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Association between weight-adjusted-waist index and long-term prognostic outcomes in cardiovascular disease patients: results from the NHANES 1999–2018 study

Diabetology & Metabolic Syndrome volume 17, Article number: 19 (2025) Cite this article

Abstract

Background

As cardiovascular disease (CVD) morbidity and mortality increase yearly, this study aimed to explore the potential of the weight-adjusted-waist index (WWI) and its relation to long-term mortality in patients with CVD.

Methods

The diagnosis of CVD was based on standardized medical condition questionnaires that incorporated participants’ self-reported physician diagnoses. WWI (cm/√kg) is a continuous variable and calculated as waist circumference (WC, cm) divided by square root of body weight (kg). For analysis purposes, the participants were divided into four groups based on the quartiles (Q1 – Q4) of the WWI. The study’s primary outcome was all-cause mortality in patients with CVD, with cardiovascular mortality as the secondary outcome, and sample weights and complex survey designs were used to ensure reliable, accurate results.

Results

The final analysis included 4,445 study participants. In the fully adjusted model, the highest quartile (WWI > 12.05 cm/√ kg) showed a higher all-cause mortality rate compared with the lowest quartile (WWI < 11.03 cm/√ kg) (HR = 1.37, 95% CI: 1.03, 1.82, P < 0.05). The risk of all-cause mortality increased with WWI and showed a linear association in patients with congestive heart failure, heart attack (P-overall < 0.05, P − nonlinear > 0.05); WWI was nonlinearly associated with the risk of all-cause mortality in patients with coronary heart disease and angina (P-overall < 0.05, P − nonlinear < 0.05). Survival curve analysis further showed that all cause and cardiovascular mortality were higher in the high WWI group (Q4) (P < 0.001). The time-dependent receiver operating characteristic (ROC) curve showed that WWI’s area under the curves (AUC) for 5- and 10-year survival rates were 0.76 and 0.792 for all-cause mortality and 0.734 and 0.757 for CVD mortality. WWI’s AUC were higher than those of body mass index (BMI) and WC (all P < 0.01).

Conclusion

Our findings indicate that a high WWI is positively associated with an increased risk of all-cause mortality. Additionally, the high AUC values for WWI strengthen its potential as a meaningful prognostic marker, underscoring its utility in clinical practice for assessing long-term survival risk in patients with CVD.

Background

Cardiovascular disease (CVD), primarily ischemic heart disease and stroke, has emerged as the predominant cause of global morbidity and mortality [1, 2]. Data from the Global Burden of Disease report indicates a steady rise in the incidence and mortality of CVD worldwide since 1990. The prevalence of CVD has nearly doubled, escalating from 271 million in 1990 to 523 million in 2019. Concurrently, the number of CVD-related deaths has surged from 12.1 million in 1990 to 18.6 million in 2019. In 2022 alone, CVD accounted for approximately 8 million deaths globally, with ischemic heart disease being the principal cause of CVD-related deaths worldwide [3], followed by cerebral hemorrhage and ischemic stroke [4]. The prevalence of CVD is expected to increase by 90.0% between 2025 and 2050; 35.6 million people will die of CVD in 2050 (20.5 million in 2025), and CVD will affect more than 184 million adults (> 61%). Similar trends are expected for children [5]. The escalating mortality rate due to CVD poses a grave threat to human life. It is rapidly becoming a significant public health concern, so prospectively predicting adverse outcomes in patients with CVD and implementing specific measures to improve them in clinical practice are of considerable significance for the prognosis of patients and alleviating the socio-economic burden [6, 7].

Obesity was previously considered to indirectly escalate cardiovascular mortality through other risk factors or other chronic diseases, but mounting evidence suggests that overweight and obesity may directly augment cardiovascular mortality [8,9,10]. A recent study by the American Heart Association reveals that from 1999 to 2020, the incidence and mortality of CVD in obese individuals were significantly higher than those in individuals of normal weight, underscoring the undeniable impact of obesity on cardiovascular health [11]. Body mass index (BMI) is a common clinical tool for assessing and diagnosing obesity/overweight. However, BMI alone cannot distinguish between obesity and muscle mass, potentially leading to inaccurate reflections of the relationship between obesity and CVD risk when applied in isolation. Similarly, because waist circumference (WC) is highly correlated with BMI, it cannot be used as a good indicator of obesity independent of BMI [12]. Consequently, there is an urgent need for a more effective adiposity index to identify individuals at high risk of adverse health outcomes.

