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Association of weight-adjusted waist index with all-cause and cardiovascular mortality in individuals with osteoarthritis

BMC Musculoskeletal Disorders volume 26, Article number: 390 (2025) Cite this article

Abstract

Background

The Weight-Adjusted waist index (WWI) is a novel obesity assessment parameter that has been shown to be associated with mortality in various chronic disease populations and is also linked to the onset of osteoarthritis (OA). The aim of this study is to investigate whether WWI is associated with all-cause and cardiovascular mortality in OA population.

Methods

The study analyzed a cohort of 3,554 OA patients drawn from the National Health and Nutrition Examination Survey (NHANES) conducted between 2005 and 2018. Kaplan-Meier survival curves, Cox proportional hazards regression models, and subgroup analyses were utilized to assess the association between WWI and mortality outcomes. The dose-response relationship was examined using a restricted cubic spline (RCS) model.

Results

Among the 3,554 OA individuals, 611 participants were determined as deceased (13%), and 26% of the deaths were due to cardiovascular causes. The fully adjusted Cox proportional hazards model revealed that elevated WWI values were significantly associated with a higher risk of all-cause mortality (HR = 1.28, 95% CI 1.07‒1.52). The association between WWI and cardiovascular mortality in OA patients was only observed in the minimally adjusted model (HR = 1.43, 95% CI 1.12‒1.81). A similar conclusion was observed when the participants were grouped according to WWI tertiles. Kaplan-Meier survival curves demonstrated elevated mortality rates among individuals with higher WWI. The dose-response analysis indicated a linear positive relationship between WWI and mortality rates. The above associations remained consistent across all subgroups.

Conclusion

Elevated WWI levels were associated with a higher risk of all-cause mortality in OA individuals independently.

Clinical trial number

Not applicable.

Peer Review reports

Introduction

Osteoarthritis (OA) is the leading degenerative joint disorder worldwide, characterized by the progressive degeneration of cartilage, inflammation in the joint, and alterations in bone structure. These changes result in pain, stiffness, and diminished joint function [1]. It mainly affects weight-bearing joints, leading to significant disability and reduced quality of life, particularly in aging populations [2, 3]. Epidemiological data show that OA currently affects 7.6% of the global population, with projections indicating an increase to 60–100% by 2050. Moreover, OA is the seventh leading cause of disability worldwide [4]. Beyond its impact on physical function, OA is increasingly recognized for its association with elevated mortality rates [5, 6]. OA frequently occurs alongside other chronic conditions, such as cardiovascular disease, metabolic disorder, diabetes, and obesity, which may increase the risk of early mortality [7, 8]. The osteoarthritis population has a higher mortality rate due to systemic inflammatory status [9], comorbidities, and mobility limitations [10], especially among those with obesity [6]. Understanding the relationship between OA and mortality is essential for enhancing clinical management and developing strategies to mitigate the long-term health risks of the disease.

Obesity is well-established as a major factor influencing the pathogenesis and prognosis of OA [11, 12]. However, current measures of obesity in OA patients have limitations, as they fail to reflect changes in body composition and muscle mass. This leads to the “obesity paradox” in the prognosis and treatment of OA [13]. A meta-analysis focusing on the elderly population showed that when BMI was used as an indicator, more than two-thirds of the studies found higher survival rates in overweight or obese elderly individuals, highlighting the limitations of using BMI to assess obesity [14]. The novel parameter weight-adjusted waist index (WWI), introduced by Park et al., integrates waist circumference measurement to emphasize abdominal obesity, providing a more precise assessment of fat distribution and muscle quality [15]. Previous studies have shown that WWI is linked to various metabolic disorders and serves as a reliable predictor of mortality in the general population [16, 17]. Previous studies have also found that, compared to other obesity indices, WWI exhibits a stronger association with mortality in the general population [18]. WWI has also been linked to the onset of OA [19, 20]; however, to date, no studies have reported its impact on the survival outcomes of OA patients.

