Association between weight-adjusted-waist index and the prevalence of gallstone disease in Minhang District, Shanghai: a cross-sectional study

Abstract

Background

Gallstone disease (GSD) is a common and costly health issue with a multifactorial etiology linked to obesity. The Weight-Adjusted-Waist Index (WWI) is a novel anthropometric measure that incorporates both weight and waist circumference, potentially offering a better assessment of GSD risk associated with adiposity. This study aims to evaluate the association between WWI and the prevalence of GSD.

Methods

We conducted a cross-sectional study involving 19,426 participants divided into stone (n = 4,398) and non-stone (n = 15,028) groups based on ultrasound-confirmed GSD. WWI was calculated, and its association with GSD was analyzed using logistic regression models, adjusting for confounders such as age, gender, and comorbid conditions.

Results

The prevalence of GSD was 22.6%. Participants with GSD had a higher mean age, a greater proportion of females, and higher Body Mass Index (BMI) compared to those without GSD. WWI was significantly higher in the stone group (p < 0.001). The prevalence of fatty liver was also higher in the stone group (p < 0.001). Laboratory findings indicated a subclinical inflammatory state in participants with GSD. The highest tertile of WWI was associated with an increased odds ratio for GSD (OR = 1.23, 95% CI: 1.13–1.35 in the fully adjusted model). WWI demonstrates superior predictive ability for gallstones compared to other obesity markers in obese populations.

Conclusions

WWI is positively associated with the prevalence of GSD, independent of traditional risk factors. These findings suggest that WWI could serve as a practical screening tool to identify individuals at higher risk for GSD, emphasizing the need for targeted interventions to address central obesity.

Introduction

Gallstone disease (GSD), characterized by the formation of calculi within the gallbladder or biliary tract, is a prevalent gastrointestinal disorder with significant health implications worldwide [1]. It is a multifactorial condition, with risk factors spanning from genetic predisposition to lifestyle choices, such as diet and physical activity [2]. In recent decades, the incidence of GSD has seen a notable increase, aligning disturbingly with the global rise in obesity rates. This parallel trajectory has prompted extensive research into the complex relationship between body weight and gallstone formation.

The traditional metrics for assessing obesity, such as Body Mass Index (BMI), have been invaluable in epidemiological studies; however, they do not account for the distribution of body fat, an aspect critical in the pathogenesis of GSD. Central obesity, or the accumulation of visceral fat, has been identified as a particularly potent risk factor for the development of gallstones, independent of overall body mass [3, 4]. This recognition has led to the proposal of alternative anthropometric measures, such as waist circumference and waist-to-hip ratio, which better represent central adiposity.

The Weight-Adjusted-Waist Index (WWI) emerges as a novel metric that integrates waist circumference with body weight, potentially offering a more accurate assessment of the risk many diseases associated with adiposity [5,6,7,8,9]. While previous studies have established a relationship between waist circumference and gallstone disease, the role of WWI in predicting the prevalence of GSD remains underexplored.

This cross-sectional study aims to bridge this knowledge gap by investigating the association between WWI and the prevalence of gallstone disease. By examining a representative sample of the adult population, we seek to determine whether WWI serves as a superior predictor of GSD prevalence compared to conventional indices of obesity, thereby providing a more nuanced understanding of the obesity-GSD nexus.

Methods

Study population

Zhuanqiao Community Health Service Center in Minhang District is a community service center covering the resident population in the area of about 256,000 individuals. It also provides basic medical and health services to migrant workers. It is an integrated health service center with diseases prevention, medical treatment, health care, rehabilitation, health education, and family planning technical service functions. There is a well-established annual health checkup center and provides annual routine checkup services to nearly 10,000 local residents every year, including lifestyle and health status assessment, physical examination, laboratory measurements and health guidance.

In this cross-sectional study, we screened healthy individuals receiving routine annual check-ups in Zhuanqiao Community Healthcare Service Center of Minhang District, Shanghai, China from 2020 to 2023. Eligible individuals were included per the following criteria: (1) aged from 18 to 90 years old; (2) with available anthropometric measurements and ultrasonography data; (3) attendance of at least 2 anually check-ups. We excluded: (1) currently pregnant women; (2) those who self-reported chronic hepatitis at each check-up; (3) incomplete data at each check-up. The study protocol was approved by the Ethics Committee of the Shanghai Fifth People’s Hospital and complied with the principles of the Helsinki Declaration (Approval No. 2023 − 135). All individuals who participated in the annual check-up signed an informed consent.