WWI is a new obesity predictor proposed by PARK based on weight-standardized WC in 2018, calculated by dividing WC by the square root of body weight [13].WWI is a more reliable predictor of hypertension incidence than measures of BMI and WC. In a Korean national cohort study, Park et al. found that WWI may offer valuable insights into CVD mortality, potentially surpassing BMI, WC, and WHtR in predictive utility. In addition, several prospective studies in China have found that higher WWI levels are associated with an increased risk of all-cause and cardiovascular mortality in patients with hypertension [14, 15]. As an anthropometric measure, WWI showed a positive correlation with fat mass and a negative correlation with muscle mass in older adults [16]. Unfortunately, the association was only verified in East Asian populations. In addition, relatively little information is available on the relationship between WWI and the prevalence of specific CVD such as coronary heart disease and stroke [17, 18]. These findings suggest that WWI may serve as a reliable, comprehensive obesity indicator, offering simple, rapid, and cost-effective measurement methods. Although WWI is a potent prognostic marker for predicting disease incidence, severity, and mortality, its predictive value for long-term mortality in patients already diagnosed with CVD has been rarely explored. Moreover, the US population has not extensively studied the relationship between WWI and cardiovascular and all-cause mortality.

This study sought to explore the relationship between WWI, a novel anthropometric measure, and all-cause mortality and cardiovascular mortality in American adults diagnosed with CVD and its subtypes. It was based on data from the National Health and Nutrition Examination Survey (NHANES) database.

Methods

Study population

All data for this study are sourced from the NHANES database, which is publicly accessible on its official website [19, 20]. NHANES, conducted biennially by the Centers for Disease Control and Prevention (CDC), is a nationally representative survey of the non-institutionalized US population. This unique survey amalgamates interviews and physical examinations. Since 1999, the health status of the population in the United States has been examined more regularly. In each survey, approximately 10,000 participants from 30 selected counties out of about 3,000 counties in the United States were invited to participate in in-home interviews, followed by physical examinations and laboratory tests at mobile examination centers (MECs). The NHANES interview encompassed demographic, socioeconomic, dietary, and health-related issues. The examination comprised medical, dental, and physiological measurements and laboratory tests conducted by trained medical personnel. Over the past two decades, NHANES has publicly released data on 10,000 participants on a 2-year cycle. Each cycle incorporated approximately 260 survey subitems and more than 1400 study variables. The CDC obtained approval from the research ethics review board for this study, and all participants provided written informed consent, obviating the need for further ethical review.

Definition of CVD, calculation of the WWI, and outcome variables

Confirmation of CVD was established through a standardized medical condition questionnaire and physicians’ diagnoses were ascertained via self-report obtained during individual interviews. As per prior studies, CVD was defined as a composite of 5 self-reported cardiovascular outcomes. Participants were posed with the question: “Has a physician or other health professional ever told you that you have congestive heart failure/coronary heart disease/angina /heart attack /stroke?” An affirmative response to any of these inquiries classified the individual as having conditions that include congestive heart failure, coronary heart disease, angina, heart attack, and stroke [21, 22]. Consequently, we compiled data for each CVD subtype to further scrutinize its association with WWI.

WWI is a novel anthropometric index formulated according to the equation [In (WC) = β0 + β1 In (weight) + ε]. Given that β1 was estimated to be 0.494 (approximating 0.5), the final formula for WWI was calculated as WC (cm) divided by the square root of body weight (kg) [13, 16, 23].

$$\:WWI(cm/\surd \:kg) = \frac{{{\text{WC}}({\text{cm}})}}{{\sqrt {Weight} (kg)}}$$

This index was derived from the routine physical examination data section of the NHANES database. To ensure data accuracy, anthropometric measurements were recorded by trained medical staff and a specialized recorder, utilizing a uniform height-weight measuring instrument and ruler. The subject’s height and weight were recorded to the nearest 0.1 cm and 0.1 kg, respectively. For WC measurement, participants were instructed to wear a uniform exam gown and then stand barefoot on a digital scale with arms near their body and gaze fixed directly in front as previously described. WC was measured using a tape measure positioned around the waist at the uppermost lateral edge of the ilium in the midaxillary line, accurate to the nearest 0.1 cm in standing subjects [24]. Our primary outcome was all-cause and cardiovascular mortality in patients with CVD. All-cause mortality refers to the total number of deaths within a specified period, irrespective of cause [25]. In contrast, cardiovascular mortality refers to the total number of deaths specifically attributable to CVD. Death data were derived from public-use linked death files from the National Health Interview Survey (NHIS) 1986–2018, NHANES, and NHANES III 1999–2018.

