Prevention through early screening remains one of the most optimal strategies against breast cancer. The primary objective of this analysis was to determine the prevalence of breast cancer screening using the clinical breast examination (CBE) and breast self-examination (BSE) methods among women aged 15-49 years, and the secondary objective was to explore demographic and socio-economic factors associated with clinical breast examination (CBE) and breast self-examination (BSE) breast cancer screening methods.the study used Demographic Health Survey data collected in 2014. The study participants were Basotho women aged 15-49 years. STATA 17 was employed for developing logistic regressions and weighting for sampling probabilities and non-response. Complex sampling procedures were also considered during testing of statistical significance.variables that were associated with significantly increased odds of having you had a breast cancer either self-examination or clinical test in last 12 months were: i) visiting a health centre in the past 12 months ; ii) completion of primary level education ; iii) being aware of breast cancer ; and iv) age ; while district of origin  was significantly associated with decreased odds of the outcome.our findings suggest that raising awareness about breast cancer is the most effective method of improving breast cancer screening among women in Lesotho. Breast cancer is associated with serious morbidity, low quality of life and mortality . TreatmeSeveral studies have investigated the factors associated with breast cancer screening. The first strand of studies investigated the factors associated with undertaking a mammography -14. The The primary objective of this study is to investigate the prevalence of breast screening among women aged 15-49 using CBE and BSE methods in Lesotho. The secondary aim, is to analyse the socio-economic and demographic factors which are associated with breast cancer screening, using CBE and BSE, among women aged 15-49 in Lesotho. Currently the prevalence of breast cancer screening is unknown for Lesotho due to absence of National Cancer Registry. Secondly, confirmed cases of breast cancer are not treated locally, they are treated in South Africa and India. In most cases, referrals are made where breast cancer is detection at an advanced stage. As a result, policy interventions which promote breast cancer screening are essential for developing countries such as Lesotho.The study participants were Basotho women aged 15-49 years from the 10 districts of Lesotho in Southern Africa.This study is an exploratory analysis of secondary data derived from the 2014 national demographic health survey (DHS) database. DHS is designed to provide estimates of national and regional health indicators for the 10 districts of Lesotho. The sampling frame used for the 2014 LDHS is an updated frame from the 2006 Lesotho Population and Housing Census (PHC) provided by the Lesotho Bureau of Statistics (BOS). The sampling frame excluded nomadic and institutional populations such as persons in hotels, barracks, and prisons.The 2014 LDHS followed a two-stage sample design and was intended to allow estimates of key indicators at the national level as well as in urban and rural areas, four ecological zones, and each of Lesotho´s 10 districts . The firWe included all women aged 15-49 years for whom we had complete data on all the variables-which included the outcome and independent variables described below. As a result, out of 9,942 participants, there were 6621 women and 2931 were men. From a sample of 6,621 women, only 6187 were eligible for this study because they had knowledge of undertaking either a CBE or BSE in the last 12 months as shown in The outcome variable was: have you had a BSE or CBE in last 12 months? (yes/no)Age (in years), wealth index, district of origin, living in a rural or urban area, owning a radio or TV set, having a health insurance policy, awareness of breast cancer, visiting a health centre in the last 12 months, were among the independent variables.We used descriptive statistics - reported as count (percentage) - to describe the characteristics of the respondents included in the analysis. We used a multivariable logistic regression to explore whether the independent factors where associated with the outcome. This analysis appropriately adjusted for the complex sampling design by using the design weights . We alsoIt is estimated that 3,887/6,187 (62.8%) of the study participants were form rural areas as shown in Prevalence of breast cancer screening: it is estimated that 38.5%   of the respondents had undertaken either a BSE or CBE in the past 12 months.Factors associated with breast cancer screening: as shown in Women from Butha Buthe district  were significantly associated with decreased odds of the outcome relative to Maseru which is capital district of Lesotho.There was no significant association between having either a BSE or CBE in last 12 months and owning a medical insurance. Similarly, the outcome variable was not significantly associated with being from a rural area, owning a radio or television set, household wealth and originating from other districts excluding the capital district which was used as a reference district.The study also investigated the relationship between district of origin, breast cancer awareness and the outcome variable. The results of the main and interaction effects shown in Key findings: based on the multivariable logistic regression estimated above, it is evident that having visited a health centre in the past 12 months, completing primary level education, awareness of breast cancer and age group 35-39 years were significantly associated with having you had either a BSE or CBE in last 12 months.The results from the interaction effects reveal that lack of knowledge about breast cancer is the main challenge for women from Butha Buthe, Mokhotlong, Thaba Tseka, Mafeteng, Mohale´s Hoek and Quthing districts to undertake either a CBE or BSE.How results compare with similar studies: these results are congruent with existing literature which indicates that breast cancer screening is highly associated with: (i) higher ager [her ager , 27-29; her ager , 28-30; her ager , 31; TheThe results further indicate that having visited a health centre in the past 12 months, having completed primary level education, and being aware of the breast cancer, were associated with significantly higher the odds of having either a BSE or CBE in last 12 months.The interaction results indicate the importance of breast cancer awareness on undertaking either a CBE or BSE. Firstly, the odds of women undertaking either a CBE or BSE are significantly increased amongst women with knowledge of breast cancer as compared to with women without knowledge of breast cancer. Secondly, women from certain districts of the country, namely Butha Buthe, Mokhotlong, Thaba Tseka, Mafeteng, Mohale´s Hoek and Quthing, are not undertaking either a CBE or BSE because they do not have knowledge of breast cancer. Thirdly, the results reveal that younger women can undertake breast cancer screening if they were aware of breast cancer. Lastly, the results reveal that awareness raising yields significant outcomes when undertaken at health centres as compared to radio or TV.Limitations: this is an exploratory analysis based on cross-sectional data and therefore we caution against inferring causal links in the results. Secondly, the study prevalence is based on self-reported data as opposed to clinical surveillance and we therefore caution its adoption as the national prevalence of breast cancer screening. Thirdly, the study focused on BSE and CBE as methods of breast cancer screening due to limited data on mammography.The prevalence rate of breast cancer screening, measured using BSE and CBE, among women aged 15-49 in Lesotho is estimated at 38.5%. In order to improve it, policy interventions should seek to improve knowledge of women on breast cancer. The interaction results indicate awareness campaigns are most effective when conducted at health centres as opposed to radios or television.Breast cancer screening in developing countries can be measured using BSE and CBE;There are socio-economic factors affecting breast cancer screening, which are country and location specific;Media advocacy is one of the most effect tool for promoting breast cancer screening.The study estimated the prevalence of breast cancer screening in Lesotho, measured using BSE and CBE, which was not known;The study presents new socio-economic variables which are associated with breast cancer screening; having visited a health centre in the last 12 months;The study proves that health education at health facilities on breast cancer is more effective than media advocacy in promoting breast cancer screening amongst women.


