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The Clínica Universidad de Navarra-Body Adiposity Estimator index is a reliable tool for screening metabolic dysfunction-associated steatotic liver disease: an analysis from a gender perspective

Lipids in Health and Disease volume 23, Article number: 311 (2024) Cite this article

Abstract

Background

The Clínica Universidad de Navarra-Body Adiposity Estimator (CUN-BAE) Index, serves as an effective tool for evaluating body fat (BF) levels. This research seeks to clarify the association between the CUN-BAE Index and metabolic dysfunction-associated steatotic liver disease (MASLD) from a gender perspective.

Methods

The study utilized data from a comprehensive health assessment initiative known as “Human Dock”, involving 14,251 participants. MASLD was diagnosed using abdominal ultrasound, primarily evaluated based on the following sonographic features: hepatorenal echo contrast, vascular blurring, deep attenuation, liver brightness. First, we evaluated the association of MASLD with the CUN-BAE Index using multivariate logistic regression. Second, we visualized this association and estimated potential threshold effect points using the restricted cubic spline (RCS) regression model. Ultimately, we evaluated the ability of the CUN-BAE Index to detect MASLD through receiver operating characteristic (ROC) curves.

Results

The female-to-male ratio was 1:1.08, with a MASLD prevalence rate of 17.59%. Following the adjustment for confounding variables, an increase of one unit in the CUN-BAE Index corresponded to a 14% increase in the risk of MASLD for males and an 18% increase for females. RCS analysis revealed an S-shaped relationship between MASLD prevalence and the CUN-BAE Index for both genders, with potential threshold effect points at approximately 30 in females and 15 in males. Beyond these threshold points, the prevalence of MASLD increased rapidly. Further subgroup analyses indicated significant differences in the relationship of the CUN-BAE Index with MASLD within age and body mass index (BMI) subgroups in females, with a stronger association observed in younger and non-obese female participants. Additionally, ROC analysis revealed that the CUN-BAE Index possesses a strong ability to distinguish MASLD in both genders, especially in females.

Conclusions

This research is the first to identify a positive relationship between the CUN-BAE Index and MASLD. The CUN-BAE Index appears to be more suitable for early screening of MASLD in females.

Background

Due to the widespread occurrence of obesity, along with changes in diet and lifestyle, the incidence of MASLD is rapidly increasing on a global scale. As of 2023, it is estimated that roughly 32.4% of the worldwide population is affected by MASLD [1, 2], and forecasts suggest that this figure may rise to 55.2% by 2040, with occurrences exceeding 60% in both Asia and Europe [3]. MASLD is well known as a systemic metabolic disease [4, 5], which leads to liver injury and elevates the likelihood of developing cardiovascular conditions, chronic kidney illnesses, and various tumors [4, 6,7,8,9,10]. More concerning than the rapidly increasing disease burden is the fact that MASLD remains largely underrecognized in most countries [11], and there are currently no targeted pharmacological treatments available [12], which may further exacerbate the societal burden associated with MASLD.

Obesity, characterized by an overabundance of body fat, is a major contributor to the rapid rise in MASLD [1, 4, 13]. While BMI has long been used as the most common obesity indicator [14], it should be noted that BMI has limited ability to distinguish between lean body mass and fat, and does not accurately reflect actual BF across different genders and age groups [14,15,16]. In light of this, Gómez-Ambrosi J and colleagues developed the CUN-BAE Index, which predicts BF by taking into account gender and age factors, building on the foundation of BMI. The CUN-BAE Index was validated by utilizing air displacement plethysmography, which serves as the gold standard for assessing body fat [17]. Additionally, another study comparing obesity measurements indicated that among various obesity indices, the CUN-BAE Index showed the strongest correlation with BF measurements acquired through the gold standard [18]. Recently, the CUN-BAE Index has also demonstrated significant utility in evaluating hypertension [19], diabetes [20], and cardiometabolic diseases [21]. However, it remains unclear whether the CUN-BAE Index is applicable for evaluating the prevalence of MASLD. Considering the important influence of obesity on the onset of MASLD and the notable variations in body fat content and distribution based on gender [22, 23], this research aims to investigate the CUN-BAE Index’s capability to assess and identify MASLD from a gender perspective, thus providing clinicians with more efficient and accurate tools for MASLD screening.

