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Epidemiology and Population Health

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

International Journal of Obesity volume 46, pages 1770–1777 (2022)Cite this article

Subjects

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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Fig. 1: STROBE diagram—study cohort creation.
Fig. 2: Multivariable logistic regression with cancer incidence as the outcome.
Fig. 3: Relationship of cancer risk and BMI.
Fig. 4: Adjusted cut-point analysis for kidney and uterine cancers.

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Data availability

Aggregated and de-identified data is available upon request. Those interested in acquiring the data will require a Data Transfer and Use Agreement (DTUA) for de-identified data between University of Wisconsin and the recipient. The form is available at https://rsp.wisc.edu/contracts/dtua.cfm under “DTUA forms.”

References

  1. National Center for Health Statistics. National Health and Nutrition Examination Survey, 2017-2018 data: CDC. 2020. https://www.cdc.gov/nchs/data/factsheets/factsheet_nhanes.pdf. Accessed 2 Nov 2020.

  2. National Center for Health Statistics. Obesity and Overweight: CDC. 2018. https://www.cdc.gov/nchs/fastats/obesity-overweight.htm. Accessed 2 Nov 2020.

  3. 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;29:749–57.

    CAS  PubMed  Google Scholar 

  4. 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:djv088.

    PubMed  Google Scholar 

  5. 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.

    PubMed  PubMed Central  Google Scholar 

  6. Lee K, Kruper L, Dieli-Conwright CM, Mortimer JE. The impact of obesity on breast cancer diagnosis and treatment. Curr Oncol Rep. 2019;21:41.

    PubMed  PubMed Central  Google Scholar 

  7. Allott EH, Masko EM, Freedland SJ. Obesity and prostate cancer: weighing the evidence. Eur Urol. 2013;63:800–9.

    CAS  PubMed  Google Scholar 

  8. Lauby-Secretan B, Scoccianti C, Loomis D, Grosse Y, Bianchini F, Straif K. Body fatness and cancer-viewpoint of the IARC Working Group. N Engl J Med. 2016;375:794–8.

    PubMed  PubMed Central  Google Scholar 

  9. Calle EE, Rodriguez C, Walker-Thurmond K, Thun MJ. Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults. N Engl J Med. 2003;348:1625–38.

    PubMed  Google Scholar 

  10. Kolb R, Sutterwala FS, Zhang W. Obesity and cancer: inflammation bridges the two. Curr Opin Pharmacol. 2016;29:77–89.

    CAS  PubMed  PubMed Central  Google Scholar 

  11. American Cancer Society. Cancer Facts & Figures. Atlanta: ACS. 2020. https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-and-statistics/annual-cancer-facts-and-figures/2020/cancer-facts-and-figures-2020.pdf. Accessed 2 Nov 2020.

  12. Ligibel JA, Alfano CM, Courneya KS, Demark-Wahnefried W, Burger RA, Chlebowski RT, et al. American Society of Clinical Oncology position statement on obesity and cancer. J Clin Oncol. 2014;32:3568–74.

    PubMed  PubMed Central  Google Scholar 

  13. Rock CL, Doyle C, Demark-Wahnefried W, Meyerhardt J, Courneya KS, Schwartz AL, et al. Nutrition and physical activity guidelines for cancer survivors. CA Cancer J Clin. 2012;62:243–74.

    PubMed  Google Scholar 

  14. Senkus E, Kyriakides S, Penault-Llorca F, Poortmans P, Thompson A, Zackrisson S, et al. Primary breast cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2013;24:vi7–23.

    PubMed  Google Scholar 

  15. Liu N, Birstler J, Hanlon B, Funk LM. BMI and chronic health conditions: where are the tipping points? J Med Internet Res. 2021;23:e24017.

    PubMed  PubMed Central  Google Scholar 

  16. Chu FL, Hsu CH, Jeng C. Lowered cutoff points of obesity indicators are better predictors of hypertension and diabetes mellitus in premenopausal Taiwanese women. Obes Res Clin Pract. 2015;9:328–35.

    PubMed  Google Scholar 

  17. Mascie-Taylor CGN, Goto R. Human variation and body mass index: a review of the universality of BMI cut-offs, gender and urban-rural differences, and secular changes. J Physiol Anthropol. 2007;26:109–12.

    PubMed  Google Scholar 

  18. Jih J, Mukherjea A, Vittinghoff E, Nguyen TT, Tsoh JY, Fukuoka Y, et al. Using appropriate body mass index cut points for overweight and obesity among Asian Americans. Prev Med. 2014;65:1–6.

    PubMed  PubMed Central  Google Scholar 

  19. Von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. 2008;61:344–9.

    Google Scholar 

  20. Cheng FW, Gao X, Mitchell DC, Wood C, Still CD, Rolston D, et al. Body mass index and all-cause mortality among older adults. Obesity. 2016;24:2232–9.

