title: Improving mortality prediction using biosocial surveys.
abstract: The authors used data from a nationally representative survey of 933 adults aged 54 years or older (mean age = 66.2 years; standard deviation, 8.0) in Taiwan to explore whether mortality prediction at older ages is improved by the use of 3 clusters of biomarkers: 1) standard cardiovascular and metabolic risk factors; 2) markers of disease progression; and 3) nonclinical (neuroendocrine and immune) markers. They also evaluated the extent to which these biomarkers account for the female advantage in survival. Estimates from logistic regression models of the probability of dying between 2000 and 2006 (162 deaths; mean length of follow-up = 5.8 years) showed that inclusion of each of the 3 sets of markers significantly (P = 0.024, P = 0.002, and P = 0.003, respectively) improved discriminatory power in comparison with a base model that adjusted for demographic characteristics, smoking, and baseline health status. The set of disease progression markers and the set of nonclinical markers each provided more discriminatory power than standard risk factors. Most of the excess male mortality resulted from the men being more likely than women to smoke, but each of 3 markers related to disease progression or inflammation (albumin, neutrophils, and interleukin-6) explained more than 10% of excess male mortality.
authors.affiliation: Office of Population Research, Princeton University, Princeton, NJ 08544-2091, USA. ngoldman@princeton.edu
authors.forename: Noreen
authors.initials: N
authors.lastname: Goldman
authors.forename: Dana A
authors.initials: DA
authors.lastname: Glei
authors.forename: Yu-Hsuan
authors.initials: YH
authors.lastname: Lin
authors.forename: Maxine
authors.initials: M
authors.lastname: Weinstein
mesh_terms: D000368:Aged; D015415:Biomarkers / Q000032:analysis*; D002318:Cardiovascular Diseases / Q000401:mortality; D003710:Demography; D018450:Disease Progression; D005260:Female; D006306:Health Surveys; D006801:Humans; D007249:Inflammation / Q000401:mortality; D016015:Logistic Models; D008297:Male; D008659:Metabolic Diseases / Q000401:mortality; D008875:Middle Aged; D009026:Mortality / Q000639:trends*; D011237:Predictive Value of Tests; D012372:ROC Curve; D012307:Risk Factors; D012907:Smoking / Q000453:epidemiology; D012931:Social Environment; D013624:Taiwan / Q000453:epidemiology
chemical_list: D015415:Biomarkers