Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.

You are viewing the site in preview mode

Skip to main content
Download PDF

Cardiometabolic index as a predictor of gallstone risk: evidence from NHANES 2017–2020

BMC Gastroenterology volume 25, Article number: 218 (2025) Cite this article

Abstract

Background

The Cardiometabolic Index (CMI), a composite marker integrating lipid profiles (triglycerides-to-HDL-C ratio) and abdominal obesity (waist-to-height ratio), we aimed to assess its association with gallstone prevalence.

Methods

We analyzed data from 2,692 participants in the NHANES 2017–2020 dataset. Gallstones were identified through self-reported data, which may introduce bias in the diagnosis. This limitation should be considered when interpreting the results. Logistic regression modelling, smoothed curve fitting and threshold effect analysis assessed the association between CMI and gallstones.

Result

Higher CMI was significantly associated with an increased risk of gallstones (OR = 1.90, 95% CI: 1.37–2.62, P < 0.0001). A threshold effect was observed at CMI = 0.85, below which risk increased significantly (OR = 2.62, 95% CI:1.34–5.12, P = 0.0049), but became non-significant above this value. The association was stronger in women.

Conclusion

Our findings support the use of CMI as a potential predictive marker for gallstone risk, suggesting its integration into clinical assessments for early detection and prevention.

Peer Review reports

Introduction

Gallstones are among the most common chronic digestive system diseases worldwide, with prevalence varying by region and ethnicity [1, 2]. In the US, gallstones affect approximately 10–20% of the population, imposing an economic burden of $6.2 billion annually in direct and indirect costs associated with gallbladder disease [3, 4]. Gallstones often cause symptoms such as abdominal discomfort, nausea, and vomiting, significantly impairing quality of life and potentially leading to life-threatening complications like cholangitis, pancreatitis, and gallbladder cancer [5, 6]. Research has also linked gallstones to coronary heart disease, diabetes mellitus, and autoimmune diseases, emphasizing the importance of predicting and preventing this condition in clinical settings [7,8,9].

Traditional markers of gallstone risk, such as body mass index (BMI) and waist circumference (WC), primarily reflect overall or central obesity but fail to capture the synergistic interplay between adiposity and metabolic dysfunction. For instance, BMI cannot distinguish between lean mass and visceral fat [10], while WC overlooks lipid abnormalities such as hypertriglyceridemia or low HDL-C levels—key drivers of biliary cholesterol supersaturation [11]. Moreover, these metrics lack sensitivity in identifying metabolically obese individuals with normal weight, a subgroup at elevated risk for gallstones [12]. The Cardiometabolic Index (CMI), which integrates waist-to-height ratio (WHtR) and the triglyceride-to-HDL-C ratio (TG/HDL-C), addresses these gaps by simultaneously quantifying abdominal adiposity and dyslipidemia. This dual assessment aligns with the multifactorial pathogenesis of gallstones, offering a more holistic risk evaluation than conventional indices [13, 14]. The Cardiometabolic Index (CMI), initially developed by Ichiro Wakabayashi and colleagues as a marker for diabetes, has gained attention as a significant metabolic health indicator [13]. This index has proven effective in depicting lipid metabolism in both obese adults and children, and is increasingly applied in studies addressing diabetes, non-alcoholic fatty liver disease, chronic kidney disease, asthma, and chronic obstructive pulmonary disease [15,16,17,18,19].

Recent advances in metabolic indices, such as the triglyceride-glucose (TyG) index, has been shown to have a strong correlation with atherosclerosis, cardiovascular disease, and cancer [20,21,22], and it has recently been suggested that higher TyG indices correlate with an increased likelihood of gallstones incidence [23].

Given the complex and multifactorial etiology of gallstones, including age, gender, obesity (especially abdominal obesity), lipid metabolism, and lifestyle, the relationship between CMI and gallstones warrants in-depth investigation [2, 24,25,26]. In this study, we hypothesized that CMI is also associated with gallstones, and compared its predictive ability with TyG index to provide new insights into early intervention and prevention strategies for gallstones.

Methods

This study utilized data from the National Health and Nutrition Examination Survey (NHANES), a nationally representative cross-sectional program conducted by the Centers for Disease Control and Prevention (CDC) to assess the health and nutritional status of the U.S. population. NHANES employs a complex, stratified, multistage sampling design, combining detailed interviews, physical examinations, and laboratory tests. The 2017–2020 cycles were selected for this analysis due to their inclusion of comprehensive gallstone-related questionnaires and metabolic biomarkers required for calculating the CMI and TyG index. The integration of demographic, clinical, and biochemical data in NHANES ensures robust generalizability to the U.S. population.

