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Association between physical activity and visceral adiposity index (VAI) in U.S. population with overweight or obesity: a cross-sectional study

BMC Public Health volume 24, Article number: 2314 (2024) Cite this article

Abstract

Background

Previous studies have revealed the effects of different physical activity (PA) types on visceral adipose tissue (VAT) accumulation in individuals with overweight/obesity. However, the independent association (especially the dose–response relationship) between PA and VAT in individuals with and without overweight/obesity remains less explored. Visceral adiposity index (VAI), calculated from waist circumference, body mass index (BMI), triglyceride and high-density lipoprotein cholesterol, is a novel indicator of VAT. This study aims to elucidate the association between PA and VAI in participants with and without overweight/obesity.

Methods

Participants who are overweight or obese and with complete data on VAI, PA, and other essential covariates from the National Health and Nutrition Examination Survey (NHANES) database (2015–2018) were included in this study. PA was evaluated by the PA questionnaire and converted into metabolic equivalent task (MET) hours per week (MET-h/wk) based on the suggested MET scores. Multivariate linear regression models were used to identify the association between PA and VAI. Subgroup analyses, combined with interaction tests and restricted cubic spline (RCS) regression analyses, were utilized to explore the stability and nonlinearity of PA-VAI association, respectively.

Results

A total of 4, 312 participants with complete data on PA and VAI was included in this study, with 3, 441 of them being overweight or obese. After adjusting for all potential covariates, increased PA was found to be significantly associated with remarkable lower level of VAI in all participants (β = -0.0004, P = 0.003), participants with (β = -0.0013, P = 0.012) and without (β = -0.0004, P = 0.003) overweight/obesity. Subgroup analyses and interaction tests revealed that the PA-VAI association was not modified by other covariates in individuals with overweight/obesity. Furthermore, RCS analyses revealed that PA was significantly, linearly and negatively associated with VAI in all participants, participants with and without overweight/obesity (all P < 0.05, all P for nonlinearity > 0.05). Noteworthily, as opposed to individuals without overweight/obesity, PA was significantly associated with lower VAI in participants with overweight/obesity after exceeding the threshold of 150 MET-h/wk.

Conclusion

Increased PA was significantly associated with lower level of VAI, but a higher level of PA (> 150 MET-h/wk) was needed to obtain significantly lower level of VAI in individuals with overweight/obesity.

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Introduction

Overweight or obesity is a multifactorial disease featured by excess fat (especially visceral fat) accumulation [1], which is responsible for various adverse consequences, such as cardiovascular disease, type 2 diabetes, arthritis, several cancers [2, 3], and so on. According to the World Health Organization (WHO), a body mass index (BMI) ≥ 25kg/m2 is considered overweight, and a BMI ≥ 30kg/m2 is defined as obesity [4]. Despite the numerous public health initiatives adopted to tackle this issue, the global prevalence of overweight or obesity is still continuing to escalate rapidly, making it a health issue that need to be addressed urgently. Therefore, studies targeted at elucidating the pathogenesis, identifying monitoring indicators, and developing management strategies for overweight or obesity are urgently needed currently.

For the management of overweight or obesity, finding an accurate and stable monitoring indicators which can effectively and reliably reflect the extent and status of overweight or obesity is one of the vital prerequisites for developing treatments and assess effectiveness. Previous studies have revealed that individuals with overweight or obesity are characterized by two outstanding features, including alterations in body anthropometrics and occurrence of metabolic abnormalities [1, 5]. Therefore, obesity indicators which incorporate metabolic and anthropometric parameters may be more reflective for assessing the onset and progression of overweight or obesity.

Visceral adiposity index (VAI), a sex-specific indicator calculated from waist circumference, BMI, triglyceride and high-density lipoprotein cholesterol (HDL-C), serves as a reliable indicator in various disease states, especially obesity. Compared with traditional body assessment parameters, such as BMI, waist circumference (WC), and waist-to-height ratio (WHtR), VAI is a novel indicator which can reflect the status of visceral adipose tissue accumulation and dysfunction with favorable performance [6, 7]. Previous studies have revealed that higher level of VAI was not only associated with severe fat accumulation [8], but also significantly associated with numerous metabolic diseases, including heart failure [9], abdominal aortic calcification [7], bone loss [10], chronic kidney disease (CKD) [11], etc. Briefly, as a novel obesity-related indicator, VAI can serve as a key parameter to evaluate the cardiometabolic risks and therapeutic effectiveness during obesity management.

