Regular physical activity has long been recognized as an important preventive measure against many chronic diseases including breast cancer. Whether the benefits differ by time in life or body composition are yet to be answered. With this research, we aimed to expand the knowledge regarding the association between physical activity and breast cancer risk. We confirmed an overall 23% lower long-term breast cancer risk among women engaging in high  vs. low physical activity and showed that physical activity exerts its greatest benefits to women during or after menopause, or among women with body compositions  in the upper-normal-to-overweight range.adj = 0.77, 95% CI 0.66–0.90) than those reporting low physical activity, being most pronounced among perimenopausal and postmenopausal women, and women with waist circumference, body fat percentage, or BMI in the upper-normal and overweight range. For premenopausal women or women having obesity or the largest body composition, high physical activity alone did not modify the breast cancer risk, suggesting additional preventive measures indicated in these groups to reduce the long-term risk of breast cancer.Being physically active as part of everyday life reduces breast cancer risk. Less is known whether the benefits of an active lifestyle differ depending on the timing of physical activity in life or anthropometric characteristics. The aim of this study was to bring further insights to the association of physical activity in relation to menopausal status and body composition with breast cancer risk by making use of a prospective Swedish cohort  with long-term follow-up. Physical activity information of 15,983 participants for the past 12 months prior to study entry was assessed according to metabolic equivalent task (MET)-hours/week to integrate duration and intensity of reported activities. During 23.2 years median follow-up, 1302 invasive breast cancers occurred. Women reporting a high physical activity at study baseline, corresponding to >1 h daily walking/week (≥28.5 MET-h/week), had a 23% lower long-term breast cancer risk (HR The almectively . Having ectively , type 2 ectively , and cerectively . The assectively . In premectively ,11,12.Leisure-time physical activity has, in addition to achieving and maintaining a healthy weight, been associated with a lower risk of at least 13 different types of cancer, including breast cancer, and may relate to its modulation of certain hormones  and the immune system ,15. The Despite the associations established so far, more links between physical activity and breast cancer risk reduction are yet to be elucidated to fill the knowledge gap whether the benefits vary with regard to time in life and body composition. These novel understandings are necessary in order to intervene and influence in a beneficial direction for the better health of women. The aim of this study was to investigate the associations between physical activity and breast cancer risk in relation to menopausal status and body composition assessed by waist circumference, body fat percentage, and BMI, by making use of a prospective Swedish cohort with long-term follow-up.The participants in the study population were recruited in Malmö, Sweden, from 1991–1996 for the observational Malmö Diet and Cancer Study (MDCS) that aimed to elucidate potential associations between dietary habits and cancer development in a prospective manner. Residents of Malmö who were born between 1923–1950 were invited to the study, and a total of 28,098 participants, of whom 17,035 were women, were enrolled and provided with a written informed consent. At study entry, baseline data regarding socioeconomic status, lifestyle  and reproductive factors of the participants were collected through a self-reported questionnaire, and anthropometric measurements were taken by trained research nurses. Ethical approvals for the MDCS (LU 51-90) and the present study  were obtained from the regional ethics committee in Lund.n = 576), missing or unrealistic physical activity reports , and missing or extreme anthropometric measures . The remaining 15,983 women were included in the present study , upper-normal (23.0–24.9 kg/m2), overweight (25.0–29.9 kg/m2), and obese (≥30.0 kg/m2). Body composition measures of total body fat mass were recorded using Bioelectrical Impedance Analyzer BIA 103 , with body fat percentage calculated as 100 * (fat weight/weight). Body fat percentage was categorized into lowest (<28.0%), lower-middle (28.0–31.9%), upper-middle (32.0–34.9%), and highest (≥35.0%), approximating body fat classifications for women aged 50–69 as good, fair, poor, and high, respectively.Women were defined according to their menopausal status at baseline and categorized as premenopausal, perimenopausal, and postmenopausal. Women were considered postmenopausal if having undergone bilateral oophorectomy or if menstruation had ceased >2 years prior to baseline; perimenopausal if menstruation had ceased <2 years prior to baseline. If the above information was unavailable (<2% of the women), the women were considered postmenopausal if 55 years or older at baseline; perimenopausal if 42–54 years at baseline. The remaining women were classified as premenopausal. Participants were further described by four categories of waist circumference: lowest (<73.0 cm), lower-middle (73.0–76.9 cm), upper-middle (77.0–83.9 cm), and highest (≥84.0 cm), corresponding to European clothing sizes small, medium, large, and extra-large, respectively. BMI was categorized into lower-normal  as an estimate of absolute risk according to the level of physical activity for all women and stratified by menopausal status or anthropometric measures. The relative risk of invasive breast cancer comparing high vs. low levels of leisure time physical activity was calculated and expressed as a difference in percentage. The Kaplan–Meier curve and LogRank test were used to compare differences between groups of women with high or low physical activity, and to visually evaluate the proportional hazards assumption. Due to signs of assumption violation in the early period, LogRank test and subsequent analyses were performed both for the complete follow-up period and for the early (0–11 years) and late (12+ years) periods, separately. Age-adjusted and multivariable-adjusted Cox regression analyses providing hazard ratios (HR) with 95% confidence interval (95% CI) for breast cancer risk according to high vs. low levels of physical activity were performed with duration of follow-up as the underlying timescale.Subgroup analyses for menopausal status, waist circumference, BMI, and body fat were also conducted. The multivariable model was adjusted for age at baseline (continuous), age at menarche , parity , age at first childbirth , oral contraceptive use , current hormone replacement therapy , socioeconomic index , and alcohol consumption . Models for all women or stratified by body composition  were additionally adjusted for age at menopause . All statistical analyses were conducted on SPSS (IBM Version 27 for Mac).n = 7990) or high levels  of physical activity  vs. 380 (IQR 228–580), respectively) and MET-h/week (26.7 (IQR 17.0–42.0) vs. 28.6 (IQR 17.3–43.9), respectively) . The difThe median follow-up time was 23.2 years (IQR 19.1–25.2) and median time to breast cancer diagnosis was 12.2 years (IQR 6.9–17.6). During follow-up, 1302 women among 15,983 participants had developed breast cancer. Of the women diagnosed with breast cancer, 699 were in the low physical activity group and 603 in the high physical activity group . Overallp = 0.002), the hazards of low and high physical activity groups followed a non-proportional pattern which had a negligible difference in the early period , whereas there were evident differences in the late period .The cumulative hazard of invasive breast cancer between women with different physical activity levels was estimated with Kaplan–Meier curves . While tadj = 0.85, 95% CI 0.76–0.95) with high compared to low levels of physical activity , or within subgroups by menopausal status or body composition  and long-term (12+ years) benefits of physical activity. In the short term, no clear associations between physical activity and breast cancer risk were observed overall  was observed for women overall  and postmenopausal  women. No association between physical activity and breast cancer risk was observed for premenopausal women  and upper-middle  waist circumferences, upper-normal  and overweight  BMI levels, and lower-middle  and upper-middle  body fat percentages  were found to be associated with a 23% lower risk of developing breast cancer. The greatest long-term benefits (12+ years) were observed among women who engaged in high levels of physical activity during or after menopause, or if they had larger body composition with lower-middle to upper-middle waist circumference or body fat percentage, or upper-normal-to-overweight BMI. High physical activity alone did not modify the breast cancer risk for premenopausal women or women with obesity and the largest body composition.The observed preventive effects of high physical activity on overall breast cancer risk are in agreement with previous studies. A meta-analysis of 31 studies with 63,786 cases found a 12% reduction in breast cancer risk among women in the 75th percentile of physical activity compared to the 25th percentile . A pooleWith regard to timing in life, the greatest long-term benefits of physical activity in our study were observed among women who engaged in high levels of physical activity during or after menopause. Although perimenopausal women had similar reduced breast cancer risk in the short term (0–11 years), the evidence was more strongly associated in the long term (12+ years). The association between high physical activity and lower breast cancer risk for postmenopausal women was only seen in the long term. A study of the NIH-AARP Diet and Health Study cohort showed a 13% risk reduction in the most active postmenopausal women compared to inactive women . Another2) benefited the most from physical activity regarding reduced long-term breast cancer risk. On the contrary, the women having the lowest and highest waist circumference, body fat percentage, or BMI did not have a modified breast cancer risk upon high physical activity alone. Our findings are similar to the results of a meta-analysis of 18 cohort studies that showed a 15% and 14% of reduction in breast cancer risk in women who had a BMI of <25 kg/m2 and ≥25 kg/m2, respectively, and engaged in high compared to low levels of physical activity [2 did not benefit from a risk-reducing effect of physical activity [2) benefited with a risk reduction of 27% from physical activity, whereas women with upper-normal (22–25 kg/m2), high (≥25 kg/m2), and very high (≥30 kg/m2) BMI benefited with risk reductions of 24%, 18%, and <1%, respectively [We also examined the effects of physical activity on breast cancer risk in relation to anthropometric measures to provide further insights into the physical activity and breast cancer association with regard to body composition and adiposity. Our study showed that women with lower-middle to upper-middle waist circumference (corresponding to European clothing sizes M–L) or body fat percentage (fair–poor), or upper-normal-to-overweight BMI  . To gainThe strengths of our study include the large study population with prospective design and long follow-up. Another strength is the availability of physical activity data comprising time spent for 17 different activities, and the combination of duration and intensity. A further strength is the comprehensive data regarding breast cancer risk factors which enabled us to perform multivariable adjustments in regression analyses. On the other hand, the limitations in our study include that the physical activity data were reported retrospectively in the baseline questionnaire for the year prior to study inclusion and not reported during the follow-up period. Therefore, it is unknown whether the participants continued the same level of physical activities that they initially reported at baseline. Although adjusting for known and potential breast cancer risk factors, it cannot be fully excluded that the remaining unknown or residual confounders could influence the results. Another limitation is the lack of information regarding body compositions at the end of the follow-up period, which could potentially have improved the interpretation of the results.Physical activity is beneficial for overall health and chronic disease prevention. Although the overall breast cancer risk-reducing effect of physical activity is previously known, it is important to identify the optimal time in life and which groups this effect is exerted upon the most. In this study, we showed that women during or after menopause, or with upper-normal-to-overweight body compositions, benefit from physical activity the most. For premenopausal women with the leanest or largest (obese) body compositions, no evident modified risk was found which implies that these women may potentially benefit from additional risk-reduction measures. Further research needs to be conducted to elucidate the associations between breast cancer prevention and lifestyle changes for women with large body compositions at an increased risk of breast cancer. The associations between physical activity-mediated breast cancer risk reduction and breast tumor characteristics should also be examined to gain an in-depth understanding of the benefits of physical activity.