Perturbations in circulating metabolites prior to a breast cancer diagnosis are not well characterised. We aimed to gain more detailed knowledge to help understand and prevent the disease.Baseline plasma samples from 791 breast cancer cases and 791 matched controls from the E3N (EPIC-France) cohort were profiled by nuclear magnetic resonance (NMR)-based untargeted metabolomics. Partial least-squares discriminant analysis (PLS-DA) models were built from NMR profiles to predict disease outcome, and odds ratios and false discovery rate (FDR)-adjusted CIs were calculated for 43 identified metabolites by conditional logistic regression.Breast cancer onset was predicted in the premenopausal subgroup with modest accuracy , and 10 metabolites associated with risk, particularly histidine , N-acetyl glycoproteins , glycerol  and ethanol . No predictive capacity or significant metabolites were found overall or for postmenopausal women.Perturbed metabolism compared to controls was observed in premenopausal but not postmenopausal cases. Histidine and NAC have known involvement in inflammatory pathways, and the robust association of ethanol with risk suggests the involvement of alcohol intake. Firstly, few of these studies have presented results by menopausal status, which may be an important determinant of both normal and pathological metabolic conditions. Secondly, alcohol intake is a likely risk factor for breast cancer, but most previous studies have relied on participant self-reported assessment. Although previous studies controlled for self-reported alcohol intake, few were able to include a biomarker surrogate for additional validation.Knowledge of pre-diagnostic metabolism can potentially help identify population subgroups at greater risk and provide insight into the mechanisms of early carcinogenesis. A small number of studies have employed metabolomics previously to estimate associations prospectively between breast cancer risk and plasma or serum metabolite concentrations.In this study, we test plasma metabolite associations with breast cancer risk using one of the largest metabolomics studies to date on the disease, in the French E3N cohort. Our study benefited from untargeted NMR metabolomics data that included the measurement of proxies of systemic inflammation (N-acetyl glycoproteins) and recent alcohol intake (free plasma ethanol), as well as a range of other endogenous metabolites. We aimed firstly to determine if full untargeted NMR plasma profiles were able to distinguish pre-diagnostic breast cancer cases from controls using multivariate predictive models, and secondly to test metabolite associations prospectively with breast cancer risk. Knowledge of how plasma metabolism is perturbed prior to a diagnosis may help disentangle the complex web of risk factors and translate into more effective disease prevention strategies.Etude Epidémiologique auprès de femmes de la MGEN  cohort (E3N), a French multicentre prospective study designed to investigate risk factors for cancer and major non-communicable diseases in women. The cohort comprised nearly 100,000 women aged 40–65 years insured through a national health system and enroled from 1990 after returning baseline self-administered questionnaires.15 Every 2–3 years after baseline, follow-up questionnaires were collected to update information about diet, lifestyle characteristics and medical events. These included detailed food-frequency questionnaires that allowed the calculation of alcohol intake in g/day. Blood samples were collected from around a quarter of all participants between 1994 and 1999. The E3N cohort was granted ethical approval by the French National Commission for Computed Data and Individual Freedom  and all E3N participants provided written consent for the use of their blood samples and all data. E3N is the French component of the European Prospective Investigation into Cancer and Nutrition (EPIC), a collaborative study of over 500,000 participants in 10 countries coordinated by the International Agency for Research on Cancer, Lyon, France.The present study is based on a case–control study nested within the n = 812) with available blood samples at baseline were identified. Each case was matched to a control who was free of cancer at the time of diagnosis. Matching factors were age at blood collection (±1 year), département (county) of residence (or collection centre), blood collection date (±9 months), menopausal status at blood collection date  and fasting status (yes or no). After the exclusion of 7 breast cancer cases with no eligible control, 1610 plasma samples (805 cases and 805 matched controls) were retained.Participants of E3N were asked to declare any new cancer event in periodic follow-up questionnaires. These reports were then investigated and validated by collecting pathological reports or clinical records from doctors. Tumour characteristics, such as stage, behaviour, histological subtype and hormone receptor status, were extracted from the reports. Incident breast cancer cases  collected from one healthy donor were prepared in parallel with experimental samples. These were placed at the beginning and end of each sample rack to evaluate analytical variability.Blood samples were collected, processed and stored as previously described.1H-NMR spectra were acquired on a Bruker Avance III spectrometer  operating at 600.55 MHz and equipped with a temperature-controlled automatic sample changer and 5-mm TCI cryo probe. Standard 1H-NMR pulse sequences, NOESY and CPMG with water pre-saturation, were applied to samples to generate raw spectra  based on full untargeted NMR profiles. Models were fit for all participants, by menopausal status at blood collection , age category at diagnosis (over or under 55 years), fasting status at blood collection (yes or no) and time to diagnosis (within 2 or 5 years of blood collection).After the exclusion of 28 participants for which spectra did not meet quality control checks, 1582 remained for statistical analysis. Relative standard deviations (RSD) were calculated for each chemical shift region as a check of analytical reproducibility, and the PC-PR2 method was employed to assess the effect of different variables upon metabolomics data.18 This matrix of residuals was used to fit a partial least-squares discriminant analysis (PLS-DA) model with case–control status as a binary response. Models were trained and refined on a random 75% of these data and tested on the remainder, and this final model used to predict case–control status for the test observations. Accuracy, as receiver-operating characteristic (ROC) area under the curve (AUC), was used to assess model performance. Analyses were performed using R statistical software, version 3.5.2 and PLS-DA models were fit using the caret package.19NMR variables were transformed to the residuals of a linear model of metabolite intensity on blood collection centre, week of laboratory analysis, biobank storage time, waiting time to sample fractionation and fasting status.P-values were adjusted using the false discovery rate (FDR) procedure and the significance threshold set at 0.05. For those associations that were significant by these criteria, FDR-adjusted confidence intervals (CI) for ORs were also calculated using the method of Benjamini and Yekutieli.20Odds ratios (OR) and 95% confidence intervals were then calculated for each of the 43 annotated metabolites using conditional logistic regression. The highest and lowest 1% of intensities were first excluded for each metabolite and data were modelled as continuous variables with odds ratios corresponding to a one-SD increase in relative concentration. Models were adjusted for sample waiting time before fractionation, BMI, diabetes status, sample-storage time, waist-to-hip ratio, daily alcohol intake (g/day) and duration of use of menopause hormonal treatment at blood collection. To account for multiple testing, The relationship between reported alcohol intake and plasma ethanol was investigated on a continuous basis by Pearson correlation and for quartiles of alcohol intake by the Kruskal–Wallis one-way analysis of variance. As a sensitivity analysis and further investigation of alcohol intake in relation to breast cancer, the premenopausal metabolite risk models were repeated treating plasma ethanol concentration as a measure of exposure to alcohol, adjusting for this variable in all other metabolite models.A sensitivity analysis was carried out excluding non-fasting matched pairs overall and by menopausal status. For the premenopausal subgroup only, further sensitivity analyses were performed adjusting for lifetime alcohol intake pattern and excluding cases diagnosed in the first two years after blood collection and their corresponding controls.Baseline characteristics of the metabolomics study participants are shown in Table The processing of raw NMR spectra produced a matrix of 8500 chemical shift regions, with a median RSD of 6.9% across QCs  or categorical basis  . Table P-values < 0.05 in at least one of the study groups. ORs and P-values for all metabolites are given overall and for pre- and postmenopausal subgroups in Supplementary Tables In the whole study, concentrations of N-acetyl glycoproteins (NAC), ethanol, hypoxanthine and dimethylamine, were positively associated with risk of breast cancer, although these associations were not significant after controlling for the false discovery rate . In the premenopausal subgroup, ORs for fasting participants remained unchanged or increased for histidine, NAC and ethanol, valine and leucine, although the association of glycerol with risk was diminished. P-values no longer met the FDR threshold due to the limited number of participants in these subgroups. ORs and P-values for all metabolites are given overall and for pre- and postmenopausal subgroups by fasting status in Supplementary Tables The exclusion of non-fasting participants  attenuated ORs in some cases, particularly in the postmenopausal subgroup. Most notably, total fatty acids became suggestively associated with breast cancer risk . NAC is involved in the activation of inflammatory pathways and is a robust indicator of systemic inflammation similar to C-reactive protein.24 Although not measured previously in prospective studies on breast cancer, it has been associated with an increased risk of all-cause and cancer-specific mortality.25 Also positively associated with risk was histidine, which has previously been implicated in breast and colorectal cancer as a necessary precursor of histamine, whose release is an early event in inflammatory responses and that is a regulator of cell proliferation.26 Other notable positive associations were observed for glutamate, which has previously been linked to insulin resistance27 and glycerol, which is perturbed in conditions of dyslipidaemia.28 The reduction is oestrogen levels post menopause and the associated physiological changes likely drive the differences in metabolite associations by menopausal status. A recent study suggested that menopause attenuates metabolism, particularly lipids and inflammatory biomarkers, independently of advancing age.29 Thus, our results on a relatively small proportion of the metabolome might reflect complex interactions between these factors and metabolic changes related to early carcinogenesis.In our study, no metabolites met the 30 In the full E3N cohort, associations were found for postmenopausal breast cancer risk only31 and the metabolomics subset under study was representative in this respect with a borderline positive association per 10 g of alcohol intake in postmenopausal women and no association in premenopausal women (data not shown). Two previous studies have linked circulating ethanol to breast cancer risk, predominantly in postmenopausal women: an NMR metabolomics study that found ethanol to be part of a profile predictive of disease development7 and a nested case–control study within the PLCO cohort that found ethanol glucuronide, a known biomarker of alcohol