In parallel with dietary, pharmaceutical, and behavioral interventions, physical activity (PA) is deemed as one of the “pillars” in controlling overweight and obesity [12]. Abundant evidences have demonstrated that PA or exercise training (ET) can trigger multiple beneficial health effects, including enhancing body weight and fat loss, maintaining body weight and fat reduction, reducing cardiovascular risks, improving metabolic fitness [13], etc. Based on previous studies, two key points can be summarized regarding the role of PA in maintaining health in individuals with overweight or obesity. One aspect is that PA can ameliorate the adverse outcomes of obesity even in the absence of weight loss, especially eliciting some beneficial metabolic alterations, such as enhancing vascular adaptations [14], alleviating insulin and leptin resistance [15, 16], increasing fat mobilization [17], etc. Previous studies have suggested that the increased risks of cardiometabolic diseases of overweight/obesity is mainly derived from its metabolic disturbance, such as insulin resistance, dyslipidemia, glucose intolerance [18], and so on. In other words, increased PA can help an overweight/obese individuals with metabolic abnormalities become an metabolically healthy obese individuals, and thus prevent the reap of additional cardiometabolic risks [19]. Except for the above-mentioned aspect, another aspect of the role PA in overweight/obese individuals is that increased PA interventions alone may only elicit modest or non-significant weight loss, a heartbreaking fact contrary to our expectation. The primary cause for such situation is that increased levels of PA may elicit diminishing returns in energy expenditure for the compensatory responses in non-PA energy expenditures [20], a phenomenon called energy compensation. It implies that increased PA can not directly and fully translate into an increase of total energy expenditure (TEE), because the TEE from other components may reduce accordingly, which mainly refers to the basal energy expenditure for the normal function of body systems and organs, including immune system, cardiovascular system, kidney, brain, liver, skeletal muscle, digestive and respiratory system, etc. Unfortunately, the degree of energy compensation may become more stronger as we get fatter, making the progressive difficulty in losing fat [20]. These facts indicated that the dose–response relationship between PA and obesity-related indicators may be quite different among individuals with and without overweight/obesity. Additionally, although abundant previous studies have explored the relationship between different PA types (such as aerobic exercise, resistance training, high-intensity interval training, and so on) and VAT accumulation [21], the independent dose–response relationship between PA and VAT-reflected indicators remained unexplored.

Given this, in this study, we aimed to explore the association (especially dose–response relationship) between physical activity and VAI in individuals with overweight/obesity. Our findings may provide useful scientific references and evidences for individuals with overweight or obesity who endeavor to reduce visceral adipose tissue accumulation by PA.

Methods

Data accession and study population

This study was performed based on the data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2015 and 2018 [22]. The NHANES is carried out in the United States by the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention (CDC). The survey is comprised of cross-sectional interviews, examinations, and laboratory data collected from a complex multistage, stratified, clustered probability sample representative of the US population. The survey protocol has been granted approval by the Institutional Review Board of the CDC, and all the participants have willingly given their informed consent [23,24,25].

A total of 19, 225 participants from two Sects. (2015–2016, 2017–2018) were preliminarily selected for analyses. After exclusion of those who with incomplete data on VAI, PA, and other essential covariates, a total of 4, 312 participants were finally included for final analyses, and 3, 441 of them with overweight/obesity and 871 of them without overweight/obesity. The flowchart of participant selection was presented in Fig. 1.

Fig. 1
figure 1

The flowchart of participant selection

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Visceral adiposity index and physical activity assessment

VAI is a sex-specific indicator derived from waist circumference (WC), BMI, triglyceride (TG) and high-density lipoprotein cholesterol (HDL-C). VAI can be calculated by the following formula [7]: with VAI = [WC/(39.68 + 1.88 × BMI)] × (TG/1.03) × (1.31/HDL-C) for males, and with VAI = [WC/(36.58 + 1.89 × BMI)] × (TG/0.81) × (1.52/HDL-C) for females, respectively.

As previously described [26], physical activity was evaluated by the weekly metabolic equivalent task (MET)-minute aggregated scores, which represented the energy expenditure used for PA. MET, served as a metabolic equivalent, reflected the ratio of metabolic rate during activity and at rest [27]. According to the NHANES recommendations [26], the weekly PA included five parts (including vigorous work-related activity, moderate work-related activity, walking or bicycling for transportation, vigorous leisure-time physical activity, and moderate leisure-time physical activity), and can be calculated by the formula presented in Fig. 2.