To determine the level of adherence and to assess the association between higher adherence to the South African food based dietary guidelines (SAFBDG) and breast cancer risk.Population-based, case–control study (the South African Breast Cancer study) matched on age and demographic settings. Validated questionnaires were used to collect dietary and epidemiological data. To assess adherence to the SAFBDG, a nine-point adherence score (out of eleven guidelines) was developed, using suggested adherence cut-points for scoring each recommendation (0 and 1). When the association between higher adherence to the SAFBDG and breast cancer risk was assessed, data-driven tertiles among controls were used as cut-points for scoring each recommendation . OR and 95 % CI were estimated using multivariate logistic regression models.Soweto, South Africa.Black urban women, 396 breast cancer cases and 396 controls.v. lower adherence (<3·5) was statistically significantly inversely associated with breast cancer risk overall , among postmenopausal women  as well as for oestrogen-positive breast cancers . Only 32·3 % of cases and 39·1 % of controls adhered to at least half (a score >4·5) of the SAFBDG.After adjusting for potential confounders, higher adherence (>5·0) to the SAFBDG Higher adherence to the SAFBDG may reduce breast cancer risk in this population. The concerning low levels of adherence to the SAFBDG emphasise the need for education campaigns and to create healthy food environments in South Africa to increase adherence to the SAFBDG. Concurrent to these dietary-and-lifestyle shifts is the continuously increasing rate of obesity, especially among South African women. Obesity and overweight are important risk factors for different cancer types, especially postmenopausal breast cancer. Breast cancer is the leading diagnosed cancer among South African women and contributes to current public health challenges in South Africa. The age-standardised incidence rate of breast cancer in South Africa is 52·6 per 100 000 women, while the age-standardised mortality rate is 16·0 per 100 000 women. Incidence rates of breast cancer are lower in black South African women (age-standardised rate of 20·8) compared with other ethnic groups , but evidence suggests that black women have higher mortality rates compared with other races. Based on data from 2020, incidence rates of all cancer cases, including breast cancer, are predicted to rise with 47 % by 2040. This is worrisome since higher incidence rates may simultaneously increase mortality rates, especially in resource poor countries such as South Africa. It is therefore important to identify specific, modifiable risk factors that could be used to implement preventive actions, especially for black South African women.South Africa, an upper-middle income country, is undergoing a rapid nutrition transition characterised by shifts in dietary-and-lifestyle patterns. Nutritious traditional meals and active lifestyles are being replaced with frequent consumption of highly processed foods, often being energy dense and nutrient poor, and high levels of physical inactivity and sedentary behaviour. The aim of country-specific FBDG is to reduce nutrition deficiencies and to assist in preventing the development of non-communicable diseases. The South African food based dietary guidelines (SAFBDG) were initially published in 2003 and revised in 2012. These recommendations consist of eleven short, simple and clear nutrition messages that promote a varied and adequate diet and consider foods that are available, culturally sensitive, affordable and environmentally sustainable. However, the level of adherence to the SAFBDG and the association thereof with breast cancer in black women from Soweto are not yet known.The FAO of the UN and WHO recommend country-specific food based dietary guidelines (FBDG) to promote healthy dietary patterns. Although the SAFBDG promote similar dietary guidelines as the 2018 WCRF/AICR Cancer prevention guidelines , there are some important differences between the two sets of recommendations to consider. Compared with the 2018 WCRF/AICR Cancer Prevention Recommendations, the SAFBDG are guidelines specifically developed for South Africa based on prevailing dietary patterns which aim to address current nutrition-related health problems within South Africa. On the other hand, the 2018 WCRF/AICR Cancer Prevention Recommendations are more lifestyle orientated and include cancer-specific recommendations, based on robust evidence from mostly higher income countries. Investigating the association between adherence to the SAFBDG and breast cancer risk will complement the previous work of Jacobs and colleagues (2021) by providing additional country-specific or context-specific insight into the diets (and variety thereof) of black women from Soweto, South Africa. The current study aims to first investigate the level of adherence to the SAFBDG  and second to assess whether higher adherence to the SAFBDG  is associated with a reduced breast cancer risk in black South African women from Soweto.A similar study, conducted in black women from Soweto, South Africa, recently showed that higher adherence to the 2018 World Cancer Research Fund/American Institution for Cancer Research’s (WCRF/AICR) Cancer Prevention Recommendations was associated with a lower breast cancer risk in this populationn 396) control (n 396) study conducted among black urban women from the greater Soweto population from 2014 to 2017, was used to conduct the current study. Breast cancer cases were newly diagnosed, prior to any cancer treatment from the Chris Hani Baragwanath Academic Hospital. Cases were recruited as soon as possible after the cancer diagnoses. Controls were healthy and unrelated to the breast cancer cases with no history of cancer diagnoses and matched by age (± 5 years) and area of residence to the cases. Information regarding inclusion and exclusion criteria of breast cancer cases and controls and recruitment of breast cancer cases was previously described elsewhere. The sample size had a sufficient power of 80 % (when type II error rate = 10 %) for OR ≥ 1·5 and type I error set at 5 %. “This study was conducted according to the guidelines laid down in the Declaration of Helsinki, and all procedures involving research study participants were approved by the International Agency for Research on Cancer, the University of the Witwatersrand and North-West University. Written informed consent was obtained from all subjects/patients.”The database from the South African Breast Cancer (SABC) study, a population-based, case , spearman rank correlation coefficients of 0·14–0·59 were obtained when macro- and micronutrients intakes and food groups were compared with a 7 d weighed food record and foods captured by a QFFQ,14. In 2011l, reproducibility of the QFFQ was evaluated again in the Prospective Urban and Rural Epidemiological-SA study, and correlations for energy, macro- and micronutrients were good (between 0·59 and 0·76), which indicated that the validity of the QFFQ stayed consistent over time. The QFFQ was administered by registered dietitians, a nutritionist and registered nurse who all received training by nutritionists with over 30 years of experience in dietary assessment to ensure consistency among the different interviewers. Nutrient content information (macro- and micronutrients) was obtained using the South African Food Composition Tables. The dietary intake frequency included the amount of times foods were consumed per d/week/month or never. Life size colour photographs of thirty-seven foods (in three portion sizes) were displayed in the food portion picture booklet. Participants were asked about their habitual dietary intake over the past month. Daily intakes of the different foods included in the QFFQ were calculated by two nutritionists to ensure accurate and quality processing of the QFFQ, using a stepwise approach. A stepwise approach was used to calculate daily intakes. Consumption frequencies were converted into number of days per month, and the amount of each portion consumed  was converted into grams, using standardised tables to convert household measurements into grams. The daily consumption was calculated by multiplying the frequency of consumption (d/month) by the portion sizes (converted to grams) divided over 30 d. The daily energy and nutrient intakes were determined by multiplying the daily intake of each food item (as consumed) by the nutrient and energy content (per 100 g), derived from the South African Food Composition tables, and then adding together the contributions from all food items.A validated and reproducible culture-specific Quantitative FFQ (QFFQ) was used together with household utensils, food portion pictures and food models to determine habitual dietary intake,18.Trained investigators and fieldworkers conducted face-to-face interviews. Detailed information on demographic factors and socio-economic status – ethnicity, history of health, family history of breast cancer, reproductive risk factors , family history of cancer, breast health , smoking habits and physical activity  – were collected. Weight, height, sitting height, hip circumference and waist circumference were measured according to standardised procedures. Measurements were always done in duplicate and redone if there was discordance. Measurements were done by the same person throughout the study to avoid inter-user variation, and all equipment was calibrated regularly. All questionnaires used to collect the anthropometry and lifestyle information were validated and proven reproducible in studies conducted in South Africa and elsewhereTo determine the level of adherence to the SAFBDG, an adherence score was developed since no standardised scoring algorithm currently exists to measure adherence to the SAFBDG. Table –27. As an example, it is advised to consume 400 g fruit and vegetables and to have <10 % of total energy from saturated fat intake/d,24.A maximum overall adherence score of nine (to the SAFBDG) was therefore possible. To determine the level of adherence to the SAFBDG, overall and for each individual SAFBDG recommendation, suggested portions sizes/d or week and percentages of total energy intake per day were used as cut-points . The lowest possible DDS score was zero (0) and the highest possible DDS nine (9). A DDS score below four (4) was considered as having a low dietary diversity.To calculate adherence to the individual SAFBDG recommendation “Enjoy a variety of foods”, a Dietary Diversity Score (DDS), based on nine food groups, was used. The nine food groups were based on the FAO of the UN guidelines and included (1) cereals, roots and tubers; (2) meat, poultry and fish; (3) diary; (4) eggs; (5) vitamin A-rich vegetables and fruit; (6) legumes; (7) other vegetables than vitamin A-rich; (8) other fruits than vitamin A-rich and (9) fats and oils. To assess adherence to this recommendation, a general guideline  was used. Starchy exchanges/units included minimally processed starches, whole grains and root vegetables . With regard to measuring adherence to the SAFBDG recommendation “Eat dry beans, spilt peas, lentils and soya regularly”, no specific amount  or portion size was stated in the SAFBDG. To establish