Evidence map›Paper›PMID 42488010›Full record

ArticleFrontiers in endocrinology2026

Discriminating prevalent type 2 diabetes among community-dwelling older adults with metabolic dysfunction-associated steatotic liver disease: a comparative analysis of 12 insulin resistance surrogates.

Xianshang Zhu, Zengrui Wang, Yan Fang, Zong Ning, Xia Yang

Abstract readComparative Study
In one paragraph

Article in Frontiers in endocrinology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Xianshang ZhuDepartment of General Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Zengrui WangDepartment of General Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Yan FangDepartment of General Medicine, Gansu Provincial Hospital, Lanzhou, Gansu, China.
Zong NingDepartment of General Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
Xia YangDepartment of General Medicine, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Metabolic dysfunction-associated steatotic liver disease frequently coexists with type 2 diabetes mellitus (T2DM). Because the gold standard for assessing insulin resistance (IR) is difficult to implement in primary care settings, this cross-sectional study aimed to compare the associations and discriminatory ability of 12 surrogate IR indexes for prevalent T2DM among community-dwelling older adults with metabolic dysfunction-associated fatty liver disease. Methods: This study was based on a health examination cohort from a community in northwestern China and included 2641 eligible participants, of whom 605 were older adults with metabolic dysfunction-associated steatotic liver disease. Multivariable logistic regression, receiver operating characteristic (ROC) curve analysis, restricted cubic spline (RCS) analysis, subgroup analysis, and exploratory internal model-discrimination analyses were performed to evaluate the associations and discriminatory ability of 12 surrogate IR indexes for prevalent T2DM. Results: After multivariable adjustment, TyG, TyG-ABSI, TyG-WWI, and TyG-WC were significantly associated with prevalent T2DM in participants with metabolic dysfunction-associated steatotic liver disease (all P < 0.05). TyG showed the highest discriminatory ability (AUC = 0.726, 95% CI: 0.686-0.766), with an optimal cut-off value of 9.225. After Bonferroni correction, it performed significantly better than the other indicators. Sensitivity analyses addressing the mathematical coupling between TyG and T2DM diagnostic criteria confirmed the robustness of these findings. Even after excluding T2DM cases defined solely by fasting plasma glucose, TyG remained the best-performing index (AUC = 0.707). RCS analyses showed significant overall associations but did not support statistically significant nonlinearity for TyG, TyG-ABSI, TyG-WWI, or TyG-WC (all P-nonlinear > 0.05). No significant interactions were observed in subgroup analyses. Conclusions: TyG, TyG-ABSI, TyG-WWI, and TyG-WC were associated with prevalent T2DM and showed moderate discriminatory ability. Sensitivity analyses addressing the shared FPG component confirmed the robustness of these associations. These findings support TyG as a simple, integrated metabolic marker for alerting to prevalent T2DM in this population.

Indexed as

BiomarkersDiabetes Mellitus, Type 2Fatty LiverInsulin ResistanceMetabolic DiseasesNon-alcoholic Fatty Liver DiseaseAgedChinaCross-Sectional StudiesFemaleHumansIndependent LivingMaleMiddle AgedPrevalenceROC CurveBiomarkersinsulin resistancemetabolic dysfunction-associated steatotic liver diseaseolder adultstriglyceride-glucose indextype 2 diabetes mellitus

Identifiers

PMID42488010
PMCPMC13388057

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.