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Association of cardiometabolic index with gallstone disease and insulin resistance based on NHANES data

BMC Gastroenterology volume 25, Article number: 354 (2025) Cite this article

Abstract

Background

Cardiometabolic index (CMI) is an index integrating visceral obesity and dyslipidemia. This study intends to scrutinize the connection between CMI and gallstone disease (GSD) and to elucidate the association between CMI and insulin resistance (IR) in patients with GSD.

Methods

To explore the potential nonlinear association and determine the inflection point, a restricted cubic spline (RCS) analysis was performed. Following categorization of CMI based on the identified inflection point, multivariate logistic regression models, subgroup analyses, and interaction tests were utilized to assess the connection between CMI and GSD, as well as between CMI and IR in GSD patients. The homeostasis model assessment for IR (HOMA-IR) and triglyceride-glucose (TyG) index was applied to evaluate IR. Spearman analysis was implemented to investigate the connection between CMI and HOMA-IR. The predictive performance of each indicator was evaluated by the receiver operating characteristic (ROC) curve and the area under the curve (AUC).

Results

The study included 2311 individuals, with a GSD prevalence of 10.90%. RCS analysis revealed a nonlinear positive correlation between CMI and GSD (nonlinear P < 0.001), as well as between CMI and IR (nonlinear P < 0.001). In the fully adjusted multivariable logistic regression analysis of covariates, compared with the low-category CMI group, the high-category CMI was significantly associated with the risk of GSD (OR = 1.547, 95% CI: 1.143–2.092, P = 0.005), IR (OR = 4.990, 95% CI: 2.517–9.892, P < 0.001). Subgroup analysis demonstrated that the correlation between CMI and GSD was stronger in females. Spearman correlation analysis showed a positive association between CMI and HOMA-IR in GSD patients (r = 0.548, P < 0.001). The ROC curve demonstrated the predictive performance of the CMI model for GSD (AUC = 0.743), which was superior to conventional indicators such as Body Mass Index and Waist Circumference; the predictive performance of CMI (AUC = 0.772) for IR was consistent with that of TyG (AUC = 0.772).

Conclusion

Our research demonstrates that CMI exhibits a nonlinear positive correlation with the incidence of GSD and IR. This suggests that CMI may serve as a novel and valuable indicator for further investigating the intricate relationships among metabolic syndrome, obesity, and GSD.

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Introduction

Gallstone disease (GSD) is a prevalent global health concern, affecting approximately 10–20% of the adult population worldwide [1]. Clinically significant complications related to GSD encompass cholecystitis, choledocholithiasis, pancreatitis, and ascending cholangitis [2]. Furthermore, GSD constitutes a considerable hazard element for the emergence of gallbladder carcinoma, a malignancy with an exceptionally poor prognosis [3, 4, 5]. Surgical intervention is often required for GSD, which has a postoperative recurrence rate ranging from 10 to 20% [6]. The pathogenesis of GSD is intricate, involving any factor that disrupts the cholesterol-bile acid-phospholipid balance or induces cholestasis [7], such as genetics, gallbladder motility disorders, intestinal factors, environmental factors, insulin resistance (IR) and abnormal lipid metabolism [1, 8]. Moreover, obesity has emerged as a critical risk factor for GSD, especially among the obese individuals who demonstrate an elevated likelihood of developing symptomatic GSD [9, 10].

The global prevalence of obesity has witnessed a marked increase in recent decades, posing a critical public health challenge [11]. Accumulating evidence indicates strong associations between obesity and numerous metabolic disorders [12]. Presently, body mass index (BMI) and waist circumference (WC) remain the most prevalently employed anthropometric measures for corpulence assessment. Nevertheless, BMI shows significant limitations as it is affected by age and sex variations, and more importantly, it is unable to distinguish between muscle mass and fat mass. Similarly, WC also fails to differentiate between visceral and subcutaneous fat [13].

In the past few decades, several novel anthropometric indices (AHIs) have been presented to distinguish the distribution of body fat and appraisal overweight, especially abdominal corpulence. AHIs are straightforward measurement indicators for assessing nutritional health and promptly ascertaining the risk of illness [14]. Among them, the Cardiometabolic index (CMI) is computed as Triglyceride (TG)/Highdensity lipid cholesterol (HDL-c) × waist-to-height ratio (WHtR) [15]. WHtR was regarded as a more accurate marker of specific health hazards compared with BMI, because it concentrated on the dispersion of body fat [16]. Furthermore, the TG/HDL-c ratio turned into a generally recognized indicator of lipid metabolism disorders [17]. By integrating these two parameters efficiently, CMI offers a comprehensive evaluation of abdominal adiposity and dyslipidemia, providing a more holistic approach to the assessment of metabolic health [18]. Several research demonstrated that CMI was a great potential indicator for metabolic syndrome, diabetes, renal dysfunction and cardiovascular disease [19, 20, 21, 22].