intake, to be associated with overall risk, as well as other metabolites originating from alcoholic drinks.10 Multiple mechanisms have been proposed to link ethanol and breast cancer risk. A portion of absorbed ethanol is converted by alcohol dehydrogenase to acetaldehyde, a carcinogen that promotes tumorigenesis by forming DNA adducts.32 Exposure to ethanol is known to disrupt endogenous metabolism. For example, blood lipids such as glycerophospholipids are perturbed by high alcohol intake,35 and amino acid profiles were perturbed by heavy alcohol intake in Japanese men.36 Levels of branched-chain amino acids, including leucine, were seen to increase in response to high wine intake.37 Metabolic changes due to alcohol intake are broad since regular ethanol exposure may disrupt the growth of gut microbiota and thus affect nutrient absorption, cause hepatocyte damage38 and react directly with endogenous metabolites.Alcohol intake is considered a risk factor for breast cancer in both pre- and postmenopausal women.39 In addition, ethanol was strongly positively correlated with some amino acids and inversely correlated with overall fatty acids, and associations between plasma concentrations and breast cancer risk for most other metabolites disappeared or became weaker when plasma ethanol was included as an additional covariate in these models. Therefore, the possibility of residual confounding by alcohol intake should be considered.We observed a strong association between plasma ethanol and premenopausal breast cancer risk only, in contrast to the associations for self-reported alcohol intake, and no detectable correlation between plasma ethanol and alcohol intake. Assuming our measurements of free plasma ethanol were representative of overall blood ethanol, this suggests that premenopausal cases consumed more alcohol than controls in the hours preceding blood collection even though no difference was observed in reported intakes between cases and controls. Also, the self-reporting of alcohol intake has been suggested to be subject to bias in observational studies.40 Also, NMR-based metabolomics does not allow the identification of metabolites to the same degree as mass spectrometry-based platforms, and fewer metabolites were included than in other recent studies, representing a small proportion of the metabolome only. The metabolite set was nonetheless diverse and included representatives of important pathways. Finally, associations for some metabolites weakened upon exclusion of diagnoses made within 2 years of baseline, suggesting the presence, to some extent, of reverse causation.The strengths of our study were its substantial size and capacity for subgroup analysis, and particularly the inclusion of more than 300 premenopausal breast cancer cases. Originating from a single country, detailed medical and lifestyle data were acquired and processed consistently. All samples were analysed on the same laboratory platform that has proven to be stable and robust, avoiding the need to account for inter-laboratory variability, and sample processing parameters were of negligible influence on metabolomics data. The study is also subject to certain limitations. Firstly, most participants had not fasted prior to blood collection and time since the last meal was not recorded. However, sensitivity analyses suggested little effect of fasting status upon the polar metabolites most strongly associated with premenopausal breast cancer, and the distinction between pre- and postmenopausal associations remained. Blood samples were only available at a single timepoint meaning that the reproducibility of metabolite measurements could not be assessed, although it is likely that most of these endogenous metabolites are biologically reproducible within a 2-year period.In summary, untargeted plasma NMR profiles at blood collection were modestly predictive of breast cancer in premenopausal women only. However, differential metabolite associations with breast cancer were found for pre- and postmenopausal women. The metabolites most associated with the disease in premenopausal women were correlated to a substantial extent with plasma ethanol, suggesting residual confounding by alcohol intake. Stratification by menopausal status and a more meticulous consideration of alcohol intake, either by measurement error correction or the use of biomarkers, is therefore needed in future studies of the disease.Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization.Supplementary material


There is substantial overlap in risk factors for the pathogenesis and progression of breast cancer (BC) and cardiovascular disease (CVD), including obesity, metabolic disturbances, and chronic inflammation. These unifying features remain prevalent after a BC diagnosis and are exacerbated by BC treatment, resulting in elevated CVD risk among survivors. Thus, therapies that target these risk factors or mechanisms are likely to be effective for the prevention or progression of both conditions. In this narrative review, we propose time-restricted eating (TRE) as a simple lifestyle therapy to address many upstream causative factors associated with both BC and CVD. TRE is simple dietary strategy that typically involves the consumption of ad libitum energy intake within 8 h, followed by a 16-h fast. We describe the feasibility and safety of TRE and the available evidence for the impact of TRE on metabolic, cardiovascular, and cancer-specific health benefits. We also highlight potential solutions for overcoming barriers to adoption and adherence and areas requiring future research. In composite, we make the case for the use of TRE as a novel, safe, and feasible intervention for primary and secondary BC prevention, as well as tertiary prevention as it relates to CVD in BC survivors. Breast cancer is the most common malignancy among women worldwide, with 1 in 8 North American women expected to be diagnosed in their lifetime ,3,4,5. MFortunately for those who receive a breast cancer diagnosis, the death rate for early stage (non-metastatic) breast cancer has dropped by over 40% in the last 40 years . ConcomiThe multi-faceted and intertwined risk profile for breast cancer and cardiovascular disease thus creates a shared opportunity to reduce the risk of both conditions by treating their shared underlying biological and behavioral mechanisms. In this context, therapies that target multiple possible biologic and behavioral mechanisms or pathways for these conditions will be the most effective for prevention. Intermittent fasting is a relatively new dietary intervention that has recently gained substantial public interest. There are multiple different formats including alternative day fasting , periodic prolonged fasting , and repeated daily fasting. The latter, referred to as Time Restricted Feeding  or Time-Restricted Eating (TRE) (for humans), involves the consumption of ad libitum energy intake within a set time window, ranging from 4–10 h, but most commonly 8 h. This is followed by a water-only fast for the remaining time in the 24-h period, typically 16 h . As 16:8 TRE requires relatively minor lifestyle changes and has simple instructions , it may 2, as defined by having overweight, is a predictor of postmenopausal breast cancer [Body mass is an important determinant of metabolic health, which is often signified by the absence of elevated cholesterol, triglycerides, blood pressure, blood sugar and waist circumference. Body mass index (BMI) is most often used to classify the level of health risk associated with body mass relative to height. Notably, having even a modest increase in body mass, such that BMI exceeds 25 kg/mt cancer , breast t cancer  and cardt cancer . Accumult cancer . Likewist cancer . In addit cancer . While wt cancer ,34. Preclinical data have highlighted the significance of the timing of food intake in weight gain ,36. The A 16:8 ad libitum TRE protocol typically results in 20–30% spontaneous caloric restriction and mild weight loss of 1–4% over 1–12 weeks without the need to count calories . A recenPreliminary evidence suggests that TRE reduces fat in ‘ectopic’ regions of the body  ,45,46,47Oxidative stress and chronic inflammation are unifying features in the pathogenesis and progression in both cancer and cardiovascular disease and their shared risk factor of obesity . OxidatiObesity is said to be a state of chronic inflammation. Chronic inflammation promotes malignant transformation of cells, carcinogenesis, and progression and is a precursor to stroke, as well as mediates all stages of atherosclerosis and other cardiovascular disease events ,64. One Metabolic syndrome is a constellation of metabolic disturbances including hyperglycemia, dyslipidemia, hypertension, and abdominal obesity, that increase the risk of heart disease, stroke, and type 2 diabetes. Metabolic syndrome also appears to play an important role in breast cancer. A recent meta-analysis showed that women with metabolic syndrome have a 52% increased risk of breast cancer . Among wGlucose metabolism specifically has also recently emerged as a key biological mechanism in breast cancer development . BiologiTwo prospective cohort studies illustrate the potential for TRE to improve chronic glucose control as a strategy for primary and tertiary prevention of breast cancer. Among 2212 women with elevated BMI, each 3-h increase in habitual overnight fast time was associated with 19% lower odds of elevated hemoglobin A1c . Among 2The individual components of metabolic syndrome are also linked to both breast cancer and cardiovascular disease. Hypertension is one of the main causal risk factors related to cardiovascular disease  with a sDyslipidemia, defined as elevated total or low-density lipoprotein (LDL) cholesterol, or low high-density lipoprotein (HDL) cholesterol, is another important metabolic risk factor. Research regarding the association between blood lipid levels and breast cancer incidence is mixed. Some studies have suggested an inverse relationship between lipid levels and breast cancer risk ,85 whileThe final component of the metabolic syndrome, abdominal obesity, is measured by elevated waist circumference (≥88 cm for women), a simple and practical anthropometric measure. The primary driver of the relationship between abdominal obesity and poor metabolic health is the volume of visceral fat. As discussed earlier, visceral adiposity is strongly linked to the development of cardiovascular disease and breast cancer ,49 and rPhysical activity is an important protective factor for breast cancer incidence ,93, breaWhen TRE or other forms of intermittent fasting or caloric restriction are combined with a purposeful exercise training intervention, additive or synergistic favorable effects on a number of health outcomes relevant to breast cancer and cardiovascular disease have been reported, including cardiorespiratory fitness, body composition, fasting insulin and glucose, and insulin-like growth factor-1 ,105,106.Shortening the eating window to follow TRE may incidentally result in dietary behavior changes that are independently linked to breast cancer incidence including reduced caloric intake as already discussed, as well as reductions in alcohol consumption and late-night snacking on sweet ,109,110.Disruption of circadian rhythms can be associated with abnormal cellular division associated with tumorigenesis . DisruptThe regular exposure to a fasting period induced by TRE also has benefits for cellular health. Regular fasting activates cell signaling pathways and integrated adaptive responses between and within organs that increase the expression of antioxidant defenses, DNA repair, protein quality control, mitochondrial biogenesis, autophagy, and reduces inflammation . This adOne compelling finding directly relevant to breast cancer is that breast cancer survivors without diabetes who reported habitually fasting overnight for less than <13 h, had a 36% higher risk of breast recurrence   . In thisThere is preliminary evidence that TRE may be