    PubMed  Google Scholar 

  21. Centers for Medicare and Medicaid Services (CMS). Chronic Conditions Data Warehouse 2021. https://www2.ccwdata.org/web/guest/condition-categories. Accessed 20 June 20 2021.

  22. Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol. 2007;165:710–8.

    PubMed  Google Scholar 

  23. Koenker R, Hallock KF. Quantile Regression. J Econ Perspect. 2001;15:143–56.

    Google Scholar 

  24. Araneta MRG, Kanaya AM, Hsu WC, Chang HK, Grandinetti A, Boyko EJ, et al. Optimum BMI cut points to screen Asian Americans for type 2 diabetes. Diabetes Care. 2015;38:814–20.

    PubMed  PubMed Central  Google Scholar 

  25. Wood S. Generalized additive models: an introduction with R. Second ed. New York: Chapman & Hall/CRC; 2006.

  26. Sollberger TL, Gavrilyuk O, Rylander C. Excess body weight and incidence of type 1 and type 2 endometrial cancer: the Norwegian Women and Cancer Study. Clin Epidemiol. 2020;12:815–24.

    PubMed  PubMed Central  Google Scholar 

  27. Haggerty AF, Sarwer DB, Schmitz KH, Ko EM, Allison KC, Chu CS. Obesity and endometrial cancer: a lack of knowledge but opportunity for intervention. Nutr Cancer. 2017;69:990–5.

    PubMed  PubMed Central  Google Scholar 

  28. Onstad MA, Schmandt RE, Lu KH. Addressing the role of obesity in endometrial cancer risk, prevention, and treatment. J Clin Oncol. 2016;34:4225–30.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. World Cancer Research Fund/American Institute for Cancer Research. Continuous Update Project Report 2018. Food: Nutrition, Physical Activity, and the Prevention of Endometrial Cancer. dietandcancerreport.org. Accessed 2 Nov 2020.

  30. Wang F, Xu Y. Body mass index and risk of renal cell cancer: a dose-response meta-analysis of published cohort studies. Int J Cancer. 2014;135:1673–86.

    CAS  PubMed  Google Scholar 

  31. World Cancer Research Fund/American Institute for Cancer Research. Continuous Update Project Expert Report 2018. Diet, nutrition, physical activity and kidney cancer. dietandcancerreport.org. Accessed 2 Nov 2020.

  32. Pischon T, Lahmann PH, Boeing H, Tjønneland A, Halkjaer J, Overvad K, et al. Body size and risk of renal cell carcinoma in the European Prospective Investigation into Cancer and Nutrition (EPIC). Int J Cancer. 2006;118:728–38.

    CAS  PubMed  Google Scholar 

  33. Nam GE, Cho KH, Han K, Kim CM, Han B, Cho SJ, et al. Obesity, abdominal obesity and subsequent risk of kidney cancer: a cohort study of 23.3 million East Asians. Br J Cancer. 2019;121:271–7.

    PubMed  PubMed Central  Google Scholar 

  34. Hunt JD, van der Hel OL, McMillan GP, Boffetta P, Brennan P. Renal cell carcinoma in relation to cigarette smoking: meta-analysis of 24 studies. Int J Cancer. 2005;114:101–8.

    CAS  PubMed  Google Scholar 

  35. Setiawan VW, Stram DO, Nomura AM, Kolonel LN, Henderson BE. Risk factors for renal cell cancer: the multiethnic cohort. Am J Epidemiol. 2007;166:932–40.

    PubMed  Google Scholar 

  36. Guh DP, Zhang W, Bansback N, Amarsi Z, Birmingham CL, Anis AH. The incidence of co-morbidities related to obesity and overweight: A systematic review and meta-analysis. BMC Public Health. 2009;9:88.

    PubMed  PubMed Central  Google Scholar 

  37. Wise LA, Palmer JR, Spiegelman D, Harlow BL, Stewart EA, Adams-Campbell LL, et al. Influence of body size and body fat distribution on risk of uterine leiomyomata in U.S. black women. Epidemiology. 2005;16:346–54.

    PubMed  PubMed Central  Google Scholar 

  38. Stafford HS, Saltzstein SL, Shimasaki S, Sanders C, Downs TM, Sadler GR. Racial/ethnic and gender disparities in renal cell carcinoma incidence and survival. J Urol. 2008;179:1704–8.

    PubMed  PubMed Central  Google Scholar 

  39. WHO expert consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–63.

  40. Pleijhuis RG, Kwast AB, Jansen L, de Vries J, Lanting R, Bart J, et al. A validated web-based nomogram for predicting positive surgical margins following breast-conserving surgery as a preoperative tool for clinical decision-making. Breast. 2013;22:773–9.