the recommended portion size to measure adherence to this guideline, legume consumption was compared with the legume consumption in other South African studies  and guidance from the Nutrition Information Centre of the University of Stellenbosch was followed–31. Sensitivity analysis was conducted without the recommendation “Eat dry beans, spilt peas, lentils and soya regularly” and “Make starchy foods part of most meals” but did not change the overall adherence result (results not shown).No specific portion size was stated in the SAFBDG to measure adherence to the individual SAFBDG recommendation “Make starchy foods part of most meals”.Adherence scores to each individual SAFBDG recommendation, using recommended portion sizes, were calculated as follows: one (1) point when adhering to the recommendation and zero (0) points for non-adherence to the recommendation. Two sub-categories were developed to measure adherence to the recommendations “Fish, chicken, lean meat or eggs can be eaten daily” and “Use fats sparingly. The two sub-categories for the recommendation “Fish, chicken, lean meat or eggs can be eaten daily” included: (1) fish, chicken and lean meat consumption can be eaten daily and (2) egg consumption can be eaten daily. The two sub-categories to measure adherence to the recommendation “Choose vegetable oils rather than hard fats” included (1) keep total fat intake between the recommended range  and (2) limit saturated fat intake <10 % of total energy intake. Adherence to these individual SAFBDG recommendations with two sub-categories, mentioned above, was scored as follows: zero (0) points for non-adherence and half a point (0·5) for adherence. To measure adherence to the individual SAFBDG recommendation “Use sugar & food & drinks high in sugar sparingly”, three sub-categories, non-adherence (0 points), partial adherence (0·5 point) and adherence (1 point) were developed. This was based on the fact that the SAFBDG, in accordance with the WHO’s healthy diet indicator, advise an added sugar intake of <10 % of total energy intake, while an added sugar intake of <6 % of total energy may have additional health benefits1). Each individual SAFBDG recommendation contributed equally to the total adherence score. The recommendations, “Use fats sparingly” and “Choose vegetable oils rather than hard fats” had two sub-recommendations that were scored individually and were divided by two to determine an average score . Finally, tertiles of control participants (33rd and 66th percentiles) were used to determine adherence to the overall score and the association with breast cancer risk, with ≤3·5 being the lowest adherence tertile and >5·0 the highest adherence tertile.The distribution of adherence to five (out of nine) of the individual SAFBDG recommendations resulted in highly skewed categories . Therefore, data-driven tertiles (33rd and 66th percentiles) were used to determine the cut-off points for assessing the association between adherence to the SAFBDG  and breast cancer risk. Cut-off points for each individual SAFBDG recommendation, using data-driven tertiles, were calculated as follows: one (1) point when adhering to the recommendation (highest tertile); half (0·5) a point for partial adherence to the recommendation (middle tertile) and zero (0) points for non-adherence to the recommendation (lowest tertile)  and Wilcoxon signed rank test  for continuous variables and paired χ2 test for categorical variables (presented as percentages). Specifications of the WHO were used to calculate BMI, using measured height and weight (kg/m2).A total of 399 breast cancer cases and 399 matched controls were recruited in the SABC study. Of those, three breast cancer cases and three matched controls were excluded due to missing dietary data information. Descriptive analyses were performed, and differences between cases and controls were assessed using paired sample t-test  and obesity (BMI ≥ 30 kg/m2). For the two latest variables, unconditional logistic regression was used. Additional analysis was conducted to determine significant interactions among strata .Conditional logistic regression models were used to compute OR and associated 95 % CI to determine the association between breast cancer risk and adherence to the SAFBDG . Adherence scores  were stratified by hormonal breast cancer receptor subtypes, menopausal status (pre v. >24 years of age), age at menopause (<48 v. >48 years of age), parity (≤ three children v. > three children), duration of exclusive breast-feeding (months), use of exogenous hormones , or hormone replacement therapy after menopause), family history of breast cancer (yes/no), HIV status (positive v. negative), total energy intake in kJ (continuous), alcohol intake in gram (continuous) and under reporting . Under reporting (13·1 % of breast cancer cases and 11·6 % of controls) and over reporting (24·0 % of breast cancer cases and 27 % of controls) cut-off points were calculated using the Goldberg and Black principle to determine over-and-under reporting of energy intake. Menopausal status, ethnicity, total energy intake, alcohol intake, individual income/month and waist circumference altered the crude OR by more than 10 % when assessing adherence to overall and individual SAFBDG recommendations and were included in our final model.The following confounders were examined for adherence to the overall- and individual SAFBDG recommendation (chosen a priory from known breast cancer risk factors): individual income , ethnicity , level of education , smoking (smokers and non-smokers), height (continuous), waist circumference (continuous data), age at menarche (continuous), full-term pregnancy (yes/no), age at first pregnancy (<24 v. less active), was examined when adherence to the individual SAFBDG recommendations was assessed. This was done as physical activity was part of the overall score and as such not included as a confounder in the overall score analyses.An additional confounder, habitual physical activity per day , with only 39·1 % of cases having a dietary diversity score above four (out of nine) compared with 51·1 % of controls. Both cases (17·2 %) and controls (22·7 %) showed low adherence to the individual SAFBDG recommendation “Eat dry beans, spilt peas and lentils regularly”. In addition, both cases (82·1 %) and controls (73 %) showed high adherence to the sub-category for fish, chicken and lean meat consumption (<90 g/d), while controls (46·7 %) were more likely to adhere to the sub-category on egg consumption (at least four eggs/week) than breast cancer cases (39·2 %). Although adherence to the individual SAFBDG “Use fats sparingly. Choose vegetable oils rather than hard fats” was low, breast cancer cases (52·5 %) were more likely to adhere to the total fat sub-recommendation  than controls (45·2 %).v. lowest adherence (≤3·5) to the SAFBDG showed a significant inverse association with breast cancer risk overall , P = 0·006), among postmenopausal women , P = 0·034) as well as in oestrogen receptor positive (ER+) breast cancer , P = 0·004). No significant association with breast cancer risk was observed in premenopausal or obese women.Table P < 0·001), in postmenopausal women , P = 0·003) and in participants with ER+ and progesterone receptor positive (PR+) breast cancers , P < 0·001 and OR = 0·51, 95 % CI , P = 0·010, respectively). With regard to the recommendation “Make starchy food part of most meals”, higher adherence showed an inverse association with ER+ breast cancer , P = 0·047). Furthermore, higher adherence to the recommendation “Eat plenty of vegetables and fruit everyday” showed an inverse association with breast cancer risk overall , P = 0·008), in postmenopausal women , P = 0·046) and in participants with ER+ and PR+ breast cancers , P = 0·005 and OR = 0·59, 95 % CI , P = 0·028, respectively). Higher consumption of milk, maas or yoghurt showed an inverse association with breast cancer risk overall , P = 0·025), in postmenopausal women , P = 0·039) and in participants with ER+ breast cancers , P = 0·006). Higher adherence to the recommendation “Fish, chicken, lean meat or eggs can be eaten daily” showed an inverse association with breast cancer risk overall , P = 0·036) and in participants with ER+ breast cancer , P = 0·010).The association between higher adherence to individual SAFBDG recommendations, using data-driven tertiles for each individual SAFBDG recommendation, with breast cancer risk is presented in Table To our knowledge, this is the first study investigating the relationship between adherence to the SAFBDG and breast cancer risk. The results indicate that higher adherence to the SAFBDG, using data-driven cut-points, may reduce the risk of developing breast cancer in this population overall, in postmenopausal women and for women with ER+ breast cancer. The strongest inverse associations with breast cancer risk were seen for higher adherence to the following individual SAFBDG recommendations “Enjoy a variety of foods”, “Eat plenty of fruit and vegetables every day”, “Have milk, maas or yoghurt every day” and “Fish, chicken, lean meat or eggs can be eaten daily”. The study results further indicate a concerning low level of adherence to the SAFBDG, whether adherence was measured as overall adherence or individual recommendation adherence.. A Dutch Cohort study showed that higher adherence to the Dutch Food Based Dietary Guidelines was inversely associated with overall mortality and non-communicable diseases such as Type 2 diabetes and colorectal cancer. The European Prospective Investigation into Cancer and Nutrition study (EPIC-Granada study conducted in Spain) showed that higher adherence to the Spanish Dietary Guidelines was associated with a lower risk of being obese. These global findings highlight the importance of adhering to national Food Based Dietary Guidelines to reduce non-communicable diseases such as breast cancer.Higher adherence to the SAFBG (measured with data-driven cut-points) may protect against the development of breast cancer in this population. Studies investigating the association between adherence to the SAFBDG (and other dietary guidelines) and nutrition-related diseases in this South African population are still limited. However, our findings are in line with results from international studies investigating adherence to national Food Based Dietary Guidelines in association with non-communicable disease and obesity risk. Although national Food Based Dietary Guidelines may differ, based on the population, Food Based Dietary Guidelines generally promote similar healthy eating behaviour/patterns globally. A Danish Cohort study of 54 305 participants showed that higher adherence to the Danish Food Based Dietary Guidelines had an inverse association with Type 2 diabetes and CVD. This is a concern as the low DDS in the current study indicates that a variety of foods and thus a variety of micronutrients, which may protect against breast cancer, are not consumed,31.Higher adherence to the individual SAFBDG recommendation “Enjoy a variety of foods”, using data-driven cut-points, showed an inverse association with breast cancer risk. This indicate that consumption of a variety of foods may play an important role in breast cancer prevention. However, more than 60 % of