Methods

Research population

The research utilized data from a comprehensive health assessment initiative known as “Human Dock” (NAGALA: NAfld in the Gifu Area, Longitudinal Analysis). This project, initiated in 1994, comprises both cross-sectional and longitudinal surveys aimed at evaluating the common chronic disease risk factors. Specifically, participants for the study were recruited who were attending health check-ups at Murakami Memorial Hospital, located in Japan. At enrollment, trained medical personnel conducted general inquiries, measured simple physical parameters, and performed abdominal ultrasound and blood tests. Detailed study design has been described elsewhere [24], and the research data accessible to the public has been made available by Okamura et al. in the DRYAD database [25]. Researchers are permitted to utilize publicly available data for further secondary analysis in alignment with new research hypotheses, as stipulated by DRYAD’s terms of service. This means that data already available in the public domain can be re-examined or re-evaluated to explore new research questions or to verify existing findings through a different analytical lens, thereby contributing to the expansion of knowledge and fostering innovation in various academic fields. The Murakami Memorial Hospital Ethics Committee [24] had previously granted approval for the NAGALA project. Additionally, in line with regional legislation and guidelines, the authors’ institution’s review board evaluated the present study protocol (No. 2021-066) and exempted the necessity for repeated informed consent from the participants.

This research seeks to explore the correlation of MASLD with the CUN-BAE Index. In line with the study objectives, the researchers extracted available data from the NAGALA project, which encompassed participants from the years 1994 to 2016. Following the application of the specified exclusion criteria, the researchers ultimately included 14,251 participants in this analysis. In accordance with the design of the earlier study [24], the criteria for exclusion in this current research were as outlined below: (1) subjects with missing baseline information; (2) according to the diagnostic requirements of MASLD, subjects with heavy drinking habits were excluded [26]; (3) to reduce the effect of medications on hepatic fat accumulation, subjects taking medications at baseline were excluded; (4) patients diagnosed with viral/alcoholic hepatitis were excluded based on diagnostic requirements for MASLD; (5) considering that abnormal blood glucose in metabolic abnormalities and its related insulin resistance are key factors and pathogenic mechanisms in the progression of MASLD, individuals with diabetes or FPG ≥ 6.1mmol/L were not included in the present study; (6) quit the project for unknown reasons.

Covariate

As detailed in a prior study [24], healthcare professionals with appropriate training gathered and documented basic information and simple measurement indices [age, gender, weight, diastolic/systolic blood pressure (D/SBP), height, waist circumference (WC), BMI], as well as histories of diabetes/hepatitis, medication use, exercise habits and drinking/smoking status by structured questionnaires. Referring to the criteria provided by the World Health Organization for obesity among the Asian population, individuals were categorized as overweight or obese if their BMI ≥ 25 kg/m², otherwise classified as non-obese [27]. The definitions of lifestyle factors were as follows: (1) smoking status: current, past, and non; (2) drinking status: non-drinkers or moderate, light, and minimal based on weekly alcohol consumption; (3) exercise habits: categorized into two groups according to the frequency of regular weekly exercise: with exercise habits and those without.

Blood specimens were obtained from participants following a minimum overnight fasting period of 8 h. The measurements for triglycerides (TG), γ-glutamyl transferase (GGT), hemoglobin A1c (HbA1c), alanine aminotransferase (ALT), total cholesterol (TC), aspartate aminotransferase (AST), fasting plasma glucose (FPG), high-density lipoprotein cholesterol (HDL-C) were conducted utilizing an automated biochemical analyzer.

Calculation

CUN-BAE Index=(0.00021 × BMI2 × age) - (0.005 × BMI2 × gender) - (0.02 × BMI × age) + (0.181 × BMI × gender) − (0.026 × BMI2) + (3.172 × BMI) + (10.689 × gender) + (0.503 × age) − 44.988; where sex is defined as male = 0, female = 1 [17].

Diagnosis of MASLD

Abdominal ultrasound was utilized as a diagnostic tool for identifying MASLD. The ultrasound procedures were carried out by skilled technicians who specialized in this type of imaging. Following the completion of the examinations, the resulting images were analyzed by a gastroenterologist. The ultrasound characteristics considered for MASLD diagnosis included deep attenuation, vascular blurring, liver brightness, hepatorenal echo contrast [28].

Statistical analysis

In this research, a bilateral p-value < 0.05 was established as the benchmark for significance, with all statistical evaluations were conducted utilizing R version 4.2.1 and Empower(R) statistical software version 2.0. Due to the specificity of body composition [22, 23], all analyses were stratified by gender.

First, the researchers divided male and female subjects into two groups depending on their diagnosis of MASLD, and then they outlined and examined the baseline characteristics of both groups (calculating standardized differences between the groups). Quantitative variables were assessed for normal distribution using QQ plots and were described using median/mean. Qualitative variables were described using n.

