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Large-Scale Proteomics-Based Risk Score for the Prediction of Incident Cardio-Kidney-Metabolic Disease Risk
Adithya K Yadalam1,2, Chang Liu3, Qin Hui3
1Division of Cardiology, Emory University School of Medicine, Atlanta, GA (A.K.Y., A.C.R., L.S.S., A.A.Q., Y.V.S.).
Insights
A novel proteomics-based risk score (ProtRS) effectively predicts cardio-kidney-metabolic (CKM) disease risk. This ProtRS improves risk prediction beyond traditional factors in individuals without baseline CKM disease.
Area of Science:
- Biomarkers and Omics
- Cardiovascular and Metabolic Diseases
- Public Health and Epidemiology
Background:
- Cardio-kidney-metabolic (CKM) disease poses a significant public health burden.
- Proteomics-based risk scores (ProtRS) show promise in enhancing cardiovascular risk prediction.
- The added value of ProtRS for predicting composite CKM outcomes beyond traditional risk factors is not well-established.
Purpose of the Study:
- To develop and validate a ProtRS for predicting incident cardio-kidney-metabolic (CKM) disease.
- To assess if ProtRS improves risk prediction for CKM disease beyond established traditional risk factors.
- To evaluate the discriminative ability of ProtRS in a large, population-based cohort.
Main Methods:
- Analysis of 23,815 UK Biobank participants without baseline CKM disease.
- Development of a ProtRS using a least absolute shrinkage and selection operator (LASSO) Cox regression model on 2913 Olink proteins.
- Validation of ProtRS association with incident CKM disease risk and its discriminative ability using competing-risk regression and C-indices.
Main Results:
- A ProtRS, derived from 238 LASSO-selected proteins, was significantly associated with incident CKM disease risk (SHR per 1-SD: 1.87; P<0.001) in the validation cohort.
- The addition of ProtRS to a traditional risk factor model significantly improved CKM disease risk discrimination (ΔC-index: 0.03; P<0.001).
- The study sample comprised 23,815 participants, with a median follow-up of 13.5 years, during which 1407 CKM events occurred in the validation cohort.
Conclusions:
- A proteomics-based risk score (ProtRS) is independently associated with incident cardio-kidney-metabolic (CKM) disease.
- ProtRS enhances the prediction of CKM disease risk beyond traditional risk factors in individuals without baseline CKM disease.
- This study highlights the potential of proteomic profiling for improving CKM disease risk stratification.
Background:
Cardio-kidney-metabolic (CKM) disease represents a significant public health challenge. While proteomics-based risk scores (ProtRS) enhance cardiovascular risk prediction, their utility in improving risk prediction for a composite CKM outcome beyond traditional risk factors remains unknown.
Methods:
We analyzed 23 815 UK Biobank participants without baseline CKM disease, defined by International Classification of Diseases-Tenth Revision codes as cardiovascular disease (coronary artery disease, heart failure, stroke, peripheral arterial disease, atrial fibrillation/flutter), kidney disease (chronic kidney disease or end-stage renal disease), or metabolic disease (type 2 diabetes or obesity). The sample was randomly divided into ProtRS training (70%, n=16 671) and validation (30%, n=7144) cohorts. A least absolute shrinkage and selection operator-based Cox regression model of 2913 Olink-based proteins was utilized to develop the ProtRS in the training cohort. We then assessed the association of the ProtRS with incident CKM disease risk in the validation cohort with competing-risk regression after adjusting for traditional risk factors and evaluated its ability to discriminate incident CKM disease risk with C-indices.
Results:
The study sample had a mean age of 56.1 years; 44% were men, and 94% were White. Over a median follow-up of 13.5 years, 3235 and 1407 incident CKM disease events occurred in the training and validation cohorts, respectively. A ProtRS based on the weighted sum of the 238 least absolute shrinkage and selection operator-selected proteins was significantly associated with incident CKM disease risk (subdistribution hazard ratio per 1-SD, 1.87 [95% CI, 1.73-2.03]; P<0.001) in the validation cohort after adjustment for traditional risk factors. The addition of the ProtRS to a traditional risk factor model significantly improved incident CKM disease risk discrimination beyond the traditional risk factor model (C-index, 0.73 [0.72-0.74] versus 0.71 [0.69-0.72]; ΔC-index, 0.03 [0.02-0.04]).
Conclusions:
A ProtRS was independently associated with incident CKM disease risk and improved risk prediction beyond traditional risk factors in a population free of CKM disease at baseline.
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