Abstract
Background: A shift towards the dynamic measurement of physiologic resilience and improved technology incorporated into experimental paradigms in aging research is producing high-resolution data. Identifying the most appropriate analysis method for this type of data is a challenge. In this work, the functional principal component analysis (fPCA) was employed to demonstrate a data-driven approach to the analysis of high-resolution data in aging research.Methods: Cerebral oxygenation during standing was measured in a large cohort [The Irish Longitudinal Study on Aging (TILDA)]. FPCA was performed on tissue saturation index (TSI) data. A regression analysis was then conducted with the functional principal component (fPC) scores as the explanatory variables and transition time as the response.Results: The mean ± SD age of the analysis sample was 64 ± 8 years. Females made up 54% of the sample and overall, 43% had tertiary education. The first PC explained 96% of the variance in cerebral oxygenation upon standing and was related to a baseline shift. Subsequent components described the recovery to before-stand levels (fPC2), drop magnitude and initial recovery (fPC3 and fPC4) as well as a temporal shift in the location of the minimum TSI value (fPC5). Transition time was associated with components describing the magnitude and timing of the nadir.Conclusions: Application of fPCA showed utility in reducing a large amount of data to a small number of parameters which summarize the inter-participant variation in TSI upon standing. A demonstration of principal component regression was provided to allow for continued use and development of data-driven approaches to high-resolution data analysis in aging research.
Highlights
Measures of physiologic resilience are of increasing interest to the field of aging as they can identify an older person’s vulnerability to negative outcomes when faced with a stressor (Hadley et al, 2017; Varadhan et al, 2018; Kuchel, 2018)
Recent publications are showing a growing interest in applying robust analysis methods for collinear, high-resolution data (Odden and Melzer, 2019; Wallace et al, 2019). An example of this type of data is generated in the study of orthostatic hypotension (OH) through monitoring of the neurovascular reaction to standing
This work aimed to describe an application of functional principal component analysis (fPCA) to assess the association between transition speed during a supine-to-stand challenge and cerebral oxygenation in a large sample of older adults
Summary
Measures of physiologic resilience are of increasing interest to the field of aging as they can identify an older person’s vulnerability to negative outcomes when faced with a stressor (Hadley et al, 2017; Varadhan et al, 2018; Kuchel, 2018). Improved technology in aging research can record stressor response at high spatial and temporal resolution While this provides an opportunity to investigate physiological phenomena occurring in shorter timeframes and at smaller scales, it presents a technical challenge for researchers in identifying the most appropriate methods to analyze the data. Recent publications are showing a growing interest in applying robust analysis methods for collinear, high-resolution data (Odden and Melzer, 2019; Wallace et al, 2019) An example of this type of data is generated in the study of orthostatic hypotension (OH) through monitoring of the neurovascular reaction to standing. The functional principal component analysis (fPCA) was employed to demonstrate a data-driven approach to the analysis of high-resolution data in aging research
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