Covariates collection

This study examined basic demographic information covariates, including age, sex (male/female), race (non-Hispanic white, non-Hispanic black, Mexican American, etc.), education (less than high school, high school or equivalent, some college or AA degrees, college graduates or more), and household poverty income ratio. Lifestyle covariates encompassed alcohol consumption and smoking habits. Based on the patient’s drinking questionnaire, we categorized patients’ drinking frequency into non-drinkers, 1–5 times/month, 5–10 times/month, and 10 + times/month. Smoking status was classified as never smoker (less than 100 cigarettes smoked in a lifetime), former smoker (more than 100 cigarettes smoked but not smoked at all now), and current smoker (more than 100 cigarettes smoked and now smoked daily or regularly). Physical examination covariates included BMI (kg/m2), calculated as weight in kilograms divided by the square of height in meters), WC (cm) systolic blood pressure (SBP, mmHg), diastolic blood pressure (DBP, mmHg). Laboratory covariates comprised fasting blood glucose (FBG, mmol/L), glycosylated hemoglobin (HbA1c, %), high-density lipoprotein cholesterol (HDL-C, mmol/L), low-density lipoprotein cholesterol (LDL-C, mmol/L), total cholesterol (TC, mmol/L), triglycerides (TG, mmol/L), platelet count(× 109/L), and lymphocyte and neutrophil counts(× 109/L). Demographic and lifestyle data were procured from a home interview questionnaire administered by trained medical staff. Anthropometric and biochemical parameters were obtained by medical examination at the MEC and subsequent laboratory assessments. A comprehensive measurement process for each variable is publicly accessible via the website www.cdc.gov/nchs/nhanes/.

Statistical analysis

Given the complex sampling design of the NHANES data, the statistical analysis of this study was conducted in compliance with the guidelines stipulated by the CDC. This included the integration of sample weights, clustering, and stratification to guarantee the precision and dependability of the findings. Analysis was conducted using complex weighting, with sample weights calculated as 2/10 of the 4-year weight for the 1999–2000 and 2001–2002 cycles and 1/10 of the 2-year weight for the 2003–2018 cycles. A normality test is first performed to determine if the data follows a normal distribution when performing statistical representation and method selection for continuous variables. Data were presented as mean and standard deviation (Mean ± SD) if they followed a normal distribution, and parametric test methods, such as analysis of variance (ANOVA), were used. Suppose the data do not follow a normal distribution. In that case, it should be presented using the median and interquartile range (Median and IQR) and a non-parametric test such as Kruskal-Wallis’s test. For data derived from complex survey designs (e.g., stratification and clustering), we use the chi-squared test with Rao & Scott’s second-order correction to compare categorical variables.

In this study, WWI scores were initially transformed from continuous variables to categorical variables (quartiles), and the included data were segregated into four groups: Q1 (< 11.03), Q2 (11.03–11.53), Q3 (11.53–12.05), and Q4 (> 12.05). We initially utilized a weighted Cox regression model analysis to explore the correlation between WWI and all-cause and cardiovascular mortality within these four groups. To analyze time-dependent data and assess the impact of covariates, we employed the Cox proportional hazards model. To control for the influence of covariates, we established three models: Model I was unadjusted; Model II was adjusted for age, gender, ethnicity, education, and household income-poverty ratios; and Model III was adjusted for age, sex, race, education, household income-poverty ratio, BMI, weight, WC, SBP, DBP, urine creatinine, FBG, HbA1c, TC, TG, HDL-c, LDL-c, history of diabetes, history of hypertension, and history of coronary heart disease. We performed a weighted analysis using the ‘survival’ package in R to calculate the Hazard ratio (HR) and 95% confidence interval (CI). The HR represents the relative change in risk per unit increase of the covariate, and the 95% CI indicates the range within which the true HR is expected to fall with 95% confidence. In a subsequent subgroup analysis, the main focus was on the association between five subgroups – congestive heart failure/coronary heart disease/angina /heart attack/stroke – and all-cause mortality and cardiovascular mortality.

