#5650 METABOLIC BLOOD BIOMARKER PROFILING FOR CHRONIC KIDNEY DISEASE PREDICTION – EVIDENCE FROM 275,000 INDIVIDUALS IN THE UK BIOBANK

Abstract

Abstract Background and Aims Early identification of individuals at high risk of developing chronic kidney disease and other chronic conditions is essential for targeted prevention. Here, we assess the utility of metabolic blood biomarkers in predicting the onset of chronic kidney disease in over 250,000 individuals from UK Biobank, beyond established risk factors and polygenic risk scores. Method Circulating blood biomarkers, including lipids, fatty acids, amino acids, glycolysis metabolites and inflammation markers were measured by a low-cost nuclear magnetic resonance (NMR) metabolomics assay in around 275,000 plasma samples from the UK Biobank. During a prospective 10-year follow-up, over 6000 incident chronic kidney disease events were obtained from national health registries. Using multivariable regression modelling, we derived a 10-year risk score for chronic kidney disease onset. We assessed the predictive performance of the biomarker risk score in comparison to standard risk factors (age, sex, prevalent diabetes, body mass index, systolic and diastolic blood pressure, smoking status and use of cholesterol lowering medication), clinical chemistry measurements (HbA1c, LDL and HDL cholesterol, total cholesterol and eGFR) and polygenic risk scores. We evaluated the performance of the risk score in two practical scenarios: in a general population screening setting and in a clinical use case of stratifying kidney disease risk among prevalent type 2 diabetics. We further illustrate applications of the biomarker risk scores in predicting the risk of other chronic diseases, such as cardiovascular outcomes, in general population settings. Results In the general population screening setting, adding the metabolic biomarkers to a simple model of standard risk factors substantially improved the predictive performance, with area under the receiver operating characteristic curve (AUC) increasing from 0.74 to 0.82. When compared to more comprehensive model including also clinical chemistry measurements and polygenic risk scores, the improvements were also significant, albeit more modest, with the AUC increasing from 0.81 to 0.83. Even more substantial gains were observed in a clinical use case scenario of stratifying risk of chronic kidney disease onset among prevalent type 2 diabetics. Among diabetics with mildly to moderately decreased kidney function at the time of blood sampling (eGFR 60-90), adding the metabolic biomarkers in a comprehensive model including standard risk factors, clinical chemistry measurements and polygenic risk scores increased the AUC from 0.60 to 0.70. Conclusion Circulating metabolic biomarkers enhanced the prediction of chronic kidney disease onset beyond standard risk factors and polygenic risk scores. From a translational perspective, this may complement the identification of high-risk individuals in both general population and clinical settings beyond current risk factor assessment, while simultaneously informing on the risk of other chronic diseases.