Despite these advancements, the connection between CMI and GSD remains ambiguous. Moreover, in GSD patients, the association between CMI and IR is also undetermined. This research utilized comprehensive data from the National Health and Nutrition Examination Survey (NHANES) database, to scrutinize the connection between CMI and GSD and to understand the association between CMI and IR in the GSD population.

Methods

Study population

The total number of 14,986 persons from the NHANES survey encompassing from 2017 to March 2020 were utilized in this study. Following the screening process (Fig. 1), we excluded those without GSD data (5,776), those lacking triglyceride and BMI data (5,351), and those deficient in other covariates (1548) participants. The final count of 2311 participants were incorporated in this study, among whom 252 cases reported gallstone cases. The data is derived from the publicly available official website of NHANES and was examined and sanctioned by The National Research Ethics Board of the United States.

Fig. 1
figure 1

Flow chart of participants selection. NHANES, National Health and Nutrition Examination Survey

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Variables

The questionnaire survey method was employed to assess whether the patients were afflicted with GSD, and the existence of GSD was regarded as the outcome variable. The question in the GSD questionnaire survey is: “Has a doctor or other health professional ever told you that you had gallstone?”

CMI = [TG (mg/dL)/HDL-C (mg/dL)]×[WC (cm)/Height (cm)] [15].

IR is assessed indirectly by means of the HOMA-IR, which is the most prevalent approach because of its simplicity in practical implementation [23–24]. HOMA-IR = [fasting plasma glucose (FPG) (mmol/L) × fasting insulin (FSI) (µU/ml)]/22.5 [25]. The threshold for HOMA-IR is regarded as 2.5, and values exceeding this suggest the existence of IR [26]. Furthermore, a novel IR surrogate marker, namely the triglyceride-glucose index (TyG index), which has been robustly validated in large-scale population studies, was also employed in this study for the assessment of the IR status [27]. TyG = ln [fasting levels of TG (mg/dL) × FPG (mg/dl)/2].

Ascertainment of other covariates

The interview determined age, sex (male/female), race/ethnicity (Mexican American, Other Hispanic, Non-Hispanic White, Non-Hispanic Black, and Other Race), educational status (less than high school/high school/more than high school), marital status (cohabitation/solitude), poverty-income ratio (PIR), smoking history (characterized as having smoked no less than 100 cigarettes during one’s lifetime) and alcohol consumption (assessed by the question: Have you ever consumed any type of alcoholic beverage? ). Moreover, the researchers took into account the existence or non-existence of comorbidities like hypertension, diabetes, coronary heart disease (CHD), asthma, and cancer. These situations are generally closely related to healthy behaviors and are of vital importance in adjusting for potential confounding factors. Meanwhile, the study encompassed physical activity and dietary factors such as water, sugar, and fat intake. All participants were obligated to offer two 24-hour dietary recalls, and the average intake calculated from these two recalls was employed for our analysis.

Statistical analysis

The independent sample t-test was employed to analyze the differences in continuous variables that were normally distributed between the two groups, while the non-parametric Mann-Whitney U test was adopted for continuous variables with skewed distribution. Categorical variables of the two groups were presented as frequency and constituent ratio (n%) and in contrast through the chi-square test. To investigate the odds ratios (ORs) and 95% confidence intervals (CIs) between CMI, GSD and IR, multivariable logistic regression was employed. Three regression models were constructed: model 1 (unadjusted), model 2 (adjusted merely for age, sex, hypertension, diabetes, CHD, smoking, and alcohol consumption), and model 3 (completely adjusted for all covariates). All the indicators are subjected to the independence test of Variance Inflation Factor (VIF). If the VIF values are all lower than 5, the collinearity issue can be disregarded. Restricted cubic spline (RCS) plot based on the Akaike Information Criterion (AIC) was applied to investigate the dose-response relationship between CMI and GSD, along with IR. The objective was to explore the potential nonlinear relationship, ascertain its inflection point and categorize CMI as a binary variable based on the cut-off point for subsequent multi-model logistic regression and trend analysis. Subsequently, subgroup analysis was carried out to assess whether potential variables modified the connection between CMI and GSD. Additionally, the receiver operating characteristic (ROC) curve and the area under the curve (AUC) were employed to assess and contrast the predictive performance of CMI, BMI, and WC for GSD, as well as that of CMI and TyG for IR. Spearman correlation analysis was utilized to explore the correlation between CMI and HOMA-IR. Statistical analyses were executed with R software. P < 0.05 is considered meaningful.