Data collection and measurements

Demographic data, including age and gender, were collected alongside anthropometric measurements. Body Mass Index (BMI) was calculated using weight in kilograms divided by the square of height in meters (kg/m²). The waist-to-hip ratio (WHR) is calculated by dividing the waist circumference by the hip circumference. The WWI was computed as the waist circumference in centimeters divided by the square root of body weight in kilograms. Waist circumference was measured at the midpoint between the lower rib and the iliac crest. The systemic immune-inflammation index (SII) is defined as the platelet count × neutrophil count / lymphocyte count.

Self-reported history of diabetes and hypertension was collected by using a standardized questionnaire.

Laboratory inspections

Blood samples were taken after an overnight fast for laboratory inspections, including complete blood count, liver function tests, renal function tests, and lipid profile. The laboratory values included white blood cell (WBC) count, neutrophil count, monocyte count, lymphocyte count, platelet count, erythrocyte count, hemoglobin levels, blood urea nitrogen (BUN), creatinine, alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C).

Ultrasonographic measurements

Non-invasive ultrasound examinations were performed as part of routine health screenings for all study participants. Individuals were asked to fast for at least 6 h before the ultrasound examination. This fasting state ensures that the gallbladder is distended, making gallstones easier to detect. Gallstones appear as echogenic structures with acoustic shadowing. The technician looked for these shadows as well as the movement of the stones and recorded the result. Hepatic ultrasound was also performed simultaneously. Fatty liver was diagnosed with increased echogenicity and the contrast in appearance between the liver and the right kidney, as was used in many other studies [10,11,12]. One technician who was blind to the study performed all the ultrasonographic measurements.

Cutoff selection

For BMI, we selected the cutoff of 28 kg/m² based on the Chinese Obesity Guidelines [13]. The cutoff for waist circumference (WC) was defined as 102 cm, according to the National Cholesterol Education Program Adult Treatment Panel III (NCEP ATP III) criteria [14].

Statistical analysis

Multiple imputation was applied for variables with less than 20% missing data. Descriptive statistics were used to summarize the demographic and clinical characteristics of the participants. Continuous variables were expressed as means with standard deviations or medians with interquartile ranges, as appropriate. Categorical variables were presented as frequencies with percentages. The associations between WWI and GSD were evaluated using logistic regression analysis, with odds ratios (OR) and 95% confidence intervals (CI) reported for the entire cohort, as well as stratified analyses by gender, age, hypertension, and diabetes status. A generalized linear model was applied in illustrating the association between WWI and GSD. A Generalized Additive Model (GAM) was used to assess the non-linear relationships between the WWI and various clinical variables. The estimated degrees of freedom (EDF) were used to represent the complexity of the smooth terms for each predictor. The adjusted R-squared values were calculated to evaluate the proportion of variance explained by each variable, with higher values indicating greater explanatory power. Deviance explained was also reported to quantify the goodness of fit of the model. The predictive ability of each obesity marker for gallstones was assessed using ROC curves. A p-value of less than 0.05 was considered statistically significant. All analyses were performed using SPSS 24.0 (IBM Corp., Armonk, NY, USA). R software (version 4.1.2). Graph plotting utilized Origin 2021.

Result

The study included 19,426 participants (Fig. 1), with 15,028 (77.4%) in the non-stone group and 4,398 (22.6%) in the stone group (Table 1). The average age of participants was significantly higher in the stone group (70 ± 6 years) compared to the non-stone group (69 ± 7 years), with a P-value of < 0.001. A higher percentage of females was observed in the stone group (61.3%) compared to the non-stone group (55.7%), which was statistically significant (P < 0.001).

