Examining the etiology of early-onset breast cancer in the Canadian Partnership for Tomorrow’s Health (CanPath)

Pader, Joy; Basmadjian, Robert B.; O’Sullivan, Dylan E.; Mealey, Nicole E.; Ruan, Yibing; Friedenreich, Christine M.; Murphy, Rachel A.; Wang, Edwin; Quan, May Lynn; Brenner, Darren R.

doi:10.1007/s10552-021-01460-y

Cited by 6 publications

(5 citation statements)

References 53 publications

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“…According to menopausal status, our findings indicate that high WC and WHR promote BC in young women, both premenopausal and postmenopausal. Regarding premenopausal status, the finding is consistent with other prospective studies that have adjusted for BMI in premenopausal women (52,57,59,62,67,76). The mechanisms underlying the increased central obesity and BC risk differ between premenopausal and postmenopausal.…”

Section: Erence In Menstrual Statussupporting

confidence: 90%

The relationship between central obesity and risk of breast cancer: a dose–response meta-analysis of 7,989,315 women

Chen,

Yuan,

Quan

et al. 2023

Front. Nutr.

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PurposeCentral obesity may contribute to breast cancer (BC); however, there is no dose–response relationship. This meta-analysis examined the effects of central obesity on BC and their potential dose–response relationship.MethodsIn the present study, PubMed, Medline, Embase, and Web of Science were searched on 1 August 2022 for published articles. We included the prospective cohort and case–control studies that reported the relationship between central obesity and BC. Summary effect size estimates were expressed as risk ratios (RRs) or odds ratios (ORs) with 95% confidence intervals (95% CI) and were evaluated using random-effect models. The inconsistency index (I2) was used to quantify the heterogeneity magnitude derived from the random-effects Mantel–Haenszel model.ResultsThis meta-analysis included 57 studies (26 case–control and 31 prospective cohort) as of August 2022. Case–control studies indicated that waist circumference (WC) (adjusted OR = 1.18; 95% CI: 1.00–1.38; P = 0.051) and waist-to-hip ratio (WHR) (adjusted OR = 1.28; 95% CI: 1.07–1.53; P = 0.008) were significantly positively related to BC. Subgroup analysis showed that central obesity measured by WC increased the premenopausal (adjusted OR = 1.15; 95% CI: 0.99–1.34; P = 0.063) and postmenopausal (adjusted OR = 1.18; 95% CI: 1.03–1.36; P = 0.018) BC risk and the same relationship appeared in WHR between premenopausal (adjusted OR = 1.38; 95% CI: 1.19–1.59; P < 0.001) and postmenopausal (adjusted OR = 1.41; 95% CI: 1.22–1.64; P < 0.001). The same relationship was observed in hormone receptor-positive (HR+) (adjusted ORWC = 1.26; 95% CI: 1.02–1.57; P = 0.035, adjusted ORWHR = 1.41; 95% CI: 1.00–1.98; P = 0.051) and hormone receptor-negative (HR–) (adjusted ORWC = 1.44; 95% CI: 1.13–1.83; P = 0.003, adjusted ORWHR = 1.42; 95% CI: 0.95–2.13; P = 0.087) BCs. Prospective cohort studies indicated that high WC (adjusted RR = 1.12; 95% CI: 1.08–1.16; P < 0.001) and WHR (adjusted RR = 1.05; 95% CI: 1.018–1.09; P = 0.017) may increase BC risk. Subgroup analysis demonstrated a significant correlation during premenopausal (adjusted RR = 1.08; 95% CI: 1.02–1.14; P = 0.007) and postmenopausal (adjusted RR = 1.14; 95% CI: 1.10–1.19; P < 0.001) between BC and central obesity measured by WC, and WHR was significantly positively related to BC both premenopausal (adjusted RRpre = 1.04; 95% CI: 0.98–1.11; P = 0.169) and postmenopausal (adjusted RRpost = 1.04; 95% CI: 1.02–1.07; P = 0.002). Regarding molecular subtype, central obesity was significantly associated with HR+ (adjusted ORWC = 1.13; 95% CI: 1.07–1.19; P < 0.001, adjusted ORWHR = 1.03; 95% CI: 0.98–1.07; P = 0.244) and HR– BCs (adjusted ORWC =1.11; 95% CI: 0.99–1.24; P = 0.086, adjusted ORWHR =1.01; 95% CI: 0.91–1.13; P = 0.808). Our dose–response analysis revealed a J-shaped trend in the relationship between central obesity and BC (measured by WC and WHR) in case–control studies and an inverted J-shaped trend between BMI (during premenopausal) and BC in the prospective cohort.ConclusionCentral obesity is a risk factor for premenopausal and postmenopausal BC, and WC and WHR may predict it. Regarding the BC subtype, central obesity is proven to be a risk of ER+ and ER- BCs. The dose–response analysis revealed that when BMI (during premenopausal) exceeded 23.40 kg/m2, the risk of BC began to decrease, and WC higher than 83.80 cm or WHR exceeded 0.78 could efficiently increase the BC risk.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42022365788.