This study aims to investigate the association between WWI and all-cause and cardiovascular mortality in OA patients using the nationally representative NHANES cohort database, providing clinical evidence for obesity management in OA patients.

Methods

NHANES is a nationally representative study that uses a multistage probability design to evaluate the nutritional and health status of the non-institutionalized civilian population in the U.S. Participants are interviewed at home, where they provide standardized information on their demographics, socioeconomic status, health behaviors, and medical history. Physical exams and laboratory tests are conducted by trained healthcare professionals at a Mobile Examination Center (MEC). All surveys received approval from the Ethics Review Board of the National Center for Health Statistics (NCHS), and written informed consent was obtained from every participant. NHANES data is publicly accessible, with all participant information anonymized and de-identified to ensure privacy.

Study population

This study used data from the NHANES survey cycle covering 2005 to 2018, including a total of 70,190 individuals from the United States. The study included participants who had a confirmed diagnosis of OA (n = 4,021). After excluding participants under 20 years of age or the pregnant individuals (n = 6), and those with missing data on WWI and mortality status (n = 461), 3,554 eligible participants were included for final analysis, representing 23.75 million non-institutionalized U.S. residents (Fig. 1).

Fig. 1
figure 1

A flow diagram of eligible participant selection in the National Health and Nutrition

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Assessment of WWI and osteoarthritis

The exposure variable in this study is the Weight-Adjusted Waist Index (WWI), calculated by dividing waist circumference (in cm) by the square root of weight (in kg). Waist circumference was measured at the level of the iliac crest while the participant stood upright. Weight was assessed using a digital scale, with participants wearing a disposable shirt, pants, and slippers. A higher WWI score indicated a higher degree of obesity. Participants were grouped into Q1, Q2, and Q3 based on the tertiles of WWI.

As per published studies, OA was assessed using the NHANES codebook questionnaire, which asked, “Has a doctor or other health professional ever told you that you have arthritis?” Participants could respond with “yes” or “no.” Only participants who selected osteoarthritis were included in the study [21].

Measurement of mortality

The primary outcomes of this study were all-cause mortality and cardiovascular mortality. Mortality data were obtained from the National Death Index (NDI) as of December 31, 2019, using strict data collection standards. Participants were required to provide complete personal information, including name, Social Security number, race or nationality, date of birth, marital status, sex, and place of residence. All causes of death were professionally classified and coded according to the International Classification of Diseases, 10th Edition (ICD-10), ensuring data accuracy and international comparability. Cardiovascular disease (CVD) mortality was defined using ICD-10 codes I00-I09, I11, I13, and I20-I51.

Covariates

This study also examined sociodemographic, lifestyle, and comorbid factors that could potentially be associated with the outcomes. Specifically, the study considered covariates such as age, gender, race, education level, marital status, poverty-to-income ratio (PIR), obesity (defined by body mass index), drinking and smoking status, hypertension, diabetes, and hyperlipidemia. The detailed definitions and grouping criteria are provided in Supplementary Table 1.

Statistical analysis

Sampling weights were applied in all statistical analyses to ensure the national representativeness of the estimated data. In this study, the “WTMEC2YR” variable was used as the weighting factor, with the new weights (2005–2018) calculated as 1/7 × WTMEC2YR. First, we conducted descriptive statistical analyses of the baseline characteristics of the population. Continuous variables are presented as means ± standard deviations, while categorical variables are reported as frequencies and percentages. Weighted t-tests were used to compare continuous variables, and weighted chi-square tests were applied to compare categorical variables. Cox proportional hazards regression models were employed to assess the relationship between WWI and all-cause and cardiovascular mortality. Three regression models were used to adjust for potential confounders: Model 1 was unadjusted, Model 2 adjusted for age, gender, education level, marital status, race, and PIR, and Model 3 further adjusted for alcohol and smoking status, diabetes, hypertension, obesity, and hyperlipidemia.

