The development and evaluation of nine obesity-based indices for gallstones in U.S. adults

Abstract

Objective:

Gallstones have gradually become a highly prevalent digestive disease worldwide. This study aimed to investigate the association of nine different obesity-related indicators (BRI, RFM, BMI, WC, LAP, CMI, VAI, AIP, TyG) with gallstones and to compare their predictive properties for screening gallstones.

Methods:

Data for this study were obtained from the National Health and Nutrition Examination Survey (NHANES) for the 2017-2020 cycle, and weighted logistic regression analyses with multi-model adjustment were conducted to explore the association of the nine indicators with gallstones. Subject working curves were analyzed to assess the screening ability of the nine indicators. In addition, the association between the most predictive indicator and gallstones was investigated with smooth curve fitting, and differences in risk across populations were explored with subgroup analyses.

Results:

In total, 3654 participants were involved in the final analysis and 383 (10.48%) carried gallstones. The results of weighted multifactorial logistic regression analysis indicated that BRI, RFM, BMI, WC, LAP, and CMI were independent risk factors for gallstones. The ORs for the highest quartile were 4.13 for RFM, 3.13 for BRI, 2.85 for BMI, 2.86 for WC, 2.45 for LAP, and 1.49 for CMI. The area under the ROC curve for RFM was 0.70. The Delong test compared the performance of different ROCs and revealed that the difference between the area under the curve of RFM and the other metrics was significant (P < 0.05). Smooth curve fitting suggested a linear positive correlation between RFM and gallstones (LLR > 0.05), especially in women, non-Hispanic White, insufficient physical activity, hypertensive, and diabetic populations.

Conclusion:

RFM, BRI, BMI, WC, LAP, and CMI were essential indicators for recognizing gallstones. By comparison, we realized that RFM was a better predictor of gallstones.

Introduction

Gallstones are a globally common disease of the biliary system, with a worldwide prevalence of approximately 6%-15% in the 21st century^[1,2]. With rising living standards (multifactorial factors such as dietary changes) and an aging population, the gallstone patient group is expanding, resulting in an enormous economic and health burden globally^[3]. Nearly 10%-20% of the U.S. adult population suffers from gallstones, and total annual healthcare expenditures for biliary tract disorders amount to a staggering $10.3 billion^[4]. The symptomatic manifestations of gallstones are highly variable, from inflammatory complications such as cholecystitis, cholangitis, and pancreatitis to more unusual ones such as gallstone ileus, while they are also associated with an increased gallbladder cancer risk and consequently increased mortality threat.^[5-7]. In recent years, emerging research has revealed that gallstones may be a risk factor for a patient’s susceptibility to other systemic diseases, such as diabetes mellitus^[8], cardiovascular diseases^[9,10], renal cancer, and colorectal cancer^[11,12]. Therefore, it is crucial to screen for effective indicators to predict the occurrence and progression of gallstones.

Obesity is usually defined as a metabolic disorder characterized by excessive accumulation and/or abnormal distribution of body fat. The prevalence of obesity has risen rapidly in recent years, and even during pandemics, its rates are still steadily increasing. Currently, approximately 70.2% of the U.S. adult population remains overweight or obese^[13]. Epidemiologic research has found that the prevalence of gallstone disease is on the rise with the increase in obese populations^[14,15]. A Mendelian study by Yuan et al demonstrates from a genetic perspective that a genetic vulnerability to obesity facilitates the development of symptomatic gallstones^[16]. Body mass index (BMI) is a well-known indicator to define obesity and is widely adopted in health examinations and clinical studies. Nevertheless, it is worth noting that traditional indicators such as BMI and waist circumference (WC) cannot properly reflect the distribution of body fat, the accumulation of visceral fat, and the metabolism of glucose and lipids. In recent years, researchers have developed new screening tools based on BMI, WC, lipid, and glucose indices to better reflect the characteristics associated with obesity from multiple perspectives. For example, relative fat mass (RFM) is a metric that evaluates the percentage of body fat throughout the whole body and provides a more accurate picture of the distribution of body fat compared to BMI^[17]. The Lipid Accumulation Product (LAP) and the Visceral Adiposity Index (VAI) are better indicators of body fat distribution and visceral fat accumulation states, with gender-specific options for calculation^[18,19]. The body roundness index (BRI), which models the human body as an ellipse and employs the theory of eccentricity to construct a formula, can also reflect the degree of visceral fat accumulation^[20]. The cardiometabolic index (CMI) was proposed by Wakabayashi et al as a modification of the LAP formula, which integrates abdominal obesity and lipid levels. The atherogenic index of plasma (AIP) is another composite index of lipid levels^[21]. The triglyceride-glucose index (TyG) is characterized as a more effective indicator of insulin resistance^[22].