In our study, we employed the Kaplan-Meier (KM) method to calculate survival probability, and the nonparametric log-rank test was utilized to estimate the survival function in the analysis to discern intergroup differences. The data fitted by this model were graphed to generate KM curves. To explore the nonlinear relationship between the continuous variable WWI and the risks of all-cause and cardiovascular mortality, we utilized restricted cubic splines (RCS). RCS is a method that uses knots to segment and fit the data with smooth curves. We selected the 5th, 35th, 65th, and 95th percentiles as knot positions using four-knot RCS. Each segment is represented by a cubic polynomial, and the knots control the smoothness and shape of the curve to optimize model fitting. Our study employed the ‘rms’ package in R to fit and plot complex regression models, incorporating weighted analysis in the Cox regression. To further illustrate the value of WWI in predicting all-cause and cardiovascular mortality at different follow-up time points, we performed time-dependent ROC curve analysis using the ‘timeROC’ package. Area under the curve (AUC) is an important indicator to evaluate the performance of the model; AUC values range from 0 to 1; the closer AUC values are to 1, the better the classification performance of the model; if AUC values are 0.5, the classification ability of the model is not different from random guess. In addition, we calculated the AUC for each predictive model to compare the predictive value of WWI with BMI and WC. Subsequently, we assessed the statistical significance of these AUC differences using DeLong’s nonparametric test to compare whether AUC under two or more ROC curves differed significantly. All analyses were performed using R (version 4.3.1). A two-sided p-value of less than 0.05 was deemed a statistically significant difference.

Results

Baseline characteristics

From 1999 to 2018, we initially selected a cohort of 101,316 participants. After excluding 46,235 individuals under the age of 20 and 50,636 patients without a diagnosis of CVD, our final study population comprised 4,445 adults diagnosed with CVD (Fig. 1). The basic characteristics of all participants included in the study, categorized according to WWI quartiles were shown in Table 1. The 4445 participants had a mean age of 64.18 ± 14.13 years. Of these, 2000 (47%) were female and 2445 (53%) were male. The mean WWI for all participants was 11.53 ± 0.74 cm/√ kg, with quartiles at 11.03,11.53,12.05 (cm/√ kg). Subjects in higher WWI quartiles were more likely to be elderly, female, and non-Hispanic whites. Meanwhile, higher WWI tended to be accompanied by higher BMI, WC, weight, FBG, HbA1c, DBP, HDL-C, and neutrophil levels. During a median follow-up period of 81 months, 2024 all-cause deaths and 674 cardiovascular deaths were recorded. In participants with a WWI greater than 12.05 cm/√kg, 655 (51%) of deaths were attributed to all causes, while 206 (14%) of deaths were attributed to CVD.

Fig. 1
figure 1

Flowchart depicting the selection process of the study population

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Table 1 Baseline characteristics stratified by the WWI quartiles
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HRs of WWI for all-cause mortality

The association of WWI with all-cause mortality is presented in Table 2. We employed three independent Cox regression models in Table 2 to examine the relationship between WWI levels and all-cause mortality, designating WWI’s lowest quartile (Q1) as a reference value 1.00. Univariate analysis (Model I) revealed an association between increasing WWI and both all-cause and cardiovascular mortality, with a 0.68-fold increase in all-cause mortality per unit increase in WWI (HR = 1.68, 95%CI = 1.53–1.84, P < 0.001). After further adjustment for age, sex, race, education, and income (Model II), all-cause mortality increased 0.26-fold (HR = 1.26, 95%CI = 1.15–1.39, P < 0.001) for each unit increase in WWI. And after adjusting for age, gender, race, education, family income-poverty ratio, smoking and drinking history, BMI, weight, WC, SBP, DBP, urine creatinine, FBG, HbA1c, TC, TG, HDL-C, LDL-C, platelet count, neutrophils count and lymphocyte count (Model III), increased WWI and increased risk of all-cause mortality remained independently associated, with a 0.28-fold (HR = 1.28, 95%CI = 1.09, 1.50, P = 0.03) increase in all-cause mortality for each unit increase in WWI; the HRs for all-cause mortality in the Q4 group compared with the Q1 group were 1.51 (95%CI = 1.23–1.87, P < 0.001) and 1.37 (95%CI = 1.03–1.82, P = 0.032) in Model II and Model III, respectively. Figure 2A presents the RCS curves for the relationship between sustained WWI index and risk of all-cause mortality in patients with CVD. The overall association was significant (P = 0.0297). In addition, the KM survival analysis for all-cause mortality (Fig. 2C) demonstrated that the high WWI group (Q4) exhibited the highest mortality risk, with the survival probability significantly lower (P < 0.0001) than that of the low WWI group (Q1-Q3). The Q1 group, in particular, showed the most favorable survival outcomes.

