Rationale VEGF impacts angiogenesis, atherosclerosis and cancer. Although the heritability of circulating VEGF levels is high, little is known about its genetic underpinnings. Objective Our aim was to identify genetic variants associated with circulating VEGF levels using an unbiased genome-wide approach and explore their functional significance with gene expression and pathway analysis. Methods and results We undertook a genome-wide association study (GWAS) of serum VEGF levels in 3,527 participants of the Framingham Heart Study (FHS), with pre-planned replication in 1,727 participants from two independent samples, the STANISLAS Family Study (SFS) and the Prospective Investigation of the Vasculature in Uppsala Seniors study (PIVUS). One hundred and forty SNPs reached genome-wide significance (p<5×10−8). We found evidence of replication for the most significant associations in both replication datasets. In a conditional GWAS 4 SNPs mapping to 3 chromosomal regions were independently associated with circulating VEGF levels: rs6921438 and rs4416670 (6p21.1, p=6.11×10−506 and p=1.47×10−12), rs6993770 (8q23.1, p=2.50×10−16) and rs10738760 (9p24.2, p=1.96×10−34). A genetic score including these four SNPs explained 48% of the heritability of serum VEGF levels. Six of the SNPs that reached genome-wide significance in the GWAS were significantly associated with VEGF mRNA levels in PBMCs. Ingenuity pathway analyses showed found plausible biological links between VEGF and 2 novel genes in these loci (ZFPM2 and VLDLR). Conclusions Genetic variants explaining up to half the heritability of serum VEGF levels were identified. These new insights provide important clues to the pathways regulating circulating VEGF levels.

Vascular endothelial growth factor (VEGF) is an angiogenic and neurotrophic factor, secreted by endothelial cells, known to impact various physiological and disease processes from cancer to cardiovascular disease and to be pharmacologically modifiable. We sought to identify novel loci associated with circulating VEGF levels through a genome-wide association meta-analysis combining data from European-ancestry individuals and using a dense variant map from 1000 genomes imputation panel. Six discovery cohorts including 13,312 samples were analyzed, followed by in-silico and de-novo replication studies including an additional 2,800 individuals. A total of 10 genome-wide significant variants were identified at 7 loci. Four were novel loci (5q14.3, 10q21.3, 16q24.2 and 18q22.3) and the leading variants at these loci were rs114694170 (MEF2C, P = 6.79x10-13), rs74506613 (JMJD1C, P = 1.17x10-19), rs4782371 (ZFPM1, P = 1.59x10-9) and rs2639990 (ZADH2, P = 1.72x10-8), respectively. We also identified two new independent variants (rs34528081, VEGFA, P = 1.52x10-18; rs7043199, VLDLR-AS1, P = 5.12x10-14) at the 3 previously identified loci and strengthened the evidence for the four previously identified SNPs (rs6921438, LOC100132354, P = 7.39x10-1467; rs1740073, C6orf223, P = 2.34x10-17; rs6993770, ZFPM2, P = 2.44x10-60; rs2375981, KCNV2, P = 1.48x10-100). These variants collectively explained up to 52% of the VEGF phenotypic variance. We explored biological links between genes in the associated loci using Ingenuity Pathway Analysis that emphasized their roles in embryonic development and function. Gene set enrichment analysis identified the ERK5 pathway as enriched in genes containing VEGF associated variants. eQTL analysis showed, in three of the identified regions, variants acting as both cis and trans eQTLs for multiple genes. Most of these genes, as well as some of those in the associated loci, were involved in platelet biogenesis and functionality, suggesting the importance of this process in regulation of VEGF levels. This work also provided new insights into the involvement of genes implicated in various angiogenesis related pathologies in determining circulating VEGF levels. The understanding of the molecular mechanisms by which the identified genes affect circulating VEGF levels could be important in the development of novel VEGF-related therapies for such diseases.

Background: Use of protein array technology over conventional assay methods has advantages that include simultaneous detection of multiple analytes, reduction in sample and reagent volumes, and high output of test results. The susceptibility of ligands to denaturation, however, has impeded production of a stable, reproducible biochip platform, limiting most array assays to manual or, at most, semiautomated processing techniques. Such limitations may be overcome by novel biochip fabrication procedures. Methods: After selection of a suitable biochip substrate, biochip surfaces were chemically modified and assessed to enable optimization of biochip fabrication procedures for different test panels. The assay procedure was then automated on a dedicated instrument, and assay performance was determined for a panel of cytokine markers. Assay results were then compared with a commercial method for measurement of cytokine markers. Results: Secondary ion mass spectrometry and x-ray photoelectron spectroscopy demonstrated appropriate and reproducible modification of the biochip surface. Contact-angle studies also confirmed generation of hydrophobic surfaces that enabled containment of droplets for fabrication of discrete test regions. Automation of the biochip assays on a dedicated instrument produced excellent cytokine marker performance with intra-and interassay imprecision <10% for most analytes. Comparison studies showed good agreement with other methods (r ‫؍‬ 0.95-0.99) for cytokines. Conclusion: Performance data from this automated biochip array analyzer provide evidence that it is now possible to produce stable and reproducible biochips for output of more than 2000 test results per hour.