To ensure the robustness of multivariable logistic regression models, the researchers began by performing a collinearity screening among the covariates (Supplementary Table 1). The researchers constructed three multivariable logistic regression models to examine the association of the CUN-BAE Index with MASLD in both male and female populations. Following a stepwise adjustment principle, Model 1 WC, height, and age adjusted; Model 2 additionally adjusted for living habits (exercise habits and drinking/smoking status) and DBP based on Model 1; and Model 3 adjusted for all non-collinear covariates, adding liver function parameters (GGT, AST, ALT), lipid parameters (HDL-C, TC, TG), and glucose parameters (FPG, HbA1c) to Model 2. Additionally, the researchers applied four-knots RCS to fit the relationship of MASLD prevalence with the CUN-BAE Index in both genders.

To delve deeper into this analysis, the researchers categorized all subjects into various subgroups according to age, BMI, and exercise habits for each gender, and employed the log-likelihood ratio test to scrutinize the variations in the correlation of MASLD and the CUN-BAE Index across these distinct subgroups. This approach was aimed at detecting the presence of any modification effect within these subpopulations.

Finally, to assess the accuracy of the CUN-BAE Index in detecting MASLD, the researchers generated the ROC curves for both genders. In addition, the researchers further evaluated the effectiveness of obesity indicators BMI and WC, as well as the most commonly used MASLD assessment systems—hepatic steatosis index (HSI) and fatty liver index (FLI)—in identifying MASLD. Using the Delong test, the researchers compared the area under the curve (AUC) values of these parameters with that of the CUN-BAE Index.

Results

Baseline characteristics by gender

The study included 14,251 participants who met the specific criteria outlined (Fig. 1). Among these participants, there were slightly more males (52%) than females (48%). The prevalence of MASLD among the participants was found to be 17.59%. The researchers cross-stratified subjects by gender and MASLD status and summarized their baseline characteristics, as shown in Table 1. Notable variations were detected among multiple covariates in both male and female subjects when comparing the MASLD and non-MASLD groups. Among female participants, the MASLD group exhibited significantly higher general measurements (age, height, weight, CUN-BAE Index, BMI, WC, SBP, and DBP) compared to the non-MASLD group. Additionally, except for HDL-C, the blood tests (HbA1c, GGT, TC, ALT, TG, AST, FPG) showed elevated levels in the MASLD group. In the male demographic, no notable variations were observed in height, age or smoking habits between the non-MASLD and MASLD groups, while other covariate differences were similar to those in female participants. Interestingly, regardless of gender, substantial differences (standardized difference > 100%) were noted in weight, CUN-BAE Index, BMI, and WC between the MASLD and non-MASLD groups, with more pronounced differences among female participants. Notably, the difference in the CUN-BAE Index between the non-MASLD and MASLD groups was the largest in females (standardized difference: 175%; Fig. 2).

Fig. 1
figure 1

Flow chart for inclusion and exclusion of study participants

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Table 1 Baseline characteristics of the study population according to gender and MASLD grouping
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Fig. 2
figure 2

Violin plots show the baseline characteristics of the CUN-BAE Index in the MASLD and non- MASLD groups

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Association of CUN-BAE index with MASLD

The results of the multivariable logistic regression analysis are presented in Table 2, which highlights a positive correlation between the CUN-BAE Index and MASLD across all three models analyzed, from Model 1 to Model 3. In Model 3, after accounting for all non-collinear variables, a one-unit rise in the CUN-BAE Index corresponded to a 193% [95% confidence interval (CI): 2.32, 3.69] increase in the risk of MASLD for males and a 136% [95% CI: 1.94, 2.88] increase for females, respectively. Furthermore, interaction analysis revealed significant differences in the relationship of the CUN-BAE Index with MASLD risk between genders across all models (all P interaction < 0.05), indicating a more pronounced connection in female subjects.

Table 2 Logistic regression analyses for the association between the CUN-BAE Index and MASLD
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The researchers also used four-knots RCS to fit dose-response curves of the CUN-BAE Index and MASLD prevalence in both genders (Fig. 3); The results showed an S-shaped relationship in both genders, with threshold effect points at approximately 30 for females and 15 for males. Beyond these thresholds, the prevalence of MASLD increased.

