Up to 40% of oestrogen-receptor-positive breast cancers could in fact be attributable to excess weight.
Excess weight may be responsible for far more postmenopausal breast cancers than we think, a Spanish research team has found, as the body-mass index is an unreliable measure of body fat in older women.
Oestrogen produced in fatty breast tissue stimulates tumour growth, and around 10% of hormone-receptor-positive breast cancers are currently attributed to being overweight, say the authors of the new study, published in BMJ’s Journal of Epidemiology & Community Health.
But the BMI, they say, underestimates body fat in older women, leading to an underestimate of the attributable cancer burden. Using a more sensitive measure that takes into account how body composition varies with age and sex, around 40% of these breast cancers can in fact be attributed to overweight and obesity.
The Clínica Universidad de Navarra–Body Adiposity Estimator (CUN-BAE) adjusts BMI for sex and age, giving a higher adiposity reading for the same BMI with female sex and older age, “and correlates better with body fat and metabolic disorders than BMI”. In a significant limitation, it was developed and validated in a white population, so the present study excluded non-white participants.
The team used data from the large population-based case-control study MCC-Spain, designed to evaluate the environmental and genetic factors associated with a range of cancers.
They took 1033 cases with first incidence breast cancer and 1143 controls, all postmenopausal women. BMI was self-reported to interviewers (a year before diagnosis for the cases, at interview for controls) and CUN-BAE calculated*.
Those in the highest CUN-BAE category (>45% body fat) had more than twice the risk of breast cancer compared to the reference category (<35%) with an odds ratio of 2.13. This relationship was not observed with BMI.
The population attributable fractions for hormone-receptor-positive cancers were 19.9% using BMI and 41.9% using CUN-BAE. For other types of cancer, the PAFs were not significantly different.
“In terms of clinical implementation, CUN-BAE has the simplicity of BMI with improved assessment of body fat, and can be used in primary care with a simple colour scale,” the paper says.
“Our findings suggest that the population impact could be underestimated when using traditional BMI estimates … This information could influence cancer prevention initiatives by highlighting the role of excess body fat in the development of breast cancer and by raising awareness among healthcare professionals and the public.”
University of Sydney Asssociate Professor Nicholas Wilcken, medical oncologist at Westmead Hospital, said the paper made sense and added to the body of evidence for the role of body fat and oestrogen-receptor-positive breast cancer in older women, given that adipose tissue is the main source of oestrogen after menopause.
“It’s reassuring that this study confirms previous findings that there’s a link between body fat and ER-positive breast cancer, at least in older women,” Professor Wilcken told Oncology Republic.
“It’s recognised that BMI is a rough and ready measure of fat content, but not entirely accurate. In older women with less muscle mass, it may underestimate the amount of fat, so we may be underestimating how much obesity is contributing to ER-positive breast cancer.”
It was not necessary to calculate CUN-BAE for patients, he said, only to tweak BMI targets to avoid high-risk territory: “You might say, instead of ‘I want to be under 30’, which is the obese mark in BMI, maybe as I get older I want to be down the 25 end.
“I think if there’s a pragmatic aspect to this story, it would be this is potentially a good news story for prevention, because it shows that if we just all work a bit harder on obesity, we potentially have quite a big effect on breast cancer.”
Journal of Epidemiology and Community Health, 17 October 2024
* The CUN-BAE formula for percentage body fat is: −44.988 + (0.503×age) + (10 689×sex) + (3.172×BMI) – (0.026×BMI2+(0.181×BMI×sex) – (0.02×BMI×age) – (0.005×BMI2×sex) + (0.00021×BMI2×age), where age is expressed in years and sex is encoded as men=0 and women=1.
