, OR Odds ratio, Q Quartiles, VAI, Visceral adiposity index.

Model 1: No covariates adjusted; Model 2: Adjusted for Age, Sex, and Race; Model 3: Adjusted for age, Sex, Race, Educational level, PIR, Smoke, Activity status, Hypertension, Hypercholesterolemia, CAD, CKD, Diabetes, Energy, Protein, Carbohydrate, Total sugars, Dietary fiber, Total fat.

Nonlinear relationship and threshold effect analysis

A substantial nonlinear connection (P-nonlinear < 0.0001) between the prevalence of sarcopenia and the VAI was found by the RCS analysis. This relationship was characterized by an inverted L-shaped positive correlation, as shown in Fig. 2. Threshold effect analysis revealed a breakpoint in the sarcopenia population at VAI = 1.51. The chance of sarcopenia rose considerably with each rise in the VAI when it was less than 1.51, according to the logistic regression analysis (Table 3). (OR 2.54, 95% CI 1.74–3.79). A log-likelihood ratio less than 0.001 showed that, however, there was no statistically significant influence on the prevalence of sarcopenia (P = 0.700) when the visceral adiposity index (VAI) was larger than 1.51.Fig. 2 RCS curve fits the Association of VAI with Sarcopenia. Adjusted for age, Sex, Race, Educational level, PIR, Smoke, Activity status, Hypertension, Hypercholesterolemia, CAD, CKD, Diabetes, Energy, Protein, Carbohydrate, Total sugars, Dietary fiber, Total fat.

Table 3 Two-stage logistic regression between VAI and Sarcopenia.

	VAI	OR (95%CI) P-value	
Sarcopenia	Standard linear model	1.03 (1.00, 1.06) 0.082	
	VAI < 1.51	2.54 (1.74, 3.79) < 0.001	
	VAI > 1.51	1.01 (0.97, 1.04) 0.700	
	Log-likelihood ratio test	< 0.001	
CI Confidence interval, OR Odds ratio, VAI Visceral adiposity index.

Subgroup analysis

To explore the potential link between the Log VAI and sarcopenia, subgroup analyses of Model 3 based on demographic factors and lifestyle factors were conducted (Fig. 3). The results showed a constant and positive relationship between the Log VAI and the occurrence of sarcopenia in the different groups. There were no statistically significant interaction tests, which further strengthens the evidence that the VAI is an independent risk factor for sarcopenia.Fig. 3 Subgroup analysis of the association between VAI and Sarcopenia. Adjusted for age, Sex, Race, Educational level, PIR, Smoke, Activity status, Hypertension, Hypercholesterolemia, CAD, CKD, Diabetes, Energy, Protein, Carbohydrate, Total sugars, Dietary fiber, Total fat.

Sensitivity analysis and additional analysis

Sensitivity analysis was used to confirm the conclusions’ validity and strength. After excluding extreme values of the VAI ± 3 SD, 4634 participants remained. After fully adjusting for covariates, the positive association between the Log VAI and sarcopenia remained stable (OR 1.48, 95% CI 1.22–1.80). When the VAI was classified as a discrete variable, the results showed a significant positive correlation between high VAI scores and the development of sarcopenia (Supplementary Table 2). To further confirm the association between the VAI and the SMI, further studies were carried out. The results of the linear regression analysis performed on the VAI and the SMI are shown in Supplementary Table 3, which shows a consistent and statistically significant negative connection. The nonlinear connection and threshold effect between the VAI and the SMI are shown in Supplementary Fig. 1, which suggests that the influence of the VAI on the SMI is more pronounced within a certain range. The additional findings were in line with the primary results of this study, providing further evidence to support the conclusion of a positive correlation between the VAI and sarcopenia.

Discussion

A cross-sectional study using NHANES data from 2011 to 2018 revealed a significant association between VAI and sarcopenia among American adults. High VAI levels were consistently associated with an increased prevalence of sarcopenia across various populations. This finding has important clinical implications, suggesting that VAI may be a risk factor for sarcopenia. By monitoring VAI levels, clinicians are able to detect and identify high-risk groups at an early stage, so that they can take early interventions to reduce the risk of sarcopenia, which provides a scientific basis for the formulation of public health policies.

