  Aging is a multidimensional process where phenotypes change at varying rates.
Longitudinal studies of aging typically involve following a cohort of
individuals over the course of several years. This design is hindered by cost,
attrition, and subsequently small sample size. Alternative methodologies are
therefore warranted. In this study, we used a variational autoencoder to
estimate rates of aging from cross-sectional data from routine laboratory tests
of 1.4 million individuals collected from 2016 to 2019. By incorporating
metrics that would ensure model's stability and distinctness of the dimensions,
we uncovered four aging dimensions that represent the following bodily
functions: 1) kidney, 2) thyroid, 3) white blood cells, and 4) liver and heart.
We then examined the relationship between rates of aging on morbidity and
health care expenditure. In general, faster agers along these dimensions are
more likely to develop chronic diseases that are related to these bodily
functions. They also had higher health care expenditures compared to the slower
agers. K-means clustering of individuals based on rate of aging revealed that
clusters with higher odds of developing morbidity had the highest cost across
all types of health care services. Results suggest that cross-sectional
laboratory data can be leveraged as an alternative methodology to understand
age along the different dimensions. Moreover, rates of aging are differentially
related to future costs, which can aid in the development of interventions to
delay disease progression.
