Association between the high-sensitivity C-reactive protein-triglyceride-glucose index and metabolic dysfunction-associated steatotic liver disease across different glycemic states

doi:10.4239/wjd.120647

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World Journal of Diabetes

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1948-9358

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

Observational Study

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 Diabetes. Jul 15, 2026; 17(7): 120647
Published online Jul 15, 2026. doi: 10.4239/wjd.120647

Association between the high-sensitivity C-reactive protein-triglyceride-glucose index and metabolic dysfunction-associated steatotic liver disease across different glycemic states

Wen-Hao Zhang, Rong-Dong-Qing Shi, Kui Chen, Xian-Ming Li, Gui-Hua Yu, Xin-Yuan Ye, Qing Xu, Yu Ding, Si-Hui Luo

Wen-Hao Zhang, Rong-Dong-Qing Shi, Xian-Ming Li, Gui-Hua Yu, Xin-Yuan Ye, Yu Ding, Si-Hui Luo, Department of Endocrinology and Metabolism, The First Affiliated Hospital of University of Science and Technology of China, University of Science and Technology of China, Hefei 230001, Anhui Province, China

Wen-Hao Zhang, Rong-Dong-Qing Shi, Xian-Ming Li, Gui-Hua Yu, Xin-Yuan Ye, Yu Ding, Si-Hui Luo, Anhui Provincial Key Laboratory of Metabolic Health and Panvascular Diseases, Hefei 230001, Anhui Province, China

Kui Chen, Health Management Medicine Center, The Third Xiangya Hospital, Central South University, Changsha 410013, Hunan Province, China

Kui Chen, State Key Laboratory of Ultrasound in Medicine and Engineering, College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China

Qing Xu, Graduate School, Bengbu Medical University, Bengbu 233030, Anhui Province, China

Co-first authors: Wen-Hao Zhang and Rong-Dong-Qing Shi.

Co-corresponding authors: Yu Ding and Si-Hui Luo.

Author contributions: Zhang WH and Shi RDQ contributed equally to the manuscript as co-first authors; Ding Y and Luo SH contributed equally to the manuscript as co-corresponding authors; Zhang WH contributed to study conceptualization, methodology, data analysis, data interpretation and visualization, wrote, and revised the manuscript; Shi RDQ reviewed the manuscript, and contributed to methodology, data analysis and data interpretation; Chen K contributed to data collection, reviewed and revised the manuscript; Li XM, Yu GH, Ye XY, and Xu Q provided insightful comments on the interpretation of the data, and the structure and revision of the manuscript; Ding Y contributed to study conceptualization, administrative support, data interpretation, reviewed and revised the manuscript; Luo SH contributed to study conceptualization, administrative support, fund acquisition, data interpretation, reviewed and revised the manuscript; and the corresponding authors attest that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted.

AI contribution statement: No part of the main text of the manuscript or the response to reviewers was Al-generated. The study rationale, methodology, data analysis, results, interpretation, and conclusions were independently developed, written, reviewed, and approved by the authors. No AI tool was used for language polishing, translation, data analysis, or writing assistance. The authors take full responsibility for the accuracy, integrity, originality, and scientific validity of all content in the manuscript and the response to reviewers.

Supported by National Natural Science Foundation of China, No. 82300904 and No. 82100857.

Institutional review board statement: The studies were reviewed and approved by the Institutional Review Board of the First Affiliated Hospital of University of Science and Technology of China (Approval No: 2021 KY-034) and conformed to the guidelines of the Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent statement: Written informed consent was obtained from all participants.

Conflict-of-interest statement: All authors declare that they have no conflicts of interest related to this work.

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 data supporting this study are available from the corresponding author upon reasonable request, subject to privacy and ethical restrictions.

Corresponding author: Si-Hui Luo, MD, Department of Endocrinology and Metabolism, The First Affiliated Hospital of University of Science and Technology of China, University of Science and Technology of China, No. 17 Lujiang Road, Hefei 230001, Anhui Province, China. luosihui@ustc.edu.cn

Received: March 5, 2026
Revised: April 9, 2026
Accepted: June 9, 2026
Published online: July 15, 2026
Processing time: 127 Days and 2.1 Hours

Abstract

BACKGROUND

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most common chronic liver disease worldwide, but non-invasive biomarkers for its detection remain limited. The high-sensitivity C-reactive protein-triglyceride-glucose index (CTI), which integrates inflammatory and metabolic parameters, has emerged as a potential marker. However, its association with MASLD and diagnostic performance across different glycemic states remain unclear.

AIM

To investigate the association between CTI and MASLD and to evaluate its diagnostic performance across different glycemic states.

METHODS

This study included 7413 adults who underwent routine health examinations between January 2020 and March 2024. Participants were categorized into three groups according to glycemic status: Normal glucose regulation (NGR; n = 2933), prediabetes mellitus (pre-DM; n = 3549), and diabetes mellitus (DM; n = 931). CTI was calculated as 0.412 × ln(high-sensitivity C-reactive protein) + ln(triglycerides × fasting plasma glucose/2). The Boruta algorithm was applied to identify variables relevant to MASLD. Multivariable logistic regression assessed CTI-MASLD associations, restricted cubic spline analyses were performed to examine dose-response relationships, and diagnostic performance was evaluated using receiver operating characteristic curves.

RESULTS

After multivariable adjustment, each unit increase in CTI was significantly associated with MASLD in the NGR (adjusted OR = 3.07; 95%CI: 2.19-4.31) and pre-DM groups (adjusted OR = 3.26; 95%CI: 1.70-6.23), but not in the DM group (adjusted OR = 1.58; 95%CI: 0.65-3.84). CTI achieved an overall area under the curve (AUC) of 0.808 (95%CI: 0.798-0.817), with AUCs of 0.792, 0.783, and 0.749 in the NGR, pre-DM, and DM groups, respectively. Optimal CTI thresholds increased progressively with worsening glycemic status (NGR: 8.596; pre-DM: 8.908; DM: 9.456).

CONCLUSION

CTI was significantly associated with MASLD in normoglycemic and prediabetic populations but not in diabetic individuals, with optimal thresholds varying by glycemic status.

Keywords: Metabolic dysfunction-associated steatotic liver disease; High-sensitivity C-reactive protein-triglyceride-glucose index; Glycemic status; Prediabetes; Diabetes; Cross-sectional study

Core Tip: This large cross-sectional study (n = 7413) evaluated the high-sensitivity C-reactive protein-triglyceride-glucose index (CTI) as a non-invasive biomarker for metabolic dysfunction-associated steatotic liver disease (MASLD) across glycemic states. The CTI showed robust diagnostic performance (area under the curve: 0.808) with strongest predictive value in normal glucose regulation (adjusted OR = 3.07) and prediabetes (OR = 3.26), but weaker association in diabetes (OR = 1.58). Optimal CTI thresholds increased progressively from normoglycemia to diabetes (8.596, 8.908, 9.456, respectively). These findings suggest CTI is a practical biomarker for early MASLD risk identification, particularly in individuals without overt diabetes.