Peripheral Blood MCEMP1 Gene Expression as a Biomarker for Stroke Prognosis.

BACKGROUND AND PURPOSE: A limitation when making early decisions on stroke management is the lack of rapid diagnostic and prognostic testing. Our study sought to identify peripheral blood RNA biomarkers associated with stroke. The secondary aims were to assess the discriminative capacity of RNA biomarkers for primary stroke type and stroke prognosis at 1-month. METHODS: Whole-blood gene expression profiling was conducted on the discovery cohort: 129 first-time stroke cases that had blood sampling within 5 days of symptom onset and 170 control participants with no history of stroke. RESULTS: Through multiple regression analysis, we determined that expression of the gene MCEMP1 had the strongest association with stroke of 11 181 genes tested. MCEMP1 increased by 2.4-fold in stroke when compared with controls (95% confidence interval, 2.0-2.8; P=8.2×10(-22)). In addition, expression was elevated in intracerebral hemorrhage when compared with ischemic stroke cases (P=3.9×10(-4)). MCEMP1 was also highest soon after symptom onset and had no association with stroke risk factors. Furthermore, MCEMP1 expression independently improved discrimination of 1-month outcome. Indeed, discrimination models for disability and mortality that included MCEMP1 expression, baseline modified Rankin Scale score, and primary stroke type improved discrimination when compared with a model without MCEMP1 (disability Net Reclassification Index, 0.76; P=3.0×10(-6) and mortality Net Reclassification Index, 1.3; P=1.1×10(-9)). Significant associations with MCEMP1 were confirmed in an independent validation cohort of 28 stroke cases and 34 controls. CONCLUSIONS: This study demonstrates that peripheral blood expression of MCEMP1 may have utility for stroke diagnosis and as a prognostic biomarker of stroke outcome at 1-month.

Gene Expression Profiles for the Identification of Prevalent Atrial Fibrillation.

BACKGROUND: Diagnosis of atrial fibrillation (AF) can be difficult, requiring cumbersome investigations. We aimed to determine the association of established whole-blood gene expression scores with prevalent AF and to evaluate their performance for the identification of AF in a SIRS (Steroids in Cardiac Surgery) trial cohort. METHODS AND RESULTS: Whole-blood, transcriptome-wide gene expression profiling was performed using the Illumina HumanHT-12 Expression BeadChip in 416 participants (65% men) before surgery, including 91 with a diagnosis of AF. An AF gene score (GS) calculated from 7 genes reported to be upregulated in AF and a validated GS for biological age based on 1254 genes related to aging were both independently associated with AF diagnosis before surgery in multivariate logistic regression analyses adjusting for known risk factors (P=0.0006 and P=0.003). Addition of AF and biological age GSs to clinical risk factors led to significant improvement in area under the receiver operating characteristic curve (from 0.77 to 0.80; P=0.03), continuous net reclassification improvement index (P<0.0001), and integrated discrimination improvement index (P=0.0002). When stratifying AF by subtype, AF GS was mainly associated with paroxysmal AF (P=0.003), whereas the biological age GS was mainly associated with permanent AF (P=0.017). CONCLUSIONS: We validated the existence of a blood gene expression signature for prevalent AF and showed that biological age derived from gene expression is significantly associated with prevalent AF. These findings suggest a potential utility of blood gene expression for the identification of patients with AF, particularly paroxysmal AF. This result could have implications for the prevention and management of cryptogenic stroke. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00427388.

Growth Differentiation Factor 15 as a Novel Biomarker for Metformin.

OBJECTIVE: Metformin is a commonly used glucose-lowering drug. However, apart from glycemic measures, no biomarker for its presence or dose has been identified. RESEARCH DESIGN AND METHODS: A total of 237 biomarkers were assayed in baseline serum from 8,401 participants (2,317 receiving metformin) in the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial. Regression models were used to identify biomarkers for metformin use. RESULTS: Growth differentiation factor 15 (GDF15) was strongly linked to metformin, such that the odds of metformin use per SD increase in level varied from 3.73 (95% CI 3.40, 4.09) to 3.94 (95% CI 3.59, 4.33) depending on the other included variables. For the remaining 25 linked biomarkers, the odds ranged from 0.71 to 1.24. A 1.64 ng/mL higher GDF15 level predicted a 188-mg higher metformin dose (P < 0.0001). CONCLUSIONS: GDF15 levels are a biomarker for the use of metformin in people with dysglycemia, and its concentration reflects the dose of metformin.

Whole Blood Gene Expression Differentiates between Atrial Fibrillation and Sinus Rhythm after Cardioversion.

