Diabetic Nephropathy

Diabetic nephropathy has become the most common cause of end-stage renal disease in the western world. Progression from normoalbuminuria to microalbuminuria (urine albumin excretion between 30 and 300 mg/day), and subsequently diabetic nephropathy usually occurs over a period of 10 to 15 years and represents a continuum of renal functional and structural abnormalities that are driven by a complex interplay between both hemodynamic and non-hemodynamic factors. Elevated urinary albumin excretion, increased blood pressure, and poor glycemic control are well-established risk factors for development of diabetic nephropathy. However, these clinical aspects explain only a minor fraction of the risk of developing diabetic nephropathy, and despite interventions to adequately reduce these risk factors, some patients develop diabetic nephropathy and progress to end-stage renal disease.

There has been an ongoing search to find markers early in the course of diabetes to better identify individuals at high risk for developing diabetic nephropathy. Such markers may also be used to discover new targets for intervention. In recent years, several new markers indicative of patho-physiologic mechanisms for diabetic renal injury have been uncovered. The online combination of a highly efficient separation technology with mass spectrometry is designed to extract the maximal information proteins present in small quantities. Capillary electrophoresis coupled to electrospray mass spectrometry (CE-MS) was recently developed to be a fast (one sample analyzed in ~60 minutes), sensitive, and automated analysis of various body fluids, including urine.

With this powerful tool several questions dealing with diabetic nephropathy can be addressed by the CE-MS analysis of urine: First, we can detect the current status of diabetic nephropathy of diabetic patients. We can also differentiate between patients with diabetes mellitus and healthy controls in general. Furthermore, we sought to evaluate if CE-MS-defined patterns derived from urinary polypeptides of patients with diabetic nephropathy differ from those of patients with other chronic renal diseases. Also we can make a statement of the progression of diabetic nephropathy. And finally, we can validate the treatment success of drugs, like candesartan in context of diabetic nephropathy.

Figure 1: CE-MS spectra from a patient with diabetic nephropathy before and after treatment with different daily doses (as indicated) of the angiotensin-receptor blocker candesartan. Mass/charge ratio is indicated on the left, migration time (in min) at the bottom, signal intensity is color-coded. Treatment resulted in a reduction of polypeptides indicative of diabetic nephropathy, consequently yielding a poorer match to the diabetic nephropathy pattern.

Figure 2: (A) Polypeptide pattern recognition for monitoring of therapeutic effects. The list of 113 diabetic renal damage marker polypeptides was applied to the samples undergoing candesartan treatment. A pattern recognition algorithm was used to specify the analogy between the sample pattern and the marker list (first bar). Since the marker list is composed of positive (present/increased in DRD samples) and negative (absent/decreased in DRD samples) markers, both types are displayed in percent in the second and the third bars. As a result of the treatment, the detection of “DRD positive markers” decreases in the samples of this patient from 74% to 15%, while the detection of “DRD negative markers” increases from 6 to 18 percent. The overall calculated factor for DRD decreases from 86% without treatment, to 74% at dose level 8, 69% at dose level 16 and down to 54 percent at dose level 32. Significant changes in polypeptide frequency and amplitude by candesartan treatment in type 2 diabetic patients with macroalbuminuria. 15 polypeptides from the list of diabetic renal damage marker were found to be significant changed during treatment with candesartan either in frequency of occurrence (B) or in amplitude (C). All amplitudes are expressed on a log scale as geometric mean with bars representing the 95 % confidence interval.