Cardiac microstructural alterations measured by echocardiography identify sex-specific risk for heart failure.

OBJECTIVE: Established preclinical imaging assessments of heart failure (HF) risk are based on macrostructural cardiac remodelling. Given that microstructural alterations may also influence HF risk, particularly in women, we examined associations between microstructural alterations and incident HF. METHODS: We studied N=2511 adult participants (mean age 65.7±8.8 years, 56% women) of the Framingham Offspring Study who were free of cardiovascular disease at baseline. We employed texture analysis of echocardiography to quantify microstructural alteration, based on the high spectrum signal intensity coefficient (HS-SIC). We examined its relations to incident HF in sex-pooled and sex-specific Cox models accounting for traditional HF risk factors and macrostructural alterations. RESULTS: We observed 94 new HF events over 7.4±1.7 years. Individuals with higher HS-SIC had increased risk for incident HF (HR 1.67 per 1-SD in HS-SIC, 95% CI 1.31 to 2.13; p<0.0001). Adjusting for age and antihypertensive medication use, this association was significant in women (p=0.02) but not men (p=0.78). Adjusting for traditional risk factors (including body mass index, total/high-density lipoprotein cholesterol, blood pressure traits, diabetes and smoking) attenuated the association in women (HR 1.30, p=0.07), with mediation of HF risk by the HS-SIC seen for a majority of these risk factors. However, the HS-SIC association with HF in women remained significant after adjusting for relative wall thickness (representing macrostructure alteration) in addition to these risk factors (HR 1.47, p=0.02). CONCLUSIONS: Cardiac microstructural alterations are associated with elevated risk for HF, particularly in women. Microstructural alteration may identify sex-specific pathways by which individuals progress from risk factors to clinical HF.

High-throughput digitization of analog human echocardiography data.

Echocardiographic imaging has been acquired in historical longitudinal cohorts of cardiovascular disease. Many cohorts were established prior to digital recording of echocardiography, and thus have preserved their archival imaging on Video Home System (VHS) tapes. These tapes require large physical storage space, are affected by physical degradation, and cannot be analyzed using modern digital techniques. We have designed and implemented a standardized methodology for digitizing analog data in historical longitudinal cohorts. The methodology creates a pipeline through critical steps of initial review, digitization, anonymization, quality control, and storage. The methodology has been implemented in the Framingham Offspring Study, a community-based epidemiological cohort study with echocardiography performed during serial examinations between 1987 and 1998. We present this method as an accessible pipeline for preserving and repurposing historical imaging data acquired from large cohort studies. The described technique:•Outlines a generalizable pipeline for digitization of analog recordings of echocardiography stored on VHS tapes•Addresses research concerns including quality control, anonymization, and storage•Expresses the authors' individual experience regarding observed image quality, training needs, and potential limitations to help readers understand the costs and benefits of this method.

A Single Visualization Technique for Displaying Multiple Metabolite-Phenotype Associations.

To assist with management and interpretation of human metabolomics data, which are rapidly increasing in quantity and complexity, we need better visualization tools. Using a dataset of several hundred metabolite measures profiled in a cohort of ~1500 individuals sampled from a population-based community study, we performed association analyses with eight demographic and clinical traits and outcomes. We compared frequently used existing graphical approaches with a novel 'rain plot' approach to display the results of these analyses. The 'rain plot' combines features of a raindrop plot and a conventional heatmap to convey results of multiple association analyses. A rain plot can simultaneously indicate effect size, directionality, and statistical significance of associations between metabolites and several traits. This approach enables visual comparison features of all metabolites examined with a given trait. The rain plot extends prior approaches and offers complementary information for data interpretation. Additional work is needed in data visualizations for metabolomics to assist investigators in the process of understanding and convey large-scale analysis results effectively, feasibly, and practically.