Assessment indicators of steatotic liver disease in non-hepatology specialty clinics: A real-world retrospective case-control study

doi:10.3748/wjg.121401

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Gastroenterol. Oct 28, 2026; 32(40): 121401
Published online Oct 28, 2026. doi: 10.3748/wjg.121401

Assessment indicators of steatotic liver disease in non-hepatology specialty clinics: A real-world retrospective case-control study

Zong-Kuo Li, Jian-Ting Cai, Pian-Hong Zhang, Lei-Lei Wang, Hui-Hui Yan

Zong-Kuo Li, Jian-Ting Cai, Hui-Hui Yan, Department of Gastroenterology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang Province, China

Pian-Hong Zhang, Lei-Lei Wang, Department of Clinical Nutrition, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, Zhejiang Province, China

Co-corresponding authors: Lei-Lei Wang and Hui-Hui Yan.

Author contributions: Yan HH and Li ZK conceptualized and designed the research; Li ZK, Cai JT, and Wang LL acquired clinical data and conducted patient follow-up; Li ZK, Cai JT, and Zhang PH performed formal data analysis and software implementation; Li ZK, Cai JT, and Yan HH wrote and revised the paper. All authors contributed to the interpretation of the study and approved the final version to be published. Both Yan HH and Wang LL have played important and indispensable roles in the experimental design, clinical data supervision, and manuscript preparation as the co-corresponding authors. Yan HH applied for and obtained the funds for this research project. She conceptualized, designed, and supervised the overall methodology of the project. During the peer-review process, Yan HH was instrumental in leading the major revision, guiding the complex data re-analysis (including bootstrapping internal validation), addressing reviewers' comments, and finalizing the submission of the revised manuscript. Wang LL was fundamentally responsible for the clinical administration of the project, meticulously supervising the patient follow-up cohort, clinical data curation, and ensuring the strict quality control of the real-world nutritional outpatient data. The collaboration and complementary clinical and methodological expertise between Yan HH and Wang LL were crucial for the successful completion and publication of this manuscript.

AI contribution statement: The authors take full responsibility and accountability for all content of this manuscript, including any portions for which AI tools were used as assistive technologies. All AI-assisted outputs were carefully reviewed, validated, and approved by the authors. AI tools were not used to generate original scientific data, perform independent scientific analyses, or draw scientific conclusions. Artificial intelligence tools (e.g., DeepL, ChatGPT) were used exclusively for English translation and language polishing of the manuscript and response documents. No AI tools were used to generate original scientific text, design the study, analyze data, interpret results, or create any images. The authors have carefully reviewed all AI-assisted outputs and take full responsibility for the authenticity and integrity of the content.

Supported by Natural Science Foundation of Zhejiang Province, No. LY22H160002.

Institutional review board statement: We confirm that all processes of this study were conducted in strict accordance with the named guidelines and regulations. The research was authorized by the Ethics Committee of the Second Affiliated Hospital of Zhejiang University School of Medicine (with approval number 20231215).

Informed consent statement: This was a retrospective investigation, which received an exemption from informed consent requirement from the ethics committee.

Conflict-of-interest statement: All the authors report having no relevant conflicts of interest for this article.

STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.

Data sharing statement: The dataset used during the current study is available from the corresponding author on reasonable request.

Corresponding author: Hui-Hui Yan, Department of Gastroenterology, The Second Affiliated Hospital of Zhejiang University School of Medicine, No. 88 Jiefang Road, Shangcheng District, Hangzhou 310009, Zhejiang Province, China. yandoublehui@zju.edu.cn

Received: March 25, 2026
Revised: May 25, 2026
Accepted: July 13, 2026
Published online: October 28, 2026
Processing time: 172 Days and 4.6 Hours

Abstract

BACKGROUND

The escalating global burden of steatotic liver disease (SLD) underscores the critical need for practical, non-invasive screening strategies, particularly in non-specialized clinical settings where advanced imaging may be inaccessible.

AIM

To evaluate the predictive value of anthropometric and metabolic indices for SLD risk stratification and fat quantification in Chinese adults.

METHODS

This retrospective case-control study enrolled 517 adults from nutrition clinics at a tertiary hospital in China over a six-month period. Participants underwent body composition analysis, routine blood tests, and liver imaging (ultrasound, computed tomography, or FibroScan) to assess steatotic changes. Univariate and multivariate logistic regression analyses were utilized to identify independent predictors of SLD. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

Univariate analysis revealed significant associations between SLD and gender, diabetes status, and seven anthropometric-metabolic indices, including the triglyceride glucose-waist-to-height ratio (TyG-WHtR) and Chinese visceral adiposity index (CVAI) (all P < 0.05). Multivariate logistic regression identified three independent predictors for SLD: male sex [odds ratio (OR) = 4.717, 95%CI: 2.114-10.523, P < 0.001]; the per 1-SD OR of TyG-WHtR = 6.689 (95%CI: 3.227-13.864, P < 0.001), and the per 1-SD OR of CVAI = 3.406 (95%CI: 2.289-5.06, P < 0.001). TyG-WHtR demonstrated robust discriminative capacity (AUC = 0.876; 95%CI: 0.846-0.905), outperforming CVAI (AUC = 0.865; 95%CI: 0.834-0.896). Furthermore, parameter distributions differed significantly across non-SLD, mild SLD, and moderate-to-severe SLD groups (P < 0.0001).

CONCLUSION

TyG-WHtR and CVAI are robust, cost-effective biomarkers for predicting SLD risk and severity in Chinese adults. They optimize primary care screening but require independent prospective cohort validation.

Keywords: Steatotic liver disease; Steatotic degree; Indicators; Non-hepatology specialty clinics; Health check-up populations

Core Tip: The escalating burden of steatotic liver disease (SLD) necessitates accessible screening tools for non-specialized settings. This study demonstrates that the triglyceride glucose-waist-to-height ratio (TyG-WHtR) and the Chinese visceral adiposity index are robust, cost-effective biomarkers for predicting SLD risk and disease severity in Chinese adults. Notably, TyG-WHtR exhibited superior discriminative capacity (area under the curve = 0.876). Implementing these non-invasive indices in primary care can significantly optimize risk stratification, reduce reliance on expensive advanced imaging, and facilitate early therapeutic interventions to mitigate SLD progression.