ObjectiveInflammation plays an important role in the pathophysiology of ischemic cardiomyopathy (ICM). We aimed to identify potential biomarkers of inflammation-related genes for ICM and build a model based on the potential biomarkers for the diagnosis of ICM.Materials and methodsThe microarray datasets and RNA-Sequencing datasets of human ICM were downloaded from the Gene Expression Omnibus database. We integrated 8 microarray datasets via the SVA package to screen the differentially expressed genes (DEGs) between ICM and non-failing control samples, then the differentially expressed inflammation-related genes (DEIRGs) were identified. The least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were utilized to screen the potential diagnostic biomarkers from the DEIRGs. The potential biomarkers were validated in the RNA-Sequencing datasets and the functional experiment of the ICM rat, respectively. A nomogram was established based on the potential biomarkers and evaluated via the area under the receiver operating characteristic curve (AUC), calibration curve, decision curve analysis (DCA), and Clinical impact curve (CIC).Results64 DEGs and 19 DEIRGs were identified, respectively. 5 potential biomarkers (SERPINA3, FCN3, PTN, CD163, and SCUBE2) were ultimately selected. The validation results showed that each of these five potential biomarkers showed good discriminant power for ICM, and their expression trends were consistent with the bioinformatics results. The results of AUC, calibration curve, DCA, and CIC showed that the nomogram demonstrated good performance, calibration, and clinical utility.ConclusionSERPINA3, FCN3, PTN, CD163, and SCUBE2 were identified as potential biomarkers associated with the inflammatory response to ICM. The proposed nomogram could potentially provide clinicians with a helpful tool to the diagnosis and treatment of ICM from an inflammatory perspective.

Background: The relationship between resting cardiac indices and exercise capacity in older adults was still not well understood. New developments in cardiac magnetic resonance imaging (MRI) enable a much fuller assessment of cardiac characteristics. Purpose/Hypothesis: To assess the association between exercise capacity and specific aspects of resting cardiac structure, function, and tissue. Study Type: Cross-sectional study. Population: A total of 112 well-functioning older adults (mean age 69 years, 52 men). Field Strength/Sequence: All participants underwent 3.0 T MRI, using scan protocols including balanced steady-state free precession cine sequence, modified look-locker inversion recovery, and T2-prepared single-shot balanced steady-state free precession. Assessment: Demographic and geriatric characteristics were collected. Blood samples were assayed for lipid and glucose related biomarkers. All participants performed a symptom-limited cardiopulmonary exercise test to achieve peakVO 2 . Cardiac MRI parameters were measured with semi-automatic software by S.Y., an 18-year experienced radiologist. Statistical Tests: Demographic, geriatric characteristics and MR measurements were compared among quartiles of peakVO2, with different methods according to the data type. Spearman's partial correlation and least absolute shrinkage selection operator regression were performed to select significant MR features associated with peakVO 2 . Mediation effect analysis was conducted to test any indirect connection between age and peakVO 2 . A two-sided P value of <0.05 was defined statistical significance. Results: Epicardial fat volume, left atrial volume indexed to height, right ventricular end-systolic volume indexed to body surface area and global circumferential strain (GCS) were correlated with peakVO 2 (regression coefficients were À0.040, À0.093, 0.127, and 0.408, respectively). Mediation analysis showed that the total effect of peakVO 2 change was 43.6% from the change of age. The proportion of indirect effect from epicardial fat volume and GCS were 11.8% and 15.1% in total effect, respectively. Data Conclusion: PeakVO 2 was associated with epicardial fat volume, left atrial volume, right ventricular volume and GCS of left ventricle.