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Section: Erence In Menstrual Statussupporting

confidence: 90%

The relationship between central obesity and risk of breast cancer: a dose–response meta-analysis of 7,989,315 women

Chen,

Yuan,

Quan

et al. 2023

Front. Nutr.

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“…In the case of normal weight or overweight (though not morbidly obese) female subjects, our study found that up to 16% had been smokers at some time during their lives. This percentage was lower than in other studies, such as the one carried out by Pader et al (2021), in which the percentage of past smokers was 27%. These differences could stem from a greater awareness of the adverse effects of cigarette consumption for women suffering from cancer.…”

Section: Discussioncontrasting

confidence: 73%

“…Furthermore, the risk of developing cancer is high in the case of obese female smokers, especially those who began smoking at a young age, and who have continued smoking over a long period of time (Byrne et al, 2010; Pader et al, 2021; Reynolds, 2013; van Kruijsdijk et al, 2013). Luo et al (2011) studied a sample population of 79,990 women, 50 to 79 years of age, and found that obese post-menopausal women who were active smokers were at greater risk of developing breast cancer than non-obese post-menopausal women.…”

Section: Introductionmentioning

confidence: 99%

Short Breastfeeding Duration is Associated With Premature Onset of Female Breast Cancer

et al. 2022

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Currently, there is controversy concerning potential factors that contribute to the development of breast cancer. Our study analyzed the possible association between weight status, cigarette consumption, lactation period, serum estrogen levels, family history of breast cancer, and age at breast cancer diagnosis. We conducted a retrospective study at a University Hospital in Granada (Spain) by consulting the medical records of 524 women aged 19 to 91 years, all of them diagnosed and treated for breast cancer from 2011 to 2019. Our findings indicated that in non-morbidly obese females who were also non-smokers, a maternal lactation period of more than 3 months ( p = .013) and the absence of family antecedents of cancer ( p = .025) were statistically significant factors that led to a more advanced age at breast cancer diagnosis. Thus, maternal lactation seems to have a potential protective effect on breast cancer.

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“…While we have shown that OTTEHR enables knowledge transfer between datasets, it would be interesting to further evaluate OTTEHR on other relevant regression and classification tasks and other demographic factors. These include predicting the time interval between consecutive visits [Poole et al, 2016] and mortality rates [Goodacre et al, 2006], using appropriate and potentially larger datasets [Pader et al, 2021, Sudlow et al, 2015]. Also, extending our method to semi-supervised transfer learning [Wei et al, 2019] and designing a unified model that simultaneously solves feature embedding and classification problems [Song et al, 2017] could improve predictive performance on target domains with limited labeled data.…”

Section: Discussionmentioning

confidence: 99%

Transport-based transfer learning on Electronic Health Records: Application to detection of treatment disparities

Li,

Park,

Duc

2024

Preprint

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Many healthcare systems increasingly recognize the opportunities Electronic Health Records (EHRs) promise. However, EHR data sampled from different population groups can easily introduce unwanted biases, rarely permit individual-level data sharing, and make the data and fitted model hardly transferable across different population groups. In this paper, we propose a novel framework that leverages unbalanced optimal transport to facilitate the unsupervised transfer learning of EHRs between different population groups using a model trained in an embedded feature space. Upon deriving a theoretical bound, we find that the generalization error of our method is governed by the Wasserstein distance and unbalancedness between the source and target domains, as well as their labeling divergence, which can be used as a guide for binary classification and regression tasks. Our experiments, conducted on experimental datasets from MIMIC-III database, show that our transfer learning strategy significantly outperforms standard and machine learning transfer learning methods, with respect to accuracy and computational efficiency. Upon applying our framework to predict hospital duration for populations with different insurance plans, we finally find significant disparities across groups, suggesting our method as a potential tool to assess fairness in healthcare treatment.

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Examining the etiology of early-onset breast cancer in the Canadian Partnership for Tomorrow’s Health (CanPath)

Cited by 6 publications

References 53 publications

The relationship between central obesity and risk of breast cancer: a dose–response meta-analysis of 7,989,315 women

The relationship between central obesity and risk of breast cancer: a dose–response meta-analysis of 7,989,315 women

Short Breastfeeding Duration is Associated With Premature Onset of Female Breast Cancer

Transport-based transfer learning on Electronic Health Records: Application to detection of treatment disparities

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