Fig. 1

The participants selection flow chart

Table 1 Baseline characteristics of participants

BMI was higher in the stone group with a median value of 25.00 kg/m² (IQR  (Interquartile Range): 23.01–27.27) as opposed to 24.49 kg/m² (IQR : 22.43–26.67) in the non-stone group (P < 0.001). Similarly, the WWI was greater in the stone group (10.85 cm/kg^1/2) compared to the non-stone group (10.75 cm/kg^1/2), with a P-value of < 0.001. The prevalence of fatty liver was significantly higher in the stone group (71.6%) in comparison to the non-stone group (62.5%), with a P-value of < 0.001. The occurrence of kidney stones did not differ significantly between the two groups, with 4.7% in the non-stone group and 4.5% in the stone group (P = 0.533). Hypertension was more prevalent in the stone group (57.3%) versus the non-stone group (54.2%), with a P-value of < 0.001. A higher percentage of participants with diabetes was found in the stone group (19.6%) compared to the non-stone group (15.2%), which was also significant (P < 0.001). Laboratory inspections revealed that the stone group had higher WBC counts (6.04 × 10^9/L vs. 5.87 × 10^9/L, P < 0.001) and neutrophil counts (3.40 × 10^9/L vs. 3.28 × 10^9/L, P < 0.001). Monocyte counts were slightly higher in the stone group (0.35 × 10^9/L), but this was only marginally significant (P = 0.023). There were no significant differences in lymphocyte counts, platelet counts, erythrocyte counts, and hemoglobin levels between the two groups. Blood urea nitrogen and creatinine levels were comparable between both groups. However, the stone group had higher ALT levels with median values of 20 U/L compared to 19 U/L in the non-stone group, with a significant P-value of < 0.001. Total bilirubin and triglyceride levels was also higher in the stone group (P = 0.002). Total cholesterol was lower in the stone group (P < 0.001), as were the HDL-C, and LDL-C, all with P-values of < 0.001.

OR for the presence of gallstones increased across the tertiles of WWI, with the highest tertile showing an OR of 1.43 (95% CI: 1.31–1.55) in model 1, 1.277 (95% CI: 1.17–1.39) in model 2, and 1.23 (95% CI: 1.13–1.35) in model 3, all compared to the lowest tertile (Table 2). We found a line-shaped relationship between WWI and gallstone (Fig. 2). Stratified analyses by gender, age, hypertension, and diabetes status all showed significant associations (P < 0.001), with ORs ranging from 1.07 to 1.19, indicating a higher risk of gallstones with increasing WWI, especially in the presence of comorbid conditions (Table 3). We found the prevalence of gallstones increases with advancing age (Fig. 3). The prevalence of gallstone was consistently higher in female participants than in male at the same WWI level. (Fig. 4). Utilizing the Youden index, we established that a WWI exceeding 10.96 is significantly associated with the increased likelihood of gallstones.

Table 2 Logistic regression analysis between WWI and gallbladder stone prevalence

Fig. 2

Density dose-response relationship between WWI and gallstone prevalence. The area between the upper and lower dashed lines is represented as the 95% CI. Each point shows the magnitude of the WWI and is connected to form a continuous line. Adjusted for all confounders

Table 3 Subgroup analysis between WWI with gallbladder stone prevalence

Fig. 3

The association between WWI and gallstone prevalence stratified by age

Fig. 4

The association between WWI and gallstone prevalence stratified by gender

Table 4 shows that many variables exhibit a p-value of less than 0.001, indicating statistically significant associations with WWI. The adjusted R-squared values reveal that, except for BMI (0.12) and WHR (0.12), other variables, such as ALT, total bilirubin, and SII, explain only a minimal amount of the variation (R-squared = 0.00), providing limited explanatory power for variations in WWI.

Table 4 Generalized additive model (GAM) analysis

We performed stratification of the obese population, with a BMI threshold of 28 kg/m² and a waist circumference threshold of 102 cm. In the obese population, as shown in Fig. 5 (b), when BMI > 28 kg/m², WWI (AUC = 0.533, P = 0.004) demonstrated significantly better predictive performance for gallstones compared to WHR (AUC = 0.452, P < 0.001) and WC (AUC = 0.504, P = 0.733). In Fig. 5 (d), when WC > 102 cm, WWI (AUC = 0.558, P = 0.015) also showed superior predictive ability compared to BMI (AUC = 0.536, P = 0.128) and WHR (AUC = 0.443, P = 0.016).

Fig. 5

ROC Curve for Predicting Gallstones. (a) BMI ≤ 28 kg/m², (b) BMI > 28 kg/m², (c) waistline ≤ 102 cm and (d) waistline > 102 cm. Abbreviation: BMI, body mass index; WC, waist circumference; WWI, Weight-Adjusted-Waist Index; WHR, the waist-to-hip ratio; AUC, area under curve

Discussion

The findings of our cross-sectional study provide compelling evidence for the association between WWI and the prevalence of GSD. In line with the existing literature, our results demonstrate that central obesity is a significant risk factor for GSD. The incremental increase in odds ratios across WWI tertiles underscores the potential of WWI as a predictive metric for gallstone formation. WWI demonstrates superior predictive ability for gallstones compared to other obesity markers in obese populations.

