In the present study we demonstrate that urinary proteome analysis based on the online combination of CE and ESI MS may contribute to the identification of patients at risk of undergoing a CAD event. The urinary protein patterns examined were associated with the development of clinical CAD, even when adjusted for known CAD risk factors and potential confounders.
In addition, there were distinct differences in the urinary polypeptide patterns among CAD cases who were individuals without diabetes compared to patients with T1D, as well as significant differences between CAD cases with normal AER and those with albuminuria.
Further investigation is needed to identify and characterize the urinary proteins in the panels that distinguished diabetes, diabetic nephropathy, and CAD events from controls. Such biomarker identification could elucidate pathophysiologic mechanisms operative in these conditions and be targets for therapeutic intervention. One identified class of biomarkers used in this study is fragments of collagen α-1(I), which are decreased in diabetes and even more so in DN. Decreased levels of urinary collagen fragments may be a result of both decreased degradation as well as increased resistance of collagen fibers towards proteolysis. The latter may be a result of increased advanced glycation end products, protecting against proteolytic processing,30
while the former may be related to decreased level or activity of elastase.11,12,31
This decrease in elastase leads to the accumulation of elastin in the macula densa, collecting ducts, and pelvicalyceal epithelia of the kidney. These changes are consistent with the theory that thickening and expansion of the extracellular matrix plays a major role in diabetes-associated complications.
The data we report are consistent with previous reports on type 1 and type 2 diabetes.10,18
Investigators at the Steno Diabetes Center identified a proteome pattern with 113 polypeptides that were able to distinguish between patients with T1D and albuminuria versus normoalbuminuric patients with T1D who were matched for age, gender, and duration of diabetes.10
The data in the present study as well as previously reported data18,28,32
indicate that peptides present in urine may reflect the turnover and dynamic changes in the extracellular matrix. Changes in these peptides may be an indicator of pathophysiologic alterations in the activity of proteases involved in the maintenance of the extracellular matrix. This dynamic balance of synthesis and degradation could not easily be accessed previously, but urinary proteome analysis may be an excellent tool to investigate this process in greater detail.
Strengths of this study include the use of previously validated biomarker panels: a well-described epidemiologic cohort; prospective data on development of CAD; and the use of well-established proteome analysis methodology performed in a highly reliable laboratory.
However, limitations include the small sample size included in this pilot study, which precludes regression analysis with adjustment for multiple variables simultaneously, and the lack of CAD events in women without diabetes. In addition, since most of the CAD events were revascularizations, it is possible that some of these “soft” events were related to other factors, including CAC score and physician characteristics. Urine samples were stored at
−80°C for 6 years on average, and so it is possible that some degradation of samples occurred. Furthermore, while these urinary proteome patterns have identified novel biomarkers, it remains to be demonstrated whether this technique can add to current clinical prognostication.
The cardiovascular pattern that has been analyzed in the present study was developed based on the differences between patients who had CAD by angiography and healthy controls.20
The prognostic value of these biomarkers consequently was unknown but has been independently validated in this study. It is to be expected that evaluation of this and additional prospective studies will result in the definition of additional urinary biomarkers that may further increase the prognostic value of the biomarker model.
In conclusion, urinary proteome patterns are associated with CAD events with statistical significance. In addition, urinary proteome patterns associated with T1D and DN have been validated in this study.
Future work will include expanding this technology to larger samples to determine whether urinary proteomics can identify diabetes, DN, and CAD earlier in the pathologic course and/or add to or improve on current diagnostic techniques. Furthermore, identification of these biomarkers could provide targets for therapeutic intervention.