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Detection of a novel, integrative aging process suggests complex physiological integration.

Cohen AA, Milot E, Li Q, Bergeron P, Poirier R, Dusseault-Bélanger F, Fülöp T, Leroux M, Legault V, Metter EJ, Fried LP, Ferrucci L - PLoS ONE (2015)

Bottom Line: The first axis was associated with anemia, inflammation, and low levels of calcium and albumin.Integrated albunemia behaves more stably across populations than its component biomarkers, and thus appears to represent a higher-order physiological process emerging from the structure of underlying regulatory networks.If this is correct, detection of this process has substantial implications for physiological organization more generally.

View Article: PubMed Central - PubMed

Affiliation: Groupe de recherche PRIMUS, Department of Family Medicine, University of Sherbrooke, 3001 12e Ave N, Sherbrooke, QC, J1H 5N4, Canada.

ABSTRACT
Many studies of aging examine biomarkers one at a time, but complex systems theory and network theory suggest that interpretations of individual markers may be context-dependent. Here, we attempted to detect underlying processes governing the levels of many biomarkers simultaneously by applying principal components analysis to 43 common clinical biomarkers measured longitudinally in 3694 humans from three longitudinal cohort studies on two continents (Women's Health and Aging I & II, InCHIANTI, and the Baltimore Longitudinal Study on Aging). The first axis was associated with anemia, inflammation, and low levels of calcium and albumin. The axis structure was precisely reproduced in all three populations and in all demographic sub-populations (by sex, race, etc.); we call the process represented by the axis "integrated albunemia." Integrated albunemia increases and accelerates with age in all populations, and predicts mortality and frailty--but not chronic disease--even after controlling for age. This suggests a role in the aging process, though causality is not yet clear. Integrated albunemia behaves more stably across populations than its component biomarkers, and thus appears to represent a higher-order physiological process emerging from the structure of underlying regulatory networks. If this is correct, detection of this process has substantial implications for physiological organization more generally.

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Related in: MedlinePlus

Age-adjusted biomarker loading order and stability for PCA1 across data sets and subsets.Loading importance is calculated as the loading divided by the sum of the absolute values of all loadings. These values are ordered from high (red, on bottom) to low (magenta, on top) for the first 20 loadings; remaining loadings are grouped together as “Other” in white. Accordingly, hemoglobin has the strongest loading, then hematocrit, then albumin, etc. The order and colors are derived from the full analysis combining the first visits of individuals in all three data sets (top-left panel, left column, “All”) and applied to all other columns in the figure. Stability of loadings is indicated by conservation of loading heights across bars. For each panel, the loadings for the full data set are at left. Numbers indicate subset sample sizes. For all panels except BLSA, the 43-variable set is used; for BLSA there was insufficient sample size to perform PCA on subsets with 43 variables, so the 34-variable analysis is presented.
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pone.0116489.g007: Age-adjusted biomarker loading order and stability for PCA1 across data sets and subsets.Loading importance is calculated as the loading divided by the sum of the absolute values of all loadings. These values are ordered from high (red, on bottom) to low (magenta, on top) for the first 20 loadings; remaining loadings are grouped together as “Other” in white. Accordingly, hemoglobin has the strongest loading, then hematocrit, then albumin, etc. The order and colors are derived from the full analysis combining the first visits of individuals in all three data sets (top-left panel, left column, “All”) and applied to all other columns in the figure. Stability of loadings is indicated by conservation of loading heights across bars. For each panel, the loadings for the full data set are at left. Numbers indicate subset sample sizes. For all panels except BLSA, the 43-variable set is used; for BLSA there was insufficient sample size to perform PCA on subsets with 43 variables, so the 34-variable analysis is presented.

Mentions: Stability and validity of the axes were tested by replicating the analyses across populations and on subgroups of the three populations by demographic traits (e.g., sex, race, income, age; Figs. 1, 2, 4–6) [27,28]. For example, if the first axis generated by an analysis of just the men is nearly identical to that generated by an analysis of just the women, this supports the hypotheses that (a) the axis represents a real biological process rather than noise, and (b) the process does not differ by sex. When comparing versions of PCA1 generated from multiple independent datasets or subsets, correlation coefficients were uniformly very high, usually greater than 0.9 (Figs. 3, 6). Similarly, biological interpretation of PCA1 was nearly identical across datasets and subsets based on the importance of the loadings of the raw variables (Fig. 1). Additionally, PCA1 was reproduced using age-adjusted levels of biomarkers rather than raw levels (Fig. 7), showing that the characteristic PCA1 biomarker signature can be extracted even from individuals of similar age. Together, these findings show that we are detecting the same PCA1 in multiple distinct populations: Italy and the USA, men and women, blacks and whites, rich and poor, etc.