The study initially included 15,560 participants. We excluded 11,173 due to incomplete data required to calculate cardiometabolic index (CMI) and TyG index. In addition, 692 participants were excluded due to missing data on gallstones and 1007 participants were excluded due to missing information on relevant covariates. After these exclusions, the study population was narrowed to 2719 individuals. Prior to analysis, we excluded extreme outliers in cardiometabolic index (CMI) to minimize their impact on the results. We identified and excluded CMI values above the 99th percentile of the distribution, and a total of 27 participants were excluded based on this criterion. The final analysis included 2692 participants. Figure 1 details the participant screening process, as well as the inclusion and exclusion criteria, and illustrates the participant screening flowchart.

Fig. 1
figure 1

Flow chart of participants selection

Full size image

Measurement of CMI and TyG

CMI was considered an exposure variable. The following metabolic profiles and body measurements of the subjects were used to calculate CMI: WHtR * [triglyceride (TG) /HDL-C].

The data of TyG index was designed as an exposure variable and was calculated as Ln [triglycerides (mg/dl) * fasting glucose (mg/dl)/2].

Definition of gallstones

Gallstones were the outcome variable. Gallstones were detected using data from the Medical Condition Questionnaire. Participants were considered to have gallstones when they were asked if they had ever been told by a doctor or professional that they had gallstones.

Relevant covariates

In this study, covariates included age, gender, race, marital status (cohabitation, solitary), educational level (below, high, and above high school), serum total cholesterol level, BMI (< 25, ≥ 25), serum total bilirubin, poverty-to-income ratio (PIR), Sedentary activities. Diabetes, alcohol consumption: individuals were categorized as drinkers if they consumed four or five drinks daily, smokers: if they smoked at least 100 cigarettes annually, hypertension, diabetes, coronary heart disease, thyroid disease, and cancer (those who replied ‘yes’ on the questionnaire were diagnosed with these conditions).

Statistical analysis

The survey respondents were divided into two groups by whether they had gallstones or not. Categorical variables were expressed as frequencies or percentages and continuous data were expressed as mean ± standard deviation. Shapiro-Wilk normality test was used to assess the distribution of continuous variables. For non-normally distributed variables, we used non-parametric tests such as the Mann-Whitney U test or the Kruskal-Wallis test. Normally distributed continuous and categorical variables were tested for between-group differences using chi-square tests and analysis of variance (ANOVA). The variance inflation factor (VIF) and tolerance were used to evaluate the variables in this study in order to remove multicollinearity. The results showed that the VIF for all covariates was less than 5, indicating that no severe multicollinearity existed. Multivariate logistic regression was used to evaluate the CMI in three models after it was split into quartiles, with the lowest quartile acting as the reference group. Model 1 remained unchanged. Model 2 adjusted for age, race, and gender. In model 3, all variables were changed and the non-linear relationship between CMI and gallstones was investigated using a smoothed curve-fitting method based on the generalised additive model (GAM), with threshold effect analysis using a segmented regression model, the Likelihood Ratio Test (LRT), and Bootstrap resampling. Through the use of subgroup analysis, the effects of heterogeneity were examined. P-values were considered statistically significant if they were less than 0.05. R software, version 4.2.3, and Empower(R) tools were used for all statistical analyses.

Results

Baseline characteristics of participants

Among the 2692 participants included in the analysis, 293 (10.9%) reported a history of gallstones. Compared to the non-gallstone group, participants with gallstones were more likely to be female (73.04% vs.26.96%, P < 0.001), older (mean age 57.74 vs. 49.12 years, P < 0.001), BMI ≥ 25 (89.08% vs.10.92%, P < 0.001), and had higher CMI values (0.82 vs. 0.68, P < 0.001). Additional significant differences were observed in hypertension prevalence (53.92% vs. (35.06%, P < 0.001) and diabetes (27.65% vs. 14.05%, P < 0.001). The baseline characteristics of participants with and without gallstones are detailed in Table 1: Baseline characteristics of participants with and without gallstones.