cases and nearly half of controls did not adhere to this recommendation when suggested adherence cut-points were used. Similar results from the South African National Health and Nutrition Examination Survey showed that 50 % of the black South African population had a DDS below fourn 355) adhered to this recommendation. Low fruit and vegetable consumption was also observed in other South African regions,36. In addition, concerning low levels of adherence to the individual SAFBDG recommendation “Eat dry beans, split peas, lentils and soya regularly” was observed when suggested adherence cut-points were used in the analysis and is in line with other studies reporting low legume consumption in South Africa. Low adherence to these respective recommendations is of concern as high fibre foods such as fruit, vegetables and legumes have been linked to a decreased risk of colorectal cancer and can help maintain a healthy weight necessary to decrease the risk for postmenopausal breast cancer.Higher adherence to the individual SAFBDG recommendation “Eat plenty of fruit and vegetables every day”, using data-driven cut-points, was inversely associated with breast cancer risk in the current study. Worrisome, however, is that adherence to the SAFBDG recommendation “Eat plenty of fruit and vegetables every day” (using suggested adherence cut-points) was low in both cases and controls in our study. The Prospective Urban and Rural Epidemiology cohort study, conducted in the North-West Province of South Africa, showed that only 31·5 % of urban women . The Prospective Urban and Rural Epidemiologica study, mentioned above, showed similar low adherence levels (11·5 %) to this recommendation amongst urban women . Despite the low consumption of minimally processed starchy foods, total carbohydrate intake for both cases (61·4 %) and controls (64·0 %) was within the recommended macronutrient distribution range . This finding indicates that not all starchy foods consumed are of high nutritional value and reflect the changes in dietary carbohydrate consumption, from high fibre and nutrient dense starchy staple food intakes to higher consumption of refined starches, lacking nutrients and having a high added sugar content.Almost 60 % of both cases and controls did not adhere to the SAFBDG recommendation “Make starchy foods part of most meals” when suggested adherence cut-points were used. This finding was unexpected since staple foods, such as fortified maize meal, bread, rice, etc., promoted in this recommendation, cost less per unit of energy than animal products, fruit and vegetables and is therefore considered affordable. With regard to added sugar intake, a review of dietary surveys in the adult South African population from 2000 to 2015 and cross-sectional studies showed that added sugar intake was greater than the recommended 10 % of total energy intake in various provinces of South Africa ,38. Diets high in saturated fat and added sugar are concerning because diets high in saturated fat and added sugar have been linked to a higher risk of being overweight or obese, which is a known risk factor for postmenopausal breast cancer.Adherence to the SAFBDG recommendations: (1) “Use fats sparingly. Choose vegetable oils rather than hard fats” and (2) “Use sugar & foods & drinks sparingly” also showed low levels of adherence in both cases and controls when suggested portions were used as adherence cut-points. Similar, low levels of adherence to the SAFBDG recommendation regarding total fat intake were observed in Cape Town, South Africa,40. The finding is alarming as physical activity may protect against breast cancer development and being overweight or obese.Apart from low adherence to the nutrition-related SAFDG recommendations, adherence to the SAFBDG recommendation “Be active” also showed worrying low adherence levels in both breast cancer cases and controls in the current study. This low physical activity level is not a new finding and is in line with several studies conducted amongst black South African women. In line with the above review, the results of our study clearly showed low adherence to the overall and individual SAFBDG recommendations.A modelling study, reviewing global adherence to national food based dietary guidelines in 85 countries, showed that South Africa is among the countries with the lowest adherence to the national food based dietary guidelines. Additionally, the Strategy for the Prevention and Control of Obesity in South Africa states that high crime rates and gender-based violence, especially in urban South African areas, contribute to the perception of it being unsafe to exercise outdoors and could also contribute to low physical activity levels . A lack of exercise space due to small plots and size of physical buildings in low-income households may also be considered a potential limitation to physical activity. Also, the many challenges associated with measuring self-reported physical activity levels in epidemiological studies such as recall bias of participants may further contribute to low physical activity levels. Furthermore, a lack of knowledge , education and skills regarding food preparation methods ; length of preparation time; taste preferences (unsweetened products v. sweetened products) and perceptions towards a healthy diet and social and cultural influences may also contribute to the low adherence levels observed. Given the effect of the COVID-19 pandemic, resulting in a higher unemployment rate and higher food prices, adherence to the SAFBDG may decrease even further. Low adherence to the recommendation “Eat dry beans, split peas, lentils and soya regularly” is not likely to be influenced by economic status since beans and other legumes are affordable in South Africa. However, the cost occurred as a result of the length of preparation of beans and other legumes may influence the consumption thereof. Therefore, more research is required to understand the drivers of consumption of specific food groups such as beans and other legumes in this population.Poverty, influencing purchasing power, and a growing obesogenic food environment are considered key barriers to healthier eating patterns in South Africa.Results of our study suggest that dietary intake that is not well aligned with the SAFBDG recommendations is associated with an increased risk of developing breast cancer in this black female population of Soweto, South Africa. It is therefore critical to also create sustainable and healthy food environments that support the affordability, availability and accessibility of healthy foods and safe environments that support physical activity in order to enable adherence to the SAFBDG in this population. However, creating sustainable and healthy food environments in South Africa is not an easy task and requires multi-sectoral and transdisciplinary public health engagement, beyond those already in place. Both sets of recommendations showed that higher consumption of fruit and vegetables may reduce breast cancer risk in this population. The 2018 WCRF/AICR Cancer Prevention Recommendations emphasised the importance of following an overall healthy lifestyle  for breast cancer prevention. Results of the current study complement these findings of Jacobs and colleagues (2021) by (1) highlighting the potential benefits of specific foods  and (2) emphasising the importance of following a diverse diet for breast cancer prevention in this population. While the findings of both studies provide valuable insights in the lifestyle and diets of black women from Soweto, more research is required to understand how the 2018 WCRF/AICR Cancer Prevention Recommendations and the SAFBDG could be used as key nutrition intervention/tool in breast cancer prevention studies.Furthermore, results of our study are in line with a recent study, conducted in the same black female population of Soweto, South Africa, which investigated the association between adherence to the 2018 WCRF/AICR Cancer Prevention Recommendations and breast cancer riskStrengths of the current study include the fact that cases were recruited prior to any breast cancer treatment and that the questionnaires used to obtain data were proven to be validated and data used in the analysis were standardised and administered by trained personnel. Limitations include the relatively limited sample size of the current study, the nature of the case–control study design which is prone to differential biases of cases and the use of a QFFQ to collect dietary data which relies on the memory of participants and is therefore more prone to recall bias. Dietary intake and physical activity were measured over the past month when habitual dietary intake of case participants could have changed due to illness and may contribute to random misclassification and under estimation of dietary intake. In addition, although dietary intakes were captured throughout the year (in different participants) and that breast cancer cases and controls were recruited little time apart, seasonal variability of foods (not adjusted for) may have influenced usual reporting of dietary intakes. It is also noteworthy that QFFQ are not ideal for measuring absolute dietary intakes as their main goal is to measure relative dietary intakes, allowing the ranking of people according to their dietary intakes. Therefore, comparison with fixed cutoffs is not recommended, especially in a population with highly skew data, which motived the use of data-driven tertiles for dividing the participants according to their dietary intakes and to measure adherence to the SAFBDG. Using such an alternative method may have influenced the overall results.The methodology used in the current study is one of the first attempts to measure quantitative adherence to the SAFBDG and requires more investigation to establish a standardised adherence algorithm. We followed the exact recommendations as stated in the technical support papers of the revised SAFBDG. But, precise recommendations were not always stated such as in the case of measuring adherence to the recommendations “Make starchy foods part of most meals” and “Eat dry beans, split peas, lentils and soya regularly”. To measure adherence to these two recommendations, assumptions were made, based on previous studies in South Africa or consulting with nutrition experts in South Africa. Such assumptions made the operationalisation of the current study challenging.In conclusion, higher adherence to the SAFBDG showed inverse associations with breast cancer risk overall, in postmenopausal women and for women with ER+ breast cancer. In particular, the increased consumption of a variety of foods, fruit and vegetables, milk, maas or yoghurt and lean meat or eggs showed strong inverse associations with breast cancer risk. However, the black female population included in the current study showed concerning low levels of adherence to the SAFBDG. It is therefore necessary to promote adherence to the SAFBDG in both preventative education campaigns/actions and the creation of sustainable and healthy food environments that enhances the affordability, availability and accessibility of healthier foods, together with safe environments that support increased physical activity in order to enable adherence to the SAFBDG.


