Predictive value of serum uric acid to high-density lipoprotein cholesterol ratio for MAFLD in non-obese type 2 diabetes patients based on nomogram.

OBJECTIVE: In non-obese type 2 diabetes (T2DM) patients, the incidence of metabolic dysfunction associated fatty liver disease (MAFLD) is very insidious and easily overlooked in clinical examinations. The aim of our article is to explore whether the serum uric acid to High-density lipoprotein cholesterol (HDL-C) ratio (UHR) can be used for independent assessment of the risk of MAFLD in non-obese T2DM patients. METHODS: 1622 T2DM patients were analyzed, and 506 non-obese patients were ultimately included in the study. Routine clinical and laboratory date were collected. In the non-obese T2DM population, the stability of UHR in predicting MAFLD was evaluated through subgroup analysis, and compared UHR with other indicators. Finally, we used logistic regression and established a nomogram model to assess the diagnostic efficacy of UHR for MAFLD. To evaluate the nomogram's predictive performance, we employed a suite of techniques including receiver operating characteristic (ROC) analysis, calibration curve assessment, and decision curve analysis (DCA). RESULTS: As UHR levels increased, the prevalence of MAFLD gradually increased in non-obese T2DM patients. Logistic regression indicated that UHR was associated with MAFLD in non-obese T2DM participants. We constructed a nomogram model using UHR, 2 hour postprandial glucose (2h-PG), 2 hour C-Peptide (2hC-P), body mass index (BMI), triglycerides (TG), serum creatinine (CRE), and C-reactive protein (CRP) as predictive factors to estimate the probability of developing MAFLD in non-obese T2DM subjects. The clinical utility of the model was supported by its strong performance on both the calibration curve and DCA. CONCLUSIONS: In non-obese T2DM patients, the morbidity rate of MAFLD was significantly higher in high level UHR subjects than that in low level UHR subjects. in non-obese T2DM patients, the nomogram model constructed based on UHR, BMI, 2h-PG, 2hC-P, TG, CRE, and CRP had good predictive ability for the risk of MAFLD.