effective in primary and secondary prevention of heart failure. An observational study of 2001 patients undergoing cardiac catheterization without prior myocardial infarction or heart failure reported that prolonged nightly fasting or religious fasting was associated with a 71% reduced incidence of heart failure  and a trend toward reduced incidence of myocardial infarction  . A prospFasting has been employed for various lengths of times safely for centuries. Fasting for religious reasons is common in two of the most prevalent religions worldwide: Judaism and Islam. Fasting durations employed by individuals following these religious practices is often much more prolonged than for TRE. For example, over the 30 days of Ramadan, individuals fast for anywhere from 10 to 21 h per day depending on their location in the world. In Judaism, personal fasting is undertaken as an act of penance. The most famous fast day of Judaism is Yom Kippur, which consists of a 25-h fast. These religious fasts differ from TRE in that they require total abstinence from food and drink, including water. Water-less fasting has been associated with a state of dehydration . NonetheA 2020 systematic review reported that TRE did not cause major adverse events or negatively impact eating disorder symptoms among adults with obesity, metabolic syndrome, or diabetes . Within Typically, in the process of losing fat mass through a calorie restricted diet, patients can experience a decrease in fat-free or lean mass that contributes to 20–35% of the total weight lost, depending on baseline weight . HoweverTypically, self-directed dietary regimens involving caloric restriction and/or macronutrient manipulation require patients to self-monitor and adjust their dietary intake, which is highly burdensome for some individuals  and can https://www.zerofasting.com/ and https://www.bodyfast.de/en/), but this is not required to be able to follow TRE. The low cost and simplicity of TRE as an intervention reduce common barriers to the adoption and maintenance of lifestyle therapies.In contrast, TRE is simple to prescribe and follow. It requires minimal instruction and no specialized training or equipment . This in turn results in minimal administrative time and costs for health care practitioners to prescribe TRE. Contrary to other caloric restrictive diets, which can include changes to the macronutrient content of the diet, TRE diets allow participants to continue enjoying the foods they habitually consume. This makes it easier to implement for patients and does not increase food-related costs. There are also free phone applications available to assist patients to track their eating window (e.g., A recent systematic review demonstrates that adherence to TRE is high, typically 80–90%, including among individuals with obesity, metabolic syndrome, and diabetes for 4–12 weeks . Other sThe primary barriers to longer-term adherence to TRE include incompatibility with family/social life and work schedules . One potA wide variety of TRE protocols with different timing and lengths of the eating window and total durations have been shown to offer metabolic health benefits ,40. ThisTRE directly improves many of the biological and behavioral mechanisms underpinning the development of breast cancer and cardiovascular disease. For example, obesity has been found to have a strong causal relationship with primary and secondary prevention of breast cancer and cardiovascular disease and related mortality. Shared features of the pathogenesis and progression of both conditions that may mediate obesity include oxidative stress and chronic inflammation. Metabolic syndrome, a constellation of metabolic disturbances including hyperglycemia, elevated triglycerides, low HDL, hypertension, and abdominal obesity, is well established to increase the risk of cardiovascular disease and has an emerging strong link to breast cancer. While further research is needed to confirm efficacy on all of these specific outcomes, the available evidence suggests that TRE has promising positive effects on inflammation, oxidative stress, and metabolic health.TRE directly addresses some of the safety and feasibility concerns associated with existing dietary interventions. Mainly, while existing weight loss interventions tend to result in loss of lean mass contributing to 20–35% of total weight loss, TRE has been found to result in decreases in fat mass while sparing lean mass. TRE also removes barriers to participating in dietary interventions, by not requiring tedious calorie counting or use of technology. Preliminary evidence and biological plausibility suggest that personalization of a TRE protocol to an individual’s preferences or lifestyle may enable long-term adherence while still offering health benefits. There are also no costs associated with this intervention. This may be why adherence rates have been reported to be much higher than other dietary interventions, with one study reporting adherence as high as 98%. To-date, most TRE studies have been 8–12 weeks in duration, but due to its simplicity and potential for high adherence, it could be an effective strategy to ameliorate the well-known issue of long-term adherence to health behaviors, especially with allowance of protocol modifications for personal preferences.TRE is safe. Many studies evaluating the practice of fasting during Ramadan and Yom Kippur suggest that, even without consuming water, it can be safe for individuals with chronic conditions such as diabetes and heart failure. It is also likely safe to perform during chemotherapy treatment for breast cancer, based on evidence that longer periods of fasting have been shown to be safe and tolerable, but this requires further research. In addition, no TRE studies have reported the occurrence of major adverse events nor hypoglycemia even among individuals with diabetes. Instead, individuals following TRE have reported positive feelings of increased energy, well-being, and self-awareness.The potential benefits of TRE as a therapy for primary and secondary breast cancer prevention, and tertiary prevention as it relates to cardiovascular disease in breast cancer survivors are three-fold:This accumulating evidence for the potential of TRE to positively impact the development and progression of breast cancer, cardiovascular, and metabolic diseases merits further research, but leaves a number of remaining questions. First, given the short duration of most TRE studies published to-date, interventions with a longer duration and a longer follow-up period are needed to determine the potential for long-term adherence and sustainability of this dietary intervention. Future research should aim to address barriers identified for TRE, such as incompatibility with social or personal life and work schedules. There are potential solutions to overcome these barriers such as personalizing the eating window length and timing and/or incorporating cheat days, that still need to be empirically tested. Lastly, TRE has not been experimentally tested in patients with cancer or cardiovascular disease. Nonetheless, this review described promising observational evidence in these populations and positive experimental evidence on the effects of TRE on biological and behavioral mechanisms underpinning these conditions. In composite, these data suggest that TRE may be an easy and novel lifestyle interventions for the primary and secondary prevention of breast cancer, as well as tertiary prevention as it relates to cardiovascular disease in breast cancer survivors.


The role of obesity and weight change in breast-cancer development is complex and incompletely understood. We investigated long-term weight change and breast-cancer risk by body mass index (BMI) at age 20 years, menopausal status, hormone replacement therapy (HRT) and hormone-receptor status.Using data on weight collected at three different time points from women who participated in the European Prospective Investigation into Cancer and Nutrition (EPIC) study, we investigated the association between weight change from age 20 years until middle adulthood and risk of breast cancer.In total, 150 257 women with a median age of 51 years at cohort entry were followed for an average of 14 years (standard deviation = 3.9) during which 6532 breast-cancer cases occurred. Compared with women with stable weight (±2.5 kg), long-term weight gain >10 kg was positively associated with postmenopausal breast-cancer risk in women who were lean at age 20  in ever HRT users , in never HRT users  and in oestrogen-and-progesterone-receptor-positive (ER+PR+) breast cancer .Long-term weight gain was positively associated with postmenopausal breast cancer in women who were lean at age 20, both in HRT ever users and non-users, and hormone-receptor-positive breast cancer. Key MessagesLong-term weight gain was positively associated with postmenopausal breast cancer in women who were lean at age 20 years, in hormone replacement therapy ever users and non-users.Long-term weight gain was positively associated with hormone-receptor-positive breast cancer.It is important to avoid weight gain from young to middle adulthood for the prevention of postmenopausal breast cancer.,,,Obesity in middle adulthood has been associated with increased breast-cancer risk in postmenopausal women,Weight change and breast cancer were previously investigated in the European Prospective Investigation into Cancer and Nutrition (EPIC) study with 5.8 years’ follow-up.With longer follow-up and more breast-cancer cases, we re-evaluated the association between long-term weight change and breast-cancer risk by investigating whether body mass index (BMI) at age 20 years or HRT use modified this association, and whether there were differences according to tumour hormone-receptor status or menopausal status at diagnosis.IJE online.The EPIC study is a large prospective cohort including >520 000 adult participants (aged 20–99 years) from 10 European countries.Weight at cohort entry was assessed by trained staff for all countries except Norway, which collected self-reported weight and height, and the UK, where weight and height were either measured or self-reported. All countries collected self-reported weight at age 20 years and weight at follow-up. Follow-up was on average 14.2 years after baseline in the current study. Weight change was calculated as the difference between weight at cohort entry and weight at age 20 years using the difference between weight at follow-up and weight at age 20 years as an update. Weight change was categorizedMenopausal status at cohort entry was defined as follows: women who reported fewer than four menses in the past year or a bilateral ovariectomy were considered postmenopausal. The rest of the women were considered pre/perimenopausal. In case of incomplete or missing questionnaire data, women <55 years old at cohort entry were considered pre/perimenopausal and women >55 years old were defined as postmenopausal.Incident breast-cancer cases were identified through population cancer registries or by active follow-up, including health-insurance records, cancer and pathology registries and contact with participants and their next-of-kin. Censoring dates depended on the dates at which cancer registries in each centre were considered complete. Women were followed up from cohort entry until diagnosis of the first incident cancer, death, emigration or end of follow-up, whichever occurred first. Cancer cases were classified according to the International Classification of Diseases for Oncology 2nd Revision (ICD-O-2) and breast cancer was defined as C50 (C50.0–C50.9). Information on hormone-receptor status  was available from pathology reports after 1997. These were classified as oestrogen-receptor-positive (ER+), oestrogen-receptor-negative (ER–), ER+ progesterone-receptor-positive (ER+PR+) and ER– progesterone-receptor-negative (ER–PR–) breast cancer. Since no data were available on menopausal status at diagnosis, age at diagnosis was used as a proxy. Premenopausal