Predicting all-cause mortality from basic physiology in the Framingham Heart Study.

Pubmed ID: 26446764

Pubmed Central ID: PMC4717277

Journal: Aging cell

Publication Date: Feb. 1, 2016

Affiliation: Department of Developmental Biology, Washington University in St. Louis, MO, 63130, USA.

MeSH Terms: Humans, Male, Adult, Female, Aged, Cardiovascular Diseases, Risk Factors, Middle Aged, Body Mass Index, Blood Pressure, Obesity, Predictive Value of Tests, Body Weight

Grants: R00 AG042487, 5T32 GM07200

Authors: Zhang WB, Pincus Z

Cite As: Zhang WB, Pincus Z. Predicting all-cause mortality from basic physiology in the Framingham Heart Study. Aging Cell 2016 Feb;15(1):39-48. Epub 2015 Oct 8.

Studies:

Abstract

Using longitudinal data from a cohort of 1349 participants in the Framingham Heart Study, we show that as early as 28-38 years of age, almost 10% of variation in future lifespan can be predicted from simple clinical parameters. Specifically, we found diastolic and systolic blood pressure, blood glucose, weight, and body mass index (BMI) to be relevant to lifespan. These and similar parameters have been well-characterized as risk factors in the relatively narrow context of cardiovascular disease and mortality in middle to old age. In contrast, we demonstrate here that such measures can be used to predict all-cause mortality from mid-adulthood onward. Further, we find that different clinical measurements are predictive of lifespan in different age regimes. Specifically, blood pressure and BMI are predictive of all-cause mortality from ages 35 to 60, while blood glucose is predictive from ages 57 to 73. Moreover, we find that several of these parameters are best considered as measures of a rate of 'damage accrual', such that total historical exposure, rather than current measurement values, is the most relevant risk factor (as with pack-years of cigarette smoking). In short, we show that simple physiological measurements have broader lifespan-predictive value than indicated by previous work and that incorporating information from multiple time points can significantly increase that predictive capacity. In general, our results apply equally to both men and women, although some differences exist.