Results

Baseline characteristics

The meticulously comprehensive baseline characteristics of the GSD group and the control group are exhibited in Table 1. Among the 2,311 enrolled participants, 252 (10.90%) were assigned to the GSD group and 2,059 (89.10%) to the control group. The CMI was notably higher in the GSD group compared to the non-GSD group (0.60 vs. 0.44, P < 0.001). Furthermore, the GSD group presented higher values in terms of age, BMI, WC, WHtR, TG, HOMA-IR, TyG, FSI, and FPG. Moreover, the GSD group had a greater proportion of female and greater prevalence of hypertensive disease, diabetes, CHD, asthma and cancer.

Table 1 Basic characteristics of participants
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The connection between CMI and GSD

As depicted in Table 2, the unadjusted model (Model 1) indicated that CMI was positively correlated with the increased prevalence of GSD (OR = 1.104, 95% CI: 1.005–1.213, P = 0.039). After adjusting for factors including age, sex, hypertension, diabetes, CHD, smoked, and alcohol use in Model 2, this positive correlation remained statistically significant (OR = 1.134, 95% CI: 1.027–1.253, P = 0.013). The dose-response relationship between CMI and GSD simulated by RCS further confirmed this correlation (nonlinear P < 0.001) (Fig. 2). The inflection point of CMI was 0.45, which was then used as the cutoff point to categorize CMI into binary variables for multifactorial logistic regression analysis. Even in the completely adjusted model (Model 3), the high category of CMI (CMI ≥ 0.45) was connected with an enhanced risk of GSD (OR = 1.547, 95% CI: 1.143–2.092, P = 0.005). All P-trends were statistically significant.

Table 2 Relationship between CMI and GSD
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Fig. 2
figure 2

The Dose–response relationship among CMI and GSD. The relationship between CMI and GSD was simulated by RCS based on the AIC. We adjusted the model fully for age, sex, hypertension, diabetes, CHD, PA, asthma, cancer, smoked, and alcohol use. The red solid line represents the curve fitting between variables, and the shaded area indicates the 95% CI of the fit. CMI, cardiometabolic index; GSD, gallstone disease; RCS, restricted cubic spline; AIC, Akaike Information Criterion; CHD, coronary heart disease; PA, Physical Activity; CI, confidence interval

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Subgroup analysis and interaction test were performed to further investigate the association between CMI and GSD. As depicted in Table 3, significant positive associations were observed in specific subgroups: females, individuals aged ≤ 60 years, individuals without CHD, individuals with hypertension, and smokers. Notably, the interaction test revealed significant sex-based effect modification (interaction P < 0.001).

Table 3 Subgroup regression analysis of the association between CMI and GSD
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To construct the ultimate predictive model, we integrated a wide range of covariates, including age, sex, hypertension, diabetes, CHD, smoking history, PIR, total sugar intake, total fat intake, total water intake, alcohol consumption, physical activity level, serum cholesterol, serum creatinine, FPG, FSI, HOMA-IR, asthma, and cancer. As shown in Fig. 3, this model presented outstanding forecasting performance. In contrast to conventional measures like BMI and WC, our model manifested significantly improved performance in predicting GSD. The AUC of the CMI model was 0.743 (95CI: 0.712–0.773), which was significantly higher than that of BMI at 0.639 (95CI: 0.604–0.674) and WC at 0.636 (95CI: 0.601–0.670).

Fig. 3
figure 3

ROC curves for CMI、BMI and WC prediction of GSD. CMI, Cardiometabolic Index; BMI, Body Mass Index; WC, Waist Circumference; GSD, gallstone disease

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The connection between CMI and IR in GSD patients

The median CMI in the IR group was notably higher compared to that in the non-IR group (0.74 vs. 0.32, P < 0.001). Furthermore, in contrast to the non-IR group, the IR group exhibited notably elevated levels of BMI, WC, WHtR, TG, FPG, FSI, TyG and HOMA-IR, while the level of HDL-c was lower (Table 4).