breast cancer was defined as having a breast-cancer diagnosis at age <55 years and postmenopausal breast cancer at age ≥55 years. Premenopausal breast cancer was studied in women who were pre- or perimenopausal at cohort entry  and postmenopausal breast cancer in all women.Cox proportional-hazard models with time-dependent covariates were used to estimate HRs and 95% confidence intervals with age as the timescale. For women with no follow-up information on weight, entry time was defined as age at cohort entry and exit time as age at cancer diagnosis or censoring. For women with information from a follow-up visit, we created two separate records for cohort entry and follow-up, accommodating the corresponding information on the time-dependent covariates. For the cohort-entry record, entry time was age at cohort entry, exit time was age on the day before the follow-up visit and the status was non-case. For the follow-up record, entry time was age at the follow-up visit and exit time was age at cancer diagnosis or censoring. All analyses were stratified by study centre and age at cohort entry (5-year categories).Models were adjusted for the following lifestyle characteristics collected at cohort entry: weight at age 20 years, height, age at menarche, age at first birth, number of full-term pregnancies, education, alcohol consumption, smoking status, physical activity, oral-contraceptive use and HRT use (the categories are shown in 20) into BMI20 <25 and ≥25 kg/m². For the cross-classification on weight change and BMI20, we generated a cross-classification variable joining together the indicators of the groups of weight change and BMI20 and ran the same model with two different reference categories: Reference 20 <25 kg/m² and ±2.5 kg) and Reference 20 ≥25 kg/m² and ±2.5 kg). We presented estimates for BMI20 ≥25 kg/m² with both reference categories to examine whether the smaller effect of weight gain for BMI20 ≥25 kg/m² was due to an already higher risk in this group compared with BMI20 <25 kg/m². For the cross-classification on weight change, BMI20 and menopausal hormone therapy, we generated a cross-classification variable pasting together the indicators of the groups for weight change, BMI20 and HRT, and ran the model with four different reference groups: Reference 1 ; Reference 2 ; Reference 3 ; Reference 4 .We divided BMI at age 20 years  was performed by fitting ordinal values corresponding to exposure categories and testing whether the slope coefficient differed from zero. A two tailed p-value of <0.05 was considered statistically significant. All statistical analyses were performed using Stata 13.1 .The IJE online). At cohort entry, 78 506 (52.3%) were pre- or perimenopausal and 71 751 (47.7%) were postmenopausal. Follow-up weight was available for 92 047 women. During follow-up, there were 6352 incident breast-cancer cases overall, which included 1461 premenopausal breast-cancer cases in women who were pre-/perimenopausal at cohort entry, 1388 postmenopausal breast-cancer cases in women who were pre-/perimenopausal at entry and 3503 postmenopausal breast-cancer cases in women who were postmenopausal at cohort entry (IJE online).In total, 150 257 women with a median age of 51 years (age range 20–99) at cohort entry were followed for an average of 14 years  (IJE online).More than 50% of the women from Italy, Denmark and Sweden gained >10 kg from age 20 years to cohort entry. The frequency of weight loss was highest in the UK (11.2%) and between 6.1% and 7.3% in other centres. We observed the same pattern of weight distribution by country for both pre- and postmenopausal women, although more postmenopausal women gained >10 kg  .There was no association with the risk of premenopausal breast cancer, but there was a positive association of postmenopausal breast cancer in postmenopausal women gaining 5–10 kg  and in both women who were pre-  and postmenopausal at baseline  and gained >10 kg compared with stable-weight women .Overall weight change was not associated with risk of premenopausal breast cancer . For pos20 <25 kg/m² and stable weight, weight gain >10 kg was positively associated with postmenopausal breast-cancer risk in women who were lean  or overweight at age 20 years  in never HRT users and in all HRT users  compared with stable weight . In HRT RT users .In this large cohort of women from six European countries, long-term weight gain was not associated with premenopausal breast cancer, but was positively associated with postmenopausal breast-cancer risk overall in women who were lean at age 20 years, and in HRT users and non-HRT users, with the strongest association in non-HRT users who were lean at age 20 years. Weight gain was associated with an increased risk of ER+ and ER+PR+ breast cancer with similar associations for hormone-receptor-negative subtypes.,,Compared with the previous EPIC analyses on long-term weight change, the current study had longer follow-up, included more breast-cancer cases and had weight at follow-up in addition to weight at age 20 years and cohort entry. Our finding of a positive association between long-term weight gain and postmenopausal breast cancer is in line with evidence from several studies.n = 28 153) reported that weight gain before or around menopause was associated with a 38–69% increased breast-cancer risk, but there was no clear risk increase associated with later weight gain.n = 99 039) reported that weight gain from age 18 years to middle adulthood was associated with increased risk of postmenopausal breast cancer in non-HRT users.Consistently with our findings, a meta-analysis of 16 studies on weight gain reported a 7% increased risk  of postmenopausal breast cancer per 5-kg weight gain.In disagreement with our result of no association of long-term weight gain with risk of premenopausal breast cancer, the Premenopausal Breast Cancer Collaborative Group found an inverse association between BMI and premenopausal breast cancer, with the strongest association for BMI at age 18–24 years.An earlier EPIC-PANACEA study on shorter-term weight change in middle adulthoodThe inconsistent findings between the current study and the EPIC-PANACEA study indicate a difference between the effect of longer-term and shorter-term weight change on risk of breast cancer. Compared with the current study, the EPIC-PANACEA study looked at weight gain at a later time point in life (women aged 40–50 years). One possible explanation for the contradictory findings could be that weight gain in middle adulthood leads to a different fat deposition than weight gain earlier in life, with more intra-abdominal fat gain in middle adulthood.,,Our findings for weight gain and risk of ER+ and ER+PR+ breast cancer, as well as ER– and ER–PR– breast cancer, are in line with several findings in other studies reporting no evidence for heterogeneity by hormone-receptor status,,Oestrogens are considered to stimulate ductal growth and cell proliferation of breast epithelial cellsOne possible explanation for our findings could be due to periods of hormonal changes through life  and the different role that body fat plays at different stages. During a menstrual cycle, fat tissue is involved in regulating hormones (oestrogen and progesterone) that make up the menstrual cycle. Premenopausal women with a high BMI have more anovulations, lower oestrogen levels during the anovulatory cycles and lower progesterone levels in the luteal phase than leaner women.Another possible explanation could be that there are differences in the location of fat deposition before and after menopause that have different effects on the breast.,A major strength of the current study is that weight assessment was standardized. Another strength is the assessment of weight at three different time points and the follow-up information on most of the covariates. A limitation of the study is that data on menopausal status at diagnosis were not available and therefore age at diagnosis was used as a proxy. This could have led to misclassification of menopausal status at breast-cancer diagnosis. Weight assessments at age 20 years and follow-up were self-reported, which could have led to misclassification of weight change. However, validation studies have been done in centres where weight at cohort entry was both self-reported and measured, and showed good agreement between self-reported and measured weight.Our findings show that long-term weight gain was associated with increased postmenopausal breast-cancer risk in women who were lean at age 20 years, HRT users and non-users, and hormone-receptor-positive breast cancer.18–21, Maria Dolores Chirlaque Lopez22, Eleni Peppa23, Antonia Trichopoulou23, Georgia Martimianaki23, Antonio Agudo24, Carmen Santiuste25–26, Eva Ardanaz20,27,28, Pilar Amiano20,29, Marie-Christine Boutron-Ruault30, Vittorio Simeon31, Franco Berrino32, Rosario Tumino33, Gianluca Severi30,34, Tanja Stocks14, Renée Turzanski-Fortner9, Krasimira Aleksandrova35, Charlotta Rylander13, Dagfinn Aune1,36,37 and Christina C Dahm6Maria Jose Sánchez18Escuela Andaluza de Salud Pública (EASP), Granada, Spain, 19Instituto de Investigación Biosanitaria ibs. GRANADA, Granada, Spain, 20Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain, 21Department of Preventive Medicine and Public Health, University of Granada, Granada, Spain, 22Department of Epidemiology, University of Murcia, Murcia, Spain, 23Hellenic Health Foundation, Athens, Greece, 24Unit of Nutrition and Cancer, Catalan Institute of Oncology—ICO, Nutrition and Cancer Group, Bellvitge Biomedical Research Institute—IDIBELL, L'Hospitalet de Llobregat, Barcelona, Spain, 25Department of Epidemiology, Murcia Regional Health Council, IMIB-Arrixaca, Murcia, Spain, 26CIBER Epidemiología y Salud Pública (CIBERESP), Spain, 27Navarra Public Health Institute, Pamplona, Spain, 28IdiSNA, Navarra Institute for Health Research, Pamplona, Spain, 29Ministry of Health of the Basque Government, Public Health Division of Gipuzkoa, Biodonostia Health Research Institute, Donostia-San Sebastian, Spain, 30Université Paris-Saclay, UVSQ, Univ. Paris-Sud, Inserm, Équipe ‘Exposome, Hérédité, Cancer et Santé’, CESP, Gustave Roussy, 94805, Villejuif, France, 31Department Of Mental, Physical Health and Preventive Medicine, Medical Statistics Unit, University of Campania ‘Luigi Vanvitelli’, Naples, Italy, 32Association La Grande Via, Villa La Mausolea, Soci, Bibbiena, Arezzo, Italy, 33Cancer Registry and Histopathology Department, Provincial Health Authority (ASP), Ragusa, Italy, 34Department of Statistics, Computer Science, and Applications ‘G. Parenti’ (DISIA), University of Florence, Italy, 35Department of Nutrition and Gerontology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany, 36Department of Nutrition, Bjørknes University College, Oslo, Norway and 37Department of Endocrinology, Morbid Obesity and Preventive Medicine, Oslo University Hospital, Oslo, NorwayIJE online.The coordination of EPIC is financially supported by the European Commission (DG-SANCO) and the International Agency for Research on Cancer. The national cohorts are supported by the Danish Cancer Society (Kræftens Bekæmpelse) (Denmark), German Cancer Aid (Deutsche Krebshilfe), German Cancer Research Center , Federal Ministry of Education and Research  (Germany), Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy and National Research Council , Swedish Cancer Society (Cancerfonden), Swedish Research Council (Vetenskapsrådet), County Councils of Skåne and Västerbotten (Sweden), Cancer Research UK , Medical Research Council  (UK). Infrastructure support for the Department of Epidemiology and Biostatistics at Imperial College London (UK) was provided by the NIHR Imperial Biomedical Research Centre (BRC). The funders had no role in the design and conduct of the study; the collection, analysis and interpretation of the data; or the preparation, review and approval of the manuscript; or in the decision to submit the manuscript for publication.dyab032_Supplementary_DataClick here for additional data file.


