Plausible biological reasons exist regarding why smoking could affect breast cancer risk, but epidemiological evidence is inconsistent.
We used serial questionnaire information from the Generations ...Study cohort (United Kingdom) to estimate HRs for breast cancer in relation to smoking adjusted for potentially confounding factors, including alcohol intake.
Among 102,927 women recruited 2003-2013, with an average of 7.7 years of follow-up, 1815 developed invasive breast cancer. The HR (reference group was never smokers) was 1.14 (95% CI 1.03-1.25; P = 0.010) for ever smokers, 1.24 (95% CI 1.08-1.43; P = 0.002) for starting smoking at ages < 17 years, and 1.23 (1.07-1.41; P = 0.004) for starting smoking 1-4 years after menarche. Breast cancer risk was not statistically associated with interval from initiation of smoking to first birth (P-trend = 0.97). Women with a family history of breast cancer (ever smoker vs never smoker HR 1.35; 95% CI 1.12-1.62; P = 0.002) had a significantly larger HR in relation to ever smokers (P for interaction = 0.039) than women without (ever smoker vs never smoker HR 1.07; 95% CI 0.96-1.20; P = 0.22). The interaction was prominent for age at starting smoking (P = 0.003) and starting smoking relative to age at menarche (P = 0.0001).
Smoking was associated with a modest but significantly increased risk of breast cancer, particularly among women who started smoking at adolescent or peri-menarcheal ages. The relative risk of breast cancer associated with smoking was greater for women with a family history of the disease.
It is plausible that night shift work could affect breast cancer risk, possibly by melatonin suppression or circadian clock disruption, but epidemiological evidence is inconclusive.
Using serial ...questionnaires from the Generations Study cohort, we estimated hazard ratios (HR) and 95% confidence intervals (95%CI) for breast cancer in relation to being a night shift worker within the last 10 years, adjusted for potential confounders.
Among 102,869 women recruited in 2003-2014, median follow-up 9.5 years, 2059 developed invasive breast cancer. The HR in relation to night shift work was 1.00 (95%CI: 0.86-1.15). There was a significant trend with average hours of night work per week (P = 0.035), but no significantly raised risks for hours worked per night, nights worked per week, average hours worked per week, cumulative years of employment, cumulative hours, time since cessation, type of occupation, age starting night shift work, or age starting in relation to first pregnancy.
The lack of overall association, and no association with all but one measure of dose, duration, and intensity in our data, does not support an increased risk of breast cancer from night shift work in women.
Purpose
Family history is an important risk factor for breast cancer incidence, but the parameters conventionally used to categorize it are based solely on numbers and/or ages of breast cancer cases ...in the family and take no account of the size and age-structure of the woman’s family.
Methods
Using data from the Generations Study, a cohort of over 113,000 women from the general UK population, we analyzed breast cancer risk in relation to first-degree family history using a family history score (FHS) that takes account of the expected number of family cases based on the family’s age-structure and national cancer incidence rates.
Results
Breast cancer risk increased significantly (
P
trend
< 0.0001) with greater FHS. There was a 3.5-fold (95% CI 2.56–4.79) range of risk between the lowest and highest FHS groups, whereas women who had two or more relatives with breast cancer, the strongest conventional familial risk factor, had a 2.5-fold (95% CI 1.83–3.47) increase in risk. Using likelihood ratio tests, the best model for determining breast cancer risk due to family history was that combining FHS and age of relative at diagnosis.
Conclusions
A family history score based on expected as well as observed breast cancers in a family can give greater risk discrimination on breast cancer incidence than conventional parameters based solely on cases in affected relatives. Our modeling suggests that a yet stronger predictor of risk might be a combination of this score and age at diagnosis in relatives.
The National Health Service England (NHS) classifies individuals as eligible for lung cancer screening using two risk prediction models, PLCOm2012 and Liverpool Lung Project-v2 (LLPv2). However, no ...study has compared the performance of lung cancer risk models in the UK.
We analysed current and former smokers aged 40-80 years in the UK Biobank (N = 217,199), EPIC-UK (N = 30,813), and Generations Study (N = 25,777). We quantified model calibration (ratio of expected to observed cases, E/O) and discrimination (AUC).
Risk discrimination in UK Biobank was best for the Lung Cancer Death Risk Assessment Tool (LCDRAT, AUC = 0.82, 95% CI = 0.81-0.84), followed by the LCRAT (AUC = 0.81, 95% CI = 0.79-0.82) and the Bach model (AUC = 0.80, 95% CI = 0.79-0.81). Results were similar in EPIC-UK and the Generations Study. All models overestimated risk in all cohorts, with E/O in UK Biobank ranging from 1.20 for LLPv3 (95% CI = 1.14-1.27) to 2.16 for LLPv2 (95% CI = 2.05-2.28). Overestimation increased with area-level socioeconomic status. In the combined cohorts, USPSTF 2013 criteria classified 50.7% of future cases as screening eligible. The LCDRAT and LCRAT identified 60.9%, followed by PLCOm2012 (58.3%), Bach (58.0%), LLPv3 (56.6%), and LLPv2 (53.7%).
