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The role of the gut microbiota in carcinogenesis is increasingly being acknowledged. Recent studies in multiple breast cancer mouse models have found that antibiotics, by altering the gut microbiota, can accelerate tumour growth. In humans, a recent cohort study restricted to triple negative breast cancer showed that breast cancer patients using a greater number of antibiotics had markedly worse survival. These studies have raised concerns about repeated antibiotic use in breast cancer patients. In this Registered Report, we investigated whether breast cancer patients using oral antibiotics had increased breast cancer-specific mortality. In population-based cohorts (n = 44,452), we did not observe a statistically significant association between antibiotic prescriptions after diagnosis and breast cancer-specific mortality (adjusted HR = 1.07 95% CI 0.87, 1.33) apart from prescriptions of 12 or more antibiotics (adjusted HR = 1.62 95% CI 1.31, 2.01). This association was weaker after adjustment for infections (adjusted HR = 1.44 95% 1.14, 1.81), when restricted to antibiotics within five years (adjusted HR = 1.33 95% 0.95, 1.84), and was similar for deaths from other causes (adjusted HR = 1.69 95% 1.19, 2.41). Frequent antibiotic users had higher cancer-specific mortality but the attenuation of associations in sensitivity analyses, and similar findings for other causes of death, suggest this increase may reflect residual confounding. Protocol registration: The Stage 1 protocol for this Registered Report was accepted in principle on 7 November 2023. The protocol, as accepted by the journal, can be found at https://doi.org/10.6084/m9.figshare.24746721.v1 . Studies in mouse models have suggested a link between antibiotic use and breast cancer but epidemiological evidence in human populations is inconsistent. Here, the authors use linked electronic health records from England and Wales to investigate the association between oral antibiotic use and survival in women with breast cancer.

Background Women with dense breasts have an increased risk of breast cancer. However, breast density is measured with variability, which may reduce the reliability and accuracy of its association with breast cancer risk. This is particularly relevant when visually assessing breast density due to variation in inter- and intra-reader assessments. To address this issue, we developed a longitudinal breast density measure which uses an individual woman’s entire history of mammographic density, and we evaluated its association with breast cancer risk as well as its predictive ability. Methods In total, 132,439 women, aged 40–73 yr, who were enrolled in Kaiser Permanente Washington and had multiple screening mammograms taken between 1996 and 2013 were followed up for invasive breast cancer through 2014. Breast Imaging Reporting and Data System (BI-RADS) density was assessed at each screen. Continuous and derived categorical longitudinal density measures were developed using a linear mixed model that allowed for longitudinal density to be updated at each screen. Predictive ability was assessed using (1) age and body mass index-adjusted hazard ratios (HR) for breast density (time-varying covariate), (2) likelihood-ratio statistics (ΔLR- χ 2 ) and (3) concordance indices. Results In total, 2704 invasive breast cancers were diagnosed during follow-up (median = 5.2 yr; median mammograms per woman = 3). When compared with an age- and body mass index-only model, the gain in statistical information provided by the continuous longitudinal density measure was 23% greater than that provided by BI-RADS density (follow-up after baseline mammogram: ΔLR- χ 2  = 379.6 (degrees of freedom ( df ) = 2) vs. 307.7 ( df  = 3)), which increased to 35% (ΔLR- χ 2  = 251.2 vs. 186.7) for follow-up after three mammograms ( n  = 76,313, 2169 cancers). There was a sixfold difference in observed risk between densest and fattiest eight-category longitudinal density (HR = 6.3, 95% CI 4.7–8.7), versus a fourfold difference with BI-RADS density (HR = 4.3, 95% CI 3.4–5.5). Discriminatory accuracy was marginally greater for longitudinal versus BI-RADS density (c-index = 0.64 vs. 0.63, mean difference = 0.008, 95% CI 0.003–0.012). Conclusions Estimating mammographic density using a woman’s history of breast density is likely to be more reliable than using the most recent observation only, which may lead to more reliable and accurate estimates of individual breast cancer risk. Longitudinal breast density has the potential to improve personal breast cancer risk estimation in women attending mammography screening.

It is uncommon for risk groups defined by statistical or artificial intelligence (AI) models to be chosen by jointly considering model performance and potential interventions available. We develop a framework to rapidly guide choice of risk groups in this manner, and apply it to guide breast cancer screening intervals using an AI model. Linear programming is used to define risk groups that minimize expected advanced cancer incidence subject to resource constraints. In the application risk stratification performance is estimated from a case–control study (2044 cases, 1:1 matching), and other parameters are taken from screening trials and the screening programme in England. Under the model, re-screening in 1 year for the highest 4% AI model risk, in 3 years for the middle 64%, and in 4 years for 32% of the population at lowest risk, was expected to reduce the number of advanced cancers diagnosed by approximately 18 advanced cancers per 1000 diagnosed with triennial screening, for the same average number of screens in the population as triennial screening for all. Sensitivity analyses found the choice of thresholds was robust to model parameters, but the estimated reduction in advanced cancers was not precise and requires further evaluation. Our framework helps define thresholds with the greatest chance of success for reducing the population health burden of cancer when used in risk-adapted screening, which should be further evaluated such as in health-economic modelling based on computer simulation models, and real-world evaluations.

We evaluated the association between short-term change in body mass index (BMI) and breast density during a 1 year weight-loss intervention (Manchester, UK). We included 65 premenopausal women (35–45 years, ≥7 kg adult weight gain, family history of breast cancer). BMI and breast density (semi-automated area-based, automated volume-based) were measured at baseline, 1 year, and 2 years after study entry (1 year post intervention). Cross-sectional (between-women) and short-term change (within-women) associations between BMI and breast density were measured using repeated-measures correlation coefficients and multivariable linear mixed models. BMI was positively correlated with dense volume between-women (r = 0.41, 95%CI: 0.17, 0.61), but less so within-women (r = 0.08, 95%CI: −0.16, 0.28). There was little association with dense area (between-women r = −0.12, 95%CI: −0.38, 0.16; within-women r = 0.01, 95%CI: −0.24, 0.25). BMI and breast fat were positively correlated (volume: between r = 0.77, 95%CI: 0.69, 0.84, within r = 0.58, 95%CI: 0.36, 0.75; area: between r = 0.74, 95%CI: 0.63, 0.82, within r = 0.45, 95%CI: 0.23, 0.63). Multivariable models reported similar associations. Exploratory analysis suggested associations between BMI gain from 20 years and density measures (standard deviation change per +5 kg/m2 BMI: dense area: +0.61 (95%CI: 0.12, 1.09); fat volume: −0.31 (95%CI: −0.62, 0.00)). Short-term BMI change is likely to be positively associated with breast fat, but we found little association with dense tissue, although power was limited by small sample size.