Additionally, the dose-response relationship between WWI and mortality rates was examined using restricted cubic spline (RCS) models. The RCS model includes three nodes, located at the 10th, 50th, and 90th percentiles of the WWI distribution. We used the rms package for performing RCS modeling, the ggplot2 package for data visualization, the scales package for adjusting the scale and labels in the graphics, and the ggrcs package to assist in the visualization.

Kaplan-Meier survival curves were plotted for different WWI groups to visually compare survival outcomes, with group comparisons conducted using the Log-rank test. Subgroup analyses were performed based on key covariates to explore potential interactions between WWI and various stratified factors. In the subgroup analysis, we further adjusted for the following covariates: age, sex, race, marital status, education level, poverty-to-income ratio (PIR), obesity, smoking, drinking, hypertension, diabetes, and hyperlipidemia. All statistical analyses were carried out using R software version 4.2.2. We used the vif() function from the “car” package in R to assess multicollinearity among covariates. A variance inflation factor (VIF) < 10 indicates no significant multicollinearity. In this study, all VIF values were below 2. A significance level of P < 0.05 was considered statistically significant.

Results

Baseline characteristics

The baseline characteristics of the individuals are presented in Table 1, which includes a total of 3,354 individuals included in this study. In the study population, 41% of participants were over the age of 65, with females representing a higher proportion than males (64% vs. 36%). The majority of the included participants were non-Hispanic white (83%), 65% were cohabiting with a partner, 87% had at least a high school education, and 16% reported poor household income.

Throughout the follow-up period, 661 individuals (13%) passed away, among which 26% were attributed to cardiovascular causes. Compared to survivors, deceased individuals were more likely to be older (p < 0.001), of non-Hispanic white ethnicity (p = 0.007), not cohabiting with a partner (p < 0.001), have at least a high school education (p < 0.001), and report poorer household income (p < 0.001). The two groups exhibited significant differences in obesity rates (p = 0.005), smoking and alcohol consumption habits, as well as the prevalence of comorbidities such as hypertension and diabetes (all p < 0.001). Additionally, survivors demonstrated significantly lower value of WWI as well as lower classification within the three WWI tertiles (both p < 0.001).

Table 1 The demographic characteristics of the osteoarthritis population in the present study were stratified by survival status, weighted
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WWI and all-cause and cardiovascular mortality

Three Cox regression models were used to examine the independent association between WWI and mortality from all causes and cardiovascular diseases (Table 2). In Model 1, WWI showed a significant positive association with all-cause mortality (HR: 1.74, 95% CI: 1.51–2.00, p < 0.001), which remained significant in both the minimally adjusted Model 2 (HR: 1.39, 95% CI: 1.19–1.63, p < 0.001) and the fully adjusted Model 3 (HR: 1.28, 95% CI: 1.07–1.52, p < 0.001). When stratified by tertiles of WWI, similar results were observed. In all three models, individuals in the highest tertile of WWI had an increased risk of all-cause mortality compared to those in the lowest tertile (Model 1: HR 2.91, 95% CI: 2.29–3.69; Model 2: HR 1.94, 95% CI: 1.49–2.53; Model 3: HR 1.66, 95% CI: 1.25–2.20; all p < 0.001). For cardiovascular mortality, both WWI and WWI tertiles were significantly positively associated with mortality in Model 1 and Model 2. However, this association between WWI and cardiovascular mortality was not significant in Model 3.

Table 2 HRs (95% CIs) for All-cause mortality and cardiovascular mortality according to WWI in the osteoarthritis population
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Restricted cubic spline (RCS) models revealed a linear positive association between WWI and both all-cause mortality (p for nonlinear = 0.224) and cardiovascular mortality (p for nonlinear = 0.776) (Fig. 2). Furthermore, Kaplan-Meier curves showed significant differences in survival patterns across WWI tertiles, with higher WWI tertiles associated with an increased risk of both all-cause mortality (Fig. 3A) and cardiovascular mortality (both p < 0.001) (Fig. 3B).

