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Epidemiology:
doi: 10.1097/EDE.0000000000000075
Methods

Obesity Paradox: Conditioning on Disease Enhances Biases in Estimating the Mortality Risks of Obesity

Preston, Samuel H.; Stokes, Andrew

Open Access
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Abstract

Background:

Many studies have documented an obesity paradox—a survival advantage of being obese—in populations diagnosed with a medical condition. Whether obesity is causally associated with improved mortality in these conditions is unresolved.

Methods:

We develop the logic of collider bias as it pertains to the association between smoking and obesity in a diseased population. Data from the National Health and Nutrition Examination Survey (NHANES) are used to investigate this bias empirically among persons with diabetes and prediabetes (dysglycemia). We also use NHANES to investigate whether reverse causal pathways are more prominent among people with dysglycemia than in the source population. Cox regression analysis is used to examine the extent of the obesity paradox among those with dysglycemia. In the regression analysis, we explore interactions between obesity and smoking, and we implement a variety of data restrictions designed to reduce the extent of reverse causality.

Results:

We find an obesity paradox among persons with dysglycemia. In this population, the inverse association between obesity and smoking is much stronger than in the source population, and the extent of illness and weight loss is greater. The obesity paradox is absent among never-smokers. Among smokers, the paradox is eliminated through successive efforts to reduce the extent of reverse causality.

Conclusion:

Higher mortality among normal-weight people with dysglycemia is not causal but is rather a product of the closer inverse association between obesity and smoking in this subpopulation.

This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives 3.0 License, where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially.

Copyright © 2014 by Lippincott Williams & Wilkins

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