Fig. 2
figure 2

The assessment of weekly PA. PA is composed of five parts, including the vigorous work-related activity (VWRA), moderate work-related activity (MWRA), walking or bicycling for transportation, vigorous leisure-time physical activity (VLTPA), and moderate leisure-time physical activity (MLTPA)

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Covariates

According to previous studies [1, 5], some essential covariates were included in this study, including demographic characteristics, behavioral risk factors, and health conditions. The demographic covariates included age, gender, race, education level, marital status, poverty income ratio (PIR) and BMI. Race was stratified into Mexican American, Non-Hispanic Black, Non-Hispanic White and other races. PIR was divided into three levels as previously described [28], including < 1.30, 1.30–3.50, and ≥ 3.50. Marital status included three categories, including married, unmarried and others (widowed, divorced, and separated). Education was stratified into five levels, which were less than 9th grade, 9th–11th grade, high school, some college and college or above, respectively. Additionally, some other essential covariates were also included in this study, including behavioral risk factors (including smoking and drinking) and some chronic diseases, such as chronic kidney disease (CKD), hypertension, hyperlipidemia, and diabetes. Smoking status, as previously described [5], was divided into three categories, including never smoker, former smoker and current smoker. Similarly, the alcohol use status was categorized into three groups [5], including never drinkers, former drinkers, current drinkers. The Chronic Kidney Disease Epidemiology Collaboration (CKD—EPI) equation to assess CKD [29]. The diagnosis of CKD was based on the estimated glomerular filtration rate (eGFR) < 60 ml/min per 1.73 m2 or urine albumin-to creatinine ratio (ACR) ≥ 30 mg/g [30]. The definition of hypertension was based on one or more of the following three conditions: (1) Participants responded “Yes” to the blood pressure questionnaire “Has a doctor or any other health professional ever told you that you had hypertension?”; (2) Participants were regarded as hypertensive if their average systolic blood pressure (SBP) was ≥ 130 mmHg or the average diastolic blood pressure (DBP) ≥ 80 mmHg; (3) Individuals were taking any antihypertensive drug were also considered hypertension. The diagnosis of diabetes was based on any of the following criteria: (1) a physician told you that you had diabetes; (2) the level of glycosylated hemoglobin (HbA1c) > 6.5%; (3) the level of fasting glucose ≥ 7.00 mmol/L; (4) the level of random blood glucose ≥ 11.10 mmol/L; (5) the level of two-hour oral glucose tolerance test (OGTT) ≥ 11.10mmo/L; (6) current use of any diabetes medication or insulin.

Statistical analyses

All statistical analyses were conducted by using R software (version 4.2.3) in this study. All analyses were conducted with the incorporation of sample weights, stratification, and cluster to account for the complex survey design. Some essential R packages were utilized to complete the data extraction, processing and analyses, including nhanesR, survery, etc.. Specifically, the baseline characteristics of all participants were presented based on the tertile grouping of PA. Continuous variables, including VAI, BMI, age, and PIR were presented as median (Q1-Q3, interquartile) for their non-normal distribution characteristics. Categorical variables were presented as numbers and percentages. The differences of baseline characteristics among different groups were compared using Rao-Scott chi-squared test or Kruskal–Wallis test. Besides, multivariate linear regression models were utilized to explore the association between PA and VAI. Additionally, subgroup analyses, interaction tests and restricted cubic spline (RCS) regression analysis were also conducted to investigate the robustness and nonlinearity of PA-VAI association, respectively. P values less than 0.05 (two sided) were considered as statistically significant.

Results

Baseline characteristics of the study population

As depicted in Fig. 1, after exclusion of participants with incomplete information on VAI, PA, and other essential covariates, including demographic characteristics (age, gender, race, education, marital status, PIR), behavioral factors (smoking and drinking), and health conditions (CKD, hypertension, hyperlipidemia and diabetes), a total of 4, 312 participants were selected with complete information on PA and VAI, and a total of 3, 441 participants with overweight and obesity were selected for final analyses.

The baseline characteristics of all enrolled participants were presented in Table 1. Variables of VAI, gender, education, marital status, PIR, alcohol use, CKD, hypertension, hyperlipidemia, and diabetes were all found to be significantly associated with PA groups (P < 0.05). Specifically, participants in the third tertile group were more likely to get lower VAI level, with age < 60 years, be male, be Non-Hispanic White, with higher education level, be married, with PIR level of 1.30–3.50, be current drinkers, without CKD and diabetes.

