A propensity-matched comparison of survival after lung resection in patients with a high versus low body mass index.
ABSTRACT An inverse relationship between body mass index (BMI) and the risk of lung cancer has been reported in several studies. In this study, we aimed to assess whether BMI can affect survival after lung resection for cancer.
We reviewed patient data for a 10-year period; 337 patients with BMI ≥30 who underwent lung resection for non-small cell lung cancer were identified. This group of patients was matched at a ratio of 1:1 to a group with BMI <30 and with similar characteristics such as sex, age, lung function test, history of smoking, diabetes, peripheral vascular disease, stroke, myocardial infarction, chronic obstructive pulmonary disease (COPD), procedure type, histology and stage of tumour. We also used the Kaplan-Meier survival curves before and after matching for the above mentioned patient characteristics.
Before adjusting for the preoperative and operative characteristics, despite more history of diabetes, hypertension and renal impairment in patients with BMI ≥30 compared to those with BMI <30 (BMI = 18.5-30 and < 8.5), the survival rate was found to be significantly higher when analysed univariately (P = 0.02). This difference remained significant after adjusting for all the characteristics, suggesting a significantly higher survival rate in the group with BMI ≥30 (P = 0.04).
Unlike in breast cancer, a high BMI in lung cancer patients after resection has protective effects. This may be due to the better nutritional status of the patient, a less aggressive cancer type that has not resulted in weight loss at the time of presentation or it may be due to certain hormones released from the adipose tissue. BMI can be a predictor of outcome after lung resection in cancer patients.
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ABSTRACT: 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. 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. 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. 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.Epidemiology (Cambridge, Mass.) 03/2014; · 5.51 Impact Factor