In UK cohorts, the ability of risk prediction models to classify future lung cancer cases as eligible for screening was best for LCDRAT/LCRAT, very good for PLCOm2012, and lowest for LLPv2. Our results highlight the importance of validating prediction tools in specific countries.
There has been a worldwide epidemic of obesity in recent decades. In animal studies, there is convincing evidence that light exposure causes weight gain, even when calorie intake and physical ...activity are held constant. Disruption of sleep and circadian rhythms by exposure to light at night (LAN) might be one mechanism contributing to the rise in obesity, but it has not been well-investigated in humans. Using multinomial logistic regression, we examined the association between exposure to LAN and obesity in questionnaire data from over 100,000 women in the Breakthrough Generations Study, a cohort study of women aged 16 years or older who were living in the United Kingdom and recruited during 2003-2012. The odds of obesity, measured using body mass index, waist:hip ratio, waist:height ratio, and waist circumference, increased with increasing levels of LAN exposure (P < 0.001), even after adjustment for potential confounders such as sleep duration, alcohol intake, physical activity, and current smoking. We found a significant association between LAN exposure and obesity which was not explained by potential confounders we could measure. While the possibility of residual confounding cannot be excluded, the pattern is intriguing, accords with the results of animal experiments, and warrants further investigation.
The Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) and the Tyrer-Cuzick breast cancer risk prediction models are commonly used in clinical practice and ...have recently been extended to include polygenic risk scores (PRS). In addition, BOADICEA has also been extended to include reproductive and lifestyle factors, which were already part of Tyrer-Cuzick model. We conducted a comparative prospective validation of these models after incorporating the recently developed 313-variant PRS.
Calibration and discrimination of 5-year absolute risk was assessed in a nested case-control sample of 1337 women of European ancestry (619 incident breast cancer cases) aged 23-75 years from the Generations Study.
The extended BOADICEA model with reproductive/lifestyle factors and PRS was well calibrated across risk deciles; expected-to-observed ratio (E/O) at the highest risk decile :0.97 (95 % CI 0.51 - 1.86) for women younger than 50 years and 1.09 (0.66 - 1.80) for women 50 years or older. Adding reproductive/lifestyle factors and PRS to the BOADICEA model improved discrimination modestly in younger women (area under the curve (AUC) 69.7 % vs. 69.1%) and substantially in older women (AUC 64.6 % vs. 56.8%). The Tyrer-Cuzick model with PRS showed evidence of overestimation at the highest risk decile: E/O = 1.54(0.81 - 2.92) for younger and 1.73 (1.03 - 2.90) for older women.
The extended BOADICEA model identified women in a European-ancestry population at elevated breast cancer risk more accurately than the Tyrer-Cuzick model with PRS. With the increasing availability of PRS, these analyses can inform choice of risk models incorporating PRS for risk stratified breast cancer prevention among women of European ancestry.
Breast development and hormonal changes at puberty might affect breast cancer risk, but epidemiological analyses have focussed largely on age at menarche and not at other pubertal stages.
We ...investigated associations between the timing of pubertal stages and breast cancer risk using data from a cohort study of 104,931 women (Breakthrough Generations Study, UK, 2003-2013). Pubertal variables were reported retrospectively at baseline. Breast cancer risk was analysed using Cox regression models with breast cancer diagnosis as the outcome of interest, attained age as the underlying time variable, and adjustment for potentially confounding variables.
During follow-up (mean = 4.1 years), 1094 breast cancers (including ductal carcinoma in situ) occurred. An increased breast cancer risk was associated with earlier thelarche (age when breast growth begins; HR 95% CI = 1.23 1.02, 1.48, 1 referent and 0.80 0.69, 0.93 for ≤10, 11-12 and ≥13 years respectively), menarche (initiation of menses; 1.06 0.93, 1.21, 1 referent and 0.78 0.62, 0.99 for ≤12, 13-14 and ≥15 years), regular periods (0.99 0.83, 1.18, 1 referent and 0.74 0.59, 0.92 for ≤12, 13-14 and ≥15 years) and age reached adult height (1.25 1.03, 1.52, 1 referent and 1.07 0.87, 1.32 for ≤14, 15-16 and ≥17 years), and with increased time between thelarche and menarche (0.87 0.65, 1.15, 1 referent, 1.14 0.96, 1.34 and 1.27 1.04, 1.55 for <0, 0, 1 and ≥2 years), and shorter time between menarche and regular periods (1 referent, 0.87 0.73, 1.04 and 0.66 0.50, 0.88 for 0, 1 and ≥2 years). These associations were generally similar when considered separately for premenopausal and postmenopausal breast cancer.