The aetiology of breast cancers diagnosed ≤ 50 years of age remains unclear. We aimed to compare reproductive risk factors between molecular subtypes of breast cancer, thereby suggesting possible aetiologic clues, using routinely collected cancer registry and maternity data in Scotland.We conducted a cross-sectional study of 4108 women aged ≤ 50 years with primary breast cancer diagnosed between 2009 and 2016 linked to maternity data. Molecular subtypes of breast cancer were defined using immunohistochemistry (IHC) tumour markers, oestrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER2), and tumour grade. Age-adjusted polytomous logistic regression models were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association of number of births, age at first birth and time since last birth with IHC-defined breast cancer subtypes. Luminal A-like was the reference compared to luminal B-like (HER2−), luminal B-like (HER2+), HER2-overexpressed and triple-negative breast cancer (TNBC).Mean (SD) for number of births, age at first birth and time since last birth was 1.4 (1.2) births, 27.2 (6.1) years and 11.0 (6.8) years, respectively. Luminal A-like was the most common subtype (40%), while HER2-overexpressed and TNBC represented 5% and 15% of cases, respectively. Larger numbers of births were recorded among women with HER2-overexpressed and TNBC compared with luminal A-like tumours . Women with their most recent birth > 10 years compared to < 2 years were less likely to have TNBC tumours compared to luminal A-like . We found limited evidence for differences by subtype with age at first birth.Number of births and time since last birth differed by molecular subtypes of breast cancer among women aged ≤ 50 years. Analyses using linked routine electronic medical records by molecularly defined tumour pathology data can be used to investigate the aetiology and prognosis of cancer. In ScotBreast cancer has been classified into ‘intrinsic’ or molecular subtypes based on mRNA expression profiling that have different treatment and survival outcomes . The chaReproductive factors have been well documented as key breast cancer risk factors with direct associations observed with early age at menarche, nulliparity, late age at menopause and first birth, and limited breastfeeding , 8. DataWithin Scotland’s renowned, high-quality routine electronic health records, the Scottish Cancer Registry (SMR06) is an excellent resource to investigate risk factors for cancer incidence. In Scotland, ER status data collection began in 1997, and PR and HER2 data collection started in 2009, almost a decade earlier than other registries in the UK. We have recently reported the high quality of these data and shown distinct temporal trends by molecular subtypes and observed increasing incidence of ER+ subtypes among women of screening age (50–70 years), among whom about half of all cases are diagnosed .In this study, we aimed to assess whether there are differences in reproductive risk factors among invasive breast cancer cases diagnosed in Scotland using a ‘case–case’ approach. A case–case analysis compares the risk factor associations of breast cancer by comparing cases of a certain molecular subtype to cases of another subtype, without also describing risk factor patterns in women without breast cancer .https://www.scotpho.org.uk/publications/overview-of-key-data-sources/scottish-national-data-schemes/isd-linked-database). Incident primary breast cancer cases were identified using data from the Scottish Cancer Registry  which attains an average of 95.4% breast cancer case ascertainment and is over 99% complete [https://www.isdscotland.org/Quality-Indicators). All tumours diagnosed in women 20+ years of age, with a primary invasive breast cancer  between 1997 and 2016 were ascertained  .All persons that are residents of Scotland are registered with a GP practice (defined as residing in UK for 3 months or longer). Records from within the UK can be added for UK residents. There are no accurate records of emigrations outside the UK (Scotland), however within the registry there is a variable that indicates whether a woman emigrated (to England or a different country). Numbers were really small (< 20 cases for the whole study period had an embarkment date recorded). The Information Services Division (ISD) of Public Health Scotland holds population-level National Health Service (NHS) data for Scotland which can be deterministically linked using the Community Health Index (CHI) number, a unique patient identifier. Probabilistic linkage providing < 4% false positive and < 2% false negative linkage  of NHS Scotland, and analyses were conducted in the Scottish National Safe Haven (PBPP Reference Number 1718-0057).CHI number and probabilistic matching were used to link cancer registry data (SMR06) to Scottish Morbidity Records maternity inpatient and day case records (SMR02) which was available from 1981. To improve completeness of maternity data, the study excluded women who were ≥ 16 years  in 1981, resulting in a cohort of women born in 1966 or thereafter. Data on number of births, age at first birth and time since last birth, including both live births and stillbirths, were calculated. The number of births was derived from the number of maternity records each woman held in SMR02. The maternal age from the first maternity record for a parous woman was considered as her age at first birth. Time since last birth was calculated as the time from the most recent birth preceding a cancer diagnosis.https://www.isdscotland.org/Cancer-Registration-Definitions). While ER status for breast cancer became available in SMR06 in 1997, recording of information on PR and HER2 status commenced only in 2009 (https://www.isdscotland.org/Cancer-Registration-Definitions). As we aimed to evaluate the subtypes based on ER, PR and HER2 status, we focused on cases diagnosed from 2009 onwards. Due to non-availability of data on Ki67 labelling index, tumour grade was employed as a proxy for distinguishing the luminal subtypes [The Scottish Cancer Registry (SMR06) records the receptor status for breast cancers using immunohistochemistry (IHC) staining for ER, PR and HER2, and for borderline IHC HER2 results the status based on fluorescence in situ hybridization  had missing subtype data and were excluded from analyses. To provide finer adjustment for age, we used 5 year age categories in regression models . Age distribution at diagnosis of breast cancer, number of births, age at first birth and time since last birth were computed for each breast cancer subtype. Pearson’s chi-square tests were used to test for differences between subtypes in the distribution of reproductive risk factors of interest. We determined the correlation of age at diagnosis and each reproductive risk factor by computing Spearman’s correlation coefficients . PolytomThe final study population included 4,108 women with breast cancer diagnosed at or below 50 years of age with data available to assign breast cancer subtype, after excluding 9.7% of the initial cohort with missing hormone status or tumour grade data (data not shown). There was a significant relationship between age at diagnosis and missingness of subtype, with patients 41–50 less likely to have missing subtype data (66.1% vs 59.6%), although the mean age was similar between those not missing subtype (mean age = 41.8 (SD = 5.3) and missing subtype (mean age = 40.9 (SD = 5.2). Luminal A-like was the most common type (40%) and HER2-overexpressed was the least common . Women with luminal A-like subtype had the highest proportion of absence of birth records (assumed nulliparity of 30.5% or 69.5% with one or more birth records) and breast cancer diagnoses that were six or more years following their most recent birth (82.8%). Women with HER2-overexpressed and TNBC had higher proportions of one or more birth records  and lower frequency diagnoses compared to luminal made six or more years after last birth . Chi-square test revealed no statistically significant differences for age at first birth by subtype (Table R2 = 0.66 p <0.001) as with age at first birth (Spearman R2 = 0.10) but not number of births (Spearman R2 = 0.19 p = 0.28).Distribution of age at diagnosis of breast cancer, number of births, age at first birth and time since last birth by the five breast cancer subtypes are presented in Table 1p = 0.05) hence we also present models adjusted for an interaction term. These results showed similar relationships, however estimates showed wider confidence intervals.Women with TNBC were significantly more likely to have at least one (relative to no birth records) in comparison to those with luminal A-like tumours Table . AlthougTable When compared to the luminal A-like subtype, TNBC cases were significantly less likely to have last given birth > 10 years ago (relative to ≤ 2 years ago) Table . Other sUsing Scottish cancer registry data linked to maternity health records, we show that parity, number of births and time since last birth to diagnosis of breast cancer differ by IHC-defined molecular subtypes of breast cancer among women ≤ 50 years of age at diagnosis of breast cancer. Breast cancer aetiology in younger women is not fully understood as few risk factors have been identified. Furthermore, few opportunities for early detection of breast cancer are available for younger women beyond genetic counselling for high-risk families.Multiple reports and pooled analyses have recently evaluated IHC and mRNA expression profiling defined molecular subtypes of breast cancer and consistently show a positive association with parity for triple-negative or basal-like breast tumours –20. InteParity confers a dual effect on the risk of breast cancer with an augmented risk observed in the initial years following pregnancy  –30, possER- breast cancers are less likely than ER+ breast cancers to be detected through screening , and preThe key strengths of our study are the high-quality longitudinal data collected within the Scottish Cancer Registry for the entire population, and the availability and high level of completeness of molecular marker and tumour grade data (≤ 10% missing data). Another strength of the study is the inclusion of women diagnosed at age 50 years or below. Although breast cancer is less common within this age range, the tumours are more aggressive with poor prognosis making it important to identify and implement effective approaches to prevention amongst this age group . MoreoveAlthough this is one of the largest studies of breast cancer among young women, a limitation is the modest number of cases for rarer tumour subtypes, especially HER2-overexpressed , potentially reducing the statistical power of analyses for these tumour subtypes. Our study did not assess incidence or risk of breast cancer, which would require comparisons to controls/general population. In addition, we cannot exclude some residual confounding by age at diagnosis since we did observe some association with age and missing subtype data. Future work including a comparison cohort of women not diagnosed with breast cancer would add further updated information about the role of reproductive history as a risk factor for breast cancer, including, in due course for whose breast cancer is diagnosed at older ages. Other limitations of our study were the potential for incomplete maternity records for women whose children were born outside Scotland, lack of availability of data for other factors such as breastfeeding as well as for a more detailed mRNA expression or mutation profiling of the cancers.In conclusion, our data highlight the value of integrating molecular data from tumours with routinely collected health records data for understanding cancer epidemiology. There is scope for future analysis using the cancer registry linked to other datasets, including community prescription records, and primary care records, to provide more detailed information on the role and patterns of key risk factors and possible new aetiologic or prognostic factors for subtypes of breast and other cancers.


