Dataset Information

Risk of Renal Failure Within 10 or 20 Years of Systemic Lupus Erythematosus Diagnosis.

ABSTRACT:

Objective

The frequency of endstage renal disease (ESRD) from systemic lupus erythematosus (SLE) in the United States has not improved over the last few decades in large population datasets. Understanding the risk factors for renal failure in SLE could lead to earlier detection of lupus nephritis and potentially more effective treatments in those with markers of poor prognosis.

Methods

The Hopkins Lupus Cohort, comprising 2528 patients was used. One hundred fifty-one patients experienced renal failure after SLE diagnosis, defined as dialysis or renal transplant. We estimated the risk of renal failure in subgroups defined by demographics, laboratory tests, and the American College of Rheumatology/Systemic Lupus International Collaborating Clinics (ACR/SLICC) classification criteria satisfied within 1 year of SLE diagnosis.

Results

The overall incidence of renal failure within 20 years of SLE diagnosis was 8.4%. The risk was much higher (20.0%) among those who experienced proteinuria within the first year of diagnosis. Demographic predictors included African American ethnicity [rate ratio (RR) 1.82, P = 0.0012] and age ≥ 40 years at SLE diagnosis (RR 0.51 vs those with diagnosis at < 30 yrs of age, P = 0.019). Among immunologic markers, low C3 was a strong predictor of renal failure (RR 2.00, P = 0.0011).

Conclusion

Proteinuria within the first year of diagnosis of SLE is one of the most important predictors of ESRD. Our data also confirm African American ethnicity, younger age at SLE diagnosis, and low C3 as strong predictors of renal failure.

SUBMITTER: Petri M

PROVIDER: S-EPMC7572868 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The diagnosis of Systemic Lupus Erythematosus (SLE) is challenging due to its heterogeneous clinical presentation and lack of robust biomarkers for laboratory testing to distinguish it from other autoimmune or infectious diseases. Current diagnostic criterion relies on a combination of clinical examination and laboratory tests, and it does not readily distinguish between those with active clinical disease from those that are clinically quiescent. Several groups have attempted to apply emerging high throughput profiling technologies to diagnose SLE. Despite showing promising diagnostic potential, many of them are expensive and technically challenging for routine clinical uses. Here we report a pilot study of applying a technically simple and highly customisable leukocyte capture antibody microarray to profile healthy (n=24) and SLE patients (n=60) of various disease activities. Our analysis reveals a set of surface antigen biomarkers that have significant association with SLE. In addition, we present a computational method to calculate a score from the entire microarray profile to obtain a score that correlate most reliably with SLE disease activity. Although the current version of our microarray alone does not match the discriminatory power of the standard laboratory tests (serum dsDNA, complements C3 and C4), the combination of the two yields a test with significantly increased discriminatory ability than what can be achieved individually. This work paves the way for a customised SLE-specific antibody microarray for accurate diagnosis and monitoring of SLE that can be readily translated to routine clinical use. Blood samples were collected from 60 patients of Systemic Lupus Erythematosus (SLE) of different disease activity (11 active, 16 semi-active, and 33 inactive), and compared it with blood samples from 24 healthy blood donors. For each subject, we extracted PBMCs and apply them to a leukocyte capture antibody microarray. The microarray was then scaned by a DotScan™ slide reader (Medsaic; Eveleigh, NSW, Australia). The number of immobilized cells was proportional to the light scattered at each antibody spot. Binding of PBMCs to each antibody dot was monitored by light scatter and averaged between the duplicate spots on each slide. We then performed statistical analysis to identify CD antigen markers that are associated with SLE, as well as determining the potential diagnostic ability of this microarray for SLE.