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An Advanced Murine Model for Nonalcoholic Steatohepatitis in Association with Type 2 Diabetes
Published on: April 26, 2019
Association between AIP and incident T2DM in patients with NAFLD: a retrospective study
Yan Chen1, Qiufang Bai2, Hao Hua3
1Department of Endocrinology, The First People's Hospital of Neijiang, Neijiang, China.
Background And Aim:
This study aimed to investigate the relationship between atherogenic index of plasma (AIP) and incident type 2 diabetes mellitus (T2DM) in non-alcoholic fatty liver disease (NAFLD) patients.
Methods And Results:
In this retrospective study, 2,370 NAFLD patients were stratified into tertiles based on AIP levels. Baseline demographic, anthropometric, and biochemical characteristics were compared across tertiles. Multivariable logistic regression models were employed to assess the association between AIP and incident T2DM, adjusting for potential confounders, including age, sex, body mass index (BMI), hemoglobin A1c (HbA1c), smoking status, high blood pressure (HBP), and liver enzymes. Restricted cubic splines (RCS) evaluated dose-response relationships, and receiver operating characteristic (ROC) curves compared the predictive performance of AIP against individual parameters. In the fully adjusted model (Model 3), the highest tertile (Q3) demonstrated a 1.99-fold increased T2DM risk (OR = 1.99, 95% CI: 1.29-3.08, P = 0.002) versus Q1. A linear dose-response relationship was observed (P for non-linearity = 0.663), with each 1-unit AiP increase corresponding to a 2.76-fold higher T2DM risk (OR = 2.76, 95% CI: 1.54-4.93, P < 0.001). AIP demonstrated superior predictive performance for incident T2DM compared to traditional markers, as evidenced by higher area under the receiver operating characteristic curve (AUC) values. The relationship between AIP and incident T2DM remained consistent across various population subgroups (all interaction P-values>0.05).
Conclusions:
Elevated AIP is independently associated with an increased risk of incident T2DM in NAFLD patients, highlighting its potential as a predictive biomarker for diabetes in this high-risk population.
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