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Phenotypic Age: a novel signature of mortality and morbidity risk

Zuyun Liu, Pei-Lun Kuo, Steve Horvath, Eileen Crimmins, Luigi Ferrucci, Morgan Levine
doi: https://doi.org/10.1101/363291
Zuyun Liu
1Department of Pathology, Yale School of Medicine, New Haven, CT,USA
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Pei-Lun Kuo
2Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, USA. Baltimore, MD, USA
3Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
4Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
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Steve Horvath
5Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
6Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA
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Eileen Crimmins
7Leonard Davis School of Gerontology, University of Southern California, Los Angeles, CA, USA
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Luigi Ferrucci
2Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, USA. Baltimore, MD, USA
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Morgan Levine
1Department of Pathology, Yale School of Medicine, New Haven, CT,USA
8Department of Epidemiology, Yale School of Public Health, New Haven, CT, USA
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Abstract

Background: A person’s rate of aging has important implications for his/her risk of death and disease, thus, quantifying aging using observable characteristics has important applications for clinical, basic, and observational research. We aimed to validate a novel aging measure, “Phenotypic Age”, constructed based on routine clinical chemistry measures, by assessing its applicability for differentiating risk for morbidity and mortality in both healthy and unhealthy populations of various ages.

Methods: A nationally representative US sample, NHANES III, was used to derive “Phenotypic Age” based on a linear combination of chronological age and nine multi-system clinical chemistry measures, selected via cox proportional elastic net. Mortality predictions were validated using an independent sample (NHANES IV), consisting of 11,432 participants, for whom we observed a total of 871 deaths, ascertained over 12.6 year of follow-up. Proportional hazard models and ROC curves were used to evaluate predictions.

Results: Phenotypic Age was significantly associated with all-cause mortality and cause-specific mortality. These results were robust to age and sex stratification, and remained even when excluding short-term mortality. Similarly, Phenotypic Age was associated with mortality among seemingly “healthy” participants—defined as those who were disease-free and had normal BMI at baseline—as well as the oldest-old (aged 85+)—a group with high disease burden.

Conclusions: Phenotypic Age is a reliable predictor of all-cause and cause-specific mortality in multiple subgroups of the population. Risk stratification by this composite measure is far superior to that of the individual measures that go into it, as well as traditional measures of health. It is able to differentiate individuals who appear healthy, who may have otherwise been missed using traditional health assessments. Further, it can differentiate risk among persons with shared disease burden. Overall, this easily measured metric may be useful in the clinical setting and facilitate secondary and tertiary prevention strategies.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted July 05, 2018.
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Phenotypic Age: a novel signature of mortality and morbidity risk
Zuyun Liu, Pei-Lun Kuo, Steve Horvath, Eileen Crimmins, Luigi Ferrucci, Morgan Levine
bioRxiv 363291; doi: https://doi.org/10.1101/363291
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Phenotypic Age: a novel signature of mortality and morbidity risk
Zuyun Liu, Pei-Lun Kuo, Steve Horvath, Eileen Crimmins, Luigi Ferrucci, Morgan Levine
bioRxiv 363291; doi: https://doi.org/10.1101/363291

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