Predicted lean body mass, fat mass and risk of lung cancer: prospective US cohort study

Su Min Jeong, Dong Hoon Lee, Edward L. Giovannucci

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

Abstract

An inverse association between body mass index (BMI) and risk of lung cancer has been reported. However, the association of body composition such as fat mass (FM) and lean body mass (LBM) with risk of lung cancer has not been fully investigated. Using two large prospective cohort studies (Nurses’ Health Study, 1986–2014; Health Professionals Follow-up Study, 1987–2012) in the United States, we included 100,985 participants who were followed for occurrence of lung cancer. Predicted FM and LBM derived from validated anthropometric prediction equations were categorized by sex-specific deciles. During an average 22.3-year follow-up, 2615 incident lung cancer cases were identified. BMI showed an inverse association with lung cancer risk. Participants in the 10th decile of predicted FM and LBM had a lower risk of lung cancer compared with those in the 1st decile, but when mutually adjusted for each other, predicted FM was not associated with lung cancer risk (adjusted hazard ratio [aHR] = 0.98, 95% confidence interval [CI] 0.72–1.35; P(trend) = 0.97) whereas predicted LBM had an inverse association (aHR = 0.73, 95% CI 0.53–1.00; P(trend) = 0.03), especially among participants who were current smokers or had smoked in the previous 10 years (aHR = 0.55, 95% CI 0.36–0.84; P(trend) = 0.008). In conclusion, BMI was inversely associated with lung cancer risk. Based on anthropometric prediction equations, low LBM rather than low FM accounted for the inverse association between BMI and lung cancer risk.

Original languageEnglish
Pages (from-to)1151-1160
Number of pages10
JournalEuropean Journal of Epidemiology
Volume34
Issue number12
DOIs
Publication statusPublished - 2019 Dec 1

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature B.V.

All Science Journal Classification (ASJC) codes

  • Epidemiology

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