Frontiers in endocrinology · 2026
Clinical study on the value of TyG index combined with systemic immune-inflammation index for screening hospitalized patients with type 2 diabetic kidney disease.
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Song Qiuyun, Yang Guangzhi, Sun Chen, Chen Xiaolong
Аннотация
OBJECTIVE: To evaluate the screening value of the triglyceride-glucose index (TyG) combined with the systemic immune-inflammation index (SII) for diabetic kidney disease (DKD) in hospitalized patients with type 2 diabetes mellitus (T2DM).
METHODS: A retrospective analysis was conducted on 335 hospitalized T2DM patients (175 with DKD, 160 without DKD) at Yancheng Third People's Hospital from 2022 to 2024. DKD was defined according to KDIGO 2022 criteria persistent urinary albumin-to-creatinine ratio (UACR) ≥30 mg/g and/or estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m² lasting for >3 months. Clinical data were collected, and the TyG index and SII were calculated. Due to severe skewness, SII was log10-transformed (lgSII). Logistic regression was used to construct five progressive prediction models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), DeLong's test, calibration curves, and decision curve analysis (DCA). Internal validation was performed via Bootstrap (2000 repetitions) and 5-fold cross-validation.
RESULTS: TyG and lgSII were significantly higher in the DKD group compared to the non-DKD group (P<0.001). The final streamlined model (including TyG, lgSII, diabetes duration, systolic blood pressure, and HbA1c) achieved an AUC of 0.850. The Hosmer-Lemeshow test indicated good calibration (P = 0.257). The optimal cut-off values were TyG >10.03 and lgSII >2.76, yielding a sensitivity of 74.3% and a specificity of 85.0%. The high-risk group (both indicators above cut-offs) had a DKD prevalence of 96.8%. Bootstrap validation yielded a mean AUC of 0.855, indicating robust model stability.
CONCLUSION: The combination of TyG and SII demonstrates favorable screening efficacy and model stability for identifying DKD among hospitalized T2DM patients. This model provides preliminary evidence for a low-cost, readily available tool for rapid risk stratification, but requires external validation before clinical implementation.
METHODS: A retrospective analysis was conducted on 335 hospitalized T2DM patients (175 with DKD, 160 without DKD) at Yancheng Third People's Hospital from 2022 to 2024. DKD was defined according to KDIGO 2022 criteria persistent urinary albumin-to-creatinine ratio (UACR) ≥30 mg/g and/or estimated glomerular filtration rate (eGFR) <60 mL/min/1.73m² lasting for >3 months. Clinical data were collected, and the TyG index and SII were calculated. Due to severe skewness, SII was log10-transformed (lgSII). Logistic regression was used to construct five progressive prediction models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), DeLong's test, calibration curves, and decision curve analysis (DCA). Internal validation was performed via Bootstrap (2000 repetitions) and 5-fold cross-validation.
RESULTS: TyG and lgSII were significantly higher in the DKD group compared to the non-DKD group (P<0.001). The final streamlined model (including TyG, lgSII, diabetes duration, systolic blood pressure, and HbA1c) achieved an AUC of 0.850. The Hosmer-Lemeshow test indicated good calibration (P = 0.257). The optimal cut-off values were TyG >10.03 and lgSII >2.76, yielding a sensitivity of 74.3% and a specificity of 85.0%. The high-risk group (both indicators above cut-offs) had a DKD prevalence of 96.8%. Bootstrap validation yielded a mean AUC of 0.855, indicating robust model stability.
CONCLUSION: The combination of TyG and SII demonstrates favorable screening efficacy and model stability for identifying DKD among hospitalized T2DM patients. This model provides preliminary evidence for a low-cost, readily available tool for rapid risk stratification, but requires external validation before clinical implementation.