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Obesity and "obesity-related" cancers: are there body mass index cut-points?
Murtha, Jacqueline A; Liu, Natalie; Birstler, Jen; Hanlon, Bret M; Venkatesh, Manasa; Hanrahan, Lawrence P; Borza, Tudor; Kushner, David M; Funk, Luke M.
Afiliação
  • Murtha JA; Department of Surgery, University of Wisconsin, Madison, WI, USA.
  • Liu N; Department of Surgery, University of Wisconsin, Madison, WI, USA.
  • Birstler J; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.
  • Hanlon BM; Department of Surgery, University of Wisconsin, Madison, WI, USA.
  • Venkatesh M; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA.
  • Hanrahan LP; Department of Surgery, University of Wisconsin, Madison, WI, USA.
  • Borza T; Department of Family Medicine and Community Health, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Kushner DM; Department of Urology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Funk LM; Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
Int J Obes (Lond) ; 46(10): 1770-1777, 2022 10.
Article em En | MEDLINE | ID: mdl-35817851
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Uterinas / Obesidade Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Uterinas / Obesidade Idioma: En Ano de publicação: 2022 Tipo de documento: Article