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

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.

Inflamm-aging does not simply reflect increases in pro-inflammatory markers.

Many biodemographic studies use biomarkers of inflammation to understand or predict chronic disease and aging. Inflamm-aging, i.e. chronic low-grade inflammation during aging, is commonly characterized by pro-inflammatory biomarkers. However, most studies use just one marker at a time, sometimes leading to conflicting results due to complex interactions among the markers. A multidimensional approach allows a more robust interpretation of the various relationships between the markers. We applied principal component analysis (PCA) to 19 inflammatory biomarkers from the InCHIANTI study. We identified a clear, stable structure among the markers, with the first axis explaining inflammatory activation (both pro- and anti-inflammatory markers loaded strongly and positively) and the second axis innate immune response. The first but not the second axis was strongly correlated with age (r=0.56, p<0.0001, r=0.08 p=0.053), and both were strongly predictive of mortality (hazard ratios per PCA unit (95% CI): 1.33 (1.16-1.53) and 0.87 (0.76-0.98) respectively) and multiple chronic diseases, but in opposite directions. Both axes were more predictive than any individual markers for baseline chronic diseases and mortality. These results show that PCA can uncover a novel biological structure in the relationships among inflammatory markers, and that key axes of this structure play important roles in chronic disease.

Validating metabolic syndrome through principal component analysis in a medically diverse, realistic cohort.

BACKGROUND: The concept of metabolic syndrome has been subject to etiological and clinical controversies in recent years. Associations among the five risk factors (obesity, hypertension, hyperglycemia, high triglyceride levels, and low high-density lipoprotein cholesterol) may help establish the validity of the concept, especially in a cohort representative of an actual population. METHODS: We used principal component analysis (PCA) to analyze the structure of the physiological components of metabolic syndrome in 7213 patients contained in an administrative database for the Centre Hospitalier Universitaire de Sherbrooke in Sherbrooke, Quebec, a realistic cohort with diverse medical histories. We validated the results by repeating the analysis on stratified and random subgroups of patients, and on different combinations of risk factors. The first axis of the PCA was used to predict coronary heart disease (CHD) and diabetes. RESULTS: The two first axes explained 53% of the variance. The first axis (33%) was associated in the expected direction with all five predictor variables, consistent with its interpretation as metabolic syndrome. The first axis was more predictive of subsequent CHD and diabetes than the formal definition of metabolic syndrome. CONCLUSIONS: These results suggest that the concept of metabolic syndrome accurately captures an existing underlying physiological process. A continuous indicator could be constructed to identify metabolic syndrome more accurately, thus improving risk assessment for CHD and diabetes mellitus. Metabolic syndrome can be measured well even without all five predictors. However, discrepancies with other studies suggest that our results may not be generalizable, perhaps because our cohort tends to be sicker.

Modified metabolic syndrome criteria for identification of patients at risk of developing diabetes and coronary heart diseases: longitudinal assessment via electronic health records.

BACKGROUND: Metabolic syndrome has been shown to predict type 2 diabetes mellitus and cardiovascular events in well-studied cohorts, but lack of appropriate measures in real-life populations has limited its use in clinical settings. We developed and tested an algorithm to identify patients at risk for future diabetes or coronary heart disease (CHD) events using electronic health records (EHRs) at the Centre Hospitalier Universitaire de Sherbrooke (CHUS). METHODS: Patients older than 18 years who had at least 1 visit (outpatient or inpatient) at the CHUS in 2002 or 2003 were included. We excluded patients with diabetes or CHD at baseline. Patients with at least 3 relevant measurements were classified as no metabolic syndrome (zero criteria met), at-risk for metabolic syndrome (1-2 criteria met), or having metabolic syndrome (≥ 3 criteria met). Incidence of diabetes and CHD were assessed through 2008. RESULTS: Data from 31,823 patients were included at baseline: 2997 (9.4%) were classified as having metabolic syndrome, while 18,686 (59%) were classified as at risk for metabolic syndrome. During the 5-year follow-up, having metabolic syndrome was associated with a 20.0% risk of developing diabetes (age- and sex-adjusted odds ratio = 5.12 [95% confidence interval, 4.57-5.74]; P < 0.0001) and a 14.7% CHD event incidence (age- and sex-adjusted odds ratio = 1.83 [95% confidence interval, 1.62-2.07]; P < 0.0001). CONCLUSIONS: An algorithm based on clinically available EHRs could identify patients at high cardiometabolic risk of future diabetes and CHD in the population receiving care at the CHUS.