We confirmed our results with a two-sample-MR of published data. In conclusion, our results suggest a protective effect of adiposity on the risk of BCa and PrCa highlighting the need to re-evaluate the role of adiposity as cancer risk factor.Breast (BCa) and prostate (PrCa) cancer are the first and second most common types of cancer in women and men, respectively. We aimed to explore the causal effect of adiposity on BCa and PrCa risk in the UK Biobank and published data. We used Mendelian randomisation (MR) to assess the causal effect of body mass index (BMI), body fat percentage (BFP), waist circumference (WC), hip circumference (HC), and waist-to-hip ratio (WHR) on BCa and PrCa risk. We found that increased BMI, WC and HC decreased the risk of breast cancer (OR 0.70 per 5.14 kg/m In 2010, the combined cost of breast and prostate cancer to the NHS was £664 million2. The number of cases are expected to rapidly increase and, by 2040, are estimated to be 20.2% higher for breast cancer and 38.5% higher for prostate cancer in comparison to 20181. Both cancer types are preventable in many cases, making robust identification of their modifiable risk factors important.Breast and prostate cancer are the most common and second most common types of cancer diagnosed worldwide in men and women, respectively3 has emphasised obesity as being a causal risk factor for cancer comparable to smoking. It has been proposed that the metabolic environment in obese people is conducive to oncogenic transformation4. However, previously published evidence on the relationship between adiposity and breast and prostate cancer does not consistently support this view7. It has been suggested that adiposity is a risk factor for breast cancer in post-menopausal women8, but not in pre-menopausal women9. A meta-analysis of 67 studies looking at the relationship between BMI and risk of prostate cancer recently showed that the relationship between BMI and prostate cancer is inconsistent10.A recent campaign by Cancer Research UK11. Persistent organic pollutant (POP) concentrations increase by 2–4% per kg of weight loss and remain elevated for up to 12 months after a weight loss intervention12. This may be one of the reasons behind the present inconsistency in the findings between BMI and breast and prostate cancer risk.Contrary to the view of obesity as cancer risk factor, previously published evidence suggest that adipose tissue may play a role in safely storing harmful chemicalsAssessment of what exposures are causal is not trivial: “correlation is not causation”. The vast majority of studies carried out to examine the impact of adiposity on breast and prostate cancer risk are observational and may be susceptible to confounding. Mendelian randomisation (MR) is a method that uses genetic variants associated with an exposure of interest, but not with any confounders, to assess the causal effect of the genetically predicted exposure on an outcome. In order for the method to provide reliable estimates of the causal effect, it is also assumed that the chosen instruments are not related to the outcome of interest independently of the exposure.16. We also aim to use the rich phenotype data collected as part of the UKB study to identify variables, including chemical exposure, that may explain the observed relationship between adiposity and breast and prostate cancer risk.With this work, we aim to explore the causal effect of adiposity on breast and prostate cancer risk in the UK Biobank (UKB), a large prospective cohort study, and published data17. The age range of the participants at the time of enrolment in the study was between 40 and 69 years of age, with a mean age of 56.63 years and 57.10 years in men and women, respectively. The use of the data for this study was approved by the UK Biobank access committee (application 44566) and data release was from April 2021. All research was performed in accordance with relevant guidelines/regulations, and informed consent was obtained from all participants by the UK Biobank.The UK Biobank (UKB) is a large prospective cohort study including information and biological samples for approximately 500,000 individuals, recruited between 2006 and 2011. The 22 UKB assessment centres throughout England, Wales and Scotland, collected baseline data from the participants in the form of questionnaires, physical and cognitive tests and blood and urine samples18. These included tests for the presence batch effects, plate effects, sex effects and array effects, as well as any departures from Hardy–Weinberg Equilibrium using a p-value threshold of 10−12—further details of these tests are available in the supplementary material provided by Bycroft et al.18 Variants with a minor allele frequency of < 0.01 and imputed variants with an INFO score of < 0.8 were excluded from any subsequent analyses in the UKB.488,377 individuals had been genotyped for up to 812,428 variants using DNA extracted from blood samples on either the UKB Axiom array  or the UK BiLEVE Axiom array . Variants that did not pass standard quality control checks were excluded from any subsequent analyses in the UKB18. Samples were excluded from the analysis if they were outliers for missingness and/or PC-corrected heterozygosity and/or if they had any sex chromosome aneuploidies as well as if the genetically inferred sex differed from the reported sex. Samples which did not have a genetically determined White British ancestry were also excluded. A list of related individuals was also provided by UKB and one individual from each related pair was excluded at random.Sample genotyping quality control metrics were provided by UKBWe used data collected at baseline for body mass index , body fat percentage  from bio-impedance, waist circumference  and hip circumference . We calculated waist-to-hip ratio (WHR) by dividing WC by HC. The variables were standardised to a mean of 0 and a variance of 1.http://biobank.ndph.ox.ac.uk/showcase/showcase/docs/CancerLinkage.pdf). Breast cancer cases are defined as females who have an ICD-10 code C50 recorded at least once (UKB field 40006). Prostate cancer cases are defined as males who have an ICD-10 code C61 recorded at least once (UKB field 40006). Females who have an ICD-10 code D05, for in situ carcinoma of the breast, without a C50 breast cancer entry were removed from the sample. Similarly, males with an ICD-10 code D075, for carcinoma in situ of prostate, without a C61 prostate cancer diagnosis were also removed from the sample.We used cancer diagnoses information from the 1970s onward obtained from linkage to national cancer registries and health records . This information was compared to the age of first breast cancer diagnosis to identify the pre- and post-menopausal cases. For women who did not have breast cancer, we used their menopause status at baseline to stratify them into pre- and post-menopausal. Women whose menopause status could not be determined were set to missing.Exposure to chemicals was based on occupation exposure information. A participant was considered to have been exposed to chemicals frequently if they answered “Often” and/or “Sometimes” at least once for any of the following UKB fields: 22609 (Workplace very dusty); 22610 ; 22611 (Workplace had a lot of cigarette smoke from other people smoking); 22612 ; 22613 ; 22614 (Worked with pesticides); and 22615 (Workplace had a lot of diesel exhaust).19 to carry out analyses and generate plots, unless stated otherwise.We used R 3.6.1For the observational correlations, we removed prevalent cases and regressed the exposures  against prostate and breast cancer cases using a logistic regression adjusting for age at baseline.20. We included all independent variants that were associated with the trait as listed in the supplementary tables provided by the authors of the studies. Please see Supplementary Table We obtained genetic instruments for BMI, WC, HC and WHR from summary statistics from publicly available sex-stratified GWAS meta-analyses of European ancestry20 does not provide sex-stratified summary statistics and could not be used to obtain instruments for BFP. We therefore carried out association analyses for the 10 variants found to be associated with BFP by Lu et al.20 in the UKB using PLINK 2.021, in males and females separately, and excluded those who had cancer to minimise any bias22. Please see Supplementary Table The most recent GWAS of BFP that does not include UKB participants21. We regressed each GRS on its respective trait to obtain R2 and F-statistics  on the fitted values using a logistic regression, adjusted for age at baseline. The confidence intervals were obtained using bootstrap with 10,000 repetitions.We carried out a one-sample MR in the UKB to assess the effect of the adiposity measures on breast and prostate cancer using the two-stage method24 to perform a chi-squared test for heterogeneity between the strata.To test whether exposure to workplace chemicals or menopause status modified the causal effect of adiposity on breast and prostate cancer, respectively, we stratified the sample by menopause status and by chemical exposure and repeated the one-sample MR analyses described above. We then used the metagen function from the meta package in RIt is assumed that the instruments selected for MR analyses are not related to the outcome of interest independently of the exposure (this is known as the “exclusion restriction” assumption). To test this assumption, we regressed the cancer outcome on each of the GRSs using a logistic regression, adjusted for the respective adiposity measure. If the assumption is valid, the GRS will not be associated with the outcome. Evidence of association means that the estimate effect may be affected by bias and methods such as MR Egger will provide a more accurate estimate of the causal effect.25 to perform a two-sample MR analysis using the inverse-variance weighted method with a multiplicative random effects model. We used the MR-Egger method as a sensitivity analysis and to detect the presence of pleiotropy . The genetic instruments for the adiposity measures were obtained as described previously. We used the extract_outcomes_data function to extract the effect sizes and standard errors for the outcomes from Michailidou et al.13 and Shumacher et al.14, which are the meta-analysis of breast cancer GWASs  and the meta-analysis of prostate cancer GWASs , respectively. Please see Supplementary Table To confirm any findings from our one-sample MR analyses, we used the TwoSampleMR package in R26. MVMR is useful when the genetic instruments used are associated with more than a single risk factor tested, as in this case of overlapping SNPs for the adiposity measures. Instruments used in the MVMR for childhood adiposity were extracted from Vogelezang et al.27 We used MVMR as implemented in the mv_multiple function from the TwoSampleMR package25, which also removes SNPs of LD > 0.001 between measures.To determine whether the associations of adiposity measures were independent of each other and of childhood adiposity, we used an extension of the MR