The role of biomarkers in neurodegenerative diseases has been emphasized by recent research. Future clinical demands for identifying diseases at an early stage may render them essential. The aim of this pilot study was to test the analytical performance of two multiplex assays of cerebral markers on a well-defined clinical material consisting of patients with various neurodegenerative diseases. We measured 10 analytes in plasma and cerebrospinal fluid (CSF) from 60 patients suffering from Alzheimer's disease (AD), vascular dementia, frontotemporal dementia, dementia with Lewy bodies, or mild cognitive impairment, as well as 20 cognitively healthy controls. We used the Randox biochip-based Evidence Investigator™ system to measure the analytes. We found it possible to measure most analytes in both plasma and CSF, and there were some interesting differences between the diagnostic groups, although with large overlaps. CSF heart-type fatty acid-binding protein was increased in AD. Glial fibrillary acidic protein and neutrophil gelatinase-associated lipocalin in CSF and D-dimer in plasma were elevated in patients with cerebrovascular disease. A multivariate statistical analysis revealed that the pattern of analytes could help to differentiate the conditions, although more studies are required to verify this.

BACKGROUND:We appraised 23 biomarkers previously associated with urothelial cancer in a case-control study. Our aim was to determine whether single biomarkers and/or multivariate algorithms significantly improved on the predictive power of an algorithm based on demographics for prediction of urothelial cancer in patients presenting with hematuria. METHODS: Twenty-two biomarkers in urine and carcinoembryonic antigen (CEA) in serum were evaluated using enzyme-linked immunosorbent assays (ELISAs) and biochip array technology in 2 patient cohorts: 80 patients with urothelial cancer, and 77 controls with confounding pathologies. We used Forward Wald binary logistic regression analyses to create algorithms based on demographic variables designated prior predicted probability (PPP) and multivariate algorithms, which included PPP as a single variable. Areas under the curve (AUC) were determined after receiver-operator characteristic (ROC) analysis for single biomarkers and algorithms. RESULTS: After univariate analysis, 9 biomarkers were differentially expressed (t test; P < .05). CEA AUC 0.74; bladder tumor antigen (BTA) AUC 0.74; and nuclear matrix protein (NMP22) 0.79. PPP included age and smoking years; AUC 0.76. An algorithm including PPP, NMP22, and epidermal growth factor (EGF) significantly improved AUC to 0.90 when compared with PPP. The algorithm including PPP, BTA, CEA, and thrombomodulin (TM) increased AUC to 0.86. Sensitivities ¼ 91%, 91%; and specificities ¼ 80%, 71%, respectively, for the algorithms. CONCLUSIONS: Addition of biomarkers representing diverse carcinogenic pathways can significantly impact on the ROC statistic based on demographics. Benign prostate hyperplasia was a significant confounding pathology and identification of nonmuscle invasive urothelial cancer remains a challenge. Cancer 2012;118:2641

No abstract

Acute kidney injury (AKI) following cardiac surgery significantly increases morbidity and mortality risks. Improving existing clinical methods of identifying patients at risk of perioperative AKI may advance management and treatment options. This study investigated whether a combination of biomarkers and clinical factors pre and post cardiac surgery could stratify patients at risk of developing AKI. Patients (n = 401) consecutively scheduled for elective cardiac surgery were prospectively studied. Clinical data was recorded and blood samples were tested for 31 biomarkers. Areas under receiver operating characteristic (AUROCs) were generated for biomarkers pre and postoperatively to stratify patients at risk of AKI. Preoperatively sTNFR1 had the highest predictive ability to identify risk of developing AKI postoperatively (AUROC 0.748). Postoperatively a combination of H-FABP, midkine and sTNFR2 had the highest predictive ability to identify AKI risk (AUROC 0.836). Preoperative clinical risk factors included patient age, body mass index and diabetes. Perioperative factors included cardio pulmonary bypass, cross-clamp and operation times, intra-aortic balloon pump, blood products and resternotomy. Combining biomarker risk score (BRS) with clinical risk score (CRS) enabled pre and postoperative assignment of patients to AKI risk categories. Combining BRS with CRS will allow better management of cardiac patients at risk of developing AKI.

No abstract