Abstract Background and Aims Chronic kidney disease (CKD) is very common in diabetes, occurring in around 40% of patients. Without treatment, CKD can lead to end stage renal disease (ESRD), which requires costly dialysis and transplantation. Current treatments aim to prevent CKD or slow down its progression to ESRD. The speed of CKD progression is patient-specific, and at present there are no biomarkers which can predict which patient with diabetes will develop CKD; level of albuminuria is an important but variably reliable biomarker of speed of CKD progression. The current methods for rapid identification of patients prone to accelerated CKD progression are limited, as they are based on measuring estimated glomerular filtration rate (eGFR) over prolonged periods of time, as well as albuminuria. Therefore, there is a clinical need for novel biomarkers that can predict early potential rapid CKD progression in patients. The aim of this project is to determine whether serum microRNAs (miRs) can be used as biomarkers to predict the speed of CKD progression in diabetes patients, and to identify potential treatment targets associated with such dysregulated miRs. Method miRs were isolated from sera of 34 patients with type 2 diabetes classed with either slow or fast progressing CKD by their eGFR (n = 17; obtained from the Salford Kidney Study sample collection). Several miRs were found to be significantly dysregulated in fast progressing patients by using Next Generation Sequencing (NGS) of miRs in individual serum samples. An additional 49 diabetic CKD patient serum samples (slow (n = 29) or fast progressors (n = 20)) were used to validate the potential miR biomarker panel by qPCR. Results The NGS miR analysis of the initial 34 CKD patient sera produced a panel of 16 miRs that were either significantly upregulated or downregulated in the fast-progressing patients compared to slow progressors. The qPCR validation of this initial panel in 49 additional CKD patient serum samples demonstrated that 6 miRs were significantly upregulated in the fast-progressing patients. The levels of these miRs correlated with demographic and clinical parameters. ROC curve analysis revealed that the individual predictive power of each of those 6 miRs to define either a slow or fast progressing CKD patient was between 63-75%. Determination of a miR combination the strongest predictive power is ongoing. Conclusion A potential biomarker panel of 6 miRs has been identified and validated by qPCR, with each miR having a predictive power of between 63-75% for fast progression of CKD. A panel of miRs that could accurately predict progression speed of CKD patients could lead to faster and earlier diagnosis of patients to enable better treatment. Work is ongoing to explore the predictive power of this miR biomarker panel, and potential novel therapeutic targets to prevent CKD or delay its progression to ESRD.