method, known as multivariable MR (MVMR)28 using a window size of 50,000 and a step size of 1000. The residuals that were used to order the data were obtained by regressing the adiposity measure on its genetic risk score, adjusted for age, age-squared2(to adjust for the non-linear effects of age), the first four genetic principal components (to account for population stratification) and genotyping array.To explore the possibility of a non-linear causal relationship between adiposity and cancer risk, we removed prevalent cases and performed non-linear MR using the sliding window method described by Burgess et al.To identify potential confounders responsible for the observed positive association between BMI and incident breast cancer cases, we developed an algorithm that used a step-wise procedure to test which variables can minimise the effect size of BMI on breast cancer when added in the model as covariates. Only variables with more than 1000 non-missing observations associated with both BMI and breast cancer (p < 0.05) were considered. Categorical phenotypes were converted to separate binary variables. Age at baseline was included in all models.UK Biobank has ethical approval from the North West Multi-centre Research Ethics Committee (16/NW/0274). The work was carried out under UK Biobank application 44566.After QC, we had 177,471 females and 154,453 males remaining in the sample. Table We first sought to examine the observational effect of the adiposity measures  on the risk of breast and prostate cancer. We only used incident cases to avoid a previous cancer diagnosis affecting any of the measures considered. We found that, in our sample, each of the adiposity measures are associated with an increased risk of breast cancer but, with the exception of HC, are associated with a decreased risk of prostate cancer Fig. .Figure 1We obtained estimates (odds ratios (OR) per SD unit increase) of the causal effect of the adiposity measures on breast and prostate cancer using one-sample MR Figs. . The var–4], OR 0.76  and OR 0.73 , respectively) and increased WC and BMI decreased the risk of prostate cancer  based on our one sample MR using the UK Biobank individual level data.We found that increased BMI, WC and HC decreased the risk of breast cancer  and repeated the one-sample MR analyses. We found that the protective effect of adiposity on breast cancer was stronger in pre-menopausal women in comparison to post-menopausal women Fig. .We hypothesised that the protective effect of adiposity may be due to adipose tissue absorbing and safely storing environmental carcinogens. We, therefore, stratified our sample, based on self-reported exposure to dust and/or chemicals and/or fumes at work and repeated the analyses. We found that the protective effect of increasing adiposity on prostate cancer was stronger in men who reported that they were frequently exposed to potentially hazardous substances at work in comparison those who were not Fig. . There iWe used a stepwise procedure to identify any confounding variables that might explain the opposite direction of effects estimated by the observational and MR associations of BMI with risk of breast cancer. Supplementary Table We sought to assess the causal effects of increased adiposity on the risk of breast and prostate cancer. We found that increased adiposity measures were associated with a lower risk of incident prostate cancer, but with an increased risk of incident female breast cancer. Both one-sample MR and two-sample MR analyses showed that increased BMI and WC were protective for both breast and prostate cancer and that HC was protective for breast cancer. Multivariable MR analyses suggest: that BMI is the independent driver of these protective associations; and that childhood BMI attenuates the association between adiposity and breast cancer, but not between adiposity and prostate cancer. Stratified analyses suggest that the protective effect of adiposity on breast cancer and on prostate cancer may be enhanced in pre-menopausal women and in men exposed to workplace chemicals, respectively. When we attempted to identify the confounders responsible for the observed detrimental association between increasing BMI and breast cancer, we found a number of variables that may be involved, but these are of a currently uncertain clinical significance.29. The inverse associations between the adiposity measures and prostate cancer were more surprising. Here the evidence are more heterogeneous, as illustrated by a recent large scale meta-analysis10, which found an overall null association between BMI and prostate cancer, but found an inverse association between BMI and prostate specific antigen concentrations. Furthermore, a number of well powered studies31 have also identified an inverse association between BMI and prostate cancer, so our results in the UK Biobank are, therefore, not unusual. Furthermore, increased adiposity has been suggested as protective for low grade prostate cancer10, the prostate cancer cases in the UKB are likely to be low grade due to the age of the sample, and this may further explain why we found an inverse observational relationship between adiposity and prostate cancer risk.In this work, we found that increases in all of the adiposity measures we tested were observationally associated with a higher number of breast cancer cases. In this respect, the UK Biobank is in agreement with a previously published large meta-analysis of 126 studies finding the same association6 using the same study but with a lower number of cases. Observationally, adiposity is reportedly protective for pre-menopausal breast cancer9 and our MR results, which show that the protective effect is stronger in pre-menopausal women, are in agreement with this. Considering all adiposity measures together, we found that BMI, a body mass measure, appears to drive the causal effect over the adiposity measures of body shape. We also show that, whilst BMI may explain the association between the other adiposity measures and breast cancer, childhood BMI may in fact explain the association between adulthood BMI and breast cancer—the latter is in agreement with Richardson et al.32 In these cases the continuity of the BMI phenotype throughout life fits well with the model of life-long exposure but also makes the interpretation of the results more difficult. It is possible that for breast cancer, childhood BMI is a better proxy of female body adiposity excluding breast fat and that this “core” adipose tissue is responsible for the protective effect seen here.Since observational studies cannot directly provide information on cause-and-effect relationships, we carried out MR analyses to see whether the associations we found were causal. We found that adiposity was causally protective for breast cancer and our results are similar to those reported by Guo et al.7 report no causal effect of BMI on prostate cancer, but this may be due to lack of statistical power because they used a smaller number of cases . Our multivariable MR results suggest that the protective association between WC and prostate cancer is driven again by BMI, a body mass measure, and that the association between BMI and WC is not attenuated by childhood BMI.We also carried out MR analyses for prostate cancer. We found that BMI and WC are causally protective for prostate cancer in the UK Biobank dataset and this is supported by our analysis of external data. Davies et al.11. Persistent organic pollutant (POP) concentrations increase by 2–4% per kg of weight loss and remain elevated for up to 12 months after a weight loss intervention12. We hypothesised that the protective effect of increasing adiposity on prostate and breast cancer risk might be explained by its ability to sequester potentially carcinogenic substances. Our results, which show that the protective effect was enhanced in men reporting more frequent exposure to potentially carcinogenic substances at work, support our hypothesis in prostate cancer. The same effect was not observed in female breast cancer, which may be due to an insufficient number of cases or due to a more complex underlying mechanism. In both breast and prostate cancer cases, it appears that the overall mass of adipose tissue, as measured by BMI, is more relevant than either % body fat or where the fat accumulates, measured by the WHR, WC and HC. This fits well with the idea of adipose tissue operating as a sink to store potentially harmful chemicals, but a more detailed investigation where exposure is accurately measured is required.There is previously published evidence which suggests that adipose tissue may play a role in safely storing harmful chemicals33. Our algorithm also selected macular degeneration 34, ankle width (which might represent swelling of the lower extremities—symptoms of diabetes and cardiovascular disease), and leukocyte count (a marker of systemic inflammation)35. These variables are likely to represent a currently undefined higher order variable, perhaps biological age or a marker of overall health, and further investigation is required to identify what this variable might be and whether or not it can be modified to minimise breast cancer risk.The direction of the observational association between BMI and breast cancer is opposite to that of the causal effect, which suggests that the former is confounded. We found that variables relating to physical activity  may be one source of confounding and this is supported by the fact that increased physical activity is protective for breast cancer36. Also, the proportion of adults who were overweight or obese among men and women in the UK population was 78% and 73%, respectively, compared to 74% and 60%, respectively, for the same age group in the UK Biobank37. The sample is, therefore, not representative of adiposity in the wider UK population. The difficulty in measuring adiposity should also be mentioned. The adiposity measures we use consider different aspects of adiposity.A number of limitations are present in our work. The UK Biobank study, despite its sample size and almost comprehensive phenotyping, does have a "healthy volunteer" selection bias. The rate of cancer is lower in comparison to the general population3 might suggest. As adiposity is a known risk factor for other age-related diseases, such as type-2-diabetes and cardiovascular disease, it is necessary to explore the mechanisms through which adiposity may protect against certain types of cancer and to identify how the former can be minimised without sacrificing the latter.In conclusion, we found that increased adiposity is causally protective for breast and prostate cancer and the effects in the prostate cancer may be modified by exposure to potentially carcinogenic substances. Further work needs to be done to identify variables that are responsible for the observed relationship between increased BMI and increased risk of breast cancer. It is clear that reduction of adiposity, in and of itself, may not reduce the risk of breast and prostate cancer as the recent campaign by Cancer Research UKSupplementary Information.