Table 4 Basic characteristics of GSD with IR and without IR
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Spearman correlation analysis revealed a significant positive correlation between CMI and HOMA-IR (r = 0.584, P < 0.001) in these GSD patients (Fig. 4). The relationship between CMI and IR is elaborated in Table 5. In the unadjusted model (Model 1), a statistically significant connection was noticed, suggesting that increased CMI levels are related to a higher occurrence of IR (OR = 8.199, 95% CI: 3.483–19.298, P < 0.001). After accounting for potential confounding factors like age, sex, hypertension, diabetes, CHD, smoked, and alcohol use in Model 2, this positive correlation stayed statistically significant (OR = 6.965, 95% CI: 2.902–16.717, P < 0.001). Additionally, as illustrated in Fig. 5, the positive association between CMI and IR in GSD patients was supported by RCS (nonlinear P < 0.001), where the inflection point value corresponding to CMI is 0.60. With this as the cut-off point, CMI was employed as a dichotomous variable for multivariate logistic regression analysis. In the adequately adjusted model (Model 3), the relationship between the high category of CMI (CMI ≥ 0.60) and IR still held statistical significance (OR = 4.990, 95% CI: 2.517–9.892, P < 0.001). Trend analysis affirmed a consistent positive correlation between the two categories (trend P < 0.001).

Fig. 4
figure 4

Spearman correlation analysis between CMI and HOMA-IR. CMI, cardiometabolic index; HOMA-IR, homeostasis model assessment for insulin resistance

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Table 5 Relationship between CMI and IR in GSD
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Fig. 5
figure 5

The Dose–response relationship among CMI and IR in GSD. The relationship between CMI and IR was simulated by RCS based on the AIC. We adjusted the model fully for age, sex, hypertension, diabetes, CHD, PA, asthma, cancer, smoked, and alcohol use. The red solid line represents the curve fitting between variables, and the shaded area indicates the 95% CI of the fit. CMI, cardiometabolic index; IR, insulin resistance; GSD, gallstone disease; RCS, restricted cubic spline; AIC, Akaike Information Criterion; CHD, coronary heart disease; PA, Physical Activity; CI, confidence interval

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To appraise the predictive performance of CMI for IR in GSD patients, the ROC curve was plotted. The results demonstrated that the AUC of CMI was 0.772 (95% CI: 0.706–0.838), which was consistent with the AUC of the TyG of 0.772 (95% CI: 0.708–0.835) (Fig. 6). Compared to TyG, the CMI model’s exhibited slightly lower sensitivity (60.00% vs. 63.53%) but higher specificity (87.43% vs. 82.04%). This implies that CMI is a potential and effective marker for predicting IR in patients with GSD.

Fig. 6
figure 6

ROC curves for CMI、TyG prediction of IR. CMI, cardiometabolic index; TyG, triglyceride-glucose; IR, insulin resistance

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Discussion

Our research reveals that a significant and robust correlation still persists between elevated CMI levels and enhanced susceptibility to GSD and IR, even after comprehensive adjustment for relevant confounding factors.

The CMI represents an extensive evaluation of corpulence, integrating TG/HDL-c and WHtR, and provides a unified measurement method combining dyslipidemia with central adiposity. Our analysis disclosed significantly higher median CMI values in the GSD group compared to the non-GSD controls (0.60 vs. 0.44, P < 0.001). While the precise mechanisms linking elevated CMI with GSD pathogenesis require further elucidation through multicenter prospective cohort studies, the existing literature suggests several potential pathways. Previous research have presented that elevated TG levels was risk factors for GSD [28]. Cavallini et al. demonstrated that hypertriglyceridemia directly correlates with an increased cholesterol saturation index (CSI) [29] and accelerated cholesterol crystallization [30], which were essential antecedents for GSD establishment. Additionally, there is evidence indicating that excessive adiposity constitutes a considerable risk for the emergence of GSD [31]. Visceral adiposity is typically accompanied by hepatic fatty infiltration, which exacerbates the disturbances of cholesterol metabolism. The existence of hepatic fatty infiltration leads to a higher cholesterol concentratedness in the bile, thus enhancing the likelihood of GSD formation [32, 33, 34]. Apart from these metabolic disorders, visceral adiposity impairs gallbladder motility. Excessive abdominal adipose tissue reduces gallbladder contractility, leading to incomplete bile evacuation and creating a milieu conducive to cholesterol deposition in the gallbladder, which significantly increases the probability of GSD formation [35, 36]. Additionally, abdominal adiposity is correlated with intestinal microbiota imbalance, a circumstance marked by diminished diversity of microorganisms and an disproportion among particular bacterial groups. Such modifications in the intestinal microbiota can have a notable consequence on the metabolic process of bile acids, reinforcing the enterohepatic circulation of cholesterol and thereby further augmenting the risk of GSD [37, 38]. The endocrinal functionality of fatty tissue being dysregulated in the situation of corpulence also has a vital position [39]. The anomalous discharge of hormones and immunological mediators disturbs normal cholesterol homeostasis and modifies bile composition, thereby further promoting GSD predisposition [40, 41, 42].