Table 1 Baseline characteristics of participants with and without gallstones
Full size table

Association of CMI with gallstones

As shown in Table 2: Multivariable logistic regression analysis of the association between CMI and gallstone risk, the results indicate a significant association between CMI and gallstone risk. In the unadjusted model, each unit increase in CMI was associated with a 50% increase in the odds of developing gallstones (OR = 1.50; 95% CI: 1.24–1.82, P < 0.0001). This association remained significant after full adjustment for potential confounders (OR = 1.28, 95% CI: 1.00–1.65, P = 0.0488).

Table 2 Multivariable logistic regression analysis of the association between CMI and gallstone risk
Full size table

Further analysis using CMI as a categorical variable revealed that individuals in the highest CMI quartile faced a substantially higher risk compared to those in the lowest quartile. Specifically, the risk was up to 3.01 times higher in Model 2 and 2.02 times higher in Model 3, reinforcing the link between higher CMI and the development of gallstones.

Threshold effect analysis

To more intuitively describe the relationship between CMI and gallstone risk, we employed smoothed curve fitting and the generalized additive model (GAM), as shown in Fig. 2. We identified a nonlinear correlation and a potential saturation effect, and conducted a threshold effect analysis using a two-piecewise linear regression model, as shown in Table 3: Threshold effect analysis of CMI on gallstone risk using a two-piecewise linear regression model. The relationship between CMI and gallstones changed at the threshold of CMI = 0.85. Below this threshold, the odds of gallstones significantly increased (OR: 2.62, 95% CI: 1.34–5.12, P = 0.0049). Above this threshold, the association became non-significant (OR: 0.92, 95% CI: 0.63–1.33, P = 0.6505).

Fig. 2
figure 2

The association between CMI and gallstones

Full size image
Table 3 Threshold effect analysis of cardiometabolic index on gallstone risk using a two-piecewise linear regression model
Full size table

Subgroup analyses

As illustrated in Fig. 3, with the exception of the gender subgroup, which exhibited a significant interaction effect (P for interaction = 0.0086), no statistically significant interactions were observed across other subgroups (e.g., age, race, BMI, diabetes status; all interaction P-values > 0.05). This indicates that the direction and magnitude of the association between CMI and gallstone risk remained consistent in these subgroups, supporting the robustness of CMI as a predictive marker.

Fig. 3
figure 3

Subgroup analysis of the association between CMI and gallstones. Adjusted for all covariates except effect modifier

Full size image

Predictive value of CMI for gallstones

The ROC curves in Fig. 4 show the diagnostic performance of CMI and TyG in identifying gallstones. CMI was slightly more accurate than TyG for gallstones, with an AUC value of 0.606 (95% CI: 0.574–0.638), compared to TyG of 0.599 (95% CI: 0.566–0.631).

Fig. 4
figure 4

Predictive value of CMI and TyG for gallstones

Full size image

Discussion

Ichiro Wakabayashi et al. proposed CMI as a novel measure for identifying diabetes in 2015 [13]. A large prospective study found that increased CMI was positively linked with all-cause mortality in older persons [27]. Several cross-sectional studies have demonstrated that a raised CMI is connected with an increased risk of depression, particularly in hypertensive populations, and Wang et al. found a 20% increase in risk of endometriosis for every unit rise in CMI when the CMI is greater than 0.67 [28]. A prospective study from China involving 117,326 subjects showed a positive correlation between CMI and the development of acute pancreatitis [29]. In addition to this, it has also been linked to diabetes, pulmonary function and stroke [15, 30, 31]. But CMI link with gallstones has not been seen yet, a study linking CMI with gallstones was carried out. The results showed that for every unit rise in the CMI index, the incidence of gallstones rose by 28% in the fully adjusted model (OR = 1.28, 95% CI: 1.00-1.65, P = 0.0488). Within a certain range, smoothed curve fitting also showed a positive correlation between gallstones and the CMI index. Based on the results of this investigation, CMI can be used to predict the risk of gallstones. We also compared the recent popular biomarker TyG, which has been shown to be significantly and positively associated with gallstone disease, we performed a ROC analysis and compared the ability of CMI to predict gallstones with TyG. We found that CMI had a statistically superior predictive ability for gallstones than TyG.

CMI Integrates abdominal obesity (via waist-to-height ratio) and dyslipidaemia (via TG/HDL-C ratio), directly addressing two key drivers of gallstone pathogenesis—visceral adiposity and cholesterol saturation [13]. TyG Focuses on the interplay between lipid and glucose metabolism, serving as a surrogate for insulin resistance, which may indirectly influence gallstone formation through hepatic cholesterol synthesis and gallbladder motility [32]. Recent evidence suggests that combining CMI and TyG could enhance risk stratification. For example, Feng et al. proposed that dual assessment of adiposity (via CMI) and insulin resistance (via TyG) improves the prediction of metabolic disorders [33]. In the context of gallstones, CMI’s emphasis on visceral fat distribution may identify individuals with obesity-driven biliary cholesterol supersaturation, while TyG could flag those with glucose-lipid dysregulation exacerbating hepatic cholesterol secretion.ero.