Fig. 2
figure 2

The association of WWI with All-cause (A) and Cardiovascular mortality (B) among Osteoarthritis visualized by restricted cubic spline. HR (solid lines) and 95% confidence levels (shaded areas) were adjusted for age, sex, education level, marital status, PIR, race, obesity, smoking, drinking, hypertension, diabetes, and Hyperlipidemia

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Fig. 3
figure 3

Kaplan–Meier analyses for mortality among the three groups. T1–T3, Tertiles 1–3; WWI, Weight-Adjusted Waist Index. (A) All-cause mortality; (B) Cardiovascular mortality

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Subgroup analyses

In the subgroup analysis, a positive correlation between WWI and all-cause mortality was observed across all subgroups (Fig. 4A. Similarly, a similar trend was seen between WWI and cardiovascular mortality in most subgroups (Fig. 4B). No significant interactions were found among these subgroup variables, indicating that the effect of WWI on all-cause and cardiovascular mortality in OA patients is robust across subgroups, with no substantial influence from these covariates.

Fig. 4
figure 4

Subgroup analysis between WWI and (A). all-cause mortality and (B). cardiovascular mortality. HRs were calculated per 1-unit increase in WWI. Analyses were adjusted for age, sex, education level, marital status, PIR, race, obesity, smoking, drinking, hypertension, diabetes, and Hyperlipidemia

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Discussion

This study explored the relationship between WWI and all-cause as well as cardiovascular mortality in OA patients based on the NHANES database. We found a strong positive association between WWI and all-cause mortality, with higher WWI linked to an increased mortality risk, even in the fully adjusted Cox models. The association between WWI and cardiovascular mortality was significant only in the minimally adjusted Model 2. Additionally, the subgroup analysis showed that the relationship between WWI and mortality was consistent across all subgroups.

Obesity has long been acknowledged as a major risk factor for the onset and further progression of OA [22, 23]. Previous research has highlighted obesity as a significant modifiable risk factor for OA, emphasizing its management as a crucial aspect of OA prevention and treatment strategies [24]. Weight loss is one of the key conservative treatments for OA, helping to alleviate symptoms and slow disease progression [25]. BMI is commonly used to measure obesity in OA patients; however, it does not account for fat distribution or muscle quality, which limits its clinical relevance in the management of OA [26]. Recent studies have also used other body composition parameters, such as waist circumference and waist-to-hip ratio, in OA populations [27]. This study uses the novel anthropometric parameter WWI, which provides a more accurate assessment of the impact of visceral fat and muscle quality, and has been proven to be a reliable and comprehensive indicator of abdominal obesity [28]. Several previous cross-sectional studies have identified the association between elevated WWI and a higher prevalence of OA [20]. However, it is still unclear whether WWI is also associated with survival rates in OA populations.

As a comprehensive indicator of body composition, high WWI has been shown to be associated with increased fat mass, reduced muscle mass, and lower bone mass [29]. Several large cohort studies have also shown that WWI is associated with overall survival prognosis in various populations. For instance, Ding et al. conducted a prospective cohort study and found that a higher WWI (≥ 11.2) was related to an increased risk of all-cause and cardiovascular mortality in the population of southern China [30]. Similarly, Cai et al. demonstrated that WWI was linked to all-cause mortality in individuals aged 60 and older in China, and its predictive performance was superior to that of traditional parameters [31]. Liu et al. compared WWI with other obesity indices, such as waist-to-hip ratio and visceral adiposity index, and found that WWI was the most accurate predictor of mortality in the general population [18]. In addition, the relationship between WWI and survival prognosis has been extensively studied in various populations, including adults in the United States [17, 32], cancer survivors [33], individuals with type 2 diabetes [16, 34], and those with metabolic-associated fatty liver disease [35] and asthma [36]. This study is the first to establish an association between WWI and both all-cause and cardiovascular mortality in the OA population, providing valuable insights for the management of OA patients. However, the association between WWI and mortality varies by population. In diabetes patients, the fully adjusted HR for all-cause mortality exceeds 1.7, likely reflecting a stronger link to metabolic dysfunction and adverse outcomes [34]. In OA patients, its impact may be more complex, involving metabolism, inflammation, and reduced mobility.