Epidemiological studies of the dietary intake of specific n‐3 polyunsaturated fatty acids (PUFA) and anatomical subsite‐specific colorectal cancer (CRC) are limited. We examined the prospective associations of total n‐3 PUFA, marine‐derived n‐3 PUFA , and alpha‐linolenic acid (ALA) as plant‐derived n‐3 PUFA with the risk of CRC by subsite in the Japan Collaborative Cohort Study.The participants completed a self‐administered food frequency questionnaire and had no prior history of CRC. Cox proportional hazards model was used to determine the associations between n‐3 PUFAs intake and CRC risk overall and by anatomical subsite.p trend = 0.01) compared with the lowest quartiles (Q1). Marine n‐3 PUFA intake was not associated with CRC risk in the overall or anatomical subsite‐specific analyses.During the median 13.8‐year follow‐up period, 699 of the 42,536 participants aged 40–79 years developed incident CRC. An inverse association was found between dietary ALA intake and the risk of distal colon cancer; the multivariable hazard ratios and 95% confidence intervals for the highest quartiles (Q4) were 0.41 (0.21–0.81; Our findings suggest that higher ALA intake may be beneficial for lowering the risk of distal colon cancer. We hypothesized different associations by anatomical subsite because the colon cancer risk varies by anatomical subsite due to different embryological and physiological features,22.1The Japan Collaborative Cohort Study for Evaluation of Cancer Risk (JACC) study was established in the late 1980s. Details of the JACC study are described in a previous publication.Our analysis included only 65,042 participants because the data on cancer incidence were collected in 24 of the 45 areas from population‐based cancer registers or by reviewing the records of major local hospitals. Furthermore, 972 participants with a previous history of cancer at baseline and 21,534 participants without dietary data were excluded. The remaining 42,536 participants were included in the final analysis. The included participants were younger; had a lower prevalence of men, smoking, and a history of diabetes mellitus; and had a higher prevalence of drinking, walking, sedentary work, higher education, and family history of CRC than the participants excluded from the analysis (Supporting Information Table 2.2p = 0.058).Food frequency questionnaire (FFQ) was used at baseline to obtain information on the intake of various food items. The FFQ included 33 food items with five multiple‐choice responses: almost never, 1–2 times/month, 1–2 times/week, 3–4 times/week, and almost daily. The responses were rated as follows: 0 (0/30 days), 0.05 (1.5/30 days), 0.214 (1.5/7 days), 0.5 (3.5/7 days), and 1.0 (30/30 days), respectively. The dietary intake of n‐3 PUFAs  was computed using the fifth edition of the Japan Food Tables to measure the amount of n‐3 PUFAs in each food. These contents were multiplied by the participants' scores for each food item, and then, the contents were summed. The portion size of the FFQ food items was estimated based on the intake reported by a subsample of the JACC study in a validation study.2.3CRC cases were defined using the codes C18 to C20, as stated in the 10th revision of the International Statistical Classification of Diseases and Related Health Problems (ICD‐10). Colon cancer was defined using CRC code C18. Colon cancer was further classified by subsite: C18.0 to C18.5 for proximal colon cancer and C18.6 and 18.7 for distal colon cancer. The codes C18.8 and C18.9 were not used for further classification by subsite because they were overlapping sites and were unspecified by subsite. C19 and C20 codes were used to define rectal cancer.2.42, 18.5 to 24.9 kg/m2, or ≥25.0 kg/ m2), smoking status , alcohol intake , walking duration , sports duration (sports <1 h/week or ≥1 h/week), education (attended school at ages ≤18 years or >18 years), sedentary work (yes or no), family CRC history in parents or siblings (yes or no), diabetes history (yes or no), and quartiles of energy, calcium, iron, saturated fatty acids, and fiber intake. Missing values for all covariates were grouped into an additional category and included in the model. In the sensitivity analyses, the HRs for any observed significant associations were calculated after excluding participants who had been diagnosed with CRC within the first 3, 5, or 10 years of follow‐up. SAS 9.4  was used to perform all analyses. Statistical significance was set at p‐value <0.05.The person‐years of follow‐up were computed from the baseline (1988–1990) to the first censoring, which included death, moving out of the study area, diagnosis of any cancer, or termination of the follow‐up. Participants who moved out of the study area were considered censored cases because the incidence rates after such moves could not be followed in our system. The follow‐up was terminated in 2009 in most areas. However, this treatment was discontinued in some areas before 2009. The lowest consumption category in quartiles (Q1) of each n‐3 PUFA energy‐adjusted intake for all participants was regarded as the reference. Multivariable‐adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for CRC, colon cancer, proximal colon cancer, distal colon cancer, and rectal cancer were calculated using Cox proportional hazards models. The confounding variables included age, sex, body mass index  of the 42,536 participants, 699 patients developed CRC, including 462 with colon cancer  and 237 with rectal cancer.Table Marine n‐3 PUFA intake  was not associated with CRC at any anatomical subsite (Table p for trend = 0.01].Table p for trend = 0.001). The respective HRs (95% CIs) after excluding incident cases within the first 10 years were as follows: Q2, 1.02 (0.46–2.27); Q3, 0.67 (0.24–1.86); and Q4, 0.44 (0.12–1.54) (p for trend = 0.18)  after excluding cases that developed during the first 5 years from the baseline were as follows: Q2, 0.86 (0.48–1.53); Q3, 0.54 (0.26–1.10); and Q4, 0.23 (0.09–0.56)  in men and 1.01 (0.65–1.57) (p for trend = 0.69) in women compared with that for the lowest (median: 1210 mg/day in men and 1350 mg/day in women) quintiles of intake.p for trend = 0.45) in men and 1.05 (0.86–1.29) (p for trend = 0.56) in women compared with those for the lowest (<900 mg/day both in men and in women) quartiles. The respective multivariable HRs (95% CIs) of proximal colon cancer were 0.81 (0.70–1.13) (p for trend = 0.45) in men and 1.04 (0.78–1.40) (p for trend = 0.89) in women. The respective multivariable HRs (95% CIs) of distal colon cancer were 1.15 (0.75–1.75) (p for trend = 0.39) in men and 1.18 (0.81–1.71) (p for trend = 0.25) in women.Previous studies evaluating the association between ALA intake and CRC risk have yielded inconsistent findings. A previous Japanese prospective study showed no associations between dietary ALA intake and colon cancer risk; the multivariable relative risks (95% CIs) for the highest (median: 2760 mg/day in men and 2640 mg/day in women) quintiles of intake were 0.84 (0.56–1.28) (p for trend = 0.001) compared with that for the lowest (median:700 mg/day) tertile.p for trend = 0.13) compared with that for the lowest (<780 mg/day) quartile of ALA intake, and a non‐significant inverse association was observed between dietary ALA intake and the risk of CRC among men.p for trend = 0.09) compared with that for the lowest (<820 mg/day) quartile of ALA intake among men.p for trend = 0.112) and 0.95 (0.65–1.41) (p for trend = 0.833), respectively, compared with that for the lowest (<840 mg/day) quartile of ALA.p for trend = 0.82), respectively, compared with that for the lowest quartile of ALA intake (amount of intake was not reported).In contrast, some prospective studies investigating the association between dietary ALA intake and CRC risk reported a positive trend.The disparity between our study and previous studies that reported a positive trend may be due to the different dietary sources and different age distributions, shorter follow‐up period, and lack of information on the anatomical subsite of CRC in previous studies. For example, the primary sources of non‐marine n‐3 PUFAs associated with a higher risk of CRCOur results indicate an inverse association between dietary ALA intake and CRC risk only in patients with distal colon cancer. In rats, the incidence of aberrant crypt foci in the distal colon was higher than that in the proximal colon,In our study, no association was found between marine n‐3 PUFA intake and CRC risk, including in distal colon cancer. However, a US study conducted among health professionals and nurses suggested a positive association between marine n‐3 PUFA intake and the risk of distal colon cancer, with the multivariable HRs (95% CIs) in the highest quartiles of intake being 1.43 (0.97–2.11) among men and 1.36 (1.03–1.80) among women compared with those in the lowest quartiles of intake.p for trend<0.001) for FOXP3 + T‐cell‐high CRC and 1.14  for FOXP3 + T‐cell‐low CRC compared with those in the lowest quartile.Moreover, no association was observed between marine n‐3 PUFA intake and risk of proximal colon cancer. Meanwhile, a previous Japanese study revealed a protective effect of marine n‐3 PUFA intake against invasive proximal colon cancer, with multivariable relative risks being 0.35 (0.14–0.88) among men and 0.59 (0.24–1.45) among women in the highest quintile compared with that in the lowest quintile.The strengths of our study were as follows: the prospective nature of the study, which avoided the occurrence of exposure recall bias; its large sample size with high follow‐up rates; and the informative analyses under sufficient quality of the cancer registry, which reduced the misclassification of outcomes. In addition, our study results were not affected by reverse causation because of the similar trend before and after excluding the early diagnosis of dietary ALA intake.Our study had several limitations. First, although we adjusted for multiple potential confounders, some confounding and other unmeasured factors, such as the use of nonsteroidal anti‐inflammatory drugs, are suggested to reduce the CRC risk due to its anti‐inflammatory effectIn conclusion, our findings provide evidence that a high ALA intake is associated with a lower risk of distal colon cancer in a dose‐dependent manner. Further studies focusing on the health benefits of ALA intake on CRC risk are warranted.Ayako Kato, Chika Okada, and Hiroyasu Iso designed the study and methods of the analyses. Ayako Kato and Chika Okada analyzed the data. Ayako Kato drafted the manuscript. Ehab S Eshak, Hiroyasu Iso, and Akiko Tamakoshi provided a critical review of the content. Akiko Tamakoshi and Hiroyasu Iso coordinated the research. All authors approved the final manuscript.The authors declare that they have no competing interests.This study was approved by the Ethical Board of Nagoya University School of Medicine, Aichi, Japan, and Osaka University Graduate School of Medicine, Osaka, Japan.Table S1Click here for additional data file.


