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Network models to capture multiple outcomes resulting from multi-component, salutogenic interventions

Network models to capture multiple outcomes resulting from multi-component, salutogenic interventions
网络模型捕捉多成分有益干预措施产生的多种结果
批准号:
10721644
负责人:
Steven Chamberlin
金额:
$15.55万
依托单位国家:
美国
项目类别:
财政年份:
2023
资助国家:
美国
项目状态:
未结题
起止时间:
2023-08-04 至 2028-07-31

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Project Summary Health care research has extensively studied disease pathogenesis, but very little is known about the complementary process of health restoration, or salutogenesis. Salutogenesis may involve the use of single or multiple interventions. These may affect multiple targets to restore specific functions as well as affect whole person health. This project aims to develop methods to study multisystem signatures of health restoration obtained through multiple interventions both at the level of the organ and the whole organism. Currently, there is considerable interest in identifying non-pharmaceutical interventions to improve resilience to cognitive decline during aging. The proposed project will use a mouse model of age-related cognitive decline to study molecular signatures and functional changes associated with restoration of healthy cognitive function. We will evaluate two interventions that have previously been demonstrated to improve cognition in aged mice i.e. exercise and a water extract (CAW) of the botanical Centella asiatica. Eighteen month old C57BL/6 male and female mice will be randomized into an exercise group (voluntary access to a running wheel), a CAW group (1000 mg/kg/day, administered in the drinking water), a group that receives both CAW and exercise, and a control group that receives neither intervention each for a period of 35 days. Untreated male and female young (4 month old) mice will serve as young controls. We will assess the impact of these two interventions, separately and combined, on four health measures: cognition, global inflammatory status, global oxidative stress and mobility using Morris Water Maze test, blood C-reactive protein, blood 8-hydroxydeoxyguanosine and DigiGait respectively. At the conclusion of treatment, hippocampal tissue from the animals will be collected and transcriptomics and metabolomics analysis will be performed. We will initially identify genes or metabolites (singly or in clusters) that are significantly altered by the treatments. This -omics data will then be integrated with the behavioral and blood data to create computational models that will be used to derive the unique molecular signatures and health data associated with improved cognition. We will also explore whether the molecular signature changes associated with cognitive improvement are a reversal of those seen in cognitive decline (young vs old untreated animals), or represent a new salutogenic pathway. A heterogenous multi-level network (HMLN) model will be used to describe and evaluate the correlations between all health outcomes described here and the molecular data. Ultimately, similar HMLN models can be used as a basis to identify the timing and types of interventions that are optimal for restoring cognitive health. The methodology developed in these experiments can then be expanded beyond cognition to encompass other aspects of whole organism health. Template Version: 9/16/2021 Research Abstract Version: 1
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