The relationship between WWI and mortality in OA populations is multifactorial, involving both metabolic and biomechanical pathways that contribute to the adverse outcomes observed in these patients [37]. Metabolic abnormalities induced by obesity can affect both the local joint environment and systemic health, thereby influencing the survival outcomes of OA patients. Firstly, WWI offers a more accurate representation of visceral fat accumulation and muscle quality, which is closely linked to glucose metabolism, lipid metabolism, and chronic low-grade inflammation [38]. Visceral adiposity, unlike subcutaneous fat, is metabolically active and releases certain pro-inflammatory cytokines, adipokines, and other bioactive molecules that contribute to chronic low-grade inflammation [39]. Adipose tissue secretes inflammatory mediators such as IL-1, IL-6, TNF-α, and adipokines, which can locally affect joint cartilage, synovial membranes, and bone marrow osteoblasts, thereby influencing the onset and progression of OA [40, 41]. Additionally, chronic inflammation is also considered to be associated with joint soft tissue fibrosis and musculoskeletal pain symptoms [42, 43]. Visceral fat accumulation promotes insulin resistance and metabolic dysfunction, thereby elevating the risk of cardiovascular diseases, diabetes, and other chronic conditions, ultimately leading to higher all-cause mortality [18]. Besides, metabolic syndrome associated with abdominal obesity has been shown to directly affect chondrocytes and macrophages within the joint, contributing to the pathogenesis of OA and influencing its prognosis [44, 45]. OA and the associated pain symptoms may, in turn, lead to reduced physical activity, resulting in difficulty losing weight and exacerbating metabolic disturbances, with the two factors mutually reinforcing each other to create a vicious cycle [46].

From a biomechanical perspective, as the degree of abdominal obesity increases, the pressure on weight-bearing joints in the lower limbs also increases, which accelerates joint wear and degeneration [47]. Previous studies have found an association between central obesity and knee OA [48]. It is hypothesized that abdominal fat exerts greater torque on the knee joint and requires excessive knee extension to maintain balance, thereby accelerating joint wear and tear. This mechanical stress not only worsens OA symptoms but may also result in reduced physical activity, thereby elevating the risk of cardiovascular diseases and other related comorbidities [49]. Another potential mechanism is the reflection of muscle mass by WWI [29]. WWI is correlated with both fat mass and lean body mass, while sarcopenia is a well-established risk factor for adverse health outcomes in OA patients [50, 51]. Research has indicated that sarcopenia and sarcopenic obesity are common in arthritis populations and significantly affect the prognosis of arthritis treatments [50, 52]. Besides, sarcopenia not only leads to functional decline, frailty, and limited physical activity but is also closely associated with metabolic dysfunction and chronic inflammation, which may further increase mortality risk [53]. Additionally, some studies suggest that WWI is associated with osteoporosis severity and fracture risk in elderly patients, reflecting bone quality in the aging population [54, 55]. Therefore, WWI offers a more comprehensive assessment of health, aiding in the identification of high-risk OA patients who may have an elevated risk of overall and cardiovascular mortality.