Breast duct development may be a time of heightened susceptibility to risk of carcinogenesis, and greater attention needs to be given to the relation of breast cancer risk to the different stages of puberty.
Abstract
Background
External validation of risk models is critical for risk-stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible ...tool for risk model development and comparative model validation and to make projections for population risk stratification.
Methods
Performance of two recently developed models, one based on the Breast and Prostate Cancer Cohort Consortium analysis (iCARE-BPC3) and another based on a literature review (iCARE-Lit), were compared with two established models (Breast Cancer Risk Assessment Tool and International Breast Cancer Intervention Study Model) based on classical risk factors in a UK-based cohort of 64 874 white non-Hispanic women (863 patients) age 35–74 years. Risk projections in a target population of US white non-Hispanic women age 50–70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS).
Results
The best calibrated models were iCARE-Lit (expected to observed number of cases E/O = 0.98, 95% confidence interval CI = 0.87 to 1.11) for women younger than 50 years, and iCARE-BPC3 (E/O = 1.00, 95% CI = 0.93 to 1.09) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify approximately 500 000 women at moderate to high risk (>3% 5-year risk) in the target population. Addition of MD and a 313-variant PRS is expected to increase this number to approximately 3.5 million women, and among them, approximately 153 000 are expected to develop invasive breast cancer within 5 years.
Conclusions
iCARE models based on classical risk factors perform similarly to or better than BCRAT or IBIS in white non-Hispanic women. Addition of MD and PRS can lead to substantial improvements in risk stratification. However, these integrated models require independent prospective validation before broad clinical applications.
Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype associated with a high rate of recurrence and poor prognosis. Recently we identified a hypermethylation in the long ...noncoding RNA 299 (LINC00299) gene in blood-derived DNA from TNBC patients compared with healthy controls implying that LINC00299 hypermethylation may serve as a circulating biomarker for TNBC. In the present study, we investigated whether LINC00299 methylation is associated with TNBC in a prospective nested breast cancer case-control study within the Generations Study. Methylation at cg06588802 in LINC00299 was measured in 154 TNBC cases and 159 breast cancer-free matched controls using MethyLight droplet digital PCR. To assess the association between methylation level and TNBC risk, logistic regression was used to calculate odd ratios and 95% confidence intervals, adjusted for smoking status. We found no evidence for association between methylation levels and TNBC overall (P = 0.062). Subgroup analysis according to age at diagnosis and age at blood draw revealed increased methylation levels in TNBC cases compared with controls in the young age groups age 26-52 (P = 0.0025) and age 22-46 (P = 0.001), respectively. Our results suggest a potential association of LINC00299 hypermethylation with TNBC in young women.
Genome-wide association studies (GWAS) have transformed our understanding of glioma susceptibility, but individual studies have had limited power to identify risk loci. We performed a meta-analysis ...of existing GWAS and two new GWAS, which totaled 12,496 cases and 18,190 controls. We identified five new loci for glioblastoma (GBM) at 1p31.3 (rs12752552; P = 2.04 × 10
, odds ratio (OR) = 1.22), 11q14.1 (rs11233250; P = 9.95 × 10
, OR = 1.24), 16p13.3 (rs2562152; P = 1.93 × 10
, OR = 1.21), 16q12.1 (rs10852606; P = 1.29 × 10
, OR = 1.18) and 22q13.1 (rs2235573; P = 1.76 × 10
, OR = 1.15), as well as eight loci for non-GBM tumors at 1q32.1 (rs4252707; P = 3.34 × 10
, OR = 1.19), 1q44 (rs12076373; P = 2.63 × 10
, OR = 1.23), 2q33.3 (rs7572263; P = 2.18 × 10
, OR = 1.20), 3p14.1 (rs11706832; P = 7.66 × 10
, OR = 1.15), 10q24.33 (rs11598018; P = 3.39 × 10
, OR = 1.14), 11q21 (rs7107785; P = 3.87 × 10
, OR = 1.16), 14q12 (rs10131032; P = 5.07 × 10
, OR = 1.33) and 16p13.3 (rs3751667; P = 2.61 × 10
, OR = 1.18). These data substantiate that genetic susceptibility to GBM and non-GBM tumors are highly distinct, which likely reflects different etiology.
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