New prognostic tool accurately predicts kidney failure risk
Researchers from the Joslin Diabetes Center (JDC) have developed a prognostic tool which they claim can accurately predict the risk of end stage renal disease (ESRD), more commonly known as kidney failure, in both types of diabetes, and could open the door for more effective treatment and prevention of the illness.
Until recently, two biomarkers – urinary albumin to creatine ratio (ACR) and estimated glomerular filtration rate – have been relied upon to assess ESRD risk. However, this has drawn criticism from those in the scientific community who argue that this view omits a large number of at-risk patients, and cannot accurately predict the onset time of the disease.
“Overall efficiency and cost-effectiveness of clinical trials depends on the diagnostic tools used to enrol study patients,” explained senior study author Andrzej S Krolewski, Head of the Section on Genetics & Epidemiology at JDC. “If you recruit people who are not at risk of progressing to ESRD during the clinical trial period, statistical power declines and you can’t prove anything.”
The foundation of the new tool, known as the classification and regression trees (CARTs), was laid with the discovery of a link between tumour necrosis factor receptor 1 (TNFR1) and failing renal function in patients with type 1 or 2 diabetes. In a bid to solidify this finding into a predictable system, the team tested CARTs using data gathered from patients enrolled in follow-up studies at JDC, all of whom were suffering from both diabetes and stage 3 or 4 chronic liver disease.
The team discovered that circulating levels of TNFR1 and ACR combined were indicative of a high risk of ESRD.
“Remarkably, when we used the TNF receptor to analyse risk of ESRD, the risk was almost identical for both type 1 and type 2 diabetes. This implies that the etiologies are similar,” continued Dr Krolewski. “This is a very important observation because in the medical community, the impression is that the progression to ESRD in type 1 is somehow different from type 2. As a result, many clinical trials do not include patients with type 1.”
The tool could have huge implications in increasing the effectiveness of data harvesting for clinical trials in this research area, as Krolewski notes: “Currently, about 80% of patients in these clinical trials provide no useful information. If our criterion is used in the recruitment of patients, you will not need two or three thousand patients for a clinical trial, you will only need 400 patients.”
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