Table 1 Baseline characteristics of the study participants
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Association between PA and VAI

Multivariate linear regression models were utilized to explore the association of PA and VAI in all participants (N = 4312, regardless of BMI), participants with (N = 3441, BMI > 25 kg/m2) and without (N = 871, BMI ≤ 25 kg/m2) overweight or obesity. After adjusting for all potential covariates, increased PA (as continuous variable) was found to be significantly associated with remarkably lower VAI in all participants (Model 3; β = -0.0004, 95%CI:-0.0007 to -0.0001, P = 0.003; STable 1), participants with overweight or obesity (Model 3; β = -0.0013, 95%CI:-0.0020 to -0.0007, P = 0.012; Table 2), and participants without overweight or obesity (Model 3; β = -0.0004, 95%CI:-0.0007 to -0.0001, P = 0.003; STable 2). Similarly, when PA was analyzed as three categorical variables, increased PA levels were also associated with significantly lower VAI in all participants (Model 3; β = -0.1183, 95%CI:-0.1972 to -0.0394, P = 0.003; STable 1), participants with (Model 3; β = -0.2496, 95%CI:-0.4469 to -0.0524, P = 0.0131; Table 2) and without (Model 3; β = -0.1361, 95%CI:-0.2402 to -0.0320, P = 0.010; STable 2) overweight or obesity.

Table 2 The Association between physical activity and visceral adiposity index in participants with overweight or obesity
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Subgroup analysis

In order to further confirm the robustness of PA-VAI association across different subgroups, subgroup analyses and interaction tests based on essential covariates were performed. As is shown in Table 3, the associations between PA and VAI remained consistent in different subgroups for covariates of age, gender, race, BMI, education, marital status, PIR, smoking, alcohol use, CKD, hypertension, hyperlipidemia, and diabetes. Additionally, interactions tests revealed that the PA-VAI association were not modified by these variables (P > 0.05).

Table 3 The association between physical activity and visceral adiposity index in different subgroups
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Exploration of the non-linear relationship between total PA and LAP

RCS analysis was employed to explore the non-linear relationship between PA and VAI in all participants, participants with and without overweight or obesity. The results, revealed a significant, linear, and negative association between PA and VAI in all participants (regardless of BMI; P = 0.001, P for nonlinearity = 0.156; SFig.1), participants with overweight or obesity (BMI > 25 kg/m2; P = 0.031, P for nonlinearity = 0.259; Fig. 3), and participants without overweight or obesity (BMI ≤ 25 kg/m2; P = 0.023, P for nonlinearity = 0.169; SFig.2). Although a similar linear PA-VAI association was found in all participants, participants with and without overweight or obesity, it should be noted that a threshold of PA level (150 MET-h/wk) existed for PA to get significantly lower VAI in participants with overweight or obesity (Fig. 3). As presented in STable 3, once the weekly PA level exceed 150 MET-h/wk, higher PA levels were obviously associated with significantly lower VAI (β = -0.540, -0.501, -0.463 for model 1, model 2 and model 3, respectively). Additionally, in contrast to PA, a significant, positive and linear relationship between sedentary time and VAI was determined by RCS analysis (Fig. 4; P = 0.027, P for nonlinearity = 0.357).

Fig. 3
figure 3

RCS analysis to determine the association between PA and VAI in participants with overweight or obesity. Variables of age, gender, race, education level, marital status, PIR, BMI, smoke, alcohol use, CKD, hypertension, hyperlipidemia, and diabetes were adjusted during RCS analyses. Abbreviations: RCS, restricted cubic spline; VAI, visceral adiposity index; PIR, ratio of family income to poverty; BMI, body mass index; CKD, chronic kidney disease

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Fig. 4
figure 4

RCS analysis to determine the association between weekly sedentary time and VAI in participants with overweight or obesity. Variables of age, gender, race, education level, marital status, PIR, BMI, smoke, alcohol use, CKD, hypertension, hyperlipidemia, and diabetes were adjusted during RCS analyses. Abbreviations: RCS, restricted cubic spline; VAI, visceral adiposity index; PIR, ratio of family income to poverty; BMI, body mass index; CKD, chronic kidney disease

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Discussion

In this study, we mainly concentrated on exploring the association between PA and VAI in participants with overweight/obesity. Our results firstly demonstrated that increased PA levels were significantly associated with remarkable VAI reduction in individuals with and without overweight/obesity. Furthermore, subgroup analyses and interaction tests revealed that the PA-VAI association was not modified by other covariates. Additionally, although a similarly linear and negative association between PA and VAI was identified by RCS analysis in participants with and without overweight/obesity, it seems to be more difficult for individuals with overweight/obesity to trigger VAI reduction as opposed to individuals without overweight/obesity. Noteworthily, only PA reaching the threshold of 150 MET-h/wk can significantly elicit VAI reduction in participants with overweight/obesity.

