Abstract
Background: The interdependence of cardiovascular and kidney health in T2D requires understanding the combination of disease risk. Proteomic surrogate models for cardiovascular risk and kidney prognosis have been developed and validated, but not in a combined manner in T2D patients. Hypothesis: Combining the results of independent proteomic risk models for 4-year cardiovascular risk and kidney prognosis will identify patients at highest risk for MACE, better than eGFR. Methods: Plasma from 1,220 participants with T2D from the Atherosclerosis Risk in Communities (ARIC) study visit 5 were assayed for >5,000 proteins using the SomaScan® assay; 34.2% had incident MACE (MI, stroke, HF hospitalization, or death). Predictions from a validated proteomic CV risk model were calculated, as well as a de novo kidney prognosis model developed and validated in the Chronic Renal Insufficiency Cohort and Fenland cohort. Patients were stratified by predefined CV risk bins and sub-stratified by 2x kidney prognosis risk, and compared to eGFR stratification. Log-odds of Kaplan-Meier (KM) event rates were compared between strata. Results: Proteomic kidney risk predictions did not sub-stratify patients at low/medium CV risk (p > 0.05), but did stratify high-risk CV patients, with highest CV and kidney risk patients (5.6% of patients; p < 0.05) having a KM-estimated 63.4% 4-year MACE rate, more than 8 times the low CV risk group. Conversely, stratification using a corresponding eGFR threshold did not significantly stratify high-risk CV patients. High CV risk MACE types were different depending on proteomic kidney risk sub-stratification (chi-square p < 0.015), and participants with increased kidney risk had more HF hospitalizations (39.7% of MACE). Conclusions: Among patients with T2D and elevated proteomic markers for CV risk, an independent proteomic risk model for kidney prognosis can be applied to identify a significantly larger proportion of patients at risk for MACE.
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