Self-perceived quality of life predicts mortality risk better than a multi-biomarker panel, but the combination of both does best

Robin Haring,Jörn Moock,You-Shan Feng,Marcus Dörr,Henry Völzke,Thomas Kohlmann,Henri Wallaschofski,Matthias Nauck

doi:10.1186/1471-2288-11-103

Robin Haring, Jörn Moock + Show 6 more

Open Access

https://doi.org/10.1186/1471-2288-11-103

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Abstract

BackgroundAssociations 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.MethodsData 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.ResultsIn 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).ConclusionAdding 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.

Highlights

Associations between measures of subjective health and mortality risk have previously been shown
Crude incidence rates of all-cause mortality decreased across quartiles of PCS-12 but not MCS-12 (Table 2)
In Cox proportional-hazards models adjusted for gender and age, we found a distinct association between low PCS-12 scores and all-cause mortality, showing that subjects with PCS-12 scores in the lowest quartile had an increased mortality risk (HR 1.75; 95% confidence interval (95% CI) 1.31-2.33) compared to subjects in the highest quartile

Summary

Introduction

Associations between measures of subjective health and mortality risk have previously been shown. Previous studies are limited by the fact that the associations between subjective health and mortality have been assessed in elderly [10,11,12] or disease-specific patient populations including conditions such as cancer [13,14], diabetes mellitus [15], coronary artery disease [16,17], respiratory disease [18,19], chronic kidney disease [20], or infection by HIV [21] Beside these limitations, the impact and comparative predictive performance of different biomarkers on the association between subjective health measures and mortality risk is largely unknown. This is even more intriguing, as the multi-biomarker approach has recently gained widespread attention as powerful predictors of clinical [22,23] and subclinical outcomes [24]

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