Despite several studies investigating the correlation between some anthropometric indices (BMI, WC, TyG) and gallstone disease^[23-25], it remains difficult to identify the best predictor. In this study, we compared the novel developed screening tool with traditional indicators, comprehensively evaluated the predictive potential of nine measures related to obesity for the risk of gallstone disease, and attempted to identify the method with the most screening power, intending to provide new perspectives for the prediction and diagnosis of gallstone disease in clinical practice.

Materials and methods

Study design and participants

The survey data utilized in this study originated from individuals who enrolled in the U.S. National Health and Nutrition Examination Survey (NHANES) during the cycle of January 2017 to March 2020. The NHANES is an ongoing epidemiological survey program conducted by the Centers for Disease Control and Prevention (CDC) to assess the health and nutritional conditions of the U.S. population^[26]. It relies on a complex multi-stage probability sampling strategy to collect information from a nationally representative sample through standardized household interviews, health screenings, and laboratory tests^[27]. The survey was approved by the Ethics Review Board of the National Center for Health Statistics (NCHS), and all participants voluntarily participated and signed an informed consent form. More information is available free of charge on the NHANES website (https://www.cdc.gov/nchs/nhanes/index.htm).

A total of 15 560 participants were enrolled in the 2017-2020 cycle of NHANES. We sequentially excluded individuals who were younger than 20 years, pregnant, lacked information on nine body measurements (BMI, WC, LAP, VAI, BRI, RFM, CMI, AIP, TyG), missing information on gallstones, and missing information on covariates. A total of 3654 subjects were ultimately selected for analysis. The detailed process of participant screening is illustrated in Figure 1.

Figure 1.

The process of participant screening.

Definition of nine obesity-related screening indicators and gallstones

In this study, the nine screening measures associated with obesity include BMI, WC, LAP, VAI, BRI, RFM, CMI, AIP, and TyG. The formulas for their calculation are summarized in Figure 2. Data related to body measurements (BMI, weight, height, and WC) were collected by trained professional technicians at the mobile examination center according to standardized protocols. In addition, HDL-C, TG, and FPG data, which are the other indicators required in the formula, were analyzed by the University of Minnesota laboratory under strict surveillance and quality management. HDL-C, FPG, and TG were measured by turbidimetric immunoassay, hexokinase (HK) assay, and automated direct chemiluminescence analyzer, respectively. Finally, we calculated these indices based on formulas and survey data.

Figure 2.

Formulas for nine different obesity-related screening indicators. Note: BMI, body mass index; WC, waist circumference; RFM, relative fat mass index; BRI, body roundness index; LAP, lipid accumulation product; VAI, visceral adiposity index; CMI, cardiometabolic index; AIP, atherogenic index of plasma (AIP); TyG, triglyceride-glucose index; TG, triglyceride; HDL-C, high density lipoprotein-C; FPG, fasting plasma glucose.

For the definition of gallstones, participants were interviewed by a well-trained specialized interviewer who asked the question, “Has a doctor or other health professional ever diagnosed you with gallstones?” The answer to this question was the basis for determining whether or not a person suffered from gallstones. This convenient and rapid approach has been utilized in previous studies^[24,28].