In subgroup analyses, the positive correlation between CMI and gallstones was more significant for women, because low estrogen levels in men reduce the expression of the 3-hydroxy-3-methyl glutaryl coenzyme A reductase(HMG-CoA-r)gene and its transcription factor sterol regulatory element-binding protein(SREBP) −2 in the liver, which affects the formation of gallstones through the reduction of cholesterol synthesis [34].

The study observed a threshold effect between CMI and gallstone risk (the risk sharply increases when CMI < 0.85, and becomes saturated when CMI > 0.85), which may be attributed to the following mechanisms: Dysregulated Lipid Metabolism: When CMI is low, mild metabolic disturbances (such as elevated blood lipids and abdominal fat accumulation) can lead to bile cholesterol supersaturation, significantly increasing the risk of gallstones. When CMI exceeds 0.85, metabolic disturbances (such as insulin resistance and chronic inflammation) reach a critical point, and bile cholesterol saturation stabilizes, thus diminishing the increase in risk [32]. Inflammation and Oxidative Stress: Abdominal obesity and dyslipidemia activate chronic inflammation (e.g., elevated IL-6 and TNF-α). At low CMI, the pro-stone effects of inflammatory mediators intensify as CMI increases, with decreasing HDL-C and increasing TG levels disrupting the oxidative-antioxidative balance, promoting cholesterol crystal nucleation. At high CMI, inflammatory signaling pathways (e.g., NF-κB) become saturated, and the risk increase slows. The depletion of the antioxidant system further limits its contribution to gallstone formation [35, 36].

CMI is a composite indicator that integrates abdominal obesity and dyslipidemia, which are key drivers of metabolic disorders. While body mass index(BMI)estimates of body fat in obese people have limitations in distinguishing between the contributions of muscle mass and adipose tissue to obesity, the WHtR, due to its greater emphasis on body fat distribution, has been proposed as a more accurate indicator of certain health risks [37, 38]. In addition, the TG/HDL-C ratio has been found to be highly correlated with coronary heart disease, diabetes mellitus, chronic kidney disease, and metabolic syndrome, and it often represents an indicator of lipid metabolism disorders [39,40,41]. Gallstone risk factors include lipid metabolism disorders and abdominal obesity. By effectively combining these two indicators, CMI is regarded as a more thorough evaluation of dyslipidemia and abdominal obesity, offering a more comprehensive approach to triglyceride evaluating metabolic health. The exact mechanism by which elevated CMI is linked to an increased risk of gallstones is unclear, but based on prior research, we propose that the causes are multifactorial: First of all, high serum triglyceride levels are linked to fast cholesterol crystal nucleation and increased biliary cholesterol saturation, both of which are significant risk factors for gallstones. Increased biliary cholesterol saturation and insufficient bile acid secretion are linked to lower serum HDL-C, which decreases cholesterol solubility in the bile and causes gallstones [14, 42, 43]. Secondly, chronic systemic inflammation and oxidative stress exists in obese and dyslipidaemic populations, and as inflammatory mediators continue to increase, such as interleukin (IL) -6 and IL-12, they can stimulate the production of tumour necrosis factor-α (TNF-α) by T-cells and natural killer cells, which directly affects the uptake, secretion, and function of the gallbladder epithelial cells, and impairs the gallbladder’s normal contractile ability, thereby increasing the risk of gallstone formation [35, 44]. Then higher HDL-C inhibits the production of various chemokines, hinders oxidative stress, suppresses inflammatory responses, and contributes to cholesterol efflux [36]. In addition, insulin resistance (IR) frequently coexists with obesity and metabolic disorders. By activating the 3-hydroxy-3-methylglutaryl coenzyme, it might accelerate hepatic cholesterol release and cholesterol supersaturation and deteriorate gallbladder dynamics, which may result in the production of gallstones [32, 45, 46]. IR is an important marker for type 2 diabetes, which explains the increased risk of developing stones among diabetics with high CMI levels in the study as well. Lastly, a high waist-to-height ratio usually implies obesity, which is an important risk factor for metabolic diseases. Obese individuals usually suffer from comorbidities such as diabetes mellitus and non-alcoholic fatty liver disease. Both of them have been shown to be associated with an increased risk of gallstones [15, 16]. The high fat content of obese patients increases leptin secretion, which regulates bile acid metabolism through the leptin receptor/AMP activated protein kinase/bile salt efflux pump (OB-Rb/AMPK/BSEP) axis resulting in the formation of gallstones [47, 48]. The positive correlation between blood pressure and leptin levels may account for the increased risk of gallstones in hypertensive people with elevated CMI levels [49]. Lastly, a high waist-to-height ratio is often indicative of obesity, a key risk factor for metabolic diseases. Obese individuals usually suffer from comorbidities such as diabetes mellitus, non-alcoholic fatty liver disease, cardiovascular disease and metabolic syndrome than non-obese individuals. These have been shown to be associated with an increased risk of gallstones.