Detection of a novel, integrative aging process suggests complex physiological integration.

Cohen AA, Milot E, Li Q, Bergeron P, Poirier R, Dusseault-Bélanger F, Fülöp T, Leroux M, Legault V, Metter EJ, Fried LP, Ferrucci L - PLoS ONE (2015)

Age-adjusted biomarker loading order and stability for PCA1 across data sets and subsets.Loading importance is calculated as the loading divided by the sum of the absolute values of all loadings. These values are ordered from high (red, on bottom) to low (magenta, on top) for the first 20 loadings; remaining loadings are grouped together as “Other” in white. Accordingly, hemoglobin has the strongest loading, then hematocrit, then albumin, etc. The order and colors are derived from the full analysis combining the first visits of individuals in all three data sets (top-left panel, left column, “All”) and applied to all other columns in the figure. Stability of loadings is indicated by conservation of loading heights across bars. For each panel, the loadings for the full data set are at left. Numbers indicate subset sample sizes. For all panels except BLSA, the 43-variable set is used; for BLSA there was insufficient sample size to perform PCA on subsets with 43 variables, so the 34-variable analysis is presented.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4356614&req=5

pone.0116489.g007: Age-adjusted biomarker loading order and stability for PCA1 across data sets and subsets.Loading importance is calculated as the loading divided by the sum of the absolute values of all loadings. These values are ordered from high (red, on bottom) to low (magenta, on top) for the first 20 loadings; remaining loadings are grouped together as “Other” in white. Accordingly, hemoglobin has the strongest loading, then hematocrit, then albumin, etc. The order and colors are derived from the full analysis combining the first visits of individuals in all three data sets (top-left panel, left column, “All”) and applied to all other columns in the figure. Stability of loadings is indicated by conservation of loading heights across bars. For each panel, the loadings for the full data set are at left. Numbers indicate subset sample sizes. For all panels except BLSA, the 43-variable set is used; for BLSA there was insufficient sample size to perform PCA on subsets with 43 variables, so the 34-variable analysis is presented.
Mentions: Stability and validity of the axes were tested by replicating the analyses across populations and on subgroups of the three populations by demographic traits (e.g., sex, race, income, age; Figs. 1, 2, 4–6) [27,28]. For example, if the first axis generated by an analysis of just the men is nearly identical to that generated by an analysis of just the women, this supports the hypotheses that (a) the axis represents a real biological process rather than noise, and (b) the process does not differ by sex. When comparing versions of PCA1 generated from multiple independent datasets or subsets, correlation coefficients were uniformly very high, usually greater than 0.9 (Figs. 3, 6). Similarly, biological interpretation of PCA1 was nearly identical across datasets and subsets based on the importance of the loadings of the raw variables (Fig. 1). Additionally, PCA1 was reproduced using age-adjusted levels of biomarkers rather than raw levels (Fig. 7), showing that the characteristic PCA1 biomarker signature can be extracted even from individuals of similar age. Together, these findings show that we are detecting the same PCA1 in multiple distinct populations: Italy and the USA, men and women, blacks and whites, rich and poor, etc.

Bottom Line: The first axis was associated with anemia, inflammation, and low levels of calcium and albumin.Integrated albunemia behaves more stably across populations than its component biomarkers, and thus appears to represent a higher-order physiological process emerging from the structure of underlying regulatory networks.If this is correct, detection of this process has substantial implications for physiological organization more generally.

View Article: PubMed Central - PubMed

Affiliation: Groupe de recherche PRIMUS, Department of Family Medicine, University of Sherbrooke, 3001 12e Ave N, Sherbrooke, QC, J1H 5N4, Canada.

ABSTRACT
Many studies of aging examine biomarkers one at a time, but complex systems theory and network theory suggest that interpretations of individual markers may be context-dependent. Here, we attempted to detect underlying processes governing the levels of many biomarkers simultaneously by applying principal components analysis to 43 common clinical biomarkers measured longitudinally in 3694 humans from three longitudinal cohort studies on two continents (Women's Health and Aging I & II, InCHIANTI, and the Baltimore Longitudinal Study on Aging). The first axis was associated with anemia, inflammation, and low levels of calcium and albumin. The axis structure was precisely reproduced in all three populations and in all demographic sub-populations (by sex, race, etc.); we call the process represented by the axis "integrated albunemia." Integrated albunemia increases and accelerates with age in all populations, and predicts mortality and frailty--but not chronic disease--even after controlling for age. This suggests a role in the aging process, though causality is not yet clear. Integrated albunemia behaves more stably across populations than its component biomarkers, and thus appears to represent a higher-order physiological process emerging from the structure of underlying regulatory networks. If this is correct, detection of this process has substantial implications for physiological organization more generally.

Show MeSH
Related in: MedlinePlus