    PubMed  Google Scholar 

  41. Pereira-Azevedo N, Osório L, Fraga A, Roobol MJ. Rotterdam prostate cancer risk calculator: development and usability testing of the mobile phone app. JMIR Cancer. 2017;3:e1–e.

    PubMed  PubMed Central  Google Scholar 

  42. Zhang JX, Song W, Chen ZH, Wei JH, Liao YJ, Lei J, et al. Prognostic and predictive value of a microRNA signature in stage II colon cancer: a microRNA expression analysis. Lancet Oncol. 2013;14:1295–306.

    CAS  PubMed  Google Scholar 

  43. Yang L, Takimoto T, Fujimoto J. Prognostic model for predicting overall survival in children and adolescents with rhabdomyosarcoma. BMC Cancer. 2014;14:654.

    PubMed  PubMed Central  Google Scholar 

  44. Feng LH, Su T, Bu KP, Ren S, Yang Z, Deng CE, et al. A clinical prediction nomogram to assess risk of colorectal cancer among patients with type 2 diabetes. Sci Rep. 2020;10:14359.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Liu A, Wang Z, Yang Y, Wang J, Dai X, Wang L, et al. Preoperative diagnosis of malignant pulmonary nodules in lung cancer screening with a radiomics nomogram. Cancer Commun. 2020;40:16–24.

    CAS  Google Scholar 

  46. Chung JW, Jang ES, Kim J, Jeong S-H, Kim N, Lee DH, et al. Development of a nomogram for screening of hepatitis B virus-associated hepatocellular carcinoma. Oncotarget. 2017;8:106499–510.

    PubMed  PubMed Central  Google Scholar 

  47. PDQ® Screening and Prevention Editorial Board. PDQ Endometrial Cancer Screening Bethesda, MD: National Cancer Institute. https://www.cancer.gov/types/uterine/hp/endometrial-screening-pdq. Accessed December 13, 2021.

  48. Muinelo-Romay L, Casas-Arozamena C, Abal M. Liquid biopsy in endometrial cancer: new opportunities for personalized oncology. Int J Mol Sci. 2018;19:2311. https://doi.org/10.3390/ijms19082311.

    Article  CAS  PubMed Central  Google Scholar 

  49. Escudier B, Porta C, Schmidinger M, Rioux-Leclercq N, Bex A, Khoo V, et al. Renal cell carcinoma: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2019;30:706–20.

    CAS  PubMed  Google Scholar 

  50. Rossi SH, Klatte T, Usher-Smith J, Stewart GD. Epidemiology and screening for renal cancer. World J Urol. 2018;36:1341–53.

    PubMed  PubMed Central  Google Scholar 

  51. Soliman PT, Bassett RL Jr., Wilson EB, Boyd-Rogers S, Schmeler KM, Milam MR, et al. Limited public knowledge of obesity and endometrial cancer risk: what women know. Obstet Gynecol. 2008;112:835–42.

    PubMed  Google Scholar 

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Funding

Effort on this study and manuscript was made possible by an American College of Surgeons George H.A. Clowes Career Development Award and a VA Career Development Award to LMF (CDA 015–060). The views in this study represent those of the authors and not those of the Department of Veterans Affairs or the U.S. Government. The project was also supported by the Clinical and Translational Science Award (CTSA) program, through the National Institutes of Health (NIH) National Center for Advancing Translational Sciences (NCATS) (grant UL1TR002373). Further funding was through the NIH T32 Surgical Oncology Research Training Program (grant CA090217–17) and the NIH Metabolism and Nutrition Training Program T32 (grant DK 007665).

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Author notes

  1. These authors contributed equally: Jacqueline A. Murtha, Natalie Liu.

Authors and Affiliations

  1. Department of Surgery, University of Wisconsin, Madison, WI, USA

    Jacqueline A. Murtha, Natalie Liu, Bret M. Hanlon, Manasa Venkatesh & Luke M. Funk

  2. Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, WI, USA

    Jen Birstler & Bret M. Hanlon

  3. Department of Family Medicine and Community Health, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

    Lawrence P. Hanrahan

  4. Department of Urology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

    Tudor Borza

  5. Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

    David M. Kushner

  6. Department of Surgery, William S. Middleton Memorial VA, Madison, WI, USA

    Luke M. Funk

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Contributions

NL, JB, BMH, MV, LPH, and LMF contributed to study design. JAM, NL, JB, BMH, MV, and LMF contributed to data collection and analysis. JAM, NL, JB, and LMF contributed to manuscript composition. All co-authors participated in the data interpretation and manuscript revisions. All co-authors approved the version to be published and agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work were appropriately investigated and resolved.

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Correspondence to Luke M. Funk.

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Murtha, J.A., Liu, N., Birstler, J. et al. Obesity and “obesity-related” cancers: are there body mass index cut-points?. Int J Obes 46, 1770–1777 (2022). https://doi.org/10.1038/s41366-022-01178-0

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