Fig. 3
figure 3

Dose-response relationship curves of CUN-BAE Index and prevalence of MASLD in both sexes

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Subgroup analysis

Subgroup and interaction tests were performed to further assess the differences in the association of MASLD with the CUN-BAE Index among various populations. The researchers conducted stratified analyses by age, BMI, and exercise habits within each gender (Table 3). The results indicated significant differences in the association of the CUN-BAE Index with MASLD only in the BMI and age subgroups among females, with a higher MASLD risk associated with the CUN-BAE Index observed in younger (18–44 years) and non-obese (< 25 kg/m²) female participants.

Table 3 Stratified association between CUN-BAE Index and MASLD by age, BMI, and Exercise habits in males and females
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ROC analysis

Studies have shown that the CUN-BAE Index exhibited high accuracy in identifying MASLD in both genders, particularly among females (Table 4; Fig. 4). Specifically, the AUC for identifying MASLD in female participants was 0.8933 (95% CI: 0.8803–0.9064), with an optimal threshold of 33.2458; in male participants, the AUC was 0.8072 (95% CI: 0.7965–0.8179), with an optimal threshold of 21.8640. In addition, the researchers further compared the identification value of CUN-BAE Index with WC, BMI, FLI, and HSI for MASLD in both genders. The findings indicated that the FLI was the most effective in identifying MASLD in women, followed by the CUN-BAE Index, HSI, BMI, WC, and. For men, the FLI also proved to be the best, followed by the HSI, BMI, WC, CUN-BAE Index and. These findings suggested that the CUN-BAE Index was superior to simply measurement obesity parameters in the diagnosis of MASLD in women and was very close to the commonly used MASLD scoring system.

Table 4 The best threshold, sensitivities, specificities, and area under the curve of the BMI, WC, CUN-BAE Index, HSI and FLI for the screening of MASLD in males and females
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Fig. 4
figure 4

ROC curves of the CUN-BAE Index, BMI, WC, FLI, and HSI in identifying MASLD in males and females

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Discussion

This research report based on the Japanese physical examination population has several key findings that are highly relevant for clinicians: (1) There is a positive correlation between CUN-BAE Index and MASLD, which is stronger in females than in males. (2) ROC analysis results show that CUN-BAE Index has high accuracy in identifying MASLD in both genders, especially in females. (3) RCS results indicated that in both genders, the relationship between the CUN-BAE Index and MASLD followed an S-shaped curve, with potential threshold effect points (approximately 15 for males and 30 for females). When the CUN-BAE Index exceeded these thresholds, the prevalence of MASLD increased rapidly. These findings clarify the association between CUN-BAE Index and MASLD and further expand its scope of clinical application.

There are various methods to assess BF, including magnetic resonance imaging, air displacement plethysmography and dual-energy X-ray absorptiometry [29, 30]. However, these methods often have limitations such as high costs, complex operations, radiation risks, and transportation issues due to their large size. These factors make them less practical for widespread clinical use [31, 32]. To address these limitations, researchers have developed various obesity-related parameters to estimate BF, with BMI being the most widely used and historically significant [14]. Despite its widespread use, BMI has notable limitations, including its inability to assess actual BF content and distribution [15, 16, 33].

The CUN-BAE Index, a new measure of BF, improves upon BMI by incorporating age and gender factors, providing higher accuracy and differentiation [17, 18]. Recent studies have shown that this index has significant value in assessing cardiovascular and metabolic diseases [19,20,21, 34]. Since MASLD is a chronic metabolic disease involving multiple organ systems, assessing its relationship with the CUN-BAE Index could be crucial for early identification and prevention. To clarify this issue, this study utilized large-scale data from the NAGALA project to evaluate this relationship. The results indicated a significant positive correlation of the CUN-BAE Index with MASLD in both genders, with a stronger association in females. Additionally, ROC analysis revealed that the CUN-BAE Index had higher accuracy in identifying MASLD in females compared to males (AUC: females 0.8933, males 0.8072). Similar gender differences have been reported in previous studies. A recent study conducted by Davila-Batista V and colleagues showed that the CUN-BAE Index demonstrated a significantly stronger relationship with cardiometabolic conditions in females than in males [21]. Additionally, evidence from the Asturias Study also indicated that the CUN-BAE Index is useful in mortality risk assessment, particularly in females [35]. Overall, the current study’s results suggest that the CUN-BAE Index may be more suitable for assessing metabolic-related diseases in females, and the researchers recommend further MASLD assessment for females with a CUN-BAE Index exceeding 30.