To the best of our knowledge, this is the first study to look into the connection between gallstones and CMI. To clarify the relationship and validate the validity and reliability of the results, we used subgroup analyses, multivariate logistic regression analysis. These findings have significant implications for future gallstone prevention and early intervention methods. Although a correlation could be seen, causality could not be concluded because this study was observational and included the impact of temporal connection ambiguity. Because asymptomatic gallstones may exist, this study relied on self-reported health information on gallstones, which is prone to reporting bias and affects the accuracy of the findings. Although we took into account as many covariates as feasible when conducting the logistic regression analyses, there may still be potential confounders that were not included in the analyses and affect the interpretation of the findings. Due to constraints in the included population, the statistical usefulness of subgroup analyses and interaction tests may be limited by the relatively small sample sizes of certain subgroups.

Conclusions

This study found that cardiometabolic index (CMI) was significantly and positively associated with the risk of developing gallstones, particularly in the female population. These findings provide new scientific rationale for utilising CMI as a component of early screening and prevention strategies for asymptomatic gallstones. However, longitudinal studies are needed to confirm our findings, and future studies should also explore advanced predictive tools, such as machine learning algorithms combining CMI, TyG and other biomarkers, to develop personalised risk stratification models. Such models could guide targeted ultrasound screening of at-risk populations to optimise early diagnosis and prevention.

Data availability

This study examines data that is available to the public. You may get the complete set of data at https://www.cdc.gov/nchs/nhanes/index.htm.

References

  1. Figueiredo JC, Haiman C, Porcel J, Buxbaum J, Stram D, Tambe N, Cozen W, Wilkens L, Le Marchand L, Setiawan VW. Sex and ethnic/racial-specific risk factors for gallbladder disease. BMC Gastroenterol. 2017;17(1):153.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Stinton LM, Shaffer EA. Epidemiology of gallbladder disease: cholelithiasis and cancer. Gut Liver. 2012;6(2):172–87.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Shaffer EA. Gallstone disease: epidemiology of gallbladder stone disease. Best Pract Res Clin Gastroenterol. 2006;20(6):981–96.

    Article  PubMed  Google Scholar 

  4. Du W, Yan C, Wang Y, Song C, Li Y, Tian Z, Liu Y, Shen W. Association between dietary magnesium intake and gallstones: the mediating role of atherogenic index of plasma. Lipids Health Dis. 2024;23(1):82.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Innes K, Hudson J, Banister K, Croal B, Ramsay C, Ahmed I, Blazeby J, Gillies K. Core outcome set for symptomatic uncomplicated gallstone disease. Br J Surg. 2022;109(6):539–44.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lammert F, Gurusamy K, Ko CW, Miquel JF, Méndez-Sánchez N, Portincasa P, van Erpecum KJ, van Laarhoven CJ, Wang DQ. Gallstones. Nat Rev Dis Primers. 2016;2:16024.

    Article  PubMed  Google Scholar 

  7. Zheng Y, Xu M, Li Y, Hruby A, Rimm EB, Hu FB, Wirth J, Albert CM, Rexrode KM, Manson JE, et al. Gallstones and risk of coronary heart disease: prospective analysis of 270 000 men and women from 3 US cohorts and Meta-Analysis. Arterioscler Thromb Vasc Biol. 2016;36(9):1997–2003.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Shabanzadeh DM, Linneberg A, Skaaby T, Sørensen LT, Jørgensen T. Screen-detected gallstone disease and autoimmune diseases - A cohort study. Dig Liver Dis. 2018;50(6):594–600.

