ALCAM predicts future cardiovascular death in acute coronary syndromes: Insights from the PLATO trial

Exploring urine:serum fractional excretion ratios as potential biomarkers for lupus nephritis

Objectives

The goal of this exploratory study is to determine if urine:serum fractional excretion ratios can outperform the corresponding urinary biomarker proteins in identifying active renal disease in systemic lupus erythematosus (SLE).

Methods

Thirty-six adult SLE patients and twelve healthy controls were examined for serum and urine levels of 8 protein markers, namely ALCAM, calpastatin, hemopexin, peroxiredoxin 6 (PRDX6), platelet factor 4 (PF4), properdin, TFPI and VCAM-1, by ELISA. Fractional excretion of analyzed biomarkers was calculated after normalizing both the urine and serum biomarker levels against creatinine. A further validation cohort of fifty SLE patients was included to validate the initial findings.

Results

The FE ratios of all 8 proteins interrogated outperformed conventional disease activity markers such as anti-dsDNA, C3 and C4 in identifying renal disease activity. All but VCAM-1^FE were superior to the corresponding urine biomarkers levels in differentiating LN activity, exhibiting positive correlation with renal SLEDAI. ALCAM^FE, PF4^FE and properdin^FE ratios exhibited the highest accuracy (AUC>0.9) in distinguishing active LN from inactive SLE. Four of the FE ratios exhibited perfect sensitivity (calpastatin, PRDX6, PF4 and properdin), while ALCAM^FE, PF4^FE and properdin^FE exhibited the highest specificity values for active LN. In addition, several of these novel biomarkers were associated with higher renal pathology activity indices. In the validation cohort ALCAM^FE, PF4^FE and properdin^FE once again exhibited higher accuracy metrics, surpassing corresponding urine and serum biomarkers levels, with ALCAM^FE exhibiting 95% accuracy in distinguishing active LN from inactive SLE.

Conclusions

With most of the tested proteins, urine:serum fractional excretion ratios outperformed corresponding urine and serum protein measurements in identifying active renal involvement in SLE. Hence, this novel class of biomarkers in SLE ought to be systemically evaluated in larger independent cohorts for their diagnostic utility in LN assessment.

Genome Wide Epistasis Study of On-Statin Cardiovascular Events with Iterative Feature Reduction and Selection

Predicting risk for major adverse cardiovascular events (MACE) is an evidence-based practice that incorporates lifestyle, history, and other risk factors. Statins reduce risk for MACE by decreasing lipids, but it is difficult to stratify risk following initiation of a statin. Genetic risk determinants for on-statin MACE are low-effect size and impossible to generalize. Our objective was to determine high-level epistatic risk factors for on-statin MACE with GWAS-scale data. Controlled-access data for 5890 subjects taking a statin collected from Vanderbilt University Medical Center's BioVU were obtained from dbGaP. We used Random Forest Iterative Feature Reduction and Selection (RF-IFRS) to select highly informative genetic and environmental features from a GWAS-scale dataset of patients taking statin medications. Variant-pairs were distilled into overlapping networks and assembled into individual decision trees to provide an interpretable set of variants and associated risk. 1718 cases who suffered MACE and 4172 controls were obtained from dbGaP. Pathway analysis showed that variants in genes related to vasculogenesis (FDR = 0.024), angiogenesis (FDR = 0.019), and carotid artery disease (FDR = 0.034) were related to risk for on-statin MACE. We identified six gene-variant networks that predicted odds of on-statin MACE. The most elevated risk was found in a small subset of patients carrying variants in COL4A2, TMEM178B, SZT2, and TBXAS1 (OR = 4.53, p < 0.001). The RF-IFRS method is a viable method for interpreting complex "black-box" findings from machine-learning. In this study, it identified epistatic networks that could be applied to risk estimation for on-statin MACE. Further study will seek to replicate these findings in other populations.

Tape-Strip Proteomic Profiling of Atopic Dermatitis on Dupilumab Identifies Minimally Invasive Biomarkers

Tape-stripping is a minimally invasive approach for skin sampling that captures the cutaneous immune/barrier abnormalities in atopic dermatitis (AD). However, tape-strips have not been used to evaluate molecular changes with therapeutic targeting. In this study, we sought to characterize the proteomic signature of tape-strips from AD patients, before and after dupilumab therapy. Twenty-six AD patients were treated with every-other-week dupilumab 300 mg for 16 weeks. Tape-strips from lesional and non-lesional skin were collected before and after treatment, and analyzed with the Olink proteomic assay. Using criteria of fold-change>1.5 and FDR < 0.05, 136 proteins significantly decreased after dupilumab treatment, corresponding to an overall mean improvement of 66.2% in the lesional vs. non-lesional AD proteome. Significant decreases after dupilumab were observed in immune markers related to general inflammation (MMP12), Th2 (CCL13/CCL17), Th17/Th22 (IL-12B, CXCL1, S100A12), and innate immunity (IL-6, IL-8, IL-17C), while the Th1 chemokines CXCL9/CXCL10 remained elevated. Proteins related to atherosclerosis/cardiovascular risk (e.g., SELE/E-selectin, IGFBP7, CHIT1/ chitotriosidase-1, AXL) also significantly decreased after treatment. Dupilumab therapy suppressed AD-related immune biomarkers and atherosclerosis/cardiovascular risk proteins. Tape-strip proteomics may be useful for monitoring therapeutic response in real-life settings, clinical trials, and longitudinal studies for AD and beyond.