BF content and its distribution are influenced by various factors, including gender, age, and ethnicity. Additionally, the prevalence of MASLD exhibits significant gender differences [1, 12]. Several studies indicate that MASLD is more common in men than in premenopausal women; however, in postmenopausal women, the prevalence often equals or exceeds that in men. The diminished protective influence of estrogen on the buildup of visceral fat following menopause is likely the cause [12, 36, 37]. In the current study, the average age of female MASLD patients is 48 years, while that of male patients is 44 years. This indicates that the female participants are generally older and likely in perimenopausal or postmenopausal stages. Therefore, the researchers consider that the observed gender differences in the relationship between MASLD and the CUN-BAE Index in this study are primarily related to age-related declines in estrogen’s protective effects.

Beyond the significant gender differences in the association between MASLD and the CUN-BAE Index, further analysis revealed a stronger association in younger (18–44 years) and non-obese women. This finding appears counterintuitive, as obesity and older age are commonly recognized as major risk factors for MASLD in women [1, 38, 39]. However, it is noteworthy that similar “unexpected findings” have been reported in previous studies. Tobari M et al. suggested that the lower risk of MASLD in obese women might be due to many of them being premenopausal. They also noted that in the Japanese population, the impact of estrogen on MASLD development could be much more significant than its impact on obesity [40].

In the present study, the researchers also considered this specific subgroup result in depth, and the main inferences are as follows: (1) Compared to older and obese women, younger and non-obese women generally have more estrogen protection, less BF content, and healthier BF distribution [40,41,42]. Thus, with the same BF increment, more risk information may be gleaned from younger and non-obese women. (2) Known Fact 1: In the current study, the older age subgroup refers to individuals aged 45–79 years, most of whom are in perimenopausal or postmenopausal stages. Typically, women in these stages experience weight gain due to declining estrogen levels, which leads to increased fat mass, particularly abdominal fat deposition [43,44,45]. Known Fact 2: Obese women have higher fat mass than non-obese women. Based on these facts, it is clear that younger and non-obese females tend to have a lower fat mass in comparison to older or obese females. Considering this relative deficiency in fat mass, the researchers hypothesize that younger and non-obese women are more sensitive to changes in fat mass. Therefore, the BF measure, CUN-BAE Index, may have better predictive value for MASLD prevalence in these populations.

In recent years, MASLD has become increasingly common around the world. Data shows that about one-third of the global population currently suffers from MASLD [1, 2]. Because the gold standard for diagnosing MASLD is invasive and unsuitable for widespread screening, many researchers in recent years have developed a variety of noninvasive assessment systems. Among these, the FLI, HSI, and nonalcoholic fatty liver screening scores are the most widely used [46, 47]. According to the developer’s description, in identifying MASLD, the accuracy of FLI is about 84% [48], the accuracy of HSI is about 87% [49], and the accuracy of nonalcoholic fatty liver screening score is about 83-86% [50]. In this research, the researchers evaluated the recognition values of the FLI, HSI, and CUN-BAE Index for MASLD in both sexes based on the available data. According to the results of the gender stratification, the accuracy of the FLI, HSI, and CUN-BAE Index in recognizing MASLD was 91.48%, 88.53%, and 89.33%, respectively, for females, and 84.22%, 83.83%, and 80.72%, respectively, for males. In contrast to previous findings [48,49,50], the value of analyzing FLI and HSI for the identification of MASLD in the current study is comparable. However, it is worth mentioning that in terms of identification of MASLD, the main evaluation factor in this study, the CUN-BAE Index, is very close to these already widely used evaluation systems. This further demonstrates that assessing obesity is of great value in diagnosing MASLD.

Study strengths and limitations

Strengths: This research presents a significant sample size of 14,251 participants and is the initial study to uncover the correlation linking MASLD with CUN-BAE Index. Additionally, this research identified that the relationship of MASLD with the CUN-BAE Index is non-linear and exhibits a threshold effect. These findings could significantly aid in the early screening of MASLD; however, it still needs to be verified in prospective studies to confirm its clinical relevance. In terms of prospects for future related research, considering the great help of machine learning to clinical research [51,52,53], the researchers believe that machine learning can be combined to further improve the diagnostic/predictive value of CUN-BAE Index for MASLD.

