Statistical Technique To Find Mix Of Biomarkers Predicting Mortality Used By Researchers

Main Category: Seniors / Aging
Also Included In: Endocrinology;  Immune System / Vaccines;  Hypertension
Article Date: 25 Sep 2006 - 5:00 PDT

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Previous research has uncovered a range of biological markers that can predict disability, morbidity and mortality in older adults. In this study, the researchers analyzed 13 biomarkers representing neuroendocrine and vascular functions and immune and metabolic activity over 12 years in 1,189 high-functioning men and women aged 70-79 enrolled in the MacArthur Study of Successful Aging. The goals were to identify biomarker combinations (or high-risk pathways) associated with high levels of mortality in men and women, determine whether the biomarkers that most closely predicted mortality differed in men and women and develop prediction rules based on combinations of biomarker conditions. The researchers also sought to present "recursive partitioning," a statistical technique for identifying multiple and interacting predictors of an outcome, as a useful analytical tool for addressing research questions in the biomedical sciences. Using recursive partitioning, they found that combinations of neuroendocrine and immune markers frequently appeared in high-risk male pathways, while systolic blood pressure was present, in combination with other biomarkers, in high-risk female pathways.

IMPACT: Clinicians and researchers may be able to use recursive partitioning to identify the biological regulatory system's importance in predicting mortality in later life.

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AUTHORS: Tara L. Gruenewald, Teresa E. Seeman and Arun S. Karlamangla of UCLA; Carol D. Ryff of the University of Wisconsin, Madison; and Burton H. Singer of the Institute on Aging at the University of Wisconsin, Madison, and the Office of Population Research, Princeton University.

JOURNAL: Proceedings of the National Academy of Sciences, Sept. 19 issue.

FUNDERS: National Institute on Aging; the John D. and Catherine T. MacArthur Foundation.

Contact: Enrique Rivero
University of California - Los Angeles

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