The relationship between obesity and sarcopenia has recently attracted widespread attention. BMI, commonly used to assess obesity, was once considered a protective factor against sarcopenia21. The limitations of BMI often lead to research results exhibiting the obesity paradox22,23. Numerous studies confirm that the VAI has significant advantages over BMI in assessing visceral fat and associated health risks24–26. Our study shows that higher VAI values are significantly associated with a higher prevalence of sarcopenia, supported by previous research. One study found that individuals with a higher weight-adjusted-waist index (WWI) are more likely to develop sarcopenia, indicating WWI as a risk factor for sarcopenia27. Another study found that visceral adipose tissue is closely related to sarcopenic obesity, with visceral fat mass predicting future muscle mass loss, further supporting the critical role of visceral fat in sarcopenia development16,28. Additionally, there is a close link between lipid metabolism and sarcopenia. A cross-sectional study showed a significant decrease in relative grip strength in older adults as the TG/HDL-C ratio increased29. These findings emphasize the validity of VAI in the assessment of sarcopenia. Additionally, subgroup analyses found that the association between VAI and the prevalence of sarcopenia remained positively correlated across different populations, supporting the universality of VAI as a tool for assessing sarcopenia. Sensitivity analyses and additional analyses further confirmed the robustness and reliability of the study results. Considering that VAI is an easily obtainable and low-cost indicator, it has the potential to serve as a convenient tool for assessing sarcopenia in clinical applications.

Several potential mechanisms can explain the relationship between VAI and the increased prevalence of sarcopenia. The increase in visceral fat significantly elevates the levels of pro-inflammatory factors, such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6)30,31. These inflammatory factors act on skeletal muscle through various signaling pathways, increasing muscle protein breakdown and reducing muscle protein synthesis, thereby accelerating the development of sarcopenia32–34. The increase in visceral fat is also closely associated with insulin resistance35,36. Insulin resistance affects the uptake and utilization of glucose by muscle cells, leading to insufficient energy supply to the muscles, which further exacerbates the development of sarcopenia37,38. Additionally, adipokines secreted by visceral fat tissue, such as leptin, play a crucial role in regulating muscle metabolism. The imbalance of these adipokines may further promote the occurrence of sarcopenia39. The above indicates that the relationship between VAI and sarcopenia is the result of multiple factors and pathways acting together, but further research is needed to explore its biological mechanisms in depth to develop more effective prevention and treatment strategies.

There are several advantages to this research. First, a technique that considers the relative significance of various groups was used to choose a sample that fairly reflects the whole country. Second, various covariates were included to control for potential confounding factors. However, this study also has several limitations. The cross-sectional design of the study limits the capacity to derive definitive conclusions regarding the causal relationship between sarcopenia and the VAI. Additionally, although complex sampling methods were used, they may still not fully reflect the real situation. Hence, future research should conduct more extensive studies to validate these findings.

Conclusion

Among American adults, the VAI is significantly associated with sarcopenia, with higher levels of the VAI potentially increasing its prevalence. Monitoring the VAI is crucial for delaying or reducing the occurrence and progression of sarcopenia.

Supplementary Information

Supplementary Information.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-72218-0.

Acknowledgements

We thank the National Center for Health Statistics of the CDC for making the National Health and Nutrition Examination Survey available.

Author contributions

J.L. conducted the study and wrote the manuscript. Y.L. analyzed the data. H.D. performed the literature search. X.S., W.F., Q.S., and K.Z. critically revised the manuscript. Each author made a substantial contribution to the forthcoming work.

Data availability

The National Center for Health Statistics Research Ethics Evaluation Committee assessed and approved the National Health and Nutrition Examination Survey (NHANES) data. Additionally, each participant gave their informed permission. The survey data are openly accessible online, providing a valuable resource for data users and researchers globally (www.cdc.gov/nchs/nhanes/).

Competing interests

The authors declare no competing interests.

Ethical approval and consent to participate

The National Center for Health Statistics (NCHS) and the Centers for Disease Control and Prevention (CDC) conducted the NHANES survey. The National Center for Health Statistics’ Research Ethics Assessment Board evaluated and approved the NHANES study protocol. This study used a de-identified publicly available dataset that did not involve direct human experimentation or further interventions, and no additional ethical approvals were required for this type of research. For further confirmation, please refer to the link to the NCHS ethics approval document for the NHANES data: https://www.cdc.gov/nchs/nhanes/irba98.htm.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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