For this research, we found a higher proportion of female in the GSD group, and subgroup analysis disclosed that the influence of CMI on GSD was more pronounced in female. The enhanced vulnerability among females might be associated with sex-dependent physiologic elements, especially those connected with hormonal disparities. The heightened estrogen levels in women, precisely during specific life phases like gestation or postmenopausal, can trigger alterations in metabolism of lipids. These hormonal variations regularly lead to an enhanced saturation of cholesterol within bile, a crucial predecessor for GSD formation. Hence, this biological procedure propelled by oestrogen could influence the greater incidence of GSD observed in females [43, 44]. In general, these mechanistic systems imply that females with raised quantities of visceral fatty indicators are more prone to GSD occurrence than male. This discovery emphasizes the significance of directed towards clinical interferences and preventive actions customized to the particular requirements of this extreme-risk group. By concentrating on the early identification and control of accumulation of visceral fatty, particularly in females, healthcare providers have the possibility to lower the occurrence of GSD and enhance the overall results for these people.

Previous studies have shown a considerable positive connection between GSD and the elevated morbidity related to chronic disorders, such as diabetes, hypertension, CHD, and cancer [13, 45, 46]. Consistent with these findings, our research detected a greater incidence of hypertensive disease, diabetes, CHD, asthma, and cancer in the GSD group. Furthermore, subgroup analysis also revealed that a statistically considerable positive correlation was present between CMI and GSD in individuals aged ≤ 60 years, individuals with hypertension, and smokers. These associations highlight the role of diverse lifestyles, dietary patterns, and metabolic states in GSD pathogenesis, emphasizing the significance of environmental factors in the progression of GSD.

The median CMI in the IR group was remarkably higher than that in the non-IR group (0.74 vs. 0.32, P < 0.001). Subsequent multivariate analysis indicated a statistically considerable association between heightened CMI levels and enhanced susceptibility to IR. Furthermore, in contrast to the non-IR group, the IR group exhibited notably elevated levels of BMI, WC, WHtR, TG, FPG, FSI, TyG and HOMA-IR, while the level of HDL-c was lower (Table 4). Previous studies have demonstrated that TG/HDL-c is closely related to IR [47]. Elevated TG concentrations may impair insulin receptor density on adipocytes and interfere with insulin-receptor binding. Concurrently, diminished HDL-c levels contribute to both impaired insulin secretion and reduced insulin sensitivity [48]. Obesity-related metabolic disturbances, particularly excessive free fatty acids and elevated WHtR, may cause IR by inhibiting the activity of glucose transporters and disrupting insulin-mediated glucose metabolism [49]. IR serves as a remarkable contributing element for the formation of GSD. Specifically, IR promotes cholesterol synthesis while reducing bile salt synthesis, disrupting the delicate balance between cholesterol and bile salts. This dual effect increases cholesterol saturation in bile, a key condition predisposing individuals to GSD formation [50, 51]. Our findings revealed that complex interactions exist among metabolic syndrome, obesity and GSD, and IR might be the central mechanism connecting these diseases.

Spearman correlation analysis in GSD patients revealed a significant positive correlation between CMI and HOMA-IR (r = 0.584, P < 0.001). The AUC for CMI was 0.772 (95% CI: 0.706–0.838), which was consistent with that of TyG, 0.772 (95% CI: 0.708–0.835). This implies that CMI is a potential and effective marker for predicting IR in GSD patients, thereby providing novel insights into the interrelationships among metabolic syndrome, obesity, and GSD.