    Article  PubMed  Google Scholar 

  9. Lv J, Yu C, Guo Y, Bian Z, Yang L, Chen Y, Li S, Huang Y, Fu Y, He P, et al. Gallstone disease and the risk of type 2 diabetes. Sci Rep. 2017;7(1):15853.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Neeland IJ, Ross R, Després JP, Matsuzawa Y, Yamashita S, Shai I, Seidell J, Magni P, Santos RD, Arsenault B, et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol. 2019;7(9):715–25.

    Article  PubMed  Google Scholar 

  11. Di Ciaula A, Garruti G, Frühbeck G, De Angelis M, de Bari O, Wang DQ, Lammert F, Portincasa P. The role of diet in the pathogenesis of cholesterol gallstones. Curr Med Chem. 2019;26(19):3620–38.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Stefan N, Häring HU, Schulze MB. Metabolically healthy obesity: the low-hanging fruit in obesity treatment? Lancet Diabetes Endocrinol. 2018;6(3):249–58.

    Article  PubMed  Google Scholar 

  13. Wakabayashi I, Daimon T. The cardiometabolic index as a new marker determined by adiposity and blood lipids for discrimination of diabetes mellitus. Clin Chim Acta. 2015;438:274–8.

    Article  PubMed  CAS  Google Scholar 

  14. Wang H, Feng X, Huang Q, Zheng X. Association between lipid accumulation products and gallstones: an analysis of the National health and nutrition examination survey 2017–2020. BMC Gastroenterol. 2024;24(1):311.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Song J, Li Y, Zhu J, Liang J, Xue S, Zhu Z. Non-linear associations of cardiometabolic index with insulin resistance, impaired fasting glucose, and type 2 diabetes among US adults: a cross-sectional study. Front Endocrinol (Lausanne). 2024;15:1341828.

    Article  PubMed  Google Scholar 

  16. Xi WF, Yang AM. Association between cardiometabolic index and controlled Attenuation parameter in U.S. Adults with NAFLD: findings from NHANES (2017–2020). Lipids Health Dis. 2024;23(1):40.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Guo Q, Wang Y, Liu Y, Wang Y, Deng L, Liao L, Lin X, Wu M, Sun M, Liao Y. Association between the cardiometabolic index and chronic kidney disease: a cross-sectional study. Int Urol Nephrol. 2024;56(5):1733–41.

    Article  PubMed  Google Scholar 

  18. Li C, Meng T, Wang B, Liu C, Jiang N, Li J, Chen H: Association between cardiometabolic index and asthma in adults: evidence from NHANES 2005-2018. J Asthma 2025;62(1):101–109.

  19. Wang L, Liu X, Du Z, Tian J, Zhang L, Yang L. Cardiometabolic index and chronic obstructive pulmonary disease: A population-based cross-sectional study. Heart Lung. 2024;68:342–9.

    Article  PubMed  Google Scholar 

  20. Yan Y, Wang D, Sun Y, Ma Q, Wang K, Liao Y, Chen C, Jia H, Chu C, Zheng W, et al. Triglyceride-glucose index trajectory and arterial stiffness: results from Hanzhong adolescent hypertension cohort study. Cardiovasc Diabetol. 2022;21(1):33.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  21. Alizargar J, Bai CH, Hsieh NC, Wu SV. Use of the triglyceride-glucose index (TyG) in cardiovascular disease patients. Cardiovasc Diabetol. 2020;19(1):8.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Nayak SS, Kuriyakose D, Polisetty LD, Patil AA, Ameen D, Bonu R, Shetty SP, Biswas P, Ulrich MT, Letafatkar N, et al. Diagnostic and prognostic value of triglyceride glucose index: a comprehensive evaluation of meta-analysis. Cardiovasc Diabetol. 2024;23(1):310.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wang J, Li H, Hu J, Shi R, Qin C, Chen X, Chen S, Zeng X, Luo H, Luo H, et al. Relationship of triglyceride-glucose index to gallstone prevalence and age at first gallstone surgery in American adults. Sci Rep. 2024;14(1):16749.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Wang J, Yang J, Chen Y, Rui J, Xu M, Chen M. Association of METS-IR index with prevalence of gallbladder stones and the age at the first gallbladder stone surgery in US adults: A cross-sectional study. Front Endocrinol (Lausanne). 2022;13:1025854.

    Article  PubMed  Google Scholar 

  25. Tsai CJ, Leitzmann MF, Willett WC, Giovannucci EL. Prospective study of abdominal adiposity and gallstone disease in US men. Am J Clin Nutr. 2004;80(1):38–44.

