Genetic, physiological, and lifestyle predictors of mortality in the general population

Abstract

Objectives: We investigated the quality of 162 variables, focusing on the contribution of genetic markers, used solely or in combination with other characteristics, when predicting mortality.

Methods: In 5974 participants from the Rotterdam Study, followed for a median of 15.1 years, 7 groups of factors including age and gender, genetics, socioeconomics, lifestyle, physiological characteristics, prevalent diseases, and indicators of general health were related to all-cause mortality. Genetic variables were identified from 8 genome-wide association scans (n = 19,033) and literature review.

Results: We observed 3174 deaths during follow-up. The fully adjusted model (C-statistic for 15-year follow-up [C15y] = 0.80; 95% confidence interval [CI] = 0.79, 0.81) predicted mortality well [corrected]. Most of the additional information apart from age and sex stemmed from physiological markers, prevalent diseases, and general health. Socioeconomic factors and lifestyle contributed meaningfully to mortality risk prediction with longer prediction horizon. Although specific genetic factors were independently associated with mortality, jointly they contributed little to mortality prediction (C(15y) = 0.56; 95% CI = 0.55, 0.57).

Conclusions: Mortality can be predicted reasonably well over a long period. Genetic factors independently predict mortality, but only modestly more than other risk indicators.

FIGURE 1—

Time-dependent receiver-operating characteristic curves for prediction of mortality for different groups of variables: The Rotterdam Study, 1990–2009. Note. AUC = area under the curve.

FIGURE 2—

Time-dependent receiver-operating characteristic curves for prediction of mortality for age and gender and differently combined models: The Rotterdam Study, 1990–2009. Note. AUC = area under the curve.