Table 2 HR of all-cause mortality and cardiovascular mortality in CVD, stratified by WWI quartiles across three models
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Fig. 2
figure 2

After adjusting for age, gender, race, education, family income-poverty ratio, smoking and drinking history, BMI, weight, WC, SBP, DBP, urine creatinine, FBG, HbA1c, TC, TG, HDL-C, LDL-C, platelet count, neutrophils count and lymphocyte count, the association of the WWI with all-cause mortality (A), cardiovascular mortality (B) in patients with CVD were explored. The Kaplan-Meier method and nonparametric log-rank test were employed to estimate survival function for both all-cause mortality (C) and cardiovascular mortality (D)

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HRs of WWI for cardiovascular mortality

The association of WWI with cardiovascular mortality is depicted in Table 2. In this table, we utilized three Cox regression models to independently test the relationship between WWI levels and cardiovascular mortality, assigning the lowest quartile Q1 of WWI as a reference value of 1.00. Univariate analysis (Model I) demonstrated that increased WWI was associated with a higher cardiovascular mortality rate (Model I: HR = 1.52, 95% CI = 1.31–1.78). The HR of cardiovascular mortality increased 0.61-fold (HR = 1.61, 95%CI = 1.07, 2.42, P = 0.022) and 0.58-fold (HR = 1.58, 95% CI = 1.09–2.29, P = 0.015) increased odds of cardiovascular mortality in Q2 and Q3, respectively, and 1.13-fold (HR = 2.13, 95% CI = 1.47–3.07, P < 0.001) increased odds of cardiovascular mortality in Q4 compared with the lowest quartile of WWI (Q1) as the reference group. Figure 2B presents the RCS curves for the relationship between sustained WWI index and risk of cardiovascular mortality in patients with CVD. The overall association was not statistically significant (P > 0.05), and the overall comparison of the curves was smooth. In addition, KM curves for cardiovascular mortality (Fig. 2D) showed that the highest probability of cardiovascular mortality was associated with the Q4 group (P < 0.0001). The probability of cardiovascular survival (log-rank P < 0.0001) was significantly lower in the high WWI (Q4) group than in the low WWI (Q1-3) group.

Examining the association between WWI and all-cause and cardiovascular mortality risk in patients with congestive heart failure, coronary heart disease, angina, heart attack, and stroke: an RCS analysis

To validate the robustness of our findings, we conducted subgroup analyses (Fig. 3) and employed RCS for flexible modeling and visualization of the association between WWI and various cardiovascular conditions, including congestive heart failure, coronary heart disease, angina, heart attack, and stroke. WWI was linearly associated with all-cause mortality in congestive heart failure (P-overall < 0.001, P-nonlinear = 0.226) (Fig. 3A), but it was not significantly associated with cardiovascular mortality (P-overall = 0.1849, P-nonlinear = 0.8445) (Fig. 3B). However, an increased risk of cardiovascular mortality with increasing WWI was observed.

Fig. 3
figure 3

Evaluating the relationship between WWI and all-cause mortality, cardiovascular mortality, with subtypes of specific cardiovascular disease subtypes, such as congestive heart failure (A, B), stroke (C, D), coronary heart disease (E, F), heart attack (G, H), and angina (I, J) using RCS method post covariate adjustment. A study depicting central estimates by blue solid lines and 95% confidence intervals by blue shaded regions

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In patients with coronary heart disease, WWI was linearly associated with the risk of all-cause mortality in patients with coronary heart disease (P-overall < 0.0001 and P-nonlinear = 0.252) (Fig. 3E). The risk of all-cause mortality escalated sharply with increasing WWI when the WWI index was less than approximately 12, and the curve ascended smoothly after WWI reached 12. WWI was linearly associated with the risk of cardiovascular mortality in patients with coronary heart disease (P-overall = 0.0071, P-non-linear = 0.4605) (Fig. 3F). In patients with heart attack, WWI was linearly associated with the risk of all-cause mortality (P-overall < 0.0001, P-nonlinear = 0.3179) (Fig. 3G), as well as with the risk of cardiovascular mortality (P-overall < 0.05, P-nonlinear = 0.9808) (Fig. 3H). In patients with angina, WWI was nonlinearly associated with the risk of all-cause mortality (P-overall < 0.0001 and P-nonlinear = 0.0252) (Fig. 3I), and the RCS curve resembled that in patients with coronary heart disease; WWI was linearly associated with the risk of cardiovascular mortality in patients with angina (P-overall = 0.0071, P-nonlinear = 0.4605) (Fig. 3J). Interestingly, in patients with stroke, WWI was not significantly associated with either all-cause mortality (Fig. 3C) or cardiovascular mortality (Fig. 3D) (all-cause mortality, P-overall = 0.6078 P-nonlinear = 0.9410; CVD mortality, P-overall = 0.5330, P-nonlinear = 0.3921).