In the UK, the National Institute for Health and Care Excellence (NICE) recommends that women at moderate or high risk of breast cancer be offered risk-reducing medication and enhanced breast screening/surveillance. In June 2022, NICE withdrew a statement recommending assessment of risk in primary care only when women present with concerns. This shift to the proactive assessment of risk substantially changes the role of primary care, in effect paving the way for a primary care-based screening programme to identify those at moderate or high risk of breast cancer. In this article, we review the literature surrounding proactive breast cancer risk assessment within primary care against the consolidated framework for screening. We find that risk assessment for women under 50 years currently satisfies many of the standard principles for screening. Most notably, there are large numbers of women at moderate or high risk currently unidentified, risk models exist that can identify those women with reasonable accuracy, and management options offer the opportunity to reduce breast cancer incidence and mortality in that group. However, there remain a number of uncertainties and research gaps, particularly around the programme/system requirements, that need to be addressed before these benefits can be realised. Breast cancer is the most common cancer worldwide and accounts for ~25% of cancers and 15% of cancer deaths in women . The. The3]. The post-screening management options are already well-defined by NICE guidelines . All womThe evidence for enhanced screening/surveillance among these women largely comes from population-based trials offering women aged 40–49 earlier mammography. From these trials, it is estimated that annual mammography would reduce mortality from breast cancer by 12–29%, with a 0.1% risk of over-diagnosis, a false-positive recall every 10 years and fatal radiation-induced breast cancer on average once every 76,000–97,000 years . There iThere is high-quality evidence that tamoxifen reduces breast cancer incidence in pre-menopausal women who do not have a diagnosis of breast cancer , 55. BasAs with the introduction of any new service within primary care, the introduction of a proactive breast cancer risk assessment would require additional funding to account for the additional time for the work undertaken by primary care –61. ThisThere is also a need for risk assessment tools that are integrated within the electronic health records to enable collection, recording and sharing of risk information between patients, professionals and care settings , 61–63 aAlongside these digital tools, additional training for primary care healthcare professionals will be required if discussions around multifactorial risk are to take place in primary care. Previous studies have consistently identified primary care providers’ lack of knowledge about breast cancer risk assessment, particularly genetic testing, and lack of confidence and access to referral guidance as significant barriers to engagement , 66–69. More generally, there will also need to be updates to the current referral pathways for those at moderate or high risk of breast cancer, and sufficient capacity and funding within the wider healthcare system to provide additional screening/surveillance and consultations to discuss risk-reducing medication for those women identified as being at moderate or high risk. Providing this additional screening/surveillance will likely require the commissioning of new services: a survey of all cancer genetics leads covering 22 regional centres in the UK in 2019 showed that 15 years after the original NICE guidance was published, 10.4 million women  were still living in regions not supplying appropriate moderate-risk surveillance and up to 42% of the population were living in areas not covered by the recommended high-risk surveillance . There wOne of the biggest challenges raised by healthcare professionals in a focus group study of incorporating cancer risk within primary care was finding the best time and place for risk assessment . SeveralIt is also unclear how frequently breast cancer risk assessment should be repeated. The current NICE guidance suggests reassessment if a woman’s family history changes . HoweverA recent systematic review showed that primary care providers are likely to accept an increased role in breast cancer risk assessment as they already collect family history information regularly and readily perceive it as their responsibility . HoweverHealthcare professionals have also identified a number of ethical implications that would need to be considered in any future programme . These iBreast cancer risk assessment itself has been shown to be widely acceptable to the public –85. WomeAs the main benefit from risk assessment arises through those women who are identified as being at moderate or high risk being offered, and subsequently taking up, earlier screening/surveillance and/or risk-reducing medication, the acceptability of these management options is key. Numerous studies have demonstrated widespread reticence by primary care practitioners to discuss and prescribe risk-reducing medication for women at high risk of breast cancer , 90–92. The acceptability of risk-reducing medication amongst women is also generally low, with uptake outside trial settings only 8.7% . A numbeIn contrast, the offer of enhanced screening/surveillance appears to be generally acceptable to women, with one study amongst women attending familial cancer clinics reporting that 77% of women aged 30–39 and 96% aged 40–49 were vigilant with respect to mammography recommendations . There aThe main rationale for breast cancer risk assessment is the offer of enhanced screening/surveillance and/or risk-reducing medications for women who are at increased risk but currently unaware of this. As described above, increased screening/surveillance or uptake of cancer-preventing drugs in this group would be expected to lead to reductions in cancer incidence, cancer-related morbidity and mortality. No studies, however, have been conducted in primary care to assess the impact of risk assessment on these “downstream” benefits  or the pIn addition to these “downstream” benefits, the risk assessment process itself potentially also has more “upstream” benefits and harms. These include the impact on future uptake of national screening, change in health-related behaviours and psychological outcomes. Quantifying these more “upstream” effects is important as any potential reduction in cancer incidence for a small proportion of the population needs to be balanced against the potential for possible net harms among the majority who will be at near-population risk, particularly if uptake of risk-reducing interventions is low amongst those at moderate or high risk.No studies have been conducted in primary care to assess the impact of breast cancer risk assessment on these more “upstream” effects. The best available evidence comes from national screening programmes. In this setting, women in receipt of risk estimates are no less likely to attend future rounds of screening , while wWith respect to psychological outcomes, although there are no data from primary care or in the age group targeted for proactive risk assessment in primary care, there are now considerable data on the effects of providing estimates of breast cancer risk to women aged 47–73 recruited via the NHS Breast Screening Programme. These data indicate that women who were told they were at higher risk were more worried about cancer, but there was no evidence of an overall effect on worry or adverse effects on anxiety . SimilarA number of analyses have assessed the cost-effectiveness of earlier mammography breast cancer screening in high-risk groups. These have shown that biennial screening from age 40 onwards in women at increased risk of breast cancer is cost-effective  and resuNo full cost-effectiveness analysis has been conducted on risk-reducing medication in pre-menopausal women. A cost-consequences analysis performed by the NICE guideline development panel estimated that offering risk-reducing medication in accordance with the recommendations would be cost-effective at a threshold of £20,000 per QALY if there was a gain of at least 1.71 QALYs per 1000 eligible women. In women over 50, it is estimated that 11 cases of breast cancer could be avoided per 1000 women who are offered risk-reducing medication and so the panel were confident that the costs of preventing a case of breast cancer were likely to be considered acceptable from an NHS perspective. The costs in women under 50 are not known.To our knowledge, no economic evaluation has been conducted of proactively offering breast cancer risk assessment within primary care.If proactive risk assessment for breast cancer risk were to be implemented on a large scale there would need to be a clear specification and set of quality assurance standards and resources for delivering and monitoring the programme. This would include protocols for who should be tested, requirements for specimens and laboratory analysis of relevant risk assessment parameters , and clinical follow-up of those identified as moderate or high risk, as well as expected internal quality control and performance criteria and published laboratory and clinical guidelines and standards. Quality assurance will also be needed with respect to the data used in risk assessment. This includes clinical accreditation of any discrete tests such as genotyping to obtain a polygenic score, and any risk tools or models used for this purpose.Developing such a programme would also require dialogue with policymakers to determine what parameters are important to monitor for quality assurance and who would be responsible for the programme oversight. Given the existence of mechanisms for monitoring existing breast cancer screening programmes, these can be used as a starting point to achieve alignment with any existing standards and guidance.Proactive breast cancer risk assessment within primary care for women under 50 years currently satisfies many of the key principles of screening. Most notably, there are large numbers of women at moderate or high risk currently unidentified, there are risk models available that are able to identify those women with reasonable accuracy, and management options exist that offer the opportunity to reduce breast cancer incidence, morbidity and mortality in that group. However, there remain a number of uncertainties and research gaps that need to be addressed before these benefits can be realised Table . These i


Breast density was classified according to BI-RADS , which defined as an ordinal scale . All body fat-driven obesity parameters were negatively associated with the grade of breast density in both groups of women (p < 0.001): The more fatty parameters are, the less dense breast is. In multivariable binary logistic regression, all body fat-driven obesity parameters also showed a negative association with grade d density . In premenopausal women, BMI was a more associated parameter with grade d density than those of the other fat-driven parameters . In postmenopausal women, WC was more associated with grade d density than the others . We found that BMI, WC, WHR, FMI, PBF and VFA were negatively correlated with dense breast, and the association degree pattern between body fat-driven obesity and dense breast differs according to menopausal status.We investigated the relationship between body fat-driven obesity and breast fat density in mammography according to menopausal status. We retrospectively analyzed 8537 women . Body fat parameters included BMI (body mass index), waist circumference (WC), waist-hip ratio (WHR), fat mass index (FMI), Percentage of body fat (PBF), and visceral fat area (VFA). Body fat-driven obesity was defined as follows: overall obesity, BMI ≥ 25 kg/m Mammographic patterns and breast cancer risks are correlated in the general population, demonstrating strong linear trends for percentage density. Even in high-risk populations, previous studies have also shown that mammographic density is a predictor for breast cancer risk4.Breast density in mammography reflects the proportion of fibroglandular tissue and fat in the breast. This is considered as one of the strongest markers for breast cancer risk in both premenopausal women and postmenopausal women5. A cohort study investigated the association of the interaction between mammographic breast density and BMI with breast cancer risk in Korean women and suggested that breast density and BMI interact synergistically to augment breast cancer risk, especially in postmenopausal women4. These correlations between BMI and breast cancer can result from the inflammatory effects of adiposity, which are mediated through increases in breast density. There is another theory that body fat aromatase activity can cause endogenous estrogens that promote breast tissue proliferation7. However, previous studies utilized BMI as an indicator of body composition, calculated as weight in kg/(height in m)2 because of its ease of measurement and calculation. However, weight may differ according to one’s body composition. BMI does not differentiate between body fat and lean mass8. Recent studies regarding obesity have shown differences between low body mass index and high body fat percentages among Asians9. These show that the relationship between body fat and BMI differs between Asians and Caucasians. The