SuPAR as a potential disease severity marker

The association between increased suPAR and mortality has been extensively documented [7]. However, most of the studies were confined to healthy subjects [12] or subjects with well-defined illnesses, such as tuberculosis [3] or HIV [15]. In this study, we aimed to confirm suPAR's association with mortality in a more heterogeneous cohort, but also to investigate if suPAR is a disease severity marker in a broader sense through association with comorbidity burden (Charlson Score), admission length and readmission rates. We found that suPAR showed a dose-response relationship with the Charlson Score, was strongly associated with 30- and 90-day mortality, even when adjusting for age, sex, Charlson Score and CRP, but not associated with readmission rates. Given that the Charlson Score is moderately associated with readmission (HR 1.37) and strongly associated with suPAR, the lack of association between suPAR and readmissions is somewhat surprising. Taken together, the association with higher mortality, longer admissions, and higher comorbidity burden suggest that suPAR actually reflects general disease severity.

SuPAR's correlation with other biomarkers and the modified Charlson groups

We found a significant positive correlation between suPAR and CRP, creatinine, ALAT, and leukocytes while there was a significant negative correlation between suPAR, hemoglobin, and albumin. These associations seem to support earlier studies. Elucidating on the kinetic relation of the pathophysiology within these associations is beyond the design of this study.

Both CRP and suPAR rise as part of an acute phase response, but CRP is produced by hepatocytes [16] whereas suPAR is produced peripherally mainly by leukocytes and activated endothelium [1], which may explain the positive association with leukocyte counts. Regarding the inverse relationship with albumin and hemoglobin, we interpret this as responses to chronic disease, for example, advanced cancer: it causes cachexia and malnutrition (low hemoglobin and albumin) as well as a systemic inflammation (high suPAR). There may be more direct signaling pathways involved, but our design makes it impossible to elucidate this further.

Regarding the nature of the association with creatinine, this is in agreement with Koch et al. who reported a similar correlation [6]. Also, there are reports that suPAR may even cause the kidney disease, focal segmental glomerulosclerosis (FSGS). Wei and coworkes recently showed that suPAR can enter the kidney glomerulus and bind and activate the β3 integrin, one of the major proteins anchoring podocytes to the glomerular basement membrane, and that increased plasma levels of suPAR lead to increased β3 integrin activation resulting in podocyte dysfunction and proteinuria [17]. However, none of the patients in this study were suffering from glomerular disease and it is currently unknown if suPAR has an active role in disease outside FSGS. Two earlier studies found a strong connection between high suPAR and decreased liver biosynthesis and cirrhosis [6, 18], and the association between ALAT and suPAR may support these findings: also, patients in the Charlson group 'Liver disease' had a mean suPAR of 11.8 ng/ml, the highest mean value among the groups. If suPAR does not play an active role in disease outside FSGS, it is likely that the suPAR level reflects a conglomerate of negative biological processes such as increased inflammation and fibrosis, organ dysfunction and decreased organ biosynthesis.

SuPAR--a true prognostic marker?

The Charlson Score is known to perform well when predicting mortality [10], but requires diagnostic information not always available upon admission, although this may be the case for more chronic patients. Based on adjusted analysis, suPAR levels appear to add information about disease severity that cannot be explained by the patient's sex, age, and diagnoses alone. However, as it also clear that interpretation of lone suPAR values is very difficult (see Figure 1), we propose that suPAR may be valuable as an addition to a prognostic model. Because of this population's heterogeneity and small size, we cannot make sound suggestions for actual cut-off values. Based on our results, such a prognostic model should include age, sex, suPAR, and, ideally, Charlson Score. A large prospective intervention trial is needed to evaluate the performance of such a model in actual clinical decision making, for example, whether to admit a patient or not.

Limitations of the study

We included half the total number of patients admitted to the Acute Medical Unit during the two month study period. Gaps in the inclusion on Fridays and Saturdays account for a fair proportion of these as patients. We did not include patients on Fridays and Saturdays as the protocol stated that plasma had to be separated from blood within 24 hours, a job that was carried out by a laboratory technician working on weekdays. Also, a large proportion of the patients met exclusion criteria or was unable to understand or sign the consent form in an ethical manner. Less than one patient per day refused to participate. According to an analysis of the unincluded patients, the study population was 11.1 years younger, but had a similar distribution of Charlson Scores and sex as the unincluded patients. There was a potential bias towards exclusion of the oldest and most ill, as these patients were not always able to sign or understand the consent form. If this were to change our results, we expect that the associations found would be similar, but with greater power as there would probably be more fatality cases.

The validity of using ICD-10 codes from the Danish Patient Registry for calculating the Charlson Score has been documented in a large Danish cohort by Thygesen et al. who found an overall positive predictive value (PPV) of 98% for the Charlson groups [19]. However, although the diagnoses from the National Patient Registry are validated for calculating the Charlson Score with a high PPV, they are more inaccurate individually. This is because the Charlson Score only incorporates more serious conditions which are more likely to be coded correctly, and affiliated diagnoses are grouped together in the Charlson groups (for example, diabetes with complications), rendering discrimination between these superfluous. Moreover, subgroup analysis based on ICD-10 diagnoses would require that we choose a main diagnosis among others based on clinical experience; the Charlson Score does this automatically, and thus the results are more reproducible. Another limitation of the study was that we did not have sufficient data to calculate SAPS, Sequential Organ Failure Assessment (SOFA) or APACHE scores. Previous studies have shown that Charlson Score has similar prognostic value, and the association between comorbidites and suPAR was the main purpose of this work. In an earlier study, suPAR and age performed similarly to these physiologic scoring systems in a cohort of 151 systemic inflammatory response syndrome (SIRS) patients [20].