ROC analysis of the predictive roles of WWI for all-cause and CVD mortality

Time-dependent ROC analysis was conducted to evaluate WWI’s predictive roles for all-cause mortality and CVD mortality. The results demonstrated that WWI exhibited AUC of 0.725 (95%CI = 0.690, 0.759), 0.74 (95%CI = 0.721, 0.760), 0.76 (95%CI = 0.744, 0.777), and 0.792 (95%CI = 0.776, 0.809) for 1-year, 3-year, 5-year, and 10-year all-cause mortality, respectively. Regarding CVD mortality, WWI achieved AUC of 0.676 (95%CI = 0.640, 0.719), 0.712 (95%CI = 0.694, 0.735), 0.734 (95%CI = 0.718, 0.753), and 0.757(95%CI = 0.740, 0.776). The results are shown in Fig. 4.

Fig. 4
figure 4

Time-dependent ROC curves of WWI for predicting. (A) all-cause mortality, (B) CVD mortality

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In our study, ROC curves demonstrated that WWI had higher AUC for all-cause (AUC (95% CI): 0.781 (0.767–0.794)) and CVD mortality (AUC (95% CI): 0.710 (0.691–0.730)) compared to BMI and WC as shown in Fig. 5 (P all < 0.01).

Fig. 5
figure 5

(A) ROC curves for predicting all-cause mortality using WWI, BMI, and WC. (B) ROC curves for predicting cardiovascular mortality using WWI, BMI, and WC

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Discussion

In this cohort study with extended follow-up, we leveraged data from the NHANES database to investigate the relationship between WWI and survival outcomes in 4,445 patients with CVD, with a median follow-up duration of 81 months. Our findings revealed a positive correlation between WWI and all-cause mortality among patients with CVD. Specifically, participants with a WWI > 12.05 cm/√kg exhibited an elevated risk of all-cause mortality compared to those with lower WWI. The KM survival curves indicated that the high WWI group had significantly lower survival probabilities, further emphasizing the detrimental impact of elevated WWI on survival. RCS analysis confirmed a linear association between WWI and all-cause mortality. WWI consistently demonstrated meaningful predictive value for all-cause mortality over various time periods, with increasing AUC values over 1, 3, 5, and 10-year follow-up periods. These results underscore the significant relationship between WWI and long-term mortality risk in patients with CVD, highlighting the importance of monitoring WWI in clinical assessments.

Obesity, a major factor impacting global public health status, is typically considered a risk factor for hypertension, diabetes, hyperlipidemia, and cardiovascular and cerebrovascular diseases [26,27,28,29]. However, in related studies of a variety of cardiovascular and cerebrovascular diseases, it has been noted that obese patients have a better prognosis, a phenomenon termed the “obesity paradox” [30, 31]. One potential explanation for this paradox could be that BMI, a commonly used obesity indicator, does not differentiate between high levels of lean body mass and weight gain resulting from fat body mass. Scholars generally agree that BMI does not accurately assess the amount of visceral fat, which has a more significant impact on the risk of metabolic and other diseases [32]. Dual-energy X-ray absorptiometry is the gold standard method for body composition analysis, but it is costly and time-consuming. Simultaneously, the measurement of visceral fat requires specialized equipment like nuclear magnetic resonance and consumes substantial social resources. In contrast, WWI serves as an easily accessible, cost-effective obesity index, which could be valuable for clinical application.

WWI is an anthropometric index recently introduced, derived by standardizing WC with body weight. It serves as an indicator of central obesity, providing a more accurate reflection of an individual’s obesity and muscle mass, thereby addressing the “obesity paradox” of BMI to a certain extent. A high WWI suggests that individuals with a larger WC possess a higher fat mass and a lower proportion of muscle mass than those of the same weight but with a smaller WC. This represents a complex condition of sarcopenic obesity and may outperform traditional measures such as BMI and WC in various health outcomes. Guo’s study discovered a significant negative correlation between WWI and Total Bone Mineral Density in adults aged 20–59, indicating that individuals with higher WWI values may exhibit an unhealthy body composition characterized by lower muscle and bone mass [33]. Wang’s findings suggest that higher WWI levels are associated with diabetic nephropathy in patients with type 2 diabetes mellitus proposing that the WWI index could be a cost-effective and straightforward method to detect diabetic nephropathy [34]. Liu’s study also found a significant positive correlation between WWI and depressive symptoms, irrespective of age, gender, race, smoking status, education level, hypertension, and diabetes. This correlation was stronger than traditional indices such as BMI and WC, demonstrating its value in identifying early depressive symptoms [32]. In addition to the above studies, the relationship between WWI and respiratory diseases, as well as dementia in hypertensive individuals, has also garnered widespread attention [35,36,37]. Therefore, WWI may serve as a critical tool for predicting long-term health risks, aiding in the early identification of high-risk patients within clinical settings, and facilitating timely and targeted intervention strategies.

