# Measuring Loss of Homeostasis in Aging

## Abstract

Individual biomarkers are often studied as indicators of abnormality, but a complex systems perspective suggests that further insight may be gained by considering biomarker values in the context of others. The concept of homeostasis implies that normal levels of a biomarker may be abnormal in relation to the levels of other biomarkers, and vice versa. On the premise that healthy physiological dynamics are constrained through regulation and thus converge towards certain profiles, results from our lab suggest that Mahalanobis distance (Dm), or the distance from the center of a distribution, can be used as a measure of physiological dysregulation. Specifically, Dm increases with age, and predicts mortality and many other health outcomes of age. Increase of signal with the inclusion of more biomarkers, and lack of sensitivity to biomarker choice confirm that dysregulation is indeed an emergent phenomenon. This approach can be applied at the organismal level or to specific physiological/biochemical systems. Here, in order to better understand the signal measured by Dm, we draw on the mathematical relationship between principal components (PCs) of the biomarkers and Mahalanobis distance. Our results characterize the relative distribution of biological information among the PCs, and suggest that careful removal of certain PCs in the calculation of Dm can significantly improve the biological signal.

## Keywords

Dysregulation Mahalanobis distance Principal components## References

- Brereton, R.G., Lloyd, G.R.: Re-evaluating the role of the Mahalanobis distance measure. J. Chemom.
**30**, 134–143 (2016)CrossRefGoogle Scholar - Cohen, A.A., Legault, V., Li, Q., Fried, L.P., Ferrucci, L.: Men sustain higher dysregulation levels than women without becoming frail. J Gerontol. A
**73**, 175–184 (2018)CrossRefGoogle Scholar - Cohen, A.A., Li, Q., Milot, E., Leroux, M., Faucher, S., Morissette-Thomas, V., Legault, V., Fried, L.P., Ferrucci, L.: Statistical distance as a measure of physiological dysregulation is largely robust to variation in its biomarker composition. PLoS ONE
**10**, e0122541 (2015a)CrossRefGoogle Scholar - Cohen, A.A., Milot, E., Li, Q., Bergeron, P., Poirier, R., Dusseault-Bélanger, F., Fülöp, T., Leroux, M., Legault, V., Metter, E.J., Fried, L.P., Ferrucci, L.: Detection of a novel, integrative aging process suggests complex physiological integration. PLoS ONE
**10**, e0116489 (2015b)CrossRefGoogle Scholar - Cohen, A.A., Milot, E., Yong, J., Seplaki, C.L., Fülöp, T., Bandeen-Roche, K., Fried, L.P.: A novel statistical approach shows evidence for multi-system physiological dysregulation during aging. Mech. Ageing Dev.
**134**, 110–117 (2013)CrossRefGoogle Scholar - Gastinel, L.N.: Principal Component Analysis in the Era of « Omics » Data. Principal Component Analysis, 23 (2012)Google Scholar
- Hubert, M., Debruyne, M.: Minimum covariance determinant. Wiley Interdisciplinary Reviews: Computational Statistics.
**2**, 36–43 (2010)CrossRefGoogle Scholar - Jolliffe, I.T.: Choosing a Subset of Principal Components or Variables. In: Principal Component Analysis, pp. 92–114. Springer, New York (1986)Google Scholar
- Kriete, A.: Robustness and aging–a systems-level perspective. BioSystems
**112**, 37–48 (2013)CrossRefGoogle Scholar - Meunier, C.L., Malzahn, A.M., Boersma, M.: A new approach to homeostatic regulation: towards a unified view of physiological and ecological concepts. PLoS ONE
**9**, e107737 (2014)ADSCrossRefGoogle Scholar - Milot, E., Morissette-Thomas, V., Li, Q., Fried, L.P., Ferrucci, L., Cohen, A.A.: Trajectories of physiological dysregulation predicts mortality and health outcomes in a consistent manner across three populations. Mech. Ageing Dev.
**141–142**, 56–63 (2014)CrossRefGoogle Scholar - Morrisette-Thomas, V., Cohen, A.A., Fülöp, T., Riesco, É., Legault, V., Li, Q., Milot, E., Dusseault-Bélanger, F., Ferrucci, L.: Inflamm-aging does not simply reflect increases in pro-inflammatory markers. Mech. Ageing Dev.
**139**, 49–57 (2014)CrossRefGoogle Scholar - Rattan, S.I.S.: Aging is not a disease: implications for intervention. Aging Dis.
**5**, 196–202 (2014)CrossRefGoogle Scholar - Reese, S.E., Archer, K.J., Therneau, T.M., Atkinson, E.J., Vachon, C.M., de Andrade, M., Kocher, J.-P.A., Eckel-Passow, J.E.: A new statistic for identifying batch effects in high-throughput genomic data that uses guided principal component analysis. Bioinformatics
**29**, 2877–2883 (2013)CrossRefGoogle Scholar - van den Berg, R.A., Hoefsloot, H.C., Westerhuis, J.A., Smilde, A.K., van der Werf, M.J.: Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genom.
**7**, 142 (2006)CrossRefGoogle Scholar