title: Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best.
abstract: BACKGROUND
  Associations between measures of subjective health and mortality risk have previously been shown. We assessed the impact and comparative predictive performance of a multi-biomarker panel on this association.
  
  
  METHODS
  Data from 4,261 individuals aged 20-79 years recruited for the population-based Study of Health in Pomerania was used. During an average 9.7 year follow-up, 456 deaths (10.7%) occurred. Subjective health was assessed by SF-12 derived physical (PCS-12) and mental component summaries (MCS-12), and a single-item self-rated health (SRH) question. We implemented Cox proportional-hazards regression models to investigate the association of subjective health with mortality and to assess the impact of a combination of 10 biomarkers on this association. Variable selection procedures were used to identify a parsimonious set of subjective health measures and biomarkers, whose predictive ability was compared using receiver operating characteristic (ROC) curves, C-statistics, and reclassification methods.
  
  
  RESULTS
  In age- and gender-adjusted Cox models, poor SRH (hazard ratio (HR), 2.07; 95% CI, 1.34-3.20) and low PCS-12 scores (lowest vs. highest quartile: HR, 1.75; 95% CI, 1.31-2.33) were significantly associated with increased risk of all-cause mortality; an association independent of various covariates and biomarkers. Furthermore, selected subjective health measures yielded a significantly higher C-statistic (0.883) compared to the selected biomarker panel (0.872), whereas a combined assessment showed the highest C-statistic (0.887) with a highly significant integrated discrimination improvement of 1.5% (p < 0.01).
  
  
  CONCLUSIONS
  Adding biomarker information did not affect the association of subjective health measures with mortality, but significantly improved risk stratification. Thus, a combined assessment of self-reported subjective health and measured biomarkers may be useful to identify high-risk individuals for intensified monitoring.
authors.affiliation: Institute for Clinical Chemistry and Laboratory Medicine, University of Greifswald, Ferdinand-Sauerbruch Str, Greifswald 17475, Germany. robin.haring@uni-greifswald.de
authors.forename: Robin
authors.initials: R
authors.lastname: Haring
authors.forename: You-Shan
authors.initials: YS
authors.lastname: Feng
authors.forename: Jörn
authors.initials: J
authors.lastname: Moock
authors.forename: Henry
authors.initials: H
authors.lastname: Völzke
authors.forename: Marcus
authors.initials: M
authors.lastname: Dörr
authors.forename: Matthias
authors.initials: M
authors.lastname: Nauck
authors.forename: Henri
authors.initials: H
authors.lastname: Wallaschofski
authors.forename: Thomas
authors.initials: T
authors.lastname: Kohlmann
mesh_terms: D000328:Adult; D000368:Aged; D015415:Biomarkers; D016000:Cluster Analysis; D059026:Diagnostic Self Evaluation; D005260:Female; D005858:Germany; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D008875:Middle Aged; D009026:Mortality; D016016:Proportional Hazards Models; D011788:Quality of Life; D012372:ROC Curve; D012307:Risk Factors; D057566:Self Report; D055815:Young Adult
chemical_list: D015415:Biomarkers