Predicting Mortality From Clinical and

Background. Few studies focus on ‘‘preclinical’ ’ warning signs associated with mortality. In this article, we investigate associations between all-cause mortality and two clusters of biological risk factors: (i) standard clinical measures related to cardiovascular disease and metabolic function; and (ii) nonclinical measures pertaining to hypothalamic–pituitary–adrenal axis activity, sympathetic nervous system activity, and inflammatory response. Methods. Data come from the 2000 Social Environment and Biomarkers of Aging Study, a national sample of Taiwanese persons aged 54 years or older; 1497 persons were interviewed in their homes, and 1023 participated in a hospital examination. The analysis is based on 927 respondents with complete information. Logistic regression models describe the association between biomarkers and the 3-year probability of dying. Results. Although both groups of biomarkers are significantly associated with mortality, the model with neuroendocrine and immune biomarkers has better explanatory and discriminatory power than the one with clinical measures. The association between these nonclinical measures and mortality remains strong after adjustment for the clinical markers, suggesting that the physiological effects of the nonclinical biomarkers are broader than those captured by the cardiovascular and metabolic system measures included here. Conclusions. Nonclinical markers are likely to provide warning signs of deteriorating health and function beyond what can be learned from conventional markers. Our findings are consistent with those of recent studies that (i) demonstrate th