In our study, a higher level of PA was identified by RCS analysis for individuals with overweight/obesity to induce the beneficial effect of VAI reduction as opposed to individuals without overweight/obesity, suggesting that it may be more difficult for overweight/obese individuals to consume fat by increased PA. The primary cause for this was energy compensation [20], a phenomenon that elevated exercise or other physical activities induced by long-term lifestyle changes didn’t contribute to commensurate increases in energy expenditure, which is widely occurred both in humans and other animals. Moreover, there existed evidence revealed that the degree of energy compensation may be more progressively aggravated as individuals become fatter [20], thus forming the huge impediment for individuals with overweight/obesity to combat obesity with increased PA. Fortunately, some interventions can be taken to alleviate energy compensation, such as changing the patterns of PA (e.g., non-structured physical activity), alter dietary composition or patterns, and so on. Previous studies have revealed that structured aerobic exercise at the dose of 20 kcal per kilogram of weight per week contributed to less weight loss than expected for the reason of energy compensation induced by behavioral adaptations [31]. Therefore, choosing the pattern of non-structured physical activity may successfully circumvent the adaptations triggered by structured PA and thus alleviate energy compensation. Moreover, abundant evidences has indicated the crucial role of dietary composition or patterns in energy expenditure and metabolism [32]. The study performed by Nina Vujović, et al. [32]. indicated that late eating was significantly associated with various adverse effects, including increased hunger, altered appetite-regulating hormones, elevated waketime and 24-h ghrelin-to-leptin ratio, and altered adipose tissue gene expression responsible for lipid metabolism (a shift of pathways from lipolysis towards adipogenesis), etc. Briefly, energy compensation hindered the beneficial effects elicited by increased PA in individuals with overweight/obesity, and thus other interventions should also be taken to optimize these effects.

Although increased PA may not contribute to successful weight loss for individuals with overweight/obesity, it indeed improved fitness via modulating metabolic health. Previous studies have indicated that metabolic health (especially in overweight/obesity) is being increasingly utilized to evaluate the risks for cardiovascular diseases (CVD), diabetes and diabetes-related complications [33]. Existing evidences have demonstrated that significant metabolic heterogeneity existed among individuals with overweight/obesity [34], and those who are metabolically healthy are defined as metabolically healthy overweight/obesity, whereas others are classified as metabolically unhealthy overweight/obesity [35, 36]. Actually, such metabolic heterogeneity also existed among individuals with normal weight, and similarly those with and without healthy metabolic conditions were classified as metabolically healthy normal weight and metabolically unhealthy normal weight [37], respectively. A series of studies have revealed that being metabolically unhealthy (even with normal weight), or the transition from metabolically healthy to metabolically unhealthy, was significantly associated with increased risk for various adverse outcomes, including elevated obesity-induced cancer risk [38], enhanced CVD incidence [39, 40], deterioration of cognitive function [41], reduced fat utilization and enhanced insulin resistance [42], etc. Given this, individuals with overweight/obesity are encouraged to complete more PA to reap more metabolic benefits instead of just concentrating on weight loss. Accumulating evidences have uncovered that PA can reshape or reborn fitness via multiple ways, such as improving insulin/leptin resistance [43, 44], activating hepatic autophagy [45], stimulating the secretion of cytokines (e.g., interleukin-13) to enhance exercise endurance [46], and so on. Briefly, the above-mentioned facts all highlighted the critical role of PA in improving metabolic status and thus lowering the risks of obesity-related diseases or complications.

According to the 2020 guidelines from the World Health Organization, all adults are recommended to complete at least 150–300 min of moderate-intensity PA (MPA), or 75–150 min of vigorous-intensity PA (VPA), or equivalent combination of MPA and VPA per week to maintain fitness [47]. This level of PA represented the minimal amount of PA which can effectively reduce cardiometabolic risks, instead of the level to arouse significant weight loss without other interventions. Since about two-thirds of overweight and obese individuals were metabolically unhealthy based on previous epidemiological surveys [36], a higher level of PA was thus needed to elicit favorable health benefits. According to the results from our study, a minimum of 150 MET-h/wk was needed to arouse significant VAI reduction in individuals with overweight/obesity.