Covariates

To explore the association between the nine measures and gallstones, we considered several confounders to be incorporated as covariates. Demographic information includes: age (≤40, over 40), sex, race (Mexican American, non-Hispanic white, non-Hispanic black, etc.), marital status, and education level (less than high school, high school or equivalent, and college or above). For smoking status, we categorized them into two groups depending on the self-reported questionnaire: smokers, and non-smokers. Alcohol consumption was defined based on the response to the question: Ever have 4/5 or more drinks every day? We also assessed leisure-time physical activity, which was categorized according to metabolic equivalents (≤500, ≤1000, >1000) as Insufficient, Moderate, and High. Information on hypertension, diabetes mellitus, and hypercholesterolemia has been obtained from professional assessment and self-reporting. More detailed descriptions of the above-mentioned covariates are publicly accessible online at the NHANES website.

Statistical analysis

All data analyses for this study were performed in strict accordance with CDC guidelines. To provide nationally representative effect estimates, we applied the recommended sample weights (WTSAFPP). Different presentation methods were adopted depending on the type of data, with continuous variables expressed as weighted means ± standard error (SE) and categorical variables as weighted proportions ± SE. In the comparison of patients with non-gallstones and gallstones, weighted linear regression was employed for the continuous variables, and weighted chi-square tests were applied to the categorical variables. To explore the relationship between nine obesity-related screening indicators and gallstones, weighted logistic regression was applied to analyze their performance in 3 different models. Model 1 remained unadjusted, model 2 was corrected for age, sex, and race, and model 3 was further adjusted for marital status, educational attainment, smoking, alcohol consumption, leisure-time physical activity, hypertension, diabetes, and hypercholesterolemia. To determine the robustness of the results, we analyzed the independent variables in both continuous and categorical forms for their association with gallstone disease. We also plotted subjects’ work characteristics (ROC) curves and computed the area under the curve (AUC) values to evaluate the predictive power of the different indicators. Furthermore, bootstrap resampling (carried out 500 times) was conducted as a sensitivity analysis to assess the stability of AUC. A comparison of the variability of AUC between the different screening parameters was performed using the Delong test. After the identification of the most predictive indicator, we developed smoothed curve fitting to visualize the association between exposure and outcome. Further subgroup analyses were conducted to identify specific risks by gender, ethnicity, lifestyle, and disease status. Statistical differences existed when the P value obtained from the significance test was less than 0.05. All analyses were performed using EmpowerStats and R4.3.2.

Results

Participants’ baseline characteristics

As illustrated in Figure 1, a total of 3654 participants were ultimately included in the analysis. The baseline characteristics of participants with or without gallstones were summarized in Table 1. There were 383 participants with gallstones and a total of 3271 participants without gallstones, with a prevalence of 10.48%. The results of the analysis of differential comparisons suggested that there were more women in the gallstone group and a higher percentage of the population over 40 years of age. In addition, there were significant differences in race, marital status, hypertension, diabetes mellitus, hypercholesterolemia, Leisure-time physical activity, height, fasting glucose, and nine obesity-related indicators between the two groups.

Table 1.