Notably, in this study, the association between WWI and cardiovascular mortality in OA patients lost significance in Model 3. This may be due to the following factors. First, chronic conditions such as obesity, diabetes, and hypertension are strongly associated with cardiovascular mortality and interact with WWI. Adjusting for these factors may attenuate WWI’s direct predictive effect on cardiovascular mortality. Second, WWI may be more strongly associated with non-cardiovascular causes of death, such as cancer and respiratory diseases, which could explain its continued relevance to overall mortality in Model 3. Finally, the inclusion of numerous variables in Model 3 may have led to overadjustment. Future research should further investigate this finding, particularly the impact of comorbidities and non-cardiovascular mortality in the OA population.

This study utilized nationally representative longitudinal data, adjusting for demographic, clinical, and laboratory variables, to examine the association between WWI and all-cause and cardiovascular mortality in individuals diagnosed with OA. This study deepens our understanding of the mechanisms underlying the relationship between WWI and mortality in OA patients, providing robust scientific evidence to inform clinical management and prevention strategies for OA. By identifying OA patients with central obesity, comprehensive management strategies such as surgical intervention, control of comorbid conditions, and targeted muscle strength training can help improve the survival prognosis of these patients.

However, this study has several limitations. First, being observational in nature, it cannot establish a causal relationship between WWI and mortality. Future research utilizing methods such as Mendelian randomization is needed to further investigate this association. Future studies could incorporate novel obesity parameters, such as the body roundness index, to further explore the relationship between obesity and prognosis of OA population. Secondly, while this study accounted for confounding variables through multivariable regression and other techniques, the potential influence of unmeasured confounders on survival outcomes cannot be ruled out. Third, this study utilized a cohort from the United States, which had a higher proportion of women and White individuals, potentially introducing confounding bias. Thus, the results may not be generalizable to other populations. To enhance the external validity of the findings, future studies should adopt a multi-center, multi-ethnic cohort design. Furthermore, the reliance on self-reported questionnaires to define OA in this study may introduce bias, as these may not precisely correspond to the participants’ clinical diagnoses. Furthermore, current diagnostic methods for OA are unable to distinguish between the affected regions and the severity of the disease, thus limiting the ability to perform further stratified analyses. Therefore, the results should be interpreted with caution. Future studies should utilize imaging data for accurate diagnosis and consider the impact of different types and stages of arthritis on the outcomes.

Conclusion

This study found that higher WWI was associated with increased all-cause and cardiovascular mortality in the OA population. The results suggest that WWI measurement may aid in mortality and prognostic prediction for OA patients and support strategies for managing and controlling obesity in this population.

Data availability

The survey data are publicly available on the internet for data users and researchers throughout the world (www.cdc.gov/nchs/nhanes/).

Abbreviations

WWI:

Weight-adjusted waist index

OA:

Osteoarthritis

NHANES:

National health and nutrition examination survey

RCS:

Restricted cubic spline

MEC:

Mobile examination center

NCHS:

National center for health statistics

NDI:

National death index

ICD:

International classification of diseases

PIR:

Poverty-to-income ratio

HR:

Hazard ratio

CI:

Confidence interval

BMI:

Body mass index.

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Authors and Affiliations

  1. Department of Orthopaedic Surgery, Beijing Jishuitan Hospital, Capital Medical University, Beijing, 100035, China

    Shaoyi Guo, Dan Chen, Yunfeng Zhang, Kaiqi Cao, Yongqi Xia & Dejin Yang

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  1. Shaoyi Guo
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  2. Dan Chen
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  3. Yunfeng Zhang
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  4. Kaiqi Cao
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  6. Dejin Yang
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Contributions

SG and DY designed the research. DC, YZ, KC and YX collected and analyzed the data, SG drafted the manuscript. DY revised the manuscript. All authors contributed to the article and approved the submitted version.

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Correspondence to Dejin Yang.

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Guo, S., Chen, D., Zhang, Y. et al. Association of weight-adjusted waist index with all-cause and cardiovascular mortality in individuals with osteoarthritis. BMC Musculoskelet Disord 26, 390 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12891-025-08638-4

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  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12891-025-08638-4

Keywords

BMC Musculoskeletal Disorders

ISSN: 1471-2474

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