Several limitations in the current should be acknowledged. Firstly, the study only included samples from specific regions or populations, which may limit the generalizability of the results to other populations. Secondly, the sample size might be relatively small, which could affect the reliability and generalizability of the study findings. Additionally, the cross-sectional design employed in this study does not allow for establishing causality between PA and visceral fat accumulation. Finally, the evaluation of the PA was based on the self-reported Global Physical Activity Questionnaire (GPAQ), instead of accelerometer, which may introduce some bias to our results. Therefore, future researches based on accelerometer-measured physical activity are needed to further confirm the conclusions of the current study. Moreover, integrated analyses based on high-throughput omics (such as metabolomics, microbiomics, proteomics, and so on) can provide a deeper insight about the association between PA and overweight/obesity.

In summary, our findings from the current study revealed the essential role of PA in controlling overweight/obesity and eliciting VAI reduction. Increased levels of PA were significantly associated with remarkable VAI reduction, but a higher level of PA (> 150 MET-h/wk) was needed to trigger remarkable VAI reduction in individuals with overweight/obesity.

Availability of data and materials

Publicly available datasets were analyzed in this study. This data can be accessible at: https://www.cdc.gov/nchs/nhanes/index.htm.

Abbreviations

BMI:

Body mass index

CKD:

Chronic kidney disease

CVD:

Cardiovascular diseases

DBP:

Diastolic blood pressure

eGFR:

Estimated glomerular filtration rate

HbA1c:

Glycosylated hemoglobin

HDL-C:

High-density lipoprotein cholesterol

MET:

Metabolic equivalent task

MPA:

Moderate-intensity physical activity

OGTT:

Oral glucose tolerance test

PA:

Physical activity

PIR:

Poverty income ratio

RCS:

Restricted cubic spline

SBP:

Systolic blood pressure

TEE:

Total energy expenditure

TG:

Triglyceride

VAI:

Visceral adiposity index

VPA:

Vigorous-intensity physical activity

WC:

Waist circumference

WHtR:

Waist-to-height ratio

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Acknowledgements

The authors thank all the volunteers for the participation and personnel for their contribution to the NHANES study. In addition, we also thank Zijiao Li, Yichong Niu and Jie Zhang for their kind help in completing this study.

Funding

This study was supported by the outstanding talent pool key support object projects of Army Medical University (Number: XZ-2019–505-021), and the project of Military key clinical specialty (Number: 41561Z23711).

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Author notes

  1. XiaoLiang Tao, Xiang Xu and YaoXin Xu contributed equally to this work and shared the co-authorship.

Authors and Affiliations

  1. Department of Orthopaedics, National & Regional United Engineering Lab of Tissue Engineering, Southwest Hospital, Army Medical University, 30 Gaotanyan Street, Shapingba District, Chongqing, 400038, China

    XiaoLiang Tao, YaoXin Xu, QianKun Yang, Hao Xue, XiaoQin Ren & Fei Luo

  2. Department of Cardiology, Southwest Hospital, Army Medical University, Chongqing, 400038, China

    Xiang Xu

  3. Department of Radiology, Southwest Hospital, Army Medical University, Chongqing, 400038, China

    TaoTao Yang

  4. College of Basic Medicine, Army Medical University, Cadet Brigade 4, Chongqing, 400038, China

    Xiang Zhou

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Contributions

Fei Luo and XiaoQin Ren guided the whole research, from research design to data analysis and article writing. XiaoLiang Tao, Xiang Xu and YaoXin Xu wrote the main manuscript text for this research article and conducted the main statistical analyses. QianKun Yang and TaoTao Yang helped to complete data analyses. Xiang Zhou and Hao Xue revised the manuscript and were responsible for writing the response letter and making the additional statistical analyses. All authors gave final approval of the version to be published and agreed to be accountable for all aspects of the work.

Corresponding authors

Correspondence to XiaoQin Ren or Fei Luo.

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Ethics approval and consent to participate

The program was approved by the National Center for Health Statistics Ethics Review Board. All of the participants provided written informed consent. No additional ethical review board approval was required to analyze the anonymized NHANES data.

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Not applicable.

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The authors declare no competing interests.

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Tao, X., Xu, X., Xu, Y. et al. Association between physical activity and visceral adiposity index (VAI) in U.S. population with overweight or obesity: a cross-sectional study. BMC Public Health 24, 2314 (2024). https://doi.org/10.1186/s12889-024-19810-1

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BMC Public Health

ISSN: 1471-2458

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