location of body fat also differs between people. Women of the same weight and height can have different breast densities. Considering this, it would be valuable to study the relationship between breast density and the body composition parameters.Breast density is considered to correlate closely with body fat, but previous studies have shown inconsistent results. Data from a UK case control study and a Norwegian cohort study show that BMI is positively correlated with non-dense volumes, and density percentage and dense volume are positively associated with breast cancer riskIn this study, we investigated the correlation of body composition parameters, including body mass index, waist circumference, waist-hip ratio, percentage of body fat, fat mass index, and visceral fat area, with breast densities, according to menopausal status.This single-center retrospective study was conducted at a health care center on women who had visited the hospital for routine checkups. We did 14,301 mammograms between January 2010 and March 2021 , smoking, alcohol intake, and medical history, including hypertension (HT), diabetes mellitus (DM), and dyslipidemia (DL). HT was defined as systolic BP ≥ 140 mmHg, diastolic BP ≥ 90 mmHg, or the use of antihypertensive medications. DM was defined as fasting plasma glucose ≥ 126 mg/dL, glycated hemoglobin level ≥ 6.5%, or the use of antidiabetic medications including insulin. DL was defined as a TG (triglyceride) level ≥ 150 mg/dL, HDL-C < 50 mg/dL, or the use of medications. The following laboratory blood investigations had been done: total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglycerides (TG), glucose, aspartate aminotransferase (HbA1c), and C-reactive protein (CRP). Medical history including obstetric gynecology—menarche, menopause, delivery—and social history—tobacco use, alcohol consumption—were also collected using a questionnaire on routine checkup.2). A trained nurse used a tape measure to measure the WC just above the standing patient’s hip bone as the patient exhaled. During the measurement, the tape measure was held flat against the body, not too tightly, in order to take a reading. Hip circumference was measured around the widest portion of the buttocks, with the tape measure parallel to the floor. WHR was calculated as WC/hip circumference. Body composition analysis was performed using the Inbody 720  by a trained nurse following the manufacturer’s protocol10. Using the Inbody 720, body composition fat parameters  were automatically calculated. The body composition parameter was divided into four parts using the quartile of each body fat parameter as follows. The PBF was divided into quartiles: quartile 1 , quartile 2 , quartile 3 , and quartile 4 . The FMI level was divided into quartiles: quartile 1 , quartile 2 , quartile 3 , and quartile 4 . The VFA level was divided into quartiles: quartile 1 , quartile 2 , quartile 3 , and quartile 4 .Height and body weight were measured on the day of the health checkup. BMI was calculated as body weight divided by height squared  of PBF; excess FMI as the highest quartile 4 (Q4) of FMI; visceral obesity as the highest quartile 4 (Q4) of VFA.We transformed numeric body fat parameters into binary body fat-driven obesity to clarify the obesity related to body fat parameters and the grade of dense breast as follows: overall obesity as BMI13. In our study group, there was 580 subjects who underwent CT examination. Pearson’s bivariate correlation analysis showed a significant and strong correlation between VFA measured by Inbody 720 and by CT  14. These are: (a) the breasts are almost entirely fatty; (b) there are scattered areas of fibroglandular density; (c) the breasts are heterogeneously dense, which may obscure small masses; (d) the breasts are extremely dense, which lowers the sensitivity of mammography. To explain baseline characteristics, grades a/b were described as fatty breast, and grades c/d were described as dense breast. Also, the breast density was used as a synonym for the BI-RADS grading system according to sentence flow.Breast density was interpreted visually by experienced radiologists with at least five years of experience. Radiologists of our center routinely graded dense breast on mammography into the four individual BI-RADS grades Continuous variables are expressed as mean ± standard deviation, and categorical variables are presented as numbers and percentages. Descriptive statistics were used to analyze the baseline characteristics of the study population. Comparison of characteristics and variables among groups with different grades of BI-RADS was performed via one-way analysis of variance (ANOVA). The association trend between body fat parameters and breast density was analyzed using linear-by-linear association, and nonparametric measures of correlation between body fat parameters and breast density were studied with Spearman’s rho. We performed multivariable binary logistic regression analyses to observe the role of body fat parameters as risk factors for grade d density (vs. a or b or c). The following clinical covariates were adjusted in this multivariable analysis: age, delivery, smoking, alcohol, TG, HDL-C, fasting glucose, and blood pressure. For postmenopausal women, the duration after menopause was additionally adjusted. Statistical analyses were conducted using IBM SPSS version 27 software .Clinical characteristics according to the grade of breast density were analyzed in two separate groups [premenopause (n = 4351) and postmenopause (n = 4186)].In premenopausal women, we evaluated clinical characteristics according to breast density. There were 462 patients with fatty breasts , 2291 patients with grade c (52.7%), and 1598 patients with grade d (36.7%). Patients with fatty breast were older (mean age: 42.0 ± 5.8) than those with grade c (mean age: 40.8 ± 5.8) or d (mean age: 38.6 ± 6.1) and associated with longer sex hormone exposure than patients with dense breasts. There were higher percentage of parous women  in the grade a/b breast group (80.5%) than in the grade c/d breast group . Other medical conditions, including high blood pressure, diabetes, dyslipidemia, and metabolic syndrome levels were significantly higher in the fatty breast group. Body fat parameters were always highest in the fatty breast and lowest in extremely dense breast groups (grade d)  and the fatty breast group’s mean age was highest (mean age: 63.08 ± 7.8) in all density groups. Also, fatty breast population had longer postmenopausal duration than dense breast . The fatty breast group had higher percentage of parous women (91.2%), and the extremely dense group had lower percentage of parous women (82.8%), as did the premenopausal women. Hormone replacement therapy was more common in the dense breast groups  than in the fatty breast groups (12.0%). The fatty breast groups had higher blood pressures, diabetes, dyslipidemia, and metabolic syndrome. Their body fat parameters also were the highest of the three groups  and weakly correlated with the grade of that in postmenopausal women (Spearman's rho = − 0.318). When the body fat parameters, including BMI, WC, FMI, PBF, and VFA were relatively higher, the grade of dense breast was relatively lower in both the premenopausal and postmenopausal group. Each fat parameter's absolute correlation magnitude was always higher in premenopausal women than that in postmenopausal women  density was significantly smaller than 1 in all body fat-driven obesity parameters, including overall obesity, central obesity, abdominal obesity, excess PBF, excessive FMI, and visceral obesity. In postmenopausal women, we also included duration following menopause as a confounding factor. In premenopausal women, overall obesity was more associated with grade d density , whereas abdominal obesity was more associated with grade d density than the others   reported heterogeneous or extreme breast density was associated with an increased risk of breast cancer in women 65–74 years of age and women aged 75 years or older with a BMI of 25 or greater. They found breast density is associated with breast cancer regardless of the BMI, but they were not looking at the association between BMI and breast density. Further, they found that extreme dense breasts were more likely to be normal weight, but this was an unadjusted model and, furthermore, in this group there was a higher proportion of Asian women21. These inconsistent results suggest the need to evaluate precise parameters regarding mammographic density. One study looked at the skeletal muscle mass index to reflect a relatively accurate fat-to-muscle ratio22. In a cross-sectional study of 143,456 women, the odds ratio for breast density was between the highest and lowest SMI  quartiles at 2.65 for premenopausal women and at 2.39 for postmenopausal women. One of the strongest factors affecting breast density is menopausal status. In a study of menopausal transition, BMI and density, the prevalence of dense breasts was negatively associated with increasing menopausal stages. In overweight women (BMI > 23 kg/m2), it was more profound23. Our study showed all body fat parameters are negatively correlated to breast density, regardless of menopausal status. But in premenopausal women, BMI was more correlated with breast density, compared with the other fat parameters, whereas WHR was more correlated with breast density compared with those in postmenopausal women. So, in premenopausal women we need to focus more on BMI than other parameters whereas, in postmenopausal women, the focus should be on WHR. Those differences may result from the fat mass changes in postmenopausal women. A meta-analysis showed a central fat increment following menopause24. This study showed a significant decrease in total leg fat and increase in central and total body fat following menopause. Our results also suggest that in menopausal women, breast fat distribution is more affected by central fat (correlated with WHR) than total body fat or BMI. There are several studies that have examined the association between body fat parameters and breast density. We summarized the results of these studies  was for BMI in early adulthood (ages 18–24 years). Another group also reported decreased risk of breast cancer in the overweight and obesity groups26. The authors suggested that these results were partly attributed to a higher rate of amenorrhea in obese women, consequently decreasing circulating estrogen levels. The International Agency for Research on Cancer (IARC) has reported that obesity (as determined by BMI) is associated with breast cancer  only in postmenopausal women, especially for estrogen receptor-positive tumors27. Waist circumference and body weight gains in adulthood were positively associated with the risk of postmenopausal breast cancer. But in premenopausal women, BMI and breast cancer were negatively correlated. However, data on associations of breast cancer with waist circumference and body weight gain were inconsistent. In a systematic review of BMI and cancer incidence, Asia-Pacific populations showed a strong association in premenopausal (p = 0.009) and postmenopausal (p = 0.06) cancers; whereas, in the general population , postmenopausal breast cancer showed only weak positive associations with BMI28. Obesity and dense breasts are the two most well-known risk factors for breast cancer. But breast density and BMI are inversely correlated and act as confounders to each other’s effect17. Our results suggest that we need to focus on the other effects that BMI and central fat have on breast density in menopausal women. To reduce breast cancer risk, both breast density and BMI need to be modified, tailored to the individual’s hormonal status.Obesity is a well-known risk factor for the growth and progression of breast cancer. But the mechanism by which adiposity affects breast cancer is still unknown. Many studies have revealed new insights into this link. The Premenopausal Breast Cancer Collaborative Group reported an inverse association of BMI and age and subsequent breast cancer risk in premenopausal womenOur study has several limitations. First, as this is a single center cross-sectional study, our results only show correlations between body fat parameters and breast density. The underlying physiological pathway is not fully understood. Second, we used BIA to measure body fat parameters. Although all body fat parameters correlated equally with breast density, these have not been widely used to assess body composition. Further research is needed to determine how breast density pathophysiology correlates with BMI and menopause. Third, we used mammographic density in BI-RADS category. Recent studies suggest the absolute volume of fibroglandular tissue in breasts may play an important role in breast density and obesity. Further studies using MRI or ultrasound may allow for the use of volume data to reveal a correlation.In summary, our results show that all body fat parameters are negatively correlated with the grade of breast density, regardless of menopausal status. Overall obesity (as represented by BMI) is a more associated risk factor than the other parameters in premenopausal women. But, central obesity (as represented by WHR) is a more associated risk factor than the others in postmenopausal women. The pattern of degree of association among body fat parameters and breast density differs according to menopausal status.Supplementary Information.