A novel vascular health index: Using data analytics and population health to facilitate mechanistic modeling of microvascular status

Abstract

The study of vascular function across conditions has been an intensive area of investigation for many years. While these efforts have revealed many factors contributing to vascular health, challenges remain for integrating results across research groups, animal models, and experimental conditions to understand integrated vascular function. As such, the insights attained in clinical/population research from linking datasets, have not been fully realized in the basic sciences, thus frustrating advanced analytics and complex modeling. To achieve comparable advances, we must address the conceptual challenge of defining/measuring integrated vascular function and the technical challenge of combining data across conditions, models, and groups. Here, we describe an approach to establish and validate a composite metric of vascular function by comparing parameters of vascular function in metabolic disease (the obese Zucker rat) to the same parameters in age-matched, “healthy” conditions, resulting in a common outcome measure which we term the vascular health index (VHI). VHI allows for the integration of datasets, thus expanding sample size and permitting advanced modeling to gain insight into the development of peripheral and cerebral vascular dysfunction. Markers of vascular reactivity, vascular wall mechanics, and microvascular network density are integrated in the VHI. We provide a detailed presentation of the development of the VHI and provide multiple measures to assess face, content, criterion, and discriminant validity of the metric. Our results demonstrate how the VHI captures multiple indices of dysfunction in the skeletal muscle and cerebral vasculature with metabolic disease and provide context for an integrated understanding of vascular health under challenged conditions.

Application of a novel index for understanding vascular health following pharmacological intervention in a pre-clinical model of metabolic disease

“…Finally, parameter weighting, by definition, requires the use of a regression-based approach designed to predict an outcome. As this was not the purpose of VHI, this was rejected in favor of the analytical approach employed ( Menon et al, 2022 ).…”

“…In evaluating the above criteria, we developed a three–component calculation for of both cerebral and peripheral VHI in LZR and OZR. The use of a three–component metric (comprised of readily collected measures) allows for larger sample sizes to be assessed while still capturing the essential aspects of vascular health, allowing for a more facile, easily integrated and broadly applicable approach for investigators ( Menon et al, 2022 ). The inclusion of additional components to the calculation of VHI will, by definition, make the determination more complicated, requiring more extensive and diverse data collection, with the potential introduction of greater error and variability, potentially confounding interpretation.…”

“…The inclusion of additional components to the calculation of VHI will, by definition, make the determination more complicated, requiring more extensive and diverse data collection, with the potential introduction of greater error and variability, potentially confounding interpretation. The initial development of the VHI was constructed using a different dataset from that for the present study (i.e., VHI was not specifically constructed for application to the treatment effects in the current dataset) ( Menon et al, 2022 ).…”

“…In response to these myriad challenges, we develop and describe a standardized, integrated measure of vascular health and dysfunction ( Menon et al, 2022 ). The Vascular Health Index (VHI) allows for the simultaneous assessment of changes to vascular reactivity/endothelial function, vascular wall mechanics, and microvessel density within skeletal muscle and cerebral vascular networks with the progression of chronic metabolic disease.…”

“…The Vascular Health Index (VHI) allows for the simultaneous assessment of changes to vascular reactivity/endothelial function, vascular wall mechanics, and microvessel density within skeletal muscle and cerebral vascular networks with the progression of chronic metabolic disease. This VHI will quantify vascular dysfunction in states of elevated peripheral vascular disease and cerebrovascular disease risk relative to the vascular function of healthy, age-matched control animals ( Menon et al, 2022 ). Further, the present study also focuses on the impact of clinically relevant pharmacological interventions to change VHI from untreated control conditions and can provide some insight as to why some interventions have more effective functional outcomes despite comparable impacts on specific risk factors.…”