Baseline characteristics of participants

Variable	Non-gallstone (n = 3271)		Gallstone (n = 383)		P value
	Mean/%	SE	Mean/%	SE
Age, %					<0.0001
≤40 years	39.39	1.75	17.96	2.73
40-80 years	60.61	1.75	82.04	2.73
Sex, %					<0.0001
Male	52.28	1.22	24.95	3.72
Female	47.72	1.22	75.05	3.72
Race, %					0.0021
Mexican American	9.35	1.44	7.93	1.22
Non-Hispanic White	62.03	2.35	69.87	3.04
Non-Hispanic Black	11.83	1.58	5.25	0.93
Other Race	16.79	1.42	16.94	2.79
Marital status, %					0.0203
Married/Living with Partner	61.93	2.60	62.36	3.73
Widowed/Divorced/Separated	18.27	1.38	24.02	3.15
Never married	19.80	1.77	13.62	2.23
Education level, %					0.5121
Less than high school	10.87	0.72	9.50	2.18
High school graduate/GED or equivalent	26.07	1.44	29.83	2.96
More than high school	63.07	1.80	60.67	2.97
Smoking status, %					0.2526
Yes	42,87	1.06	47.33	4.25
No	57.13	1.06	52.67	4.25
Alcohol consumption, %					0.5372
Yes	12.07	1.10	15.58	4.05
No	78.70	1.21	74.92	3.39
Not recorded	9.23	1.09	9.50	1.68
Hypertension, %					0.0019
Yes	30.67	1.57	45.94	5.27
No	69.33	1.57	54.06	5.27
Diabetes, %					0.0006
Yes	10.21	0.94	18.36	1.92
No	87.77	1.03	78.12	2.14
Threshold value	2.03	0.34	3.52	1.46
Hypercholesterolemia, %					0.0034
Yes	33.99	1.06	46.98	4.35
No	66.01	1.06	53.02	4.35
Leisure-time physical activity, %					0.0020
Insufficient	58.55	2.06	72.85	3.69
Moderate	12.60	0.91	8.14	1.92
High	28.86	1.73	19.01	2.95
Fasting plasma glucose, mmol/L	6.04	0.06	6.51	0.09	0.0002
Height, cm	168.73	0.18	164.81	0.83	0.0001
TC, mmol/L	4.80	0.04	4.84	0.09	0.6075
HDL, mmol/L	1.39	0.02	1.39	0.03	0.9980
TG, mmol/L	1.24	0.03	1.36	0.06	0.0591
BMI	29.27	0.20	32.61	0.65	<0.0001
WC	99.61	0.51	107.07	1.48	<0.0001
BRI	5.43	0.07	6.84	0.23	<0.0001
RFM	34.96	0.22	41.50	0.73	<0.0001
AIP	0.24	0.01	0.29	0.02	0.0216
CMI	0.65	0.02	0.78	0.05	0.0129
LAP	50.18	1.36	67.74	3.94	<0.0001
VAI	1.54	0.04	2.03	0.11	0.0001
TyG	8.48	0.02	8.66	0.04	0.0004

TC, total cholesterol; TG, triglyceride; HDL-C, high density lipoprotein-C;

Association of nine obesity-related screening indicators and risk of gallstones

The association of the nine indicators with gallstones was explored using weighted multifactorial logistic regression, and the indicators were analyzed as continuous variables, quartiles, and their trend pairs. All indicators were significantly associated with gallstone risk in Model 1 and Model 2. However, in Model 3, five indicators, including RFM, BRI, BMI, WC, and LAP, still showed positive correlations with gallstones. In Model 3, the differences were statistically significant when CMI was analyzed as a categorical variable and trend. When the independent variables were analyzed as categorical variables, RFM had the highest OR of 4.13. It was followed by BRI (OR = 3.13, 95% CI:1.70-5.77), WC (OR = 2.86, 95% CI: 1.14-7.17), BMI (OR = 2.85, 95% CI: 1.17-6.92), LAP (OR = 2.45, 95% CI: 1.00-6.38), and CMI (OR = 2.02,95%CI: 1.00-4.32). Figure 3 visually portrays the relationship between the nine indicators and the risk of gallstones under the 3 models and the two variable types, and more detailed information on the data can be available in the Supplementary Table 1, available at: http://links.lww.com/JS9/D764.

Figure 3.

Relationship between nine obesity-related indicators and gallstones in different models. Note: Model 1 was not adjusted for other variables, Model 2 was adjusted for age, gender, and race, and Model 3 was further corrected for marital status, education level, smoking, alcohol consumption, leisure-time physical activity, hypertension, diabetes, and hypercholesterolemia based on Model 2. The nine indicators were put into the model as continuous and categorical variables, and when they were treated as categorical variables, the OR values represented the results of comparing the fourth quartile with the first quartile.