The observed higher prevalence of GSD with increasing age, particularly noted in the stone group, may reflect the cumulative effect of metabolic risk factors over time. The significant association between GSD and female gender in our study corroborates previous research that has identified women as being at a higher risk, possibly due to hormonal influences on bile composition and gallbladder motility [15, 16]. BMI, a traditional marker of obesity, was indeed higher in the stone group, reinforcing the notion that overall body mass is a factor in gallstone pathogenesis. However, the distinct correlation between WWI and GSD prevalence, even after controlling for BMI, suggests that the distribution of body fat may be more relevant than previously appreciated. This is particularly salient considering the limitations of BMI, which does not differentiate between lean and fat mass or provide information on fat distribution [17]. However, WWI is associated with high fat mass, low muscle mass, and low bone mass [5]. The significant relationship between fatty liver and GSD in our cohort is noteworthy, as both conditions share pathophysiological mechanisms linked to metabolic syndrome. The presence of fatty liver could serve as an additional clinical indicator for GSD risk assessment.

The association between the WWI and GSD can be elucidated through several potential mechanisms that intertwine metabolic, genetic, and environmental factors. Laboratory findings, including elevated WBC and neutrophil counts, are suggestive of a subclinical inflammatory state in participants with gallstones, which aligns with the hypothesis that inflammation contributes to gallstone formation [18, 19]. The higher ALT levels observed in the stone group also hint at a possible hepatic involvement that could be related to cholesterol metabolism and bile saturation, though the clinical implications of this finding require further exploration [20, 21]. The lack of a significant difference in kidney stone prevalence between the groups points towards a more specific pathogenic pathway for gallstones, which may not be shared with renal calculi, despite some overlapping risk factors. Visceral adiposity is closely linked to insulin resistance, which could be a mediating factor in the relationship between WWI and GSD [22]. Insulin resistance leads to increased hepatic de novo lipogenesis and secretion of very-low-density lipoprotein (VLDL), enhancing cholesterol availability for bile synthesis [23]. Moreover, hyperinsulinemia can decrease the solubility of cholesterol in bile by increasing the secretion of biliary cholesterol while simultaneously decreasing bile acids and phospholipids, thus promoting gallstone formation [24, 25].

Previous studies have reported a strong correlation between traditional markers of obesity, such as WC and BMI, and visceral adipose tissue measured by CT [26]. In our study, WWI proved to be a more sensitive indicator of central obesity than traditional markers, especially in obese populations.

Although several studies have investigated the relationship between WWI and GSD, most have been based on the National Health and Nutrition Examination Survey (NHANES) database, with limited large-scale data from Chinese populations [27]. Moreover, while prior research highlighted a non-linear positive correlation between WWI and GSD [27, 28], our findings reveal a linear positive correlation, possibly due to ethnic, dietary patterns or lifestyle behaviors differences. Beyond predicting central obesity, WWI has been shown to predict stroke incidence [29], osteoporosis [30], cognitive decline [31], and infertility in women [32]. WWI is thus an indispensable marker of obesity. In future studies, we plan to conduct cohort research on high-WWI populations within our hospital and develop composite scores integrating other metabolic syndrome components for managing central obesity.

The limitations of our study include its cross-sectional design, which does not allow for causal inferences. Additionally, while ultrasound is an effective tool for GSD diagnosis, it is not infallible, and some cases of gallstones may have been missed, potentially leading to underestimation of prevalence. Future longitudinal studies could provide more insight into the causal relationship between WWI and GSD development. This study is limited to the Chinese population, ethnicity has also been associated with gallbladder stones, a high-carbohydrate diet may reduce gallbladder volume and increase crystal mass, while a sedentary lifestyle may lead to gallbladder stasis [33,34,35]. We lack information regarding the size and morphology of gallstones. Finally, although not directly measured in our study, dietary patterns and lifestyle behaviors could indirectly contribute to gallstone risk [36, 37]. Diets high in refined carbohydrates and fats may increase cholesterol saturation in bile, while a sedentary lifestyle may contribute to gallbladder stasis. We would like to collect data on lifestyle information in the near future.

Conclusions

Our study advocates for the inclusion of WWI in the evaluation of patients at risk for GSD. The simplicity and non-invasiveness of measuring WWI make it a practical tool in clinical settings. By identifying individuals with elevated WWI, particularly among obese individuals, clinicians can target modifiable risk factors, such as central obesity, to mitigate the risk of GSD. Our findings also call for a re-evaluation of current screening protocols, with a view towards integrating WWI as a routine measure in the assessment of metabolic health.