    Article  PubMed  CAS  Google Scholar 

  26. Zhang M, Mao M, Zhang C, Hu F, Cui P, Li G, Shi J, Wang X, Shan X. Blood lipid metabolism and the risk of gallstone disease: a multi-center study and meta-analysis. Lipids Health Dis. 2022;21(1):26.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Xu B, Wu Q, La R, Lu L, Abdu FA, Yin G, Zhang W, Ding W, Ling Y, He Z, et al. Is systemic inflammation a missing link between cardiometabolic index with mortality? Evidence from a large population-based study. Cardiovasc Diabetol. 2024;23(1):212.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Wang J, Wang B, Liu T, Shang J, Gu X, Zhang T, Cong H. Association between cardiometabolic index (CMI) and endometriosis: a cross-sectional study on NHANES. Lipids Health Dis. 2024;23(1):328.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Sun Q, Ren Q, Du L, Chen S, Wu S, Zhang B, Wang B. Cardiometabolic index (CMI), lipid accumulation products (LAP), waist triglyceride index (WTI) and the risk of acute pancreatitis: a prospective study in adults of North China. Lipids Health Dis. 2023;22(1):190.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  30. Mo CY, Pu JL, Zheng YF, Li YL. The relationship between cardiometabolic index and pulmonary function among U.S. Adults: insights from the National health and nutrition examination survey (2007–2012). Lipids Health Dis. 2024;23(1):246.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Li FE, Luo Y, Zhang FL, Zhang P, Liu D, Ta S, Yu Y, Guo ZN, Yang Y. Association between cardiometabolic index and stroke: A Population- based Cross-sectional study. Curr Neurovasc Res. 2021;18(3):324–32.

    Article  PubMed  CAS  Google Scholar 

  32. Di Ciaula A, Wang DQ, Portincasa P. An update on the pathogenesis of cholesterol gallstone disease. Curr Opin Gastroenterol. 2018;34(2):71–80.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Yin JL, Yang J, Song XJ, Qin X, Chang YJ, Chen X, Liu FH, Li YZ, Xu HL, Wei YF, et al. Triglyceride-glucose index and health outcomes: an umbrella review of systematic reviews with meta-analyses of observational studies. Cardiovasc Diabetol. 2024;23(1):177.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Lavoie JM. Dynamics of hepatic and intestinal cholesterol and bile acid pathways: the impact of the animal model of Estrogen deficiency and exercise training. World J Hepatol. 2016;8(23):961–75.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Liu Z, Kemp TJ, Gao YT, Corbel A, McGee EE, Wang B, Shen MC, Rashid A, Hsing AW, Hildesheim A, et al. Association of Circulating inflammation proteins and gallstone disease. J Gastroenterol Hepatol. 2018;33(11):1920–4.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Zhou Y, Dan H, Bai L, Jia L, Lu B, Cui W. Nonlinear relationship with saturation effect observed between neutrophil to high-density lipoprotein cholesterol ratio and atherosclerosis in a health examination population: a cross-sectional study. BMC Cardiovasc Disord. 2022;22(1):424.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Hajian-Tilaki K, Heidari B. Variations in the pattern and distribution of non-obese components of metabolic syndrome across different obesity phenotypes among Iranian adults’ population. Diabetes Metab Syndr. 2019;13(4):2419–24.

    Article  PubMed  Google Scholar 

  38. Orsi E, Solini A, Penno G, Bonora E, Fondelli C, Trevisan R, Vedovato M, Cavalot F, Lamacchia O, Haxhi J, et al. Body mass index versus surrogate measures of central adiposity as independent predictors of mortality in type 2 diabetes. Cardiovasc Diabetol. 2022;21(1):266.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Cheng B, Yi Y, Chen M, Wei Y, Su X, Chen P, Lin X, Gu Y, Li T, Xu C, et al. TG/HDL-C ratio is positively associated with risk and severity of CHD among NAFLD patients: a case control study. Front Endocrinol (Lausanne). 2024;15:1383489.

    Article  PubMed  Google Scholar 

  40. Liu H, Liu J, Liu J, Xin S, Lyu Z, Fu X. Triglyceride to High-Density lipoprotein cholesterol (TG/HDL-C) ratio, a simple but effective indicator in predicting type 2 diabetes mellitus in older adults. Front Endocrinol (Lausanne). 2022;13:828581.

