Cell populations and molecular biomarkers in blood and urine characterise nephropathy in type 1 diabetes

Background and aims: Diabetic nephropathy (DN) is a lethal complication of type 1 diabetes (T1D). Nevertheless, the pathogenesis behind DN is still not fully understood. Inflammation and a dysregulation of the immune systems response to injury have been shown to be important pathways associated with the disease. We aim to perform a detailed phenotyping of T1D patients, with and without progressive renal complications, through flow cytometry analysis of cell populations in blood and urine. We hypothesize that the combined data of flow cytometry, biomarker and clinical measurements may provide new insight into DN and its progression. Materials and methods: The cross-sectional study (n=150) consisted of healthy non-diabetic controls (Healthy, n=22) and 128 persons with T1D stratified according to historic levels of urinary albumin excretion rate or urinary albumin creatinine ratio: Normoalbuminuria (<30 mg/g or mg/d; n=50), Microalbuminuria (30-299 mg/g or mg/d; n=45), and Macroalbuminuria (≥300 mg/g or mg/d, n=33). Flow cytometry was used to record 278 features relating to immune cell populations in the blood and urine of study subjects. ELISA/multiplexing was used to measure an additional 34 molecular biomarker features, many of which had previously been associated with DN. Urinary flow cytometry and biomarker features were standardised according to creatinine levels. Variables were transformed using specialised functions written in R to decide whether features should be log transformed and if left, right or no censoring should be carried out. Transformed flow cytometry and biomarker features were analysed with Tukey or Games Howell one-way posthoc tests comparing difference of means for all combinations of the four groups and p-values were converted to False Discovery Rate (FDR). Effect size was calculated according to the Cohen D formula. Results: A total of 1872 one-way posthoc tests were carried out on the data (204 for biomarker features and 1668 for flow cytometry features) of which 163 were found to be significant (FDR < 0.05). Of the 34 biomarker features, 25 showed significance in one or more tests while 48 of the 278 flow cytometry variables generated one or more significant test results. The results of these 48 flow cytometry features are summarised in the figure showing effect sizes. Significance is indicated with asterisks as explained in the footnote. The figure also shows dendrograms for the hierarchical clustering of features (left) and tests (top). Conclusion: The results of the urine and blood biomarkers were comparable to previous studies in the literature suggesting good reproducibility of our data in relation to DN. Further data analysis will be needed to explore novel relationships between clinical, flow cytometry and biomarker features, while follow up results on study participants may eventually lead to improved diagnosis of DN progression.