Predictive value of nine indicators for gallstones

Figure 4 as well as Table 2 provided the AUC levels and 95% confidence intervals for the nine indices screening for gallstones in U.S. adults. Specifically, RFM was the most discriminating indicator for gallstones (AUC = 0.70), followed by BRI (AUC = 0.67). The excellent thresholds for RFM and BRI were identified as 41.76 and 5.20, respectively. The comparison of the differences in the predictive ability of RFM with other indicators for gallstones by DeLong’s test indicated that the differences in AUC between BRI and other obesity-related screening indicators were statistically significant (P < 0.05) (Supplementary Table 2, available at: http://links.lww.com/JS9/D764). For sensitivity analysis, bootstrap resampling analysis supported the robustness of the AUC results (Supplementary Table 3, available at: http://links.lww.com/JS9/D764).

Figure 4.

ROC curves of nine indices for gallstone screening in U.S. adults.

Table 2.

Results of ROC analysis of nine screening tools

	AUC	95%CI low	95%CI up	Best threshold	Specificity	Sensitivity
RFM	0.70	0.67	0.73	41.76	0.72	0.58
BRI	0.67	0.64	0.70	5.20	0.50	0.75
BMI	0.64	0.61	0.67	28.7	0.53	0.68
LAP	0.64	0.61	0.67	49.93	0.64	0.57
WC	0.64	0.61	0.66	101.45	0.57	0.64
VAI	0.63	0.60	0.65	0.96	0.43	0.78
CMI	0.60	0.57	0.63	0.44	0.50	0.67
TyG	0.59	0.57	0.62	8.61	0.60	0.55
AIP	0.57	0.54	0.60	0.04	0.28	0.85

RFM, relative fat mass index; BRI, body roundness index; BMI, body mass index; LAP, lipid accumulation product; WC, waist circumference; VAI, visceral adiposity index; CMI, cardiometabolic index; TyG, triglyceride-glucose index; AIP, atherogenic Index of Plasma (AIP);

Exploring the exposure-risk relationship between RFM and gallstones

From the above analysis, we determined that RFM was the strongest predictor of gallstones. To further explore whether there was a linear or nonlinear relationship between them, we performed smoothed curve fitting as well as threshold effect and saturation effect analyses. Figure 5 and Table 3 demonstrated that there was a linear positive correlation between RFM and gallstones (LLR = 0.19), and the risk of gallstones increased by approximately 9% for each unit increase in RFM. Further subgroup analyses supported the identification of specific populations at higher risk, including non-Hispanic white, female, insufficient physical activity, and hypertensive and diabetic patients. The results of the threshold effect and saturation effect analyses of the subgroup analyses were presented in Supplementary Tables 4-8, available at: http://links.lww.com/JS9/D764.

Figure 5.

Smooth curve fitting and subgroup analysis of RFM and Gallstones. Note: (a) the association of RFM with the risk of gallstones in total participants, and subgroups stratified by race (b), sex (c), leisure-time physical activity (d), hypertension (e), and diabetes (f).

Table 3.

Results of the threshold and saturation effect analysis of relative fat mass (RFM) and gallstones

Outcome	Gallstone
Model 1
Linear effect	1.09(1.08,1.10) < 0.0001
Model 2
breaking point (K)	21.63
<K-segment effect 1	1.49(0.80,2.77) 0.2072
>K-segment effect 2	1.09(1.07, 1.10) < 0.0001
Difference between 2 and 1 effects	0.73(0.39,1.36) 0.3207
LLR	0.190

Models 1 and 2 are evaluations of linear and nonlinear relationships, respectively. It is usually assumed that when the likelihood ratio (LLR) is less than 0.05, there is a nonlinear association between the independent and dependent variables.

Discussion

To our knowledge, this is the first study to explore the association of nine obesity-related anthropometric measures with gallstones in the U.S. population. Through cross-sectional analysis with the 2017-2020 NHANES database, we found that the prevalence of gallstones in the U.S. adult population was 10.48%. Although gallstones are a benign surgical condition, they are strongly associated with pancreatitis, NAFLD, cardiovascular disease, and cancer^[8-12]. Moreover, gallstones significantly increased the risk of death from hepatobiliary (HR = 2.74) and gallbladder (HR = 7.35) cancers^[7]. Therefore, early prevention and health care through simple body measurements are crucial before clinical disease emerges.