BACKGROUND: Treatment to restore sinus rhythm among patients with atrial fibrillation (AF) has limited long-term success rates. Gene expression profiling may provide new insights into AF pathophysiology. OBJECTIVE: To identify biomarkers and improve our understanding of AF pathophysiology by comparing whole blood gene expression before and after electrical cardioversion (ECV). METHODS: In 46 patients with persistent AF that underwent ECV, whole blood samples were collected 1-2 hours before and 4 to 6 weeks after successful cardioversion. The paired samples were sent for microarray and plasma biomarker comparison. RESULTS: Of 13,942 genes tested, expression of SLC25A20 and PDK4 had the strongest associations with AF. Post-cardioversion, SLC25A20 and PDK4 expression decreased by 0.8 (CI 0.7-0.8, p = 2.0x10-6) and 0.7 (CI 0.6-0.8, p = 3.0x10-5) fold respectively. Median N-terminal pro B-type natriuretic peptide (NT-proBNP) concentrations decreased from 127.7 pg/mL to 44.9 pg/mL (p = 2.3x10-13) after cardioversion. AF discrimination models combining NT-proBNP and gene expression (NT-proBNP + SLC25A20 area under the curve = 0.88, NT-proBNP + PDK4 AUC = 0.86) had greater discriminative capacity as compared with NT-proBNP alone (AUC = 0.82). Moreover, a model including NT-proBNP, SLC25A20 and PDK4 significantly improved AF discrimination as compared with other models (AUC = 0.87, Net Reclassification Index >0.56, p<5.8x10-3). We validated the association between SLC25A20 and PDK4 with AF in an independent sample of 17 patients. CONCLUSION: This study demonstrates that SLC25A20, PDK4, and NT-proBNP have incremental utility as biomarkers discriminating AF from sinus rhythm. Elevated SLC25A20 and PDK4 expression during AF indicates an important role for energy metabolism in AF.

Overlap Chronic Placental Inflammation Is Associated with a Unique Gene Expression Pattern.

Breakdown of the balance between maternal pro- and anti-inflammatory pathways is thought to allow an anti-fetal maternal immune response that underlies development of chronic placental inflammation. Chronic placental inflammation is manifested by the influx of maternal inflammatory cells, including lymphocytes, histiocytes, and plasma cells, into the placental membranes, villi, and decidua. These infiltrates are recognized pathologically as chronic chorioamnionitis, chronic villitis of unknown etiology, and chronic deciduitis. Each of these histological entities is associated with adverse fetal outcomes including intrauterine growth restriction and preterm birth. Studying the gene expression patterns in chronically inflamed placenta, particularly when overlapping histologies are present, may lead to a better understanding of the underlying mechanism(s). Therefore, this study compared tissue with and without chronic placental inflammation, manifested as overlapping chronic chorioamnionitis, chronic villitis of unknown etiology, and chronic deciduitis. RNA expression profiling was conducted on formalin fixed, paraffin embedded placental tissue using Illumina microarrays. IGJ was the most significant differentially expressed gene identified and had increased expression in the inflamed tissue. In addition, IGLL1, CXCL13, CD27, CXCL9, ICOS, and KLRC1 had increased expression in the inflamed placental samples. These differentially expressed genes are associated with T follicular helper cells, natural killer cells, and B cells. Furthermore, these genes differ from those typically associated with the individual components of chronic placental inflammation, such as chronic villitis, suggesting that the inflammatory infiltrate associated with overlapping chronic chorioamnionitis, chronic villitis of unknown etiology, and chronic deciduitis differs is unique. To further explore and validate gene expression findings, we conducted immunohistochemical assessment of protein level expression and demonstrate that IgJ expression was largely attributable to the presence of plasma cells as part of chronic deciduitis and that IgA positive plasma cells are associated with chronic deciduitis occurring in combination with chronic chorioamnionitis and chronic villitis of unknown etiology but not with isolated chronic deciduitis.

Genetic markers of inflammation and their role in cardiovascular disease.

Atherosclerosis and associated cardiovascular diseases (CVD) are multifaceted disorders, influenced by environmental and heritable risk factors. Inflammation plays a significant role in each stage of atherosclerosis and as such, discovery and characterization of inflammatory biomarkers associated with risk of CVD is an active area of research. Because of the strong predicted genetic components of both CVD and inflammatory biomarkers, there is interest in identifying genetic determinants of inflammatory markers and characterizing their role in CVD. Recent developments in the methodological approaches of genetic epidemiology, especially genome-wide association studies and Mendelian randomization studies, have been effective in identifying novel gene associations and determining the causality of these genes with CVD. In this review, we will summarize the current understanding of the genetic architecture of inflammatory markers. The markers selected for this review include C-reactive protein, soluble intercellular adhesion molecule-1, interleukin-6, and P-selectin.