Key Points

Proteomics analyses consistently identified nine independent protein predictors for CKD in both Chinese and European participants with type 2 diabetes.A novel prediction model, “eGFR+five-protein panel predicting CKD,” reliably predicted CKD with good performance (area under the receiver operating characteristic curve ≥0.80).The new “eGFR+five-protein panel predicting CKD” model exhibited superior performance over traditional clinical models.

Background

Currently, there is a lack of reliable biomarkers for noninvasive prediction of CKD in patients with diabetes. This study aimed to identify novel protein predictors and construct a prediction model for incident CKD in participants with type 2 diabetes applicable across different populations.

Methods

A targeted Olink plasma proteomics profiling analysis, involving 368 proteins, was conducted in a nested case-control study comprising 132 incident CKD cases and 132 non-CKD controls, matched for age, sex, duration of diabetes, and eGFR, recruited from a long-term prospective cohort of Chinese type 2 diabetes participants (median approximately 9-year follow-up). False discovery rate was applied for multiple testing corrections. A q-value

Results

Among the 18 identified protein features predictive of incident CKD, 12 showed significant associations with consistent direction of effects in both cohorts. A prediction model (“eGFR+five-protein panel predicting CKD [eGFR+FPPC]”) combining eGFR and five proteins (α-1-microglobulin/bikunin precursor, matrix metallopeptidase 7, placental growth factor, TNF-related apoptosis-inducing ligand receptor 2, and kidney injury molecule-1) was constructed and validated in the testing and training sets of the UKB-PPP, respectively. The “eGFR+FPPC” model achieved superior predictive performance in both training (area under the receiver operating characteristic curve [95% confidence interval]: 0.82 [0.79 to 0.85]) and testing (area under the receiver operating characteristic curve [95% confidence interval]: 0.80 [0.74 to 0.86]) sets of UKB-PPP and yielded fewer indeterminate results compared with conventional clinical models.

Conclusions

The “eGFR+FPPC” model performed better than conventional clinical models in predicting incident CKD in type 2 diabetes participants across different populations.