Gallstones are recognized as a common disorder influenced by a combination of genetic and multiple environmental factors, as well as an important cause of hospital admissions for digestive disorders in the U.S. population^[29]. Its risk factors frequently include advanced age, females, pregnancy, obesity, and race, among which obesity exerts a rather central effect on gallstone formation^[15,16]. The pathophysiologic mechanisms underlying the increased risk of gallstones due to obesity have not been fully identified. The widely accepted perspective is that obesity promotes excessive cholesterol secretion by the liver, which leads to a relative oversaturation of cholesterol in the bile, thus making gallstone deposits more susceptible to formation^[30]. Insulin serves an important biological function in mediating obesity and hepatic metabolism, and obesity may lead to increased insulin levels and decreased insulin sensitivity. Higher insulin levels can trigger an increase in cholesterol secretion through both activation of HMG-CoA reductase and upregulation of low-density lipoprotein receptor expression^[31]. Obesity may also affect gallbladder motility, and although postprandial plasma cholecystokinin (CCK) levels are normal in obese individuals, hyperinsulinemia may mediate a decreased sensitivity to CCK in obese individuals, resulting in decreased motility that is strongly associated with cholestasis^[32]. Leptin is a hormone released by adipocytes that functions in the regulation of energy homeostasis. In most obese patients, leptin levels are often significantly elevated, but the role of leptin is weakened or disappeared, which is also known as leptin resistance. This is another vital reason why obese patients are prone to form gallstones^[33]. Leptin can affect stone formation through several mechanisms such as affecting gallbladder contractility and regulating bile acid metabolism^[34,35]. However, rapid weight loss after metabolic bariatric surgery is also associated with gallstone formation due to cholesterol-supersaturated bile, making overall obesity a challenging risk factor for gallstones^[36].

Obesity has long been one of the critical pathogenic mechanisms of gallstones, and the effect of obesity-related indicators on screening for gallstones has received extensive attention from researchers. In this study, by analyzing the Nhanes data, we found that the six indicators BRI, RFM, BMI, WC, LAP, and CMI were effective in differentiating among patients with gallstones in American adults. The outcome of the ROC curve analysis implied that RFM was the most predictive parameter. RFM is characterized by the fact that it combines height and waist circumference, which provides a better reflection of the overall body fat distribution. Its predictive ability in cardiovascular disease^[37], depression^[38], and diabetes mellitus^[17] has been confirmed. In our study, we found that it also distinguished excellently between patients suffering from gallstones.

According to the ROC curve results, the optimal threshold for RFM is 41.76. When the RFM value exceeds 41.76, the risk of gallstones is considerably higher, and we suggest that the patient should receive further abdominal ultrasonography for a definitive diagnosis. In clinical practice, this may help in the initial screening of gallstones in the general population, improve the early diagnosis of gallstones, and thus reduce the pain of patients, and avoid complications, which is beneficial to the health and quality of patients’ lives. Smooth curve fitting more visually reflects the fact that there is a linear relationship between RFM and gallstones. With each unit increase in RFM, the risk of gallstones rises by 9%. Another advantage of utilizing RFM as an optimal predictor is that it is a non-invasive tool that does not require invasive testing procedures, which greatly increases its applicability to gallstone prevention and intervention, providing a friendlier and more efficient option for patients. Further subgroup analyses suggest that for better prevention of gallstones, RFM indices in special populations should receive more attention, including females, non-Hispanic whites, physically inactive, hypertensive, and diabetic individuals.