Limitations: (1) Cross-sectional design limits assessment of causal relationships. (2) The study’s diagnosis of MASLD relied on abdominal ultrasound instead of the gold standard, potentially resulting in underdiagnosis [54]; At present, magnetic resonance spectroscopy, ultrasound elastography, magnetic resonance elastography, and magnetic resonance imaging are considered to be better tools for judging MASLD and its severity. It is recommended that subsequent related research refer to the standards of magnetic resonance and ultrasound elastography to diagnose MASLD. (3) Despite taking into account multiple well-known variables that could influence the outcomes, there might still be other confounding factors that were not measured or identified. These unmeasured or unidentified variables could potentially introduce bias into the study’s results [55]. (4) This study is the first to examine the connection of the CUN-BAE Index with MASLD, lacking comparisons with similar studies. Therefore, more research involving different populations is needed to validate the reliability of these conclusions. (5) Some of the data in the current study relied on patient self-report, which may introduce recall bias and affect the accuracy of the study results. (6) The current study also does not account for potential variations in the CUN-BAE Index and MASLD association across different ethnicities, limiting the applicability of the results to diverse populations; additionally, the study’s findings may not be applicable to populations outside the specific demographic and health characteristics of the “Human Dock” health examination program participants. (7) Since the NAGALA project has excluded subjects with diabetes, impaired glucose tolerance, and drug use in the initial design stage, which may reduce the prevalence of MASLD in the current study, because the above exclusion factors are all high-risk factors for MASLD; Furthermore, it is important to highlight that this study results also do not apply to the excluded populations listed above.

Conclusion

This research represents the initial evidence of a direct relationship of MASLD with the CUN-BAE Index. Looking at it through a gender lens, the CUN-BAE Index seems to be better suited for the early detection of MASLD among females. Considering the ease of obtaining the components of the CUN-BAE Index, the researchers suggest that the assessment of this parameter can be strengthened among the general population.

Data availability

The data set supporting the results of this study has been uploaded to Dryad database.

Abbreviations

CUN-BAE Index:

Clínica Universidad de Navarra-Body Adiposity Estimator Index

BF:

Body fat

MASLD:

Metabolic dysfunction-associated steatotic liver disease

RCS:

Restricted cubic spline

ROC:

Receiver operating characteristic

BMI:

Body mass index

AUC:

Area under the curve

WC:

Waist circumference

S/DBP:

Systolic/diastolic blood pressure

FPG:

Fasting plasma glucose

ALT:

Alanine aminotransferase

TC:

Total cholesterol

GGT:

γ-glutamyl transferase

TG:

Triglycerides

HbA1c:

Hemoglobin A1c

HDL-C:

High-density lipoprotein cholesterol

AST:

Aspartate aminotransferase

CI:

Confidence interval

fli:

Fatty liver index

HSI:

Hepatic steatosis index

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Acknowledgements

Thanks to the data sharing plan of the DRYAD public database and the data support of Professor Okamura’s team.

Funding

This work was supported by Natural Science Foundation of Jiangxi Province [No. 20232BAB216004 to YZ].

Author information

Author notes

  1. Chao Wang, Xin Huang and Shiming He equally to this work and share first authorship.

Authors and Affiliations

  1. Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Provincial, China

    Chao Wang, Xin Huang, Shiming He & Maobin Kuang

  2. Jiangxi Cardiovascular Research Institute, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi Provincial, China

    Chao Wang, Xin Huang, Shiming He, Maobin Kuang & Yang Zou

  3. Jiangxi Provincial Geriatric Hospital, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi Provincial, China

    Guobo Xie & Guotai Sheng

  4. Department of Cardiology, Jiangxi Provincial People’s Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi Provincial, China

    Guobo Xie & Guotai Sheng

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  1. Chao Wang
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Contributions

GT-S and YZ: Conceptualization, methodology, supervision, and project administration; CW, XH and SM-H: writing-original draft preparation.MB-K, GT-S, YZ and GB-X: writing-reviewing and editing.CW, XH and YZ: Software.SM-H, MB-K, GT-S, YZ and GB-X: formal analysis and validation.YZ: data curation.All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Guotai Sheng or Yang Zou.

Ethics declarations

Ethics approval and consent to participate

The NAGALA project was previously approved by Murakami Memorial Hospital Ethics Committee, complied with the Helsinki Declaration, and all participants provided written informed consent. Additionally, in accordance with local laws and regulations, the institutional review board of the authors’ institution reviewed the current study protocol (No. 2021-066) and waived the requirement for repeated informed consent from the subjects.

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Wang, C., Huang, X., He, S. et al. The Clínica Universidad de Navarra-Body Adiposity Estimator index is a reliable tool for screening metabolic dysfunction-associated steatotic liver disease: an analysis from a gender perspective. Lipids Health Dis 23, 311 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12944-024-02299-3

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Keywords

Lipids in Health and Disease

ISSN: 1476-511X