    Article  PubMed  Google Scholar 

  41. Wen J, Chen Y, Huang Y, Lu Y, Liu X, Zhou H, Yuan H. Association of the TG/HDL-C and Non-HDL-C/HDL-C ratios with chronic kidney disease in an adult Chinese population. Kidney Blood Press Res. 2017;42(6):1141–54.

    Article  PubMed  CAS  Google Scholar 

  42. Andreotti G, Chen J, Gao YT, Rashid A, Chang SC, Shen MC, Wang BS, Han TQ, Zhang BH, Danforth KN, et al. Serum lipid levels and the risk of biliary tract cancers and biliary stones: A population-based study in China. Int J Cancer. 2008;122(10):2322–9.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Hung MC, Chen CF, Tsou MT, Lin HH, Hwang LC, Hsu CP. Relationship between gallstone disease and cardiometabolic risk factors in elderly people with Non-Alcoholic fatty liver disease. Diabetes Metab Syndr Obes. 2020;13:3579–85.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Maurer KJ, Carey MC, Fox JG. Roles of infection, inflammation, and the immune system in cholesterol gallstone formation. Gastroenterology. 2009;136(2):425–40.

    Article  PubMed  CAS  Google Scholar 

  45. Ahmed B, Sultana R, Greene MW. Adipose tissue and insulin resistance in obese. Biomed Pharmacother. 2021;137:111315.

    Article  PubMed  CAS  Google Scholar 

  46. Tsai CJ, Leitzmann MF, Willett WC, Giovannucci EL. Macronutrients and insulin resistance in cholesterol gallstone disease. Am J Gastroenterol. 2008;103(11):2932–9.

    Article  PubMed  CAS  Google Scholar 

  47. Perakakis N, Farr OM, Mantzoros CS. Leptin in leanness and obesity: JACC State-of-the-Art review. J Am Coll Cardiol. 2021;77(6):745–60.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Wen J, Jiang Y, Lei Z, He J, Ye M, Fu W. Leptin influence cholelithiasis formation by regulating bile acid metabolism. Turk J Gastroenterol. 2021;32(1):97–105.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Zhang Y, Sun L, Wang X, Chen Z. The association between hypertension and the risk of gallstone disease: a cross-sectional study. BMC Gastroenterol. 2022;22(1):138.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We appreciate the National Center for Health Statistics of the Centers for Disease Control staff for collecting the NHANES data and creating the public database.

Funding

The paper was not funded.

Author information

Author notes

  1. Huachao Zheng and Bo Wu contributed equally to this work.

Authors and Affiliations

  1. Department of General Surgery, The Third Clinical College of Wenzhou Medical University, Wenzhou People’s Hospital, Wenzhou, Zhejiang, China

    Huachao Zheng, Bo Wu, Min li, Yuncheng Luo, Lidong Huang, Sisi Lin, Feiyang Zhao & Yiren Hu

  2. Department of Neurology, Postgraduate Training Base Alliance of Wenzhou Medical University (WenzhouPeople’s Hospital), Wenzhou, Zhejiang, China

    Caixiang Zhuang & Jiesheng Mao

Authors
  1. Huachao Zheng
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Bo Wu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Caixiang Zhuang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jiesheng Mao
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Min li
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Yuncheng Luo
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Lidong Huang
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Sisi Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  9. Feiyang Zhao
    View author publications

    You can also search for this author inPubMed Google Scholar

  10. Yiren Hu
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Huachao Zheng: Conceptualization, Methodology, Formal analysis, Writing – original draft, Visualization. Bo Wu: Conceptualization, Software, Writing – review & editing. Caixiang Zhuang, Jiesheng Mao and Min li: Conceptualization, Software. Yuncheng Luo and Lidong Huang: Software, Visualization, Data curation. Feiyang Zhao and Sisi Lin: Validation, Data curation, Formal analysis. Yiren Hu: Writing – review & editing, Supervision, Project administration.

Corresponding author

Correspondence to Yiren Hu.

Ethics declarations

Ethics approval

The datasets were obtained from the NHANES database, and all data were under ethics approval before recorded in the database.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, H., Wu, B., Zhuang, C. et al. Cardiometabolic index as a predictor of gallstone risk: evidence from NHANES 2017–2020. BMC Gastroenterol 25, 218 (2025). https://doi.org/10.1186/s12876-025-03777-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12876-025-03777-3

Keywords

BMC Gastroenterology

ISSN: 1471-230X

Contact us