BRI is another novel obesity index that is also an effective indicator for identifying gallstones. In the present study, we also came to the consistent conclusion that for every 1-unit change in BRI, there was a 1.14-fold increase in the risk of gallstones. BMI and WC are popularly evaluated as obesity indicators, and a study has proposed that a higher risk of gallstones accompanies rising BMI and WC, even in a relatively metabolically healthy adult population^[39,40]. Similarly, the present study confirms that they are independent risk factors for the prevalence of gallstones. LAP is a good indicator of fat accumulation in the organism and its calculation is based on WC and TG with different formulas according to gender. Many studies have demonstrated a positive correlation between LAP and various metabolic disorders^[41,42]. However, its effect on gallstones has not been explored, and our results revealed that LAP was significantly linked to an increased risk of gallstones. In the U.S. population, its optimal threshold was 49.9302. CMI is a newly established composite lipid index consisting of lipids and waist-to-hip ratio. Distinct from AIP, CMI is not only computed according to TG and HDL-C but also incorporates WC and height. In the current study, CMI is positively linked to gallstone prevalence, but its predictive power does not seem to be prominent. Besides, the results of multifactor-adjusted regression analysis suggested that AIP, TyG, and VAI were not correlated with the prevalence of gallstones significantly, and they were not favorable in the identification of gallstones. Our findings indicated that RFM and BRI exhibited higher levels of AUC compared to other indices. Interestingly, they have the same parameters in their formula, i.e., height and waist circumference. Therefore, it is reasonable to assume that combining waist circumference and height is more advantageous in predicting gallstones. This is a novel discovery and further validation in larger studies is warranted.

Strengths and limitations of the research

Our study provides the first relatively comprehensive insight into the potential value of nine obesity-related indicators in predicting gallstones, contributing to the research in this area. Diverse sensitivity analyses were also conducted to examine the robustness of the relations. Among the many obesity-related indicators, we determined that RFM may be the best predictor for the development of gallstones. In future clinical practice, individuals with RFM above the threshold in the general population may be recommended for further ultrasound examination, which will not only improve the early diagnosis of gallstones, benefiting patients, health, but also save the consumption of healthcare resources related to gallstone diagnosis. In addition, NHANES is a strictly supervised database by the Centers for Disease Control and Prevention, which provides detailed, high-quality information. Our findings reflect the relationship between obesity-related indices and gallstones among adults across the United States and suggest that non-Hispanic whites, physically inactive individuals, and hypertensive or diabetic patients are populations that deserve focused attention. However, there are some limitations to this study. First, this was a cross-sectional study and could not determine a causal relationship between obesity-related measures and gallstones. Although the RFM was found to be a better predictor for identifying gallstone risk compared with other indicators, further prospective investigations are warranted in the future to confirm its value for clinical application. Second, the diagnosis regarding the outcome of gallstones was based on participants’ responses to a medical questionnaire rather than a more accurate ultrasound diagnosis. Third, although we adjusted for multiple covariates, there remains the influence of other potential confounders, such as family histories of gallstones, genetic susceptibility to cholesterol metabolism-related abnormalities, and other hereditary factors. Finally, the conclusions of this study are based on U.S. adults, and their applicability to other populations remains to be further explored.

Conclusion

Our study revealed that six screening tools, RFM, BRI, BMI, WC, LAP, and CMI, were positively associated with gallstone risk. Among them, RFM exhibited better predictive performance in identifying patients with gallstones in the U.S. adult population. Females, non-Hispanic whites, insufficient physical activity, hypertension, and diabetes are focus groups of concern. Furthermore, the application of RFM, a non-invasive method, as a tool for gallstone risk assessment is likely to be a powerful and convenient option in clinical practice.

Ethical approval

The dataset was obtained from the NHANES database and all data were ethically approved before recording into the database.

Consent

Not applicable.

Sources of funding

This study did not receive any specific funding from granting agencies in the public, commercial, or non-profit sectors.

Author’s contribution

X.W. designed, conceptualized, and wrote the first draft of the paper, and Y.S. provided significant assistance in data analysis and graph production. They worked together to complete the whole research project. The entire process of designing, writing, and revising the article was supervised and guided by S.W. All authors contributed to the article and agreed to the final submitted version.

Conflicts of interest disclosure

All the authors declare to have no conflicts of interest relevant to this study.

Research registration unique identifying number (UIN)

Not applicable.

Guarantor

Xiaodong Wu and Shuodong Wu.

Provenance and peer review

Not applicable.

Data availability statement

The datasets that were obtained in this study can be made available by the corresponding author upon reasonable request.