Our findings align with previous studies. Park et al. demonstrate that WWI may serve as a valuable indicator for assessing cardiometabolic morbidity and mortality in the Korean population [13]. Similarly, Ding et al. found a nonlinear positive correlation between WWI and cardiovascular mortality, as well as all-cause mortality in Southern China [14]. They proposed that the mechanism by which elevated WWI leads to poor prognosis may be due to adipose tissue dysfunction [38]. Adipose tissue is now recognized as an immune organ characterized by high heterogeneity, plasticity, and dynamic secretion of various bioactive substances [39]. In states of obesity, where there is an excess of energy, adipose tissue enters a state of chronic inflammation. Many immune cells adopt a pro-inflammatory phenotype, M1 macrophages infiltrate more, and inflammatory cytokines such as IL-1β, IL-6, and TNF-α are secreted in higher quantities. Conversely, anti-inflammatory M2 macrophages decrease, and anti-inflammatory cytokines such as IL-4, IL-5, and IL-13 are secreted less. These reactions subsequently lead to various downstream effects [40]. For instance, in cancer patients receiving cytokine therapy, the pro-inflammatory cytokine IL-2 can lead to decreased tryptophan levels, resulting in reduced serum hydroxytryptamine production and an increased risk of depressive symptoms. An increased WWI reflects adipose tissue dysfunction, indicating abnormal lipid levels and lipid metabolism disorders [41, 42]. Numerous studies have shown that adipose tissue dysfunction is associated with cancer prognosis. For example, adipose tissue dysfunction strongly contributes to the pathogenesis of obesity-related breast cancer [43]. It is also associated with hypertension, diabetes, metabolic syndrome, and CVD [17, 44, 45], as well as an increased risk of subsequent death. This has important implications for clinical practice and public health.

In our study, we further investigated the dose-response relationship between WWI and all-cause mortality in patients with CVD. We found a linear association between WWI and all-cause mortality in these patients. In subsequent subgroup analyses, a linear association was observed between WWI and the risk of all-cause mortality in patients with congestive heart failure and heart attack, with a significant increase in the risk of all-cause mortality correlating with WWI levels. However, in patients with coronary heart disease and angina, the association between WWI and the risk of all-cause mortality was nonlinear. Our findings indicate that special attention should be given to patients with high WWI levels in CVD during clinical prognostic care, which can promote the use of WWI in clinical practice. Furthermore, in our study, after adjusting for BMI and WC as covariates, we found that elevated WWI levels were independently associated with a significant increase in all-cause mortality among patients with CVD. Approximately 20% of people with normal BMI are classified as metabolically obese and are characterized by metabolic disorders [46]. Additionally, WC does not adequately identify visceral or subcutaneous fat accumulation, leading to a higher prevalence of visceral adiposity in subjects with normal WC [47]. Compared to BMI and WC, WWI showed significantly higher AUC for both all-cause and cardiovascular mortality, indicating its potential as a superior predictive marker for mortality outcomes in patients with CVD. Therefore, WWI may serve as a meaningful prognostic marker for assessing mortality risk in patients with CVD. However, our study primarily focuses on the relationship between WWI and long-term mortality risk, and future studies should further investigate the impact of WWI on multiple organ comorbidity. WWI, a simple and effective index, has certain advantages as a tool to assess obesity and related health risks; it requires more research support in estimating fat and muscle mass. Therefore, the application of WWI in these specific measurements requires further investigation and predictive modeling.

Our study carries significant implications for clinical practice. Using WWI as a screening tool in clinical settings can greatly enhance the identification of high-risk individuals. Early detection through WWI allows for timely intervention, potentially mitigating mortality outcomes. Owing to its simplicity, cost-effectiveness, and accuracy, WWI can be utilized in various races or ethnicities and healthcare facilities at all levels. The primary advantages of our study include the large sample size of our subjects and a median follow-up period of 81 months, which lend more reliability to our conclusions. Second, we used the NHANES cohort, which was established and collected in a very tightly controlled manner. Exposure data were collected from study subjects before the onset of outcomes, obtained by the investigator’s personal observation. Our analysis adjusted for potential confounders and conducted detailed subgroup analyses for the main outcome. However, the limitations of our study cannot be ignored. Firstly, because the subjects were derived from the NHANES database, the participants came from the NHANES coronary heart disease population in the United States, and the individuals included in the analysis were from the same country. This may limit the generalizability of our conclusions to other populations or ethnic groups. In future cohort studies, we plan to expand the study population and increase the sample size to enhance the statistical power of our analyses. We also propose conducting multicenter collaborations to acquire additional sample data from various research centers, thereby improving the representativeness and reliability of our findings. Secondly, we recognized that there may be internal validity issues in the self-reported definition of CVD outcomes. Due to research resources and design constraints, we selected self-reporting as a data collection method. To further enhance the reliability and validity of our study, we plan to collect more clinical diagnostic data in future research to supplement and validate the self-reported data. This approach will enable us to assess CVD outcomes more accurately and improve the credibility of our research findings. Thirdly, although we adjusted for covariates that may affect the COX regression model, we could not eliminate the influence of other potential confounders inherent in observational studies. Fourth, this study only assessed the prognostic value of baseline WWI, did not investigate the association between changes in WWI over time and mortality, and may not fully reflect long-term trends and changes, suggesting that our future studies need to capture dynamic changes in individual health status and improve the accuracy of cardiovascular disease mortality prediction. Lastly, NHANES data are collected in a variety of ways, including interviews, physical examinations, and laboratory tests, and the quality and accuracy of different data sources may vary.