This research possesses several crucial advantages. NHANES and its representative American sample strictly comply with the elaborately formulated research protocol, encompassing strict quality control and guarantee approaches, thereby reinforcing the dependability of our research results. Secondly, NHANES offers a substantial amount of larithmic and metabolic information, enabling comprehensive modifications for the main confusing factors in our multivariate model. Subgroup analysis further affirmed the signification of CMI in particular patient groups, providing novel approaches and perspectives for the progress of personalized therapeutics. Additionally, our discoveries unveiled a nonlinear positive correlation between CMI and GSD as well as IR. In contrast to conventional indicators like BMI and WC, the CMI model demonstrated outstanding predictive capability in forecasting GSD, with an AUC of 0.743. Spearman correlation analysis indicated that CMI was positively correlated with HOMA-IR (r = 0.584, P < 0.001). Regarding the prediction of IR, the predictive performance of CMI was consistent with that of TyG, with both AUCs being 0.722. This implies that CMI is a potential indicator for investigating obesity, GSD, and IR, providing a fresh perspective for exploring the relationship among metabolic syndrome, obesity, and GSD.

Nevertheless, this investigation possesses certain restrictions. As this constituted a cross-sectional investigation lacking time series data, there are constraints in demonstrating causal relationships between variables. This research employed self-reported outcome variables and was devoid of imaging diagnosis. Given that the majority of GSD are asymptomatic clinically, the study results were affected by whether the participants had received medical care. Participants might have been misdiagnosed as having or not having GSD, thereby potentially introducing research biases in this report. Additionally, this report lacked information regarding the type of GSD, and we were precluded from conducting further subgroup analyses stratified by the composition of gallstones. Future studies could clarify the diagnosis of GSD via imaging examinations such as ultrasound, computed tomography, and magnetic resonance cholangio-pancreatographg, and carry out quantitative analysis of stone components in patients undergoing surgical lithotomy to further explore the association between CMI and different types of GSD. Through constructing a verification system integrating “imaging - biochemistry - clinical” aspects, this issue can be effectively addressed. When it comes to analyzing IR in GSD patients, the small sample size might have an impact on the reliability of the results. The research results are derived from the US population sample. It is proposed that this relationship be explored more comprehensively in other populations in the future. Finally, despite the inclusion of numerous concomitant variables in the multivariate regression analysis, there could still be some remanent confounder. Notwithstanding these restrictions, our study still constitutes the initial exploration of the correlation between CMI and GSD as well as IR in patients with GSD. In this regard, subsequent multicenter prospective longitudinal studies are requisite to further investigate the capacity of CMI as a risk predictor and explore the specific mechanisms of the causal pathways among CMI, GSD, and IR.

Conclusions

Our research demonstrates that CMI exhibits a nonlinear positive correlation with the incidence of GSD and IR. This suggests that CMI may serve as a novel and valuable indicator for further investigating the intricate relationships among metabolic syndrome, obesity, and GSD.

Data availability

The publicly accessible data sets were analyzed in this research. These data are available at https://www.cdc.gov/nchs/nhanes/index.htm.

Abbreviations

CMI:

cardiometabolic index

GSD:

gallstone disease

RCS:

restricted cubic spline

AHIs:

anthropometric indices

TG:

triglycerides

HDL-c:

high-density lipoprotein cholesterol

BMI:

body mass index

WC:

Waist Circumference

HC:

Hip Circumference

WHpR:

waist-to-hip ratio

WHtR:

waist-to-height ratio

CHD:

coronary heart disease

CSI:

cholesterol saturation index

ROC:

Receiver operating characteristic

AUC:

Area under the curve

CI:

Confidence interval

OR:

Odds ratio

AIC:

Akaike Information Criterion

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Acknowledgements

We would like to thank all participants in this study.

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  1. Liu Yuan and Shuqi Wang have contributed equally to this work.

Authors and Affiliations

  1. China Medical University, Shenyang, China

    Liu Yuan

  2. Central Hospital of Dalian University of Technology (Dalian Municipal Central Hospital), Dalian, China

    Liu Yuan, Shuqi Wang, Dong Wang & Enbo Wang

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Contributions

LY methodology, investigation, writing original draft, data curation, formal analysis, validation, visualization. SW methodology, investigation, conceptualization, formal analysis. DW and EW conceptualization, supervision, review and editing. All authors reviewed the manuscript.

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Correspondence to Enbo Wang.

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The protocol of NHANES was approved by the Ethical Review Board of the National Center for Health Statistics, and the participants provided written informed consent prior to their participation.

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The authors declare no competing interests.

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Yuan, L., Wang, S., Wang, D. et al. Association of cardiometabolic index with gallstone disease and insulin resistance based on NHANES data. BMC Gastroenterol 25, 354 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12876-025-03950-8

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Keywords

BMC Gastroenterology

ISSN: 1471-230X

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