Jacqueline A Murtha1, Natalie Liu1, Jen Birstler2, Bret M Hanlon1,2, Manasa Venkatesh1, Lawrence P Hanrahan3, Tudor Borza4, David M Kushner5, Luke M Funk6,7. 1. Department of Surgery, University of Wisconsin, Madison, WI, USA. 2. Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA. 3. Department of Family Medicine and Community Health, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. 4. Department of Urology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. 5. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. 6. Department of Surgery, University of Wisconsin, Madison, WI, USA. funk@surgery.wisc.edu. 7. Department of Surgery, William S. Middleton Memorial VA, Madison, WI, USA. funk@surgery.wisc.edu.
Abstract
BACKGROUND: Despite compelling links between excess body weight and cancer, body mass index (BMI) cut-points, or thresholds above which cancer incidence increased, have not been identified. The objective of this study was to determine if BMI cut-points exist for 14 obesity-related cancers. SUBJECTS/ METHODS: In this retrospective cohort study, patients 18-75 years old were included if they had ≥2 clinical encounters with BMI measurements in the electronic health record (EHR) at a single academic medical center from 2008 to 2018. Patients who were pregnant, had a history of cancer, or had undergone bariatric surgery were excluded. Adjusted logistic regression was performed to identify cancers that were associated with increasing BMI. For those cancers, BMI cut-points were calculated using adjusted quantile regression for cancer incidence at 80% sensitivity. Logistic and quantile regression models were adjusted for age, sex, race/ethnicity, and smoking status. RESULTS: A total of 7079 cancer patients (mean age 58.5 years, mean BMI 30.5 kg/m2) and 270,441 non-cancer patients (mean age 43.8 years, mean BMI 28.8 kg/m2) were included in the study. In adjusted logistic regression analyses, statistically significant associations were identified between increasing BMI and the incidence of kidney, thyroid, and uterine cancer. BMI cut-points were identified for kidney (26.3 kg/m2) and uterine (26.9 kg/m2) cancer. CONCLUSIONS: BMI cut-points that accurately predicted development kidney and uterine cancer occurred in the overweight category. Analysis of multi-institutional EHR data may help determine if these relationships are generalizable to other health care settings. If they are, incorporation of BMI into the screening algorithms for these cancers may be warranted.
BACKGROUND: Despite compelling links between excess body weight and cancer, body mass index (BMI) cut-points, or thresholds above which cancer incidence increased, have not been identified. The objective of this study was to determine if BMI cut-points exist for 14 obesity-related cancers. SUBJECTS/ METHODS: In this retrospective cohort study, patients 18-75 years old were included if they had ≥2 clinical encounters with BMI measurements in the electronic health record (EHR) at a single academic medical center from 2008 to 2018. Patients who were pregnant, had a history of cancer, or had undergone bariatric surgery were excluded. Adjusted logistic regression was performed to identify cancers that were associated with increasing BMI. For those cancers, BMI cut-points were calculated using adjusted quantile regression for cancer incidence at 80% sensitivity. Logistic and quantile regression models were adjusted for age, sex, race/ethnicity, and smoking status. RESULTS: A total of 7079 cancer patients (mean age 58.5 years, mean BMI 30.5 kg/m2) and 270,441 non-cancer patients (mean age 43.8 years, mean BMI 28.8 kg/m2) were included in the study. In adjusted logistic regression analyses, statistically significant associations were identified between increasing BMI and the incidence of kidney, thyroid, and uterine cancer. BMI cut-points were identified for kidney (26.3 kg/m2) and uterine (26.9 kg/m2) cancer. CONCLUSIONS: BMI cut-points that accurately predicted development kidney and uterine cancer occurred in the overweight category. Analysis of multi-institutional EHR data may help determine if these relationships are generalizable to other health care settings. If they are, incorporation of BMI into the screening algorithms for these cancers may be warranted.
Authors: Cheryl L Rock; Colleen Doyle; Wendy Demark-Wahnefried; Jeffrey Meyerhardt; Kerry S Courneya; Anna L Schwartz; Elisa V Bandera; Kathryn K Hamilton; Barbara Grant; Marji McCullough; Tim Byers; Ted Gansler Journal: CA Cancer J Clin Date: 2012-04-26 Impact factor: 508.702
Authors: E K Choi; H B Park; K H Lee; J H Park; M Eisenhut; H J van der Vliet; G Kim; J I Shin Journal: Ann Oncol Date: 2018-03-01 Impact factor: 32.976
Authors: NaNa Keum; Darren C Greenwood; Dong Hoon Lee; Rockli Kim; Dagfinn Aune; Woong Ju; Frank B Hu; Edward L Giovannucci Journal: J Natl Cancer Inst Date: 2015-03-10 Impact factor: 13.506
Authors: Jennifer A Ligibel; Catherine M Alfano; Kerry S Courneya; Wendy Demark-Wahnefried; Robert A Burger; Rowan T Chlebowski; Carol J Fabian; Ayca Gucalp; Dawn L Hershman; Melissa M Hudson; Lee W Jones; Madhuri Kakarala; Kirsten K Ness; Janette K Merrill; Dana S Wollins; Clifford A Hudis Journal: J Clin Oncol Date: 2014-10-01 Impact factor: 44.544
Authors: Béatrice Lauby-Secretan; Chiara Scoccianti; Dana Loomis; Yann Grosse; Franca Bianchini; Kurt Straif Journal: N Engl J Med Date: 2016-08-25 Impact factor: 91.245
Authors: Maria Kyrgiou; Ilkka Kalliala; Georgios Markozannes; Marc J Gunter; Evangelos Paraskevaidis; Hani Gabra; Pierre Martin-Hirsch; Konstantinos K Tsilidis Journal: BMJ Date: 2017-02-28