Conclusion

In our secondary analysis of NHANES 1999–2018 data, we identified a positive linear correlation between increased WWI and elevated all-cause mortality. Participants with higher WWI exhibited increased mortality risk. This trend was consistent across different time periods, demonstrating WWI’s meaningful predictive value. The clinical utility of WWI, owing to its simplicity, cost-effectiveness, and broad applicability, further emphasizes the need for its integration into clinical practice. Multicenter, multinational studies are imperative to generalize these conclusions to diverse populations and ethnicities.

Data availability

The datasets used and/or analyzed during the current study were publicly available. This data can be found here: https://www.cdc.gov/nchs/nhanes/index.htm.

Abbreviations

CVD:

cardiovascular disease

WWI:

weight-adjusted-waist index

BMI:

body mass index

WC:

waist circumference

TC:

total cholesterol

HDL-C:

high-density lipoprotein cholesterol

LDL-C:

low-density lipoprotein cholesterol

TG:

triglycerides

FBG:

fasting blood glucose

HbA1c:

glycated hemoglobin

SBP:

systolic blood pressure

DBP:

diastolic blood pressure

HR:

hazard ratio

CI:

confidence interval

KM:

Kaplan-Meier

RCS:

restricted cubic spline

ROC:

receiver operating characteristic

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Acknowledgements

A special thanks to all of the NHANES participants who freely gave their time to make this and other studies possible.

Funding

This work was supported by the National Key R&D Program of China (Grant No. 2023YFC3603800 and 2023YFC3603801) and National Natural Science Foundation of China (Grant No. 82372574, 82172534, 82202792, 82202793) and Sichuan Science and Technology Program (Grant No. 2023NSFSC1999, No. 2023NSFSC1495) and 1·3·5 Project for Disciplines of Excellence, West China Hospital, Sichuan University (Grant No. ZYJC21038).

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  1. Hanbin Li and Wen Zhong contributed equally to this work.

Authors and Affiliations

  1. Rehabilitation Medicine Center, Institute of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, 610041, China

    Hanbin Li, Wen Zhong, Hongxin Cheng, Shiqi Wang, Ran Li, Lu Wang, Chengqi He & Quan Wei

  2. Key Laboratory of Rehabilitation Medicine in Sichuan Province, West China Hospital, Sichuan University, Chengdu, 610041, China

    Hanbin Li, Wen Zhong, Hongxin Cheng, Shiqi Wang, Ran Li, Lu Wang, Chengqi He & Quan Wei

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Contributions

H.B.L., W.Z., and Q.W. designed and wrote the manuscript. H.B.L., W.Z., H.X.C., S.Q.W., L.W., R.L., and Q.W. revised the manuscript. H.B.L. and W.Z. drew the figures. C.Q.H. and Q.W. provided critical feedback and helped to shape the manuscript. All authors listed have made a substantial contribution to the work. The authors have no potential conflicts of interest that are directly relevant to the content of this review.

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Correspondence to Quan Wei.

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Data collection for NHANES was approved by the NCHS Research Ethics Review Board. Analysis of de-identified data from the survey is exempt from the federal regulations for the protection of human research participants. Analysis of restricted data through the NCHS Research Data Center is also approved by the NCHS ERB. (https://www.cdc.gov/nchs/nhanes/irba98.htm).

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All participants in the study provided written informed consent.

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Li, H., Zhong, W., Cheng, H. et al. Association between weight-adjusted-waist index and long-term prognostic outcomes in cardiovascular disease patients: results from the NHANES 1999–2018 study. Diabetol Metab Syndr 17, 19 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13098-025-01590-2

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Keywords

Diabetology & Metabolic Syndrome

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