Here, we integrate prominent alternate models to help guide future research on this very important topic concerning human health.Known genetic mutations and familial hereditary factors account for less than 20–25% of breast cancer cases in women, therefore, most instances have been classified as sporadic cases of unknown aetiologies. Single nucleotide polymorphisms (SNPs) were considered as breast cancer risk factors, but numerous studies have failed to support this assertion. Recent evidence correlates aberrant epigenetic mechanisms in the development and metastatic progression of breast cancer, yet there has been limited progress made to identify the primary aetiology underlying sporadic cases of breast cancer. This has led some researchers to consider alternative hypotheses including  The aetiology of cancer, including breast cancer, is often discussed in the context of genetic and environmental risk factors. Skin cancer is the major class of cancer that occurs both in men and women and its risk factor is predominantly considered to be environmental exposure to sun. On the other hand, genetic risk factors are considered key to the development of breast cancer, the second most common cancer, accounting for nearly one in three cases diagnosed in United States women . The AmeBRCA1 and BRCA2 genes with low penetrance act as predisposing factors for hereditary breast/ovarian cancer (HBOC); that is, not all subjects carrying disease susceptibility mutations even in homozygous conditions develop disease. These mutations are also associated with an increased incidence of pancreatic, colon, and prostate cancers in men [TP53, PTEN, STK11, and CDH1, may also have an increased risk of developing breast cancer, but these genetic defects account for a very small percentage of cases[CHEK2, ATM, NBS1, RAD50, BRIP1, and PALB2 have been shortlisted based on subtle sequence variants or polymorphisms that could be associated with a small to moderate increased risk for breast cancer [According to the American Society of Clinical Oncology, autosomal dominant mutations of the s in men . Individ of cases,5. For e of cases. Charact of cases. Overallt cancer .Single Nucleotide Polymorphisms (SNPs) make up about 90% of all human genetic variation, with numerous SNPs linked to various diseases including breast cancer. For example, three SNPs of the MBD2 transcriptional repressor gene are associated with increased breast cancer risk yet another three seem to offer marginal protection . The rolBRCA1, BRCA2), cell-signaling pathways, and estrogen-α and progesterone receptors [Having failed to discern the aetiology of sporadic cases by familial genetics and genome-wide SNP mapping studies, researchers have searched for other causes. There has been relatively recent recognition that epigenetic factors may be significant in the development and progression of cancers. DNA methylation of CpG islands in promoter regions can generally remodel chromatin. Both hypermethylated and hypomethylated regions in the genome have been proposed to exert epigenetic influences in breast cancer . Promoteeceptors . Abnormaeceptors . Histoneeceptors . In addieceptors . Diminiseceptors . In a steceptors . We surmeceptors –23.in utero changes and an increased stem cell population as the keys to understanding breast cancer [The immense amount of information unearthed in the study of cancer in general, and breast cancer in particular, indicates that allelic and epigenetic factors are intertwined in complex mechanistic interactions. Advancements in the field, including technological innovations, are expected to resolve these interactions towards a meaningful understanding of breast cancer. It is justifiably contended that there has been limited progress in understanding the basis of the disease . Notablyt cancer –27. A tht cancer –29.in-utero are at increased risk of developing clear-cell adenocarcinoma (CCA) of the vagina and cervix as well as structural abnormalitiesin the anatomy of the reproductive tract [in utero have been shown to experience epididymal cysts, microphallism, and cryptorchidism, among other abnormalities [in utero may be linked to breast cancer susceptibility. A large-cohort epidemiological study showed that women with prenatal DES exposure have an increased risk of breast cancer after 40 years of age [in utero DES and an unexposed control group of 1,927 women was undertaken to assess the risks of 12 adverse outcomes linked to DES exposure. The cumulative risk for breast cancer in DES-exposed and -unexposed women was 3.9% and 2.2%, respectively [There have been many, often conflicting, studies about estrogen vis-à-vis breast cancer tumorigenesis and metastases. In 2003, the National Institute of Environmental Health Sciences (NIEHS) added estrogen to its list of known cancer-causing agents . Howeverve tract . In addimalities . Howevermalities . In addis of age . In 2011ectively .inutero), as well as in the human breast adenocarcinoma cell line MCF-7 [in situ) in the mammary glands of 33% of the exposed rats compared to none in unexposed fetuses. In addition, animals developed mammary tumors when a sub carcinogenic dose of a chemical carcinogen was administered to rats at puberty [in utero hormonal exposure with an increased risk of breast cancer of the progeny. We suggest that such experimental manipulations possibly interfere with normal biological mechanisms to cause increased disease incidence, and therefore these studies should not necessarily be construed to favor in utero hormonal exposure being the cause of sporadic cases found in women at large.Bisphenol-A (BPA), a chemical used in many commonly used plastics, is another known EDC. BPA and DES are nonsteroidal, display similar chemical structures, and have estrogenic effects. Both DES and BPA have been shown to interfere with the developmental programming of breast tissue in mice  is responsible for coupling the development of right- versus left-hand-use preference for unimanual tasks  and brain hemispheric structural and functional asymmetry (r/r genotype), uncoupling of the brain laterality and handedness traits occurs so that they are distributed randomly and independently from one another to the left- or right-side of an individual. By investigating only right-handed women subjects with breast cancer, Sandson and coworkersmade a truly remarkable observation in that there was 49% incidence of brain laterality reversal in breast cancer patients [r/r genetic constitution according to the random-recessive model. Originally, the RGHT1 gene was proposed to promote asymmetric cell division for establishing brain laterality. It was also proposed that breast cancer may be caused by random errors in asymmetric cell division in some r/r individuals. Specifically, the r/r genotype may cause Loss of Heterozygosity (LOH) through increased mitotic recombination operating later in adult life to cause breast disease [r/r genotype increases the rate of mitotic recombination, as compared to that of the R/R and R/r genotype, leading to LOH across the genome, including chromosome regions where genes mutations or TSGs implicated in breast cancer development lie. Our thesis concerns the reason why initially cancer originates, not later biochemical events associated with cancer progression. The genetics of the Strand-Specific Imprinting and Selective chromatid segregation model (SSIS) have been described previously [r/r genotype is a predisposing factor for somatic recombination in breast tissue, and separately, for brain hemispheric randomization in utero through asymmetric cell division. These explanations provide a new basis for the amazing association of sporadic breast cancer predisposition to brain laterality development [After remaining largely ignored for almost two decades, data from the above study were explained by employing a different hypothesis . Accordisymmetry . Howeverpatients ; in othe disease . We suggeviously . When LOeviously ,72. Howeeviously ,74. Furtelopment . Models elopment .r/r genetic constitution [r/r genotype initiates the process of carcinogenesis that is restricted to a minority of the population.The mechanism of body laterality development continues to remain controversial and is an exceptionally active field of research ,77. Constitution , the latin utero is supported by studies indicating that the number of mammary stem cells is increased by in utero exposure to estrogen and IGF-1. Moreover, higher breast density in breast cancer patients has been proposed to be a consequence of the increased number of stem cells in the breast tissue, yet this idea remains controversial with increasing evidence contrary to the previously suggested cause-effect relationship between breast density and cancer. Our hypothesis involves consideration of the biological consequences of laterality specifying genetics. Sandson and coworkers’ defining 1992 study showed that right-handed women with breast cancer have a remarkably increased prevalence of reversed functional laterality of the brain, as compared to right-handed subjects with typical brain laterality [r/r) allelic form confers random brain hemispheric laterality during embryonic development [r/r genotype also constitutes a predisposing factor for breast cancer development in adult life [in utero with breast cancer development occurring later in adult life.The aetiology of sporadic breast cancer cases remains unknown. A number of large studies aimed at defining breast cancer aetiology using whole-genome sequencing of diseased and healthy breast tissues have unearthed thousands of genomic variations , but theterality . A previelopment . The enhult life . Notablyr/r genotype constitutes a primary cancer predisposition factor has the potential to help uncover the disease aetiology. To be noted, the existence of WW:: CC segregation pattern was recently spotted by Sauer and Klar, 2013 [Drosophila autosomes by Yadlapalli and Yamashita, 2013 [Clearly, further research is required to establish a definitive association between the two seemingly unrelated traits of brain laterality and breast cancer explored in our perspective. It is interesting to note that gliomas and melanomas have also been reported to correlate with atypical laterality –81. Cairar, 2013  in a stuta, 2013 . The keyta, 2013  study pu