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. 2022 Oct;46(10):1770-1777.
doi: 10.1038/s41366-022-01178-0. Epub 2022 Jul 11.

Obesity and "obesity-related" cancers: are there body mass index cut-points?

Jacqueline A Murtha #  1 , Natalie Liu #  1 , Jen Birstler  2 , Bret M Hanlon  1   2 , Manasa Venkatesh  1 , Lawrence P Hanrahan  3 , Tudor Borza  4 , David M Kushner  5 , Luke M Funk  6   7
Affiliations

Obesity and "obesity-related" cancers: are there body mass index cut-points?

Jacqueline A Murtha et al. Int J Obes (Lond). 2022 Oct.

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.

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Conflict of interest statement

Competing Interests:

The authors declare no conflicts of interest related to these funding sources.

Figures

Figure 1.
Figure 1.. STROBE Diagram – Study Cohort Creation
BMI: body mass index; EHR: electronic health record
Figure 2.
Figure 2.. Multivariable Logistic Regression with Cancer Incidence as the Outcome
Adjusted odds ratio for new cancer diagnosis per 5 kg/m2 increase and 95% confidence interval. The model is adjusted for age, sex (for non-sex specific cancers), race/ethnicity, and smoking status.
Figure 3.
Figure 3.. Relationship of Cancer Risk and BMI
Generalized additive model of the distribution of cancer cases with increasing BMI. The y-axis represents the overall percent of cancer cases diagnosed at a given BMI with BMI functioning as the denominator. The gray shaded area represents the 95% confidence interval.
Figure 4.
Figure 4.. Adjusted Cut-Point Analysis for Kidney and Uterine Cancers
Quantile regression models were adjusted for age, sex (for non-sex specific cancers), race/ethnicity, and smoking status. Estimates represent the change in cut-points at 80% sensitivity when accounting for race/ethnicity, age, smoking status, and sex. For example, from the identified cut-point of 26.3 kg/m2 for kidney cancer, an Asian patient will have a cut-point 1.40 kg/m2 less while a male will have a cut-point 1.73 kg/m2 higher when compared to the referent. Kidney cancer: Referent is a female, white, non-Hispanic who never smoked. BMI is per 5 kg/m2 from the mean BMI, 22.70 kg/m2, and age per 5 years from the mean age of 43.7 years. Uterine cancer: Referent is a female, white, non-Hispanic who never smoked. BMI is per 5 kg/m2 from the mean BMI, 22.74 kg/m2, and age per 5 years from the mean age of 43.7 years.
See this image and copyright information in PMC

References

    1. National Center for Health Statistics. National Health and Nutrition Examination Survey, 2017-2018 data: CDC. 2020. Available from: https://www.cdc.gov/nchs/data/factsheets/factsheet_nhanes.pdf.Date Accessed November 2, 2020.
    1. National Center for Health Statistics. Obesity and Overweight: CDC. 2018. Available from: https://www.cdc.gov/nchs/fastats/obesity-overweight.htm.Date Accessed November 2, 2020.
    1. Choi EK, Park HB, Lee KH, Park JH, Eisenhut M, van der Vliet HJ, et al. Body mass index and 20 specific cancers: re-analyses of dose-response meta-analyses of observational studies. Ann Oncol. 2018;29(3):749–57. - PubMed
    1. Keum N, Greenwood DC, Lee DH, Kim R, Aune D, Ju W, et al. Adult weight gain and adiposity-related cancers: a dose-response meta-analysis of prospective observational studies. J Natl Cancer Inst. 2015;107(2). - PubMed
    1. Kyrgiou M, Kalliala I, Markozannes G, Gunter MJ, Paraskevaidis E, Gabra H, et al. Adiposity and cancer at major anatomical sites: umbrella review of the literature. BMJ. 2017;356:j477–j. - PMC - PubMed

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